Namespace(batch_size=64, data_shape=416, dataset='coco', epochs=280, gpus='0,1,2,3,4,5,6,7', log_interval=100, lr=0.001, lr_decay=0.1, lr_decay_epoch='220,250', momentum=0.9, network='darknet53', num_samples=117266, num_workers=48, resume='', save_interval=10, save_prefix='yolo3_darknet53_coco', seed=233, start_epoch=0, syncbn=True, val_interval=1, wd=0.0005) Start training from [Epoch 0] [Epoch 0][Batch 99], LR: 2.70E-05, Speed: 102.511 samples/sec, ObjLoss=1210.310, BoxCenterLoss=17.306, BoxScaleLoss=14.587, ClassLoss=334.813 [Epoch 0][Batch 199], LR: 5.43E-05, Speed: 59.584 samples/sec, ObjLoss=631.317, BoxCenterLoss=17.376, BoxScaleLoss=13.136, ClassLoss=243.959 [Epoch 0][Batch 299], LR: 8.16E-05, Speed: 10.676 samples/sec, ObjLoss=434.385, BoxCenterLoss=17.162, BoxScaleLoss=11.886, ClassLoss=179.192 [Epoch 0][Batch 399], LR: 1.09E-04, Speed: 8.311 samples/sec, ObjLoss=335.187, BoxCenterLoss=17.005, BoxScaleLoss=11.148, ClassLoss=143.556 [Epoch 0][Batch 499], LR: 1.36E-04, Speed: 9.467 samples/sec, ObjLoss=275.173, BoxCenterLoss=16.696, BoxScaleLoss=10.381, ClassLoss=120.671 [Epoch 0][Batch 599], LR: 1.63E-04, Speed: 8.538 samples/sec, ObjLoss=235.245, BoxCenterLoss=16.557, BoxScaleLoss=9.904, ClassLoss=105.309 [Epoch 0][Batch 699], LR: 1.91E-04, Speed: 114.205 samples/sec, ObjLoss=206.661, BoxCenterLoss=16.523, BoxScaleLoss=9.610, ClassLoss=94.187 [Epoch 0][Batch 799], LR: 2.18E-04, Speed: 9.296 samples/sec, ObjLoss=185.305, BoxCenterLoss=16.518, BoxScaleLoss=9.350, ClassLoss=85.873 [Epoch 0][Batch 899], LR: 2.45E-04, Speed: 9.777 samples/sec, ObjLoss=168.766, BoxCenterLoss=16.538, BoxScaleLoss=9.148, ClassLoss=79.260 [Epoch 0][Batch 999], LR: 2.73E-04, Speed: 124.848 samples/sec, ObjLoss=155.298, BoxCenterLoss=16.484, BoxScaleLoss=8.932, ClassLoss=73.832 [Epoch 0][Batch 1099], LR: 3.00E-04, Speed: 10.544 samples/sec, ObjLoss=144.121, BoxCenterLoss=16.347, BoxScaleLoss=8.708, ClassLoss=69.236 [Epoch 0][Batch 1199], LR: 3.27E-04, Speed: 7.300 samples/sec, ObjLoss=134.727, BoxCenterLoss=16.200, BoxScaleLoss=8.508, ClassLoss=65.366 [Epoch 0][Batch 1299], LR: 3.55E-04, Speed: 9.516 samples/sec, ObjLoss=126.902, BoxCenterLoss=16.155, BoxScaleLoss=8.376, ClassLoss=62.190 [Epoch 0][Batch 1399], LR: 3.82E-04, Speed: 112.544 samples/sec, ObjLoss=120.094, BoxCenterLoss=16.121, BoxScaleLoss=8.328, ClassLoss=59.433 [Epoch 0][Batch 1499], LR: 4.09E-04, Speed: 11.919 samples/sec, ObjLoss=114.176, BoxCenterLoss=16.059, BoxScaleLoss=8.217, ClassLoss=56.987 [Epoch 0][Batch 1599], LR: 4.36E-04, Speed: 9.972 samples/sec, ObjLoss=109.011, BoxCenterLoss=15.988, BoxScaleLoss=8.092, ClassLoss=54.805 [Epoch 0][Batch 1699], LR: 4.64E-04, Speed: 8.737 samples/sec, ObjLoss=104.421, BoxCenterLoss=15.924, BoxScaleLoss=8.000, ClassLoss=52.850 [Epoch 0][Batch 1799], LR: 4.91E-04, Speed: 118.228 samples/sec, ObjLoss=100.484, BoxCenterLoss=15.933, BoxScaleLoss=7.943, ClassLoss=51.162 [Epoch 0] Training cost: 2179.576, ObjLoss=99.285, BoxCenterLoss=15.928, BoxScaleLoss=7.934, ClassLoss=50.659 [Epoch 0] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011 person=7.2 bicycle=0.0 car=2.7 motorcycle=0.8 airplane=0.2 bus=1.7 train=0.0 truck=0.6 boat=0.0 traffic light=0.0 fire hydrant=0.0 stop sign=0.0 parking meter=0.0 bench=0.0 bird=0.0 cat=0.0 dog=0.0 horse=0.0 sheep=0.0 cow=0.0 elephant=0.0 bear=0.0 zebra=1.2 giraffe=1.0 backpack=0.0 umbrella=0.0 handbag=0.0 tie=0.0 suitcase=0.0 frisbee=0.0 skis=0.8 snowboard=0.0 sports ball=0.4 kite=0.6 baseball bat=0.0 baseball glove=0.0 skateboard=0.0 surfboard=0.0 tennis racket=0.0 bottle=0.2 wine glass=0.0 cup=0.4 fork=0.0 knife=0.0 spoon=0.0 bowl=0.7 banana=0.0 apple=0.0 sandwich=0.0 orange=0.0 broccoli=0.0 carrot=0.0 hot dog=0.0 pizza=0.5 donut=0.0 cake=0.0 chair=0.1 couch=0.0 potted plant=0.0 bed=0.0 dining table=2.5 toilet=0.7 tv=0.0 laptop=0.0 mouse=0.0 remote=0.0 keyboard=0.0 cell phone=0.0 microwave=0.0 oven=0.0 toaster=0.0 sink=0.0 refrigerator=0.0 book=0.1 clock=0.0 vase=0.0 scissors=0.0 teddy bear=0.0 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =0.3 [Epoch 1][Batch 99], LR: 5.27E-04, Speed: 11.364 samples/sec, ObjLoss=95.721, BoxCenterLoss=15.855, BoxScaleLoss=7.839, ClassLoss=49.102 [Epoch 1][Batch 199], LR: 5.54E-04, Speed: 97.907 samples/sec, ObjLoss=92.574, BoxCenterLoss=15.827, BoxScaleLoss=7.778, ClassLoss=47.766 [Epoch 1][Batch 299], LR: 5.82E-04, Speed: 8.800 samples/sec, ObjLoss=89.728, BoxCenterLoss=15.798, BoxScaleLoss=7.729, ClassLoss=46.555 [Epoch 1][Batch 399], LR: 6.09E-04, Speed: 9.613 samples/sec, ObjLoss=87.048, BoxCenterLoss=15.734, BoxScaleLoss=7.658, ClassLoss=45.384 [Epoch 1][Batch 499], LR: 6.36E-04, Speed: 7.365 samples/sec, ObjLoss=84.655, BoxCenterLoss=15.720, BoxScaleLoss=7.621, ClassLoss=44.359 [Epoch 1][Batch 599], LR: 6.63E-04, Speed: 12.801 samples/sec, ObjLoss=82.402, BoxCenterLoss=15.663, BoxScaleLoss=7.557, ClassLoss=43.339 [Epoch 1][Batch 699], LR: 6.91E-04, Speed: 9.142 samples/sec, ObjLoss=80.389, BoxCenterLoss=15.647, BoxScaleLoss=7.521, ClassLoss=42.442 [Epoch 1][Batch 799], LR: 7.18E-04, Speed: 9.891 samples/sec, ObjLoss=78.556, BoxCenterLoss=15.643, BoxScaleLoss=7.495, ClassLoss=41.607 [Epoch 1][Batch 899], LR: 7.45E-04, Speed: 110.022 samples/sec, ObjLoss=76.787, BoxCenterLoss=15.620, BoxScaleLoss=7.471, ClassLoss=40.820 [Epoch 1][Batch 999], LR: 7.73E-04, Speed: 10.215 samples/sec, ObjLoss=75.158, BoxCenterLoss=15.623, BoxScaleLoss=7.461, ClassLoss=40.088 [Epoch 1][Batch 1099], LR: 8.00E-04, Speed: 9.729 samples/sec, ObjLoss=73.645, BoxCenterLoss=15.606, BoxScaleLoss=7.433, ClassLoss=39.383 [Epoch 1][Batch 1199], LR: 8.27E-04, Speed: 11.004 samples/sec, ObjLoss=72.225, BoxCenterLoss=15.601, BoxScaleLoss=7.433, ClassLoss=38.729 [Epoch 1][Batch 1299], LR: 8.55E-04, Speed: 7.328 samples/sec, ObjLoss=70.948, BoxCenterLoss=15.602, BoxScaleLoss=7.421, ClassLoss=38.121 [Epoch 1][Batch 1399], LR: 8.82E-04, Speed: 10.830 samples/sec, ObjLoss=69.725, BoxCenterLoss=15.586, BoxScaleLoss=7.394, ClassLoss=37.530 [Epoch 1][Batch 1499], LR: 9.09E-04, Speed: 111.391 samples/sec, ObjLoss=68.541, BoxCenterLoss=15.567, BoxScaleLoss=7.366, ClassLoss=36.977 [Epoch 1][Batch 1599], LR: 9.36E-04, Speed: 108.353 samples/sec, ObjLoss=67.448, BoxCenterLoss=15.558, BoxScaleLoss=7.344, ClassLoss=36.445 [Epoch 1][Batch 1699], LR: 9.64E-04, Speed: 9.988 samples/sec, ObjLoss=66.430, BoxCenterLoss=15.560, BoxScaleLoss=7.333, ClassLoss=35.949 [Epoch 1][Batch 1799], LR: 9.91E-04, Speed: 12.471 samples/sec, ObjLoss=65.425, BoxCenterLoss=15.537, BoxScaleLoss=7.309, ClassLoss=35.441 [Epoch 1] Training cost: 2068.898, ObjLoss=65.093, BoxCenterLoss=15.522, BoxScaleLoss=7.300, ClassLoss=35.280 [Epoch 1] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.017 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.049 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.008 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.012 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.031 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.028 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.034 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.034 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.016 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.063 person=10.8 bicycle=0.0 car=4.2 motorcycle=4.6 airplane=5.0 bus=9.0 train=10.5 truck=0.9 boat=0.1 traffic light=0.4 fire hydrant=0.0 stop sign=2.3 parking meter=0.0 bench=0.4 bird=0.6 cat=3.5 dog=2.5 horse=2.5 sheep=0.7 cow=1.4 elephant=2.4 bear=0.0 zebra=10.5 giraffe=10.9 backpack=0.0 umbrella=1.4 handbag=0.0 tie=0.2 suitcase=0.0 frisbee=0.0 skis=1.0 snowboard=0.0 sports ball=2.5 kite=1.5 baseball bat=0.0 baseball glove=0.0 skateboard=0.8 surfboard=0.1 tennis racket=0.0 bottle=1.5 wine glass=0.3 cup=1.5 fork=0.0 knife=0.0 spoon=0.0 bowl=2.1 banana=0.0 apple=0.0 sandwich=0.5 orange=0.8 broccoli=0.0 carrot=0.1 hot dog=0.0 pizza=3.7 donut=0.1 cake=0.5 chair=1.4 couch=2.3 potted plant=0.0 bed=4.8 dining table=6.1 toilet=4.3 tv=5.7 laptop=6.4 mouse=0.0 remote=0.0 keyboard=0.7 cell phone=0.0 microwave=0.0 oven=0.0 toaster=0.0 sink=1.0 refrigerator=0.0 book=0.0 clock=3.4 vase=0.1 scissors=0.0 teddy bear=1.4 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =1.7 [Epoch 2][Batch 99], LR: 1.00E-03, Speed: 10.290 samples/sec, ObjLoss=64.161, BoxCenterLoss=15.500, BoxScaleLoss=7.276, ClassLoss=34.808 [Epoch 2][Batch 199], LR: 1.00E-03, Speed: 8.331 samples/sec, ObjLoss=63.277, BoxCenterLoss=15.484, BoxScaleLoss=7.256, ClassLoss=34.370 [Epoch 2][Batch 299], LR: 1.00E-03, Speed: 7.674 samples/sec, ObjLoss=62.418, BoxCenterLoss=15.464, BoxScaleLoss=7.242, ClassLoss=33.950 [Epoch 2][Batch 399], LR: 1.00E-03, Speed: 88.881 samples/sec, ObjLoss=61.597, BoxCenterLoss=15.439, BoxScaleLoss=7.217, ClassLoss=33.528 [Epoch 2][Batch 499], LR: 1.00E-03, Speed: 10.324 samples/sec, ObjLoss=60.826, BoxCenterLoss=15.409, BoxScaleLoss=7.192, ClassLoss=33.121 [Epoch 2][Batch 599], LR: 1.00E-03, Speed: 110.380 samples/sec, ObjLoss=60.077, BoxCenterLoss=15.382, BoxScaleLoss=7.169, ClassLoss=32.728 [Epoch 2][Batch 699], LR: 1.00E-03, Speed: 11.186 samples/sec, ObjLoss=59.416, BoxCenterLoss=15.382, BoxScaleLoss=7.151, ClassLoss=32.377 [Epoch 2][Batch 799], LR: 1.00E-03, Speed: 8.304 samples/sec, ObjLoss=58.788, BoxCenterLoss=15.394, BoxScaleLoss=7.141, ClassLoss=32.040 [Epoch 2][Batch 899], LR: 1.00E-03, Speed: 7.392 samples/sec, ObjLoss=58.138, BoxCenterLoss=15.375, BoxScaleLoss=7.126, ClassLoss=31.702 [Epoch 2][Batch 999], LR: 1.00E-03, Speed: 9.193 samples/sec, ObjLoss=57.514, BoxCenterLoss=15.362, BoxScaleLoss=7.112, ClassLoss=31.389 [Epoch 2][Batch 1099], LR: 1.00E-03, Speed: 10.809 samples/sec, ObjLoss=56.958, BoxCenterLoss=15.362, BoxScaleLoss=7.098, ClassLoss=31.080 [Epoch 2][Batch 1199], LR: 1.00E-03, Speed: 10.198 samples/sec, ObjLoss=56.415, BoxCenterLoss=15.360, BoxScaleLoss=7.088, ClassLoss=30.774 [Epoch 2][Batch 1299], LR: 1.00E-03, Speed: 11.151 samples/sec, ObjLoss=55.847, BoxCenterLoss=15.332, BoxScaleLoss=7.065, ClassLoss=30.473 [Epoch 2][Batch 1399], LR: 1.00E-03, Speed: 11.791 samples/sec, ObjLoss=55.321, BoxCenterLoss=15.329, BoxScaleLoss=7.064, ClassLoss=30.202 [Epoch 2][Batch 1499], LR: 1.00E-03, Speed: 10.619 samples/sec, ObjLoss=54.821, BoxCenterLoss=15.321, BoxScaleLoss=7.046, ClassLoss=29.926 [Epoch 2][Batch 1599], LR: 1.00E-03, Speed: 9.736 samples/sec, ObjLoss=54.316, BoxCenterLoss=15.311, BoxScaleLoss=7.041, ClassLoss=29.667 [Epoch 2][Batch 1699], LR: 1.00E-03, Speed: 82.946 samples/sec, ObjLoss=53.867, BoxCenterLoss=15.317, BoxScaleLoss=7.037, ClassLoss=29.423 [Epoch 2][Batch 1799], LR: 1.00E-03, Speed: 8.529 samples/sec, ObjLoss=53.414, BoxCenterLoss=15.311, BoxScaleLoss=7.026, ClassLoss=29.183 [Epoch 2] Training cost: 2172.419, ObjLoss=53.279, BoxCenterLoss=15.308, BoxScaleLoss=7.020, ClassLoss=29.106 [Epoch 2] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.027 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.009 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.040 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.041 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.057 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.058 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.015 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.051 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.089 person=12.4 bicycle=1.7 car=6.0 motorcycle=4.9 airplane=3.3 bus=9.8 train=7.9 truck=1.7 boat=1.2 traffic light=2.3 fire hydrant=11.5 stop sign=4.3 parking meter=0.6 bench=1.1 bird=0.4 cat=6.4 dog=1.7 horse=3.5 sheep=2.2 cow=3.2 elephant=4.3 bear=0.9 zebra=15.2 giraffe=12.2 backpack=0.0 umbrella=1.9 handbag=0.0 tie=0.7 suitcase=0.0 frisbee=1.3 skis=0.9 snowboard=0.7 sports ball=6.6 kite=3.4 baseball bat=0.0 baseball glove=0.0 skateboard=0.7 surfboard=0.4 tennis racket=2.7 bottle=3.3 wine glass=1.9 cup=4.1 fork=0.0 knife=0.1 spoon=0.2 bowl=3.1 banana=0.2 apple=0.3 sandwich=1.2 orange=0.5 broccoli=0.7 carrot=0.2 hot dog=0.0 pizza=5.2 donut=1.4 cake=0.0 chair=1.5 couch=6.3 potted plant=0.3 bed=3.3 dining table=1.6 toilet=10.5 tv=7.0 laptop=7.1 mouse=0.0 remote=0.0 keyboard=1.5 cell phone=0.1 microwave=0.0 oven=1.0 toaster=0.0 sink=2.3 refrigerator=1.7 book=0.4 clock=7.7 vase=0.9 scissors=0.0 teddy bear=2.9 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =2.7 [Epoch 3][Batch 99], LR: 1.00E-03, Speed: 10.689 samples/sec, ObjLoss=52.860, BoxCenterLoss=15.308, BoxScaleLoss=7.016, ClassLoss=28.880 [Epoch 3][Batch 199], LR: 1.00E-03, Speed: 8.889 samples/sec, ObjLoss=52.480, BoxCenterLoss=15.319, BoxScaleLoss=7.015, ClassLoss=28.659 [Epoch 3][Batch 299], LR: 1.00E-03, Speed: 9.127 samples/sec, ObjLoss=52.061, BoxCenterLoss=15.304, BoxScaleLoss=7.006, ClassLoss=28.436 [Epoch 3][Batch 399], LR: 1.00E-03, Speed: 10.477 samples/sec, ObjLoss=51.669, BoxCenterLoss=15.290, BoxScaleLoss=6.986, ClassLoss=28.221 [Epoch 3][Batch 499], LR: 1.00E-03, Speed: 9.475 samples/sec, ObjLoss=51.299, BoxCenterLoss=15.285, BoxScaleLoss=6.976, ClassLoss=28.012 [Epoch 3][Batch 599], LR: 1.00E-03, Speed: 8.256 samples/sec, ObjLoss=50.930, BoxCenterLoss=15.276, BoxScaleLoss=6.964, ClassLoss=27.808 [Epoch 3][Batch 699], LR: 1.00E-03, Speed: 91.543 samples/sec, ObjLoss=50.588, BoxCenterLoss=15.274, BoxScaleLoss=6.958, ClassLoss=27.624 [Epoch 3][Batch 799], LR: 1.00E-03, Speed: 12.288 samples/sec, ObjLoss=50.276, BoxCenterLoss=15.282, BoxScaleLoss=6.951, ClassLoss=27.446 [Epoch 3][Batch 899], LR: 1.00E-03, Speed: 10.211 samples/sec, ObjLoss=49.957, BoxCenterLoss=15.273, BoxScaleLoss=6.935, ClassLoss=27.252 [Epoch 3][Batch 999], LR: 1.00E-03, Speed: 10.849 samples/sec, ObjLoss=49.663, BoxCenterLoss=15.276, BoxScaleLoss=6.925, ClassLoss=27.077 [Epoch 3][Batch 1099], LR: 1.00E-03, Speed: 9.989 samples/sec, ObjLoss=49.367, BoxCenterLoss=15.277, BoxScaleLoss=6.920, ClassLoss=26.906 [Epoch 3][Batch 1199], LR: 1.00E-03, Speed: 11.435 samples/sec, ObjLoss=49.104, BoxCenterLoss=15.287, BoxScaleLoss=6.913, ClassLoss=26.746 [Epoch 3][Batch 1299], LR: 1.00E-03, Speed: 8.773 samples/sec, ObjLoss=48.832, BoxCenterLoss=15.294, BoxScaleLoss=6.911, ClassLoss=26.584 [Epoch 3][Batch 1399], LR: 1.00E-03, Speed: 8.894 samples/sec, ObjLoss=48.554, BoxCenterLoss=15.291, BoxScaleLoss=6.899, ClassLoss=26.420 [Epoch 3][Batch 1499], LR: 1.00E-03, Speed: 8.235 samples/sec, ObjLoss=48.279, BoxCenterLoss=15.280, BoxScaleLoss=6.882, ClassLoss=26.255 [Epoch 3][Batch 1599], LR: 1.00E-03, Speed: 9.850 samples/sec, ObjLoss=48.007, BoxCenterLoss=15.276, BoxScaleLoss=6.872, ClassLoss=26.099 [Epoch 3][Batch 1699], LR: 1.00E-03, Speed: 11.086 samples/sec, ObjLoss=47.745, BoxCenterLoss=15.271, BoxScaleLoss=6.863, ClassLoss=25.949 [Epoch 3][Batch 1799], LR: 1.00E-03, Speed: 92.709 samples/sec, ObjLoss=47.490, BoxCenterLoss=15.268, BoxScaleLoss=6.855, ClassLoss=25.802 [Epoch 3] Training cost: 2192.816, ObjLoss=47.403, BoxCenterLoss=15.265, BoxScaleLoss=6.852, ClassLoss=25.754 [Epoch 3] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.058 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.152 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.016 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.087 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.076 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.109 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.110 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.029 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.102 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.160 person=20.2 bicycle=2.2 car=10.5 motorcycle=8.7 airplane=11.5 bus=19.0 train=18.5 truck=5.3 boat=2.6 traffic light=2.9 fire hydrant=16.3 stop sign=16.2 parking meter=0.0 bench=2.1 bird=5.0 cat=13.7 dog=7.2 horse=6.4 sheep=7.7 cow=7.6 elephant=13.3 bear=3.5 zebra=24.0 giraffe=25.5 backpack=0.0 umbrella=7.0 handbag=0.2 tie=2.1 suitcase=0.8 frisbee=4.3 skis=1.6 snowboard=0.5 sports ball=9.1 kite=6.5 baseball bat=0.5 baseball glove=0.7 skateboard=3.7 surfboard=1.8 tennis racket=5.6 bottle=4.9 wine glass=2.5 cup=7.7 fork=0.3 knife=0.3 spoon=0.2 bowl=6.4 banana=1.5 apple=0.6 sandwich=3.2 orange=3.3 broccoli=2.0 carrot=0.5 hot dog=1.2 pizza=11.0 donut=7.7 cake=3.3 chair=3.5 couch=9.6 potted plant=1.5 bed=7.0 dining table=5.4 toilet=14.7 tv=11.3 laptop=11.7 mouse=0.9 remote=0.1 keyboard=7.9 cell phone=2.8 microwave=2.4 oven=1.9 toaster=0.0 sink=6.4 refrigerator=2.4 book=1.5 clock=11.2 vase=2.2 scissors=0.0 teddy bear=8.4 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =5.8 [Epoch 4][Batch 99], LR: 1.00E-03, Speed: 110.813 samples/sec, ObjLoss=47.171, BoxCenterLoss=15.268, BoxScaleLoss=6.842, ClassLoss=25.608 [Epoch 4][Batch 199], LR: 1.00E-03, Speed: 9.717 samples/sec, ObjLoss=46.934, BoxCenterLoss=15.263, BoxScaleLoss=6.830, ClassLoss=25.470 [Epoch 4][Batch 299], LR: 1.00E-03, Speed: 10.098 samples/sec, ObjLoss=46.707, BoxCenterLoss=15.264, BoxScaleLoss=6.827, ClassLoss=25.337 [Epoch 4][Batch 399], LR: 1.00E-03, Speed: 9.644 samples/sec, ObjLoss=46.465, BoxCenterLoss=15.253, BoxScaleLoss=6.817, ClassLoss=25.199 [Epoch 4][Batch 499], LR: 1.00E-03, Speed: 100.118 samples/sec, ObjLoss=46.251, BoxCenterLoss=15.249, BoxScaleLoss=6.813, ClassLoss=25.075 [Epoch 4][Batch 599], LR: 1.00E-03, Speed: 10.244 samples/sec, ObjLoss=46.029, BoxCenterLoss=15.243, BoxScaleLoss=6.806, ClassLoss=24.947 [Epoch 4][Batch 699], LR: 1.00E-03, Speed: 8.922 samples/sec, ObjLoss=45.831, BoxCenterLoss=15.246, BoxScaleLoss=6.799, ClassLoss=24.835 [Epoch 4][Batch 799], LR: 1.00E-03, Speed: 11.840 samples/sec, ObjLoss=45.633, BoxCenterLoss=15.244, BoxScaleLoss=6.790, ClassLoss=24.719 [Epoch 4][Batch 899], LR: 1.00E-03, Speed: 8.640 samples/sec, ObjLoss=45.401, BoxCenterLoss=15.225, BoxScaleLoss=6.773, ClassLoss=24.587 [Epoch 4][Batch 999], LR: 1.00E-03, Speed: 10.121 samples/sec, ObjLoss=45.190, BoxCenterLoss=15.211, BoxScaleLoss=6.759, ClassLoss=24.456 [Epoch 4][Batch 1099], LR: 1.00E-03, Speed: 7.745 samples/sec, ObjLoss=45.009, BoxCenterLoss=15.213, BoxScaleLoss=6.751, ClassLoss=24.341 [Epoch 4][Batch 1199], LR: 1.00E-03, Speed: 10.048 samples/sec, ObjLoss=44.806, BoxCenterLoss=15.203, BoxScaleLoss=6.742, ClassLoss=24.222 [Epoch 4][Batch 1299], LR: 1.00E-03, Speed: 11.420 samples/sec, ObjLoss=44.637, BoxCenterLoss=15.208, BoxScaleLoss=6.736, ClassLoss=24.117 [Epoch 4][Batch 1399], LR: 1.00E-03, Speed: 10.429 samples/sec, ObjLoss=44.469, BoxCenterLoss=15.200, BoxScaleLoss=6.727, ClassLoss=24.012 [Epoch 4][Batch 1499], LR: 1.00E-03, Speed: 9.511 samples/sec, ObjLoss=44.302, BoxCenterLoss=15.198, BoxScaleLoss=6.720, ClassLoss=23.909 [Epoch 4][Batch 1599], LR: 1.00E-03, Speed: 8.702 samples/sec, ObjLoss=44.135, BoxCenterLoss=15.203, BoxScaleLoss=6.717, ClassLoss=23.815 [Epoch 4][Batch 1699], LR: 1.00E-03, Speed: 101.035 samples/sec, ObjLoss=43.964, BoxCenterLoss=15.200, BoxScaleLoss=6.708, ClassLoss=23.710 [Epoch 4][Batch 1799], LR: 1.00E-03, Speed: 12.112 samples/sec, ObjLoss=43.799, BoxCenterLoss=15.199, BoxScaleLoss=6.706, ClassLoss=23.619 [Epoch 4] Training cost: 2119.334, ObjLoss=43.748, BoxCenterLoss=15.200, BoxScaleLoss=6.704, ClassLoss=23.589 [Epoch 4] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.032 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.094 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.081 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.109 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.109 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.090 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.178 person=19.3 bicycle=3.9 car=10.4 motorcycle=9.3 airplane=13.7 bus=17.4 train=21.2 truck=5.1 boat=2.2 traffic light=3.8 fire hydrant=14.8 stop sign=18.0 parking meter=0.0 bench=1.9 bird=4.6 cat=19.4 dog=9.6 horse=8.9 sheep=10.8 cow=7.6 elephant=15.2 bear=14.2 zebra=24.6 giraffe=12.0 backpack=0.0 umbrella=5.1 handbag=0.6 tie=1.0 suitcase=2.4 frisbee=3.9 skis=2.6 snowboard=2.6 sports ball=9.0 kite=5.1 baseball bat=0.4 baseball glove=1.1 skateboard=6.0 surfboard=2.5 tennis racket=6.1 bottle=3.7 wine glass=1.7 cup=5.0 fork=0.2 knife=0.3 spoon=0.4 bowl=6.7 banana=2.5 apple=0.7 sandwich=3.6 orange=4.2 broccoli=1.4 carrot=1.6 hot dog=3.1 pizza=13.6 donut=6.2 cake=2.5 chair=2.5 couch=11.4 potted plant=1.6 bed=7.1 dining table=7.8 toilet=12.9 tv=10.2 laptop=14.4 mouse=2.1 remote=0.2 keyboard=7.5 cell phone=2.8 microwave=3.6 oven=0.7 toaster=0.0 sink=3.3 refrigerator=5.2 book=1.0 clock=15.3 vase=1.8 scissors=0.0 teddy bear=10.2 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =6.2 [Epoch 5][Batch 99], LR: 1.00E-03, Speed: 10.145 samples/sec, ObjLoss=43.597, BoxCenterLoss=15.195, BoxScaleLoss=6.699, ClassLoss=23.492 [Epoch 5][Batch 199], LR: 1.00E-03, Speed: 6.625 samples/sec, ObjLoss=43.441, BoxCenterLoss=15.191, BoxScaleLoss=6.691, ClassLoss=23.393 [Epoch 5][Batch 299], LR: 1.00E-03, Speed: 9.339 samples/sec, ObjLoss=43.292, BoxCenterLoss=15.191, BoxScaleLoss=6.685, ClassLoss=23.300 [Epoch 5][Batch 399], LR: 1.00E-03, Speed: 91.864 samples/sec, ObjLoss=43.144, BoxCenterLoss=15.192, BoxScaleLoss=6.679, ClassLoss=23.206 [Epoch 5][Batch 499], LR: 1.00E-03, Speed: 8.097 samples/sec, ObjLoss=42.991, BoxCenterLoss=15.186, BoxScaleLoss=6.670, ClassLoss=23.109 [Epoch 5][Batch 599], LR: 1.00E-03, Speed: 9.186 samples/sec, ObjLoss=42.854, BoxCenterLoss=15.190, BoxScaleLoss=6.666, ClassLoss=23.025 [Epoch 5][Batch 699], LR: 1.00E-03, Speed: 7.904 samples/sec, ObjLoss=42.722, BoxCenterLoss=15.192, BoxScaleLoss=6.661, ClassLoss=22.937 [Epoch 5][Batch 799], LR: 1.00E-03, Speed: 8.929 samples/sec, ObjLoss=42.575, BoxCenterLoss=15.186, BoxScaleLoss=6.654, ClassLoss=22.847 [Epoch 5][Batch 899], LR: 1.00E-03, Speed: 8.504 samples/sec, ObjLoss=42.432, BoxCenterLoss=15.176, BoxScaleLoss=6.646, ClassLoss=22.760 [Epoch 5][Batch 999], LR: 1.00E-03, Speed: 9.471 samples/sec, ObjLoss=42.303, BoxCenterLoss=15.177, BoxScaleLoss=6.642, ClassLoss=22.681 [Epoch 5][Batch 1099], LR: 1.00E-03, Speed: 93.235 samples/sec, ObjLoss=42.182, BoxCenterLoss=15.176, BoxScaleLoss=6.633, ClassLoss=22.600 [Epoch 5][Batch 1199], LR: 1.00E-03, Speed: 9.802 samples/sec, ObjLoss=42.053, BoxCenterLoss=15.174, BoxScaleLoss=6.625, ClassLoss=22.516 [Epoch 5][Batch 1299], LR: 1.00E-03, Speed: 12.379 samples/sec, ObjLoss=41.927, BoxCenterLoss=15.173, BoxScaleLoss=6.620, ClassLoss=22.433 [Epoch 5][Batch 1399], LR: 1.00E-03, Speed: 95.012 samples/sec, ObjLoss=41.795, BoxCenterLoss=15.164, BoxScaleLoss=6.613, ClassLoss=22.353 [Epoch 5][Batch 1499], LR: 1.00E-03, Speed: 117.502 samples/sec, ObjLoss=41.648, BoxCenterLoss=15.148, BoxScaleLoss=6.601, ClassLoss=22.269 [Epoch 5][Batch 1599], LR: 1.00E-03, Speed: 8.381 samples/sec, ObjLoss=41.532, BoxCenterLoss=15.146, BoxScaleLoss=6.595, ClassLoss=22.195 [Epoch 5][Batch 1699], LR: 1.00E-03, Speed: 110.812 samples/sec, ObjLoss=41.422, BoxCenterLoss=15.145, BoxScaleLoss=6.590, ClassLoss=22.124 [Epoch 5][Batch 1799], LR: 1.00E-03, Speed: 11.729 samples/sec, ObjLoss=41.306, BoxCenterLoss=15.143, BoxScaleLoss=6.584, ClassLoss=22.053 [Epoch 5] Training cost: 2136.719, ObjLoss=41.272, BoxCenterLoss=15.142, BoxScaleLoss=6.581, ClassLoss=22.029 [Epoch 5] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.067 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.027 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.024 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.075 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.092 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.088 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.129 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.132 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.042 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.176 person=19.2 bicycle=3.8 car=12.3 motorcycle=5.7 airplane=13.5 bus=18.0 train=19.2 truck=4.6 boat=3.5 traffic light=3.0 fire hydrant=19.1 stop sign=16.7 parking meter=2.5 bench=3.0 bird=5.0 cat=15.6 dog=9.1 horse=9.1 sheep=9.2 cow=7.4 elephant=13.2 bear=15.8 zebra=26.6 giraffe=22.4 backpack=0.2 umbrella=6.3 handbag=1.0 tie=3.4 suitcase=3.3 frisbee=7.9 skis=2.2 snowboard=2.0 sports ball=3.0 kite=6.9 baseball bat=1.0 baseball glove=4.6 skateboard=6.4 surfboard=3.2 tennis racket=5.6 bottle=6.4 wine glass=3.1 cup=8.3 fork=0.5 knife=0.5 spoon=1.0 bowl=7.5 banana=1.7 apple=1.7 sandwich=5.5 orange=3.1 broccoli=3.4 carrot=2.0 hot dog=1.6 pizza=10.2 donut=6.6 cake=3.5 chair=3.2 couch=11.6 potted plant=2.6 bed=9.4 dining table=2.7 toilet=14.5 tv=12.7 laptop=10.4 mouse=4.6 remote=0.2 keyboard=8.9 cell phone=4.4 microwave=5.8 oven=3.2 toaster=0.0 sink=3.5 refrigerator=4.8 book=1.3 clock=15.4 vase=4.0 scissors=0.0 teddy bear=12.5 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =6.7 [Epoch 6][Batch 99], LR: 1.00E-03, Speed: 12.272 samples/sec, ObjLoss=41.142, BoxCenterLoss=15.134, BoxScaleLoss=6.574, ClassLoss=21.955 [Epoch 6][Batch 199], LR: 1.00E-03, Speed: 129.016 samples/sec, ObjLoss=41.008, BoxCenterLoss=15.122, BoxScaleLoss=6.565, ClassLoss=21.874 [Epoch 6][Batch 299], LR: 1.00E-03, Speed: 113.515 samples/sec, ObjLoss=40.887, BoxCenterLoss=15.114, BoxScaleLoss=6.559, ClassLoss=21.803 [Epoch 6][Batch 399], LR: 1.00E-03, Speed: 108.054 samples/sec, ObjLoss=40.784, BoxCenterLoss=15.112, BoxScaleLoss=6.552, ClassLoss=21.736 [Epoch 6][Batch 499], LR: 1.00E-03, Speed: 60.302 samples/sec, ObjLoss=40.673, BoxCenterLoss=15.107, BoxScaleLoss=6.545, ClassLoss=21.667 [Epoch 6][Batch 599], LR: 1.00E-03, Speed: 9.157 samples/sec, ObjLoss=40.558, BoxCenterLoss=15.101, BoxScaleLoss=6.539, ClassLoss=21.598 [Epoch 6][Batch 699], LR: 1.00E-03, Speed: 10.125 samples/sec, ObjLoss=40.467, BoxCenterLoss=15.108, BoxScaleLoss=6.538, ClassLoss=21.534 [Epoch 6][Batch 799], LR: 1.00E-03, Speed: 11.001 samples/sec, ObjLoss=40.347, BoxCenterLoss=15.097, BoxScaleLoss=6.528, ClassLoss=21.464 [Epoch 6][Batch 899], LR: 1.00E-03, Speed: 10.400 samples/sec, ObjLoss=40.250, BoxCenterLoss=15.096, BoxScaleLoss=6.523, ClassLoss=21.404 [Epoch 6][Batch 999], LR: 1.00E-03, Speed: 10.060 samples/sec, ObjLoss=40.149, BoxCenterLoss=15.098, BoxScaleLoss=6.520, ClassLoss=21.339 [Epoch 6][Batch 1099], LR: 1.00E-03, Speed: 10.327 samples/sec, ObjLoss=40.061, BoxCenterLoss=15.101, BoxScaleLoss=6.517, ClassLoss=21.278 [Epoch 6][Batch 1199], LR: 1.00E-03, Speed: 122.579 samples/sec, ObjLoss=39.964, BoxCenterLoss=15.097, BoxScaleLoss=6.510, ClassLoss=21.217 [Epoch 6][Batch 1299], LR: 1.00E-03, Speed: 8.926 samples/sec, ObjLoss=39.879, BoxCenterLoss=15.100, BoxScaleLoss=6.506, ClassLoss=21.161 [Epoch 6][Batch 1399], LR: 1.00E-03, Speed: 10.313 samples/sec, ObjLoss=39.800, BoxCenterLoss=15.097, BoxScaleLoss=6.501, ClassLoss=21.104 [Epoch 6][Batch 1499], LR: 1.00E-03, Speed: 9.386 samples/sec, ObjLoss=39.725, BoxCenterLoss=15.100, BoxScaleLoss=6.499, ClassLoss=21.052 [Epoch 6][Batch 1599], LR: 1.00E-03, Speed: 10.212 samples/sec, ObjLoss=39.634, BoxCenterLoss=15.095, BoxScaleLoss=6.492, ClassLoss=20.992 [Epoch 6][Batch 1699], LR: 1.00E-03, Speed: 8.668 samples/sec, ObjLoss=39.561, BoxCenterLoss=15.104, BoxScaleLoss=6.492, ClassLoss=20.943 [Epoch 6][Batch 1799], LR: 1.00E-03, Speed: 12.199 samples/sec, ObjLoss=39.485, BoxCenterLoss=15.100, BoxScaleLoss=6.486, ClassLoss=20.890 [Epoch 6] Training cost: 2146.050, ObjLoss=39.456, BoxCenterLoss=15.098, BoxScaleLoss=6.484, ClassLoss=20.871 [Epoch 6] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.063 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.022 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.081 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.080 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.117 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.118 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.152 person=16.0 bicycle=3.7 car=11.7 motorcycle=7.1 airplane=12.2 bus=18.1 train=15.8 truck=4.8 boat=4.0 traffic light=4.9 fire hydrant=11.2 stop sign=20.1 parking meter=0.0 bench=2.4 bird=4.4 cat=10.9 dog=11.0 horse=11.8 sheep=12.9 cow=11.0 elephant=15.0 bear=15.2 zebra=16.9 giraffe=17.6 backpack=0.6 umbrella=6.0 handbag=0.8 tie=4.6 suitcase=2.1 frisbee=7.3 skis=1.9 snowboard=2.5 sports ball=8.4 kite=10.0 baseball bat=1.8 baseball glove=2.2 skateboard=7.3 surfboard=5.8 tennis racket=5.5 bottle=5.6 wine glass=3.4 cup=8.0 fork=0.5 knife=0.6 spoon=0.4 bowl=7.4 banana=3.6 apple=0.5 sandwich=2.4 orange=4.7 broccoli=1.5 carrot=1.5 hot dog=0.8 pizza=11.7 donut=6.3 cake=2.4 chair=3.5 couch=3.7 potted plant=2.6 bed=7.6 dining table=6.1 toilet=9.5 tv=8.1 laptop=6.5 mouse=1.8 remote=0.8 keyboard=11.0 cell phone=4.8 microwave=7.5 oven=0.6 toaster=0.0 sink=6.8 refrigerator=4.4 book=0.9 clock=17.9 vase=2.7 scissors=0.0 teddy bear=10.5 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =6.3 [Epoch 7][Batch 99], LR: 1.00E-03, Speed: 9.371 samples/sec, ObjLoss=39.359, BoxCenterLoss=15.088, BoxScaleLoss=6.475, ClassLoss=20.810 [Epoch 7][Batch 199], LR: 1.00E-03, Speed: 9.295 samples/sec, ObjLoss=39.273, BoxCenterLoss=15.086, BoxScaleLoss=6.470, ClassLoss=20.753 [Epoch 7][Batch 299], LR: 1.00E-03, Speed: 89.805 samples/sec, ObjLoss=39.187, BoxCenterLoss=15.080, BoxScaleLoss=6.462, ClassLoss=20.696 [Epoch 7][Batch 399], LR: 1.00E-03, Speed: 91.296 samples/sec, ObjLoss=39.116, BoxCenterLoss=15.084, BoxScaleLoss=6.460, ClassLoss=20.647 [Epoch 7][Batch 499], LR: 1.00E-03, Speed: 8.471 samples/sec, ObjLoss=39.026, BoxCenterLoss=15.079, BoxScaleLoss=6.454, ClassLoss=20.593 [Epoch 7][Batch 599], LR: 1.00E-03, Speed: 6.938 samples/sec, ObjLoss=38.947, BoxCenterLoss=15.077, BoxScaleLoss=6.448, ClassLoss=20.539 [Epoch 7][Batch 699], LR: 1.00E-03, Speed: 8.320 samples/sec, ObjLoss=38.873, BoxCenterLoss=15.079, BoxScaleLoss=6.444, ClassLoss=20.485 [Epoch 7][Batch 799], LR: 1.00E-03, Speed: 9.912 samples/sec, ObjLoss=38.796, BoxCenterLoss=15.075, BoxScaleLoss=6.438, ClassLoss=20.430 [Epoch 7][Batch 899], LR: 1.00E-03, Speed: 10.780 samples/sec, ObjLoss=38.712, BoxCenterLoss=15.069, BoxScaleLoss=6.430, ClassLoss=20.377 [Epoch 7][Batch 999], LR: 1.00E-03, Speed: 117.812 samples/sec, ObjLoss=38.632, BoxCenterLoss=15.065, BoxScaleLoss=6.426, ClassLoss=20.327 [Epoch 7][Batch 1099], LR: 1.00E-03, Speed: 77.111 samples/sec, ObjLoss=38.553, BoxCenterLoss=15.062, BoxScaleLoss=6.421, ClassLoss=20.274 [Epoch 7][Batch 1199], LR: 1.00E-03, Speed: 8.536 samples/sec, ObjLoss=38.477, BoxCenterLoss=15.061, BoxScaleLoss=6.418, ClassLoss=20.227 [Epoch 7][Batch 1299], LR: 1.00E-03, Speed: 11.030 samples/sec, ObjLoss=38.419, BoxCenterLoss=15.065, BoxScaleLoss=6.417, ClassLoss=20.185 [Epoch 7][Batch 1399], LR: 1.00E-03, Speed: 11.492 samples/sec, ObjLoss=38.360, BoxCenterLoss=15.070, BoxScaleLoss=6.416, ClassLoss=20.143 [Epoch 7][Batch 1499], LR: 1.00E-03, Speed: 10.096 samples/sec, ObjLoss=38.284, BoxCenterLoss=15.066, BoxScaleLoss=6.413, ClassLoss=20.099 [Epoch 7][Batch 1599], LR: 1.00E-03, Speed: 9.895 samples/sec, ObjLoss=38.213, BoxCenterLoss=15.064, BoxScaleLoss=6.408, ClassLoss=20.049 [Epoch 7][Batch 1699], LR: 1.00E-03, Speed: 9.996 samples/sec, ObjLoss=38.139, BoxCenterLoss=15.061, BoxScaleLoss=6.403, ClassLoss=19.999 [Epoch 7][Batch 1799], LR: 1.00E-03, Speed: 9.997 samples/sec, ObjLoss=38.074, BoxCenterLoss=15.061, BoxScaleLoss=6.399, ClassLoss=19.955 [Epoch 7] Training cost: 2179.160, ObjLoss=38.052, BoxCenterLoss=15.060, BoxScaleLoss=6.396, ClassLoss=19.938 [Epoch 7] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.048 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.024 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.107 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.039 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.243 person=20.3 bicycle=5.4 car=15.2 motorcycle=10.7 airplane=20.6 bus=24.8 train=25.0 truck=6.4 boat=4.0 traffic light=6.3 fire hydrant=25.0 stop sign=25.6 parking meter=1.1 bench=3.0 bird=8.2 cat=19.8 dog=15.7 horse=15.1 sheep=7.8 cow=13.1 elephant=17.7 bear=17.4 zebra=19.9 giraffe=20.9 backpack=0.3 umbrella=9.6 handbag=0.6 tie=6.5 suitcase=4.0 frisbee=5.7 skis=3.6 snowboard=4.1 sports ball=10.3 kite=11.0 baseball bat=1.7 baseball glove=5.2 skateboard=11.6 surfboard=7.6 tennis racket=9.0 bottle=9.1 wine glass=7.9 cup=11.3 fork=1.8 knife=0.8 spoon=0.7 bowl=10.4 banana=4.3 apple=1.4 sandwich=7.2 orange=7.0 broccoli=5.3 carrot=1.5 hot dog=1.8 pizza=17.3 donut=9.8 cake=5.0 chair=5.0 couch=7.4 potted plant=3.8 bed=15.5 dining table=6.4 toilet=18.1 tv=15.8 laptop=17.0 mouse=3.5 remote=0.7 keyboard=11.1 cell phone=6.2 microwave=10.2 oven=5.5 toaster=0.0 sink=6.9 refrigerator=8.9 book=2.4 clock=17.5 vase=7.4 scissors=0.9 teddy bear=12.3 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =9.1 [Epoch 8][Batch 99], LR: 1.00E-03, Speed: 8.707 samples/sec, ObjLoss=37.991, BoxCenterLoss=15.064, BoxScaleLoss=6.394, ClassLoss=19.896 [Epoch 8][Batch 199], LR: 1.00E-03, Speed: 117.129 samples/sec, ObjLoss=37.920, BoxCenterLoss=15.065, BoxScaleLoss=6.392, ClassLoss=19.853 [Epoch 8][Batch 299], LR: 1.00E-03, Speed: 108.727 samples/sec, ObjLoss=37.857, BoxCenterLoss=15.067, BoxScaleLoss=6.390, ClassLoss=19.812 [Epoch 8][Batch 399], LR: 1.00E-03, Speed: 125.285 samples/sec, ObjLoss=37.803, BoxCenterLoss=15.071, BoxScaleLoss=6.389, ClassLoss=19.773 [Epoch 8][Batch 499], LR: 1.00E-03, Speed: 126.670 samples/sec, ObjLoss=37.731, BoxCenterLoss=15.062, BoxScaleLoss=6.383, ClassLoss=19.728 [Epoch 8][Batch 599], LR: 1.00E-03, Speed: 8.259 samples/sec, ObjLoss=37.672, BoxCenterLoss=15.065, BoxScaleLoss=6.380, ClassLoss=19.686 [Epoch 8][Batch 699], LR: 1.00E-03, Speed: 9.507 samples/sec, ObjLoss=37.618, BoxCenterLoss=15.068, BoxScaleLoss=6.377, ClassLoss=19.646 [Epoch 8][Batch 799], LR: 1.00E-03, Speed: 10.103 samples/sec, ObjLoss=37.550, BoxCenterLoss=15.064, BoxScaleLoss=6.372, ClassLoss=19.605 [Epoch 8][Batch 899], LR: 1.00E-03, Speed: 10.531 samples/sec, ObjLoss=37.489, BoxCenterLoss=15.064, BoxScaleLoss=6.369, ClassLoss=19.566 [Epoch 8][Batch 999], LR: 1.00E-03, Speed: 12.418 samples/sec, ObjLoss=37.435, BoxCenterLoss=15.067, BoxScaleLoss=6.367, ClassLoss=19.527 [Epoch 8][Batch 1099], LR: 1.00E-03, Speed: 90.639 samples/sec, ObjLoss=37.372, BoxCenterLoss=15.066, BoxScaleLoss=6.364, ClassLoss=19.491 [Epoch 8][Batch 1199], LR: 1.00E-03, Speed: 9.544 samples/sec, ObjLoss=37.311, BoxCenterLoss=15.061, BoxScaleLoss=6.358, ClassLoss=19.448 [Epoch 8][Batch 1299], LR: 1.00E-03, Speed: 12.594 samples/sec, ObjLoss=37.256, BoxCenterLoss=15.059, BoxScaleLoss=6.355, ClassLoss=19.408 [Epoch 8][Batch 1399], LR: 1.00E-03, Speed: 111.273 samples/sec, ObjLoss=37.197, BoxCenterLoss=15.057, BoxScaleLoss=6.351, ClassLoss=19.371 [Epoch 8][Batch 1499], LR: 1.00E-03, Speed: 10.422 samples/sec, ObjLoss=37.135, BoxCenterLoss=15.054, BoxScaleLoss=6.347, ClassLoss=19.332 [Epoch 8][Batch 1599], LR: 1.00E-03, Speed: 9.746 samples/sec, ObjLoss=37.077, BoxCenterLoss=15.052, BoxScaleLoss=6.343, ClassLoss=19.293 [Epoch 8][Batch 1699], LR: 1.00E-03, Speed: 7.891 samples/sec, ObjLoss=37.009, BoxCenterLoss=15.043, BoxScaleLoss=6.337, ClassLoss=19.251 [Epoch 8][Batch 1799], LR: 1.00E-03, Speed: 13.017 samples/sec, ObjLoss=36.957, BoxCenterLoss=15.042, BoxScaleLoss=6.334, ClassLoss=19.215 [Epoch 8] Training cost: 2129.357, ObjLoss=36.942, BoxCenterLoss=15.043, BoxScaleLoss=6.334, ClassLoss=19.205 [Epoch 8] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.104 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.066 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.030 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.158 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.119 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.165 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.167 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.044 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.263 person=23.5 bicycle=4.7 car=14.6 motorcycle=12.8 airplane=18.8 bus=29.5 train=27.3 truck=12.0 boat=4.8 traffic light=7.0 fire hydrant=27.6 stop sign=27.1 parking meter=4.7 bench=3.6 bird=8.5 cat=23.8 dog=19.8 horse=17.6 sheep=15.6 cow=17.2 elephant=22.8 bear=23.2 zebra=28.9 giraffe=23.3 backpack=0.3 umbrella=8.9 handbag=1.0 tie=5.8 suitcase=4.7 frisbee=12.8 skis=2.4 snowboard=4.2 sports ball=15.0 kite=11.7 baseball bat=2.4 baseball glove=3.3 skateboard=10.3 surfboard=5.7 tennis racket=8.4 bottle=7.5 wine glass=5.9 cup=11.7 fork=1.0 knife=0.4 spoon=1.0 bowl=9.5 banana=3.1 apple=1.1 sandwich=8.0 orange=5.5 broccoli=3.7 carrot=1.4 hot dog=4.2 pizza=17.3 donut=11.5 cake=4.4 chair=6.0 couch=15.2 potted plant=3.1 bed=20.3 dining table=11.0 toilet=20.7 tv=15.5 laptop=21.2 mouse=9.2 remote=0.4 keyboard=11.8 cell phone=5.3 microwave=7.6 oven=6.1 toaster=0.0 sink=9.2 refrigerator=8.0 book=1.8 clock=20.1 vase=5.7 scissors=4.0 teddy bear=18.6 hair drier=0.0 toothbrush=0.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =10.4 [Epoch 9][Batch 99], LR: 1.00E-03, Speed: 91.211 samples/sec, ObjLoss=36.895, BoxCenterLoss=15.044, BoxScaleLoss=6.334, ClassLoss=19.173 [Epoch 9][Batch 199], LR: 1.00E-03, Speed: 10.406 samples/sec, ObjLoss=36.838, BoxCenterLoss=15.042, BoxScaleLoss=6.329, ClassLoss=19.136 [Epoch 9][Batch 299], LR: 1.00E-03, Speed: 11.425 samples/sec, ObjLoss=36.788, BoxCenterLoss=15.038, BoxScaleLoss=6.324, ClassLoss=19.100 [Epoch 9][Batch 399], LR: 1.00E-03, Speed: 97.357 samples/sec, ObjLoss=36.746, BoxCenterLoss=15.042, BoxScaleLoss=6.323, ClassLoss=19.072 [Epoch 9][Batch 499], LR: 1.00E-03, Speed: 93.096 samples/sec, ObjLoss=36.699, BoxCenterLoss=15.043, BoxScaleLoss=6.321, ClassLoss=19.040 [Epoch 9][Batch 599], LR: 1.00E-03, Speed: 8.206 samples/sec, ObjLoss=36.649, BoxCenterLoss=15.039, BoxScaleLoss=6.316, ClassLoss=19.006 [Epoch 9][Batch 699], LR: 1.00E-03, Speed: 9.595 samples/sec, ObjLoss=36.600, BoxCenterLoss=15.036, BoxScaleLoss=6.309, ClassLoss=18.969 [Epoch 9][Batch 799], LR: 1.00E-03, Speed: 10.229 samples/sec, ObjLoss=36.554, BoxCenterLoss=15.035, BoxScaleLoss=6.305, ClassLoss=18.932 [Epoch 9][Batch 899], LR: 1.00E-03, Speed: 9.146 samples/sec, ObjLoss=36.509, BoxCenterLoss=15.036, BoxScaleLoss=6.301, ClassLoss=18.897 [Epoch 9][Batch 999], LR: 1.00E-03, Speed: 12.993 samples/sec, ObjLoss=36.462, BoxCenterLoss=15.035, BoxScaleLoss=6.297, ClassLoss=18.865 [Epoch 9][Batch 1099], LR: 1.00E-03, Speed: 101.813 samples/sec, ObjLoss=36.407, BoxCenterLoss=15.031, BoxScaleLoss=6.293, ClassLoss=18.831 [Epoch 9][Batch 1199], LR: 1.00E-03, Speed: 11.234 samples/sec, ObjLoss=36.345, BoxCenterLoss=15.022, BoxScaleLoss=6.287, ClassLoss=18.792 [Epoch 9][Batch 1299], LR: 1.00E-03, Speed: 12.367 samples/sec, ObjLoss=36.298, BoxCenterLoss=15.022, BoxScaleLoss=6.285, ClassLoss=18.763 [Epoch 9][Batch 1399], LR: 1.00E-03, Speed: 8.473 samples/sec, ObjLoss=36.242, BoxCenterLoss=15.018, BoxScaleLoss=6.281, ClassLoss=18.726 [Epoch 9][Batch 1499], LR: 1.00E-03, Speed: 10.755 samples/sec, ObjLoss=36.193, BoxCenterLoss=15.018, BoxScaleLoss=6.279, ClassLoss=18.693 [Epoch 9][Batch 1599], LR: 1.00E-03, Speed: 11.923 samples/sec, ObjLoss=36.152, BoxCenterLoss=15.021, BoxScaleLoss=6.277, ClassLoss=18.661 [Epoch 9][Batch 1699], LR: 1.00E-03, Speed: 103.122 samples/sec, ObjLoss=36.104, BoxCenterLoss=15.019, BoxScaleLoss=6.274, ClassLoss=18.630 [Epoch 9][Batch 1799], LR: 1.00E-03, Speed: 10.183 samples/sec, ObjLoss=36.058, BoxCenterLoss=15.017, BoxScaleLoss=6.271, ClassLoss=18.601 [Epoch 9] Training cost: 2124.731, ObjLoss=36.044, BoxCenterLoss=15.016, BoxScaleLoss=6.270, ClassLoss=18.592 [Epoch 9] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.092 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.042 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.107 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.106 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.045 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.156 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.226 person=21.4 bicycle=6.1 car=15.0 motorcycle=13.8 airplane=18.0 bus=19.2 train=22.2 truck=9.6 boat=5.4 traffic light=6.4 fire hydrant=25.2 stop sign=14.8 parking meter=2.9 bench=4.2 bird=7.9 cat=19.9 dog=14.3 horse=11.9 sheep=16.4 cow=12.6 elephant=14.9 bear=20.4 zebra=19.3 giraffe=21.5 backpack=0.7 umbrella=6.6 handbag=0.8 tie=7.9 suitcase=4.4 frisbee=13.3 skis=2.5 snowboard=5.1 sports ball=10.9 kite=13.6 baseball bat=1.2 baseball glove=5.0 skateboard=15.1 surfboard=7.5 tennis racket=7.5 bottle=8.0 wine glass=6.9 cup=11.3 fork=0.9 knife=0.9 spoon=1.0 bowl=9.5 banana=3.8 apple=1.2 sandwich=3.2 orange=6.9 broccoli=3.6 carrot=1.4 hot dog=3.9 pizza=12.8 donut=10.1 cake=6.1 chair=4.5 couch=6.9 potted plant=3.7 bed=14.0 dining table=11.0 toilet=18.2 tv=16.3 laptop=19.3 mouse=6.7 remote=0.7 keyboard=9.6 cell phone=7.2 microwave=15.3 oven=4.7 toaster=0.0 sink=9.5 refrigerator=13.8 book=1.8 clock=14.9 vase=5.0 scissors=0.9 teddy bear=11.1 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =9.2 [Epoch 10][Batch 99], LR: 1.00E-03, Speed: 12.170 samples/sec, ObjLoss=35.996, BoxCenterLoss=15.014, BoxScaleLoss=6.267, ClassLoss=18.560 [Epoch 10][Batch 199], LR: 1.00E-03, Speed: 8.394 samples/sec, ObjLoss=35.947, BoxCenterLoss=15.011, BoxScaleLoss=6.263, ClassLoss=18.529 [Epoch 10][Batch 299], LR: 1.00E-03, Speed: 10.215 samples/sec, ObjLoss=35.898, BoxCenterLoss=15.005, BoxScaleLoss=6.257, ClassLoss=18.496 [Epoch 10][Batch 399], LR: 1.00E-03, Speed: 10.864 samples/sec, ObjLoss=35.852, BoxCenterLoss=15.003, BoxScaleLoss=6.255, ClassLoss=18.469 [Epoch 10][Batch 499], LR: 1.00E-03, Speed: 11.550 samples/sec, ObjLoss=35.810, BoxCenterLoss=15.003, BoxScaleLoss=6.253, ClassLoss=18.441 [Epoch 10][Batch 599], LR: 1.00E-03, Speed: 8.644 samples/sec, ObjLoss=35.769, BoxCenterLoss=15.005, BoxScaleLoss=6.251, ClassLoss=18.413 [Epoch 10][Batch 699], LR: 1.00E-03, Speed: 8.173 samples/sec, ObjLoss=35.729, BoxCenterLoss=15.005, BoxScaleLoss=6.249, ClassLoss=18.385 [Epoch 10][Batch 799], LR: 1.00E-03, Speed: 9.198 samples/sec, ObjLoss=35.690, BoxCenterLoss=15.005, BoxScaleLoss=6.244, ClassLoss=18.354 [Epoch 10][Batch 899], LR: 1.00E-03, Speed: 9.691 samples/sec, ObjLoss=35.655, BoxCenterLoss=15.005, BoxScaleLoss=6.241, ClassLoss=18.323 [Epoch 10][Batch 999], LR: 1.00E-03, Speed: 10.657 samples/sec, ObjLoss=35.609, BoxCenterLoss=15.002, BoxScaleLoss=6.237, ClassLoss=18.293 [Epoch 10][Batch 1099], LR: 1.00E-03, Speed: 10.329 samples/sec, ObjLoss=35.568, BoxCenterLoss=15.002, BoxScaleLoss=6.234, ClassLoss=18.264 [Epoch 10][Batch 1199], LR: 1.00E-03, Speed: 8.390 samples/sec, ObjLoss=35.520, BoxCenterLoss=14.998, BoxScaleLoss=6.230, ClassLoss=18.234 [Epoch 10][Batch 1299], LR: 1.00E-03, Speed: 10.476 samples/sec, ObjLoss=35.487, BoxCenterLoss=14.999, BoxScaleLoss=6.230, ClassLoss=18.209 [Epoch 10][Batch 1399], LR: 1.00E-03, Speed: 9.053 samples/sec, ObjLoss=35.446, BoxCenterLoss=14.996, BoxScaleLoss=6.226, ClassLoss=18.180 [Epoch 10][Batch 1499], LR: 1.00E-03, Speed: 9.064 samples/sec, ObjLoss=35.408, BoxCenterLoss=14.997, BoxScaleLoss=6.224, ClassLoss=18.153 [Epoch 10][Batch 1599], LR: 1.00E-03, Speed: 9.427 samples/sec, ObjLoss=35.361, BoxCenterLoss=14.992, BoxScaleLoss=6.220, ClassLoss=18.124 [Epoch 10][Batch 1699], LR: 1.00E-03, Speed: 8.195 samples/sec, ObjLoss=35.316, BoxCenterLoss=14.990, BoxScaleLoss=6.217, ClassLoss=18.098 [Epoch 10][Batch 1799], LR: 1.00E-03, Speed: 12.714 samples/sec, ObjLoss=35.276, BoxCenterLoss=14.987, BoxScaleLoss=6.214, ClassLoss=18.069 [Epoch 10] Training cost: 2078.978, ObjLoss=35.264, BoxCenterLoss=14.985, BoxScaleLoss=6.213, ClassLoss=18.060 [Epoch 10] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.062 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.029 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.166 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.111 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.160 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.163 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.051 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.158 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.258 person=24.2 bicycle=7.5 car=12.0 motorcycle=14.7 airplane=14.8 bus=24.4 train=25.0 truck=8.2 boat=4.2 traffic light=5.1 fire hydrant=18.2 stop sign=26.4 parking meter=4.4 bench=2.8 bird=9.4 cat=22.9 dog=18.8 horse=18.5 sheep=10.3 cow=17.2 elephant=23.7 bear=24.7 zebra=31.8 giraffe=31.4 backpack=0.8 umbrella=9.8 handbag=0.6 tie=5.1 suitcase=5.3 frisbee=13.2 skis=1.3 snowboard=3.2 sports ball=7.7 kite=12.2 baseball bat=3.4 baseball glove=4.7 skateboard=9.1 surfboard=9.0 tennis racket=10.3 bottle=8.6 wine glass=8.7 cup=10.8 fork=0.9 knife=0.4 spoon=1.1 bowl=11.8 banana=4.5 apple=2.3 sandwich=5.4 orange=7.4 broccoli=4.8 carrot=2.6 hot dog=4.7 pizza=14.8 donut=14.3 cake=6.9 chair=5.1 couch=12.5 potted plant=3.8 bed=6.9 dining table=3.8 toilet=18.5 tv=20.4 laptop=18.5 mouse=9.9 remote=0.8 keyboard=10.0 cell phone=7.1 microwave=11.5 oven=5.5 toaster=0.0 sink=6.5 refrigerator=12.6 book=1.8 clock=21.3 vase=5.6 scissors=3.6 teddy bear=13.5 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =10.1 [Epoch 11][Batch 99], LR: 1.00E-03, Speed: 10.792 samples/sec, ObjLoss=35.222, BoxCenterLoss=14.983, BoxScaleLoss=6.208, ClassLoss=18.032 [Epoch 11][Batch 199], LR: 1.00E-03, Speed: 119.635 samples/sec, ObjLoss=35.192, BoxCenterLoss=14.987, BoxScaleLoss=6.207, ClassLoss=18.010 [Epoch 11][Batch 299], LR: 1.00E-03, Speed: 7.773 samples/sec, ObjLoss=35.150, BoxCenterLoss=14.983, BoxScaleLoss=6.204, ClassLoss=17.981 [Epoch 11][Batch 399], LR: 1.00E-03, Speed: 10.957 samples/sec, ObjLoss=35.110, BoxCenterLoss=14.981, BoxScaleLoss=6.199, ClassLoss=17.951 [Epoch 11][Batch 499], LR: 1.00E-03, Speed: 9.849 samples/sec, ObjLoss=35.074, BoxCenterLoss=14.979, BoxScaleLoss=6.195, ClassLoss=17.925 [Epoch 11][Batch 599], LR: 1.00E-03, Speed: 100.052 samples/sec, ObjLoss=35.038, BoxCenterLoss=14.979, BoxScaleLoss=6.193, ClassLoss=17.900 [Epoch 11][Batch 699], LR: 1.00E-03, Speed: 101.821 samples/sec, ObjLoss=35.003, BoxCenterLoss=14.979, BoxScaleLoss=6.190, ClassLoss=17.876 [Epoch 11][Batch 799], LR: 1.00E-03, Speed: 10.381 samples/sec, ObjLoss=34.958, BoxCenterLoss=14.975, BoxScaleLoss=6.186, ClassLoss=17.851 [Epoch 11][Batch 899], LR: 1.00E-03, Speed: 103.893 samples/sec, ObjLoss=34.918, BoxCenterLoss=14.974, BoxScaleLoss=6.185, ClassLoss=17.829 [Epoch 11][Batch 999], LR: 1.00E-03, Speed: 124.292 samples/sec, ObjLoss=34.885, BoxCenterLoss=14.978, BoxScaleLoss=6.184, ClassLoss=17.805 [Epoch 11][Batch 1099], LR: 1.00E-03, Speed: 9.929 samples/sec, ObjLoss=34.855, BoxCenterLoss=14.981, BoxScaleLoss=6.183, ClassLoss=17.783 [Epoch 11][Batch 1199], LR: 1.00E-03, Speed: 7.425 samples/sec, ObjLoss=34.812, BoxCenterLoss=14.975, BoxScaleLoss=6.180, ClassLoss=17.759 [Epoch 11][Batch 1299], LR: 1.00E-03, Speed: 12.275 samples/sec, ObjLoss=34.781, BoxCenterLoss=14.975, BoxScaleLoss=6.177, ClassLoss=17.736 [Epoch 11][Batch 1399], LR: 1.00E-03, Speed: 8.781 samples/sec, ObjLoss=34.748, BoxCenterLoss=14.976, BoxScaleLoss=6.175, ClassLoss=17.712 [Epoch 11][Batch 1499], LR: 1.00E-03, Speed: 117.756 samples/sec, ObjLoss=34.716, BoxCenterLoss=14.977, BoxScaleLoss=6.173, ClassLoss=17.689 [Epoch 11][Batch 1599], LR: 1.00E-03, Speed: 9.192 samples/sec, ObjLoss=34.680, BoxCenterLoss=14.976, BoxScaleLoss=6.170, ClassLoss=17.665 [Epoch 11][Batch 1699], LR: 1.00E-03, Speed: 13.104 samples/sec, ObjLoss=34.646, BoxCenterLoss=14.975, BoxScaleLoss=6.168, ClassLoss=17.643 [Epoch 11][Batch 1799], LR: 1.00E-03, Speed: 8.958 samples/sec, ObjLoss=34.612, BoxCenterLoss=14.974, BoxScaleLoss=6.165, ClassLoss=17.619 [Epoch 11] Training cost: 2188.111, ObjLoss=34.601, BoxCenterLoss=14.973, BoxScaleLoss=6.163, ClassLoss=17.611 [Epoch 11] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.113 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.271 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.069 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.040 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.166 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.124 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.176 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.267 person=24.8 bicycle=6.0 car=16.6 motorcycle=11.3 airplane=23.5 bus=31.5 train=26.9 truck=10.3 boat=6.5 traffic light=6.4 fire hydrant=26.5 stop sign=26.2 parking meter=2.9 bench=5.1 bird=9.7 cat=25.2 dog=17.0 horse=14.7 sheep=18.5 cow=20.2 elephant=26.0 bear=21.4 zebra=31.5 giraffe=34.3 backpack=1.0 umbrella=9.5 handbag=0.9 tie=6.4 suitcase=5.4 frisbee=13.7 skis=2.5 snowboard=2.5 sports ball=13.6 kite=15.7 baseball bat=3.1 baseball glove=5.8 skateboard=13.4 surfboard=8.2 tennis racket=11.6 bottle=9.0 wine glass=8.1 cup=13.1 fork=1.7 knife=1.3 spoon=0.8 bowl=12.1 banana=4.2 apple=1.3 sandwich=6.5 orange=5.5 broccoli=2.5 carrot=1.1 hot dog=2.8 pizza=15.5 donut=14.8 cake=6.0 chair=5.8 couch=14.6 potted plant=5.7 bed=17.6 dining table=10.7 toilet=22.3 tv=16.3 laptop=21.7 mouse=13.7 remote=1.1 keyboard=12.9 cell phone=7.8 microwave=13.4 oven=5.8 toaster=0.0 sink=10.4 refrigerator=13.4 book=2.1 clock=20.9 vase=9.1 scissors=2.6 teddy bear=16.4 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =11.3 [Epoch 12][Batch 99], LR: 1.00E-03, Speed: 12.254 samples/sec, ObjLoss=34.563, BoxCenterLoss=14.969, BoxScaleLoss=6.159, ClassLoss=17.585 [Epoch 12][Batch 199], LR: 1.00E-03, Speed: 9.702 samples/sec, ObjLoss=34.526, BoxCenterLoss=14.967, BoxScaleLoss=6.157, ClassLoss=17.564 [Epoch 12][Batch 299], LR: 1.00E-03, Speed: 8.946 samples/sec, ObjLoss=34.488, BoxCenterLoss=14.963, BoxScaleLoss=6.153, ClassLoss=17.541 [Epoch 12][Batch 399], LR: 1.00E-03, Speed: 8.851 samples/sec, ObjLoss=34.460, BoxCenterLoss=14.965, BoxScaleLoss=6.151, ClassLoss=17.519 [Epoch 12][Batch 499], LR: 1.00E-03, Speed: 11.587 samples/sec, ObjLoss=34.429, BoxCenterLoss=14.963, BoxScaleLoss=6.148, ClassLoss=17.496 [Epoch 12][Batch 599], LR: 1.00E-03, Speed: 8.982 samples/sec, ObjLoss=34.395, BoxCenterLoss=14.960, BoxScaleLoss=6.145, ClassLoss=17.474 [Epoch 12][Batch 699], LR: 1.00E-03, Speed: 100.202 samples/sec, ObjLoss=34.358, BoxCenterLoss=14.957, BoxScaleLoss=6.142, ClassLoss=17.452 [Epoch 12][Batch 799], LR: 1.00E-03, Speed: 113.196 samples/sec, ObjLoss=34.329, BoxCenterLoss=14.959, BoxScaleLoss=6.142, ClassLoss=17.430 [Epoch 12][Batch 899], LR: 1.00E-03, Speed: 10.355 samples/sec, ObjLoss=34.297, BoxCenterLoss=14.958, BoxScaleLoss=6.140, ClassLoss=17.409 [Epoch 12][Batch 999], LR: 1.00E-03, Speed: 10.303 samples/sec, ObjLoss=34.265, BoxCenterLoss=14.956, BoxScaleLoss=6.137, ClassLoss=17.388 [Epoch 12][Batch 1099], LR: 1.00E-03, Speed: 7.306 samples/sec, ObjLoss=34.226, BoxCenterLoss=14.951, BoxScaleLoss=6.133, ClassLoss=17.365 [Epoch 12][Batch 1199], LR: 1.00E-03, Speed: 11.987 samples/sec, ObjLoss=34.193, BoxCenterLoss=14.948, BoxScaleLoss=6.131, ClassLoss=17.345 [Epoch 12][Batch 1299], LR: 1.00E-03, Speed: 10.520 samples/sec, ObjLoss=34.162, BoxCenterLoss=14.949, BoxScaleLoss=6.129, ClassLoss=17.325 [Epoch 12][Batch 1399], LR: 1.00E-03, Speed: 8.324 samples/sec, ObjLoss=34.127, BoxCenterLoss=14.947, BoxScaleLoss=6.128, ClassLoss=17.305 [Epoch 12][Batch 1499], LR: 1.00E-03, Speed: 129.679 samples/sec, ObjLoss=34.092, BoxCenterLoss=14.944, BoxScaleLoss=6.125, ClassLoss=17.284 [Epoch 12][Batch 1599], LR: 1.00E-03, Speed: 9.603 samples/sec, ObjLoss=34.066, BoxCenterLoss=14.945, BoxScaleLoss=6.124, ClassLoss=17.265 [Epoch 12][Batch 1699], LR: 1.00E-03, Speed: 9.973 samples/sec, ObjLoss=34.039, BoxCenterLoss=14.947, BoxScaleLoss=6.123, ClassLoss=17.246 [Epoch 12][Batch 1799], LR: 1.00E-03, Speed: 10.389 samples/sec, ObjLoss=34.017, BoxCenterLoss=14.948, BoxScaleLoss=6.122, ClassLoss=17.227 [Epoch 12] Training cost: 2127.912, ObjLoss=34.009, BoxCenterLoss=14.949, BoxScaleLoss=6.122, ClassLoss=17.221 [Epoch 12] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.111 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.064 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.171 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.121 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.175 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.055 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.184 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.266 person=24.1 bicycle=4.2 car=17.0 motorcycle=12.1 airplane=22.3 bus=28.0 train=29.7 truck=12.1 boat=5.6 traffic light=6.6 fire hydrant=27.3 stop sign=27.0 parking meter=6.6 bench=5.2 bird=8.1 cat=21.6 dog=16.2 horse=15.8 sheep=16.9 cow=20.2 elephant=25.6 bear=24.1 zebra=29.9 giraffe=25.4 backpack=1.2 umbrella=9.5 handbag=0.5 tie=4.8 suitcase=5.1 frisbee=13.7 skis=2.4 snowboard=1.7 sports ball=14.6 kite=14.9 baseball bat=1.9 baseball glove=5.5 skateboard=10.1 surfboard=7.8 tennis racket=10.6 bottle=7.2 wine glass=6.6 cup=8.5 fork=0.4 knife=0.6 spoon=0.8 bowl=11.3 banana=3.4 apple=1.9 sandwich=5.4 orange=9.1 broccoli=4.6 carrot=4.3 hot dog=6.0 pizza=15.6 donut=12.7 cake=7.7 chair=5.8 couch=15.5 potted plant=3.7 bed=10.3 dining table=6.3 toilet=25.0 tv=22.5 laptop=20.9 mouse=13.7 remote=1.5 keyboard=11.6 cell phone=7.7 microwave=16.9 oven=6.6 toaster=0.0 sink=9.0 refrigerator=14.7 book=1.9 clock=21.4 vase=10.2 scissors=4.2 teddy bear=13.9 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =11.1 [Epoch 13][Batch 99], LR: 1.00E-03, Speed: 110.208 samples/sec, ObjLoss=33.979, BoxCenterLoss=14.950, BoxScaleLoss=6.121, ClassLoss=17.202 [Epoch 13][Batch 199], LR: 1.00E-03, Speed: 91.636 samples/sec, ObjLoss=33.951, BoxCenterLoss=14.949, BoxScaleLoss=6.119, ClassLoss=17.183 [Epoch 13][Batch 299], LR: 1.00E-03, Speed: 10.002 samples/sec, ObjLoss=33.924, BoxCenterLoss=14.949, BoxScaleLoss=6.117, ClassLoss=17.164 [Epoch 13][Batch 399], LR: 1.00E-03, Speed: 9.423 samples/sec, ObjLoss=33.891, BoxCenterLoss=14.946, BoxScaleLoss=6.115, ClassLoss=17.144 [Epoch 13][Batch 499], LR: 1.00E-03, Speed: 10.896 samples/sec, ObjLoss=33.862, BoxCenterLoss=14.945, BoxScaleLoss=6.114, ClassLoss=17.126 [Epoch 13][Batch 599], LR: 1.00E-03, Speed: 11.305 samples/sec, ObjLoss=33.830, BoxCenterLoss=14.943, BoxScaleLoss=6.112, ClassLoss=17.106 [Epoch 13][Batch 699], LR: 1.00E-03, Speed: 8.872 samples/sec, ObjLoss=33.806, BoxCenterLoss=14.942, BoxScaleLoss=6.110, ClassLoss=17.087 [Epoch 13][Batch 799], LR: 1.00E-03, Speed: 12.040 samples/sec, ObjLoss=33.774, BoxCenterLoss=14.941, BoxScaleLoss=6.109, ClassLoss=17.068 [Epoch 13][Batch 899], LR: 1.00E-03, Speed: 11.337 samples/sec, ObjLoss=33.754, BoxCenterLoss=14.945, BoxScaleLoss=6.109, ClassLoss=17.053 [Epoch 13][Batch 999], LR: 1.00E-03, Speed: 98.664 samples/sec, ObjLoss=33.730, BoxCenterLoss=14.948, BoxScaleLoss=6.110, ClassLoss=17.038 [Epoch 13][Batch 1099], LR: 1.00E-03, Speed: 11.632 samples/sec, ObjLoss=33.701, BoxCenterLoss=14.947, BoxScaleLoss=6.107, ClassLoss=17.019 [Epoch 13][Batch 1199], LR: 1.00E-03, Speed: 105.920 samples/sec, ObjLoss=33.677, BoxCenterLoss=14.948, BoxScaleLoss=6.106, ClassLoss=17.001 [Epoch 13][Batch 1299], LR: 1.00E-03, Speed: 9.998 samples/sec, ObjLoss=33.652, BoxCenterLoss=14.949, BoxScaleLoss=6.105, ClassLoss=16.985 [Epoch 13][Batch 1399], LR: 1.00E-03, Speed: 8.951 samples/sec, ObjLoss=33.629, BoxCenterLoss=14.949, BoxScaleLoss=6.103, ClassLoss=16.966 [Epoch 13][Batch 1499], LR: 1.00E-03, Speed: 10.535 samples/sec, ObjLoss=33.601, BoxCenterLoss=14.946, BoxScaleLoss=6.100, ClassLoss=16.944 [Epoch 13][Batch 1599], LR: 1.00E-03, Speed: 102.198 samples/sec, ObjLoss=33.572, BoxCenterLoss=14.944, BoxScaleLoss=6.097, ClassLoss=16.925 [Epoch 13][Batch 1699], LR: 1.00E-03, Speed: 10.928 samples/sec, ObjLoss=33.547, BoxCenterLoss=14.943, BoxScaleLoss=6.095, ClassLoss=16.907 [Epoch 13][Batch 1799], LR: 1.00E-03, Speed: 13.497 samples/sec, ObjLoss=33.532, BoxCenterLoss=14.947, BoxScaleLoss=6.095, ClassLoss=16.893 [Epoch 13] Training cost: 2149.652, ObjLoss=33.527, BoxCenterLoss=14.948, BoxScaleLoss=6.095, ClassLoss=16.889 [Epoch 13] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.114 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.067 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.043 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.123 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.124 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.179 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.072 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.240 person=22.6 bicycle=6.4 car=16.9 motorcycle=14.8 airplane=21.1 bus=23.8 train=23.3 truck=10.5 boat=5.1 traffic light=6.9 fire hydrant=25.6 stop sign=24.9 parking meter=6.2 bench=4.4 bird=9.0 cat=17.6 dog=20.3 horse=20.1 sheep=17.8 cow=20.3 elephant=24.1 bear=28.2 zebra=28.8 giraffe=28.8 backpack=1.8 umbrella=9.5 handbag=1.0 tie=7.1 suitcase=4.6 frisbee=16.9 skis=3.5 snowboard=1.7 sports ball=14.7 kite=12.4 baseball bat=2.8 baseball glove=10.1 skateboard=14.2 surfboard=7.3 tennis racket=15.0 bottle=8.2 wine glass=7.8 cup=11.1 fork=1.4 knife=1.0 spoon=0.5 bowl=11.3 banana=4.1 apple=1.0 sandwich=4.5 orange=7.2 broccoli=5.1 carrot=3.2 hot dog=3.8 pizza=21.7 donut=11.5 cake=9.1 chair=6.0 couch=13.7 potted plant=5.6 bed=17.8 dining table=6.1 toilet=19.5 tv=22.5 laptop=21.0 mouse=20.9 remote=2.1 keyboard=16.7 cell phone=7.5 microwave=12.0 oven=3.8 toaster=0.0 sink=8.5 refrigerator=14.6 book=2.6 clock=21.7 vase=9.3 scissors=3.1 teddy bear=13.6 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =11.4 [Epoch 14][Batch 99], LR: 1.00E-03, Speed: 9.761 samples/sec, ObjLoss=33.506, BoxCenterLoss=14.949, BoxScaleLoss=6.093, ClassLoss=16.874 [Epoch 14][Batch 199], LR: 1.00E-03, Speed: 123.644 samples/sec, ObjLoss=33.472, BoxCenterLoss=14.944, BoxScaleLoss=6.090, ClassLoss=16.852 [Epoch 14][Batch 299], LR: 1.00E-03, Speed: 8.351 samples/sec, ObjLoss=33.449, BoxCenterLoss=14.943, BoxScaleLoss=6.087, ClassLoss=16.833 [Epoch 14][Batch 399], LR: 1.00E-03, Speed: 9.386 samples/sec, ObjLoss=33.427, BoxCenterLoss=14.944, BoxScaleLoss=6.085, ClassLoss=16.818 [Epoch 14][Batch 499], LR: 1.00E-03, Speed: 10.070 samples/sec, ObjLoss=33.398, BoxCenterLoss=14.941, BoxScaleLoss=6.083, ClassLoss=16.799 [Epoch 14][Batch 599], LR: 1.00E-03, Speed: 9.783 samples/sec, ObjLoss=33.371, BoxCenterLoss=14.938, BoxScaleLoss=6.081, ClassLoss=16.779 [Epoch 14][Batch 699], LR: 1.00E-03, Speed: 10.321 samples/sec, ObjLoss=33.354, BoxCenterLoss=14.940, BoxScaleLoss=6.079, ClassLoss=16.764 [Epoch 14][Batch 799], LR: 1.00E-03, Speed: 8.684 samples/sec, ObjLoss=33.334, BoxCenterLoss=14.944, BoxScaleLoss=6.079, ClassLoss=16.750 [Epoch 14][Batch 899], LR: 1.00E-03, Speed: 9.054 samples/sec, ObjLoss=33.313, BoxCenterLoss=14.946, BoxScaleLoss=6.077, ClassLoss=16.733 [Epoch 14][Batch 999], LR: 1.00E-03, Speed: 7.776 samples/sec, ObjLoss=33.282, BoxCenterLoss=14.941, BoxScaleLoss=6.074, ClassLoss=16.713 [Epoch 14][Batch 1099], LR: 1.00E-03, Speed: 7.802 samples/sec, ObjLoss=33.256, BoxCenterLoss=14.940, BoxScaleLoss=6.072, ClassLoss=16.696 [Epoch 14][Batch 1199], LR: 1.00E-03, Speed: 10.158 samples/sec, ObjLoss=33.231, BoxCenterLoss=14.939, BoxScaleLoss=6.071, ClassLoss=16.681 [Epoch 14][Batch 1299], LR: 1.00E-03, Speed: 9.018 samples/sec, ObjLoss=33.205, BoxCenterLoss=14.939, BoxScaleLoss=6.071, ClassLoss=16.665 [Epoch 14][Batch 1399], LR: 1.00E-03, Speed: 11.501 samples/sec, ObjLoss=33.182, BoxCenterLoss=14.939, BoxScaleLoss=6.068, ClassLoss=16.648 [Epoch 14][Batch 1499], LR: 1.00E-03, Speed: 10.281 samples/sec, ObjLoss=33.154, BoxCenterLoss=14.937, BoxScaleLoss=6.067, ClassLoss=16.632 [Epoch 14][Batch 1599], LR: 1.00E-03, Speed: 8.705 samples/sec, ObjLoss=33.130, BoxCenterLoss=14.935, BoxScaleLoss=6.064, ClassLoss=16.615 [Epoch 14][Batch 1699], LR: 1.00E-03, Speed: 101.286 samples/sec, ObjLoss=33.111, BoxCenterLoss=14.935, BoxScaleLoss=6.062, ClassLoss=16.598 [Epoch 14][Batch 1799], LR: 1.00E-03, Speed: 10.669 samples/sec, ObjLoss=33.093, BoxCenterLoss=14.937, BoxScaleLoss=6.061, ClassLoss=16.582 [Epoch 14] Training cost: 2121.444, ObjLoss=33.085, BoxCenterLoss=14.936, BoxScaleLoss=6.060, ClassLoss=16.578 [Epoch 14] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.296 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.037 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.126 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.195 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.198 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.053 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.192 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.309 person=28.0 bicycle=8.3 car=17.3 motorcycle=17.1 airplane=22.8 bus=35.1 train=31.0 truck=14.3 boat=4.8 traffic light=5.0 fire hydrant=27.5 stop sign=26.7 parking meter=7.8 bench=5.3 bird=11.4 cat=29.2 dog=20.4 horse=18.8 sheep=21.0 cow=16.9 elephant=32.0 bear=34.8 zebra=31.2 giraffe=32.4 backpack=1.8 umbrella=11.3 handbag=0.8 tie=7.7 suitcase=6.3 frisbee=18.6 skis=3.4 snowboard=3.6 sports ball=15.6 kite=13.6 baseball bat=2.6 baseball glove=6.1 skateboard=14.2 surfboard=9.1 tennis racket=13.0 bottle=11.4 wine glass=8.7 cup=14.2 fork=1.6 knife=1.2 spoon=1.2 bowl=12.2 banana=4.9 apple=2.4 sandwich=7.2 orange=7.6 broccoli=4.8 carrot=3.7 hot dog=6.1 pizza=22.7 donut=13.9 cake=10.0 chair=6.5 couch=14.8 potted plant=5.8 bed=21.9 dining table=10.4 toilet=25.2 tv=25.8 laptop=26.1 mouse=19.3 remote=3.1 keyboard=18.3 cell phone=8.2 microwave=13.1 oven=8.1 toaster=0.0 sink=11.7 refrigerator=18.2 book=3.1 clock=22.6 vase=10.1 scissors=5.6 teddy bear=20.8 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.2 [Epoch 15][Batch 99], LR: 1.00E-03, Speed: 10.212 samples/sec, ObjLoss=33.061, BoxCenterLoss=14.934, BoxScaleLoss=6.057, ClassLoss=16.560 [Epoch 15][Batch 199], LR: 1.00E-03, Speed: 9.460 samples/sec, ObjLoss=33.034, BoxCenterLoss=14.931, BoxScaleLoss=6.055, ClassLoss=16.544 [Epoch 15][Batch 299], LR: 1.00E-03, Speed: 8.475 samples/sec, ObjLoss=33.005, BoxCenterLoss=14.926, BoxScaleLoss=6.053, ClassLoss=16.528 [Epoch 15][Batch 399], LR: 1.00E-03, Speed: 9.738 samples/sec, ObjLoss=32.984, BoxCenterLoss=14.926, BoxScaleLoss=6.050, ClassLoss=16.511 [Epoch 15][Batch 499], LR: 1.00E-03, Speed: 103.980 samples/sec, ObjLoss=32.961, BoxCenterLoss=14.925, BoxScaleLoss=6.048, ClassLoss=16.494 [Epoch 15][Batch 599], LR: 1.00E-03, Speed: 10.582 samples/sec, ObjLoss=32.940, BoxCenterLoss=14.925, BoxScaleLoss=6.046, ClassLoss=16.479 [Epoch 15][Batch 699], LR: 1.00E-03, Speed: 12.821 samples/sec, ObjLoss=32.917, BoxCenterLoss=14.925, BoxScaleLoss=6.044, ClassLoss=16.463 [Epoch 15][Batch 799], LR: 1.00E-03, Speed: 10.430 samples/sec, ObjLoss=32.894, BoxCenterLoss=14.922, BoxScaleLoss=6.042, ClassLoss=16.447 [Epoch 15][Batch 899], LR: 1.00E-03, Speed: 10.099 samples/sec, ObjLoss=32.872, BoxCenterLoss=14.921, BoxScaleLoss=6.040, ClassLoss=16.431 [Epoch 15][Batch 999], LR: 1.00E-03, Speed: 10.778 samples/sec, ObjLoss=32.846, BoxCenterLoss=14.917, BoxScaleLoss=6.037, ClassLoss=16.413 [Epoch 15][Batch 1099], LR: 1.00E-03, Speed: 12.638 samples/sec, ObjLoss=32.830, BoxCenterLoss=14.919, BoxScaleLoss=6.035, ClassLoss=16.398 [Epoch 15][Batch 1199], LR: 1.00E-03, Speed: 9.030 samples/sec, ObjLoss=32.814, BoxCenterLoss=14.922, BoxScaleLoss=6.036, ClassLoss=16.387 [Epoch 15][Batch 1299], LR: 1.00E-03, Speed: 9.769 samples/sec, ObjLoss=32.798, BoxCenterLoss=14.925, BoxScaleLoss=6.034, ClassLoss=16.372 [Epoch 15][Batch 1399], LR: 1.00E-03, Speed: 9.693 samples/sec, ObjLoss=32.778, BoxCenterLoss=14.924, BoxScaleLoss=6.032, ClassLoss=16.357 [Epoch 15][Batch 1499], LR: 1.00E-03, Speed: 111.170 samples/sec, ObjLoss=32.757, BoxCenterLoss=14.923, BoxScaleLoss=6.031, ClassLoss=16.342 [Epoch 15][Batch 1599], LR: 1.00E-03, Speed: 11.343 samples/sec, ObjLoss=32.735, BoxCenterLoss=14.923, BoxScaleLoss=6.031, ClassLoss=16.329 [Epoch 15][Batch 1699], LR: 1.00E-03, Speed: 13.483 samples/sec, ObjLoss=32.713, BoxCenterLoss=14.921, BoxScaleLoss=6.029, ClassLoss=16.312 [Epoch 15][Batch 1799], LR: 1.00E-03, Speed: 13.065 samples/sec, ObjLoss=32.691, BoxCenterLoss=14.919, BoxScaleLoss=6.026, ClassLoss=16.297 [Epoch 15] Training cost: 2138.029, ObjLoss=32.685, BoxCenterLoss=14.919, BoxScaleLoss=6.025, ClassLoss=16.292 [Epoch 15] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.302 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.041 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.136 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.191 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.136 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.193 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.197 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.063 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.195 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.297 person=28.3 bicycle=7.9 car=17.7 motorcycle=14.0 airplane=21.9 bus=31.8 train=33.2 truck=12.2 boat=4.5 traffic light=7.6 fire hydrant=31.7 stop sign=24.9 parking meter=9.5 bench=5.4 bird=10.0 cat=22.9 dog=22.0 horse=18.2 sheep=23.2 cow=20.8 elephant=27.5 bear=28.7 zebra=32.4 giraffe=36.5 backpack=2.0 umbrella=11.3 handbag=0.8 tie=8.0 suitcase=5.6 frisbee=16.9 skis=3.6 snowboard=3.5 sports ball=16.4 kite=16.2 baseball bat=3.8 baseball glove=6.8 skateboard=12.7 surfboard=5.4 tennis racket=13.5 bottle=11.8 wine glass=9.9 cup=15.5 fork=2.7 knife=0.8 spoon=0.9 bowl=13.2 banana=4.7 apple=2.4 sandwich=11.5 orange=6.0 broccoli=5.7 carrot=3.1 hot dog=6.5 pizza=24.1 donut=16.2 cake=10.6 chair=6.4 couch=18.0 potted plant=4.7 bed=17.6 dining table=10.1 toilet=23.1 tv=24.3 laptop=22.3 mouse=14.8 remote=1.5 keyboard=15.1 cell phone=9.4 microwave=14.4 oven=7.8 toaster=0.0 sink=8.5 refrigerator=15.7 book=1.8 clock=21.1 vase=11.6 scissors=2.3 teddy bear=18.7 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.9 [Epoch 16][Batch 99], LR: 1.00E-03, Speed: 111.418 samples/sec, ObjLoss=32.667, BoxCenterLoss=14.918, BoxScaleLoss=6.022, ClassLoss=16.276 [Epoch 16][Batch 199], LR: 1.00E-03, Speed: 9.202 samples/sec, ObjLoss=32.647, BoxCenterLoss=14.919, BoxScaleLoss=6.021, ClassLoss=16.264 [Epoch 16][Batch 299], LR: 1.00E-03, Speed: 7.973 samples/sec, ObjLoss=32.633, BoxCenterLoss=14.922, BoxScaleLoss=6.021, ClassLoss=16.251 [Epoch 16][Batch 399], LR: 1.00E-03, Speed: 10.672 samples/sec, ObjLoss=32.618, BoxCenterLoss=14.923, BoxScaleLoss=6.020, ClassLoss=16.238 [Epoch 16][Batch 499], LR: 1.00E-03, Speed: 109.434 samples/sec, ObjLoss=32.598, BoxCenterLoss=14.924, BoxScaleLoss=6.019, ClassLoss=16.225 [Epoch 16][Batch 599], LR: 1.00E-03, Speed: 111.794 samples/sec, ObjLoss=32.577, BoxCenterLoss=14.923, BoxScaleLoss=6.018, ClassLoss=16.211 [Epoch 16][Batch 699], LR: 1.00E-03, Speed: 8.716 samples/sec, ObjLoss=32.557, BoxCenterLoss=14.923, BoxScaleLoss=6.016, ClassLoss=16.195 [Epoch 16][Batch 799], LR: 1.00E-03, Speed: 9.982 samples/sec, ObjLoss=32.536, BoxCenterLoss=14.921, BoxScaleLoss=6.013, ClassLoss=16.181 [Epoch 16][Batch 899], LR: 1.00E-03, Speed: 11.397 samples/sec, ObjLoss=32.516, BoxCenterLoss=14.921, BoxScaleLoss=6.011, ClassLoss=16.166 [Epoch 16][Batch 999], LR: 1.00E-03, Speed: 120.421 samples/sec, ObjLoss=32.496, BoxCenterLoss=14.922, BoxScaleLoss=6.011, ClassLoss=16.155 [Epoch 16][Batch 1099], LR: 1.00E-03, Speed: 10.878 samples/sec, ObjLoss=32.475, BoxCenterLoss=14.920, BoxScaleLoss=6.008, ClassLoss=16.140 [Epoch 16][Batch 1199], LR: 1.00E-03, Speed: 8.763 samples/sec, ObjLoss=32.449, BoxCenterLoss=14.916, BoxScaleLoss=6.005, ClassLoss=16.125 [Epoch 16][Batch 1299], LR: 1.00E-03, Speed: 11.236 samples/sec, ObjLoss=32.434, BoxCenterLoss=14.917, BoxScaleLoss=6.006, ClassLoss=16.114 [Epoch 16][Batch 1399], LR: 1.00E-03, Speed: 7.986 samples/sec, ObjLoss=32.415, BoxCenterLoss=14.917, BoxScaleLoss=6.004, ClassLoss=16.100 [Epoch 16][Batch 1499], LR: 1.00E-03, Speed: 10.437 samples/sec, ObjLoss=32.398, BoxCenterLoss=14.918, BoxScaleLoss=6.003, ClassLoss=16.086 [Epoch 16][Batch 1599], LR: 1.00E-03, Speed: 10.612 samples/sec, ObjLoss=32.378, BoxCenterLoss=14.917, BoxScaleLoss=6.002, ClassLoss=16.074 [Epoch 16][Batch 1699], LR: 1.00E-03, Speed: 77.791 samples/sec, ObjLoss=32.356, BoxCenterLoss=14.914, BoxScaleLoss=5.998, ClassLoss=16.059 [Epoch 16][Batch 1799], LR: 1.00E-03, Speed: 10.658 samples/sec, ObjLoss=32.340, BoxCenterLoss=14.915, BoxScaleLoss=5.997, ClassLoss=16.045 [Epoch 16] Training cost: 2136.160, ObjLoss=32.335, BoxCenterLoss=14.915, BoxScaleLoss=5.997, ClassLoss=16.041 [Epoch 16] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.303 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.045 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.189 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.203 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.071 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.294 person=25.9 bicycle=9.7 car=16.7 motorcycle=16.1 airplane=24.7 bus=31.3 train=26.0 truck=13.3 boat=5.8 traffic light=8.0 fire hydrant=21.4 stop sign=31.1 parking meter=8.5 bench=5.2 bird=12.3 cat=25.8 dog=21.7 horse=21.4 sheep=22.1 cow=20.8 elephant=30.6 bear=30.0 zebra=29.9 giraffe=33.4 backpack=1.6 umbrella=12.0 handbag=0.9 tie=5.0 suitcase=6.8 frisbee=16.7 skis=3.3 snowboard=5.9 sports ball=8.8 kite=16.2 baseball bat=5.8 baseball glove=9.5 skateboard=15.7 surfboard=7.7 tennis racket=13.0 bottle=11.5 wine glass=9.5 cup=13.2 fork=1.8 knife=1.2 spoon=0.4 bowl=13.3 banana=5.4 apple=2.6 sandwich=8.0 orange=9.2 broccoli=4.5 carrot=4.8 hot dog=6.9 pizza=18.9 donut=15.2 cake=9.1 chair=6.8 couch=19.9 potted plant=4.5 bed=24.5 dining table=11.7 toilet=24.6 tv=21.1 laptop=19.3 mouse=19.4 remote=2.4 keyboard=13.6 cell phone=7.6 microwave=13.5 oven=8.4 toaster=0.0 sink=11.2 refrigerator=15.8 book=2.1 clock=21.1 vase=9.8 scissors=0.7 teddy bear=21.9 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.9 [Epoch 17][Batch 99], LR: 1.00E-03, Speed: 9.836 samples/sec, ObjLoss=32.318, BoxCenterLoss=14.915, BoxScaleLoss=5.995, ClassLoss=16.028 [Epoch 17][Batch 199], LR: 1.00E-03, Speed: 8.984 samples/sec, ObjLoss=32.303, BoxCenterLoss=14.916, BoxScaleLoss=5.994, ClassLoss=16.017 [Epoch 17][Batch 299], LR: 1.00E-03, Speed: 10.164 samples/sec, ObjLoss=32.285, BoxCenterLoss=14.915, BoxScaleLoss=5.992, ClassLoss=16.005 [Epoch 17][Batch 399], LR: 1.00E-03, Speed: 8.346 samples/sec, ObjLoss=32.271, BoxCenterLoss=14.916, BoxScaleLoss=5.992, ClassLoss=15.994 [Epoch 17][Batch 499], LR: 1.00E-03, Speed: 116.303 samples/sec, ObjLoss=32.251, BoxCenterLoss=14.914, BoxScaleLoss=5.990, ClassLoss=15.980 [Epoch 17][Batch 599], LR: 1.00E-03, Speed: 9.110 samples/sec, ObjLoss=32.231, BoxCenterLoss=14.913, BoxScaleLoss=5.988, ClassLoss=15.968 [Epoch 17][Batch 699], LR: 1.00E-03, Speed: 8.634 samples/sec, ObjLoss=32.209, BoxCenterLoss=14.911, BoxScaleLoss=5.987, ClassLoss=15.955 [Epoch 17][Batch 799], LR: 1.00E-03, Speed: 10.480 samples/sec, ObjLoss=32.187, BoxCenterLoss=14.909, BoxScaleLoss=5.985, ClassLoss=15.942 [Epoch 17][Batch 899], LR: 1.00E-03, Speed: 9.679 samples/sec, ObjLoss=32.167, BoxCenterLoss=14.907, BoxScaleLoss=5.984, ClassLoss=15.929 [Epoch 17][Batch 999], LR: 1.00E-03, Speed: 9.197 samples/sec, ObjLoss=32.149, BoxCenterLoss=14.906, BoxScaleLoss=5.982, ClassLoss=15.916 [Epoch 17][Batch 1099], LR: 1.00E-03, Speed: 10.694 samples/sec, ObjLoss=32.128, BoxCenterLoss=14.905, BoxScaleLoss=5.981, ClassLoss=15.904 [Epoch 17][Batch 1199], LR: 1.00E-03, Speed: 9.511 samples/sec, ObjLoss=32.111, BoxCenterLoss=14.903, BoxScaleLoss=5.978, ClassLoss=15.890 [Epoch 17][Batch 1299], LR: 1.00E-03, Speed: 9.320 samples/sec, ObjLoss=32.090, BoxCenterLoss=14.901, BoxScaleLoss=5.976, ClassLoss=15.875 [Epoch 17][Batch 1399], LR: 1.00E-03, Speed: 92.848 samples/sec, ObjLoss=32.072, BoxCenterLoss=14.900, BoxScaleLoss=5.974, ClassLoss=15.862 [Epoch 17][Batch 1499], LR: 1.00E-03, Speed: 85.273 samples/sec, ObjLoss=32.055, BoxCenterLoss=14.900, BoxScaleLoss=5.973, ClassLoss=15.849 [Epoch 17][Batch 1599], LR: 1.00E-03, Speed: 112.332 samples/sec, ObjLoss=32.035, BoxCenterLoss=14.898, BoxScaleLoss=5.972, ClassLoss=15.837 [Epoch 17][Batch 1699], LR: 1.00E-03, Speed: 8.201 samples/sec, ObjLoss=32.014, BoxCenterLoss=14.895, BoxScaleLoss=5.969, ClassLoss=15.823 [Epoch 17][Batch 1799], LR: 1.00E-03, Speed: 14.123 samples/sec, ObjLoss=31.999, BoxCenterLoss=14.895, BoxScaleLoss=5.969, ClassLoss=15.812 [Epoch 17] Training cost: 2037.107, ObjLoss=31.994, BoxCenterLoss=14.895, BoxScaleLoss=5.968, ClassLoss=15.808 [Epoch 17] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.142 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.317 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.047 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.070 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321 person=28.8 bicycle=10.6 car=19.6 motorcycle=18.2 airplane=25.6 bus=37.7 train=35.5 truck=16.4 boat=7.6 traffic light=8.3 fire hydrant=33.5 stop sign=33.0 parking meter=13.9 bench=6.6 bird=15.0 cat=21.4 dog=22.2 horse=19.2 sheep=20.9 cow=24.5 elephant=31.0 bear=32.6 zebra=36.3 giraffe=30.8 backpack=1.8 umbrella=14.4 handbag=1.5 tie=6.8 suitcase=7.1 frisbee=16.8 skis=4.5 snowboard=5.7 sports ball=15.1 kite=18.2 baseball bat=4.3 baseball glove=8.0 skateboard=15.9 surfboard=10.7 tennis racket=16.2 bottle=12.0 wine glass=10.8 cup=14.1 fork=3.0 knife=0.7 spoon=0.8 bowl=16.4 banana=6.8 apple=3.2 sandwich=9.4 orange=8.4 broccoli=6.3 carrot=2.8 hot dog=8.3 pizza=22.1 donut=16.7 cake=10.4 chair=7.3 couch=15.4 potted plant=5.1 bed=15.5 dining table=8.8 toilet=30.4 tv=24.1 laptop=21.9 mouse=17.0 remote=3.4 keyboard=19.3 cell phone=8.6 microwave=17.8 oven=5.5 toaster=0.0 sink=11.6 refrigerator=21.5 book=2.8 clock=23.3 vase=12.9 scissors=2.9 teddy bear=15.3 hair drier=0.0 toothbrush=0.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.2 [Epoch 18][Batch 99], LR: 1.00E-03, Speed: 101.256 samples/sec, ObjLoss=31.982, BoxCenterLoss=14.897, BoxScaleLoss=5.969, ClassLoss=15.798 [Epoch 18][Batch 199], LR: 1.00E-03, Speed: 8.360 samples/sec, ObjLoss=31.967, BoxCenterLoss=14.898, BoxScaleLoss=5.968, ClassLoss=15.789 [Epoch 18][Batch 299], LR: 1.00E-03, Speed: 109.523 samples/sec, ObjLoss=31.950, BoxCenterLoss=14.896, BoxScaleLoss=5.966, ClassLoss=15.777 [Epoch 18][Batch 399], LR: 1.00E-03, Speed: 8.585 samples/sec, ObjLoss=31.928, BoxCenterLoss=14.893, BoxScaleLoss=5.964, ClassLoss=15.764 [Epoch 18][Batch 499], LR: 1.00E-03, Speed: 9.435 samples/sec, ObjLoss=31.914, BoxCenterLoss=14.893, BoxScaleLoss=5.962, ClassLoss=15.751 [Epoch 18][Batch 599], LR: 1.00E-03, Speed: 114.451 samples/sec, ObjLoss=31.897, BoxCenterLoss=14.893, BoxScaleLoss=5.961, ClassLoss=15.741 [Epoch 18][Batch 699], LR: 1.00E-03, Speed: 7.139 samples/sec, ObjLoss=31.882, BoxCenterLoss=14.894, BoxScaleLoss=5.961, ClassLoss=15.730 [Epoch 18][Batch 799], LR: 1.00E-03, Speed: 8.914 samples/sec, ObjLoss=31.867, BoxCenterLoss=14.893, BoxScaleLoss=5.959, ClassLoss=15.716 [Epoch 18][Batch 899], LR: 1.00E-03, Speed: 8.536 samples/sec, ObjLoss=31.849, BoxCenterLoss=14.892, BoxScaleLoss=5.958, ClassLoss=15.704 [Epoch 18][Batch 999], LR: 1.00E-03, Speed: 8.039 samples/sec, ObjLoss=31.831, BoxCenterLoss=14.892, BoxScaleLoss=5.957, ClassLoss=15.694 [Epoch 18][Batch 1099], LR: 1.00E-03, Speed: 11.093 samples/sec, ObjLoss=31.814, BoxCenterLoss=14.891, BoxScaleLoss=5.956, ClassLoss=15.683 [Epoch 18][Batch 1199], LR: 1.00E-03, Speed: 8.247 samples/sec, ObjLoss=31.794, BoxCenterLoss=14.888, BoxScaleLoss=5.954, ClassLoss=15.669 [Epoch 18][Batch 1299], LR: 1.00E-03, Speed: 8.670 samples/sec, ObjLoss=31.777, BoxCenterLoss=14.888, BoxScaleLoss=5.952, ClassLoss=15.655 [Epoch 18][Batch 1399], LR: 1.00E-03, Speed: 9.052 samples/sec, ObjLoss=31.762, BoxCenterLoss=14.886, BoxScaleLoss=5.949, ClassLoss=15.642 [Epoch 18][Batch 1499], LR: 1.00E-03, Speed: 9.165 samples/sec, ObjLoss=31.746, BoxCenterLoss=14.885, BoxScaleLoss=5.948, ClassLoss=15.631 [Epoch 18][Batch 1599], LR: 1.00E-03, Speed: 9.467 samples/sec, ObjLoss=31.727, BoxCenterLoss=14.883, BoxScaleLoss=5.946, ClassLoss=15.619 [Epoch 18][Batch 1699], LR: 1.00E-03, Speed: 113.634 samples/sec, ObjLoss=31.713, BoxCenterLoss=14.885, BoxScaleLoss=5.946, ClassLoss=15.610 [Epoch 18][Batch 1799], LR: 1.00E-03, Speed: 10.510 samples/sec, ObjLoss=31.700, BoxCenterLoss=14.886, BoxScaleLoss=5.945, ClassLoss=15.599 [Epoch 18] Training cost: 2175.155, ObjLoss=31.697, BoxCenterLoss=14.887, BoxScaleLoss=5.945, ClassLoss=15.595 [Epoch 18] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.305 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.045 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.133 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.198 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.072 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.195 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.304 person=24.7 bicycle=10.2 car=18.0 motorcycle=17.3 airplane=22.8 bus=32.7 train=31.1 truck=13.1 boat=6.0 traffic light=7.1 fire hydrant=32.5 stop sign=29.7 parking meter=9.4 bench=5.9 bird=10.6 cat=30.3 dog=22.3 horse=17.4 sheep=18.5 cow=21.3 elephant=31.1 bear=35.4 zebra=33.2 giraffe=31.5 backpack=1.5 umbrella=12.7 handbag=0.9 tie=8.7 suitcase=5.6 frisbee=16.9 skis=2.9 snowboard=3.8 sports ball=12.1 kite=17.4 baseball bat=3.2 baseball glove=4.0 skateboard=13.8 surfboard=8.4 tennis racket=12.7 bottle=12.8 wine glass=9.9 cup=13.4 fork=1.7 knife=1.1 spoon=0.8 bowl=12.0 banana=5.4 apple=1.7 sandwich=7.0 orange=9.2 broccoli=5.0 carrot=1.9 hot dog=5.9 pizza=21.6 donut=14.8 cake=10.3 chair=7.1 couch=17.0 potted plant=6.1 bed=21.8 dining table=11.1 toilet=22.0 tv=19.3 laptop=20.0 mouse=13.3 remote=2.0 keyboard=14.6 cell phone=6.9 microwave=14.3 oven=8.9 toaster=0.0 sink=10.5 refrigerator=17.3 book=2.4 clock=23.2 vase=13.0 scissors=4.2 teddy bear=14.7 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.0 [Epoch 19][Batch 99], LR: 1.00E-03, Speed: 8.390 samples/sec, ObjLoss=31.680, BoxCenterLoss=14.886, BoxScaleLoss=5.944, ClassLoss=15.585 [Epoch 19][Batch 199], LR: 1.00E-03, Speed: 7.614 samples/sec, ObjLoss=31.666, BoxCenterLoss=14.887, BoxScaleLoss=5.943, ClassLoss=15.575 [Epoch 19][Batch 299], LR: 1.00E-03, Speed: 9.708 samples/sec, ObjLoss=31.651, BoxCenterLoss=14.886, BoxScaleLoss=5.941, ClassLoss=15.562 [Epoch 19][Batch 399], LR: 1.00E-03, Speed: 9.837 samples/sec, ObjLoss=31.635, BoxCenterLoss=14.886, BoxScaleLoss=5.939, ClassLoss=15.551 [Epoch 19][Batch 499], LR: 1.00E-03, Speed: 89.942 samples/sec, ObjLoss=31.617, BoxCenterLoss=14.883, BoxScaleLoss=5.937, ClassLoss=15.540 [Epoch 19][Batch 599], LR: 1.00E-03, Speed: 7.547 samples/sec, ObjLoss=31.601, BoxCenterLoss=14.882, BoxScaleLoss=5.935, ClassLoss=15.528 [Epoch 19][Batch 699], LR: 1.00E-03, Speed: 10.474 samples/sec, ObjLoss=31.584, BoxCenterLoss=14.880, BoxScaleLoss=5.934, ClassLoss=15.516 [Epoch 19][Batch 799], LR: 1.00E-03, Speed: 9.792 samples/sec, ObjLoss=31.564, BoxCenterLoss=14.878, BoxScaleLoss=5.931, ClassLoss=15.505 [Epoch 19][Batch 899], LR: 1.00E-03, Speed: 8.468 samples/sec, ObjLoss=31.549, BoxCenterLoss=14.878, BoxScaleLoss=5.931, ClassLoss=15.495 [Epoch 19][Batch 999], LR: 1.00E-03, Speed: 124.580 samples/sec, ObjLoss=31.533, BoxCenterLoss=14.876, BoxScaleLoss=5.928, ClassLoss=15.483 [Epoch 19][Batch 1099], LR: 1.00E-03, Speed: 86.575 samples/sec, ObjLoss=31.520, BoxCenterLoss=14.875, BoxScaleLoss=5.927, ClassLoss=15.472 [Epoch 19][Batch 1199], LR: 1.00E-03, Speed: 10.315 samples/sec, ObjLoss=31.504, BoxCenterLoss=14.874, BoxScaleLoss=5.925, ClassLoss=15.461 [Epoch 19][Batch 1299], LR: 1.00E-03, Speed: 11.530 samples/sec, ObjLoss=31.489, BoxCenterLoss=14.873, BoxScaleLoss=5.924, ClassLoss=15.451 [Epoch 19][Batch 1399], LR: 1.00E-03, Speed: 59.040 samples/sec, ObjLoss=31.476, BoxCenterLoss=14.874, BoxScaleLoss=5.923, ClassLoss=15.440 [Epoch 19][Batch 1499], LR: 1.00E-03, Speed: 9.401 samples/sec, ObjLoss=31.458, BoxCenterLoss=14.871, BoxScaleLoss=5.920, ClassLoss=15.428 [Epoch 19][Batch 1599], LR: 1.00E-03, Speed: 10.548 samples/sec, ObjLoss=31.441, BoxCenterLoss=14.869, BoxScaleLoss=5.918, ClassLoss=15.416 [Epoch 19][Batch 1699], LR: 1.00E-03, Speed: 10.552 samples/sec, ObjLoss=31.426, BoxCenterLoss=14.868, BoxScaleLoss=5.916, ClassLoss=15.404 [Epoch 19][Batch 1799], LR: 1.00E-03, Speed: 12.886 samples/sec, ObjLoss=31.409, BoxCenterLoss=14.866, BoxScaleLoss=5.914, ClassLoss=15.392 [Epoch 19] Training cost: 2137.721, ObjLoss=31.403, BoxCenterLoss=14.865, BoxScaleLoss=5.914, ClassLoss=15.389 [Epoch 19] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.143 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.311 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.044 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.133 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.209 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.190 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333 person=29.3 bicycle=9.0 car=14.7 motorcycle=20.3 airplane=26.3 bus=37.2 train=32.6 truck=14.0 boat=5.9 traffic light=8.1 fire hydrant=31.8 stop sign=29.6 parking meter=13.6 bench=6.5 bird=12.4 cat=30.5 dog=25.7 horse=22.3 sheep=23.8 cow=22.2 elephant=35.4 bear=34.1 zebra=30.8 giraffe=37.4 backpack=1.7 umbrella=14.0 handbag=1.2 tie=10.7 suitcase=5.6 frisbee=18.1 skis=2.6 snowboard=2.7 sports ball=12.0 kite=15.1 baseball bat=3.7 baseball glove=9.5 skateboard=8.1 surfboard=7.1 tennis racket=12.2 bottle=12.5 wine glass=10.1 cup=16.0 fork=3.3 knife=1.1 spoon=1.7 bowl=15.0 banana=5.9 apple=2.6 sandwich=10.5 orange=6.0 broccoli=3.9 carrot=1.7 hot dog=8.9 pizza=18.3 donut=15.7 cake=7.9 chair=8.9 couch=21.1 potted plant=6.5 bed=21.3 dining table=14.6 toilet=28.0 tv=26.8 laptop=27.4 mouse=20.2 remote=3.2 keyboard=19.3 cell phone=9.4 microwave=20.0 oven=9.8 toaster=0.0 sink=8.5 refrigerator=19.2 book=2.0 clock=22.9 vase=11.4 scissors=1.6 teddy bear=22.0 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.3 [Epoch 20][Batch 99], LR: 1.00E-03, Speed: 10.228 samples/sec, ObjLoss=31.390, BoxCenterLoss=14.866, BoxScaleLoss=5.913, ClassLoss=15.378 [Epoch 20][Batch 199], LR: 1.00E-03, Speed: 88.789 samples/sec, ObjLoss=31.375, BoxCenterLoss=14.865, BoxScaleLoss=5.910, ClassLoss=15.366 [Epoch 20][Batch 299], LR: 1.00E-03, Speed: 108.717 samples/sec, ObjLoss=31.365, BoxCenterLoss=14.866, BoxScaleLoss=5.909, ClassLoss=15.357 [Epoch 20][Batch 399], LR: 1.00E-03, Speed: 10.086 samples/sec, ObjLoss=31.350, BoxCenterLoss=14.866, BoxScaleLoss=5.908, ClassLoss=15.346 [Epoch 20][Batch 499], LR: 1.00E-03, Speed: 12.436 samples/sec, ObjLoss=31.335, BoxCenterLoss=14.865, BoxScaleLoss=5.907, ClassLoss=15.337 [Epoch 20][Batch 599], LR: 1.00E-03, Speed: 9.309 samples/sec, ObjLoss=31.321, BoxCenterLoss=14.864, BoxScaleLoss=5.905, ClassLoss=15.326 [Epoch 20][Batch 699], LR: 1.00E-03, Speed: 10.108 samples/sec, ObjLoss=31.311, BoxCenterLoss=14.865, BoxScaleLoss=5.904, ClassLoss=15.317 [Epoch 20][Batch 799], LR: 1.00E-03, Speed: 9.807 samples/sec, ObjLoss=31.303, BoxCenterLoss=14.867, BoxScaleLoss=5.902, ClassLoss=15.307 [Epoch 20][Batch 899], LR: 1.00E-03, Speed: 8.818 samples/sec, ObjLoss=31.290, BoxCenterLoss=14.867, BoxScaleLoss=5.901, ClassLoss=15.297 [Epoch 20][Batch 999], LR: 1.00E-03, Speed: 9.820 samples/sec, ObjLoss=31.276, BoxCenterLoss=14.866, BoxScaleLoss=5.900, ClassLoss=15.286 [Epoch 20][Batch 1099], LR: 1.00E-03, Speed: 10.245 samples/sec, ObjLoss=31.263, BoxCenterLoss=14.865, BoxScaleLoss=5.898, ClassLoss=15.274 [Epoch 20][Batch 1199], LR: 1.00E-03, Speed: 118.550 samples/sec, ObjLoss=31.247, BoxCenterLoss=14.862, BoxScaleLoss=5.896, ClassLoss=15.263 [Epoch 20][Batch 1299], LR: 1.00E-03, Speed: 10.861 samples/sec, ObjLoss=31.233, BoxCenterLoss=14.863, BoxScaleLoss=5.895, ClassLoss=15.255 [Epoch 20][Batch 1399], LR: 1.00E-03, Speed: 88.648 samples/sec, ObjLoss=31.219, BoxCenterLoss=14.862, BoxScaleLoss=5.894, ClassLoss=15.244 [Epoch 20][Batch 1499], LR: 1.00E-03, Speed: 87.614 samples/sec, ObjLoss=31.205, BoxCenterLoss=14.861, BoxScaleLoss=5.893, ClassLoss=15.233 [Epoch 20][Batch 1599], LR: 1.00E-03, Speed: 9.174 samples/sec, ObjLoss=31.191, BoxCenterLoss=14.861, BoxScaleLoss=5.891, ClassLoss=15.223 [Epoch 20][Batch 1699], LR: 1.00E-03, Speed: 10.092 samples/sec, ObjLoss=31.177, BoxCenterLoss=14.861, BoxScaleLoss=5.890, ClassLoss=15.212 [Epoch 20][Batch 1799], LR: 1.00E-03, Speed: 12.234 samples/sec, ObjLoss=31.162, BoxCenterLoss=14.859, BoxScaleLoss=5.889, ClassLoss=15.203 [Epoch 20] Training cost: 2116.340, ObjLoss=31.157, BoxCenterLoss=14.859, BoxScaleLoss=5.889, ClassLoss=15.200 [Epoch 20] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.139 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.323 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.050 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.131 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.081 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333 person=29.6 bicycle=10.6 car=16.4 motorcycle=18.9 airplane=30.6 bus=29.5 train=31.5 truck=14.7 boat=7.8 traffic light=8.7 fire hydrant=29.0 stop sign=27.7 parking meter=14.7 bench=6.6 bird=13.9 cat=29.2 dog=23.2 horse=18.4 sheep=19.2 cow=22.8 elephant=32.3 bear=28.0 zebra=33.7 giraffe=39.0 backpack=2.1 umbrella=14.0 handbag=0.8 tie=10.4 suitcase=6.9 frisbee=19.8 skis=4.5 snowboard=5.0 sports ball=16.9 kite=18.3 baseball bat=3.4 baseball glove=9.4 skateboard=14.6 surfboard=9.9 tennis racket=13.1 bottle=10.8 wine glass=11.2 cup=12.1 fork=3.5 knife=1.7 spoon=1.1 bowl=11.8 banana=7.5 apple=3.3 sandwich=10.1 orange=9.4 broccoli=7.2 carrot=2.3 hot dog=10.4 pizza=20.9 donut=15.8 cake=9.8 chair=8.9 couch=20.8 potted plant=4.7 bed=26.2 dining table=14.7 toilet=24.4 tv=23.3 laptop=23.1 mouse=15.7 remote=2.8 keyboard=11.9 cell phone=8.5 microwave=5.5 oven=10.2 toaster=0.0 sink=8.3 refrigerator=14.0 book=2.7 clock=16.7 vase=12.8 scissors=1.6 teddy bear=17.4 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.9 [Epoch 21][Batch 99], LR: 1.00E-03, Speed: 10.754 samples/sec, ObjLoss=31.145, BoxCenterLoss=14.858, BoxScaleLoss=5.887, ClassLoss=15.190 [Epoch 21][Batch 199], LR: 1.00E-03, Speed: 8.346 samples/sec, ObjLoss=31.128, BoxCenterLoss=14.854, BoxScaleLoss=5.885, ClassLoss=15.179 [Epoch 21][Batch 299], LR: 1.00E-03, Speed: 85.923 samples/sec, ObjLoss=31.112, BoxCenterLoss=14.852, BoxScaleLoss=5.883, ClassLoss=15.168 [Epoch 21][Batch 399], LR: 1.00E-03, Speed: 7.964 samples/sec, ObjLoss=31.100, BoxCenterLoss=14.853, BoxScaleLoss=5.882, ClassLoss=15.158 [Epoch 21][Batch 499], LR: 1.00E-03, Speed: 123.705 samples/sec, ObjLoss=31.087, BoxCenterLoss=14.852, BoxScaleLoss=5.881, ClassLoss=15.151 [Epoch 21][Batch 599], LR: 1.00E-03, Speed: 9.468 samples/sec, ObjLoss=31.075, BoxCenterLoss=14.852, BoxScaleLoss=5.880, ClassLoss=15.142 [Epoch 21][Batch 699], LR: 1.00E-03, Speed: 15.062 samples/sec, ObjLoss=31.061, BoxCenterLoss=14.851, BoxScaleLoss=5.879, ClassLoss=15.134 [Epoch 21][Batch 799], LR: 1.00E-03, Speed: 11.020 samples/sec, ObjLoss=31.045, BoxCenterLoss=14.850, BoxScaleLoss=5.878, ClassLoss=15.124 [Epoch 21][Batch 899], LR: 1.00E-03, Speed: 10.321 samples/sec, ObjLoss=31.030, BoxCenterLoss=14.847, BoxScaleLoss=5.876, ClassLoss=15.113 [Epoch 21][Batch 999], LR: 1.00E-03, Speed: 12.023 samples/sec, ObjLoss=31.015, BoxCenterLoss=14.846, BoxScaleLoss=5.875, ClassLoss=15.103 [Epoch 21][Batch 1099], LR: 1.00E-03, Speed: 91.566 samples/sec, ObjLoss=31.003, BoxCenterLoss=14.846, BoxScaleLoss=5.874, ClassLoss=15.093 [Epoch 21][Batch 1199], LR: 1.00E-03, Speed: 9.472 samples/sec, ObjLoss=30.992, BoxCenterLoss=14.847, BoxScaleLoss=5.873, ClassLoss=15.085 [Epoch 21][Batch 1299], LR: 1.00E-03, Speed: 13.538 samples/sec, ObjLoss=30.979, BoxCenterLoss=14.847, BoxScaleLoss=5.872, ClassLoss=15.075 [Epoch 21][Batch 1399], LR: 1.00E-03, Speed: 12.004 samples/sec, ObjLoss=30.966, BoxCenterLoss=14.846, BoxScaleLoss=5.870, ClassLoss=15.065 [Epoch 21][Batch 1499], LR: 1.00E-03, Speed: 8.235 samples/sec, ObjLoss=30.952, BoxCenterLoss=14.845, BoxScaleLoss=5.869, ClassLoss=15.055 [Epoch 21][Batch 1599], LR: 1.00E-03, Speed: 105.447 samples/sec, ObjLoss=30.940, BoxCenterLoss=14.845, BoxScaleLoss=5.868, ClassLoss=15.046 [Epoch 21][Batch 1699], LR: 1.00E-03, Speed: 7.340 samples/sec, ObjLoss=30.925, BoxCenterLoss=14.844, BoxScaleLoss=5.868, ClassLoss=15.038 [Epoch 21][Batch 1799], LR: 1.00E-03, Speed: 12.722 samples/sec, ObjLoss=30.914, BoxCenterLoss=14.845, BoxScaleLoss=5.867, ClassLoss=15.029 [Epoch 21] Training cost: 2095.318, ObjLoss=30.910, BoxCenterLoss=14.845, BoxScaleLoss=5.867, ClassLoss=15.026 [Epoch 21] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.147 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.333 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.052 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.079 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.330 person=30.9 bicycle=12.0 car=17.7 motorcycle=19.7 airplane=27.4 bus=36.5 train=37.7 truck=15.0 boat=7.3 traffic light=9.4 fire hydrant=36.2 stop sign=29.5 parking meter=15.6 bench=6.6 bird=12.8 cat=27.6 dog=25.4 horse=20.9 sheep=20.4 cow=25.1 elephant=34.2 bear=29.0 zebra=37.9 giraffe=40.2 backpack=2.6 umbrella=12.5 handbag=1.5 tie=9.2 suitcase=7.2 frisbee=12.4 skis=5.2 snowboard=5.1 sports ball=16.6 kite=17.1 baseball bat=5.9 baseball glove=9.4 skateboard=14.5 surfboard=7.8 tennis racket=15.6 bottle=13.1 wine glass=12.7 cup=15.7 fork=3.9 knife=2.4 spoon=1.3 bowl=13.5 banana=5.9 apple=3.5 sandwich=9.8 orange=11.5 broccoli=4.5 carrot=3.6 hot dog=4.7 pizza=21.0 donut=16.5 cake=10.8 chair=8.3 couch=17.5 potted plant=6.9 bed=22.9 dining table=12.9 toilet=29.7 tv=24.0 laptop=24.8 mouse=12.0 remote=3.3 keyboard=12.5 cell phone=9.6 microwave=14.7 oven=10.5 toaster=0.0 sink=9.9 refrigerator=16.1 book=2.9 clock=23.4 vase=17.0 scissors=2.4 teddy bear=20.5 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.7 [Epoch 22][Batch 99], LR: 1.00E-03, Speed: 8.883 samples/sec, ObjLoss=30.897, BoxCenterLoss=14.844, BoxScaleLoss=5.866, ClassLoss=15.018 [Epoch 22][Batch 199], LR: 1.00E-03, Speed: 8.621 samples/sec, ObjLoss=30.888, BoxCenterLoss=14.845, BoxScaleLoss=5.865, ClassLoss=15.009 [Epoch 22][Batch 299], LR: 1.00E-03, Speed: 107.844 samples/sec, ObjLoss=30.876, BoxCenterLoss=14.844, BoxScaleLoss=5.864, ClassLoss=15.000 [Epoch 22][Batch 399], LR: 1.00E-03, Speed: 10.161 samples/sec, ObjLoss=30.864, BoxCenterLoss=14.844, BoxScaleLoss=5.863, ClassLoss=14.992 [Epoch 22][Batch 499], LR: 1.00E-03, Speed: 8.931 samples/sec, ObjLoss=30.854, BoxCenterLoss=14.845, BoxScaleLoss=5.863, ClassLoss=14.985 [Epoch 22][Batch 599], LR: 1.00E-03, Speed: 8.154 samples/sec, ObjLoss=30.844, BoxCenterLoss=14.845, BoxScaleLoss=5.862, ClassLoss=14.977 [Epoch 22][Batch 699], LR: 1.00E-03, Speed: 11.123 samples/sec, ObjLoss=30.837, BoxCenterLoss=14.847, BoxScaleLoss=5.862, ClassLoss=14.970 [Epoch 22][Batch 799], LR: 1.00E-03, Speed: 10.729 samples/sec, ObjLoss=30.825, BoxCenterLoss=14.846, BoxScaleLoss=5.861, ClassLoss=14.960 [Epoch 22][Batch 899], LR: 1.00E-03, Speed: 9.959 samples/sec, ObjLoss=30.813, BoxCenterLoss=14.846, BoxScaleLoss=5.860, ClassLoss=14.952 [Epoch 22][Batch 999], LR: 1.00E-03, Speed: 94.813 samples/sec, ObjLoss=30.798, BoxCenterLoss=14.844, BoxScaleLoss=5.858, ClassLoss=14.942 [Epoch 22][Batch 1099], LR: 1.00E-03, Speed: 10.821 samples/sec, ObjLoss=30.787, BoxCenterLoss=14.843, BoxScaleLoss=5.856, ClassLoss=14.933 [Epoch 22][Batch 1199], LR: 1.00E-03, Speed: 10.489 samples/sec, ObjLoss=30.773, BoxCenterLoss=14.841, BoxScaleLoss=5.854, ClassLoss=14.923 [Epoch 22][Batch 1299], LR: 1.00E-03, Speed: 11.046 samples/sec, ObjLoss=30.765, BoxCenterLoss=14.841, BoxScaleLoss=5.854, ClassLoss=14.916 [Epoch 22][Batch 1399], LR: 1.00E-03, Speed: 11.734 samples/sec, ObjLoss=30.752, BoxCenterLoss=14.840, BoxScaleLoss=5.852, ClassLoss=14.906 [Epoch 22][Batch 1499], LR: 1.00E-03, Speed: 10.327 samples/sec, ObjLoss=30.744, BoxCenterLoss=14.842, BoxScaleLoss=5.852, ClassLoss=14.899 [Epoch 22][Batch 1599], LR: 1.00E-03, Speed: 10.172 samples/sec, ObjLoss=30.731, BoxCenterLoss=14.841, BoxScaleLoss=5.851, ClassLoss=14.891 [Epoch 22][Batch 1699], LR: 1.00E-03, Speed: 90.805 samples/sec, ObjLoss=30.723, BoxCenterLoss=14.842, BoxScaleLoss=5.850, ClassLoss=14.883 [Epoch 22][Batch 1799], LR: 1.00E-03, Speed: 11.731 samples/sec, ObjLoss=30.714, BoxCenterLoss=14.843, BoxScaleLoss=5.850, ClassLoss=14.876 [Epoch 22] Training cost: 2221.650, ObjLoss=30.710, BoxCenterLoss=14.843, BoxScaleLoss=5.850, ClassLoss=14.873 [Epoch 22] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.341 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.089 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.050 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.225 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.324 person=27.7 bicycle=9.4 car=19.6 motorcycle=19.8 airplane=28.3 bus=29.9 train=28.3 truck=12.3 boat=7.5 traffic light=7.5 fire hydrant=29.7 stop sign=23.2 parking meter=13.7 bench=6.0 bird=12.0 cat=31.3 dog=22.7 horse=22.9 sheep=25.3 cow=25.9 elephant=30.5 bear=26.5 zebra=35.4 giraffe=30.4 backpack=2.8 umbrella=11.2 handbag=1.4 tie=10.5 suitcase=9.0 frisbee=21.2 skis=5.4 snowboard=6.3 sports ball=17.1 kite=18.4 baseball bat=4.9 baseball glove=10.0 skateboard=15.3 surfboard=10.2 tennis racket=12.7 bottle=12.2 wine glass=10.0 cup=14.8 fork=5.7 knife=2.0 spoon=3.0 bowl=16.2 banana=5.0 apple=2.2 sandwich=11.4 orange=10.1 broccoli=8.0 carrot=2.8 hot dog=7.5 pizza=25.3 donut=16.5 cake=13.6 chair=7.3 couch=15.3 potted plant=6.5 bed=20.7 dining table=12.6 toilet=24.0 tv=15.5 laptop=21.9 mouse=23.8 remote=4.5 keyboard=21.6 cell phone=11.2 microwave=14.8 oven=11.1 toaster=0.0 sink=13.7 refrigerator=20.5 book=2.8 clock=21.5 vase=13.7 scissors=1.9 teddy bear=19.2 hair drier=0.0 toothbrush=0.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.4 [Epoch 23][Batch 99], LR: 1.00E-03, Speed: 11.377 samples/sec, ObjLoss=30.703, BoxCenterLoss=14.844, BoxScaleLoss=5.849, ClassLoss=14.866 [Epoch 23][Batch 199], LR: 1.00E-03, Speed: 99.392 samples/sec, ObjLoss=30.691, BoxCenterLoss=14.843, BoxScaleLoss=5.847, ClassLoss=14.856 [Epoch 23][Batch 299], LR: 1.00E-03, Speed: 9.261 samples/sec, ObjLoss=30.678, BoxCenterLoss=14.842, BoxScaleLoss=5.845, ClassLoss=14.847 [Epoch 23][Batch 399], LR: 1.00E-03, Speed: 8.801 samples/sec, ObjLoss=30.670, BoxCenterLoss=14.843, BoxScaleLoss=5.844, ClassLoss=14.838 [Epoch 23][Batch 499], LR: 1.00E-03, Speed: 10.465 samples/sec, ObjLoss=30.658, BoxCenterLoss=14.842, BoxScaleLoss=5.843, ClassLoss=14.829 [Epoch 23][Batch 599], LR: 1.00E-03, Speed: 11.268 samples/sec, ObjLoss=30.644, BoxCenterLoss=14.840, BoxScaleLoss=5.841, ClassLoss=14.820 [Epoch 23][Batch 699], LR: 1.00E-03, Speed: 10.610 samples/sec, ObjLoss=30.632, BoxCenterLoss=14.840, BoxScaleLoss=5.840, ClassLoss=14.811 [Epoch 23][Batch 799], LR: 1.00E-03, Speed: 8.489 samples/sec, ObjLoss=30.622, BoxCenterLoss=14.840, BoxScaleLoss=5.839, ClassLoss=14.803 [Epoch 23][Batch 899], LR: 1.00E-03, Speed: 11.909 samples/sec, ObjLoss=30.612, BoxCenterLoss=14.841, BoxScaleLoss=5.839, ClassLoss=14.796 [Epoch 23][Batch 999], LR: 1.00E-03, Speed: 8.090 samples/sec, ObjLoss=30.602, BoxCenterLoss=14.842, BoxScaleLoss=5.840, ClassLoss=14.789 [Epoch 23][Batch 1099], LR: 1.00E-03, Speed: 9.609 samples/sec, ObjLoss=30.591, BoxCenterLoss=14.841, BoxScaleLoss=5.838, ClassLoss=14.781 [Epoch 23][Batch 1199], LR: 1.00E-03, Speed: 10.408 samples/sec, ObjLoss=30.583, BoxCenterLoss=14.843, BoxScaleLoss=5.838, ClassLoss=14.773 [Epoch 23][Batch 1299], LR: 1.00E-03, Speed: 119.135 samples/sec, ObjLoss=30.569, BoxCenterLoss=14.841, BoxScaleLoss=5.837, ClassLoss=14.765 [Epoch 23][Batch 1399], LR: 1.00E-03, Speed: 10.798 samples/sec, ObjLoss=30.558, BoxCenterLoss=14.840, BoxScaleLoss=5.835, ClassLoss=14.756 [Epoch 23][Batch 1499], LR: 1.00E-03, Speed: 8.162 samples/sec, ObjLoss=30.549, BoxCenterLoss=14.840, BoxScaleLoss=5.834, ClassLoss=14.747 [Epoch 23][Batch 1599], LR: 1.00E-03, Speed: 7.968 samples/sec, ObjLoss=30.541, BoxCenterLoss=14.842, BoxScaleLoss=5.834, ClassLoss=14.741 [Epoch 23][Batch 1699], LR: 1.00E-03, Speed: 85.912 samples/sec, ObjLoss=30.534, BoxCenterLoss=14.843, BoxScaleLoss=5.834, ClassLoss=14.733 [Epoch 23][Batch 1799], LR: 1.00E-03, Speed: 131.176 samples/sec, ObjLoss=30.523, BoxCenterLoss=14.842, BoxScaleLoss=5.832, ClassLoss=14.724 [Epoch 23] Training cost: 2119.603, ObjLoss=30.518, BoxCenterLoss=14.841, BoxScaleLoss=5.832, ClassLoss=14.721 [Epoch 23] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.153 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.341 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.113 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.052 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.224 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.160 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 person=28.6 bicycle=10.3 car=14.9 motorcycle=20.2 airplane=31.8 bus=34.7 train=36.7 truck=12.6 boat=6.0 traffic light=6.3 fire hydrant=22.9 stop sign=31.9 parking meter=15.1 bench=7.0 bird=10.4 cat=32.3 dog=26.5 horse=19.6 sheep=22.8 cow=20.4 elephant=31.5 bear=32.2 zebra=35.8 giraffe=35.9 backpack=2.6 umbrella=14.0 handbag=1.7 tie=8.6 suitcase=6.7 frisbee=25.7 skis=4.2 snowboard=7.0 sports ball=17.3 kite=17.0 baseball bat=7.5 baseball glove=11.5 skateboard=16.0 surfboard=13.1 tennis racket=14.0 bottle=12.9 wine glass=13.0 cup=15.6 fork=5.5 knife=0.5 spoon=1.1 bowl=17.3 banana=6.9 apple=6.3 sandwich=11.8 orange=12.9 broccoli=6.7 carrot=5.7 hot dog=9.8 pizza=23.6 donut=18.6 cake=11.8 chair=8.8 couch=20.4 potted plant=8.5 bed=26.8 dining table=14.5 toilet=23.6 tv=24.5 laptop=27.1 mouse=27.2 remote=4.8 keyboard=17.2 cell phone=10.5 microwave=16.1 oven=7.0 toaster=0.0 sink=10.9 refrigerator=16.0 book=2.4 clock=23.4 vase=14.2 scissors=7.3 teddy bear=20.7 hair drier=0.0 toothbrush=0.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.3 [Epoch 24][Batch 99], LR: 1.00E-03, Speed: 82.525 samples/sec, ObjLoss=30.508, BoxCenterLoss=14.841, BoxScaleLoss=5.831, ClassLoss=14.715 [Epoch 24][Batch 199], LR: 1.00E-03, Speed: 9.351 samples/sec, ObjLoss=30.498, BoxCenterLoss=14.841, BoxScaleLoss=5.830, ClassLoss=14.707 [Epoch 24][Batch 299], LR: 1.00E-03, Speed: 9.767 samples/sec, ObjLoss=30.489, BoxCenterLoss=14.841, BoxScaleLoss=5.829, ClassLoss=14.700 [Epoch 24][Batch 399], LR: 1.00E-03, Speed: 8.916 samples/sec, ObjLoss=30.480, BoxCenterLoss=14.841, BoxScaleLoss=5.828, ClassLoss=14.693 [Epoch 24][Batch 499], LR: 1.00E-03, Speed: 8.089 samples/sec, ObjLoss=30.468, BoxCenterLoss=14.840, BoxScaleLoss=5.827, ClassLoss=14.684 [Epoch 24][Batch 599], LR: 1.00E-03, Speed: 12.939 samples/sec, ObjLoss=30.457, BoxCenterLoss=14.839, BoxScaleLoss=5.826, ClassLoss=14.676 [Epoch 24][Batch 699], LR: 1.00E-03, Speed: 8.993 samples/sec, ObjLoss=30.445, BoxCenterLoss=14.838, BoxScaleLoss=5.825, ClassLoss=14.667 [Epoch 24][Batch 799], LR: 1.00E-03, Speed: 93.665 samples/sec, ObjLoss=30.439, BoxCenterLoss=14.840, BoxScaleLoss=5.824, ClassLoss=14.659 [Epoch 24][Batch 899], LR: 1.00E-03, Speed: 11.501 samples/sec, ObjLoss=30.427, BoxCenterLoss=14.839, BoxScaleLoss=5.823, ClassLoss=14.652 [Epoch 24][Batch 999], LR: 1.00E-03, Speed: 113.928 samples/sec, ObjLoss=30.419, BoxCenterLoss=14.840, BoxScaleLoss=5.823, ClassLoss=14.647 [Epoch 24][Batch 1099], LR: 1.00E-03, Speed: 10.292 samples/sec, ObjLoss=30.408, BoxCenterLoss=14.839, BoxScaleLoss=5.822, ClassLoss=14.640 [Epoch 24][Batch 1199], LR: 1.00E-03, Speed: 9.001 samples/sec, ObjLoss=30.400, BoxCenterLoss=14.840, BoxScaleLoss=5.822, ClassLoss=14.633 [Epoch 24][Batch 1299], LR: 1.00E-03, Speed: 12.074 samples/sec, ObjLoss=30.394, BoxCenterLoss=14.842, BoxScaleLoss=5.822, ClassLoss=14.628 [Epoch 24][Batch 1399], LR: 1.00E-03, Speed: 10.244 samples/sec, ObjLoss=30.384, BoxCenterLoss=14.843, BoxScaleLoss=5.821, ClassLoss=14.620 [Epoch 24][Batch 1499], LR: 1.00E-03, Speed: 11.414 samples/sec, ObjLoss=30.373, BoxCenterLoss=14.841, BoxScaleLoss=5.819, ClassLoss=14.611 [Epoch 24][Batch 1599], LR: 1.00E-03, Speed: 10.581 samples/sec, ObjLoss=30.361, BoxCenterLoss=14.840, BoxScaleLoss=5.817, ClassLoss=14.603 [Epoch 24][Batch 1699], LR: 1.00E-03, Speed: 7.649 samples/sec, ObjLoss=30.351, BoxCenterLoss=14.839, BoxScaleLoss=5.816, ClassLoss=14.596 [Epoch 24][Batch 1799], LR: 1.00E-03, Speed: 9.366 samples/sec, ObjLoss=30.344, BoxCenterLoss=14.841, BoxScaleLoss=5.816, ClassLoss=14.588 [Epoch 24] Training cost: 2127.171, ObjLoss=30.342, BoxCenterLoss=14.841, BoxScaleLoss=5.816, ClassLoss=14.586 [Epoch 24] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.114 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.057 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.160 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.340 person=29.6 bicycle=9.0 car=17.7 motorcycle=19.1 airplane=26.6 bus=34.5 train=31.3 truck=15.0 boat=6.8 traffic light=9.2 fire hydrant=31.2 stop sign=36.4 parking meter=15.8 bench=7.4 bird=12.6 cat=33.6 dog=24.2 horse=22.1 sheep=19.7 cow=24.4 elephant=31.4 bear=34.4 zebra=39.8 giraffe=37.3 backpack=2.6 umbrella=13.9 handbag=1.6 tie=7.4 suitcase=10.7 frisbee=22.6 skis=2.9 snowboard=4.0 sports ball=17.9 kite=14.2 baseball bat=4.4 baseball glove=11.1 skateboard=17.5 surfboard=11.0 tennis racket=13.3 bottle=11.1 wine glass=11.1 cup=16.2 fork=4.8 knife=1.6 spoon=2.2 bowl=14.1 banana=6.9 apple=2.1 sandwich=11.9 orange=12.4 broccoli=8.2 carrot=4.8 hot dog=7.7 pizza=21.2 donut=21.7 cake=12.4 chair=8.5 couch=19.8 potted plant=6.5 bed=26.9 dining table=16.1 toilet=27.8 tv=26.7 laptop=26.0 mouse=29.0 remote=4.3 keyboard=17.6 cell phone=11.0 microwave=15.4 oven=9.4 toaster=0.0 sink=13.2 refrigerator=19.5 book=3.0 clock=27.3 vase=12.7 scissors=1.7 teddy bear=17.5 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.5 [Epoch 25][Batch 99], LR: 1.00E-03, Speed: 19.215 samples/sec, ObjLoss=30.330, BoxCenterLoss=14.839, BoxScaleLoss=5.814, ClassLoss=14.578 [Epoch 25][Batch 199], LR: 1.00E-03, Speed: 42.797 samples/sec, ObjLoss=30.319, BoxCenterLoss=14.837, BoxScaleLoss=5.813, ClassLoss=14.571 [Epoch 25][Batch 299], LR: 1.00E-03, Speed: 9.018 samples/sec, ObjLoss=30.309, BoxCenterLoss=14.837, BoxScaleLoss=5.811, ClassLoss=14.564 [Epoch 25][Batch 399], LR: 1.00E-03, Speed: 9.593 samples/sec, ObjLoss=30.297, BoxCenterLoss=14.836, BoxScaleLoss=5.810, ClassLoss=14.555 [Epoch 25][Batch 499], LR: 1.00E-03, Speed: 11.602 samples/sec, ObjLoss=30.286, BoxCenterLoss=14.835, BoxScaleLoss=5.809, ClassLoss=14.548 [Epoch 25][Batch 599], LR: 1.00E-03, Speed: 8.663 samples/sec, ObjLoss=30.273, BoxCenterLoss=14.832, BoxScaleLoss=5.808, ClassLoss=14.540 [Epoch 25][Batch 699], LR: 1.00E-03, Speed: 9.107 samples/sec, ObjLoss=30.262, BoxCenterLoss=14.831, BoxScaleLoss=5.806, ClassLoss=14.532 [Epoch 25][Batch 799], LR: 1.00E-03, Speed: 10.700 samples/sec, ObjLoss=30.250, BoxCenterLoss=14.830, BoxScaleLoss=5.806, ClassLoss=14.525 [Epoch 25][Batch 899], LR: 1.00E-03, Speed: 12.828 samples/sec, ObjLoss=30.242, BoxCenterLoss=14.831, BoxScaleLoss=5.805, ClassLoss=14.519 [Epoch 25][Batch 999], LR: 1.00E-03, Speed: 128.609 samples/sec, ObjLoss=30.234, BoxCenterLoss=14.831, BoxScaleLoss=5.805, ClassLoss=14.514 [Epoch 25][Batch 1099], LR: 1.00E-03, Speed: 106.907 samples/sec, ObjLoss=30.222, BoxCenterLoss=14.830, BoxScaleLoss=5.805, ClassLoss=14.507 [Epoch 25][Batch 1199], LR: 1.00E-03, Speed: 9.006 samples/sec, ObjLoss=30.214, BoxCenterLoss=14.830, BoxScaleLoss=5.804, ClassLoss=14.500 [Epoch 25][Batch 1299], LR: 1.00E-03, Speed: 8.433 samples/sec, ObjLoss=30.207, BoxCenterLoss=14.831, BoxScaleLoss=5.804, ClassLoss=14.494 [Epoch 25][Batch 1399], LR: 1.00E-03, Speed: 129.935 samples/sec, ObjLoss=30.195, BoxCenterLoss=14.829, BoxScaleLoss=5.802, ClassLoss=14.487 [Epoch 25][Batch 1499], LR: 1.00E-03, Speed: 8.270 samples/sec, ObjLoss=30.183, BoxCenterLoss=14.827, BoxScaleLoss=5.801, ClassLoss=14.479 [Epoch 25][Batch 1599], LR: 1.00E-03, Speed: 11.791 samples/sec, ObjLoss=30.175, BoxCenterLoss=14.826, BoxScaleLoss=5.800, ClassLoss=14.473 [Epoch 25][Batch 1699], LR: 1.00E-03, Speed: 9.521 samples/sec, ObjLoss=30.164, BoxCenterLoss=14.826, BoxScaleLoss=5.799, ClassLoss=14.466 [Epoch 25][Batch 1799], LR: 1.00E-03, Speed: 13.253 samples/sec, ObjLoss=30.153, BoxCenterLoss=14.824, BoxScaleLoss=5.798, ClassLoss=14.458 [Epoch 25] Training cost: 2174.519, ObjLoss=30.148, BoxCenterLoss=14.823, BoxScaleLoss=5.798, ClassLoss=14.456 [Epoch 25] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.152 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.106 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.059 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.311 person=30.0 bicycle=11.2 car=17.0 motorcycle=19.1 airplane=31.9 bus=38.9 train=39.6 truck=14.9 boat=8.1 traffic light=9.0 fire hydrant=34.8 stop sign=28.2 parking meter=13.7 bench=7.6 bird=15.5 cat=30.6 dog=24.4 horse=25.4 sheep=22.2 cow=24.9 elephant=27.7 bear=27.8 zebra=37.8 giraffe=40.1 backpack=2.6 umbrella=11.2 handbag=1.6 tie=11.1 suitcase=8.4 frisbee=22.7 skis=5.4 snowboard=6.8 sports ball=23.0 kite=16.6 baseball bat=5.9 baseball glove=10.1 skateboard=16.1 surfboard=10.1 tennis racket=15.9 bottle=14.4 wine glass=12.1 cup=16.5 fork=4.4 knife=1.7 spoon=1.2 bowl=13.2 banana=6.7 apple=3.1 sandwich=10.7 orange=8.4 broccoli=6.9 carrot=3.8 hot dog=6.3 pizza=26.2 donut=18.9 cake=9.9 chair=8.7 couch=16.3 potted plant=6.4 bed=23.1 dining table=12.5 toilet=20.4 tv=25.6 laptop=22.6 mouse=22.2 remote=4.7 keyboard=12.2 cell phone=10.6 microwave=16.2 oven=10.4 toaster=0.0 sink=8.7 refrigerator=15.6 book=2.4 clock=26.0 vase=12.9 scissors=3.6 teddy bear=21.9 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.2 [Epoch 26][Batch 99], LR: 1.00E-03, Speed: 10.763 samples/sec, ObjLoss=30.139, BoxCenterLoss=14.822, BoxScaleLoss=5.796, ClassLoss=14.449 [Epoch 26][Batch 199], LR: 1.00E-03, Speed: 10.850 samples/sec, ObjLoss=30.132, BoxCenterLoss=14.822, BoxScaleLoss=5.795, ClassLoss=14.442 [Epoch 26][Batch 299], LR: 1.00E-03, Speed: 10.218 samples/sec, ObjLoss=30.121, BoxCenterLoss=14.821, BoxScaleLoss=5.793, ClassLoss=14.433 [Epoch 26][Batch 399], LR: 1.00E-03, Speed: 10.032 samples/sec, ObjLoss=30.112, BoxCenterLoss=14.822, BoxScaleLoss=5.793, ClassLoss=14.426 [Epoch 26][Batch 499], LR: 1.00E-03, Speed: 11.405 samples/sec, ObjLoss=30.104, BoxCenterLoss=14.822, BoxScaleLoss=5.791, ClassLoss=14.419 [Epoch 26][Batch 599], LR: 1.00E-03, Speed: 11.328 samples/sec, ObjLoss=30.095, BoxCenterLoss=14.821, BoxScaleLoss=5.790, ClassLoss=14.411 [Epoch 26][Batch 699], LR: 1.00E-03, Speed: 117.998 samples/sec, ObjLoss=30.085, BoxCenterLoss=14.819, BoxScaleLoss=5.788, ClassLoss=14.403 [Epoch 26][Batch 799], LR: 1.00E-03, Speed: 10.744 samples/sec, ObjLoss=30.075, BoxCenterLoss=14.819, BoxScaleLoss=5.787, ClassLoss=14.396 [Epoch 26][Batch 899], LR: 1.00E-03, Speed: 13.738 samples/sec, ObjLoss=30.063, BoxCenterLoss=14.817, BoxScaleLoss=5.786, ClassLoss=14.389 [Epoch 26][Batch 999], LR: 1.00E-03, Speed: 7.773 samples/sec, ObjLoss=30.055, BoxCenterLoss=14.818, BoxScaleLoss=5.786, ClassLoss=14.384 [Epoch 26][Batch 1099], LR: 1.00E-03, Speed: 9.982 samples/sec, ObjLoss=30.045, BoxCenterLoss=14.817, BoxScaleLoss=5.785, ClassLoss=14.377 [Epoch 26][Batch 1199], LR: 1.00E-03, Speed: 59.954 samples/sec, ObjLoss=30.036, BoxCenterLoss=14.816, BoxScaleLoss=5.784, ClassLoss=14.369 [Epoch 26][Batch 1299], LR: 1.00E-03, Speed: 93.375 samples/sec, ObjLoss=30.029, BoxCenterLoss=14.817, BoxScaleLoss=5.784, ClassLoss=14.364 [Epoch 26][Batch 1399], LR: 1.00E-03, Speed: 11.358 samples/sec, ObjLoss=30.020, BoxCenterLoss=14.817, BoxScaleLoss=5.784, ClassLoss=14.358 [Epoch 26][Batch 1499], LR: 1.00E-03, Speed: 9.772 samples/sec, ObjLoss=30.012, BoxCenterLoss=14.817, BoxScaleLoss=5.783, ClassLoss=14.351 [Epoch 26][Batch 1599], LR: 1.00E-03, Speed: 7.959 samples/sec, ObjLoss=30.002, BoxCenterLoss=14.816, BoxScaleLoss=5.781, ClassLoss=14.343 [Epoch 26][Batch 1699], LR: 1.00E-03, Speed: 8.457 samples/sec, ObjLoss=29.992, BoxCenterLoss=14.815, BoxScaleLoss=5.780, ClassLoss=14.337 [Epoch 26][Batch 1799], LR: 1.00E-03, Speed: 12.825 samples/sec, ObjLoss=29.984, BoxCenterLoss=14.816, BoxScaleLoss=5.780, ClassLoss=14.331 [Epoch 26] Training cost: 2195.202, ObjLoss=29.980, BoxCenterLoss=14.815, BoxScaleLoss=5.779, ClassLoss=14.328 [Epoch 26] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.156 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.348 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.113 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.055 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.159 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.092 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.338 person=30.6 bicycle=7.7 car=16.6 motorcycle=19.8 airplane=29.5 bus=35.2 train=33.3 truck=11.9 boat=7.9 traffic light=9.0 fire hydrant=36.4 stop sign=33.1 parking meter=16.8 bench=6.6 bird=13.3 cat=32.0 dog=27.1 horse=27.3 sheep=21.0 cow=27.4 elephant=29.5 bear=35.6 zebra=34.2 giraffe=38.7 backpack=2.9 umbrella=11.2 handbag=2.1 tie=8.8 suitcase=7.8 frisbee=20.3 skis=3.3 snowboard=6.8 sports ball=19.2 kite=17.7 baseball bat=4.1 baseball glove=11.7 skateboard=12.7 surfboard=10.7 tennis racket=12.0 bottle=12.0 wine glass=11.8 cup=15.2 fork=5.4 knife=1.6 spoon=2.5 bowl=13.9 banana=7.4 apple=5.3 sandwich=12.7 orange=14.2 broccoli=6.8 carrot=5.3 hot dog=8.9 pizza=26.9 donut=18.5 cake=11.0 chair=8.9 couch=23.0 potted plant=6.4 bed=23.8 dining table=11.4 toilet=25.5 tv=20.8 laptop=26.6 mouse=22.3 remote=5.0 keyboard=17.3 cell phone=12.3 microwave=21.7 oven=6.8 toaster=0.0 sink=12.7 refrigerator=20.1 book=3.1 clock=26.5 vase=13.5 scissors=4.5 teddy bear=23.3 hair drier=0.0 toothbrush=0.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.6 [Epoch 27][Batch 99], LR: 1.00E-03, Speed: 10.016 samples/sec, ObjLoss=29.970, BoxCenterLoss=14.814, BoxScaleLoss=5.778, ClassLoss=14.322 [Epoch 27][Batch 199], LR: 1.00E-03, Speed: 11.088 samples/sec, ObjLoss=29.961, BoxCenterLoss=14.814, BoxScaleLoss=5.778, ClassLoss=14.316 [Epoch 27][Batch 299], LR: 1.00E-03, Speed: 9.503 samples/sec, ObjLoss=29.953, BoxCenterLoss=14.814, BoxScaleLoss=5.778, ClassLoss=14.311 [Epoch 27][Batch 399], LR: 1.00E-03, Speed: 9.897 samples/sec, ObjLoss=29.945, BoxCenterLoss=14.815, BoxScaleLoss=5.778, ClassLoss=14.306 [Epoch 27][Batch 499], LR: 1.00E-03, Speed: 7.988 samples/sec, ObjLoss=29.936, BoxCenterLoss=14.814, BoxScaleLoss=5.777, ClassLoss=14.300 [Epoch 27][Batch 599], LR: 1.00E-03, Speed: 89.634 samples/sec, ObjLoss=29.925, BoxCenterLoss=14.812, BoxScaleLoss=5.775, ClassLoss=14.292 [Epoch 27][Batch 699], LR: 1.00E-03, Speed: 10.223 samples/sec, ObjLoss=29.918, BoxCenterLoss=14.813, BoxScaleLoss=5.774, ClassLoss=14.285 [Epoch 27][Batch 799], LR: 1.00E-03, Speed: 8.406 samples/sec, ObjLoss=29.908, BoxCenterLoss=14.811, BoxScaleLoss=5.773, ClassLoss=14.278 [Epoch 27][Batch 899], LR: 1.00E-03, Speed: 7.449 samples/sec, ObjLoss=29.900, BoxCenterLoss=14.811, BoxScaleLoss=5.772, ClassLoss=14.271 [Epoch 27][Batch 999], LR: 1.00E-03, Speed: 7.656 samples/sec, ObjLoss=29.890, BoxCenterLoss=14.809, BoxScaleLoss=5.770, ClassLoss=14.263 [Epoch 27][Batch 1099], LR: 1.00E-03, Speed: 10.031 samples/sec, ObjLoss=29.879, BoxCenterLoss=14.808, BoxScaleLoss=5.769, ClassLoss=14.257 [Epoch 27][Batch 1199], LR: 1.00E-03, Speed: 8.391 samples/sec, ObjLoss=29.871, BoxCenterLoss=14.808, BoxScaleLoss=5.769, ClassLoss=14.251 [Epoch 27][Batch 1299], LR: 1.00E-03, Speed: 11.643 samples/sec, ObjLoss=29.863, BoxCenterLoss=14.808, BoxScaleLoss=5.768, ClassLoss=14.245 [Epoch 27][Batch 1399], LR: 1.00E-03, Speed: 10.225 samples/sec, ObjLoss=29.853, BoxCenterLoss=14.807, BoxScaleLoss=5.767, ClassLoss=14.240 [Epoch 27][Batch 1499], LR: 1.00E-03, Speed: 120.284 samples/sec, ObjLoss=29.845, BoxCenterLoss=14.806, BoxScaleLoss=5.766, ClassLoss=14.233 [Epoch 27][Batch 1599], LR: 1.00E-03, Speed: 10.752 samples/sec, ObjLoss=29.836, BoxCenterLoss=14.804, BoxScaleLoss=5.765, ClassLoss=14.226 [Epoch 27][Batch 1699], LR: 1.00E-03, Speed: 9.193 samples/sec, ObjLoss=29.827, BoxCenterLoss=14.804, BoxScaleLoss=5.764, ClassLoss=14.220 [Epoch 27][Batch 1799], LR: 1.00E-03, Speed: 13.151 samples/sec, ObjLoss=29.819, BoxCenterLoss=14.804, BoxScaleLoss=5.763, ClassLoss=14.214 [Epoch 27] Training cost: 2159.388, ObjLoss=29.817, BoxCenterLoss=14.804, BoxScaleLoss=5.763, ClassLoss=14.212 [Epoch 27] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.167 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.057 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.167 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.262 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.166 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363 person=29.3 bicycle=12.0 car=20.0 motorcycle=18.7 airplane=33.5 bus=37.8 train=37.3 truck=18.0 boat=8.1 traffic light=7.8 fire hydrant=35.6 stop sign=28.7 parking meter=15.4 bench=8.6 bird=13.1 cat=32.2 dog=22.3 horse=29.4 sheep=25.5 cow=26.5 elephant=35.3 bear=34.9 zebra=38.3 giraffe=39.1 backpack=3.7 umbrella=15.2 handbag=1.7 tie=9.8 suitcase=7.7 frisbee=24.6 skis=4.7 snowboard=5.9 sports ball=18.8 kite=17.4 baseball bat=6.2 baseball glove=12.5 skateboard=19.7 surfboard=10.5 tennis racket=16.0 bottle=13.5 wine glass=13.0 cup=17.1 fork=5.5 knife=2.2 spoon=1.4 bowl=17.0 banana=7.4 apple=4.6 sandwich=13.8 orange=14.1 broccoli=7.7 carrot=5.2 hot dog=7.8 pizza=22.7 donut=18.8 cake=12.7 chair=10.0 couch=21.3 potted plant=8.3 bed=22.5 dining table=12.9 toilet=29.2 tv=29.9 laptop=27.4 mouse=26.9 remote=5.5 keyboard=21.3 cell phone=14.7 microwave=24.7 oven=13.1 toaster=0.0 sink=12.8 refrigerator=20.9 book=3.0 clock=26.4 vase=13.8 scissors=6.0 teddy bear=22.3 hair drier=0.0 toothbrush=0.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.7 [Epoch 28][Batch 99], LR: 1.00E-03, Speed: 10.814 samples/sec, ObjLoss=29.810, BoxCenterLoss=14.805, BoxScaleLoss=5.762, ClassLoss=14.207 [Epoch 28][Batch 199], LR: 1.00E-03, Speed: 7.793 samples/sec, ObjLoss=29.802, BoxCenterLoss=14.804, BoxScaleLoss=5.762, ClassLoss=14.201 [Epoch 28][Batch 299], LR: 1.00E-03, Speed: 8.763 samples/sec, ObjLoss=29.794, BoxCenterLoss=14.803, BoxScaleLoss=5.760, ClassLoss=14.194 [Epoch 28][Batch 399], LR: 1.00E-03, Speed: 9.536 samples/sec, ObjLoss=29.789, BoxCenterLoss=14.805, BoxScaleLoss=5.760, ClassLoss=14.188 [Epoch 28][Batch 499], LR: 1.00E-03, Speed: 10.055 samples/sec, ObjLoss=29.781, BoxCenterLoss=14.805, BoxScaleLoss=5.759, ClassLoss=14.183 [Epoch 28][Batch 599], LR: 1.00E-03, Speed: 9.300 samples/sec, ObjLoss=29.773, BoxCenterLoss=14.805, BoxScaleLoss=5.759, ClassLoss=14.178 [Epoch 28][Batch 699], LR: 1.00E-03, Speed: 10.411 samples/sec, ObjLoss=29.768, BoxCenterLoss=14.805, BoxScaleLoss=5.758, ClassLoss=14.172 [Epoch 28][Batch 799], LR: 1.00E-03, Speed: 91.809 samples/sec, ObjLoss=29.756, BoxCenterLoss=14.803, BoxScaleLoss=5.756, ClassLoss=14.164 [Epoch 28][Batch 899], LR: 1.00E-03, Speed: 10.295 samples/sec, ObjLoss=29.745, BoxCenterLoss=14.800, BoxScaleLoss=5.754, ClassLoss=14.157 [Epoch 28][Batch 999], LR: 1.00E-03, Speed: 84.980 samples/sec, ObjLoss=29.735, BoxCenterLoss=14.799, BoxScaleLoss=5.753, ClassLoss=14.150 [Epoch 28][Batch 1099], LR: 1.00E-03, Speed: 9.799 samples/sec, ObjLoss=29.726, BoxCenterLoss=14.799, BoxScaleLoss=5.752, ClassLoss=14.144 [Epoch 28][Batch 1199], LR: 1.00E-03, Speed: 11.949 samples/sec, ObjLoss=29.718, BoxCenterLoss=14.798, BoxScaleLoss=5.751, ClassLoss=14.138 [Epoch 28][Batch 1299], LR: 1.00E-03, Speed: 13.823 samples/sec, ObjLoss=29.709, BoxCenterLoss=14.797, BoxScaleLoss=5.750, ClassLoss=14.132 [Epoch 28][Batch 1399], LR: 1.00E-03, Speed: 8.638 samples/sec, ObjLoss=29.702, BoxCenterLoss=14.797, BoxScaleLoss=5.749, ClassLoss=14.126 [Epoch 28][Batch 1499], LR: 1.00E-03, Speed: 11.327 samples/sec, ObjLoss=29.694, BoxCenterLoss=14.797, BoxScaleLoss=5.748, ClassLoss=14.119 [Epoch 28][Batch 1599], LR: 1.00E-03, Speed: 10.518 samples/sec, ObjLoss=29.687, BoxCenterLoss=14.798, BoxScaleLoss=5.748, ClassLoss=14.116 [Epoch 28][Batch 1699], LR: 1.00E-03, Speed: 10.030 samples/sec, ObjLoss=29.678, BoxCenterLoss=14.797, BoxScaleLoss=5.748, ClassLoss=14.110 [Epoch 28][Batch 1799], LR: 1.00E-03, Speed: 11.202 samples/sec, ObjLoss=29.671, BoxCenterLoss=14.797, BoxScaleLoss=5.747, ClassLoss=14.103 [Epoch 28] Training cost: 2196.405, ObjLoss=29.670, BoxCenterLoss=14.797, BoxScaleLoss=5.747, ClassLoss=14.102 [Epoch 28] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.053 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.203 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.301 person=27.3 bicycle=11.8 car=18.1 motorcycle=17.4 airplane=24.5 bus=34.4 train=35.0 truck=14.4 boat=6.9 traffic light=6.7 fire hydrant=30.1 stop sign=27.7 parking meter=14.9 bench=7.4 bird=10.5 cat=28.6 dog=20.3 horse=24.0 sheep=20.8 cow=22.1 elephant=27.8 bear=26.3 zebra=31.2 giraffe=31.8 backpack=2.6 umbrella=12.6 handbag=1.9 tie=7.0 suitcase=9.0 frisbee=25.8 skis=3.7 snowboard=6.9 sports ball=17.7 kite=16.5 baseball bat=5.7 baseball glove=11.9 skateboard=14.5 surfboard=9.3 tennis racket=17.0 bottle=11.9 wine glass=13.7 cup=16.6 fork=6.0 knife=1.1 spoon=2.2 bowl=13.3 banana=3.9 apple=3.1 sandwich=7.6 orange=9.3 broccoli=5.3 carrot=3.7 hot dog=6.5 pizza=21.5 donut=13.5 cake=10.4 chair=8.7 couch=16.0 potted plant=9.3 bed=21.8 dining table=10.5 toilet=25.7 tv=25.3 laptop=28.0 mouse=29.0 remote=4.1 keyboard=14.6 cell phone=10.6 microwave=21.2 oven=11.2 toaster=0.0 sink=10.5 refrigerator=19.9 book=2.3 clock=24.9 vase=12.4 scissors=5.9 teddy bear=11.8 hair drier=0.0 toothbrush=0.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.4 [Epoch 29][Batch 99], LR: 1.00E-03, Speed: 8.828 samples/sec, ObjLoss=29.664, BoxCenterLoss=14.799, BoxScaleLoss=5.747, ClassLoss=14.097 [Epoch 29][Batch 199], LR: 1.00E-03, Speed: 9.763 samples/sec, ObjLoss=29.658, BoxCenterLoss=14.798, BoxScaleLoss=5.746, ClassLoss=14.090 [Epoch 29][Batch 299], LR: 1.00E-03, Speed: 9.955 samples/sec, ObjLoss=29.651, BoxCenterLoss=14.798, BoxScaleLoss=5.745, ClassLoss=14.085 [Epoch 29][Batch 399], LR: 1.00E-03, Speed: 89.347 samples/sec, ObjLoss=29.644, BoxCenterLoss=14.798, BoxScaleLoss=5.744, ClassLoss=14.078 [Epoch 29][Batch 499], LR: 1.00E-03, Speed: 9.007 samples/sec, ObjLoss=29.637, BoxCenterLoss=14.798, BoxScaleLoss=5.743, ClassLoss=14.073 [Epoch 29][Batch 599], LR: 1.00E-03, Speed: 8.599 samples/sec, ObjLoss=29.629, BoxCenterLoss=14.798, BoxScaleLoss=5.743, ClassLoss=14.067 [Epoch 29][Batch 699], LR: 1.00E-03, Speed: 9.428 samples/sec, ObjLoss=29.624, BoxCenterLoss=14.799, BoxScaleLoss=5.742, ClassLoss=14.062 [Epoch 29][Batch 799], LR: 1.00E-03, Speed: 14.350 samples/sec, ObjLoss=29.619, BoxCenterLoss=14.801, BoxScaleLoss=5.742, ClassLoss=14.057 [Epoch 29][Batch 899], LR: 1.00E-03, Speed: 11.055 samples/sec, ObjLoss=29.611, BoxCenterLoss=14.801, BoxScaleLoss=5.743, ClassLoss=14.053 [Epoch 29][Batch 999], LR: 1.00E-03, Speed: 122.791 samples/sec, ObjLoss=29.605, BoxCenterLoss=14.802, BoxScaleLoss=5.743, ClassLoss=14.049 [Epoch 29][Batch 1099], LR: 1.00E-03, Speed: 11.354 samples/sec, ObjLoss=29.596, BoxCenterLoss=14.801, BoxScaleLoss=5.742, ClassLoss=14.044 [Epoch 29][Batch 1199], LR: 1.00E-03, Speed: 11.059 samples/sec, ObjLoss=29.589, BoxCenterLoss=14.801, BoxScaleLoss=5.742, ClassLoss=14.038 [Epoch 29][Batch 1299], LR: 1.00E-03, Speed: 9.681 samples/sec, ObjLoss=29.583, BoxCenterLoss=14.801, BoxScaleLoss=5.741, ClassLoss=14.032 [Epoch 29][Batch 1399], LR: 1.00E-03, Speed: 12.361 samples/sec, ObjLoss=29.576, BoxCenterLoss=14.801, BoxScaleLoss=5.740, ClassLoss=14.026 [Epoch 29][Batch 1499], LR: 1.00E-03, Speed: 9.940 samples/sec, ObjLoss=29.569, BoxCenterLoss=14.801, BoxScaleLoss=5.739, ClassLoss=14.021 [Epoch 29][Batch 1599], LR: 1.00E-03, Speed: 10.374 samples/sec, ObjLoss=29.559, BoxCenterLoss=14.799, BoxScaleLoss=5.738, ClassLoss=14.014 [Epoch 29][Batch 1699], LR: 1.00E-03, Speed: 10.549 samples/sec, ObjLoss=29.549, BoxCenterLoss=14.796, BoxScaleLoss=5.736, ClassLoss=14.008 [Epoch 29][Batch 1799], LR: 1.00E-03, Speed: 12.785 samples/sec, ObjLoss=29.541, BoxCenterLoss=14.796, BoxScaleLoss=5.736, ClassLoss=14.002 [Epoch 29] Training cost: 2183.276, ObjLoss=29.539, BoxCenterLoss=14.797, BoxScaleLoss=5.736, ClassLoss=14.001 [Epoch 29] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.350 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.053 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.170 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.166 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363 person=30.7 bicycle=11.7 car=18.9 motorcycle=19.6 airplane=27.0 bus=34.9 train=41.5 truck=17.0 boat=7.8 traffic light=9.5 fire hydrant=30.1 stop sign=37.2 parking meter=18.2 bench=7.7 bird=14.3 cat=34.2 dog=27.9 horse=24.4 sheep=25.0 cow=27.6 elephant=33.6 bear=36.6 zebra=38.8 giraffe=39.2 backpack=3.2 umbrella=13.3 handbag=1.8 tie=10.4 suitcase=10.6 frisbee=22.4 skis=5.8 snowboard=7.3 sports ball=11.7 kite=18.3 baseball bat=4.7 baseball glove=9.5 skateboard=18.8 surfboard=11.4 tennis racket=14.2 bottle=13.8 wine glass=13.2 cup=17.7 fork=4.9 knife=1.7 spoon=2.2 bowl=16.2 banana=7.4 apple=4.2 sandwich=12.0 orange=8.1 broccoli=6.8 carrot=3.5 hot dog=9.7 pizza=22.4 donut=16.4 cake=15.0 chair=8.5 couch=19.9 potted plant=7.2 bed=26.8 dining table=13.5 toilet=28.1 tv=27.1 laptop=26.1 mouse=22.4 remote=4.6 keyboard=19.2 cell phone=13.0 microwave=27.8 oven=13.7 toaster=0.0 sink=13.2 refrigerator=22.9 book=2.4 clock=23.8 vase=14.9 scissors=1.9 teddy bear=22.1 hair drier=0.0 toothbrush=0.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.4 [Epoch 30][Batch 99], LR: 1.00E-03, Speed: 8.627 samples/sec, ObjLoss=29.532, BoxCenterLoss=14.796, BoxScaleLoss=5.735, ClassLoss=13.995 [Epoch 30][Batch 199], LR: 1.00E-03, Speed: 9.738 samples/sec, ObjLoss=29.526, BoxCenterLoss=14.796, BoxScaleLoss=5.734, ClassLoss=13.989 [Epoch 30][Batch 299], LR: 1.00E-03, Speed: 11.030 samples/sec, ObjLoss=29.519, BoxCenterLoss=14.796, BoxScaleLoss=5.734, ClassLoss=13.984 [Epoch 30][Batch 399], LR: 1.00E-03, Speed: 8.827 samples/sec, ObjLoss=29.513, BoxCenterLoss=14.796, BoxScaleLoss=5.733, ClassLoss=13.979 [Epoch 30][Batch 499], LR: 1.00E-03, Speed: 9.885 samples/sec, ObjLoss=29.508, BoxCenterLoss=14.797, BoxScaleLoss=5.733, ClassLoss=13.975 [Epoch 30][Batch 599], LR: 1.00E-03, Speed: 12.034 samples/sec, ObjLoss=29.500, BoxCenterLoss=14.797, BoxScaleLoss=5.732, ClassLoss=13.970 [Epoch 30][Batch 699], LR: 1.00E-03, Speed: 9.808 samples/sec, ObjLoss=29.493, BoxCenterLoss=14.797, BoxScaleLoss=5.732, ClassLoss=13.965 [Epoch 30][Batch 799], LR: 1.00E-03, Speed: 11.489 samples/sec, ObjLoss=29.485, BoxCenterLoss=14.796, BoxScaleLoss=5.731, ClassLoss=13.959 [Epoch 30][Batch 899], LR: 1.00E-03, Speed: 91.550 samples/sec, ObjLoss=29.476, BoxCenterLoss=14.795, BoxScaleLoss=5.730, ClassLoss=13.954 [Epoch 30][Batch 999], LR: 1.00E-03, Speed: 8.808 samples/sec, ObjLoss=29.468, BoxCenterLoss=14.793, BoxScaleLoss=5.729, ClassLoss=13.949 [Epoch 30][Batch 1099], LR: 1.00E-03, Speed: 86.237 samples/sec, ObjLoss=29.460, BoxCenterLoss=14.792, BoxScaleLoss=5.728, ClassLoss=13.943 [Epoch 30][Batch 1199], LR: 1.00E-03, Speed: 14.112 samples/sec, ObjLoss=29.455, BoxCenterLoss=14.793, BoxScaleLoss=5.727, ClassLoss=13.937 [Epoch 30][Batch 1299], LR: 1.00E-03, Speed: 10.395 samples/sec, ObjLoss=29.448, BoxCenterLoss=14.793, BoxScaleLoss=5.727, ClassLoss=13.931 [Epoch 30][Batch 1399], LR: 1.00E-03, Speed: 10.426 samples/sec, ObjLoss=29.440, BoxCenterLoss=14.792, BoxScaleLoss=5.725, ClassLoss=13.925 [Epoch 30][Batch 1499], LR: 1.00E-03, Speed: 13.684 samples/sec, ObjLoss=29.430, BoxCenterLoss=14.790, BoxScaleLoss=5.724, ClassLoss=13.919 [Epoch 30][Batch 1599], LR: 1.00E-03, Speed: 12.746 samples/sec, ObjLoss=29.422, BoxCenterLoss=14.790, BoxScaleLoss=5.723, ClassLoss=13.912 [Epoch 30][Batch 1699], LR: 1.00E-03, Speed: 9.970 samples/sec, ObjLoss=29.415, BoxCenterLoss=14.789, BoxScaleLoss=5.722, ClassLoss=13.906 [Epoch 30][Batch 1799], LR: 1.00E-03, Speed: 11.439 samples/sec, ObjLoss=29.409, BoxCenterLoss=14.789, BoxScaleLoss=5.721, ClassLoss=13.900 [Epoch 30] Training cost: 2157.707, ObjLoss=29.406, BoxCenterLoss=14.789, BoxScaleLoss=5.721, ClassLoss=13.898 [Epoch 30] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.157 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.351 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.114 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.059 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.161 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.229 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.344 person=29.5 bicycle=11.7 car=20.2 motorcycle=17.5 airplane=26.7 bus=37.0 train=33.9 truck=15.5 boat=8.9 traffic light=10.8 fire hydrant=27.2 stop sign=30.1 parking meter=16.5 bench=7.3 bird=14.4 cat=29.0 dog=26.3 horse=19.1 sheep=24.7 cow=24.7 elephant=31.0 bear=30.2 zebra=32.0 giraffe=34.0 backpack=2.3 umbrella=13.4 handbag=1.9 tie=8.6 suitcase=8.3 frisbee=26.3 skis=5.9 snowboard=8.2 sports ball=18.3 kite=17.4 baseball bat=6.5 baseball glove=10.4 skateboard=15.9 surfboard=10.9 tennis racket=19.4 bottle=12.0 wine glass=11.5 cup=18.0 fork=5.3 knife=2.0 spoon=2.1 bowl=17.1 banana=5.4 apple=3.4 sandwich=11.8 orange=8.7 broccoli=4.2 carrot=3.2 hot dog=9.2 pizza=25.0 donut=19.4 cake=14.9 chair=8.1 couch=20.3 potted plant=7.5 bed=24.1 dining table=11.2 toilet=31.3 tv=26.0 laptop=28.5 mouse=21.7 remote=4.1 keyboard=18.5 cell phone=10.7 microwave=30.7 oven=11.9 toaster=0.0 sink=13.3 refrigerator=23.0 book=2.8 clock=21.5 vase=15.7 scissors=3.1 teddy bear=19.5 hair drier=0.0 toothbrush=0.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.7 [Epoch 31][Batch 99], LR: 1.00E-03, Speed: 121.401 samples/sec, ObjLoss=29.399, BoxCenterLoss=14.789, BoxScaleLoss=5.721, ClassLoss=13.892 [Epoch 31][Batch 199], LR: 1.00E-03, Speed: 9.942 samples/sec, ObjLoss=29.393, BoxCenterLoss=14.790, BoxScaleLoss=5.720, ClassLoss=13.888 [Epoch 31][Batch 299], LR: 1.00E-03, Speed: 9.880 samples/sec, ObjLoss=29.386, BoxCenterLoss=14.789, BoxScaleLoss=5.720, ClassLoss=13.882 [Epoch 31][Batch 399], LR: 1.00E-03, Speed: 89.140 samples/sec, ObjLoss=29.375, BoxCenterLoss=14.787, BoxScaleLoss=5.718, ClassLoss=13.875 [Epoch 31][Batch 499], LR: 1.00E-03, Speed: 8.369 samples/sec, ObjLoss=29.369, BoxCenterLoss=14.786, BoxScaleLoss=5.717, ClassLoss=13.870 [Epoch 31][Batch 599], LR: 1.00E-03, Speed: 100.768 samples/sec, ObjLoss=29.361, BoxCenterLoss=14.785, BoxScaleLoss=5.716, ClassLoss=13.864 [Epoch 31][Batch 699], LR: 1.00E-03, Speed: 134.170 samples/sec, ObjLoss=29.351, BoxCenterLoss=14.783, BoxScaleLoss=5.715, ClassLoss=13.858 [Epoch 31][Batch 799], LR: 1.00E-03, Speed: 8.530 samples/sec, ObjLoss=29.342, BoxCenterLoss=14.782, BoxScaleLoss=5.715, ClassLoss=13.853 [Epoch 31][Batch 899], LR: 1.00E-03, Speed: 10.941 samples/sec, ObjLoss=29.336, BoxCenterLoss=14.782, BoxScaleLoss=5.714, ClassLoss=13.848 [Epoch 31][Batch 999], LR: 1.00E-03, Speed: 123.482 samples/sec, ObjLoss=29.329, BoxCenterLoss=14.782, BoxScaleLoss=5.713, ClassLoss=13.842 [Epoch 31][Batch 1099], LR: 1.00E-03, Speed: 107.311 samples/sec, ObjLoss=29.322, BoxCenterLoss=14.782, BoxScaleLoss=5.713, ClassLoss=13.836 [Epoch 31][Batch 1199], LR: 1.00E-03, Speed: 9.453 samples/sec, ObjLoss=29.317, BoxCenterLoss=14.782, BoxScaleLoss=5.712, ClassLoss=13.831 [Epoch 31][Batch 1299], LR: 1.00E-03, Speed: 11.045 samples/sec, ObjLoss=29.306, BoxCenterLoss=14.779, BoxScaleLoss=5.710, ClassLoss=13.824 [Epoch 31][Batch 1399], LR: 1.00E-03, Speed: 10.029 samples/sec, ObjLoss=29.297, BoxCenterLoss=14.778, BoxScaleLoss=5.709, ClassLoss=13.819 [Epoch 31][Batch 1499], LR: 1.00E-03, Speed: 9.770 samples/sec, ObjLoss=29.290, BoxCenterLoss=14.778, BoxScaleLoss=5.709, ClassLoss=13.813 [Epoch 31][Batch 1599], LR: 1.00E-03, Speed: 13.299 samples/sec, ObjLoss=29.284, BoxCenterLoss=14.777, BoxScaleLoss=5.708, ClassLoss=13.808 [Epoch 31][Batch 1699], LR: 1.00E-03, Speed: 6.922 samples/sec, ObjLoss=29.277, BoxCenterLoss=14.777, BoxScaleLoss=5.708, ClassLoss=13.805 [Epoch 31][Batch 1799], LR: 1.00E-03, Speed: 10.281 samples/sec, ObjLoss=29.271, BoxCenterLoss=14.778, BoxScaleLoss=5.708, ClassLoss=13.801 [Epoch 31] Training cost: 2116.843, ObjLoss=29.269, BoxCenterLoss=14.777, BoxScaleLoss=5.708, ClassLoss=13.799 [Epoch 31] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.157 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.351 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.112 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.051 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.242 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.159 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.225 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.079 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.342 person=28.1 bicycle=11.4 car=18.8 motorcycle=18.9 airplane=32.5 bus=35.1 train=35.1 truck=13.7 boat=8.8 traffic light=8.6 fire hydrant=27.6 stop sign=29.9 parking meter=15.0 bench=9.0 bird=8.8 cat=32.4 dog=25.3 horse=19.1 sheep=20.2 cow=22.7 elephant=34.0 bear=32.5 zebra=38.5 giraffe=36.8 backpack=2.8 umbrella=15.2 handbag=1.3 tie=11.4 suitcase=8.7 frisbee=23.1 skis=4.3 snowboard=7.0 sports ball=16.2 kite=17.1 baseball bat=6.1 baseball glove=12.7 skateboard=19.1 surfboard=11.3 tennis racket=15.8 bottle=11.7 wine glass=12.1 cup=16.8 fork=6.4 knife=1.0 spoon=2.7 bowl=15.3 banana=6.2 apple=4.4 sandwich=10.3 orange=14.1 broccoli=7.8 carrot=4.4 hot dog=8.2 pizza=23.2 donut=21.9 cake=10.8 chair=9.1 couch=19.5 potted plant=6.2 bed=16.2 dining table=9.4 toilet=30.0 tv=29.2 laptop=26.1 mouse=25.1 remote=4.3 keyboard=16.4 cell phone=11.5 microwave=24.1 oven=13.8 toaster=0.0 sink=12.6 refrigerator=20.0 book=2.1 clock=27.1 vase=15.7 scissors=6.6 teddy bear=22.3 hair drier=0.0 toothbrush=0.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.7 [Epoch 32][Batch 99], LR: 1.00E-03, Speed: 9.055 samples/sec, ObjLoss=29.266, BoxCenterLoss=14.780, BoxScaleLoss=5.708, ClassLoss=13.794 [Epoch 32][Batch 199], LR: 1.00E-03, Speed: 9.056 samples/sec, ObjLoss=29.256, BoxCenterLoss=14.778, BoxScaleLoss=5.706, ClassLoss=13.788 [Epoch 32][Batch 299], LR: 1.00E-03, Speed: 109.740 samples/sec, ObjLoss=29.249, BoxCenterLoss=14.777, BoxScaleLoss=5.705, ClassLoss=13.782 [Epoch 32][Batch 399], LR: 1.00E-03, Speed: 9.254 samples/sec, ObjLoss=29.245, BoxCenterLoss=14.778, BoxScaleLoss=5.704, ClassLoss=13.777 [Epoch 32][Batch 499], LR: 1.00E-03, Speed: 12.194 samples/sec, ObjLoss=29.238, BoxCenterLoss=14.777, BoxScaleLoss=5.703, ClassLoss=13.772 [Epoch 32][Batch 599], LR: 1.00E-03, Speed: 9.643 samples/sec, ObjLoss=29.232, BoxCenterLoss=14.779, BoxScaleLoss=5.703, ClassLoss=13.767 [Epoch 32][Batch 699], LR: 1.00E-03, Speed: 8.048 samples/sec, ObjLoss=29.225, BoxCenterLoss=14.778, BoxScaleLoss=5.702, ClassLoss=13.762 [Epoch 32][Batch 799], LR: 1.00E-03, Speed: 7.437 samples/sec, ObjLoss=29.217, BoxCenterLoss=14.777, BoxScaleLoss=5.702, ClassLoss=13.757 [Epoch 32][Batch 899], LR: 1.00E-03, Speed: 11.284 samples/sec, ObjLoss=29.208, BoxCenterLoss=14.775, BoxScaleLoss=5.701, ClassLoss=13.752 [Epoch 32][Batch 999], LR: 1.00E-03, Speed: 10.589 samples/sec, ObjLoss=29.206, BoxCenterLoss=14.778, BoxScaleLoss=5.701, ClassLoss=13.749 [Epoch 32][Batch 1099], LR: 1.00E-03, Speed: 7.792 samples/sec, ObjLoss=29.200, BoxCenterLoss=14.778, BoxScaleLoss=5.701, ClassLoss=13.744 [Epoch 32][Batch 1199], LR: 1.00E-03, Speed: 10.276 samples/sec, ObjLoss=29.191, BoxCenterLoss=14.776, BoxScaleLoss=5.699, ClassLoss=13.738 [Epoch 32][Batch 1299], LR: 1.00E-03, Speed: 13.047 samples/sec, ObjLoss=29.188, BoxCenterLoss=14.778, BoxScaleLoss=5.699, ClassLoss=13.733 [Epoch 32][Batch 1399], LR: 1.00E-03, Speed: 8.047 samples/sec, ObjLoss=29.181, BoxCenterLoss=14.778, BoxScaleLoss=5.699, ClassLoss=13.728 [Epoch 32][Batch 1499], LR: 1.00E-03, Speed: 10.199 samples/sec, ObjLoss=29.175, BoxCenterLoss=14.778, BoxScaleLoss=5.698, ClassLoss=13.723 [Epoch 32][Batch 1599], LR: 1.00E-03, Speed: 7.996 samples/sec, ObjLoss=29.166, BoxCenterLoss=14.775, BoxScaleLoss=5.697, ClassLoss=13.718 [Epoch 32][Batch 1699], LR: 1.00E-03, Speed: 8.649 samples/sec, ObjLoss=29.158, BoxCenterLoss=14.774, BoxScaleLoss=5.696, ClassLoss=13.712 [Epoch 32][Batch 1799], LR: 1.00E-03, Speed: 10.812 samples/sec, ObjLoss=29.154, BoxCenterLoss=14.775, BoxScaleLoss=5.695, ClassLoss=13.707 [Epoch 32] Training cost: 2157.961, ObjLoss=29.153, BoxCenterLoss=14.776, BoxScaleLoss=5.695, ClassLoss=13.705 [Epoch 32] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.170 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.367 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.064 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.172 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.255 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.378 person=29.9 bicycle=11.8 car=19.8 motorcycle=21.3 airplane=29.4 bus=32.5 train=35.8 truck=15.2 boat=8.8 traffic light=7.2 fire hydrant=30.9 stop sign=34.2 parking meter=21.2 bench=7.8 bird=14.2 cat=36.2 dog=26.2 horse=27.3 sheep=23.9 cow=25.8 elephant=36.4 bear=37.0 zebra=32.5 giraffe=40.9 backpack=2.8 umbrella=15.8 handbag=2.5 tie=14.3 suitcase=10.9 frisbee=23.8 skis=4.9 snowboard=5.5 sports ball=23.4 kite=19.0 baseball bat=5.7 baseball glove=12.3 skateboard=17.7 surfboard=10.2 tennis racket=15.4 bottle=14.5 wine glass=12.4 cup=16.9 fork=6.2 knife=2.4 spoon=1.8 bowl=15.3 banana=7.6 apple=3.5 sandwich=12.7 orange=11.6 broccoli=7.2 carrot=5.3 hot dog=8.8 pizza=24.5 donut=21.9 cake=18.6 chair=10.0 couch=23.4 potted plant=6.9 bed=24.7 dining table=16.0 toilet=31.5 tv=31.3 laptop=32.2 mouse=26.7 remote=4.5 keyboard=18.2 cell phone=13.1 microwave=19.3 oven=14.9 toaster=0.0 sink=13.5 refrigerator=21.9 book=3.0 clock=24.2 vase=15.9 scissors=7.9 teddy bear=21.5 hair drier=0.0 toothbrush=0.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.0 [Epoch 33][Batch 99], LR: 1.00E-03, Speed: 11.412 samples/sec, ObjLoss=29.145, BoxCenterLoss=14.775, BoxScaleLoss=5.694, ClassLoss=13.701 [Epoch 33][Batch 199], LR: 1.00E-03, Speed: 96.821 samples/sec, ObjLoss=29.138, BoxCenterLoss=14.774, BoxScaleLoss=5.693, ClassLoss=13.696 [Epoch 33][Batch 299], LR: 1.00E-03, Speed: 103.383 samples/sec, ObjLoss=29.130, BoxCenterLoss=14.773, BoxScaleLoss=5.692, ClassLoss=13.691 [Epoch 33][Batch 399], LR: 1.00E-03, Speed: 11.839 samples/sec, ObjLoss=29.122, BoxCenterLoss=14.772, BoxScaleLoss=5.691, ClassLoss=13.686 [Epoch 33][Batch 499], LR: 1.00E-03, Speed: 8.115 samples/sec, ObjLoss=29.118, BoxCenterLoss=14.773, BoxScaleLoss=5.691, ClassLoss=13.682 [Epoch 33][Batch 599], LR: 1.00E-03, Speed: 7.675 samples/sec, ObjLoss=29.115, BoxCenterLoss=14.774, BoxScaleLoss=5.691, ClassLoss=13.678 [Epoch 33][Batch 699], LR: 1.00E-03, Speed: 8.277 samples/sec, ObjLoss=29.107, BoxCenterLoss=14.773, BoxScaleLoss=5.690, ClassLoss=13.672 [Epoch 33][Batch 799], LR: 1.00E-03, Speed: 9.799 samples/sec, ObjLoss=29.101, BoxCenterLoss=14.772, BoxScaleLoss=5.689, ClassLoss=13.667 [Epoch 33][Batch 899], LR: 1.00E-03, Speed: 12.652 samples/sec, ObjLoss=29.094, BoxCenterLoss=14.771, BoxScaleLoss=5.688, ClassLoss=13.662 [Epoch 33][Batch 999], LR: 1.00E-03, Speed: 12.080 samples/sec, ObjLoss=29.086, BoxCenterLoss=14.769, BoxScaleLoss=5.686, ClassLoss=13.656 [Epoch 33][Batch 1099], LR: 1.00E-03, Speed: 9.307 samples/sec, ObjLoss=29.078, BoxCenterLoss=14.768, BoxScaleLoss=5.685, ClassLoss=13.651 [Epoch 33][Batch 1199], LR: 1.00E-03, Speed: 130.085 samples/sec, ObjLoss=29.075, BoxCenterLoss=14.770, BoxScaleLoss=5.685, ClassLoss=13.647 [Epoch 33][Batch 1299], LR: 1.00E-03, Speed: 9.871 samples/sec, ObjLoss=29.068, BoxCenterLoss=14.769, BoxScaleLoss=5.685, ClassLoss=13.643 [Epoch 33][Batch 1399], LR: 1.00E-03, Speed: 10.890 samples/sec, ObjLoss=29.062, BoxCenterLoss=14.769, BoxScaleLoss=5.685, ClassLoss=13.639 [Epoch 33][Batch 1499], LR: 1.00E-03, Speed: 10.379 samples/sec, ObjLoss=29.056, BoxCenterLoss=14.769, BoxScaleLoss=5.683, ClassLoss=13.634 [Epoch 33][Batch 1599], LR: 1.00E-03, Speed: 8.463 samples/sec, ObjLoss=29.051, BoxCenterLoss=14.768, BoxScaleLoss=5.683, ClassLoss=13.630 [Epoch 33][Batch 1699], LR: 1.00E-03, Speed: 7.729 samples/sec, ObjLoss=29.046, BoxCenterLoss=14.769, BoxScaleLoss=5.682, ClassLoss=13.626 [Epoch 33][Batch 1799], LR: 1.00E-03, Speed: 130.882 samples/sec, ObjLoss=29.039, BoxCenterLoss=14.768, BoxScaleLoss=5.682, ClassLoss=13.622 [Epoch 33] Training cost: 2163.549, ObjLoss=29.038, BoxCenterLoss=14.768, BoxScaleLoss=5.682, ClassLoss=13.621 [Epoch 33] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.170 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.140 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.059 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.174 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.260 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.170 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363 person=32.4 bicycle=11.3 car=18.7 motorcycle=20.4 airplane=28.3 bus=42.4 train=41.6 truck=17.8 boat=8.8 traffic light=9.3 fire hydrant=30.7 stop sign=35.6 parking meter=22.5 bench=8.8 bird=14.8 cat=35.2 dog=29.2 horse=30.0 sheep=26.1 cow=27.5 elephant=34.2 bear=37.6 zebra=38.2 giraffe=38.5 backpack=3.2 umbrella=15.5 handbag=2.6 tie=10.5 suitcase=10.6 frisbee=28.5 skis=4.6 snowboard=10.3 sports ball=14.4 kite=17.8 baseball bat=7.0 baseball glove=13.1 skateboard=18.5 surfboard=13.7 tennis racket=19.5 bottle=12.3 wine glass=14.0 cup=18.7 fork=5.6 knife=1.7 spoon=2.3 bowl=15.1 banana=9.1 apple=7.0 sandwich=14.8 orange=12.8 broccoli=4.7 carrot=4.7 hot dog=7.2 pizza=18.4 donut=17.8 cake=14.2 chair=9.9 couch=20.1 potted plant=6.2 bed=26.0 dining table=17.2 toilet=27.3 tv=25.9 laptop=27.3 mouse=25.3 remote=4.0 keyboard=18.5 cell phone=10.8 microwave=19.2 oven=11.3 toaster=0.0 sink=13.0 refrigerator=18.9 book=1.6 clock=25.4 vase=14.8 scissors=4.4 teddy bear=23.6 hair drier=0.0 toothbrush=1.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.0 [Epoch 34][Batch 99], LR: 1.00E-03, Speed: 10.409 samples/sec, ObjLoss=29.031, BoxCenterLoss=14.767, BoxScaleLoss=5.681, ClassLoss=13.616 [Epoch 34][Batch 199], LR: 1.00E-03, Speed: 8.102 samples/sec, ObjLoss=29.024, BoxCenterLoss=14.766, BoxScaleLoss=5.679, ClassLoss=13.610 [Epoch 34][Batch 299], LR: 1.00E-03, Speed: 6.203 samples/sec, ObjLoss=29.017, BoxCenterLoss=14.766, BoxScaleLoss=5.678, ClassLoss=13.604 [Epoch 34][Batch 399], LR: 1.00E-03, Speed: 9.069 samples/sec, ObjLoss=29.011, BoxCenterLoss=14.766, BoxScaleLoss=5.678, ClassLoss=13.601 [Epoch 34][Batch 499], LR: 1.00E-03, Speed: 96.934 samples/sec, ObjLoss=29.006, BoxCenterLoss=14.767, BoxScaleLoss=5.678, ClassLoss=13.597 [Epoch 34][Batch 599], LR: 1.00E-03, Speed: 7.910 samples/sec, ObjLoss=28.998, BoxCenterLoss=14.765, BoxScaleLoss=5.677, ClassLoss=13.592 [Epoch 34][Batch 699], LR: 1.00E-03, Speed: 10.631 samples/sec, ObjLoss=28.991, BoxCenterLoss=14.765, BoxScaleLoss=5.677, ClassLoss=13.586 [Epoch 34][Batch 799], LR: 1.00E-03, Speed: 10.268 samples/sec, ObjLoss=28.983, BoxCenterLoss=14.764, BoxScaleLoss=5.675, ClassLoss=13.580 [Epoch 34][Batch 899], LR: 1.00E-03, Speed: 12.938 samples/sec, ObjLoss=28.977, BoxCenterLoss=14.764, BoxScaleLoss=5.675, ClassLoss=13.577 [Epoch 34][Batch 999], LR: 1.00E-03, Speed: 10.665 samples/sec, ObjLoss=28.970, BoxCenterLoss=14.763, BoxScaleLoss=5.675, ClassLoss=13.573 [Epoch 34][Batch 1099], LR: 1.00E-03, Speed: 7.822 samples/sec, ObjLoss=28.964, BoxCenterLoss=14.764, BoxScaleLoss=5.675, ClassLoss=13.569 [Epoch 34][Batch 1199], LR: 1.00E-03, Speed: 10.168 samples/sec, ObjLoss=28.959, BoxCenterLoss=14.764, BoxScaleLoss=5.674, ClassLoss=13.565 [Epoch 34][Batch 1299], LR: 1.00E-03, Speed: 10.199 samples/sec, ObjLoss=28.955, BoxCenterLoss=14.764, BoxScaleLoss=5.674, ClassLoss=13.561 [Epoch 34][Batch 1399], LR: 1.00E-03, Speed: 12.271 samples/sec, ObjLoss=28.948, BoxCenterLoss=14.763, BoxScaleLoss=5.673, ClassLoss=13.556 [Epoch 34][Batch 1499], LR: 1.00E-03, Speed: 8.323 samples/sec, ObjLoss=28.942, BoxCenterLoss=14.763, BoxScaleLoss=5.672, ClassLoss=13.551 [Epoch 34][Batch 1599], LR: 1.00E-03, Speed: 93.078 samples/sec, ObjLoss=28.933, BoxCenterLoss=14.761, BoxScaleLoss=5.671, ClassLoss=13.546 [Epoch 34][Batch 1699], LR: 1.00E-03, Speed: 8.715 samples/sec, ObjLoss=28.925, BoxCenterLoss=14.759, BoxScaleLoss=5.670, ClassLoss=13.540 [Epoch 34][Batch 1799], LR: 1.00E-03, Speed: 10.498 samples/sec, ObjLoss=28.920, BoxCenterLoss=14.759, BoxScaleLoss=5.669, ClassLoss=13.536 [Epoch 34] Training cost: 2116.574, ObjLoss=28.918, BoxCenterLoss=14.759, BoxScaleLoss=5.669, ClassLoss=13.535 [Epoch 34] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.177 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.145 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.186 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.174 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.257 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.260 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377 person=30.7 bicycle=12.8 car=20.9 motorcycle=23.1 airplane=33.5 bus=36.9 train=34.4 truck=18.2 boat=8.7 traffic light=7.9 fire hydrant=39.5 stop sign=35.0 parking meter=18.0 bench=8.7 bird=16.6 cat=34.9 dog=27.4 horse=30.9 sheep=25.3 cow=28.8 elephant=36.7 bear=33.1 zebra=39.0 giraffe=45.7 backpack=2.5 umbrella=15.4 handbag=1.5 tie=10.8 suitcase=9.1 frisbee=24.3 skis=5.8 snowboard=4.3 sports ball=21.1 kite=18.4 baseball bat=5.6 baseball glove=13.5 skateboard=22.5 surfboard=13.6 tennis racket=19.7 bottle=13.0 wine glass=13.5 cup=18.3 fork=7.4 knife=1.8 spoon=1.8 bowl=17.4 banana=8.2 apple=5.0 sandwich=15.9 orange=13.1 broccoli=8.1 carrot=5.5 hot dog=9.7 pizza=26.9 donut=21.4 cake=14.7 chair=11.4 couch=19.6 potted plant=9.0 bed=25.3 dining table=16.3 toilet=29.2 tv=30.1 laptop=31.7 mouse=30.3 remote=4.2 keyboard=21.0 cell phone=12.2 microwave=24.9 oven=12.4 toaster=0.0 sink=13.2 refrigerator=18.4 book=2.1 clock=25.2 vase=15.1 scissors=7.4 teddy bear=21.9 hair drier=0.0 toothbrush=0.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.7 [Epoch 35][Batch 99], LR: 1.00E-03, Speed: 10.136 samples/sec, ObjLoss=28.911, BoxCenterLoss=14.758, BoxScaleLoss=5.668, ClassLoss=13.530 [Epoch 35][Batch 199], LR: 1.00E-03, Speed: 107.541 samples/sec, ObjLoss=28.905, BoxCenterLoss=14.757, BoxScaleLoss=5.667, ClassLoss=13.525 [Epoch 35][Batch 299], LR: 1.00E-03, Speed: 9.048 samples/sec, ObjLoss=28.898, BoxCenterLoss=14.757, BoxScaleLoss=5.666, ClassLoss=13.520 [Epoch 35][Batch 399], LR: 1.00E-03, Speed: 9.308 samples/sec, ObjLoss=28.891, BoxCenterLoss=14.756, BoxScaleLoss=5.665, ClassLoss=13.515 [Epoch 35][Batch 499], LR: 1.00E-03, Speed: 121.566 samples/sec, ObjLoss=28.885, BoxCenterLoss=14.755, BoxScaleLoss=5.665, ClassLoss=13.510 [Epoch 35][Batch 599], LR: 1.00E-03, Speed: 9.866 samples/sec, ObjLoss=28.881, BoxCenterLoss=14.756, BoxScaleLoss=5.664, ClassLoss=13.505 [Epoch 35][Batch 699], LR: 1.00E-03, Speed: 10.976 samples/sec, ObjLoss=28.874, BoxCenterLoss=14.756, BoxScaleLoss=5.663, ClassLoss=13.501 [Epoch 35][Batch 799], LR: 1.00E-03, Speed: 9.440 samples/sec, ObjLoss=28.869, BoxCenterLoss=14.755, BoxScaleLoss=5.663, ClassLoss=13.497 [Epoch 35][Batch 899], LR: 1.00E-03, Speed: 8.627 samples/sec, ObjLoss=28.862, BoxCenterLoss=14.754, BoxScaleLoss=5.662, ClassLoss=13.493 [Epoch 35][Batch 999], LR: 1.00E-03, Speed: 99.914 samples/sec, ObjLoss=28.856, BoxCenterLoss=14.754, BoxScaleLoss=5.662, ClassLoss=13.489 [Epoch 35][Batch 1099], LR: 1.00E-03, Speed: 11.237 samples/sec, ObjLoss=28.852, BoxCenterLoss=14.755, BoxScaleLoss=5.661, ClassLoss=13.485 [Epoch 35][Batch 1199], LR: 1.00E-03, Speed: 136.006 samples/sec, ObjLoss=28.847, BoxCenterLoss=14.755, BoxScaleLoss=5.661, ClassLoss=13.480 [Epoch 35][Batch 1299], LR: 1.00E-03, Speed: 9.549 samples/sec, ObjLoss=28.840, BoxCenterLoss=14.754, BoxScaleLoss=5.660, ClassLoss=13.475 [Epoch 35][Batch 1399], LR: 1.00E-03, Speed: 9.206 samples/sec, ObjLoss=28.833, BoxCenterLoss=14.753, BoxScaleLoss=5.659, ClassLoss=13.470 [Epoch 35][Batch 1499], LR: 1.00E-03, Speed: 9.114 samples/sec, ObjLoss=28.827, BoxCenterLoss=14.752, BoxScaleLoss=5.658, ClassLoss=13.466 [Epoch 35][Batch 1599], LR: 1.00E-03, Speed: 11.212 samples/sec, ObjLoss=28.822, BoxCenterLoss=14.751, BoxScaleLoss=5.657, ClassLoss=13.461 [Epoch 35][Batch 1699], LR: 1.00E-03, Speed: 8.009 samples/sec, ObjLoss=28.817, BoxCenterLoss=14.752, BoxScaleLoss=5.657, ClassLoss=13.457 [Epoch 35][Batch 1799], LR: 1.00E-03, Speed: 9.386 samples/sec, ObjLoss=28.812, BoxCenterLoss=14.751, BoxScaleLoss=5.656, ClassLoss=13.452 [Epoch 35] Training cost: 2230.316, ObjLoss=28.810, BoxCenterLoss=14.751, BoxScaleLoss=5.656, ClassLoss=13.451 [Epoch 35] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.178 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.060 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.186 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.174 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.246 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.376 person=31.2 bicycle=13.5 car=21.0 motorcycle=23.1 airplane=35.2 bus=39.8 train=38.2 truck=17.2 boat=8.9 traffic light=7.4 fire hydrant=34.8 stop sign=35.4 parking meter=24.8 bench=8.2 bird=13.7 cat=35.3 dog=30.1 horse=28.2 sheep=26.7 cow=29.1 elephant=36.3 bear=39.3 zebra=40.6 giraffe=43.8 backpack=2.8 umbrella=16.6 handbag=1.7 tie=11.2 suitcase=8.3 frisbee=29.4 skis=6.1 snowboard=9.3 sports ball=21.4 kite=14.6 baseball bat=6.1 baseball glove=12.8 skateboard=19.5 surfboard=13.6 tennis racket=17.6 bottle=11.1 wine glass=12.2 cup=17.9 fork=8.3 knife=2.2 spoon=1.7 bowl=15.0 banana=7.5 apple=4.9 sandwich=13.0 orange=11.7 broccoli=8.2 carrot=4.6 hot dog=12.4 pizza=26.4 donut=20.2 cake=13.3 chair=10.0 couch=19.6 potted plant=8.6 bed=24.1 dining table=12.9 toilet=32.0 tv=31.3 laptop=33.3 mouse=29.2 remote=6.3 keyboard=23.0 cell phone=13.8 microwave=23.2 oven=13.8 toaster=0.0 sink=12.4 refrigerator=21.3 book=2.7 clock=27.3 vase=15.4 scissors=5.0 teddy bear=18.4 hair drier=0.0 toothbrush=0.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.8 [Epoch 36][Batch 99], LR: 1.00E-03, Speed: 10.818 samples/sec, ObjLoss=28.804, BoxCenterLoss=14.751, BoxScaleLoss=5.655, ClassLoss=13.447 [Epoch 36][Batch 199], LR: 1.00E-03, Speed: 118.408 samples/sec, ObjLoss=28.799, BoxCenterLoss=14.751, BoxScaleLoss=5.655, ClassLoss=13.443 [Epoch 36][Batch 299], LR: 1.00E-03, Speed: 10.775 samples/sec, ObjLoss=28.792, BoxCenterLoss=14.750, BoxScaleLoss=5.654, ClassLoss=13.438 [Epoch 36][Batch 399], LR: 1.00E-03, Speed: 9.624 samples/sec, ObjLoss=28.786, BoxCenterLoss=14.749, BoxScaleLoss=5.653, ClassLoss=13.434 [Epoch 36][Batch 499], LR: 1.00E-03, Speed: 8.918 samples/sec, ObjLoss=28.779, BoxCenterLoss=14.747, BoxScaleLoss=5.652, ClassLoss=13.429 [Epoch 36][Batch 599], LR: 1.00E-03, Speed: 7.062 samples/sec, ObjLoss=28.772, BoxCenterLoss=14.746, BoxScaleLoss=5.652, ClassLoss=13.425 [Epoch 36][Batch 699], LR: 1.00E-03, Speed: 10.102 samples/sec, ObjLoss=28.767, BoxCenterLoss=14.747, BoxScaleLoss=5.651, ClassLoss=13.421 [Epoch 36][Batch 799], LR: 1.00E-03, Speed: 8.018 samples/sec, ObjLoss=28.759, BoxCenterLoss=14.745, BoxScaleLoss=5.650, ClassLoss=13.415 [Epoch 36][Batch 899], LR: 1.00E-03, Speed: 10.697 samples/sec, ObjLoss=28.753, BoxCenterLoss=14.744, BoxScaleLoss=5.649, ClassLoss=13.411 [Epoch 36][Batch 999], LR: 1.00E-03, Speed: 9.466 samples/sec, ObjLoss=28.749, BoxCenterLoss=14.745, BoxScaleLoss=5.648, ClassLoss=13.407 [Epoch 36][Batch 1099], LR: 1.00E-03, Speed: 10.221 samples/sec, ObjLoss=28.745, BoxCenterLoss=14.745, BoxScaleLoss=5.648, ClassLoss=13.402 [Epoch 36][Batch 1199], LR: 1.00E-03, Speed: 126.434 samples/sec, ObjLoss=28.739, BoxCenterLoss=14.745, BoxScaleLoss=5.647, ClassLoss=13.398 [Epoch 36][Batch 1299], LR: 1.00E-03, Speed: 12.029 samples/sec, ObjLoss=28.732, BoxCenterLoss=14.743, BoxScaleLoss=5.646, ClassLoss=13.393 [Epoch 36][Batch 1399], LR: 1.00E-03, Speed: 8.602 samples/sec, ObjLoss=28.724, BoxCenterLoss=14.742, BoxScaleLoss=5.645, ClassLoss=13.388 [Epoch 36][Batch 1499], LR: 1.00E-03, Speed: 10.443 samples/sec, ObjLoss=28.719, BoxCenterLoss=14.742, BoxScaleLoss=5.644, ClassLoss=13.384 [Epoch 36][Batch 1599], LR: 1.00E-03, Speed: 9.957 samples/sec, ObjLoss=28.714, BoxCenterLoss=14.742, BoxScaleLoss=5.644, ClassLoss=13.380 [Epoch 36][Batch 1699], LR: 1.00E-03, Speed: 9.591 samples/sec, ObjLoss=28.708, BoxCenterLoss=14.741, BoxScaleLoss=5.643, ClassLoss=13.376 [Epoch 36][Batch 1799], LR: 1.00E-03, Speed: 129.772 samples/sec, ObjLoss=28.701, BoxCenterLoss=14.740, BoxScaleLoss=5.642, ClassLoss=13.371 [Epoch 36] Training cost: 2125.426, ObjLoss=28.700, BoxCenterLoss=14.740, BoxScaleLoss=5.642, ClassLoss=13.370 [Epoch 36] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.375 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.128 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.064 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.169 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.246 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358 person=32.3 bicycle=14.2 car=19.9 motorcycle=19.5 airplane=31.1 bus=39.1 train=40.1 truck=18.0 boat=9.5 traffic light=8.9 fire hydrant=25.6 stop sign=28.7 parking meter=16.1 bench=10.5 bird=14.7 cat=34.4 dog=30.5 horse=29.5 sheep=24.9 cow=28.1 elephant=35.0 bear=34.8 zebra=37.4 giraffe=37.9 backpack=4.5 umbrella=13.7 handbag=3.0 tie=11.1 suitcase=9.8 frisbee=23.1 skis=4.6 snowboard=7.7 sports ball=21.7 kite=20.1 baseball bat=5.2 baseball glove=14.8 skateboard=19.8 surfboard=12.0 tennis racket=18.7 bottle=14.7 wine glass=13.5 cup=16.6 fork=4.6 knife=1.9 spoon=0.8 bowl=15.3 banana=8.4 apple=6.3 sandwich=11.6 orange=11.7 broccoli=5.8 carrot=4.5 hot dog=12.0 pizza=23.8 donut=19.6 cake=13.1 chair=10.8 couch=19.6 potted plant=8.1 bed=24.0 dining table=14.0 toilet=29.1 tv=26.7 laptop=27.8 mouse=24.3 remote=5.8 keyboard=17.8 cell phone=12.1 microwave=26.7 oven=10.7 toaster=0.0 sink=12.5 refrigerator=19.6 book=3.4 clock=26.2 vase=13.9 scissors=4.5 teddy bear=21.7 hair drier=0.0 toothbrush=0.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.9 [Epoch 37][Batch 99], LR: 1.00E-03, Speed: 10.024 samples/sec, ObjLoss=28.696, BoxCenterLoss=14.740, BoxScaleLoss=5.642, ClassLoss=13.367 [Epoch 37][Batch 199], LR: 1.00E-03, Speed: 10.407 samples/sec, ObjLoss=28.693, BoxCenterLoss=14.741, BoxScaleLoss=5.642, ClassLoss=13.363 [Epoch 37][Batch 299], LR: 1.00E-03, Speed: 120.727 samples/sec, ObjLoss=28.687, BoxCenterLoss=14.740, BoxScaleLoss=5.641, ClassLoss=13.359 [Epoch 37][Batch 399], LR: 1.00E-03, Speed: 9.430 samples/sec, ObjLoss=28.681, BoxCenterLoss=14.740, BoxScaleLoss=5.641, ClassLoss=13.356 [Epoch 37][Batch 499], LR: 1.00E-03, Speed: 9.673 samples/sec, ObjLoss=28.675, BoxCenterLoss=14.739, BoxScaleLoss=5.640, ClassLoss=13.352 [Epoch 37][Batch 599], LR: 1.00E-03, Speed: 12.866 samples/sec, ObjLoss=28.671, BoxCenterLoss=14.739, BoxScaleLoss=5.639, ClassLoss=13.348 [Epoch 37][Batch 699], LR: 1.00E-03, Speed: 8.976 samples/sec, ObjLoss=28.666, BoxCenterLoss=14.739, BoxScaleLoss=5.639, ClassLoss=13.343 [Epoch 37][Batch 799], LR: 1.00E-03, Speed: 9.780 samples/sec, ObjLoss=28.660, BoxCenterLoss=14.738, BoxScaleLoss=5.639, ClassLoss=13.339 [Epoch 37][Batch 899], LR: 1.00E-03, Speed: 9.462 samples/sec, ObjLoss=28.655, BoxCenterLoss=14.739, BoxScaleLoss=5.638, ClassLoss=13.335 [Epoch 37][Batch 999], LR: 1.00E-03, Speed: 11.288 samples/sec, ObjLoss=28.649, BoxCenterLoss=14.738, BoxScaleLoss=5.638, ClassLoss=13.331 [Epoch 37][Batch 1099], LR: 1.00E-03, Speed: 11.473 samples/sec, ObjLoss=28.644, BoxCenterLoss=14.738, BoxScaleLoss=5.637, ClassLoss=13.326 [Epoch 37][Batch 1199], LR: 1.00E-03, Speed: 88.907 samples/sec, ObjLoss=28.637, BoxCenterLoss=14.737, BoxScaleLoss=5.636, ClassLoss=13.322 [Epoch 37][Batch 1299], LR: 1.00E-03, Speed: 12.726 samples/sec, ObjLoss=28.631, BoxCenterLoss=14.736, BoxScaleLoss=5.635, ClassLoss=13.317 [Epoch 37][Batch 1399], LR: 1.00E-03, Speed: 9.243 samples/sec, ObjLoss=28.629, BoxCenterLoss=14.737, BoxScaleLoss=5.635, ClassLoss=13.314 [Epoch 37][Batch 1499], LR: 1.00E-03, Speed: 10.666 samples/sec, ObjLoss=28.624, BoxCenterLoss=14.738, BoxScaleLoss=5.635, ClassLoss=13.311 [Epoch 37][Batch 1599], LR: 1.00E-03, Speed: 7.219 samples/sec, ObjLoss=28.619, BoxCenterLoss=14.738, BoxScaleLoss=5.636, ClassLoss=13.308 [Epoch 37][Batch 1699], LR: 1.00E-03, Speed: 10.396 samples/sec, ObjLoss=28.615, BoxCenterLoss=14.738, BoxScaleLoss=5.635, ClassLoss=13.305 [Epoch 37][Batch 1799], LR: 1.00E-03, Speed: 11.055 samples/sec, ObjLoss=28.607, BoxCenterLoss=14.736, BoxScaleLoss=5.634, ClassLoss=13.301 [Epoch 37] Training cost: 2182.560, ObjLoss=28.606, BoxCenterLoss=14.736, BoxScaleLoss=5.634, ClassLoss=13.299 [Epoch 37] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.385 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.115 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.066 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.171 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.255 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.269 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357 person=33.5 bicycle=10.7 car=21.7 motorcycle=20.9 airplane=30.9 bus=34.0 train=39.2 truck=15.4 boat=9.5 traffic light=10.8 fire hydrant=22.4 stop sign=27.7 parking meter=19.4 bench=8.9 bird=12.4 cat=36.8 dog=27.9 horse=23.7 sheep=21.3 cow=20.3 elephant=35.2 bear=31.5 zebra=31.1 giraffe=35.6 backpack=3.1 umbrella=18.9 handbag=2.4 tie=12.2 suitcase=9.2 frisbee=22.5 skis=7.5 snowboard=8.0 sports ball=20.5 kite=21.6 baseball bat=7.8 baseball glove=15.0 skateboard=20.7 surfboard=13.9 tennis racket=17.2 bottle=16.0 wine glass=9.0 cup=19.6 fork=5.6 knife=2.4 spoon=3.1 bowl=20.5 banana=7.4 apple=6.7 sandwich=14.0 orange=13.9 broccoli=7.5 carrot=6.3 hot dog=13.7 pizza=27.5 donut=20.4 cake=13.2 chair=12.0 couch=20.9 potted plant=7.6 bed=29.6 dining table=17.3 toilet=24.6 tv=25.9 laptop=22.7 mouse=26.2 remote=5.2 keyboard=21.2 cell phone=11.7 microwave=21.2 oven=13.5 toaster=0.0 sink=13.2 refrigerator=17.3 book=2.3 clock=26.2 vase=14.3 scissors=7.2 teddy bear=24.4 hair drier=0.0 toothbrush=2.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.9 [Epoch 38][Batch 99], LR: 1.00E-03, Speed: 9.152 samples/sec, ObjLoss=28.604, BoxCenterLoss=14.738, BoxScaleLoss=5.634, ClassLoss=13.296 [Epoch 38][Batch 199], LR: 1.00E-03, Speed: 106.193 samples/sec, ObjLoss=28.603, BoxCenterLoss=14.740, BoxScaleLoss=5.634, ClassLoss=13.293 [Epoch 38][Batch 299], LR: 1.00E-03, Speed: 8.786 samples/sec, ObjLoss=28.597, BoxCenterLoss=14.739, BoxScaleLoss=5.633, ClassLoss=13.288 [Epoch 38][Batch 399], LR: 1.00E-03, Speed: 10.419 samples/sec, ObjLoss=28.591, BoxCenterLoss=14.738, BoxScaleLoss=5.632, ClassLoss=13.284 [Epoch 38][Batch 499], LR: 1.00E-03, Speed: 7.850 samples/sec, ObjLoss=28.586, BoxCenterLoss=14.738, BoxScaleLoss=5.631, ClassLoss=13.278 [Epoch 38][Batch 599], LR: 1.00E-03, Speed: 10.835 samples/sec, ObjLoss=28.583, BoxCenterLoss=14.738, BoxScaleLoss=5.631, ClassLoss=13.275 [Epoch 38][Batch 699], LR: 1.00E-03, Speed: 10.697 samples/sec, ObjLoss=28.577, BoxCenterLoss=14.737, BoxScaleLoss=5.630, ClassLoss=13.272 [Epoch 38][Batch 799], LR: 1.00E-03, Speed: 94.679 samples/sec, ObjLoss=28.574, BoxCenterLoss=14.737, BoxScaleLoss=5.630, ClassLoss=13.268 [Epoch 38][Batch 899], LR: 1.00E-03, Speed: 10.382 samples/sec, ObjLoss=28.570, BoxCenterLoss=14.738, BoxScaleLoss=5.629, ClassLoss=13.264 [Epoch 38][Batch 999], LR: 1.00E-03, Speed: 7.894 samples/sec, ObjLoss=28.566, BoxCenterLoss=14.739, BoxScaleLoss=5.629, ClassLoss=13.260 [Epoch 38][Batch 1099], LR: 1.00E-03, Speed: 9.783 samples/sec, ObjLoss=28.562, BoxCenterLoss=14.738, BoxScaleLoss=5.628, ClassLoss=13.256 [Epoch 38][Batch 1199], LR: 1.00E-03, Speed: 9.961 samples/sec, ObjLoss=28.557, BoxCenterLoss=14.738, BoxScaleLoss=5.627, ClassLoss=13.252 [Epoch 38][Batch 1299], LR: 1.00E-03, Speed: 9.807 samples/sec, ObjLoss=28.552, BoxCenterLoss=14.738, BoxScaleLoss=5.627, ClassLoss=13.248 [Epoch 38][Batch 1399], LR: 1.00E-03, Speed: 8.874 samples/sec, ObjLoss=28.547, BoxCenterLoss=14.738, BoxScaleLoss=5.626, ClassLoss=13.245 [Epoch 38][Batch 1499], LR: 1.00E-03, Speed: 9.164 samples/sec, ObjLoss=28.542, BoxCenterLoss=14.737, BoxScaleLoss=5.625, ClassLoss=13.241 [Epoch 38][Batch 1599], LR: 1.00E-03, Speed: 9.532 samples/sec, ObjLoss=28.538, BoxCenterLoss=14.737, BoxScaleLoss=5.625, ClassLoss=13.237 [Epoch 38][Batch 1699], LR: 1.00E-03, Speed: 110.124 samples/sec, ObjLoss=28.533, BoxCenterLoss=14.737, BoxScaleLoss=5.624, ClassLoss=13.234 [Epoch 38][Batch 1799], LR: 1.00E-03, Speed: 99.366 samples/sec, ObjLoss=28.526, BoxCenterLoss=14.736, BoxScaleLoss=5.624, ClassLoss=13.230 [Epoch 38] Training cost: 2137.347, ObjLoss=28.525, BoxCenterLoss=14.736, BoxScaleLoss=5.623, ClassLoss=13.228 [Epoch 38] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.179 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.375 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.059 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.175 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.257 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 person=31.5 bicycle=13.3 car=21.2 motorcycle=20.5 airplane=34.0 bus=41.8 train=43.1 truck=18.6 boat=8.5 traffic light=9.1 fire hydrant=40.5 stop sign=34.4 parking meter=16.3 bench=10.4 bird=14.2 cat=33.2 dog=29.2 horse=25.9 sheep=27.2 cow=22.9 elephant=30.7 bear=41.6 zebra=37.0 giraffe=40.7 backpack=2.6 umbrella=19.5 handbag=2.4 tie=12.3 suitcase=11.7 frisbee=30.3 skis=4.2 snowboard=8.1 sports ball=17.1 kite=20.2 baseball bat=7.1 baseball glove=9.4 skateboard=16.1 surfboard=13.1 tennis racket=15.7 bottle=15.6 wine glass=12.6 cup=17.8 fork=6.3 knife=3.2 spoon=2.1 bowl=16.4 banana=7.2 apple=5.6 sandwich=12.4 orange=9.7 broccoli=7.8 carrot=4.0 hot dog=9.3 pizza=26.6 donut=21.7 cake=17.3 chair=12.1 couch=24.7 potted plant=7.7 bed=27.6 dining table=13.7 toilet=31.8 tv=29.1 laptop=29.9 mouse=30.3 remote=5.3 keyboard=27.1 cell phone=13.7 microwave=25.4 oven=12.0 toaster=0.0 sink=14.0 refrigerator=21.6 book=2.5 clock=25.0 vase=16.3 scissors=6.0 teddy bear=20.1 hair drier=0.0 toothbrush=2.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.9 [Epoch 39][Batch 99], LR: 1.00E-03, Speed: 106.676 samples/sec, ObjLoss=28.518, BoxCenterLoss=14.735, BoxScaleLoss=5.622, ClassLoss=13.224 [Epoch 39][Batch 199], LR: 1.00E-03, Speed: 90.969 samples/sec, ObjLoss=28.514, BoxCenterLoss=14.734, BoxScaleLoss=5.622, ClassLoss=13.221 [Epoch 39][Batch 299], LR: 1.00E-03, Speed: 8.390 samples/sec, ObjLoss=28.509, BoxCenterLoss=14.734, BoxScaleLoss=5.621, ClassLoss=13.217 [Epoch 39][Batch 399], LR: 1.00E-03, Speed: 7.531 samples/sec, ObjLoss=28.504, BoxCenterLoss=14.734, BoxScaleLoss=5.620, ClassLoss=13.213 [Epoch 39][Batch 499], LR: 1.00E-03, Speed: 9.670 samples/sec, ObjLoss=28.498, BoxCenterLoss=14.733, BoxScaleLoss=5.620, ClassLoss=13.209 [Epoch 39][Batch 599], LR: 1.00E-03, Speed: 9.887 samples/sec, ObjLoss=28.494, BoxCenterLoss=14.734, BoxScaleLoss=5.620, ClassLoss=13.206 [Epoch 39][Batch 699], LR: 1.00E-03, Speed: 12.154 samples/sec, ObjLoss=28.487, BoxCenterLoss=14.732, BoxScaleLoss=5.619, ClassLoss=13.203 [Epoch 39][Batch 799], LR: 1.00E-03, Speed: 8.938 samples/sec, ObjLoss=28.483, BoxCenterLoss=14.732, BoxScaleLoss=5.619, ClassLoss=13.199 [Epoch 39][Batch 899], LR: 1.00E-03, Speed: 12.022 samples/sec, ObjLoss=28.479, BoxCenterLoss=14.733, BoxScaleLoss=5.618, ClassLoss=13.195 [Epoch 39][Batch 999], LR: 1.00E-03, Speed: 13.102 samples/sec, ObjLoss=28.474, BoxCenterLoss=14.732, BoxScaleLoss=5.617, ClassLoss=13.191 [Epoch 39][Batch 1099], LR: 1.00E-03, Speed: 10.493 samples/sec, ObjLoss=28.469, BoxCenterLoss=14.732, BoxScaleLoss=5.617, ClassLoss=13.187 [Epoch 39][Batch 1199], LR: 1.00E-03, Speed: 10.544 samples/sec, ObjLoss=28.463, BoxCenterLoss=14.731, BoxScaleLoss=5.616, ClassLoss=13.182 [Epoch 39][Batch 1299], LR: 1.00E-03, Speed: 10.378 samples/sec, ObjLoss=28.459, BoxCenterLoss=14.731, BoxScaleLoss=5.615, ClassLoss=13.179 [Epoch 39][Batch 1399], LR: 1.00E-03, Speed: 8.350 samples/sec, ObjLoss=28.454, BoxCenterLoss=14.730, BoxScaleLoss=5.614, ClassLoss=13.174 [Epoch 39][Batch 1499], LR: 1.00E-03, Speed: 9.103 samples/sec, ObjLoss=28.449, BoxCenterLoss=14.729, BoxScaleLoss=5.613, ClassLoss=13.170 [Epoch 39][Batch 1599], LR: 1.00E-03, Speed: 10.211 samples/sec, ObjLoss=28.444, BoxCenterLoss=14.729, BoxScaleLoss=5.612, ClassLoss=13.165 [Epoch 39][Batch 1699], LR: 1.00E-03, Speed: 9.906 samples/sec, ObjLoss=28.441, BoxCenterLoss=14.730, BoxScaleLoss=5.611, ClassLoss=13.162 [Epoch 39][Batch 1799], LR: 1.00E-03, Speed: 11.206 samples/sec, ObjLoss=28.436, BoxCenterLoss=14.729, BoxScaleLoss=5.611, ClassLoss=13.157 [Epoch 39] Training cost: 2169.008, ObjLoss=28.435, BoxCenterLoss=14.729, BoxScaleLoss=5.610, ClassLoss=13.156 [Epoch 39] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.067 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.174 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.252 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.168 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357 person=30.5 bicycle=12.6 car=22.6 motorcycle=20.1 airplane=24.9 bus=34.4 train=34.5 truck=15.8 boat=8.7 traffic light=11.8 fire hydrant=22.5 stop sign=29.4 parking meter=14.9 bench=8.8 bird=13.1 cat=37.3 dog=27.3 horse=30.6 sheep=21.7 cow=26.2 elephant=37.6 bear=26.6 zebra=37.3 giraffe=35.2 backpack=3.5 umbrella=14.4 handbag=2.2 tie=9.5 suitcase=11.0 frisbee=18.7 skis=7.7 snowboard=8.4 sports ball=21.2 kite=23.0 baseball bat=7.5 baseball glove=10.4 skateboard=24.0 surfboard=11.8 tennis racket=18.4 bottle=15.3 wine glass=13.3 cup=16.5 fork=8.0 knife=2.5 spoon=1.6 bowl=15.3 banana=6.6 apple=3.5 sandwich=12.9 orange=11.2 broccoli=7.2 carrot=4.6 hot dog=10.9 pizza=25.2 donut=22.9 cake=13.9 chair=10.3 couch=23.8 potted plant=7.9 bed=28.3 dining table=13.8 toilet=26.8 tv=31.9 laptop=26.3 mouse=32.0 remote=5.0 keyboard=21.5 cell phone=10.5 microwave=22.8 oven=11.4 toaster=0.0 sink=13.4 refrigerator=21.7 book=3.6 clock=28.8 vase=18.6 scissors=4.0 teddy bear=23.4 hair drier=0.0 toothbrush=1.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.9 [Epoch 40][Batch 99], LR: 1.00E-03, Speed: 10.353 samples/sec, ObjLoss=28.431, BoxCenterLoss=14.730, BoxScaleLoss=5.610, ClassLoss=13.152 [Epoch 40][Batch 199], LR: 1.00E-03, Speed: 102.233 samples/sec, ObjLoss=28.426, BoxCenterLoss=14.729, BoxScaleLoss=5.609, ClassLoss=13.149 [Epoch 40][Batch 299], LR: 1.00E-03, Speed: 8.988 samples/sec, ObjLoss=28.421, BoxCenterLoss=14.728, BoxScaleLoss=5.609, ClassLoss=13.144 [Epoch 40][Batch 399], LR: 1.00E-03, Speed: 6.403 samples/sec, ObjLoss=28.418, BoxCenterLoss=14.729, BoxScaleLoss=5.608, ClassLoss=13.141 [Epoch 40][Batch 499], LR: 1.00E-03, Speed: 8.891 samples/sec, ObjLoss=28.413, BoxCenterLoss=14.729, BoxScaleLoss=5.607, ClassLoss=13.137 [Epoch 40][Batch 599], LR: 1.00E-03, Speed: 106.280 samples/sec, ObjLoss=28.407, BoxCenterLoss=14.728, BoxScaleLoss=5.606, ClassLoss=13.132 [Epoch 40][Batch 699], LR: 1.00E-03, Speed: 9.446 samples/sec, ObjLoss=28.402, BoxCenterLoss=14.727, BoxScaleLoss=5.606, ClassLoss=13.129 [Epoch 40][Batch 799], LR: 1.00E-03, Speed: 9.498 samples/sec, ObjLoss=28.397, BoxCenterLoss=14.727, BoxScaleLoss=5.605, ClassLoss=13.125 [Epoch 40][Batch 899], LR: 1.00E-03, Speed: 12.391 samples/sec, ObjLoss=28.393, BoxCenterLoss=14.727, BoxScaleLoss=5.604, ClassLoss=13.121 [Epoch 40][Batch 999], LR: 1.00E-03, Speed: 10.621 samples/sec, ObjLoss=28.389, BoxCenterLoss=14.727, BoxScaleLoss=5.604, ClassLoss=13.117 [Epoch 40][Batch 1099], LR: 1.00E-03, Speed: 10.068 samples/sec, ObjLoss=28.387, BoxCenterLoss=14.728, BoxScaleLoss=5.604, ClassLoss=13.114 [Epoch 40][Batch 1199], LR: 1.00E-03, Speed: 9.859 samples/sec, ObjLoss=28.383, BoxCenterLoss=14.728, BoxScaleLoss=5.603, ClassLoss=13.111 [Epoch 40][Batch 1299], LR: 1.00E-03, Speed: 134.210 samples/sec, ObjLoss=28.379, BoxCenterLoss=14.728, BoxScaleLoss=5.603, ClassLoss=13.107 [Epoch 40][Batch 1399], LR: 1.00E-03, Speed: 11.877 samples/sec, ObjLoss=28.374, BoxCenterLoss=14.728, BoxScaleLoss=5.602, ClassLoss=13.103 [Epoch 40][Batch 1499], LR: 1.00E-03, Speed: 9.063 samples/sec, ObjLoss=28.370, BoxCenterLoss=14.729, BoxScaleLoss=5.602, ClassLoss=13.101 [Epoch 40][Batch 1599], LR: 1.00E-03, Speed: 6.396 samples/sec, ObjLoss=28.366, BoxCenterLoss=14.729, BoxScaleLoss=5.602, ClassLoss=13.097 [Epoch 40][Batch 1699], LR: 1.00E-03, Speed: 8.326 samples/sec, ObjLoss=28.362, BoxCenterLoss=14.729, BoxScaleLoss=5.601, ClassLoss=13.093 [Epoch 40][Batch 1799], LR: 1.00E-03, Speed: 10.869 samples/sec, ObjLoss=28.357, BoxCenterLoss=14.729, BoxScaleLoss=5.601, ClassLoss=13.090 [Epoch 40] Training cost: 2193.392, ObjLoss=28.356, BoxCenterLoss=14.728, BoxScaleLoss=5.601, ClassLoss=13.088 [Epoch 40] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.186 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.384 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.391 person=32.2 bicycle=13.1 car=18.5 motorcycle=23.9 airplane=34.1 bus=40.5 train=41.6 truck=17.5 boat=8.6 traffic light=9.7 fire hydrant=37.2 stop sign=38.8 parking meter=16.2 bench=9.7 bird=15.9 cat=38.5 dog=30.9 horse=27.4 sheep=24.8 cow=26.7 elephant=37.2 bear=33.3 zebra=38.7 giraffe=41.7 backpack=3.0 umbrella=19.4 handbag=2.4 tie=9.8 suitcase=13.6 frisbee=30.0 skis=7.9 snowboard=6.5 sports ball=19.6 kite=21.4 baseball bat=7.9 baseball glove=14.5 skateboard=20.4 surfboard=12.2 tennis racket=20.7 bottle=15.0 wine glass=14.7 cup=15.4 fork=8.7 knife=1.7 spoon=2.5 bowl=17.9 banana=7.4 apple=6.4 sandwich=16.1 orange=13.7 broccoli=8.8 carrot=4.0 hot dog=12.5 pizza=27.7 donut=21.7 cake=16.0 chair=10.7 couch=25.8 potted plant=9.2 bed=29.4 dining table=13.7 toilet=34.3 tv=28.5 laptop=30.9 mouse=26.8 remote=5.3 keyboard=24.0 cell phone=10.7 microwave=29.8 oven=12.1 toaster=0.0 sink=15.4 refrigerator=24.1 book=3.1 clock=28.2 vase=15.6 scissors=7.7 teddy bear=23.9 hair drier=0.0 toothbrush=0.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.6 [Epoch 41][Batch 99], LR: 1.00E-03, Speed: 92.397 samples/sec, ObjLoss=28.350, BoxCenterLoss=14.727, BoxScaleLoss=5.600, ClassLoss=13.084 [Epoch 41][Batch 199], LR: 1.00E-03, Speed: 9.008 samples/sec, ObjLoss=28.345, BoxCenterLoss=14.727, BoxScaleLoss=5.599, ClassLoss=13.080 [Epoch 41][Batch 299], LR: 1.00E-03, Speed: 9.814 samples/sec, ObjLoss=28.343, BoxCenterLoss=14.728, BoxScaleLoss=5.598, ClassLoss=13.076 [Epoch 41][Batch 399], LR: 1.00E-03, Speed: 73.355 samples/sec, ObjLoss=28.338, BoxCenterLoss=14.727, BoxScaleLoss=5.597, ClassLoss=13.072 [Epoch 41][Batch 499], LR: 1.00E-03, Speed: 9.556 samples/sec, ObjLoss=28.335, BoxCenterLoss=14.727, BoxScaleLoss=5.597, ClassLoss=13.069 [Epoch 41][Batch 599], LR: 1.00E-03, Speed: 10.838 samples/sec, ObjLoss=28.329, BoxCenterLoss=14.726, BoxScaleLoss=5.596, ClassLoss=13.065 [Epoch 41][Batch 699], LR: 1.00E-03, Speed: 10.444 samples/sec, ObjLoss=28.324, BoxCenterLoss=14.725, BoxScaleLoss=5.595, ClassLoss=13.061 [Epoch 41][Batch 799], LR: 1.00E-03, Speed: 8.601 samples/sec, ObjLoss=28.318, BoxCenterLoss=14.724, BoxScaleLoss=5.595, ClassLoss=13.057 [Epoch 41][Batch 899], LR: 1.00E-03, Speed: 10.439 samples/sec, ObjLoss=28.312, BoxCenterLoss=14.723, BoxScaleLoss=5.594, ClassLoss=13.053 [Epoch 41][Batch 999], LR: 1.00E-03, Speed: 9.264 samples/sec, ObjLoss=28.307, BoxCenterLoss=14.722, BoxScaleLoss=5.593, ClassLoss=13.049 [Epoch 41][Batch 1099], LR: 1.00E-03, Speed: 86.450 samples/sec, ObjLoss=28.305, BoxCenterLoss=14.723, BoxScaleLoss=5.593, ClassLoss=13.046 [Epoch 41][Batch 1199], LR: 1.00E-03, Speed: 89.255 samples/sec, ObjLoss=28.301, BoxCenterLoss=14.723, BoxScaleLoss=5.592, ClassLoss=13.043 [Epoch 41][Batch 1299], LR: 1.00E-03, Speed: 9.892 samples/sec, ObjLoss=28.296, BoxCenterLoss=14.722, BoxScaleLoss=5.592, ClassLoss=13.039 [Epoch 41][Batch 1399], LR: 1.00E-03, Speed: 9.637 samples/sec, ObjLoss=28.290, BoxCenterLoss=14.721, BoxScaleLoss=5.591, ClassLoss=13.035 [Epoch 41][Batch 1499], LR: 1.00E-03, Speed: 9.409 samples/sec, ObjLoss=28.286, BoxCenterLoss=14.721, BoxScaleLoss=5.590, ClassLoss=13.031 [Epoch 41][Batch 1599], LR: 1.00E-03, Speed: 96.133 samples/sec, ObjLoss=28.282, BoxCenterLoss=14.721, BoxScaleLoss=5.590, ClassLoss=13.029 [Epoch 41][Batch 1699], LR: 1.00E-03, Speed: 94.191 samples/sec, ObjLoss=28.280, BoxCenterLoss=14.722, BoxScaleLoss=5.590, ClassLoss=13.025 [Epoch 41][Batch 1799], LR: 1.00E-03, Speed: 11.780 samples/sec, ObjLoss=28.277, BoxCenterLoss=14.722, BoxScaleLoss=5.589, ClassLoss=13.022 [Epoch 41] Training cost: 2132.528, ObjLoss=28.276, BoxCenterLoss=14.722, BoxScaleLoss=5.589, ClassLoss=13.021 [Epoch 41] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.187 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.387 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.157 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.179 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.265 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389 person=30.2 bicycle=13.6 car=21.6 motorcycle=24.2 airplane=29.1 bus=40.6 train=39.0 truck=19.3 boat=8.5 traffic light=11.1 fire hydrant=37.1 stop sign=38.2 parking meter=24.5 bench=8.9 bird=17.0 cat=37.4 dog=31.2 horse=30.9 sheep=27.2 cow=30.4 elephant=36.2 bear=42.8 zebra=39.4 giraffe=38.6 backpack=2.5 umbrella=13.9 handbag=2.0 tie=12.0 suitcase=10.1 frisbee=26.7 skis=6.0 snowboard=7.4 sports ball=20.0 kite=18.8 baseball bat=7.3 baseball glove=12.0 skateboard=22.7 surfboard=15.2 tennis racket=18.0 bottle=16.1 wine glass=13.9 cup=18.4 fork=8.2 knife=2.9 spoon=3.2 bowl=20.1 banana=9.4 apple=5.0 sandwich=15.4 orange=16.5 broccoli=8.9 carrot=6.5 hot dog=9.5 pizza=30.5 donut=22.0 cake=16.4 chair=9.7 couch=22.3 potted plant=9.7 bed=28.2 dining table=15.9 toilet=28.3 tv=32.0 laptop=29.8 mouse=26.1 remote=6.2 keyboard=20.8 cell phone=13.3 microwave=22.1 oven=15.0 toaster=0.0 sink=15.0 refrigerator=23.0 book=3.3 clock=26.0 vase=16.4 scissors=5.9 teddy bear=25.7 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.7 [Epoch 42][Batch 99], LR: 1.00E-03, Speed: 123.128 samples/sec, ObjLoss=28.271, BoxCenterLoss=14.722, BoxScaleLoss=5.589, ClassLoss=13.017 [Epoch 42][Batch 199], LR: 1.00E-03, Speed: 11.584 samples/sec, ObjLoss=28.267, BoxCenterLoss=14.722, BoxScaleLoss=5.588, ClassLoss=13.014 [Epoch 42][Batch 299], LR: 1.00E-03, Speed: 10.022 samples/sec, ObjLoss=28.263, BoxCenterLoss=14.722, BoxScaleLoss=5.587, ClassLoss=13.010 [Epoch 42][Batch 399], LR: 1.00E-03, Speed: 10.118 samples/sec, ObjLoss=28.259, BoxCenterLoss=14.722, BoxScaleLoss=5.587, ClassLoss=13.006 [Epoch 42][Batch 499], LR: 1.00E-03, Speed: 10.472 samples/sec, ObjLoss=28.254, BoxCenterLoss=14.721, BoxScaleLoss=5.586, ClassLoss=13.003 [Epoch 42][Batch 599], LR: 1.00E-03, Speed: 8.095 samples/sec, ObjLoss=28.251, BoxCenterLoss=14.721, BoxScaleLoss=5.586, ClassLoss=13.000 [Epoch 42][Batch 699], LR: 1.00E-03, Speed: 6.731 samples/sec, ObjLoss=28.246, BoxCenterLoss=14.721, BoxScaleLoss=5.586, ClassLoss=12.997 [Epoch 42][Batch 799], LR: 1.00E-03, Speed: 9.992 samples/sec, ObjLoss=28.243, BoxCenterLoss=14.721, BoxScaleLoss=5.585, ClassLoss=12.995 [Epoch 42][Batch 899], LR: 1.00E-03, Speed: 13.717 samples/sec, ObjLoss=28.241, BoxCenterLoss=14.722, BoxScaleLoss=5.585, ClassLoss=12.992 [Epoch 42][Batch 999], LR: 1.00E-03, Speed: 93.014 samples/sec, ObjLoss=28.235, BoxCenterLoss=14.721, BoxScaleLoss=5.584, ClassLoss=12.987 [Epoch 42][Batch 1099], LR: 1.00E-03, Speed: 9.195 samples/sec, ObjLoss=28.231, BoxCenterLoss=14.721, BoxScaleLoss=5.583, ClassLoss=12.983 [Epoch 42][Batch 1199], LR: 1.00E-03, Speed: 11.610 samples/sec, ObjLoss=28.228, BoxCenterLoss=14.722, BoxScaleLoss=5.583, ClassLoss=12.981 [Epoch 42][Batch 1299], LR: 1.00E-03, Speed: 8.747 samples/sec, ObjLoss=28.222, BoxCenterLoss=14.721, BoxScaleLoss=5.583, ClassLoss=12.977 [Epoch 42][Batch 1399], LR: 1.00E-03, Speed: 11.208 samples/sec, ObjLoss=28.218, BoxCenterLoss=14.721, BoxScaleLoss=5.582, ClassLoss=12.974 [Epoch 42][Batch 1499], LR: 1.00E-03, Speed: 10.927 samples/sec, ObjLoss=28.216, BoxCenterLoss=14.721, BoxScaleLoss=5.582, ClassLoss=12.970 [Epoch 42][Batch 1599], LR: 1.00E-03, Speed: 10.108 samples/sec, ObjLoss=28.208, BoxCenterLoss=14.719, BoxScaleLoss=5.580, ClassLoss=12.966 [Epoch 42][Batch 1699], LR: 1.00E-03, Speed: 10.320 samples/sec, ObjLoss=28.203, BoxCenterLoss=14.718, BoxScaleLoss=5.580, ClassLoss=12.963 [Epoch 42][Batch 1799], LR: 1.00E-03, Speed: 125.138 samples/sec, ObjLoss=28.197, BoxCenterLoss=14.717, BoxScaleLoss=5.579, ClassLoss=12.959 [Epoch 42] Training cost: 2129.646, ObjLoss=28.195, BoxCenterLoss=14.717, BoxScaleLoss=5.579, ClassLoss=12.958 [Epoch 42] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.188 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.383 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.162 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.069 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.265 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.111 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.395 person=33.6 bicycle=13.6 car=22.1 motorcycle=22.8 airplane=34.9 bus=44.3 train=42.1 truck=19.4 boat=8.0 traffic light=10.0 fire hydrant=38.2 stop sign=37.7 parking meter=18.7 bench=9.2 bird=11.1 cat=33.2 dog=32.2 horse=30.4 sheep=25.4 cow=28.2 elephant=39.1 bear=38.7 zebra=40.4 giraffe=42.4 backpack=3.8 umbrella=17.3 handbag=3.2 tie=13.2 suitcase=10.6 frisbee=30.3 skis=5.6 snowboard=6.9 sports ball=16.5 kite=17.0 baseball bat=9.1 baseball glove=14.2 skateboard=21.5 surfboard=12.5 tennis racket=21.8 bottle=14.1 wine glass=13.8 cup=17.5 fork=8.8 knife=3.3 spoon=2.5 bowl=17.3 banana=8.0 apple=6.3 sandwich=16.2 orange=15.3 broccoli=9.0 carrot=6.6 hot dog=15.1 pizza=26.8 donut=23.9 cake=18.0 chair=11.3 couch=23.3 potted plant=10.7 bed=26.5 dining table=15.9 toilet=31.8 tv=31.1 laptop=29.7 mouse=32.0 remote=5.4 keyboard=18.9 cell phone=14.9 microwave=22.4 oven=14.8 toaster=0.0 sink=14.3 refrigerator=23.8 book=2.3 clock=22.6 vase=16.6 scissors=4.8 teddy bear=27.1 hair drier=0.0 toothbrush=1.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.8 [Epoch 43][Batch 99], LR: 1.00E-03, Speed: 9.378 samples/sec, ObjLoss=28.193, BoxCenterLoss=14.718, BoxScaleLoss=5.579, ClassLoss=12.956 [Epoch 43][Batch 199], LR: 1.00E-03, Speed: 8.892 samples/sec, ObjLoss=28.188, BoxCenterLoss=14.717, BoxScaleLoss=5.578, ClassLoss=12.953 [Epoch 43][Batch 299], LR: 1.00E-03, Speed: 10.323 samples/sec, ObjLoss=28.184, BoxCenterLoss=14.717, BoxScaleLoss=5.577, ClassLoss=12.949 [Epoch 43][Batch 399], LR: 1.00E-03, Speed: 9.448 samples/sec, ObjLoss=28.179, BoxCenterLoss=14.716, BoxScaleLoss=5.576, ClassLoss=12.944 [Epoch 43][Batch 499], LR: 1.00E-03, Speed: 79.889 samples/sec, ObjLoss=28.176, BoxCenterLoss=14.715, BoxScaleLoss=5.575, ClassLoss=12.940 [Epoch 43][Batch 599], LR: 1.00E-03, Speed: 8.727 samples/sec, ObjLoss=28.171, BoxCenterLoss=14.715, BoxScaleLoss=5.574, ClassLoss=12.936 [Epoch 43][Batch 699], LR: 1.00E-03, Speed: 9.862 samples/sec, ObjLoss=28.167, BoxCenterLoss=14.715, BoxScaleLoss=5.574, ClassLoss=12.932 [Epoch 43][Batch 799], LR: 1.00E-03, Speed: 9.623 samples/sec, ObjLoss=28.162, BoxCenterLoss=14.714, BoxScaleLoss=5.574, ClassLoss=12.929 [Epoch 43][Batch 899], LR: 1.00E-03, Speed: 10.781 samples/sec, ObjLoss=28.157, BoxCenterLoss=14.714, BoxScaleLoss=5.573, ClassLoss=12.925 [Epoch 43][Batch 999], LR: 1.00E-03, Speed: 11.378 samples/sec, ObjLoss=28.153, BoxCenterLoss=14.714, BoxScaleLoss=5.572, ClassLoss=12.922 [Epoch 43][Batch 1099], LR: 1.00E-03, Speed: 11.997 samples/sec, ObjLoss=28.148, BoxCenterLoss=14.713, BoxScaleLoss=5.572, ClassLoss=12.919 [Epoch 43][Batch 1199], LR: 1.00E-03, Speed: 11.535 samples/sec, ObjLoss=28.144, BoxCenterLoss=14.713, BoxScaleLoss=5.571, ClassLoss=12.915 [Epoch 43][Batch 1299], LR: 1.00E-03, Speed: 9.705 samples/sec, ObjLoss=28.140, BoxCenterLoss=14.712, BoxScaleLoss=5.571, ClassLoss=12.912 [Epoch 43][Batch 1399], LR: 1.00E-03, Speed: 22.252 samples/sec, ObjLoss=28.133, BoxCenterLoss=14.710, BoxScaleLoss=5.569, ClassLoss=12.907 [Epoch 43][Batch 1499], LR: 1.00E-03, Speed: 8.899 samples/sec, ObjLoss=28.128, BoxCenterLoss=14.709, BoxScaleLoss=5.569, ClassLoss=12.904 [Epoch 43][Batch 1599], LR: 1.00E-03, Speed: 10.688 samples/sec, ObjLoss=28.122, BoxCenterLoss=14.708, BoxScaleLoss=5.567, ClassLoss=12.900 [Epoch 43][Batch 1699], LR: 1.00E-03, Speed: 89.856 samples/sec, ObjLoss=28.115, BoxCenterLoss=14.706, BoxScaleLoss=5.567, ClassLoss=12.896 [Epoch 43][Batch 1799], LR: 1.00E-03, Speed: 123.275 samples/sec, ObjLoss=28.111, BoxCenterLoss=14.706, BoxScaleLoss=5.566, ClassLoss=12.893 [Epoch 43] Training cost: 2147.831, ObjLoss=28.110, BoxCenterLoss=14.706, BoxScaleLoss=5.566, ClassLoss=12.892 [Epoch 43] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.389 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.072 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.269 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.111 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.398 person=33.2 bicycle=13.3 car=21.7 motorcycle=22.8 airplane=32.2 bus=46.2 train=41.8 truck=17.9 boat=8.5 traffic light=9.4 fire hydrant=34.7 stop sign=34.6 parking meter=20.1 bench=10.8 bird=12.8 cat=36.8 dog=29.5 horse=27.3 sheep=27.6 cow=26.5 elephant=39.8 bear=39.4 zebra=42.3 giraffe=44.2 backpack=4.6 umbrella=20.2 handbag=2.2 tie=14.1 suitcase=11.8 frisbee=30.3 skis=6.8 snowboard=11.2 sports ball=17.8 kite=20.2 baseball bat=8.3 baseball glove=12.0 skateboard=22.3 surfboard=17.3 tennis racket=21.7 bottle=15.7 wine glass=16.3 cup=18.9 fork=7.4 knife=2.2 spoon=2.1 bowl=18.7 banana=9.0 apple=8.5 sandwich=18.0 orange=14.5 broccoli=6.5 carrot=6.1 hot dog=11.5 pizza=28.5 donut=21.6 cake=15.2 chair=10.7 couch=26.7 potted plant=9.4 bed=30.2 dining table=17.0 toilet=30.2 tv=31.2 laptop=33.7 mouse=29.1 remote=6.1 keyboard=21.6 cell phone=14.0 microwave=24.4 oven=13.5 toaster=0.0 sink=14.8 refrigerator=23.1 book=3.5 clock=26.3 vase=15.7 scissors=6.7 teddy bear=26.6 hair drier=0.0 toothbrush=2.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.1 [Epoch 44][Batch 99], LR: 1.00E-03, Speed: 10.268 samples/sec, ObjLoss=28.106, BoxCenterLoss=14.706, BoxScaleLoss=5.566, ClassLoss=12.889 [Epoch 44][Batch 199], LR: 1.00E-03, Speed: 10.632 samples/sec, ObjLoss=28.100, BoxCenterLoss=14.705, BoxScaleLoss=5.566, ClassLoss=12.886 [Epoch 44][Batch 299], LR: 1.00E-03, Speed: 8.114 samples/sec, ObjLoss=28.097, BoxCenterLoss=14.705, BoxScaleLoss=5.565, ClassLoss=12.884 [Epoch 44][Batch 399], LR: 1.00E-03, Speed: 11.537 samples/sec, ObjLoss=28.093, BoxCenterLoss=14.704, BoxScaleLoss=5.565, ClassLoss=12.881 [Epoch 44][Batch 499], LR: 1.00E-03, Speed: 10.633 samples/sec, ObjLoss=28.090, BoxCenterLoss=14.705, BoxScaleLoss=5.565, ClassLoss=12.878 [Epoch 44][Batch 599], LR: 1.00E-03, Speed: 10.579 samples/sec, ObjLoss=28.087, BoxCenterLoss=14.706, BoxScaleLoss=5.564, ClassLoss=12.875 [Epoch 44][Batch 699], LR: 1.00E-03, Speed: 9.227 samples/sec, ObjLoss=28.083, BoxCenterLoss=14.706, BoxScaleLoss=5.564, ClassLoss=12.872 [Epoch 44][Batch 799], LR: 1.00E-03, Speed: 10.175 samples/sec, ObjLoss=28.078, BoxCenterLoss=14.705, BoxScaleLoss=5.563, ClassLoss=12.869 [Epoch 44][Batch 899], LR: 1.00E-03, Speed: 8.929 samples/sec, ObjLoss=28.074, BoxCenterLoss=14.705, BoxScaleLoss=5.563, ClassLoss=12.865 [Epoch 44][Batch 999], LR: 1.00E-03, Speed: 10.899 samples/sec, ObjLoss=28.071, BoxCenterLoss=14.705, BoxScaleLoss=5.562, ClassLoss=12.862 [Epoch 44][Batch 1099], LR: 1.00E-03, Speed: 11.234 samples/sec, ObjLoss=28.065, BoxCenterLoss=14.705, BoxScaleLoss=5.562, ClassLoss=12.858 [Epoch 44][Batch 1199], LR: 1.00E-03, Speed: 8.373 samples/sec, ObjLoss=28.060, BoxCenterLoss=14.704, BoxScaleLoss=5.562, ClassLoss=12.856 [Epoch 44][Batch 1299], LR: 1.00E-03, Speed: 11.194 samples/sec, ObjLoss=28.056, BoxCenterLoss=14.704, BoxScaleLoss=5.561, ClassLoss=12.853 [Epoch 44][Batch 1399], LR: 1.00E-03, Speed: 90.326 samples/sec, ObjLoss=28.052, BoxCenterLoss=14.703, BoxScaleLoss=5.561, ClassLoss=12.849 [Epoch 44][Batch 1499], LR: 1.00E-03, Speed: 68.997 samples/sec, ObjLoss=28.046, BoxCenterLoss=14.702, BoxScaleLoss=5.559, ClassLoss=12.845 [Epoch 44][Batch 1599], LR: 1.00E-03, Speed: 10.618 samples/sec, ObjLoss=28.042, BoxCenterLoss=14.701, BoxScaleLoss=5.559, ClassLoss=12.841 [Epoch 44][Batch 1699], LR: 1.00E-03, Speed: 114.223 samples/sec, ObjLoss=28.036, BoxCenterLoss=14.700, BoxScaleLoss=5.558, ClassLoss=12.837 [Epoch 44][Batch 1799], LR: 1.00E-03, Speed: 10.629 samples/sec, ObjLoss=28.032, BoxCenterLoss=14.700, BoxScaleLoss=5.557, ClassLoss=12.835 [Epoch 44] Training cost: 2177.864, ObjLoss=28.030, BoxCenterLoss=14.700, BoxScaleLoss=5.557, ClassLoss=12.834 [Epoch 44] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.187 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.391 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.069 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.201 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.181 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.261 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.379 person=32.4 bicycle=11.4 car=21.3 motorcycle=23.5 airplane=25.5 bus=37.5 train=38.4 truck=17.8 boat=10.1 traffic light=11.3 fire hydrant=38.0 stop sign=37.3 parking meter=19.2 bench=9.3 bird=14.6 cat=39.8 dog=35.9 horse=29.7 sheep=29.5 cow=30.3 elephant=37.9 bear=42.5 zebra=41.4 giraffe=46.6 backpack=3.0 umbrella=17.6 handbag=2.6 tie=12.2 suitcase=11.5 frisbee=27.7 skis=7.9 snowboard=8.3 sports ball=16.7 kite=19.5 baseball bat=8.0 baseball glove=15.4 skateboard=23.3 surfboard=15.2 tennis racket=17.9 bottle=16.0 wine glass=15.7 cup=20.5 fork=8.8 knife=2.9 spoon=1.8 bowl=17.5 banana=9.2 apple=5.4 sandwich=11.7 orange=9.7 broccoli=7.9 carrot=5.4 hot dog=9.0 pizza=24.3 donut=12.0 cake=14.7 chair=11.2 couch=21.8 potted plant=7.8 bed=24.1 dining table=16.0 toilet=31.5 tv=28.5 laptop=31.5 mouse=32.3 remote=5.7 keyboard=23.8 cell phone=13.3 microwave=23.5 oven=15.3 toaster=0.0 sink=17.3 refrigerator=23.7 book=3.0 clock=29.3 vase=17.6 scissors=12.1 teddy bear=23.2 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.7 [Epoch 45][Batch 99], LR: 1.00E-03, Speed: 10.613 samples/sec, ObjLoss=28.027, BoxCenterLoss=14.700, BoxScaleLoss=5.557, ClassLoss=12.832 [Epoch 45][Batch 199], LR: 1.00E-03, Speed: 9.139 samples/sec, ObjLoss=28.023, BoxCenterLoss=14.700, BoxScaleLoss=5.556, ClassLoss=12.828 [Epoch 45][Batch 299], LR: 1.00E-03, Speed: 9.125 samples/sec, ObjLoss=28.020, BoxCenterLoss=14.700, BoxScaleLoss=5.556, ClassLoss=12.825 [Epoch 45][Batch 399], LR: 1.00E-03, Speed: 9.020 samples/sec, ObjLoss=28.015, BoxCenterLoss=14.700, BoxScaleLoss=5.556, ClassLoss=12.822 [Epoch 45][Batch 499], LR: 1.00E-03, Speed: 7.007 samples/sec, ObjLoss=28.012, BoxCenterLoss=14.700, BoxScaleLoss=5.556, ClassLoss=12.820 [Epoch 45][Batch 599], LR: 1.00E-03, Speed: 7.897 samples/sec, ObjLoss=28.006, BoxCenterLoss=14.699, BoxScaleLoss=5.555, ClassLoss=12.816 [Epoch 45][Batch 699], LR: 1.00E-03, Speed: 9.498 samples/sec, ObjLoss=28.002, BoxCenterLoss=14.699, BoxScaleLoss=5.555, ClassLoss=12.813 [Epoch 45][Batch 799], LR: 1.00E-03, Speed: 7.717 samples/sec, ObjLoss=27.996, BoxCenterLoss=14.697, BoxScaleLoss=5.554, ClassLoss=12.810 [Epoch 45][Batch 899], LR: 1.00E-03, Speed: 9.382 samples/sec, ObjLoss=27.993, BoxCenterLoss=14.698, BoxScaleLoss=5.553, ClassLoss=12.806 [Epoch 45][Batch 999], LR: 1.00E-03, Speed: 11.234 samples/sec, ObjLoss=27.989, BoxCenterLoss=14.698, BoxScaleLoss=5.553, ClassLoss=12.803 [Epoch 45][Batch 1099], LR: 1.00E-03, Speed: 97.287 samples/sec, ObjLoss=27.983, BoxCenterLoss=14.696, BoxScaleLoss=5.552, ClassLoss=12.799 [Epoch 45][Batch 1199], LR: 1.00E-03, Speed: 63.465 samples/sec, ObjLoss=27.977, BoxCenterLoss=14.695, BoxScaleLoss=5.551, ClassLoss=12.796 [Epoch 45][Batch 1299], LR: 1.00E-03, Speed: 9.415 samples/sec, ObjLoss=27.972, BoxCenterLoss=14.694, BoxScaleLoss=5.550, ClassLoss=12.792 [Epoch 45][Batch 1399], LR: 1.00E-03, Speed: 15.107 samples/sec, ObjLoss=27.967, BoxCenterLoss=14.693, BoxScaleLoss=5.549, ClassLoss=12.789 [Epoch 45][Batch 1499], LR: 1.00E-03, Speed: 10.598 samples/sec, ObjLoss=27.961, BoxCenterLoss=14.691, BoxScaleLoss=5.548, ClassLoss=12.785 [Epoch 45][Batch 1599], LR: 1.00E-03, Speed: 9.714 samples/sec, ObjLoss=27.956, BoxCenterLoss=14.691, BoxScaleLoss=5.548, ClassLoss=12.782 [Epoch 45][Batch 1699], LR: 1.00E-03, Speed: 9.384 samples/sec, ObjLoss=27.953, BoxCenterLoss=14.691, BoxScaleLoss=5.548, ClassLoss=12.779 [Epoch 45][Batch 1799], LR: 1.00E-03, Speed: 11.505 samples/sec, ObjLoss=27.949, BoxCenterLoss=14.691, BoxScaleLoss=5.548, ClassLoss=12.777 [Epoch 45] Training cost: 2092.278, ObjLoss=27.948, BoxCenterLoss=14.691, BoxScaleLoss=5.548, ClassLoss=12.776 [Epoch 45] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.178 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.390 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.064 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.188 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.174 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.253 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372 person=30.9 bicycle=14.1 car=19.9 motorcycle=24.2 airplane=30.5 bus=41.5 train=34.6 truck=18.3 boat=8.0 traffic light=13.3 fire hydrant=39.2 stop sign=29.9 parking meter=17.3 bench=10.3 bird=14.6 cat=33.8 dog=24.9 horse=31.0 sheep=24.3 cow=28.9 elephant=38.0 bear=34.9 zebra=38.4 giraffe=39.9 backpack=4.2 umbrella=17.2 handbag=3.4 tie=13.3 suitcase=11.9 frisbee=22.4 skis=7.4 snowboard=10.6 sports ball=10.4 kite=21.0 baseball bat=7.8 baseball glove=10.0 skateboard=20.4 surfboard=14.1 tennis racket=20.5 bottle=15.4 wine glass=13.2 cup=18.8 fork=6.6 knife=2.9 spoon=1.5 bowl=17.1 banana=9.2 apple=5.4 sandwich=11.1 orange=13.6 broccoli=7.4 carrot=6.5 hot dog=11.8 pizza=27.9 donut=20.0 cake=14.2 chair=10.4 couch=19.7 potted plant=9.9 bed=30.1 dining table=15.8 toilet=28.2 tv=30.2 laptop=29.7 mouse=16.7 remote=3.8 keyboard=22.3 cell phone=10.3 microwave=27.8 oven=13.1 toaster=0.0 sink=16.5 refrigerator=25.2 book=4.0 clock=25.0 vase=15.8 scissors=10.5 teddy bear=22.7 hair drier=0.0 toothbrush=1.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.8 [Epoch 46][Batch 99], LR: 1.00E-03, Speed: 11.717 samples/sec, ObjLoss=27.946, BoxCenterLoss=14.692, BoxScaleLoss=5.547, ClassLoss=12.773 [Epoch 46][Batch 199], LR: 1.00E-03, Speed: 11.448 samples/sec, ObjLoss=27.943, BoxCenterLoss=14.692, BoxScaleLoss=5.547, ClassLoss=12.770 [Epoch 46][Batch 299], LR: 1.00E-03, Speed: 113.728 samples/sec, ObjLoss=27.938, BoxCenterLoss=14.691, BoxScaleLoss=5.547, ClassLoss=12.767 [Epoch 46][Batch 399], LR: 1.00E-03, Speed: 115.274 samples/sec, ObjLoss=27.931, BoxCenterLoss=14.689, BoxScaleLoss=5.546, ClassLoss=12.763 [Epoch 46][Batch 499], LR: 1.00E-03, Speed: 8.266 samples/sec, ObjLoss=27.927, BoxCenterLoss=14.689, BoxScaleLoss=5.545, ClassLoss=12.761 [Epoch 46][Batch 599], LR: 1.00E-03, Speed: 11.434 samples/sec, ObjLoss=27.923, BoxCenterLoss=14.688, BoxScaleLoss=5.544, ClassLoss=12.757 [Epoch 46][Batch 699], LR: 1.00E-03, Speed: 10.024 samples/sec, ObjLoss=27.919, BoxCenterLoss=14.688, BoxScaleLoss=5.544, ClassLoss=12.754 [Epoch 46][Batch 799], LR: 1.00E-03, Speed: 10.777 samples/sec, ObjLoss=27.914, BoxCenterLoss=14.687, BoxScaleLoss=5.543, ClassLoss=12.751 [Epoch 46][Batch 899], LR: 1.00E-03, Speed: 11.388 samples/sec, ObjLoss=27.910, BoxCenterLoss=14.686, BoxScaleLoss=5.542, ClassLoss=12.747 [Epoch 46][Batch 999], LR: 1.00E-03, Speed: 11.155 samples/sec, ObjLoss=27.908, BoxCenterLoss=14.687, BoxScaleLoss=5.542, ClassLoss=12.744 [Epoch 46][Batch 1099], LR: 1.00E-03, Speed: 9.695 samples/sec, ObjLoss=27.904, BoxCenterLoss=14.686, BoxScaleLoss=5.541, ClassLoss=12.741 [Epoch 46][Batch 1199], LR: 1.00E-03, Speed: 11.812 samples/sec, ObjLoss=27.900, BoxCenterLoss=14.686, BoxScaleLoss=5.541, ClassLoss=12.738 [Epoch 46][Batch 1299], LR: 1.00E-03, Speed: 8.603 samples/sec, ObjLoss=27.897, BoxCenterLoss=14.686, BoxScaleLoss=5.540, ClassLoss=12.736 [Epoch 46][Batch 1399], LR: 1.00E-03, Speed: 116.598 samples/sec, ObjLoss=27.895, BoxCenterLoss=14.687, BoxScaleLoss=5.540, ClassLoss=12.733 [Epoch 46][Batch 1499], LR: 1.00E-03, Speed: 121.877 samples/sec, ObjLoss=27.891, BoxCenterLoss=14.687, BoxScaleLoss=5.540, ClassLoss=12.730 [Epoch 46][Batch 1599], LR: 1.00E-03, Speed: 9.254 samples/sec, ObjLoss=27.888, BoxCenterLoss=14.687, BoxScaleLoss=5.540, ClassLoss=12.727 [Epoch 46][Batch 1699], LR: 1.00E-03, Speed: 113.079 samples/sec, ObjLoss=27.885, BoxCenterLoss=14.688, BoxScaleLoss=5.540, ClassLoss=12.724 [Epoch 46][Batch 1799], LR: 1.00E-03, Speed: 103.557 samples/sec, ObjLoss=27.884, BoxCenterLoss=14.689, BoxScaleLoss=5.539, ClassLoss=12.722 [Epoch 46] Training cost: 2145.519, ObjLoss=27.883, BoxCenterLoss=14.689, BoxScaleLoss=5.539, ClassLoss=12.720 [Epoch 46] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.183 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.377 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.067 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.254 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.260 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.261 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.374 person=30.1 bicycle=12.3 car=23.4 motorcycle=21.2 airplane=36.2 bus=40.8 train=40.7 truck=19.5 boat=10.2 traffic light=10.3 fire hydrant=32.4 stop sign=32.8 parking meter=15.4 bench=9.6 bird=13.4 cat=35.7 dog=27.6 horse=29.8 sheep=25.5 cow=28.2 elephant=37.0 bear=36.8 zebra=36.3 giraffe=39.8 backpack=3.6 umbrella=17.4 handbag=2.6 tie=12.0 suitcase=11.6 frisbee=28.9 skis=6.0 snowboard=8.7 sports ball=22.3 kite=19.6 baseball bat=7.0 baseball glove=13.8 skateboard=19.4 surfboard=11.5 tennis racket=17.2 bottle=13.3 wine glass=12.0 cup=19.0 fork=7.5 knife=3.6 spoon=2.4 bowl=20.4 banana=6.9 apple=7.0 sandwich=13.5 orange=11.2 broccoli=7.4 carrot=3.8 hot dog=8.5 pizza=27.8 donut=23.3 cake=17.8 chair=11.2 couch=19.7 potted plant=9.2 bed=27.1 dining table=16.4 toilet=33.7 tv=32.8 laptop=32.0 mouse=28.2 remote=5.6 keyboard=23.9 cell phone=12.5 microwave=28.7 oven=12.3 toaster=0.0 sink=16.9 refrigerator=22.8 book=3.0 clock=26.4 vase=16.2 scissors=7.7 teddy bear=22.6 hair drier=0.0 toothbrush=1.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.3 [Epoch 47][Batch 99], LR: 1.00E-03, Speed: 119.855 samples/sec, ObjLoss=27.878, BoxCenterLoss=14.688, BoxScaleLoss=5.538, ClassLoss=12.717 [Epoch 47][Batch 199], LR: 1.00E-03, Speed: 8.428 samples/sec, ObjLoss=27.874, BoxCenterLoss=14.688, BoxScaleLoss=5.538, ClassLoss=12.715 [Epoch 47][Batch 299], LR: 1.00E-03, Speed: 10.806 samples/sec, ObjLoss=27.870, BoxCenterLoss=14.688, BoxScaleLoss=5.537, ClassLoss=12.712 [Epoch 47][Batch 399], LR: 1.00E-03, Speed: 9.518 samples/sec, ObjLoss=27.865, BoxCenterLoss=14.687, BoxScaleLoss=5.537, ClassLoss=12.708 [Epoch 47][Batch 499], LR: 1.00E-03, Speed: 11.390 samples/sec, ObjLoss=27.863, BoxCenterLoss=14.687, BoxScaleLoss=5.536, ClassLoss=12.705 [Epoch 47][Batch 599], LR: 1.00E-03, Speed: 112.947 samples/sec, ObjLoss=27.859, BoxCenterLoss=14.687, BoxScaleLoss=5.536, ClassLoss=12.703 [Epoch 47][Batch 699], LR: 1.00E-03, Speed: 10.429 samples/sec, ObjLoss=27.855, BoxCenterLoss=14.686, BoxScaleLoss=5.536, ClassLoss=12.700 [Epoch 47][Batch 799], LR: 1.00E-03, Speed: 69.403 samples/sec, ObjLoss=27.852, BoxCenterLoss=14.686, BoxScaleLoss=5.535, ClassLoss=12.697 [Epoch 47][Batch 899], LR: 1.00E-03, Speed: 8.723 samples/sec, ObjLoss=27.849, BoxCenterLoss=14.687, BoxScaleLoss=5.535, ClassLoss=12.694 [Epoch 47][Batch 999], LR: 1.00E-03, Speed: 10.599 samples/sec, ObjLoss=27.845, BoxCenterLoss=14.686, BoxScaleLoss=5.534, ClassLoss=12.691 [Epoch 47][Batch 1099], LR: 1.00E-03, Speed: 9.110 samples/sec, ObjLoss=27.842, BoxCenterLoss=14.687, BoxScaleLoss=5.534, ClassLoss=12.688 [Epoch 47][Batch 1199], LR: 1.00E-03, Speed: 9.707 samples/sec, ObjLoss=27.837, BoxCenterLoss=14.685, BoxScaleLoss=5.533, ClassLoss=12.685 [Epoch 47][Batch 1299], LR: 1.00E-03, Speed: 9.818 samples/sec, ObjLoss=27.833, BoxCenterLoss=14.684, BoxScaleLoss=5.532, ClassLoss=12.681 [Epoch 47][Batch 1399], LR: 1.00E-03, Speed: 139.689 samples/sec, ObjLoss=27.829, BoxCenterLoss=14.685, BoxScaleLoss=5.533, ClassLoss=12.680 [Epoch 47][Batch 1499], LR: 1.00E-03, Speed: 114.366 samples/sec, ObjLoss=27.825, BoxCenterLoss=14.685, BoxScaleLoss=5.532, ClassLoss=12.677 [Epoch 47][Batch 1599], LR: 1.00E-03, Speed: 8.479 samples/sec, ObjLoss=27.820, BoxCenterLoss=14.683, BoxScaleLoss=5.531, ClassLoss=12.673 [Epoch 47][Batch 1699], LR: 1.00E-03, Speed: 11.159 samples/sec, ObjLoss=27.816, BoxCenterLoss=14.683, BoxScaleLoss=5.531, ClassLoss=12.670 [Epoch 47][Batch 1799], LR: 1.00E-03, Speed: 12.609 samples/sec, ObjLoss=27.810, BoxCenterLoss=14.681, BoxScaleLoss=5.530, ClassLoss=12.667 [Epoch 47] Training cost: 2080.939, ObjLoss=27.809, BoxCenterLoss=14.681, BoxScaleLoss=5.530, ClassLoss=12.666 [Epoch 47] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.405 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.070 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.112 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367 person=31.0 bicycle=13.8 car=21.8 motorcycle=23.5 airplane=32.2 bus=37.2 train=38.8 truck=18.5 boat=10.0 traffic light=10.9 fire hydrant=34.9 stop sign=33.5 parking meter=22.3 bench=10.8 bird=16.0 cat=31.6 dog=28.5 horse=30.5 sheep=24.4 cow=32.6 elephant=31.6 bear=36.0 zebra=38.1 giraffe=40.1 backpack=3.8 umbrella=19.4 handbag=3.0 tie=12.6 suitcase=13.6 frisbee=26.2 skis=6.4 snowboard=8.8 sports ball=15.1 kite=23.3 baseball bat=9.6 baseball glove=13.5 skateboard=22.1 surfboard=10.9 tennis racket=23.3 bottle=15.3 wine glass=16.2 cup=18.7 fork=8.8 knife=2.7 spoon=2.2 bowl=18.9 banana=5.9 apple=6.5 sandwich=15.3 orange=13.6 broccoli=7.2 carrot=7.7 hot dog=13.3 pizza=26.9 donut=18.3 cake=18.0 chair=11.3 couch=23.7 potted plant=10.0 bed=27.9 dining table=13.7 toilet=31.0 tv=30.2 laptop=32.3 mouse=20.4 remote=5.0 keyboard=27.5 cell phone=12.1 microwave=25.0 oven=10.9 toaster=0.0 sink=16.8 refrigerator=24.7 book=3.3 clock=29.7 vase=16.6 scissors=8.9 teddy bear=24.1 hair drier=0.0 toothbrush=2.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.5 [Epoch 48][Batch 99], LR: 1.00E-03, Speed: 10.698 samples/sec, ObjLoss=27.804, BoxCenterLoss=14.680, BoxScaleLoss=5.529, ClassLoss=12.663 [Epoch 48][Batch 199], LR: 1.00E-03, Speed: 117.398 samples/sec, ObjLoss=27.799, BoxCenterLoss=14.679, BoxScaleLoss=5.528, ClassLoss=12.660 [Epoch 48][Batch 299], LR: 1.00E-03, Speed: 10.344 samples/sec, ObjLoss=27.797, BoxCenterLoss=14.680, BoxScaleLoss=5.528, ClassLoss=12.658 [Epoch 48][Batch 399], LR: 1.00E-03, Speed: 10.931 samples/sec, ObjLoss=27.793, BoxCenterLoss=14.680, BoxScaleLoss=5.528, ClassLoss=12.655 [Epoch 48][Batch 499], LR: 1.00E-03, Speed: 10.792 samples/sec, ObjLoss=27.790, BoxCenterLoss=14.680, BoxScaleLoss=5.528, ClassLoss=12.653 [Epoch 48][Batch 599], LR: 1.00E-03, Speed: 9.325 samples/sec, ObjLoss=27.785, BoxCenterLoss=14.679, BoxScaleLoss=5.528, ClassLoss=12.650 [Epoch 48][Batch 699], LR: 1.00E-03, Speed: 8.672 samples/sec, ObjLoss=27.782, BoxCenterLoss=14.679, BoxScaleLoss=5.528, ClassLoss=12.648 [Epoch 48][Batch 799], LR: 1.00E-03, Speed: 12.247 samples/sec, ObjLoss=27.779, BoxCenterLoss=14.680, BoxScaleLoss=5.528, ClassLoss=12.646 [Epoch 48][Batch 899], LR: 1.00E-03, Speed: 11.406 samples/sec, ObjLoss=27.775, BoxCenterLoss=14.679, BoxScaleLoss=5.527, ClassLoss=12.642 [Epoch 48][Batch 999], LR: 1.00E-03, Speed: 12.454 samples/sec, ObjLoss=27.770, BoxCenterLoss=14.678, BoxScaleLoss=5.526, ClassLoss=12.639 [Epoch 48][Batch 1099], LR: 1.00E-03, Speed: 8.264 samples/sec, ObjLoss=27.767, BoxCenterLoss=14.678, BoxScaleLoss=5.526, ClassLoss=12.636 [Epoch 48][Batch 1199], LR: 1.00E-03, Speed: 7.736 samples/sec, ObjLoss=27.762, BoxCenterLoss=14.677, BoxScaleLoss=5.525, ClassLoss=12.633 [Epoch 48][Batch 1299], LR: 1.00E-03, Speed: 9.854 samples/sec, ObjLoss=27.760, BoxCenterLoss=14.678, BoxScaleLoss=5.525, ClassLoss=12.631 [Epoch 48][Batch 1399], LR: 1.00E-03, Speed: 9.738 samples/sec, ObjLoss=27.756, BoxCenterLoss=14.678, BoxScaleLoss=5.524, ClassLoss=12.628 [Epoch 48][Batch 1499], LR: 1.00E-03, Speed: 12.377 samples/sec, ObjLoss=27.753, BoxCenterLoss=14.678, BoxScaleLoss=5.524, ClassLoss=12.625 [Epoch 48][Batch 1599], LR: 1.00E-03, Speed: 8.075 samples/sec, ObjLoss=27.750, BoxCenterLoss=14.679, BoxScaleLoss=5.524, ClassLoss=12.623 [Epoch 48][Batch 1699], LR: 1.00E-03, Speed: 7.717 samples/sec, ObjLoss=27.747, BoxCenterLoss=14.679, BoxScaleLoss=5.524, ClassLoss=12.620 [Epoch 48][Batch 1799], LR: 1.00E-03, Speed: 120.593 samples/sec, ObjLoss=27.743, BoxCenterLoss=14.678, BoxScaleLoss=5.523, ClassLoss=12.617 [Epoch 48] Training cost: 2169.187, ObjLoss=27.742, BoxCenterLoss=14.678, BoxScaleLoss=5.523, ClassLoss=12.616 [Epoch 48] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.176 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.389 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.082 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.183 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.172 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.254 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.261 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.354 person=31.0 bicycle=11.3 car=22.4 motorcycle=20.6 airplane=32.3 bus=35.0 train=41.8 truck=16.6 boat=10.7 traffic light=10.8 fire hydrant=29.6 stop sign=27.9 parking meter=19.3 bench=9.3 bird=15.7 cat=33.5 dog=28.7 horse=25.6 sheep=23.1 cow=27.1 elephant=35.1 bear=31.9 zebra=37.7 giraffe=36.5 backpack=4.3 umbrella=17.3 handbag=3.9 tie=15.1 suitcase=11.9 frisbee=30.9 skis=6.8 snowboard=8.7 sports ball=26.0 kite=20.5 baseball bat=7.6 baseball glove=14.9 skateboard=21.3 surfboard=14.6 tennis racket=18.1 bottle=14.7 wine glass=14.4 cup=17.7 fork=7.3 knife=2.9 spoon=2.2 bowl=16.4 banana=7.5 apple=4.7 sandwich=7.9 orange=11.5 broccoli=8.1 carrot=6.2 hot dog=9.7 pizza=25.4 donut=22.9 cake=16.4 chair=12.1 couch=21.7 potted plant=8.4 bed=21.7 dining table=10.0 toilet=26.1 tv=30.3 laptop=27.3 mouse=30.4 remote=6.1 keyboard=22.2 cell phone=12.0 microwave=29.0 oven=12.4 toaster=0.0 sink=14.7 refrigerator=17.7 book=4.0 clock=29.4 vase=15.7 scissors=5.3 teddy bear=20.6 hair drier=0.0 toothbrush=2.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.6 [Epoch 49][Batch 99], LR: 1.00E-03, Speed: 10.095 samples/sec, ObjLoss=27.739, BoxCenterLoss=14.679, BoxScaleLoss=5.523, ClassLoss=12.613 [Epoch 49][Batch 199], LR: 1.00E-03, Speed: 10.861 samples/sec, ObjLoss=27.733, BoxCenterLoss=14.677, BoxScaleLoss=5.522, ClassLoss=12.609 [Epoch 49][Batch 299], LR: 1.00E-03, Speed: 9.823 samples/sec, ObjLoss=27.729, BoxCenterLoss=14.677, BoxScaleLoss=5.521, ClassLoss=12.606 [Epoch 49][Batch 399], LR: 1.00E-03, Speed: 9.041 samples/sec, ObjLoss=27.725, BoxCenterLoss=14.677, BoxScaleLoss=5.521, ClassLoss=12.604 [Epoch 49][Batch 499], LR: 1.00E-03, Speed: 11.136 samples/sec, ObjLoss=27.722, BoxCenterLoss=14.676, BoxScaleLoss=5.521, ClassLoss=12.602 [Epoch 49][Batch 599], LR: 1.00E-03, Speed: 11.165 samples/sec, ObjLoss=27.720, BoxCenterLoss=14.677, BoxScaleLoss=5.521, ClassLoss=12.600 [Epoch 49][Batch 699], LR: 1.00E-03, Speed: 128.436 samples/sec, ObjLoss=27.716, BoxCenterLoss=14.677, BoxScaleLoss=5.520, ClassLoss=12.597 [Epoch 49][Batch 799], LR: 1.00E-03, Speed: 89.363 samples/sec, ObjLoss=27.712, BoxCenterLoss=14.676, BoxScaleLoss=5.520, ClassLoss=12.594 [Epoch 49][Batch 899], LR: 1.00E-03, Speed: 97.883 samples/sec, ObjLoss=27.709, BoxCenterLoss=14.677, BoxScaleLoss=5.519, ClassLoss=12.591 [Epoch 49][Batch 999], LR: 1.00E-03, Speed: 105.579 samples/sec, ObjLoss=27.704, BoxCenterLoss=14.676, BoxScaleLoss=5.519, ClassLoss=12.588 [Epoch 49][Batch 1099], LR: 1.00E-03, Speed: 119.035 samples/sec, ObjLoss=27.700, BoxCenterLoss=14.675, BoxScaleLoss=5.518, ClassLoss=12.586 [Epoch 49][Batch 1199], LR: 1.00E-03, Speed: 7.741 samples/sec, ObjLoss=27.696, BoxCenterLoss=14.675, BoxScaleLoss=5.518, ClassLoss=12.583 [Epoch 49][Batch 1299], LR: 1.00E-03, Speed: 8.715 samples/sec, ObjLoss=27.694, BoxCenterLoss=14.675, BoxScaleLoss=5.518, ClassLoss=12.581 [Epoch 49][Batch 1399], LR: 1.00E-03, Speed: 7.754 samples/sec, ObjLoss=27.693, BoxCenterLoss=14.676, BoxScaleLoss=5.518, ClassLoss=12.579 [Epoch 49][Batch 1499], LR: 1.00E-03, Speed: 9.457 samples/sec, ObjLoss=27.689, BoxCenterLoss=14.676, BoxScaleLoss=5.518, ClassLoss=12.577 [Epoch 49][Batch 1599], LR: 1.00E-03, Speed: 10.174 samples/sec, ObjLoss=27.685, BoxCenterLoss=14.675, BoxScaleLoss=5.517, ClassLoss=12.574 [Epoch 49][Batch 1699], LR: 1.00E-03, Speed: 9.793 samples/sec, ObjLoss=27.682, BoxCenterLoss=14.675, BoxScaleLoss=5.517, ClassLoss=12.571 [Epoch 49][Batch 1799], LR: 1.00E-03, Speed: 11.286 samples/sec, ObjLoss=27.680, BoxCenterLoss=14.676, BoxScaleLoss=5.517, ClassLoss=12.569 [Epoch 49] Training cost: 2136.005, ObjLoss=27.679, BoxCenterLoss=14.676, BoxScaleLoss=5.516, ClassLoss=12.568 [Epoch 49] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.393 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.074 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.188 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.269 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389 person=32.0 bicycle=14.0 car=21.3 motorcycle=24.9 airplane=35.1 bus=40.5 train=41.9 truck=17.5 boat=8.5 traffic light=10.7 fire hydrant=34.3 stop sign=36.5 parking meter=20.0 bench=8.2 bird=14.9 cat=37.9 dog=30.5 horse=29.0 sheep=24.3 cow=28.7 elephant=37.4 bear=39.1 zebra=44.6 giraffe=38.5 backpack=4.1 umbrella=17.9 handbag=3.0 tie=12.6 suitcase=13.1 frisbee=31.1 skis=5.3 snowboard=7.5 sports ball=22.3 kite=21.5 baseball bat=9.3 baseball glove=14.9 skateboard=23.5 surfboard=14.4 tennis racket=22.4 bottle=15.5 wine glass=13.7 cup=18.9 fork=9.0 knife=3.4 spoon=2.3 bowl=18.0 banana=8.6 apple=5.2 sandwich=14.9 orange=11.9 broccoli=8.6 carrot=3.5 hot dog=14.4 pizza=30.1 donut=24.2 cake=17.0 chair=10.5 couch=23.8 potted plant=7.2 bed=30.3 dining table=14.7 toilet=33.6 tv=29.5 laptop=29.1 mouse=32.9 remote=8.2 keyboard=20.0 cell phone=15.6 microwave=28.5 oven=14.4 toaster=0.0 sink=15.1 refrigerator=25.3 book=3.5 clock=31.1 vase=17.8 scissors=8.8 teddy bear=23.3 hair drier=0.0 toothbrush=1.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.2 [Epoch 50][Batch 99], LR: 1.00E-03, Speed: 8.862 samples/sec, ObjLoss=27.675, BoxCenterLoss=14.675, BoxScaleLoss=5.516, ClassLoss=12.565 [Epoch 50][Batch 199], LR: 1.00E-03, Speed: 8.967 samples/sec, ObjLoss=27.672, BoxCenterLoss=14.675, BoxScaleLoss=5.516, ClassLoss=12.562 [Epoch 50][Batch 299], LR: 1.00E-03, Speed: 8.985 samples/sec, ObjLoss=27.668, BoxCenterLoss=14.675, BoxScaleLoss=5.515, ClassLoss=12.560 [Epoch 50][Batch 399], LR: 1.00E-03, Speed: 8.543 samples/sec, ObjLoss=27.664, BoxCenterLoss=14.675, BoxScaleLoss=5.515, ClassLoss=12.558 [Epoch 50][Batch 499], LR: 1.00E-03, Speed: 11.609 samples/sec, ObjLoss=27.663, BoxCenterLoss=14.675, BoxScaleLoss=5.515, ClassLoss=12.555 [Epoch 50][Batch 599], LR: 1.00E-03, Speed: 9.559 samples/sec, ObjLoss=27.660, BoxCenterLoss=14.675, BoxScaleLoss=5.514, ClassLoss=12.552 [Epoch 50][Batch 699], LR: 1.00E-03, Speed: 11.727 samples/sec, ObjLoss=27.657, BoxCenterLoss=14.676, BoxScaleLoss=5.514, ClassLoss=12.550 [Epoch 50][Batch 799], LR: 1.00E-03, Speed: 114.834 samples/sec, ObjLoss=27.655, BoxCenterLoss=14.676, BoxScaleLoss=5.514, ClassLoss=12.548 [Epoch 50][Batch 899], LR: 1.00E-03, Speed: 11.681 samples/sec, ObjLoss=27.653, BoxCenterLoss=14.677, BoxScaleLoss=5.513, ClassLoss=12.545 [Epoch 50][Batch 999], LR: 1.00E-03, Speed: 12.991 samples/sec, ObjLoss=27.649, BoxCenterLoss=14.676, BoxScaleLoss=5.513, ClassLoss=12.542 [Epoch 50][Batch 1099], LR: 1.00E-03, Speed: 11.457 samples/sec, ObjLoss=27.646, BoxCenterLoss=14.677, BoxScaleLoss=5.512, ClassLoss=12.539 [Epoch 50][Batch 1199], LR: 1.00E-03, Speed: 10.865 samples/sec, ObjLoss=27.644, BoxCenterLoss=14.677, BoxScaleLoss=5.512, ClassLoss=12.537 [Epoch 50][Batch 1299], LR: 1.00E-03, Speed: 100.963 samples/sec, ObjLoss=27.639, BoxCenterLoss=14.676, BoxScaleLoss=5.511, ClassLoss=12.534 [Epoch 50][Batch 1399], LR: 1.00E-03, Speed: 10.747 samples/sec, ObjLoss=27.635, BoxCenterLoss=14.675, BoxScaleLoss=5.511, ClassLoss=12.531 [Epoch 50][Batch 1499], LR: 1.00E-03, Speed: 9.043 samples/sec, ObjLoss=27.631, BoxCenterLoss=14.674, BoxScaleLoss=5.510, ClassLoss=12.527 [Epoch 50][Batch 1599], LR: 1.00E-03, Speed: 9.738 samples/sec, ObjLoss=27.627, BoxCenterLoss=14.674, BoxScaleLoss=5.510, ClassLoss=12.525 [Epoch 50][Batch 1699], LR: 1.00E-03, Speed: 8.465 samples/sec, ObjLoss=27.625, BoxCenterLoss=14.675, BoxScaleLoss=5.509, ClassLoss=12.523 [Epoch 50][Batch 1799], LR: 1.00E-03, Speed: 12.794 samples/sec, ObjLoss=27.623, BoxCenterLoss=14.675, BoxScaleLoss=5.509, ClassLoss=12.520 [Epoch 50] Training cost: 2134.711, ObjLoss=27.622, BoxCenterLoss=14.675, BoxScaleLoss=5.509, ClassLoss=12.519 [Epoch 50] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.404 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.114 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410 person=32.5 bicycle=15.0 car=22.2 motorcycle=25.5 airplane=36.0 bus=42.9 train=40.7 truck=20.1 boat=9.6 traffic light=11.8 fire hydrant=40.5 stop sign=36.6 parking meter=19.4 bench=11.3 bird=16.9 cat=41.0 dog=35.2 horse=31.4 sheep=29.2 cow=30.6 elephant=38.6 bear=38.6 zebra=40.6 giraffe=43.9 backpack=4.2 umbrella=18.0 handbag=2.8 tie=15.0 suitcase=13.2 frisbee=31.4 skis=5.7 snowboard=8.9 sports ball=23.2 kite=22.3 baseball bat=9.5 baseball glove=14.2 skateboard=24.0 surfboard=15.2 tennis racket=23.6 bottle=15.2 wine glass=14.5 cup=20.1 fork=9.3 knife=3.8 spoon=2.6 bowl=18.5 banana=10.7 apple=7.0 sandwich=17.0 orange=16.1 broccoli=10.2 carrot=7.4 hot dog=10.8 pizza=30.5 donut=26.4 cake=17.1 chair=10.7 couch=22.3 potted plant=9.2 bed=23.0 dining table=17.6 toilet=35.4 tv=30.9 laptop=30.5 mouse=34.1 remote=6.7 keyboard=24.5 cell phone=15.0 microwave=29.3 oven=17.0 toaster=0.0 sink=19.1 refrigerator=24.9 book=3.5 clock=33.0 vase=17.9 scissors=11.3 teddy bear=25.3 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.3 [Epoch 51][Batch 99], LR: 1.00E-03, Speed: 10.191 samples/sec, ObjLoss=27.619, BoxCenterLoss=14.675, BoxScaleLoss=5.508, ClassLoss=12.516 [Epoch 51][Batch 199], LR: 1.00E-03, Speed: 113.084 samples/sec, ObjLoss=27.616, BoxCenterLoss=14.675, BoxScaleLoss=5.508, ClassLoss=12.514 [Epoch 51][Batch 299], LR: 1.00E-03, Speed: 9.298 samples/sec, ObjLoss=27.612, BoxCenterLoss=14.675, BoxScaleLoss=5.507, ClassLoss=12.511 [Epoch 51][Batch 399], LR: 1.00E-03, Speed: 8.741 samples/sec, ObjLoss=27.609, BoxCenterLoss=14.674, BoxScaleLoss=5.507, ClassLoss=12.508 [Epoch 51][Batch 499], LR: 1.00E-03, Speed: 10.535 samples/sec, ObjLoss=27.605, BoxCenterLoss=14.674, BoxScaleLoss=5.506, ClassLoss=12.505 [Epoch 51][Batch 599], LR: 1.00E-03, Speed: 8.605 samples/sec, ObjLoss=27.601, BoxCenterLoss=14.673, BoxScaleLoss=5.506, ClassLoss=12.502 [Epoch 51][Batch 699], LR: 1.00E-03, Speed: 7.690 samples/sec, ObjLoss=27.599, BoxCenterLoss=14.673, BoxScaleLoss=5.505, ClassLoss=12.500 [Epoch 51][Batch 799], LR: 1.00E-03, Speed: 9.299 samples/sec, ObjLoss=27.596, BoxCenterLoss=14.673, BoxScaleLoss=5.505, ClassLoss=12.497 [Epoch 51][Batch 899], LR: 1.00E-03, Speed: 10.868 samples/sec, ObjLoss=27.593, BoxCenterLoss=14.673, BoxScaleLoss=5.505, ClassLoss=12.495 [Epoch 51][Batch 999], LR: 1.00E-03, Speed: 9.861 samples/sec, ObjLoss=27.590, BoxCenterLoss=14.673, BoxScaleLoss=5.504, ClassLoss=12.492 [Epoch 51][Batch 1099], LR: 1.00E-03, Speed: 141.498 samples/sec, ObjLoss=27.588, BoxCenterLoss=14.673, BoxScaleLoss=5.504, ClassLoss=12.490 [Epoch 51][Batch 1199], LR: 1.00E-03, Speed: 9.383 samples/sec, ObjLoss=27.583, BoxCenterLoss=14.673, BoxScaleLoss=5.503, ClassLoss=12.487 [Epoch 51][Batch 1299], LR: 1.00E-03, Speed: 6.584 samples/sec, ObjLoss=27.578, BoxCenterLoss=14.672, BoxScaleLoss=5.503, ClassLoss=12.484 [Epoch 51][Batch 1399], LR: 1.00E-03, Speed: 10.947 samples/sec, ObjLoss=27.574, BoxCenterLoss=14.670, BoxScaleLoss=5.502, ClassLoss=12.481 [Epoch 51][Batch 1499], LR: 1.00E-03, Speed: 9.110 samples/sec, ObjLoss=27.571, BoxCenterLoss=14.671, BoxScaleLoss=5.502, ClassLoss=12.479 [Epoch 51][Batch 1599], LR: 1.00E-03, Speed: 85.916 samples/sec, ObjLoss=27.568, BoxCenterLoss=14.670, BoxScaleLoss=5.501, ClassLoss=12.476 [Epoch 51][Batch 1699], LR: 1.00E-03, Speed: 8.592 samples/sec, ObjLoss=27.566, BoxCenterLoss=14.671, BoxScaleLoss=5.501, ClassLoss=12.474 [Epoch 51][Batch 1799], LR: 1.00E-03, Speed: 11.813 samples/sec, ObjLoss=27.563, BoxCenterLoss=14.670, BoxScaleLoss=5.500, ClassLoss=12.471 [Epoch 51] Training cost: 2147.421, ObjLoss=27.561, BoxCenterLoss=14.670, BoxScaleLoss=5.500, ClassLoss=12.469 [Epoch 51] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.398 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.082 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.385 person=33.3 bicycle=15.1 car=22.6 motorcycle=24.5 airplane=35.8 bus=32.4 train=40.0 truck=17.4 boat=11.4 traffic light=13.2 fire hydrant=36.2 stop sign=35.8 parking meter=26.8 bench=9.8 bird=18.7 cat=36.7 dog=30.1 horse=31.7 sheep=28.3 cow=30.4 elephant=35.9 bear=34.8 zebra=38.2 giraffe=43.2 backpack=4.2 umbrella=20.3 handbag=3.3 tie=15.5 suitcase=11.7 frisbee=28.1 skis=5.9 snowboard=9.9 sports ball=22.6 kite=24.9 baseball bat=9.1 baseball glove=14.0 skateboard=21.0 surfboard=14.4 tennis racket=23.4 bottle=16.3 wine glass=16.9 cup=19.8 fork=9.7 knife=3.6 spoon=3.0 bowl=17.4 banana=9.2 apple=5.9 sandwich=15.8 orange=12.3 broccoli=8.3 carrot=5.5 hot dog=13.1 pizza=25.2 donut=22.4 cake=17.0 chair=11.2 couch=20.5 potted plant=10.2 bed=26.1 dining table=17.8 toilet=31.9 tv=31.9 laptop=30.2 mouse=27.4 remote=7.0 keyboard=18.3 cell phone=15.0 microwave=28.4 oven=15.1 toaster=0.0 sink=18.0 refrigerator=20.8 book=3.6 clock=28.2 vase=19.0 scissors=12.1 teddy bear=25.5 hair drier=0.0 toothbrush=1.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.4 [Epoch 52][Batch 99], LR: 1.00E-03, Speed: 11.651 samples/sec, ObjLoss=27.558, BoxCenterLoss=14.669, BoxScaleLoss=5.499, ClassLoss=12.466 [Epoch 52][Batch 199], LR: 1.00E-03, Speed: 107.358 samples/sec, ObjLoss=27.555, BoxCenterLoss=14.669, BoxScaleLoss=5.499, ClassLoss=12.464 [Epoch 52][Batch 299], LR: 1.00E-03, Speed: 10.905 samples/sec, ObjLoss=27.551, BoxCenterLoss=14.669, BoxScaleLoss=5.498, ClassLoss=12.461 [Epoch 52][Batch 399], LR: 1.00E-03, Speed: 8.339 samples/sec, ObjLoss=27.549, BoxCenterLoss=14.669, BoxScaleLoss=5.498, ClassLoss=12.459 [Epoch 52][Batch 499], LR: 1.00E-03, Speed: 8.608 samples/sec, ObjLoss=27.546, BoxCenterLoss=14.669, BoxScaleLoss=5.498, ClassLoss=12.457 [Epoch 52][Batch 599], LR: 1.00E-03, Speed: 80.883 samples/sec, ObjLoss=27.544, BoxCenterLoss=14.669, BoxScaleLoss=5.497, ClassLoss=12.454 [Epoch 52][Batch 699], LR: 1.00E-03, Speed: 11.331 samples/sec, ObjLoss=27.541, BoxCenterLoss=14.669, BoxScaleLoss=5.497, ClassLoss=12.451 [Epoch 52][Batch 799], LR: 1.00E-03, Speed: 10.408 samples/sec, ObjLoss=27.538, BoxCenterLoss=14.669, BoxScaleLoss=5.496, ClassLoss=12.449 [Epoch 52][Batch 899], LR: 1.00E-03, Speed: 8.424 samples/sec, ObjLoss=27.534, BoxCenterLoss=14.669, BoxScaleLoss=5.496, ClassLoss=12.446 [Epoch 52][Batch 999], LR: 1.00E-03, Speed: 10.556 samples/sec, ObjLoss=27.531, BoxCenterLoss=14.668, BoxScaleLoss=5.496, ClassLoss=12.443 [Epoch 52][Batch 1099], LR: 1.00E-03, Speed: 8.859 samples/sec, ObjLoss=27.528, BoxCenterLoss=14.668, BoxScaleLoss=5.495, ClassLoss=12.441 [Epoch 52][Batch 1199], LR: 1.00E-03, Speed: 11.769 samples/sec, ObjLoss=27.524, BoxCenterLoss=14.667, BoxScaleLoss=5.494, ClassLoss=12.438 [Epoch 52][Batch 1299], LR: 1.00E-03, Speed: 125.009 samples/sec, ObjLoss=27.520, BoxCenterLoss=14.667, BoxScaleLoss=5.494, ClassLoss=12.435 [Epoch 52][Batch 1399], LR: 1.00E-03, Speed: 10.179 samples/sec, ObjLoss=27.517, BoxCenterLoss=14.667, BoxScaleLoss=5.494, ClassLoss=12.433 [Epoch 52][Batch 1499], LR: 1.00E-03, Speed: 8.061 samples/sec, ObjLoss=27.512, BoxCenterLoss=14.666, BoxScaleLoss=5.493, ClassLoss=12.430 [Epoch 52][Batch 1599], LR: 1.00E-03, Speed: 13.741 samples/sec, ObjLoss=27.507, BoxCenterLoss=14.664, BoxScaleLoss=5.493, ClassLoss=12.427 [Epoch 52][Batch 1699], LR: 1.00E-03, Speed: 9.715 samples/sec, ObjLoss=27.503, BoxCenterLoss=14.664, BoxScaleLoss=5.492, ClassLoss=12.424 [Epoch 52][Batch 1799], LR: 1.00E-03, Speed: 10.517 samples/sec, ObjLoss=27.499, BoxCenterLoss=14.663, BoxScaleLoss=5.492, ClassLoss=12.421 [Epoch 52] Training cost: 2186.806, ObjLoss=27.498, BoxCenterLoss=14.663, BoxScaleLoss=5.492, ClassLoss=12.421 [Epoch 52] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.193 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.187 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.402 person=31.8 bicycle=14.0 car=23.6 motorcycle=25.5 airplane=37.1 bus=42.5 train=39.7 truck=18.6 boat=11.2 traffic light=11.9 fire hydrant=34.1 stop sign=38.2 parking meter=20.6 bench=11.6 bird=16.3 cat=39.4 dog=29.8 horse=31.1 sheep=27.5 cow=30.3 elephant=37.2 bear=43.3 zebra=42.9 giraffe=48.6 backpack=3.9 umbrella=17.7 handbag=3.2 tie=12.9 suitcase=12.5 frisbee=26.3 skis=7.2 snowboard=10.3 sports ball=13.7 kite=20.2 baseball bat=8.3 baseball glove=16.0 skateboard=24.7 surfboard=15.1 tennis racket=17.1 bottle=14.4 wine glass=16.0 cup=18.9 fork=8.6 knife=4.6 spoon=3.2 bowl=18.7 banana=8.6 apple=3.8 sandwich=15.6 orange=15.7 broccoli=8.0 carrot=6.6 hot dog=9.4 pizza=30.4 donut=22.7 cake=18.9 chair=12.8 couch=22.5 potted plant=7.7 bed=31.6 dining table=17.2 toilet=32.9 tv=29.4 laptop=28.6 mouse=33.5 remote=7.1 keyboard=21.7 cell phone=16.1 microwave=27.1 oven=17.0 toaster=0.0 sink=17.1 refrigerator=25.6 book=3.7 clock=31.6 vase=17.8 scissors=13.2 teddy bear=26.5 hair drier=0.0 toothbrush=3.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.8 [Epoch 53][Batch 99], LR: 1.00E-03, Speed: 8.722 samples/sec, ObjLoss=27.495, BoxCenterLoss=14.663, BoxScaleLoss=5.491, ClassLoss=12.418 [Epoch 53][Batch 199], LR: 1.00E-03, Speed: 7.526 samples/sec, ObjLoss=27.490, BoxCenterLoss=14.662, BoxScaleLoss=5.491, ClassLoss=12.415 [Epoch 53][Batch 299], LR: 1.00E-03, Speed: 57.185 samples/sec, ObjLoss=27.486, BoxCenterLoss=14.661, BoxScaleLoss=5.490, ClassLoss=12.412 [Epoch 53][Batch 399], LR: 1.00E-03, Speed: 9.102 samples/sec, ObjLoss=27.483, BoxCenterLoss=14.660, BoxScaleLoss=5.489, ClassLoss=12.409 [Epoch 53][Batch 499], LR: 1.00E-03, Speed: 12.968 samples/sec, ObjLoss=27.480, BoxCenterLoss=14.660, BoxScaleLoss=5.489, ClassLoss=12.406 [Epoch 53][Batch 599], LR: 1.00E-03, Speed: 13.141 samples/sec, ObjLoss=27.477, BoxCenterLoss=14.661, BoxScaleLoss=5.489, ClassLoss=12.404 [Epoch 53][Batch 699], LR: 1.00E-03, Speed: 9.669 samples/sec, ObjLoss=27.474, BoxCenterLoss=14.660, BoxScaleLoss=5.488, ClassLoss=12.402 [Epoch 53][Batch 799], LR: 1.00E-03, Speed: 9.952 samples/sec, ObjLoss=27.470, BoxCenterLoss=14.659, BoxScaleLoss=5.487, ClassLoss=12.399 [Epoch 53][Batch 899], LR: 1.00E-03, Speed: 9.301 samples/sec, ObjLoss=27.467, BoxCenterLoss=14.659, BoxScaleLoss=5.487, ClassLoss=12.396 [Epoch 53][Batch 999], LR: 1.00E-03, Speed: 8.296 samples/sec, ObjLoss=27.464, BoxCenterLoss=14.659, BoxScaleLoss=5.487, ClassLoss=12.395 [Epoch 53][Batch 1099], LR: 1.00E-03, Speed: 7.714 samples/sec, ObjLoss=27.463, BoxCenterLoss=14.660, BoxScaleLoss=5.486, ClassLoss=12.392 [Epoch 53][Batch 1199], LR: 1.00E-03, Speed: 10.911 samples/sec, ObjLoss=27.458, BoxCenterLoss=14.659, BoxScaleLoss=5.486, ClassLoss=12.390 [Epoch 53][Batch 1299], LR: 1.00E-03, Speed: 10.570 samples/sec, ObjLoss=27.455, BoxCenterLoss=14.659, BoxScaleLoss=5.486, ClassLoss=12.388 [Epoch 53][Batch 1399], LR: 1.00E-03, Speed: 107.450 samples/sec, ObjLoss=27.452, BoxCenterLoss=14.659, BoxScaleLoss=5.485, ClassLoss=12.385 [Epoch 53][Batch 1499], LR: 1.00E-03, Speed: 89.786 samples/sec, ObjLoss=27.449, BoxCenterLoss=14.658, BoxScaleLoss=5.484, ClassLoss=12.382 [Epoch 53][Batch 1599], LR: 1.00E-03, Speed: 7.614 samples/sec, ObjLoss=27.446, BoxCenterLoss=14.658, BoxScaleLoss=5.484, ClassLoss=12.379 [Epoch 53][Batch 1699], LR: 1.00E-03, Speed: 7.331 samples/sec, ObjLoss=27.442, BoxCenterLoss=14.657, BoxScaleLoss=5.483, ClassLoss=12.377 [Epoch 53][Batch 1799], LR: 1.00E-03, Speed: 147.114 samples/sec, ObjLoss=27.438, BoxCenterLoss=14.656, BoxScaleLoss=5.483, ClassLoss=12.374 [Epoch 53] Training cost: 2120.804, ObjLoss=27.437, BoxCenterLoss=14.656, BoxScaleLoss=5.483, ClassLoss=12.373 [Epoch 53] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.202 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.395 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.071 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.213 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.281 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 person=34.1 bicycle=14.7 car=21.7 motorcycle=23.1 airplane=35.7 bus=44.2 train=48.5 truck=20.2 boat=11.6 traffic light=10.2 fire hydrant=42.3 stop sign=37.7 parking meter=23.3 bench=11.7 bird=16.4 cat=33.9 dog=32.2 horse=32.7 sheep=29.7 cow=32.7 elephant=42.7 bear=48.7 zebra=39.9 giraffe=41.7 backpack=3.9 umbrella=18.1 handbag=2.6 tie=14.1 suitcase=13.3 frisbee=29.0 skis=5.6 snowboard=10.4 sports ball=9.8 kite=17.7 baseball bat=8.7 baseball glove=12.1 skateboard=25.0 surfboard=16.9 tennis racket=21.6 bottle=16.9 wine glass=18.7 cup=20.5 fork=9.9 knife=3.6 spoon=3.1 bowl=19.3 banana=10.0 apple=6.6 sandwich=16.7 orange=16.2 broccoli=5.6 carrot=5.9 hot dog=15.3 pizza=30.7 donut=20.3 cake=16.3 chair=11.2 couch=23.0 potted plant=9.5 bed=32.1 dining table=17.9 toilet=35.7 tv=33.7 laptop=33.4 mouse=30.0 remote=7.2 keyboard=26.6 cell phone=13.3 microwave=20.7 oven=16.0 toaster=0.0 sink=16.5 refrigerator=27.1 book=2.5 clock=29.4 vase=16.5 scissors=11.3 teddy bear=25.2 hair drier=0.0 toothbrush=3.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.2 [Epoch 54][Batch 99], LR: 1.00E-03, Speed: 9.601 samples/sec, ObjLoss=27.434, BoxCenterLoss=14.657, BoxScaleLoss=5.483, ClassLoss=12.371 [Epoch 54][Batch 199], LR: 1.00E-03, Speed: 12.974 samples/sec, ObjLoss=27.430, BoxCenterLoss=14.656, BoxScaleLoss=5.482, ClassLoss=12.368 [Epoch 54][Batch 299], LR: 1.00E-03, Speed: 7.363 samples/sec, ObjLoss=27.427, BoxCenterLoss=14.655, BoxScaleLoss=5.481, ClassLoss=12.366 [Epoch 54][Batch 399], LR: 1.00E-03, Speed: 7.653 samples/sec, ObjLoss=27.423, BoxCenterLoss=14.655, BoxScaleLoss=5.481, ClassLoss=12.363 [Epoch 54][Batch 499], LR: 1.00E-03, Speed: 8.293 samples/sec, ObjLoss=27.419, BoxCenterLoss=14.654, BoxScaleLoss=5.480, ClassLoss=12.360 [Epoch 54][Batch 599], LR: 1.00E-03, Speed: 10.377 samples/sec, ObjLoss=27.415, BoxCenterLoss=14.653, BoxScaleLoss=5.480, ClassLoss=12.358 [Epoch 54][Batch 699], LR: 1.00E-03, Speed: 9.364 samples/sec, ObjLoss=27.413, BoxCenterLoss=14.654, BoxScaleLoss=5.480, ClassLoss=12.356 [Epoch 54][Batch 799], LR: 1.00E-03, Speed: 103.660 samples/sec, ObjLoss=27.410, BoxCenterLoss=14.653, BoxScaleLoss=5.479, ClassLoss=12.353 [Epoch 54][Batch 899], LR: 1.00E-03, Speed: 45.301 samples/sec, ObjLoss=27.407, BoxCenterLoss=14.653, BoxScaleLoss=5.479, ClassLoss=12.352 [Epoch 54][Batch 999], LR: 1.00E-03, Speed: 9.894 samples/sec, ObjLoss=27.403, BoxCenterLoss=14.653, BoxScaleLoss=5.479, ClassLoss=12.349 [Epoch 54][Batch 1099], LR: 1.00E-03, Speed: 10.468 samples/sec, ObjLoss=27.399, BoxCenterLoss=14.652, BoxScaleLoss=5.478, ClassLoss=12.347 [Epoch 54][Batch 1199], LR: 1.00E-03, Speed: 8.053 samples/sec, ObjLoss=27.396, BoxCenterLoss=14.652, BoxScaleLoss=5.478, ClassLoss=12.344 [Epoch 54][Batch 1299], LR: 1.00E-03, Speed: 11.015 samples/sec, ObjLoss=27.394, BoxCenterLoss=14.653, BoxScaleLoss=5.478, ClassLoss=12.342 [Epoch 54][Batch 1399], LR: 1.00E-03, Speed: 107.384 samples/sec, ObjLoss=27.391, BoxCenterLoss=14.652, BoxScaleLoss=5.477, ClassLoss=12.339 [Epoch 54][Batch 1499], LR: 1.00E-03, Speed: 7.783 samples/sec, ObjLoss=27.387, BoxCenterLoss=14.652, BoxScaleLoss=5.477, ClassLoss=12.336 [Epoch 54][Batch 1599], LR: 1.00E-03, Speed: 9.687 samples/sec, ObjLoss=27.383, BoxCenterLoss=14.651, BoxScaleLoss=5.476, ClassLoss=12.333 [Epoch 54][Batch 1699], LR: 1.00E-03, Speed: 7.478 samples/sec, ObjLoss=27.381, BoxCenterLoss=14.651, BoxScaleLoss=5.476, ClassLoss=12.331 [Epoch 54][Batch 1799], LR: 1.00E-03, Speed: 11.498 samples/sec, ObjLoss=27.378, BoxCenterLoss=14.651, BoxScaleLoss=5.476, ClassLoss=12.328 [Epoch 54] Training cost: 2135.494, ObjLoss=27.377, BoxCenterLoss=14.651, BoxScaleLoss=5.475, ClassLoss=12.328 [Epoch 54] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.414 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.188 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.195 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 person=34.3 bicycle=15.1 car=24.5 motorcycle=22.1 airplane=40.1 bus=43.7 train=43.8 truck=18.2 boat=12.3 traffic light=12.3 fire hydrant=37.6 stop sign=37.8 parking meter=25.7 bench=10.0 bird=17.2 cat=38.4 dog=30.8 horse=29.6 sheep=32.6 cow=31.9 elephant=41.7 bear=40.2 zebra=43.7 giraffe=42.4 backpack=3.9 umbrella=19.5 handbag=3.2 tie=13.7 suitcase=14.6 frisbee=29.3 skis=7.6 snowboard=10.3 sports ball=18.8 kite=23.5 baseball bat=12.3 baseball glove=13.6 skateboard=24.9 surfboard=18.0 tennis racket=26.0 bottle=14.6 wine glass=17.8 cup=23.7 fork=10.0 knife=4.2 spoon=3.5 bowl=20.8 banana=9.1 apple=6.4 sandwich=15.2 orange=15.1 broccoli=9.7 carrot=6.6 hot dog=14.9 pizza=26.3 donut=25.6 cake=18.1 chair=12.5 couch=26.7 potted plant=11.2 bed=29.6 dining table=17.0 toilet=35.6 tv=35.2 laptop=32.4 mouse=25.0 remote=6.6 keyboard=23.5 cell phone=13.3 microwave=30.9 oven=18.0 toaster=0.0 sink=18.2 refrigerator=28.0 book=3.1 clock=27.3 vase=17.7 scissors=8.7 teddy bear=25.6 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.7 [Epoch 55][Batch 99], LR: 1.00E-03, Speed: 7.548 samples/sec, ObjLoss=27.373, BoxCenterLoss=14.651, BoxScaleLoss=5.475, ClassLoss=12.325 [Epoch 55][Batch 199], LR: 1.00E-03, Speed: 10.218 samples/sec, ObjLoss=27.370, BoxCenterLoss=14.650, BoxScaleLoss=5.474, ClassLoss=12.322 [Epoch 55][Batch 299], LR: 1.00E-03, Speed: 123.991 samples/sec, ObjLoss=27.367, BoxCenterLoss=14.650, BoxScaleLoss=5.474, ClassLoss=12.321 [Epoch 55][Batch 399], LR: 1.00E-03, Speed: 8.703 samples/sec, ObjLoss=27.363, BoxCenterLoss=14.649, BoxScaleLoss=5.473, ClassLoss=12.318 [Epoch 55][Batch 499], LR: 1.00E-03, Speed: 11.594 samples/sec, ObjLoss=27.360, BoxCenterLoss=14.648, BoxScaleLoss=5.473, ClassLoss=12.316 [Epoch 55][Batch 599], LR: 1.00E-03, Speed: 10.654 samples/sec, ObjLoss=27.358, BoxCenterLoss=14.648, BoxScaleLoss=5.473, ClassLoss=12.314 [Epoch 55][Batch 699], LR: 1.00E-03, Speed: 11.778 samples/sec, ObjLoss=27.355, BoxCenterLoss=14.649, BoxScaleLoss=5.473, ClassLoss=12.312 [Epoch 55][Batch 799], LR: 1.00E-03, Speed: 10.073 samples/sec, ObjLoss=27.352, BoxCenterLoss=14.648, BoxScaleLoss=5.472, ClassLoss=12.310 [Epoch 55][Batch 899], LR: 1.00E-03, Speed: 8.342 samples/sec, ObjLoss=27.350, BoxCenterLoss=14.648, BoxScaleLoss=5.472, ClassLoss=12.308 [Epoch 55][Batch 999], LR: 1.00E-03, Speed: 8.855 samples/sec, ObjLoss=27.347, BoxCenterLoss=14.649, BoxScaleLoss=5.472, ClassLoss=12.306 [Epoch 55][Batch 1099], LR: 1.00E-03, Speed: 9.640 samples/sec, ObjLoss=27.346, BoxCenterLoss=14.649, BoxScaleLoss=5.471, ClassLoss=12.303 [Epoch 55][Batch 1199], LR: 1.00E-03, Speed: 7.374 samples/sec, ObjLoss=27.343, BoxCenterLoss=14.649, BoxScaleLoss=5.471, ClassLoss=12.301 [Epoch 55][Batch 1299], LR: 1.00E-03, Speed: 8.181 samples/sec, ObjLoss=27.340, BoxCenterLoss=14.649, BoxScaleLoss=5.471, ClassLoss=12.299 [Epoch 55][Batch 1399], LR: 1.00E-03, Speed: 12.202 samples/sec, ObjLoss=27.336, BoxCenterLoss=14.648, BoxScaleLoss=5.470, ClassLoss=12.296 [Epoch 55][Batch 1499], LR: 1.00E-03, Speed: 119.494 samples/sec, ObjLoss=27.334, BoxCenterLoss=14.648, BoxScaleLoss=5.470, ClassLoss=12.294 [Epoch 55][Batch 1599], LR: 1.00E-03, Speed: 8.704 samples/sec, ObjLoss=27.330, BoxCenterLoss=14.647, BoxScaleLoss=5.469, ClassLoss=12.291 [Epoch 55][Batch 1699], LR: 1.00E-03, Speed: 8.411 samples/sec, ObjLoss=27.327, BoxCenterLoss=14.647, BoxScaleLoss=5.469, ClassLoss=12.289 [Epoch 55][Batch 1799], LR: 1.00E-03, Speed: 15.818 samples/sec, ObjLoss=27.324, BoxCenterLoss=14.647, BoxScaleLoss=5.468, ClassLoss=12.287 [Epoch 55] Training cost: 2203.278, ObjLoss=27.324, BoxCenterLoss=14.647, BoxScaleLoss=5.468, ClassLoss=12.286 [Epoch 55] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.204 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.406 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.079 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.193 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.281 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426 person=34.2 bicycle=14.9 car=22.8 motorcycle=24.8 airplane=36.1 bus=44.3 train=45.1 truck=18.5 boat=9.5 traffic light=11.7 fire hydrant=42.6 stop sign=37.0 parking meter=20.0 bench=11.6 bird=17.5 cat=41.0 dog=36.0 horse=33.6 sheep=26.3 cow=30.7 elephant=38.9 bear=40.9 zebra=42.4 giraffe=45.2 backpack=4.0 umbrella=20.1 handbag=3.0 tie=14.7 suitcase=12.0 frisbee=34.7 skis=7.8 snowboard=9.2 sports ball=18.5 kite=19.4 baseball bat=7.8 baseball glove=15.6 skateboard=24.0 surfboard=16.6 tennis racket=22.7 bottle=15.4 wine glass=16.5 cup=21.3 fork=9.7 knife=1.9 spoon=1.7 bowl=19.7 banana=8.8 apple=3.7 sandwich=14.1 orange=17.5 broccoli=9.2 carrot=8.7 hot dog=14.7 pizza=29.5 donut=27.7 cake=17.5 chair=11.8 couch=24.0 potted plant=10.3 bed=29.5 dining table=17.2 toilet=36.8 tv=31.9 laptop=28.9 mouse=31.1 remote=7.5 keyboard=24.9 cell phone=13.1 microwave=27.3 oven=15.9 toaster=0.0 sink=16.5 refrigerator=25.2 book=4.0 clock=28.4 vase=18.2 scissors=8.4 teddy bear=27.5 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 56][Batch 99], LR: 1.00E-03, Speed: 10.420 samples/sec, ObjLoss=27.321, BoxCenterLoss=14.647, BoxScaleLoss=5.468, ClassLoss=12.283 [Epoch 56][Batch 199], LR: 1.00E-03, Speed: 82.114 samples/sec, ObjLoss=27.318, BoxCenterLoss=14.647, BoxScaleLoss=5.468, ClassLoss=12.282 [Epoch 56][Batch 299], LR: 1.00E-03, Speed: 7.949 samples/sec, ObjLoss=27.313, BoxCenterLoss=14.646, BoxScaleLoss=5.467, ClassLoss=12.279 [Epoch 56][Batch 399], LR: 1.00E-03, Speed: 10.796 samples/sec, ObjLoss=27.311, BoxCenterLoss=14.646, BoxScaleLoss=5.467, ClassLoss=12.277 [Epoch 56][Batch 499], LR: 1.00E-03, Speed: 15.106 samples/sec, ObjLoss=27.308, BoxCenterLoss=14.645, BoxScaleLoss=5.467, ClassLoss=12.274 [Epoch 56][Batch 599], LR: 1.00E-03, Speed: 10.311 samples/sec, ObjLoss=27.304, BoxCenterLoss=14.645, BoxScaleLoss=5.466, ClassLoss=12.272 [Epoch 56][Batch 699], LR: 1.00E-03, Speed: 10.234 samples/sec, ObjLoss=27.302, BoxCenterLoss=14.645, BoxScaleLoss=5.466, ClassLoss=12.269 [Epoch 56][Batch 799], LR: 1.00E-03, Speed: 9.279 samples/sec, ObjLoss=27.299, BoxCenterLoss=14.645, BoxScaleLoss=5.465, ClassLoss=12.267 [Epoch 56][Batch 899], LR: 1.00E-03, Speed: 11.946 samples/sec, ObjLoss=27.298, BoxCenterLoss=14.646, BoxScaleLoss=5.465, ClassLoss=12.265 [Epoch 56][Batch 999], LR: 1.00E-03, Speed: 115.204 samples/sec, ObjLoss=27.294, BoxCenterLoss=14.645, BoxScaleLoss=5.465, ClassLoss=12.262 [Epoch 56][Batch 1099], LR: 1.00E-03, Speed: 10.485 samples/sec, ObjLoss=27.293, BoxCenterLoss=14.646, BoxScaleLoss=5.465, ClassLoss=12.261 [Epoch 56][Batch 1199], LR: 1.00E-03, Speed: 7.875 samples/sec, ObjLoss=27.290, BoxCenterLoss=14.647, BoxScaleLoss=5.465, ClassLoss=12.259 [Epoch 56][Batch 1299], LR: 1.00E-03, Speed: 93.988 samples/sec, ObjLoss=27.288, BoxCenterLoss=14.647, BoxScaleLoss=5.465, ClassLoss=12.258 [Epoch 56][Batch 1399], LR: 1.00E-03, Speed: 7.876 samples/sec, ObjLoss=27.284, BoxCenterLoss=14.645, BoxScaleLoss=5.464, ClassLoss=12.255 [Epoch 56][Batch 1499], LR: 1.00E-03, Speed: 11.401 samples/sec, ObjLoss=27.280, BoxCenterLoss=14.645, BoxScaleLoss=5.464, ClassLoss=12.252 [Epoch 56][Batch 1599], LR: 1.00E-03, Speed: 8.333 samples/sec, ObjLoss=27.277, BoxCenterLoss=14.644, BoxScaleLoss=5.463, ClassLoss=12.251 [Epoch 56][Batch 1699], LR: 1.00E-03, Speed: 13.297 samples/sec, ObjLoss=27.275, BoxCenterLoss=14.644, BoxScaleLoss=5.463, ClassLoss=12.248 [Epoch 56][Batch 1799], LR: 1.00E-03, Speed: 11.556 samples/sec, ObjLoss=27.271, BoxCenterLoss=14.644, BoxScaleLoss=5.462, ClassLoss=12.246 [Epoch 56] Training cost: 2177.863, ObjLoss=27.270, BoxCenterLoss=14.644, BoxScaleLoss=5.462, ClassLoss=12.245 [Epoch 56] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.201 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.206 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.193 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.119 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.406 person=33.6 bicycle=16.1 car=22.5 motorcycle=22.6 airplane=31.0 bus=44.2 train=47.3 truck=20.3 boat=9.4 traffic light=11.0 fire hydrant=31.1 stop sign=41.6 parking meter=20.9 bench=12.0 bird=14.8 cat=36.9 dog=32.8 horse=34.6 sheep=30.3 cow=28.6 elephant=42.6 bear=37.8 zebra=39.5 giraffe=42.1 backpack=5.8 umbrella=20.0 handbag=3.1 tie=15.4 suitcase=16.4 frisbee=29.8 skis=7.5 snowboard=8.2 sports ball=22.9 kite=17.9 baseball bat=10.1 baseball glove=16.7 skateboard=24.5 surfboard=15.4 tennis racket=24.4 bottle=15.3 wine glass=12.3 cup=18.7 fork=7.7 knife=3.3 spoon=2.3 bowl=19.5 banana=8.6 apple=6.2 sandwich=16.0 orange=14.7 broccoli=9.3 carrot=6.9 hot dog=13.7 pizza=26.2 donut=21.8 cake=15.9 chair=12.4 couch=26.6 potted plant=9.3 bed=29.8 dining table=18.3 toilet=35.7 tv=33.2 laptop=33.0 mouse=28.3 remote=7.2 keyboard=26.4 cell phone=15.7 microwave=27.7 oven=16.0 toaster=0.0 sink=17.1 refrigerator=23.2 book=3.0 clock=29.1 vase=16.9 scissors=10.4 teddy bear=27.4 hair drier=0.0 toothbrush=0.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.1 [Epoch 57][Batch 99], LR: 1.00E-03, Speed: 10.880 samples/sec, ObjLoss=27.269, BoxCenterLoss=14.644, BoxScaleLoss=5.462, ClassLoss=12.243 [Epoch 57][Batch 199], LR: 1.00E-03, Speed: 7.877 samples/sec, ObjLoss=27.265, BoxCenterLoss=14.644, BoxScaleLoss=5.461, ClassLoss=12.241 [Epoch 57][Batch 299], LR: 1.00E-03, Speed: 103.141 samples/sec, ObjLoss=27.263, BoxCenterLoss=14.644, BoxScaleLoss=5.461, ClassLoss=12.239 [Epoch 57][Batch 399], LR: 1.00E-03, Speed: 10.504 samples/sec, ObjLoss=27.261, BoxCenterLoss=14.643, BoxScaleLoss=5.460, ClassLoss=12.236 [Epoch 57][Batch 499], LR: 1.00E-03, Speed: 8.122 samples/sec, ObjLoss=27.256, BoxCenterLoss=14.642, BoxScaleLoss=5.460, ClassLoss=12.233 [Epoch 57][Batch 599], LR: 1.00E-03, Speed: 9.029 samples/sec, ObjLoss=27.255, BoxCenterLoss=14.642, BoxScaleLoss=5.459, ClassLoss=12.231 [Epoch 57][Batch 699], LR: 1.00E-03, Speed: 123.007 samples/sec, ObjLoss=27.252, BoxCenterLoss=14.642, BoxScaleLoss=5.459, ClassLoss=12.229 [Epoch 57][Batch 799], LR: 1.00E-03, Speed: 11.373 samples/sec, ObjLoss=27.249, BoxCenterLoss=14.642, BoxScaleLoss=5.458, ClassLoss=12.227 [Epoch 57][Batch 899], LR: 1.00E-03, Speed: 14.109 samples/sec, ObjLoss=27.247, BoxCenterLoss=14.643, BoxScaleLoss=5.459, ClassLoss=12.225 [Epoch 57][Batch 999], LR: 1.00E-03, Speed: 8.838 samples/sec, ObjLoss=27.245, BoxCenterLoss=14.643, BoxScaleLoss=5.459, ClassLoss=12.224 [Epoch 57][Batch 1099], LR: 1.00E-03, Speed: 19.404 samples/sec, ObjLoss=27.242, BoxCenterLoss=14.643, BoxScaleLoss=5.458, ClassLoss=12.221 [Epoch 57][Batch 1199], LR: 1.00E-03, Speed: 9.405 samples/sec, ObjLoss=27.239, BoxCenterLoss=14.642, BoxScaleLoss=5.458, ClassLoss=12.219 [Epoch 57][Batch 1299], LR: 1.00E-03, Speed: 8.201 samples/sec, ObjLoss=27.236, BoxCenterLoss=14.642, BoxScaleLoss=5.457, ClassLoss=12.217 [Epoch 57][Batch 1399], LR: 1.00E-03, Speed: 11.761 samples/sec, ObjLoss=27.234, BoxCenterLoss=14.643, BoxScaleLoss=5.457, ClassLoss=12.215 [Epoch 57][Batch 1499], LR: 1.00E-03, Speed: 90.647 samples/sec, ObjLoss=27.231, BoxCenterLoss=14.642, BoxScaleLoss=5.457, ClassLoss=12.212 [Epoch 57][Batch 1599], LR: 1.00E-03, Speed: 8.812 samples/sec, ObjLoss=27.229, BoxCenterLoss=14.643, BoxScaleLoss=5.457, ClassLoss=12.210 [Epoch 57][Batch 1699], LR: 1.00E-03, Speed: 9.100 samples/sec, ObjLoss=27.226, BoxCenterLoss=14.642, BoxScaleLoss=5.456, ClassLoss=12.208 [Epoch 57][Batch 1799], LR: 1.00E-03, Speed: 12.655 samples/sec, ObjLoss=27.223, BoxCenterLoss=14.642, BoxScaleLoss=5.456, ClassLoss=12.206 [Epoch 57] Training cost: 2206.918, ObjLoss=27.222, BoxCenterLoss=14.641, BoxScaleLoss=5.455, ClassLoss=12.205 [Epoch 57] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.419 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.195 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.118 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413 person=34.5 bicycle=16.6 car=22.0 motorcycle=25.1 airplane=38.1 bus=46.1 train=41.6 truck=19.1 boat=10.8 traffic light=10.7 fire hydrant=38.7 stop sign=36.5 parking meter=23.4 bench=11.7 bird=17.4 cat=40.1 dog=33.3 horse=35.3 sheep=30.9 cow=30.9 elephant=39.6 bear=42.1 zebra=41.6 giraffe=48.4 backpack=4.1 umbrella=21.4 handbag=3.8 tie=16.4 suitcase=15.0 frisbee=26.2 skis=7.7 snowboard=10.1 sports ball=16.3 kite=20.5 baseball bat=10.7 baseball glove=12.8 skateboard=28.2 surfboard=17.3 tennis racket=23.1 bottle=15.6 wine glass=18.3 cup=22.1 fork=9.5 knife=3.2 spoon=2.5 bowl=20.8 banana=10.0 apple=6.1 sandwich=12.9 orange=13.7 broccoli=11.2 carrot=6.8 hot dog=14.2 pizza=27.9 donut=28.3 cake=18.0 chair=13.2 couch=21.7 potted plant=10.5 bed=30.1 dining table=16.4 toilet=34.3 tv=29.8 laptop=35.5 mouse=33.6 remote=7.0 keyboard=25.5 cell phone=14.4 microwave=29.6 oven=13.8 toaster=0.0 sink=19.3 refrigerator=28.2 book=3.7 clock=28.4 vase=20.3 scissors=13.0 teddy bear=28.3 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 58][Batch 99], LR: 1.00E-03, Speed: 8.358 samples/sec, ObjLoss=27.219, BoxCenterLoss=14.641, BoxScaleLoss=5.455, ClassLoss=12.202 [Epoch 58][Batch 199], LR: 1.00E-03, Speed: 9.958 samples/sec, ObjLoss=27.215, BoxCenterLoss=14.640, BoxScaleLoss=5.454, ClassLoss=12.200 [Epoch 58][Batch 299], LR: 1.00E-03, Speed: 10.078 samples/sec, ObjLoss=27.213, BoxCenterLoss=14.640, BoxScaleLoss=5.454, ClassLoss=12.197 [Epoch 58][Batch 399], LR: 1.00E-03, Speed: 9.152 samples/sec, ObjLoss=27.212, BoxCenterLoss=14.641, BoxScaleLoss=5.454, ClassLoss=12.196 [Epoch 58][Batch 499], LR: 1.00E-03, Speed: 9.997 samples/sec, ObjLoss=27.210, BoxCenterLoss=14.641, BoxScaleLoss=5.453, ClassLoss=12.193 [Epoch 58][Batch 599], LR: 1.00E-03, Speed: 10.969 samples/sec, ObjLoss=27.207, BoxCenterLoss=14.641, BoxScaleLoss=5.453, ClassLoss=12.191 [Epoch 58][Batch 699], LR: 1.00E-03, Speed: 11.844 samples/sec, ObjLoss=27.204, BoxCenterLoss=14.640, BoxScaleLoss=5.452, ClassLoss=12.189 [Epoch 58][Batch 799], LR: 1.00E-03, Speed: 11.404 samples/sec, ObjLoss=27.200, BoxCenterLoss=14.640, BoxScaleLoss=5.452, ClassLoss=12.186 [Epoch 58][Batch 899], LR: 1.00E-03, Speed: 9.480 samples/sec, ObjLoss=27.197, BoxCenterLoss=14.639, BoxScaleLoss=5.452, ClassLoss=12.184 [Epoch 58][Batch 999], LR: 1.00E-03, Speed: 112.097 samples/sec, ObjLoss=27.195, BoxCenterLoss=14.640, BoxScaleLoss=5.451, ClassLoss=12.182 [Epoch 58][Batch 1099], LR: 1.00E-03, Speed: 8.187 samples/sec, ObjLoss=27.193, BoxCenterLoss=14.640, BoxScaleLoss=5.451, ClassLoss=12.180 [Epoch 58][Batch 1199], LR: 1.00E-03, Speed: 10.201 samples/sec, ObjLoss=27.189, BoxCenterLoss=14.639, BoxScaleLoss=5.450, ClassLoss=12.177 [Epoch 58][Batch 1299], LR: 1.00E-03, Speed: 10.636 samples/sec, ObjLoss=27.185, BoxCenterLoss=14.638, BoxScaleLoss=5.449, ClassLoss=12.175 [Epoch 58][Batch 1399], LR: 1.00E-03, Speed: 10.072 samples/sec, ObjLoss=27.182, BoxCenterLoss=14.637, BoxScaleLoss=5.449, ClassLoss=12.172 [Epoch 58][Batch 1499], LR: 1.00E-03, Speed: 8.637 samples/sec, ObjLoss=27.180, BoxCenterLoss=14.637, BoxScaleLoss=5.448, ClassLoss=12.169 [Epoch 58][Batch 1599], LR: 1.00E-03, Speed: 9.751 samples/sec, ObjLoss=27.178, BoxCenterLoss=14.637, BoxScaleLoss=5.448, ClassLoss=12.167 [Epoch 58][Batch 1699], LR: 1.00E-03, Speed: 8.946 samples/sec, ObjLoss=27.175, BoxCenterLoss=14.637, BoxScaleLoss=5.448, ClassLoss=12.165 [Epoch 58][Batch 1799], LR: 1.00E-03, Speed: 13.368 samples/sec, ObjLoss=27.171, BoxCenterLoss=14.636, BoxScaleLoss=5.447, ClassLoss=12.162 [Epoch 58] Training cost: 2249.618, ObjLoss=27.169, BoxCenterLoss=14.635, BoxScaleLoss=5.447, ClassLoss=12.161 [Epoch 58] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.415 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.082 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.118 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424 person=34.6 bicycle=15.7 car=23.2 motorcycle=25.5 airplane=36.1 bus=43.4 train=43.5 truck=20.6 boat=11.1 traffic light=11.6 fire hydrant=39.5 stop sign=32.8 parking meter=26.9 bench=11.3 bird=14.4 cat=39.3 dog=33.3 horse=32.7 sheep=29.5 cow=30.0 elephant=41.3 bear=38.8 zebra=39.4 giraffe=48.0 backpack=4.6 umbrella=19.6 handbag=3.7 tie=15.7 suitcase=13.0 frisbee=27.3 skis=7.5 snowboard=9.4 sports ball=25.5 kite=19.8 baseball bat=11.1 baseball glove=15.8 skateboard=26.1 surfboard=13.3 tennis racket=22.7 bottle=17.7 wine glass=19.3 cup=23.1 fork=11.3 knife=3.6 spoon=2.8 bowl=21.2 banana=9.4 apple=7.1 sandwich=17.0 orange=16.9 broccoli=9.5 carrot=7.0 hot dog=13.8 pizza=25.9 donut=22.9 cake=19.0 chair=12.8 couch=26.6 potted plant=9.1 bed=31.6 dining table=18.3 toilet=30.3 tv=32.4 laptop=33.9 mouse=33.5 remote=7.6 keyboard=25.6 cell phone=15.1 microwave=30.5 oven=16.5 toaster=0.0 sink=16.2 refrigerator=26.9 book=4.3 clock=28.1 vase=20.5 scissors=10.2 teddy bear=22.7 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 59][Batch 99], LR: 1.00E-03, Speed: 9.608 samples/sec, ObjLoss=27.166, BoxCenterLoss=14.635, BoxScaleLoss=5.446, ClassLoss=12.159 [Epoch 59][Batch 199], LR: 1.00E-03, Speed: 7.452 samples/sec, ObjLoss=27.164, BoxCenterLoss=14.635, BoxScaleLoss=5.446, ClassLoss=12.157 [Epoch 59][Batch 299], LR: 1.00E-03, Speed: 9.411 samples/sec, ObjLoss=27.161, BoxCenterLoss=14.635, BoxScaleLoss=5.445, ClassLoss=12.155 [Epoch 59][Batch 399], LR: 1.00E-03, Speed: 9.488 samples/sec, ObjLoss=27.158, BoxCenterLoss=14.634, BoxScaleLoss=5.445, ClassLoss=12.152 [Epoch 59][Batch 499], LR: 1.00E-03, Speed: 8.769 samples/sec, ObjLoss=27.156, BoxCenterLoss=14.634, BoxScaleLoss=5.444, ClassLoss=12.150 [Epoch 59][Batch 599], LR: 1.00E-03, Speed: 10.293 samples/sec, ObjLoss=27.154, BoxCenterLoss=14.634, BoxScaleLoss=5.444, ClassLoss=12.147 [Epoch 59][Batch 699], LR: 1.00E-03, Speed: 8.750 samples/sec, ObjLoss=27.151, BoxCenterLoss=14.634, BoxScaleLoss=5.444, ClassLoss=12.146 [Epoch 59][Batch 799], LR: 1.00E-03, Speed: 7.640 samples/sec, ObjLoss=27.148, BoxCenterLoss=14.633, BoxScaleLoss=5.443, ClassLoss=12.143 [Epoch 59][Batch 899], LR: 1.00E-03, Speed: 10.131 samples/sec, ObjLoss=27.145, BoxCenterLoss=14.632, BoxScaleLoss=5.442, ClassLoss=12.141 [Epoch 59][Batch 999], LR: 1.00E-03, Speed: 11.736 samples/sec, ObjLoss=27.143, BoxCenterLoss=14.632, BoxScaleLoss=5.442, ClassLoss=12.138 [Epoch 59][Batch 1099], LR: 1.00E-03, Speed: 12.313 samples/sec, ObjLoss=27.141, BoxCenterLoss=14.632, BoxScaleLoss=5.441, ClassLoss=12.136 [Epoch 59][Batch 1199], LR: 1.00E-03, Speed: 10.932 samples/sec, ObjLoss=27.139, BoxCenterLoss=14.633, BoxScaleLoss=5.441, ClassLoss=12.134 [Epoch 59][Batch 1299], LR: 1.00E-03, Speed: 9.116 samples/sec, ObjLoss=27.136, BoxCenterLoss=14.632, BoxScaleLoss=5.441, ClassLoss=12.133 [Epoch 59][Batch 1399], LR: 1.00E-03, Speed: 8.547 samples/sec, ObjLoss=27.134, BoxCenterLoss=14.633, BoxScaleLoss=5.441, ClassLoss=12.131 [Epoch 59][Batch 1499], LR: 1.00E-03, Speed: 38.735 samples/sec, ObjLoss=27.130, BoxCenterLoss=14.632, BoxScaleLoss=5.440, ClassLoss=12.128 [Epoch 59][Batch 1599], LR: 1.00E-03, Speed: 9.127 samples/sec, ObjLoss=27.127, BoxCenterLoss=14.632, BoxScaleLoss=5.440, ClassLoss=12.125 [Epoch 59][Batch 1699], LR: 1.00E-03, Speed: 126.279 samples/sec, ObjLoss=27.124, BoxCenterLoss=14.631, BoxScaleLoss=5.439, ClassLoss=12.123 [Epoch 59][Batch 1799], LR: 1.00E-03, Speed: 12.262 samples/sec, ObjLoss=27.122, BoxCenterLoss=14.631, BoxScaleLoss=5.439, ClassLoss=12.121 [Epoch 59] Training cost: 2223.806, ObjLoss=27.122, BoxCenterLoss=14.632, BoxScaleLoss=5.439, ClassLoss=12.121 [Epoch 59] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.206 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.211 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.432 person=33.3 bicycle=15.2 car=23.2 motorcycle=25.4 airplane=38.1 bus=48.2 train=45.9 truck=19.7 boat=11.7 traffic light=10.7 fire hydrant=33.4 stop sign=32.5 parking meter=27.5 bench=10.9 bird=14.2 cat=36.9 dog=32.8 horse=32.2 sheep=27.2 cow=30.4 elephant=39.6 bear=40.1 zebra=44.4 giraffe=45.5 backpack=4.3 umbrella=21.5 handbag=3.1 tie=15.6 suitcase=15.0 frisbee=27.9 skis=6.6 snowboard=9.1 sports ball=23.6 kite=20.0 baseball bat=12.2 baseball glove=11.2 skateboard=27.7 surfboard=15.1 tennis racket=23.1 bottle=17.8 wine glass=18.1 cup=18.8 fork=9.5 knife=4.1 spoon=2.7 bowl=19.7 banana=11.0 apple=6.7 sandwich=15.2 orange=15.2 broccoli=7.8 carrot=7.1 hot dog=17.2 pizza=29.4 donut=18.5 cake=18.8 chair=12.9 couch=26.4 potted plant=10.7 bed=32.8 dining table=16.8 toilet=35.0 tv=28.5 laptop=33.7 mouse=26.6 remote=6.9 keyboard=25.4 cell phone=13.7 microwave=27.7 oven=17.6 toaster=0.0 sink=17.5 refrigerator=30.0 book=3.6 clock=26.6 vase=21.1 scissors=11.3 teddy bear=24.8 hair drier=0.0 toothbrush=1.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.6 [Epoch 60][Batch 99], LR: 1.00E-03, Speed: 9.563 samples/sec, ObjLoss=27.119, BoxCenterLoss=14.632, BoxScaleLoss=5.438, ClassLoss=12.118 [Epoch 60][Batch 199], LR: 1.00E-03, Speed: 9.691 samples/sec, ObjLoss=27.118, BoxCenterLoss=14.632, BoxScaleLoss=5.438, ClassLoss=12.116 [Epoch 60][Batch 299], LR: 1.00E-03, Speed: 7.352 samples/sec, ObjLoss=27.116, BoxCenterLoss=14.632, BoxScaleLoss=5.438, ClassLoss=12.114 [Epoch 60][Batch 399], LR: 1.00E-03, Speed: 117.882 samples/sec, ObjLoss=27.113, BoxCenterLoss=14.632, BoxScaleLoss=5.437, ClassLoss=12.112 [Epoch 60][Batch 499], LR: 1.00E-03, Speed: 7.720 samples/sec, ObjLoss=27.111, BoxCenterLoss=14.632, BoxScaleLoss=5.437, ClassLoss=12.110 [Epoch 60][Batch 599], LR: 1.00E-03, Speed: 7.641 samples/sec, ObjLoss=27.107, BoxCenterLoss=14.631, BoxScaleLoss=5.437, ClassLoss=12.108 [Epoch 60][Batch 699], LR: 1.00E-03, Speed: 9.324 samples/sec, ObjLoss=27.104, BoxCenterLoss=14.630, BoxScaleLoss=5.436, ClassLoss=12.106 [Epoch 60][Batch 799], LR: 1.00E-03, Speed: 110.701 samples/sec, ObjLoss=27.102, BoxCenterLoss=14.631, BoxScaleLoss=5.436, ClassLoss=12.104 [Epoch 60][Batch 899], LR: 1.00E-03, Speed: 10.979 samples/sec, ObjLoss=27.101, BoxCenterLoss=14.631, BoxScaleLoss=5.436, ClassLoss=12.102 [Epoch 60][Batch 999], LR: 1.00E-03, Speed: 11.891 samples/sec, ObjLoss=27.098, BoxCenterLoss=14.631, BoxScaleLoss=5.436, ClassLoss=12.100 [Epoch 60][Batch 1099], LR: 1.00E-03, Speed: 93.808 samples/sec, ObjLoss=27.095, BoxCenterLoss=14.631, BoxScaleLoss=5.435, ClassLoss=12.098 [Epoch 60][Batch 1199], LR: 1.00E-03, Speed: 9.102 samples/sec, ObjLoss=27.092, BoxCenterLoss=14.630, BoxScaleLoss=5.435, ClassLoss=12.095 [Epoch 60][Batch 1299], LR: 1.00E-03, Speed: 9.698 samples/sec, ObjLoss=27.089, BoxCenterLoss=14.629, BoxScaleLoss=5.434, ClassLoss=12.093 [Epoch 60][Batch 1399], LR: 1.00E-03, Speed: 8.165 samples/sec, ObjLoss=27.087, BoxCenterLoss=14.629, BoxScaleLoss=5.433, ClassLoss=12.091 [Epoch 60][Batch 1499], LR: 1.00E-03, Speed: 8.461 samples/sec, ObjLoss=27.084, BoxCenterLoss=14.629, BoxScaleLoss=5.433, ClassLoss=12.088 [Epoch 60][Batch 1599], LR: 1.00E-03, Speed: 8.110 samples/sec, ObjLoss=27.081, BoxCenterLoss=14.629, BoxScaleLoss=5.433, ClassLoss=12.087 [Epoch 60][Batch 1699], LR: 1.00E-03, Speed: 10.094 samples/sec, ObjLoss=27.078, BoxCenterLoss=14.628, BoxScaleLoss=5.432, ClassLoss=12.084 [Epoch 60][Batch 1799], LR: 1.00E-03, Speed: 13.112 samples/sec, ObjLoss=27.076, BoxCenterLoss=14.629, BoxScaleLoss=5.433, ClassLoss=12.083 [Epoch 60] Training cost: 2213.212, ObjLoss=27.075, BoxCenterLoss=14.629, BoxScaleLoss=5.432, ClassLoss=12.082 [Epoch 60] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.204 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.424 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.169 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.216 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 person=32.0 bicycle=14.7 car=22.8 motorcycle=23.5 airplane=33.3 bus=35.8 train=39.9 truck=17.0 boat=10.9 traffic light=12.3 fire hydrant=37.3 stop sign=36.3 parking meter=27.5 bench=10.8 bird=17.3 cat=40.9 dog=33.1 horse=32.2 sheep=26.6 cow=32.8 elephant=36.4 bear=39.4 zebra=37.2 giraffe=41.2 backpack=5.7 umbrella=20.2 handbag=3.3 tie=10.9 suitcase=14.3 frisbee=31.8 skis=8.3 snowboard=11.5 sports ball=18.1 kite=21.6 baseball bat=9.8 baseball glove=15.8 skateboard=27.7 surfboard=15.8 tennis racket=23.8 bottle=18.5 wine glass=15.6 cup=21.4 fork=10.7 knife=4.3 spoon=2.9 bowl=20.9 banana=10.7 apple=5.3 sandwich=15.3 orange=15.2 broccoli=10.3 carrot=6.7 hot dog=13.1 pizza=30.4 donut=25.5 cake=17.2 chair=12.5 couch=24.3 potted plant=11.9 bed=28.8 dining table=17.8 toilet=36.4 tv=32.2 laptop=35.4 mouse=29.3 remote=8.5 keyboard=24.0 cell phone=13.2 microwave=29.2 oven=15.4 toaster=0.0 sink=17.6 refrigerator=24.5 book=3.9 clock=29.6 vase=19.0 scissors=13.6 teddy bear=29.1 hair drier=0.0 toothbrush=4.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 61][Batch 99], LR: 1.00E-03, Speed: 8.020 samples/sec, ObjLoss=27.073, BoxCenterLoss=14.629, BoxScaleLoss=5.432, ClassLoss=12.080 [Epoch 61][Batch 199], LR: 1.00E-03, Speed: 10.034 samples/sec, ObjLoss=27.070, BoxCenterLoss=14.628, BoxScaleLoss=5.431, ClassLoss=12.077 [Epoch 61][Batch 299], LR: 1.00E-03, Speed: 10.480 samples/sec, ObjLoss=27.067, BoxCenterLoss=14.628, BoxScaleLoss=5.431, ClassLoss=12.075 [Epoch 61][Batch 399], LR: 1.00E-03, Speed: 7.991 samples/sec, ObjLoss=27.065, BoxCenterLoss=14.628, BoxScaleLoss=5.430, ClassLoss=12.073 [Epoch 61][Batch 499], LR: 1.00E-03, Speed: 90.013 samples/sec, ObjLoss=27.062, BoxCenterLoss=14.627, BoxScaleLoss=5.430, ClassLoss=12.070 [Epoch 61][Batch 599], LR: 1.00E-03, Speed: 105.552 samples/sec, ObjLoss=27.059, BoxCenterLoss=14.627, BoxScaleLoss=5.429, ClassLoss=12.068 [Epoch 61][Batch 699], LR: 1.00E-03, Speed: 10.496 samples/sec, ObjLoss=27.056, BoxCenterLoss=14.626, BoxScaleLoss=5.429, ClassLoss=12.066 [Epoch 61][Batch 799], LR: 1.00E-03, Speed: 9.125 samples/sec, ObjLoss=27.053, BoxCenterLoss=14.626, BoxScaleLoss=5.428, ClassLoss=12.064 [Epoch 61][Batch 899], LR: 1.00E-03, Speed: 8.042 samples/sec, ObjLoss=27.049, BoxCenterLoss=14.625, BoxScaleLoss=5.428, ClassLoss=12.061 [Epoch 61][Batch 999], LR: 1.00E-03, Speed: 87.199 samples/sec, ObjLoss=27.046, BoxCenterLoss=14.625, BoxScaleLoss=5.427, ClassLoss=12.059 [Epoch 61][Batch 1099], LR: 1.00E-03, Speed: 9.520 samples/sec, ObjLoss=27.044, BoxCenterLoss=14.625, BoxScaleLoss=5.427, ClassLoss=12.057 [Epoch 61][Batch 1199], LR: 1.00E-03, Speed: 115.582 samples/sec, ObjLoss=27.042, BoxCenterLoss=14.625, BoxScaleLoss=5.427, ClassLoss=12.056 [Epoch 61][Batch 1299], LR: 1.00E-03, Speed: 8.786 samples/sec, ObjLoss=27.038, BoxCenterLoss=14.624, BoxScaleLoss=5.427, ClassLoss=12.053 [Epoch 61][Batch 1399], LR: 1.00E-03, Speed: 10.833 samples/sec, ObjLoss=27.034, BoxCenterLoss=14.623, BoxScaleLoss=5.426, ClassLoss=12.051 [Epoch 61][Batch 1499], LR: 1.00E-03, Speed: 7.594 samples/sec, ObjLoss=27.030, BoxCenterLoss=14.622, BoxScaleLoss=5.426, ClassLoss=12.049 [Epoch 61][Batch 1599], LR: 1.00E-03, Speed: 9.066 samples/sec, ObjLoss=27.027, BoxCenterLoss=14.621, BoxScaleLoss=5.425, ClassLoss=12.047 [Epoch 61][Batch 1699], LR: 1.00E-03, Speed: 9.472 samples/sec, ObjLoss=27.025, BoxCenterLoss=14.621, BoxScaleLoss=5.425, ClassLoss=12.044 [Epoch 61][Batch 1799], LR: 1.00E-03, Speed: 126.591 samples/sec, ObjLoss=27.022, BoxCenterLoss=14.621, BoxScaleLoss=5.424, ClassLoss=12.043 [Epoch 61] Training cost: 2240.434, ObjLoss=27.021, BoxCenterLoss=14.621, BoxScaleLoss=5.424, ClassLoss=12.042 [Epoch 61] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.416 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.183 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.197 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.293 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412 person=35.2 bicycle=16.3 car=21.0 motorcycle=25.6 airplane=34.2 bus=41.9 train=44.7 truck=19.1 boat=11.4 traffic light=10.8 fire hydrant=36.5 stop sign=38.2 parking meter=26.3 bench=10.6 bird=16.9 cat=43.4 dog=36.0 horse=33.9 sheep=26.8 cow=31.7 elephant=36.5 bear=41.7 zebra=42.3 giraffe=45.9 backpack=4.6 umbrella=18.7 handbag=3.1 tie=13.1 suitcase=13.2 frisbee=32.8 skis=9.5 snowboard=9.1 sports ball=23.4 kite=22.4 baseball bat=11.9 baseball glove=16.5 skateboard=24.3 surfboard=10.4 tennis racket=25.9 bottle=16.0 wine glass=18.6 cup=22.4 fork=9.6 knife=4.2 spoon=2.7 bowl=20.5 banana=10.3 apple=6.1 sandwich=15.4 orange=14.4 broccoli=7.4 carrot=4.4 hot dog=14.1 pizza=31.7 donut=24.7 cake=18.2 chair=14.3 couch=26.5 potted plant=12.7 bed=30.6 dining table=15.7 toilet=34.4 tv=29.6 laptop=31.4 mouse=30.2 remote=8.2 keyboard=21.9 cell phone=14.5 microwave=27.1 oven=17.9 toaster=4.8 sink=16.2 refrigerator=25.8 book=4.3 clock=27.9 vase=20.6 scissors=13.0 teddy bear=29.8 hair drier=0.0 toothbrush=2.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 62][Batch 99], LR: 1.00E-03, Speed: 10.259 samples/sec, ObjLoss=27.019, BoxCenterLoss=14.621, BoxScaleLoss=5.424, ClassLoss=12.040 [Epoch 62][Batch 199], LR: 1.00E-03, Speed: 9.622 samples/sec, ObjLoss=27.014, BoxCenterLoss=14.619, BoxScaleLoss=5.423, ClassLoss=12.037 [Epoch 62][Batch 299], LR: 1.00E-03, Speed: 119.213 samples/sec, ObjLoss=27.013, BoxCenterLoss=14.620, BoxScaleLoss=5.424, ClassLoss=12.036 [Epoch 62][Batch 399], LR: 1.00E-03, Speed: 110.700 samples/sec, ObjLoss=27.010, BoxCenterLoss=14.620, BoxScaleLoss=5.423, ClassLoss=12.034 [Epoch 62][Batch 499], LR: 1.00E-03, Speed: 8.618 samples/sec, ObjLoss=27.008, BoxCenterLoss=14.620, BoxScaleLoss=5.423, ClassLoss=12.032 [Epoch 62][Batch 599], LR: 1.00E-03, Speed: 10.103 samples/sec, ObjLoss=27.005, BoxCenterLoss=14.619, BoxScaleLoss=5.422, ClassLoss=12.029 [Epoch 62][Batch 699], LR: 1.00E-03, Speed: 8.341 samples/sec, ObjLoss=27.002, BoxCenterLoss=14.618, BoxScaleLoss=5.422, ClassLoss=12.027 [Epoch 62][Batch 799], LR: 1.00E-03, Speed: 7.083 samples/sec, ObjLoss=26.999, BoxCenterLoss=14.618, BoxScaleLoss=5.421, ClassLoss=12.025 [Epoch 62][Batch 899], LR: 1.00E-03, Speed: 7.832 samples/sec, ObjLoss=26.997, BoxCenterLoss=14.618, BoxScaleLoss=5.421, ClassLoss=12.023 [Epoch 62][Batch 999], LR: 1.00E-03, Speed: 10.046 samples/sec, ObjLoss=26.995, BoxCenterLoss=14.618, BoxScaleLoss=5.421, ClassLoss=12.021 [Epoch 62][Batch 1099], LR: 1.00E-03, Speed: 9.394 samples/sec, ObjLoss=26.992, BoxCenterLoss=14.618, BoxScaleLoss=5.421, ClassLoss=12.019 [Epoch 62][Batch 1199], LR: 1.00E-03, Speed: 10.841 samples/sec, ObjLoss=26.990, BoxCenterLoss=14.618, BoxScaleLoss=5.421, ClassLoss=12.017 [Epoch 62][Batch 1299], LR: 1.00E-03, Speed: 10.899 samples/sec, ObjLoss=26.987, BoxCenterLoss=14.617, BoxScaleLoss=5.420, ClassLoss=12.015 [Epoch 62][Batch 1399], LR: 1.00E-03, Speed: 8.002 samples/sec, ObjLoss=26.984, BoxCenterLoss=14.618, BoxScaleLoss=5.420, ClassLoss=12.013 [Epoch 62][Batch 1499], LR: 1.00E-03, Speed: 99.927 samples/sec, ObjLoss=26.983, BoxCenterLoss=14.619, BoxScaleLoss=5.419, ClassLoss=12.011 [Epoch 62][Batch 1599], LR: 1.00E-03, Speed: 11.816 samples/sec, ObjLoss=26.981, BoxCenterLoss=14.618, BoxScaleLoss=5.419, ClassLoss=12.009 [Epoch 62][Batch 1699], LR: 1.00E-03, Speed: 8.679 samples/sec, ObjLoss=26.979, BoxCenterLoss=14.619, BoxScaleLoss=5.419, ClassLoss=12.007 [Epoch 62][Batch 1799], LR: 1.00E-03, Speed: 11.602 samples/sec, ObjLoss=26.977, BoxCenterLoss=14.619, BoxScaleLoss=5.418, ClassLoss=12.005 [Epoch 62] Training cost: 2216.633, ObjLoss=26.976, BoxCenterLoss=14.618, BoxScaleLoss=5.418, ClassLoss=12.004 [Epoch 62] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.204 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.073 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.193 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420 person=34.1 bicycle=15.5 car=21.0 motorcycle=25.5 airplane=33.0 bus=41.5 train=43.2 truck=19.0 boat=11.6 traffic light=12.4 fire hydrant=37.8 stop sign=35.7 parking meter=24.0 bench=9.6 bird=16.1 cat=35.6 dog=33.6 horse=35.8 sheep=26.6 cow=32.7 elephant=42.2 bear=43.3 zebra=43.9 giraffe=42.9 backpack=4.2 umbrella=21.2 handbag=3.5 tie=15.3 suitcase=13.6 frisbee=30.4 skis=9.5 snowboard=15.8 sports ball=21.9 kite=23.4 baseball bat=9.7 baseball glove=12.0 skateboard=27.9 surfboard=16.5 tennis racket=20.5 bottle=16.7 wine glass=17.6 cup=21.6 fork=11.0 knife=3.4 spoon=2.7 bowl=20.8 banana=11.6 apple=6.0 sandwich=16.2 orange=13.4 broccoli=7.9 carrot=5.4 hot dog=13.0 pizza=28.1 donut=21.2 cake=18.4 chair=12.1 couch=24.9 potted plant=8.5 bed=30.8 dining table=15.9 toilet=32.0 tv=32.7 laptop=36.4 mouse=27.1 remote=6.3 keyboard=24.5 cell phone=13.9 microwave=26.0 oven=16.9 toaster=0.0 sink=16.6 refrigerator=24.1 book=3.5 clock=28.8 vase=19.4 scissors=9.8 teddy bear=21.7 hair drier=0.0 toothbrush=3.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 63][Batch 99], LR: 1.00E-03, Speed: 9.089 samples/sec, ObjLoss=26.973, BoxCenterLoss=14.618, BoxScaleLoss=5.418, ClassLoss=12.002 [Epoch 63][Batch 199], LR: 1.00E-03, Speed: 8.389 samples/sec, ObjLoss=26.972, BoxCenterLoss=14.618, BoxScaleLoss=5.417, ClassLoss=12.000 [Epoch 63][Batch 299], LR: 1.00E-03, Speed: 9.050 samples/sec, ObjLoss=26.970, BoxCenterLoss=14.618, BoxScaleLoss=5.417, ClassLoss=11.998 [Epoch 63][Batch 399], LR: 1.00E-03, Speed: 7.138 samples/sec, ObjLoss=26.968, BoxCenterLoss=14.618, BoxScaleLoss=5.417, ClassLoss=11.996 [Epoch 63][Batch 499], LR: 1.00E-03, Speed: 8.908 samples/sec, ObjLoss=26.964, BoxCenterLoss=14.618, BoxScaleLoss=5.416, ClassLoss=11.994 [Epoch 63][Batch 599], LR: 1.00E-03, Speed: 11.585 samples/sec, ObjLoss=26.961, BoxCenterLoss=14.617, BoxScaleLoss=5.416, ClassLoss=11.992 [Epoch 63][Batch 699], LR: 1.00E-03, Speed: 8.986 samples/sec, ObjLoss=26.958, BoxCenterLoss=14.617, BoxScaleLoss=5.415, ClassLoss=11.990 [Epoch 63][Batch 799], LR: 1.00E-03, Speed: 8.640 samples/sec, ObjLoss=26.956, BoxCenterLoss=14.617, BoxScaleLoss=5.415, ClassLoss=11.989 [Epoch 63][Batch 899], LR: 1.00E-03, Speed: 8.845 samples/sec, ObjLoss=26.954, BoxCenterLoss=14.617, BoxScaleLoss=5.415, ClassLoss=11.986 [Epoch 63][Batch 999], LR: 1.00E-03, Speed: 10.337 samples/sec, ObjLoss=26.952, BoxCenterLoss=14.616, BoxScaleLoss=5.414, ClassLoss=11.984 [Epoch 63][Batch 1099], LR: 1.00E-03, Speed: 110.881 samples/sec, ObjLoss=26.949, BoxCenterLoss=14.616, BoxScaleLoss=5.414, ClassLoss=11.982 [Epoch 63][Batch 1199], LR: 1.00E-03, Speed: 10.023 samples/sec, ObjLoss=26.946, BoxCenterLoss=14.615, BoxScaleLoss=5.413, ClassLoss=11.980 [Epoch 63][Batch 1299], LR: 1.00E-03, Speed: 13.722 samples/sec, ObjLoss=26.943, BoxCenterLoss=14.615, BoxScaleLoss=5.413, ClassLoss=11.978 [Epoch 63][Batch 1399], LR: 1.00E-03, Speed: 9.056 samples/sec, ObjLoss=26.942, BoxCenterLoss=14.616, BoxScaleLoss=5.413, ClassLoss=11.977 [Epoch 63][Batch 1499], LR: 1.00E-03, Speed: 113.693 samples/sec, ObjLoss=26.940, BoxCenterLoss=14.615, BoxScaleLoss=5.413, ClassLoss=11.976 [Epoch 63][Batch 1599], LR: 1.00E-03, Speed: 123.570 samples/sec, ObjLoss=26.937, BoxCenterLoss=14.615, BoxScaleLoss=5.413, ClassLoss=11.973 [Epoch 63][Batch 1699], LR: 1.00E-03, Speed: 120.065 samples/sec, ObjLoss=26.935, BoxCenterLoss=14.615, BoxScaleLoss=5.412, ClassLoss=11.971 [Epoch 63][Batch 1799], LR: 1.00E-03, Speed: 10.969 samples/sec, ObjLoss=26.933, BoxCenterLoss=14.615, BoxScaleLoss=5.412, ClassLoss=11.969 [Epoch 63] Training cost: 2183.456, ObjLoss=26.931, BoxCenterLoss=14.614, BoxScaleLoss=5.412, ClassLoss=11.968 [Epoch 63] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.187 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.419 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.261 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.179 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.355 person=33.2 bicycle=13.3 car=24.4 motorcycle=21.1 airplane=30.5 bus=33.8 train=35.2 truck=16.5 boat=11.0 traffic light=13.3 fire hydrant=36.0 stop sign=30.2 parking meter=21.7 bench=10.9 bird=16.1 cat=35.0 dog=26.8 horse=25.0 sheep=27.1 cow=28.2 elephant=37.2 bear=36.4 zebra=31.5 giraffe=35.5 backpack=4.2 umbrella=19.1 handbag=5.0 tie=14.6 suitcase=12.2 frisbee=35.4 skis=9.0 snowboard=12.5 sports ball=15.2 kite=22.6 baseball bat=10.5 baseball glove=17.3 skateboard=24.7 surfboard=17.0 tennis racket=24.6 bottle=16.0 wine glass=17.1 cup=20.9 fork=10.4 knife=4.5 spoon=3.6 bowl=21.0 banana=9.3 apple=6.1 sandwich=12.2 orange=12.9 broccoli=9.4 carrot=5.5 hot dog=13.0 pizza=28.5 donut=19.3 cake=17.3 chair=12.4 couch=21.6 potted plant=11.2 bed=25.4 dining table=12.4 toilet=33.6 tv=24.2 laptop=26.9 mouse=35.1 remote=8.9 keyboard=20.7 cell phone=14.3 microwave=26.4 oven=12.8 toaster=0.0 sink=15.7 refrigerator=22.2 book=3.4 clock=29.8 vase=17.0 scissors=5.9 teddy bear=15.1 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.7 [Epoch 64][Batch 99], LR: 1.00E-03, Speed: 8.563 samples/sec, ObjLoss=26.929, BoxCenterLoss=14.614, BoxScaleLoss=5.411, ClassLoss=11.966 [Epoch 64][Batch 199], LR: 1.00E-03, Speed: 10.019 samples/sec, ObjLoss=26.927, BoxCenterLoss=14.614, BoxScaleLoss=5.410, ClassLoss=11.964 [Epoch 64][Batch 299], LR: 1.00E-03, Speed: 11.041 samples/sec, ObjLoss=26.924, BoxCenterLoss=14.613, BoxScaleLoss=5.410, ClassLoss=11.962 [Epoch 64][Batch 399], LR: 1.00E-03, Speed: 8.401 samples/sec, ObjLoss=26.922, BoxCenterLoss=14.613, BoxScaleLoss=5.410, ClassLoss=11.960 [Epoch 64][Batch 499], LR: 1.00E-03, Speed: 9.121 samples/sec, ObjLoss=26.920, BoxCenterLoss=14.614, BoxScaleLoss=5.410, ClassLoss=11.958 [Epoch 64][Batch 599], LR: 1.00E-03, Speed: 8.427 samples/sec, ObjLoss=26.918, BoxCenterLoss=14.614, BoxScaleLoss=5.410, ClassLoss=11.957 [Epoch 64][Batch 699], LR: 1.00E-03, Speed: 93.636 samples/sec, ObjLoss=26.917, BoxCenterLoss=14.614, BoxScaleLoss=5.410, ClassLoss=11.955 [Epoch 64][Batch 799], LR: 1.00E-03, Speed: 8.338 samples/sec, ObjLoss=26.914, BoxCenterLoss=14.614, BoxScaleLoss=5.409, ClassLoss=11.954 [Epoch 64][Batch 899], LR: 1.00E-03, Speed: 10.296 samples/sec, ObjLoss=26.912, BoxCenterLoss=14.614, BoxScaleLoss=5.409, ClassLoss=11.952 [Epoch 64][Batch 999], LR: 1.00E-03, Speed: 10.923 samples/sec, ObjLoss=26.910, BoxCenterLoss=14.614, BoxScaleLoss=5.409, ClassLoss=11.950 [Epoch 64][Batch 1099], LR: 1.00E-03, Speed: 12.850 samples/sec, ObjLoss=26.907, BoxCenterLoss=14.613, BoxScaleLoss=5.408, ClassLoss=11.948 [Epoch 64][Batch 1199], LR: 1.00E-03, Speed: 93.815 samples/sec, ObjLoss=26.905, BoxCenterLoss=14.613, BoxScaleLoss=5.408, ClassLoss=11.946 [Epoch 64][Batch 1299], LR: 1.00E-03, Speed: 9.359 samples/sec, ObjLoss=26.902, BoxCenterLoss=14.613, BoxScaleLoss=5.408, ClassLoss=11.944 [Epoch 64][Batch 1399], LR: 1.00E-03, Speed: 109.319 samples/sec, ObjLoss=26.898, BoxCenterLoss=14.612, BoxScaleLoss=5.407, ClassLoss=11.942 [Epoch 64][Batch 1499], LR: 1.00E-03, Speed: 10.865 samples/sec, ObjLoss=26.896, BoxCenterLoss=14.612, BoxScaleLoss=5.407, ClassLoss=11.940 [Epoch 64][Batch 1599], LR: 1.00E-03, Speed: 109.895 samples/sec, ObjLoss=26.894, BoxCenterLoss=14.612, BoxScaleLoss=5.407, ClassLoss=11.938 [Epoch 64][Batch 1699], LR: 1.00E-03, Speed: 12.202 samples/sec, ObjLoss=26.891, BoxCenterLoss=14.612, BoxScaleLoss=5.406, ClassLoss=11.936 [Epoch 64][Batch 1799], LR: 1.00E-03, Speed: 9.904 samples/sec, ObjLoss=26.889, BoxCenterLoss=14.611, BoxScaleLoss=5.406, ClassLoss=11.934 [Epoch 64] Training cost: 2112.409, ObjLoss=26.888, BoxCenterLoss=14.611, BoxScaleLoss=5.406, ClassLoss=11.933 [Epoch 64] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.200 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.162 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.081 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.190 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.123 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.380 person=32.8 bicycle=14.4 car=24.4 motorcycle=24.7 airplane=32.4 bus=40.4 train=38.9 truck=17.7 boat=11.9 traffic light=12.3 fire hydrant=35.7 stop sign=34.8 parking meter=15.2 bench=11.2 bird=16.5 cat=36.0 dog=34.8 horse=34.1 sheep=27.5 cow=34.7 elephant=36.7 bear=42.4 zebra=40.7 giraffe=46.8 backpack=5.3 umbrella=20.1 handbag=4.3 tie=13.6 suitcase=10.9 frisbee=32.8 skis=9.0 snowboard=7.6 sports ball=19.7 kite=22.3 baseball bat=11.1 baseball glove=16.1 skateboard=25.4 surfboard=16.1 tennis racket=20.9 bottle=17.1 wine glass=17.7 cup=18.9 fork=8.0 knife=3.7 spoon=3.0 bowl=17.4 banana=9.8 apple=7.8 sandwich=14.9 orange=13.7 broccoli=8.5 carrot=7.0 hot dog=13.8 pizza=28.8 donut=25.6 cake=19.5 chair=12.1 couch=22.4 potted plant=10.8 bed=27.7 dining table=16.0 toilet=27.7 tv=27.1 laptop=28.5 mouse=30.2 remote=9.6 keyboard=24.9 cell phone=12.7 microwave=32.0 oven=13.6 toaster=0.0 sink=18.8 refrigerator=23.4 book=4.3 clock=28.9 vase=18.2 scissors=16.1 teddy bear=25.0 hair drier=0.0 toothbrush=2.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.0 [Epoch 65][Batch 99], LR: 1.00E-03, Speed: 9.961 samples/sec, ObjLoss=26.886, BoxCenterLoss=14.611, BoxScaleLoss=5.405, ClassLoss=11.932 [Epoch 65][Batch 199], LR: 1.00E-03, Speed: 9.640 samples/sec, ObjLoss=26.883, BoxCenterLoss=14.611, BoxScaleLoss=5.405, ClassLoss=11.930 [Epoch 65][Batch 299], LR: 1.00E-03, Speed: 9.430 samples/sec, ObjLoss=26.880, BoxCenterLoss=14.610, BoxScaleLoss=5.405, ClassLoss=11.928 [Epoch 65][Batch 399], LR: 1.00E-03, Speed: 10.433 samples/sec, ObjLoss=26.877, BoxCenterLoss=14.610, BoxScaleLoss=5.405, ClassLoss=11.927 [Epoch 65][Batch 499], LR: 1.00E-03, Speed: 88.710 samples/sec, ObjLoss=26.875, BoxCenterLoss=14.610, BoxScaleLoss=5.404, ClassLoss=11.924 [Epoch 65][Batch 599], LR: 1.00E-03, Speed: 87.662 samples/sec, ObjLoss=26.872, BoxCenterLoss=14.609, BoxScaleLoss=5.404, ClassLoss=11.922 [Epoch 65][Batch 699], LR: 1.00E-03, Speed: 10.499 samples/sec, ObjLoss=26.870, BoxCenterLoss=14.609, BoxScaleLoss=5.403, ClassLoss=11.920 [Epoch 65][Batch 799], LR: 1.00E-03, Speed: 8.065 samples/sec, ObjLoss=26.867, BoxCenterLoss=14.609, BoxScaleLoss=5.403, ClassLoss=11.919 [Epoch 65][Batch 899], LR: 1.00E-03, Speed: 92.960 samples/sec, ObjLoss=26.865, BoxCenterLoss=14.609, BoxScaleLoss=5.403, ClassLoss=11.916 [Epoch 65][Batch 999], LR: 1.00E-03, Speed: 7.966 samples/sec, ObjLoss=26.862, BoxCenterLoss=14.608, BoxScaleLoss=5.402, ClassLoss=11.914 [Epoch 65][Batch 1099], LR: 1.00E-03, Speed: 105.744 samples/sec, ObjLoss=26.860, BoxCenterLoss=14.609, BoxScaleLoss=5.402, ClassLoss=11.912 [Epoch 65][Batch 1199], LR: 1.00E-03, Speed: 112.377 samples/sec, ObjLoss=26.859, BoxCenterLoss=14.609, BoxScaleLoss=5.402, ClassLoss=11.911 [Epoch 65][Batch 1299], LR: 1.00E-03, Speed: 9.527 samples/sec, ObjLoss=26.855, BoxCenterLoss=14.608, BoxScaleLoss=5.401, ClassLoss=11.909 [Epoch 65][Batch 1399], LR: 1.00E-03, Speed: 124.508 samples/sec, ObjLoss=26.853, BoxCenterLoss=14.608, BoxScaleLoss=5.401, ClassLoss=11.907 [Epoch 65][Batch 1499], LR: 1.00E-03, Speed: 9.778 samples/sec, ObjLoss=26.851, BoxCenterLoss=14.608, BoxScaleLoss=5.401, ClassLoss=11.905 [Epoch 65][Batch 1599], LR: 1.00E-03, Speed: 9.951 samples/sec, ObjLoss=26.848, BoxCenterLoss=14.608, BoxScaleLoss=5.400, ClassLoss=11.904 [Epoch 65][Batch 1699], LR: 1.00E-03, Speed: 8.721 samples/sec, ObjLoss=26.845, BoxCenterLoss=14.607, BoxScaleLoss=5.400, ClassLoss=11.902 [Epoch 65][Batch 1799], LR: 1.00E-03, Speed: 8.316 samples/sec, ObjLoss=26.843, BoxCenterLoss=14.607, BoxScaleLoss=5.400, ClassLoss=11.900 [Epoch 65] Training cost: 2243.302, ObjLoss=26.843, BoxCenterLoss=14.607, BoxScaleLoss=5.400, ClassLoss=11.899 [Epoch 65] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.420 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.203 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 person=34.3 bicycle=15.4 car=23.4 motorcycle=26.0 airplane=38.8 bus=45.1 train=45.2 truck=19.9 boat=12.2 traffic light=11.5 fire hydrant=42.4 stop sign=40.4 parking meter=23.5 bench=11.3 bird=16.2 cat=39.5 dog=32.4 horse=31.1 sheep=26.7 cow=30.7 elephant=39.5 bear=44.0 zebra=42.5 giraffe=44.2 backpack=5.3 umbrella=19.7 handbag=3.0 tie=13.6 suitcase=14.3 frisbee=34.9 skis=8.6 snowboard=10.7 sports ball=23.2 kite=24.3 baseball bat=7.8 baseball glove=16.2 skateboard=25.6 surfboard=17.8 tennis racket=23.0 bottle=18.2 wine glass=15.4 cup=20.9 fork=10.0 knife=3.4 spoon=3.0 bowl=20.8 banana=10.9 apple=5.2 sandwich=17.0 orange=15.9 broccoli=8.3 carrot=6.5 hot dog=14.7 pizza=30.4 donut=24.8 cake=19.0 chair=12.7 couch=24.1 potted plant=10.8 bed=29.7 dining table=20.1 toilet=37.8 tv=35.1 laptop=32.9 mouse=38.3 remote=7.6 keyboard=25.7 cell phone=15.2 microwave=31.4 oven=17.6 toaster=0.0 sink=18.4 refrigerator=29.6 book=3.9 clock=30.5 vase=18.6 scissors=13.7 teddy bear=25.5 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.4 [Epoch 66][Batch 99], LR: 1.00E-03, Speed: 95.581 samples/sec, ObjLoss=26.841, BoxCenterLoss=14.607, BoxScaleLoss=5.400, ClassLoss=11.898 [Epoch 66][Batch 199], LR: 1.00E-03, Speed: 10.014 samples/sec, ObjLoss=26.839, BoxCenterLoss=14.607, BoxScaleLoss=5.399, ClassLoss=11.896 [Epoch 66][Batch 299], LR: 1.00E-03, Speed: 9.276 samples/sec, ObjLoss=26.836, BoxCenterLoss=14.606, BoxScaleLoss=5.399, ClassLoss=11.894 [Epoch 66][Batch 399], LR: 1.00E-03, Speed: 122.150 samples/sec, ObjLoss=26.833, BoxCenterLoss=14.606, BoxScaleLoss=5.398, ClassLoss=11.892 [Epoch 66][Batch 499], LR: 1.00E-03, Speed: 15.102 samples/sec, ObjLoss=26.829, BoxCenterLoss=14.605, BoxScaleLoss=5.398, ClassLoss=11.890 [Epoch 66][Batch 599], LR: 1.00E-03, Speed: 10.809 samples/sec, ObjLoss=26.828, BoxCenterLoss=14.605, BoxScaleLoss=5.398, ClassLoss=11.889 [Epoch 66][Batch 699], LR: 1.00E-03, Speed: 11.031 samples/sec, ObjLoss=26.824, BoxCenterLoss=14.604, BoxScaleLoss=5.397, ClassLoss=11.886 [Epoch 66][Batch 799], LR: 1.00E-03, Speed: 10.206 samples/sec, ObjLoss=26.822, BoxCenterLoss=14.604, BoxScaleLoss=5.397, ClassLoss=11.885 [Epoch 66][Batch 899], LR: 1.00E-03, Speed: 124.079 samples/sec, ObjLoss=26.820, BoxCenterLoss=14.604, BoxScaleLoss=5.397, ClassLoss=11.883 [Epoch 66][Batch 999], LR: 1.00E-03, Speed: 54.290 samples/sec, ObjLoss=26.817, BoxCenterLoss=14.604, BoxScaleLoss=5.396, ClassLoss=11.881 [Epoch 66][Batch 1099], LR: 1.00E-03, Speed: 130.835 samples/sec, ObjLoss=26.815, BoxCenterLoss=14.604, BoxScaleLoss=5.396, ClassLoss=11.880 [Epoch 66][Batch 1199], LR: 1.00E-03, Speed: 92.967 samples/sec, ObjLoss=26.812, BoxCenterLoss=14.603, BoxScaleLoss=5.396, ClassLoss=11.877 [Epoch 66][Batch 1299], LR: 1.00E-03, Speed: 11.969 samples/sec, ObjLoss=26.810, BoxCenterLoss=14.603, BoxScaleLoss=5.395, ClassLoss=11.875 [Epoch 66][Batch 1399], LR: 1.00E-03, Speed: 10.999 samples/sec, ObjLoss=26.806, BoxCenterLoss=14.602, BoxScaleLoss=5.395, ClassLoss=11.873 [Epoch 66][Batch 1499], LR: 1.00E-03, Speed: 15.862 samples/sec, ObjLoss=26.805, BoxCenterLoss=14.602, BoxScaleLoss=5.395, ClassLoss=11.872 [Epoch 66][Batch 1599], LR: 1.00E-03, Speed: 9.651 samples/sec, ObjLoss=26.802, BoxCenterLoss=14.602, BoxScaleLoss=5.394, ClassLoss=11.870 [Epoch 66][Batch 1699], LR: 1.00E-03, Speed: 7.962 samples/sec, ObjLoss=26.801, BoxCenterLoss=14.602, BoxScaleLoss=5.394, ClassLoss=11.868 [Epoch 66][Batch 1799], LR: 1.00E-03, Speed: 17.188 samples/sec, ObjLoss=26.799, BoxCenterLoss=14.602, BoxScaleLoss=5.394, ClassLoss=11.866 [Epoch 66] Training cost: 2140.320, ObjLoss=26.798, BoxCenterLoss=14.601, BoxScaleLoss=5.393, ClassLoss=11.866 [Epoch 66] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.199 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.424 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.188 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382 person=34.7 bicycle=13.4 car=23.2 motorcycle=24.6 airplane=40.2 bus=37.1 train=34.5 truck=17.4 boat=11.0 traffic light=12.0 fire hydrant=41.3 stop sign=32.2 parking meter=22.5 bench=9.4 bird=18.0 cat=38.1 dog=32.7 horse=31.0 sheep=30.5 cow=33.0 elephant=36.8 bear=35.4 zebra=39.6 giraffe=40.5 backpack=4.3 umbrella=18.8 handbag=3.4 tie=14.4 suitcase=11.4 frisbee=22.8 skis=8.8 snowboard=10.9 sports ball=22.4 kite=22.9 baseball bat=9.2 baseball glove=18.4 skateboard=22.9 surfboard=15.2 tennis racket=23.5 bottle=15.3 wine glass=19.6 cup=19.9 fork=10.6 knife=2.0 spoon=2.4 bowl=18.0 banana=12.5 apple=5.9 sandwich=14.4 orange=15.0 broccoli=8.9 carrot=6.4 hot dog=14.2 pizza=30.6 donut=23.4 cake=17.4 chair=12.9 couch=23.0 potted plant=11.6 bed=26.7 dining table=16.2 toilet=37.3 tv=29.2 laptop=26.8 mouse=28.9 remote=6.6 keyboard=23.7 cell phone=14.6 microwave=27.5 oven=19.0 toaster=0.0 sink=15.5 refrigerator=28.0 book=3.3 clock=27.1 vase=19.5 scissors=10.6 teddy bear=24.0 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.9 [Epoch 67][Batch 99], LR: 1.00E-03, Speed: 9.201 samples/sec, ObjLoss=26.796, BoxCenterLoss=14.601, BoxScaleLoss=5.393, ClassLoss=11.864 [Epoch 67][Batch 199], LR: 1.00E-03, Speed: 91.010 samples/sec, ObjLoss=26.793, BoxCenterLoss=14.601, BoxScaleLoss=5.393, ClassLoss=11.862 [Epoch 67][Batch 299], LR: 1.00E-03, Speed: 10.891 samples/sec, ObjLoss=26.791, BoxCenterLoss=14.601, BoxScaleLoss=5.392, ClassLoss=11.860 [Epoch 67][Batch 399], LR: 1.00E-03, Speed: 8.008 samples/sec, ObjLoss=26.789, BoxCenterLoss=14.601, BoxScaleLoss=5.392, ClassLoss=11.858 [Epoch 67][Batch 499], LR: 1.00E-03, Speed: 9.099 samples/sec, ObjLoss=26.786, BoxCenterLoss=14.601, BoxScaleLoss=5.392, ClassLoss=11.856 [Epoch 67][Batch 599], LR: 1.00E-03, Speed: 10.589 samples/sec, ObjLoss=26.784, BoxCenterLoss=14.600, BoxScaleLoss=5.391, ClassLoss=11.854 [Epoch 67][Batch 699], LR: 1.00E-03, Speed: 10.399 samples/sec, ObjLoss=26.782, BoxCenterLoss=14.600, BoxScaleLoss=5.391, ClassLoss=11.852 [Epoch 67][Batch 799], LR: 1.00E-03, Speed: 8.467 samples/sec, ObjLoss=26.779, BoxCenterLoss=14.599, BoxScaleLoss=5.390, ClassLoss=11.850 [Epoch 67][Batch 899], LR: 1.00E-03, Speed: 8.499 samples/sec, ObjLoss=26.777, BoxCenterLoss=14.599, BoxScaleLoss=5.389, ClassLoss=11.847 [Epoch 67][Batch 999], LR: 1.00E-03, Speed: 8.908 samples/sec, ObjLoss=26.774, BoxCenterLoss=14.599, BoxScaleLoss=5.389, ClassLoss=11.845 [Epoch 67][Batch 1099], LR: 1.00E-03, Speed: 11.512 samples/sec, ObjLoss=26.771, BoxCenterLoss=14.598, BoxScaleLoss=5.388, ClassLoss=11.843 [Epoch 67][Batch 1199], LR: 1.00E-03, Speed: 7.815 samples/sec, ObjLoss=26.767, BoxCenterLoss=14.597, BoxScaleLoss=5.388, ClassLoss=11.841 [Epoch 67][Batch 1299], LR: 1.00E-03, Speed: 8.197 samples/sec, ObjLoss=26.764, BoxCenterLoss=14.597, BoxScaleLoss=5.388, ClassLoss=11.839 [Epoch 67][Batch 1399], LR: 1.00E-03, Speed: 10.524 samples/sec, ObjLoss=26.761, BoxCenterLoss=14.596, BoxScaleLoss=5.387, ClassLoss=11.837 [Epoch 67][Batch 1499], LR: 1.00E-03, Speed: 9.041 samples/sec, ObjLoss=26.758, BoxCenterLoss=14.595, BoxScaleLoss=5.387, ClassLoss=11.835 [Epoch 67][Batch 1599], LR: 1.00E-03, Speed: 8.893 samples/sec, ObjLoss=26.756, BoxCenterLoss=14.595, BoxScaleLoss=5.387, ClassLoss=11.833 [Epoch 67][Batch 1699], LR: 1.00E-03, Speed: 9.146 samples/sec, ObjLoss=26.753, BoxCenterLoss=14.595, BoxScaleLoss=5.386, ClassLoss=11.831 [Epoch 67][Batch 1799], LR: 1.00E-03, Speed: 10.577 samples/sec, ObjLoss=26.752, BoxCenterLoss=14.595, BoxScaleLoss=5.386, ClassLoss=11.829 [Epoch 67] Training cost: 2185.004, ObjLoss=26.752, BoxCenterLoss=14.596, BoxScaleLoss=5.386, ClassLoss=11.829 [Epoch 67] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.415 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.112 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.409 person=32.6 bicycle=15.5 car=23.6 motorcycle=24.8 airplane=37.0 bus=41.7 train=45.2 truck=18.7 boat=10.4 traffic light=12.2 fire hydrant=36.0 stop sign=38.3 parking meter=24.7 bench=9.0 bird=17.7 cat=39.9 dog=34.8 horse=30.3 sheep=30.0 cow=28.8 elephant=35.6 bear=42.6 zebra=44.4 giraffe=45.3 backpack=3.8 umbrella=18.9 handbag=2.8 tie=13.5 suitcase=11.5 frisbee=29.8 skis=10.1 snowboard=13.2 sports ball=23.1 kite=22.9 baseball bat=9.8 baseball glove=20.0 skateboard=25.7 surfboard=17.3 tennis racket=22.5 bottle=16.6 wine glass=17.5 cup=21.9 fork=10.6 knife=2.1 spoon=2.9 bowl=20.5 banana=9.9 apple=6.0 sandwich=17.9 orange=13.1 broccoli=9.7 carrot=4.3 hot dog=16.1 pizza=27.3 donut=25.7 cake=19.1 chair=10.0 couch=26.6 potted plant=11.3 bed=33.8 dining table=16.8 toilet=28.5 tv=33.0 laptop=35.1 mouse=35.8 remote=8.7 keyboard=24.5 cell phone=15.2 microwave=24.9 oven=15.4 toaster=0.0 sink=15.3 refrigerator=24.2 book=3.9 clock=28.2 vase=20.5 scissors=9.6 teddy bear=23.3 hair drier=0.0 toothbrush=5.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.7 [Epoch 68][Batch 99], LR: 1.00E-03, Speed: 9.894 samples/sec, ObjLoss=26.750, BoxCenterLoss=14.596, BoxScaleLoss=5.386, ClassLoss=11.827 [Epoch 68][Batch 199], LR: 1.00E-03, Speed: 9.282 samples/sec, ObjLoss=26.749, BoxCenterLoss=14.596, BoxScaleLoss=5.386, ClassLoss=11.826 [Epoch 68][Batch 299], LR: 1.00E-03, Speed: 86.567 samples/sec, ObjLoss=26.745, BoxCenterLoss=14.595, BoxScaleLoss=5.386, ClassLoss=11.824 [Epoch 68][Batch 399], LR: 1.00E-03, Speed: 9.831 samples/sec, ObjLoss=26.745, BoxCenterLoss=14.596, BoxScaleLoss=5.385, ClassLoss=11.822 [Epoch 68][Batch 499], LR: 1.00E-03, Speed: 9.346 samples/sec, ObjLoss=26.743, BoxCenterLoss=14.596, BoxScaleLoss=5.385, ClassLoss=11.821 [Epoch 68][Batch 599], LR: 1.00E-03, Speed: 9.548 samples/sec, ObjLoss=26.740, BoxCenterLoss=14.595, BoxScaleLoss=5.384, ClassLoss=11.818 [Epoch 68][Batch 699], LR: 1.00E-03, Speed: 91.094 samples/sec, ObjLoss=26.738, BoxCenterLoss=14.595, BoxScaleLoss=5.384, ClassLoss=11.816 [Epoch 68][Batch 799], LR: 1.00E-03, Speed: 9.703 samples/sec, ObjLoss=26.736, BoxCenterLoss=14.595, BoxScaleLoss=5.384, ClassLoss=11.814 [Epoch 68][Batch 899], LR: 1.00E-03, Speed: 9.941 samples/sec, ObjLoss=26.735, BoxCenterLoss=14.596, BoxScaleLoss=5.384, ClassLoss=11.813 [Epoch 68][Batch 999], LR: 1.00E-03, Speed: 9.138 samples/sec, ObjLoss=26.733, BoxCenterLoss=14.596, BoxScaleLoss=5.384, ClassLoss=11.811 [Epoch 68][Batch 1099], LR: 1.00E-03, Speed: 10.272 samples/sec, ObjLoss=26.731, BoxCenterLoss=14.596, BoxScaleLoss=5.383, ClassLoss=11.810 [Epoch 68][Batch 1199], LR: 1.00E-03, Speed: 10.673 samples/sec, ObjLoss=26.729, BoxCenterLoss=14.596, BoxScaleLoss=5.383, ClassLoss=11.808 [Epoch 68][Batch 1299], LR: 1.00E-03, Speed: 9.393 samples/sec, ObjLoss=26.728, BoxCenterLoss=14.597, BoxScaleLoss=5.383, ClassLoss=11.807 [Epoch 68][Batch 1399], LR: 1.00E-03, Speed: 26.297 samples/sec, ObjLoss=26.725, BoxCenterLoss=14.596, BoxScaleLoss=5.383, ClassLoss=11.805 [Epoch 68][Batch 1499], LR: 1.00E-03, Speed: 9.611 samples/sec, ObjLoss=26.724, BoxCenterLoss=14.596, BoxScaleLoss=5.383, ClassLoss=11.804 [Epoch 68][Batch 1599], LR: 1.00E-03, Speed: 86.408 samples/sec, ObjLoss=26.723, BoxCenterLoss=14.597, BoxScaleLoss=5.383, ClassLoss=11.802 [Epoch 68][Batch 1699], LR: 1.00E-03, Speed: 9.990 samples/sec, ObjLoss=26.721, BoxCenterLoss=14.597, BoxScaleLoss=5.383, ClassLoss=11.800 [Epoch 68][Batch 1799], LR: 1.00E-03, Speed: 11.079 samples/sec, ObjLoss=26.718, BoxCenterLoss=14.596, BoxScaleLoss=5.382, ClassLoss=11.798 [Epoch 68] Training cost: 2173.626, ObjLoss=26.718, BoxCenterLoss=14.596, BoxScaleLoss=5.382, ClassLoss=11.797 [Epoch 68] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.126 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.434 person=35.1 bicycle=14.9 car=25.2 motorcycle=26.6 airplane=37.5 bus=44.7 train=43.0 truck=18.4 boat=11.2 traffic light=10.0 fire hydrant=40.2 stop sign=40.6 parking meter=19.4 bench=10.5 bird=18.2 cat=42.0 dog=33.4 horse=35.0 sheep=30.1 cow=35.2 elephant=41.9 bear=44.4 zebra=46.5 giraffe=47.5 backpack=4.5 umbrella=20.1 handbag=4.2 tie=15.7 suitcase=13.5 frisbee=34.9 skis=7.6 snowboard=10.2 sports ball=23.7 kite=21.1 baseball bat=9.8 baseball glove=16.8 skateboard=23.0 surfboard=17.6 tennis racket=25.9 bottle=17.6 wine glass=19.4 cup=23.0 fork=10.8 knife=3.4 spoon=1.7 bowl=21.8 banana=9.5 apple=5.6 sandwich=17.9 orange=12.6 broccoli=9.6 carrot=7.3 hot dog=17.7 pizza=32.2 donut=24.6 cake=20.0 chair=12.1 couch=23.7 potted plant=11.2 bed=32.6 dining table=17.9 toilet=33.3 tv=34.1 laptop=35.4 mouse=37.7 remote=7.8 keyboard=22.3 cell phone=14.1 microwave=27.1 oven=16.1 toaster=0.0 sink=18.2 refrigerator=29.8 book=4.5 clock=27.8 vase=19.6 scissors=13.2 teddy bear=26.7 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.5 [Epoch 69][Batch 99], LR: 1.00E-03, Speed: 107.879 samples/sec, ObjLoss=26.714, BoxCenterLoss=14.595, BoxScaleLoss=5.381, ClassLoss=11.795 [Epoch 69][Batch 199], LR: 1.00E-03, Speed: 9.603 samples/sec, ObjLoss=26.713, BoxCenterLoss=14.596, BoxScaleLoss=5.381, ClassLoss=11.793 [Epoch 69][Batch 299], LR: 1.00E-03, Speed: 8.786 samples/sec, ObjLoss=26.712, BoxCenterLoss=14.596, BoxScaleLoss=5.381, ClassLoss=11.791 [Epoch 69][Batch 399], LR: 1.00E-03, Speed: 10.882 samples/sec, ObjLoss=26.711, BoxCenterLoss=14.597, BoxScaleLoss=5.381, ClassLoss=11.790 [Epoch 69][Batch 499], LR: 1.00E-03, Speed: 9.850 samples/sec, ObjLoss=26.709, BoxCenterLoss=14.597, BoxScaleLoss=5.380, ClassLoss=11.788 [Epoch 69][Batch 599], LR: 1.00E-03, Speed: 10.933 samples/sec, ObjLoss=26.707, BoxCenterLoss=14.597, BoxScaleLoss=5.380, ClassLoss=11.787 [Epoch 69][Batch 699], LR: 1.00E-03, Speed: 9.483 samples/sec, ObjLoss=26.705, BoxCenterLoss=14.597, BoxScaleLoss=5.380, ClassLoss=11.785 [Epoch 69][Batch 799], LR: 1.00E-03, Speed: 10.249 samples/sec, ObjLoss=26.703, BoxCenterLoss=14.597, BoxScaleLoss=5.380, ClassLoss=11.784 [Epoch 69][Batch 899], LR: 1.00E-03, Speed: 7.684 samples/sec, ObjLoss=26.701, BoxCenterLoss=14.596, BoxScaleLoss=5.379, ClassLoss=11.782 [Epoch 69][Batch 999], LR: 1.00E-03, Speed: 11.469 samples/sec, ObjLoss=26.699, BoxCenterLoss=14.596, BoxScaleLoss=5.379, ClassLoss=11.780 [Epoch 69][Batch 1099], LR: 1.00E-03, Speed: 8.363 samples/sec, ObjLoss=26.696, BoxCenterLoss=14.596, BoxScaleLoss=5.379, ClassLoss=11.779 [Epoch 69][Batch 1199], LR: 1.00E-03, Speed: 11.286 samples/sec, ObjLoss=26.694, BoxCenterLoss=14.595, BoxScaleLoss=5.378, ClassLoss=11.777 [Epoch 69][Batch 1299], LR: 1.00E-03, Speed: 105.236 samples/sec, ObjLoss=26.691, BoxCenterLoss=14.594, BoxScaleLoss=5.378, ClassLoss=11.775 [Epoch 69][Batch 1399], LR: 1.00E-03, Speed: 10.928 samples/sec, ObjLoss=26.687, BoxCenterLoss=14.593, BoxScaleLoss=5.377, ClassLoss=11.773 [Epoch 69][Batch 1499], LR: 1.00E-03, Speed: 11.868 samples/sec, ObjLoss=26.686, BoxCenterLoss=14.594, BoxScaleLoss=5.377, ClassLoss=11.771 [Epoch 69][Batch 1599], LR: 1.00E-03, Speed: 8.305 samples/sec, ObjLoss=26.683, BoxCenterLoss=14.593, BoxScaleLoss=5.377, ClassLoss=11.769 [Epoch 69][Batch 1699], LR: 1.00E-03, Speed: 10.371 samples/sec, ObjLoss=26.680, BoxCenterLoss=14.593, BoxScaleLoss=5.376, ClassLoss=11.767 [Epoch 69][Batch 1799], LR: 1.00E-03, Speed: 11.981 samples/sec, ObjLoss=26.678, BoxCenterLoss=14.593, BoxScaleLoss=5.376, ClassLoss=11.766 [Epoch 69] Training cost: 2144.940, ObjLoss=26.677, BoxCenterLoss=14.593, BoxScaleLoss=5.376, ClassLoss=11.765 [Epoch 69] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.408 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.148 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.075 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.216 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.269 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.114 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.380 person=33.8 bicycle=15.2 car=23.8 motorcycle=21.1 airplane=32.9 bus=32.5 train=25.3 truck=14.8 boat=10.7 traffic light=10.8 fire hydrant=36.3 stop sign=28.7 parking meter=21.9 bench=11.7 bird=16.2 cat=40.5 dog=32.2 horse=28.9 sheep=31.0 cow=32.7 elephant=33.3 bear=30.8 zebra=36.5 giraffe=41.8 backpack=4.1 umbrella=18.6 handbag=3.2 tie=14.7 suitcase=11.9 frisbee=32.8 skis=6.7 snowboard=11.6 sports ball=18.5 kite=22.6 baseball bat=9.9 baseball glove=15.9 skateboard=24.8 surfboard=15.7 tennis racket=24.2 bottle=15.0 wine glass=16.3 cup=19.8 fork=9.7 knife=3.2 spoon=2.9 bowl=21.3 banana=9.7 apple=5.3 sandwich=17.3 orange=13.6 broccoli=8.2 carrot=6.8 hot dog=14.9 pizza=28.5 donut=17.9 cake=16.1 chair=12.9 couch=24.1 potted plant=11.2 bed=14.1 dining table=8.8 toilet=34.4 tv=31.9 laptop=32.5 mouse=31.7 remote=8.5 keyboard=31.2 cell phone=15.5 microwave=22.8 oven=16.0 toaster=0.0 sink=18.8 refrigerator=19.4 book=2.8 clock=27.5 vase=14.8 scissors=13.7 teddy bear=24.6 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.1 [Epoch 70][Batch 99], LR: 1.00E-03, Speed: 10.433 samples/sec, ObjLoss=26.677, BoxCenterLoss=14.594, BoxScaleLoss=5.376, ClassLoss=11.764 [Epoch 70][Batch 199], LR: 1.00E-03, Speed: 9.010 samples/sec, ObjLoss=26.674, BoxCenterLoss=14.594, BoxScaleLoss=5.376, ClassLoss=11.762 [Epoch 70][Batch 299], LR: 1.00E-03, Speed: 82.221 samples/sec, ObjLoss=26.673, BoxCenterLoss=14.594, BoxScaleLoss=5.375, ClassLoss=11.761 [Epoch 70][Batch 399], LR: 1.00E-03, Speed: 8.755 samples/sec, ObjLoss=26.671, BoxCenterLoss=14.594, BoxScaleLoss=5.375, ClassLoss=11.759 [Epoch 70][Batch 499], LR: 1.00E-03, Speed: 8.814 samples/sec, ObjLoss=26.669, BoxCenterLoss=14.593, BoxScaleLoss=5.375, ClassLoss=11.757 [Epoch 70][Batch 599], LR: 1.00E-03, Speed: 110.151 samples/sec, ObjLoss=26.667, BoxCenterLoss=14.593, BoxScaleLoss=5.375, ClassLoss=11.756 [Epoch 70][Batch 699], LR: 1.00E-03, Speed: 93.859 samples/sec, ObjLoss=26.665, BoxCenterLoss=14.593, BoxScaleLoss=5.375, ClassLoss=11.755 [Epoch 70][Batch 799], LR: 1.00E-03, Speed: 10.344 samples/sec, ObjLoss=26.663, BoxCenterLoss=14.593, BoxScaleLoss=5.374, ClassLoss=11.753 [Epoch 70][Batch 899], LR: 1.00E-03, Speed: 128.096 samples/sec, ObjLoss=26.659, BoxCenterLoss=14.592, BoxScaleLoss=5.374, ClassLoss=11.751 [Epoch 70][Batch 999], LR: 1.00E-03, Speed: 9.959 samples/sec, ObjLoss=26.656, BoxCenterLoss=14.591, BoxScaleLoss=5.373, ClassLoss=11.749 [Epoch 70][Batch 1099], LR: 1.00E-03, Speed: 10.180 samples/sec, ObjLoss=26.654, BoxCenterLoss=14.591, BoxScaleLoss=5.373, ClassLoss=11.747 [Epoch 70][Batch 1199], LR: 1.00E-03, Speed: 10.193 samples/sec, ObjLoss=26.652, BoxCenterLoss=14.591, BoxScaleLoss=5.373, ClassLoss=11.745 [Epoch 70][Batch 1299], LR: 1.00E-03, Speed: 9.624 samples/sec, ObjLoss=26.650, BoxCenterLoss=14.591, BoxScaleLoss=5.373, ClassLoss=11.744 [Epoch 70][Batch 1399], LR: 1.00E-03, Speed: 9.169 samples/sec, ObjLoss=26.647, BoxCenterLoss=14.590, BoxScaleLoss=5.372, ClassLoss=11.742 [Epoch 70][Batch 1499], LR: 1.00E-03, Speed: 113.661 samples/sec, ObjLoss=26.644, BoxCenterLoss=14.590, BoxScaleLoss=5.372, ClassLoss=11.740 [Epoch 70][Batch 1599], LR: 1.00E-03, Speed: 10.162 samples/sec, ObjLoss=26.641, BoxCenterLoss=14.589, BoxScaleLoss=5.371, ClassLoss=11.738 [Epoch 70][Batch 1699], LR: 1.00E-03, Speed: 11.239 samples/sec, ObjLoss=26.639, BoxCenterLoss=14.589, BoxScaleLoss=5.371, ClassLoss=11.737 [Epoch 70][Batch 1799], LR: 1.00E-03, Speed: 10.151 samples/sec, ObjLoss=26.636, BoxCenterLoss=14.588, BoxScaleLoss=5.371, ClassLoss=11.735 [Epoch 70] Training cost: 2130.937, ObjLoss=26.636, BoxCenterLoss=14.588, BoxScaleLoss=5.371, ClassLoss=11.734 [Epoch 70] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.213 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.080 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.198 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 person=33.6 bicycle=15.5 car=23.8 motorcycle=25.0 airplane=39.7 bus=44.2 train=43.3 truck=20.6 boat=10.4 traffic light=12.4 fire hydrant=35.5 stop sign=42.2 parking meter=28.6 bench=8.2 bird=15.4 cat=44.1 dog=31.8 horse=30.2 sheep=28.1 cow=29.6 elephant=39.9 bear=44.0 zebra=43.4 giraffe=47.8 backpack=3.7 umbrella=21.4 handbag=3.5 tie=15.4 suitcase=15.5 frisbee=36.8 skis=8.5 snowboard=10.9 sports ball=17.2 kite=21.6 baseball bat=9.4 baseball glove=17.9 skateboard=23.9 surfboard=14.8 tennis racket=24.8 bottle=16.5 wine glass=16.3 cup=22.6 fork=10.2 knife=4.1 spoon=2.4 bowl=22.0 banana=11.0 apple=7.1 sandwich=21.2 orange=14.8 broccoli=8.7 carrot=8.3 hot dog=13.2 pizza=32.9 donut=25.2 cake=22.7 chair=12.7 couch=24.7 potted plant=10.8 bed=28.4 dining table=17.4 toilet=37.6 tv=31.1 laptop=36.4 mouse=31.8 remote=9.2 keyboard=23.4 cell phone=16.1 microwave=29.3 oven=15.9 toaster=0.0 sink=17.7 refrigerator=21.1 book=4.0 clock=31.6 vase=18.9 scissors=15.5 teddy bear=25.2 hair drier=0.0 toothbrush=4.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.3 [Epoch 71][Batch 99], LR: 1.00E-03, Speed: 132.815 samples/sec, ObjLoss=26.634, BoxCenterLoss=14.588, BoxScaleLoss=5.370, ClassLoss=11.732 [Epoch 71][Batch 199], LR: 1.00E-03, Speed: 104.834 samples/sec, ObjLoss=26.633, BoxCenterLoss=14.589, BoxScaleLoss=5.370, ClassLoss=11.731 [Epoch 71][Batch 299], LR: 1.00E-03, Speed: 6.770 samples/sec, ObjLoss=26.631, BoxCenterLoss=14.589, BoxScaleLoss=5.371, ClassLoss=11.730 [Epoch 71][Batch 399], LR: 1.00E-03, Speed: 8.956 samples/sec, ObjLoss=26.630, BoxCenterLoss=14.590, BoxScaleLoss=5.371, ClassLoss=11.728 [Epoch 71][Batch 499], LR: 1.00E-03, Speed: 10.809 samples/sec, ObjLoss=26.628, BoxCenterLoss=14.590, BoxScaleLoss=5.370, ClassLoss=11.727 [Epoch 71][Batch 599], LR: 1.00E-03, Speed: 7.185 samples/sec, ObjLoss=26.626, BoxCenterLoss=14.589, BoxScaleLoss=5.370, ClassLoss=11.725 [Epoch 71][Batch 699], LR: 1.00E-03, Speed: 8.492 samples/sec, ObjLoss=26.623, BoxCenterLoss=14.589, BoxScaleLoss=5.370, ClassLoss=11.723 [Epoch 71][Batch 799], LR: 1.00E-03, Speed: 114.023 samples/sec, ObjLoss=26.622, BoxCenterLoss=14.589, BoxScaleLoss=5.369, ClassLoss=11.722 [Epoch 71][Batch 899], LR: 1.00E-03, Speed: 10.325 samples/sec, ObjLoss=26.619, BoxCenterLoss=14.589, BoxScaleLoss=5.369, ClassLoss=11.720 [Epoch 71][Batch 999], LR: 1.00E-03, Speed: 11.581 samples/sec, ObjLoss=26.615, BoxCenterLoss=14.587, BoxScaleLoss=5.369, ClassLoss=11.718 [Epoch 71][Batch 1099], LR: 1.00E-03, Speed: 8.682 samples/sec, ObjLoss=26.613, BoxCenterLoss=14.587, BoxScaleLoss=5.368, ClassLoss=11.716 [Epoch 71][Batch 1199], LR: 1.00E-03, Speed: 12.419 samples/sec, ObjLoss=26.611, BoxCenterLoss=14.587, BoxScaleLoss=5.368, ClassLoss=11.715 [Epoch 71][Batch 1299], LR: 1.00E-03, Speed: 13.255 samples/sec, ObjLoss=26.607, BoxCenterLoss=14.586, BoxScaleLoss=5.368, ClassLoss=11.713 [Epoch 71][Batch 1399], LR: 1.00E-03, Speed: 9.900 samples/sec, ObjLoss=26.606, BoxCenterLoss=14.586, BoxScaleLoss=5.367, ClassLoss=11.711 [Epoch 71][Batch 1499], LR: 1.00E-03, Speed: 11.338 samples/sec, ObjLoss=26.604, BoxCenterLoss=14.586, BoxScaleLoss=5.368, ClassLoss=11.710 [Epoch 71][Batch 1599], LR: 1.00E-03, Speed: 29.141 samples/sec, ObjLoss=26.601, BoxCenterLoss=14.586, BoxScaleLoss=5.367, ClassLoss=11.709 [Epoch 71][Batch 1699], LR: 1.00E-03, Speed: 9.033 samples/sec, ObjLoss=26.599, BoxCenterLoss=14.586, BoxScaleLoss=5.367, ClassLoss=11.707 [Epoch 71][Batch 1799], LR: 1.00E-03, Speed: 128.351 samples/sec, ObjLoss=26.597, BoxCenterLoss=14.586, BoxScaleLoss=5.367, ClassLoss=11.705 [Epoch 71] Training cost: 2083.492, ObjLoss=26.596, BoxCenterLoss=14.586, BoxScaleLoss=5.367, ClassLoss=11.705 [Epoch 71] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.423 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.118 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.084 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.177 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.265 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358 person=33.6 bicycle=13.8 car=24.0 motorcycle=20.4 airplane=30.6 bus=37.6 train=32.6 truck=18.8 boat=12.0 traffic light=7.9 fire hydrant=32.7 stop sign=32.0 parking meter=17.1 bench=9.9 bird=13.3 cat=34.6 dog=25.1 horse=22.1 sheep=28.3 cow=25.6 elephant=35.9 bear=34.6 zebra=30.9 giraffe=33.2 backpack=3.8 umbrella=20.5 handbag=3.3 tie=12.2 suitcase=12.3 frisbee=28.5 skis=7.9 snowboard=11.8 sports ball=17.6 kite=22.9 baseball bat=9.7 baseball glove=17.3 skateboard=23.7 surfboard=18.1 tennis racket=23.1 bottle=16.8 wine glass=17.2 cup=20.5 fork=8.6 knife=3.2 spoon=2.9 bowl=20.3 banana=8.4 apple=7.9 sandwich=13.9 orange=14.3 broccoli=8.8 carrot=6.9 hot dog=12.7 pizza=23.1 donut=25.6 cake=17.1 chair=13.2 couch=19.5 potted plant=11.0 bed=14.9 dining table=11.0 toilet=26.3 tv=31.8 laptop=29.1 mouse=36.5 remote=7.0 keyboard=25.7 cell phone=13.3 microwave=31.7 oven=14.1 toaster=0.0 sink=14.7 refrigerator=21.6 book=4.1 clock=28.8 vase=17.3 scissors=11.4 teddy bear=21.4 hair drier=0.0 toothbrush=4.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.5 [Epoch 72][Batch 99], LR: 1.00E-03, Speed: 9.334 samples/sec, ObjLoss=26.595, BoxCenterLoss=14.586, BoxScaleLoss=5.366, ClassLoss=11.703 [Epoch 72][Batch 199], LR: 1.00E-03, Speed: 9.546 samples/sec, ObjLoss=26.593, BoxCenterLoss=14.586, BoxScaleLoss=5.366, ClassLoss=11.701 [Epoch 72][Batch 299], LR: 1.00E-03, Speed: 123.462 samples/sec, ObjLoss=26.593, BoxCenterLoss=14.586, BoxScaleLoss=5.366, ClassLoss=11.700 [Epoch 72][Batch 399], LR: 1.00E-03, Speed: 10.001 samples/sec, ObjLoss=26.590, BoxCenterLoss=14.586, BoxScaleLoss=5.366, ClassLoss=11.699 [Epoch 72][Batch 499], LR: 1.00E-03, Speed: 10.826 samples/sec, ObjLoss=26.588, BoxCenterLoss=14.586, BoxScaleLoss=5.365, ClassLoss=11.697 [Epoch 72][Batch 599], LR: 1.00E-03, Speed: 10.731 samples/sec, ObjLoss=26.585, BoxCenterLoss=14.585, BoxScaleLoss=5.365, ClassLoss=11.695 [Epoch 72][Batch 699], LR: 1.00E-03, Speed: 11.024 samples/sec, ObjLoss=26.583, BoxCenterLoss=14.585, BoxScaleLoss=5.365, ClassLoss=11.693 [Epoch 72][Batch 799], LR: 1.00E-03, Speed: 12.713 samples/sec, ObjLoss=26.580, BoxCenterLoss=14.584, BoxScaleLoss=5.364, ClassLoss=11.691 [Epoch 72][Batch 899], LR: 1.00E-03, Speed: 8.497 samples/sec, ObjLoss=26.577, BoxCenterLoss=14.583, BoxScaleLoss=5.364, ClassLoss=11.689 [Epoch 72][Batch 999], LR: 1.00E-03, Speed: 8.381 samples/sec, ObjLoss=26.576, BoxCenterLoss=14.584, BoxScaleLoss=5.363, ClassLoss=11.688 [Epoch 72][Batch 1099], LR: 1.00E-03, Speed: 8.577 samples/sec, ObjLoss=26.574, BoxCenterLoss=14.584, BoxScaleLoss=5.363, ClassLoss=11.686 [Epoch 72][Batch 1199], LR: 1.00E-03, Speed: 8.187 samples/sec, ObjLoss=26.572, BoxCenterLoss=14.583, BoxScaleLoss=5.363, ClassLoss=11.685 [Epoch 72][Batch 1299], LR: 1.00E-03, Speed: 8.835 samples/sec, ObjLoss=26.570, BoxCenterLoss=14.583, BoxScaleLoss=5.363, ClassLoss=11.683 [Epoch 72][Batch 1399], LR: 1.00E-03, Speed: 11.757 samples/sec, ObjLoss=26.568, BoxCenterLoss=14.583, BoxScaleLoss=5.362, ClassLoss=11.682 [Epoch 72][Batch 1499], LR: 1.00E-03, Speed: 8.877 samples/sec, ObjLoss=26.566, BoxCenterLoss=14.583, BoxScaleLoss=5.362, ClassLoss=11.680 [Epoch 72][Batch 1599], LR: 1.00E-03, Speed: 10.899 samples/sec, ObjLoss=26.563, BoxCenterLoss=14.583, BoxScaleLoss=5.362, ClassLoss=11.678 [Epoch 72][Batch 1699], LR: 1.00E-03, Speed: 8.790 samples/sec, ObjLoss=26.560, BoxCenterLoss=14.582, BoxScaleLoss=5.361, ClassLoss=11.676 [Epoch 72][Batch 1799], LR: 1.00E-03, Speed: 12.310 samples/sec, ObjLoss=26.558, BoxCenterLoss=14.582, BoxScaleLoss=5.361, ClassLoss=11.674 [Epoch 72] Training cost: 2278.293, ObjLoss=26.557, BoxCenterLoss=14.581, BoxScaleLoss=5.360, ClassLoss=11.674 [Epoch 72] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.424 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.214 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.080 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441 person=35.4 bicycle=13.8 car=24.1 motorcycle=25.7 airplane=40.8 bus=48.2 train=48.3 truck=21.3 boat=11.1 traffic light=8.4 fire hydrant=40.5 stop sign=44.5 parking meter=27.2 bench=10.8 bird=15.8 cat=40.1 dog=35.4 horse=35.3 sheep=24.9 cow=33.8 elephant=43.0 bear=44.8 zebra=46.7 giraffe=47.3 backpack=4.2 umbrella=21.9 handbag=3.4 tie=14.9 suitcase=15.4 frisbee=39.0 skis=8.1 snowboard=10.3 sports ball=25.9 kite=21.2 baseball bat=10.1 baseball glove=14.1 skateboard=27.1 surfboard=18.9 tennis racket=23.6 bottle=17.8 wine glass=19.6 cup=24.3 fork=9.3 knife=2.3 spoon=3.2 bowl=22.0 banana=11.2 apple=6.7 sandwich=16.7 orange=16.0 broccoli=8.0 carrot=8.8 hot dog=13.4 pizza=31.2 donut=26.2 cake=23.2 chair=14.0 couch=28.4 potted plant=10.4 bed=33.5 dining table=19.9 toilet=30.9 tv=38.1 laptop=35.3 mouse=35.1 remote=9.4 keyboard=30.7 cell phone=15.0 microwave=31.7 oven=19.3 toaster=0.0 sink=20.6 refrigerator=28.5 book=4.7 clock=29.1 vase=20.1 scissors=12.1 teddy bear=29.8 hair drier=0.0 toothbrush=6.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.4 [Epoch 73][Batch 99], LR: 1.00E-03, Speed: 10.180 samples/sec, ObjLoss=26.557, BoxCenterLoss=14.582, BoxScaleLoss=5.360, ClassLoss=11.673 [Epoch 73][Batch 199], LR: 1.00E-03, Speed: 8.984 samples/sec, ObjLoss=26.555, BoxCenterLoss=14.582, BoxScaleLoss=5.360, ClassLoss=11.671 [Epoch 73][Batch 299], LR: 1.00E-03, Speed: 8.250 samples/sec, ObjLoss=26.553, BoxCenterLoss=14.582, BoxScaleLoss=5.360, ClassLoss=11.669 [Epoch 73][Batch 399], LR: 1.00E-03, Speed: 7.740 samples/sec, ObjLoss=26.551, BoxCenterLoss=14.582, BoxScaleLoss=5.360, ClassLoss=11.668 [Epoch 73][Batch 499], LR: 1.00E-03, Speed: 9.921 samples/sec, ObjLoss=26.548, BoxCenterLoss=14.581, BoxScaleLoss=5.359, ClassLoss=11.666 [Epoch 73][Batch 599], LR: 1.00E-03, Speed: 10.681 samples/sec, ObjLoss=26.547, BoxCenterLoss=14.581, BoxScaleLoss=5.359, ClassLoss=11.665 [Epoch 73][Batch 699], LR: 1.00E-03, Speed: 7.280 samples/sec, ObjLoss=26.545, BoxCenterLoss=14.581, BoxScaleLoss=5.359, ClassLoss=11.663 [Epoch 73][Batch 799], LR: 1.00E-03, Speed: 7.892 samples/sec, ObjLoss=26.543, BoxCenterLoss=14.581, BoxScaleLoss=5.359, ClassLoss=11.661 [Epoch 73][Batch 899], LR: 1.00E-03, Speed: 92.440 samples/sec, ObjLoss=26.541, BoxCenterLoss=14.581, BoxScaleLoss=5.358, ClassLoss=11.660 [Epoch 73][Batch 999], LR: 1.00E-03, Speed: 12.052 samples/sec, ObjLoss=26.539, BoxCenterLoss=14.580, BoxScaleLoss=5.358, ClassLoss=11.658 [Epoch 73][Batch 1099], LR: 1.00E-03, Speed: 9.607 samples/sec, ObjLoss=26.537, BoxCenterLoss=14.580, BoxScaleLoss=5.357, ClassLoss=11.656 [Epoch 73][Batch 1199], LR: 1.00E-03, Speed: 9.122 samples/sec, ObjLoss=26.535, BoxCenterLoss=14.580, BoxScaleLoss=5.357, ClassLoss=11.654 [Epoch 73][Batch 1299], LR: 1.00E-03, Speed: 7.602 samples/sec, ObjLoss=26.533, BoxCenterLoss=14.580, BoxScaleLoss=5.357, ClassLoss=11.653 [Epoch 73][Batch 1399], LR: 1.00E-03, Speed: 11.720 samples/sec, ObjLoss=26.532, BoxCenterLoss=14.580, BoxScaleLoss=5.357, ClassLoss=11.651 [Epoch 73][Batch 1499], LR: 1.00E-03, Speed: 9.709 samples/sec, ObjLoss=26.530, BoxCenterLoss=14.580, BoxScaleLoss=5.356, ClassLoss=11.649 [Epoch 73][Batch 1599], LR: 1.00E-03, Speed: 10.112 samples/sec, ObjLoss=26.528, BoxCenterLoss=14.580, BoxScaleLoss=5.356, ClassLoss=11.648 [Epoch 73][Batch 1699], LR: 1.00E-03, Speed: 8.522 samples/sec, ObjLoss=26.525, BoxCenterLoss=14.579, BoxScaleLoss=5.356, ClassLoss=11.646 [Epoch 73][Batch 1799], LR: 1.00E-03, Speed: 9.514 samples/sec, ObjLoss=26.522, BoxCenterLoss=14.579, BoxScaleLoss=5.355, ClassLoss=11.645 [Epoch 73] Training cost: 2188.505, ObjLoss=26.521, BoxCenterLoss=14.578, BoxScaleLoss=5.355, ClassLoss=11.644 [Epoch 73] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.213 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.426 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.188 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 person=35.6 bicycle=15.3 car=26.0 motorcycle=24.7 airplane=38.7 bus=40.2 train=43.8 truck=19.7 boat=12.9 traffic light=13.5 fire hydrant=43.8 stop sign=37.8 parking meter=23.9 bench=11.6 bird=15.4 cat=41.6 dog=35.6 horse=28.1 sheep=30.1 cow=33.7 elephant=37.2 bear=44.1 zebra=37.8 giraffe=46.8 backpack=4.9 umbrella=19.9 handbag=2.9 tie=15.0 suitcase=15.4 frisbee=33.0 skis=8.9 snowboard=11.0 sports ball=22.0 kite=23.2 baseball bat=10.2 baseball glove=19.7 skateboard=21.5 surfboard=15.4 tennis racket=23.1 bottle=16.6 wine glass=16.5 cup=22.4 fork=10.0 knife=3.1 spoon=3.8 bowl=21.3 banana=11.9 apple=6.5 sandwich=15.9 orange=14.5 broccoli=7.2 carrot=7.0 hot dog=14.2 pizza=31.6 donut=24.6 cake=19.2 chair=13.1 couch=24.5 potted plant=9.6 bed=32.7 dining table=18.6 toilet=30.4 tv=29.9 laptop=31.0 mouse=35.8 remote=7.6 keyboard=30.4 cell phone=13.1 microwave=32.9 oven=18.8 toaster=0.0 sink=18.8 refrigerator=27.6 book=4.1 clock=32.4 vase=18.3 scissors=11.8 teddy bear=27.7 hair drier=0.0 toothbrush=5.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.3 [Epoch 74][Batch 99], LR: 1.00E-03, Speed: 10.302 samples/sec, ObjLoss=26.520, BoxCenterLoss=14.578, BoxScaleLoss=5.355, ClassLoss=11.642 [Epoch 74][Batch 199], LR: 1.00E-03, Speed: 31.769 samples/sec, ObjLoss=26.519, BoxCenterLoss=14.579, BoxScaleLoss=5.355, ClassLoss=11.641 [Epoch 74][Batch 299], LR: 1.00E-03, Speed: 10.759 samples/sec, ObjLoss=26.518, BoxCenterLoss=14.579, BoxScaleLoss=5.354, ClassLoss=11.639 [Epoch 74][Batch 399], LR: 1.00E-03, Speed: 10.056 samples/sec, ObjLoss=26.515, BoxCenterLoss=14.579, BoxScaleLoss=5.354, ClassLoss=11.637 [Epoch 74][Batch 499], LR: 1.00E-03, Speed: 11.048 samples/sec, ObjLoss=26.514, BoxCenterLoss=14.579, BoxScaleLoss=5.354, ClassLoss=11.635 [Epoch 74][Batch 599], LR: 1.00E-03, Speed: 11.312 samples/sec, ObjLoss=26.512, BoxCenterLoss=14.579, BoxScaleLoss=5.353, ClassLoss=11.634 [Epoch 74][Batch 699], LR: 1.00E-03, Speed: 9.825 samples/sec, ObjLoss=26.511, BoxCenterLoss=14.579, BoxScaleLoss=5.353, ClassLoss=11.632 [Epoch 74][Batch 799], LR: 1.00E-03, Speed: 9.950 samples/sec, ObjLoss=26.508, BoxCenterLoss=14.578, BoxScaleLoss=5.353, ClassLoss=11.631 [Epoch 74][Batch 899], LR: 1.00E-03, Speed: 91.394 samples/sec, ObjLoss=26.507, BoxCenterLoss=14.579, BoxScaleLoss=5.352, ClassLoss=11.629 [Epoch 74][Batch 999], LR: 1.00E-03, Speed: 8.450 samples/sec, ObjLoss=26.505, BoxCenterLoss=14.578, BoxScaleLoss=5.352, ClassLoss=11.628 [Epoch 74][Batch 1099], LR: 1.00E-03, Speed: 10.634 samples/sec, ObjLoss=26.503, BoxCenterLoss=14.578, BoxScaleLoss=5.352, ClassLoss=11.626 [Epoch 74][Batch 1199], LR: 1.00E-03, Speed: 9.783 samples/sec, ObjLoss=26.502, BoxCenterLoss=14.578, BoxScaleLoss=5.352, ClassLoss=11.625 [Epoch 74][Batch 1299], LR: 1.00E-03, Speed: 9.884 samples/sec, ObjLoss=26.501, BoxCenterLoss=14.578, BoxScaleLoss=5.351, ClassLoss=11.623 [Epoch 74][Batch 1399], LR: 1.00E-03, Speed: 103.300 samples/sec, ObjLoss=26.497, BoxCenterLoss=14.578, BoxScaleLoss=5.351, ClassLoss=11.621 [Epoch 74][Batch 1499], LR: 1.00E-03, Speed: 10.079 samples/sec, ObjLoss=26.495, BoxCenterLoss=14.577, BoxScaleLoss=5.351, ClassLoss=11.620 [Epoch 74][Batch 1599], LR: 1.00E-03, Speed: 9.042 samples/sec, ObjLoss=26.493, BoxCenterLoss=14.577, BoxScaleLoss=5.350, ClassLoss=11.618 [Epoch 74][Batch 1699], LR: 1.00E-03, Speed: 11.016 samples/sec, ObjLoss=26.489, BoxCenterLoss=14.576, BoxScaleLoss=5.350, ClassLoss=11.616 [Epoch 74][Batch 1799], LR: 1.00E-03, Speed: 11.548 samples/sec, ObjLoss=26.486, BoxCenterLoss=14.575, BoxScaleLoss=5.350, ClassLoss=11.615 [Epoch 74] Training cost: 2175.632, ObjLoss=26.485, BoxCenterLoss=14.575, BoxScaleLoss=5.349, ClassLoss=11.614 [Epoch 74] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.214 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.230 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.113 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.451 person=35.4 bicycle=17.2 car=23.9 motorcycle=26.3 airplane=41.6 bus=47.8 train=47.6 truck=21.6 boat=11.6 traffic light=9.7 fire hydrant=39.5 stop sign=43.3 parking meter=27.2 bench=10.7 bird=16.9 cat=43.1 dog=33.8 horse=32.1 sheep=28.9 cow=28.6 elephant=43.6 bear=41.5 zebra=46.1 giraffe=49.2 backpack=4.3 umbrella=23.6 handbag=4.5 tie=12.7 suitcase=15.1 frisbee=29.7 skis=9.3 snowboard=10.9 sports ball=20.3 kite=22.5 baseball bat=12.6 baseball glove=16.3 skateboard=27.9 surfboard=18.7 tennis racket=28.1 bottle=15.7 wine glass=19.7 cup=23.1 fork=11.3 knife=4.6 spoon=3.0 bowl=18.3 banana=10.8 apple=7.0 sandwich=19.1 orange=16.6 broccoli=9.8 carrot=6.4 hot dog=16.0 pizza=32.7 donut=21.0 cake=17.9 chair=14.1 couch=28.0 potted plant=12.5 bed=33.3 dining table=20.9 toilet=36.7 tv=36.3 laptop=36.3 mouse=31.0 remote=7.7 keyboard=31.9 cell phone=16.3 microwave=32.9 oven=19.5 toaster=0.0 sink=18.6 refrigerator=25.7 book=4.1 clock=29.7 vase=19.9 scissors=11.5 teddy bear=24.9 hair drier=0.0 toothbrush=8.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.2 [Epoch 75][Batch 99], LR: 1.00E-03, Speed: 8.176 samples/sec, ObjLoss=26.483, BoxCenterLoss=14.575, BoxScaleLoss=5.349, ClassLoss=11.613 [Epoch 75][Batch 199], LR: 1.00E-03, Speed: 90.311 samples/sec, ObjLoss=26.480, BoxCenterLoss=14.574, BoxScaleLoss=5.349, ClassLoss=11.611 [Epoch 75][Batch 299], LR: 1.00E-03, Speed: 85.091 samples/sec, ObjLoss=26.479, BoxCenterLoss=14.574, BoxScaleLoss=5.349, ClassLoss=11.610 [Epoch 75][Batch 399], LR: 1.00E-03, Speed: 10.159 samples/sec, ObjLoss=26.479, BoxCenterLoss=14.575, BoxScaleLoss=5.349, ClassLoss=11.609 [Epoch 75][Batch 499], LR: 1.00E-03, Speed: 8.806 samples/sec, ObjLoss=26.476, BoxCenterLoss=14.575, BoxScaleLoss=5.348, ClassLoss=11.607 [Epoch 75][Batch 599], LR: 1.00E-03, Speed: 10.424 samples/sec, ObjLoss=26.475, BoxCenterLoss=14.574, BoxScaleLoss=5.348, ClassLoss=11.605 [Epoch 75][Batch 699], LR: 1.00E-03, Speed: 8.838 samples/sec, ObjLoss=26.473, BoxCenterLoss=14.575, BoxScaleLoss=5.348, ClassLoss=11.604 [Epoch 75][Batch 799], LR: 1.00E-03, Speed: 8.452 samples/sec, ObjLoss=26.470, BoxCenterLoss=14.574, BoxScaleLoss=5.347, ClassLoss=11.602 [Epoch 75][Batch 899], LR: 1.00E-03, Speed: 7.862 samples/sec, ObjLoss=26.469, BoxCenterLoss=14.574, BoxScaleLoss=5.347, ClassLoss=11.601 [Epoch 75][Batch 999], LR: 1.00E-03, Speed: 10.012 samples/sec, ObjLoss=26.466, BoxCenterLoss=14.574, BoxScaleLoss=5.347, ClassLoss=11.599 [Epoch 75][Batch 1099], LR: 1.00E-03, Speed: 9.875 samples/sec, ObjLoss=26.464, BoxCenterLoss=14.573, BoxScaleLoss=5.347, ClassLoss=11.597 [Epoch 75][Batch 1199], LR: 1.00E-03, Speed: 99.327 samples/sec, ObjLoss=26.462, BoxCenterLoss=14.573, BoxScaleLoss=5.346, ClassLoss=11.596 [Epoch 75][Batch 1299], LR: 1.00E-03, Speed: 9.031 samples/sec, ObjLoss=26.459, BoxCenterLoss=14.573, BoxScaleLoss=5.346, ClassLoss=11.594 [Epoch 75][Batch 1399], LR: 1.00E-03, Speed: 7.075 samples/sec, ObjLoss=26.458, BoxCenterLoss=14.573, BoxScaleLoss=5.346, ClassLoss=11.592 [Epoch 75][Batch 1499], LR: 1.00E-03, Speed: 11.058 samples/sec, ObjLoss=26.456, BoxCenterLoss=14.573, BoxScaleLoss=5.346, ClassLoss=11.591 [Epoch 75][Batch 1599], LR: 1.00E-03, Speed: 9.276 samples/sec, ObjLoss=26.455, BoxCenterLoss=14.573, BoxScaleLoss=5.346, ClassLoss=11.590 [Epoch 75][Batch 1699], LR: 1.00E-03, Speed: 7.826 samples/sec, ObjLoss=26.453, BoxCenterLoss=14.573, BoxScaleLoss=5.345, ClassLoss=11.588 [Epoch 75][Batch 1799], LR: 1.00E-03, Speed: 8.586 samples/sec, ObjLoss=26.451, BoxCenterLoss=14.573, BoxScaleLoss=5.345, ClassLoss=11.587 [Epoch 75] Training cost: 2209.951, ObjLoss=26.450, BoxCenterLoss=14.573, BoxScaleLoss=5.345, ClassLoss=11.586 [Epoch 75] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.413 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.080 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.198 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.114 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420 person=34.4 bicycle=16.4 car=25.5 motorcycle=27.2 airplane=38.2 bus=42.7 train=44.1 truck=18.3 boat=11.9 traffic light=13.6 fire hydrant=38.1 stop sign=39.3 parking meter=25.7 bench=11.6 bird=14.2 cat=40.0 dog=31.3 horse=27.9 sheep=31.7 cow=32.2 elephant=40.3 bear=36.6 zebra=41.1 giraffe=41.9 backpack=4.5 umbrella=22.1 handbag=4.1 tie=13.1 suitcase=13.7 frisbee=32.6 skis=6.9 snowboard=14.9 sports ball=20.3 kite=22.3 baseball bat=9.2 baseball glove=18.2 skateboard=24.3 surfboard=16.9 tennis racket=23.6 bottle=16.1 wine glass=17.6 cup=20.6 fork=13.1 knife=3.0 spoon=3.1 bowl=20.1 banana=10.8 apple=5.4 sandwich=13.9 orange=13.5 broccoli=8.2 carrot=5.2 hot dog=14.4 pizza=31.9 donut=21.7 cake=14.8 chair=14.9 couch=25.9 potted plant=11.8 bed=28.5 dining table=15.2 toilet=35.8 tv=34.5 laptop=35.9 mouse=30.1 remote=6.5 keyboard=23.9 cell phone=15.9 microwave=29.0 oven=16.9 toaster=0.0 sink=19.7 refrigerator=27.5 book=2.0 clock=31.5 vase=19.1 scissors=16.0 teddy bear=23.7 hair drier=0.0 toothbrush=5.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 76][Batch 99], LR: 1.00E-03, Speed: 120.425 samples/sec, ObjLoss=26.448, BoxCenterLoss=14.572, BoxScaleLoss=5.344, ClassLoss=11.585 [Epoch 76][Batch 199], LR: 1.00E-03, Speed: 9.544 samples/sec, ObjLoss=26.445, BoxCenterLoss=14.572, BoxScaleLoss=5.344, ClassLoss=11.582 [Epoch 76][Batch 299], LR: 1.00E-03, Speed: 8.936 samples/sec, ObjLoss=26.443, BoxCenterLoss=14.571, BoxScaleLoss=5.343, ClassLoss=11.581 [Epoch 76][Batch 399], LR: 1.00E-03, Speed: 11.014 samples/sec, ObjLoss=26.441, BoxCenterLoss=14.571, BoxScaleLoss=5.343, ClassLoss=11.579 [Epoch 76][Batch 499], LR: 1.00E-03, Speed: 7.537 samples/sec, ObjLoss=26.439, BoxCenterLoss=14.571, BoxScaleLoss=5.343, ClassLoss=11.578 [Epoch 76][Batch 599], LR: 1.00E-03, Speed: 10.008 samples/sec, ObjLoss=26.438, BoxCenterLoss=14.571, BoxScaleLoss=5.343, ClassLoss=11.576 [Epoch 76][Batch 699], LR: 1.00E-03, Speed: 11.353 samples/sec, ObjLoss=26.437, BoxCenterLoss=14.571, BoxScaleLoss=5.342, ClassLoss=11.575 [Epoch 76][Batch 799], LR: 1.00E-03, Speed: 11.353 samples/sec, ObjLoss=26.436, BoxCenterLoss=14.571, BoxScaleLoss=5.342, ClassLoss=11.573 [Epoch 76][Batch 899], LR: 1.00E-03, Speed: 37.790 samples/sec, ObjLoss=26.434, BoxCenterLoss=14.571, BoxScaleLoss=5.342, ClassLoss=11.572 [Epoch 76][Batch 999], LR: 1.00E-03, Speed: 10.578 samples/sec, ObjLoss=26.432, BoxCenterLoss=14.571, BoxScaleLoss=5.342, ClassLoss=11.570 [Epoch 76][Batch 1099], LR: 1.00E-03, Speed: 9.570 samples/sec, ObjLoss=26.430, BoxCenterLoss=14.571, BoxScaleLoss=5.341, ClassLoss=11.569 [Epoch 76][Batch 1199], LR: 1.00E-03, Speed: 90.049 samples/sec, ObjLoss=26.428, BoxCenterLoss=14.571, BoxScaleLoss=5.341, ClassLoss=11.567 [Epoch 76][Batch 1299], LR: 1.00E-03, Speed: 8.716 samples/sec, ObjLoss=26.426, BoxCenterLoss=14.571, BoxScaleLoss=5.341, ClassLoss=11.565 [Epoch 76][Batch 1399], LR: 1.00E-03, Speed: 9.562 samples/sec, ObjLoss=26.425, BoxCenterLoss=14.571, BoxScaleLoss=5.340, ClassLoss=11.564 [Epoch 76][Batch 1499], LR: 1.00E-03, Speed: 10.078 samples/sec, ObjLoss=26.423, BoxCenterLoss=14.571, BoxScaleLoss=5.340, ClassLoss=11.562 [Epoch 76][Batch 1599], LR: 1.00E-03, Speed: 105.178 samples/sec, ObjLoss=26.422, BoxCenterLoss=14.571, BoxScaleLoss=5.340, ClassLoss=11.561 [Epoch 76][Batch 1699], LR: 1.00E-03, Speed: 10.356 samples/sec, ObjLoss=26.421, BoxCenterLoss=14.571, BoxScaleLoss=5.340, ClassLoss=11.559 [Epoch 76][Batch 1799], LR: 1.00E-03, Speed: 11.033 samples/sec, ObjLoss=26.419, BoxCenterLoss=14.571, BoxScaleLoss=5.340, ClassLoss=11.558 [Epoch 76] Training cost: 2172.710, ObjLoss=26.419, BoxCenterLoss=14.571, BoxScaleLoss=5.340, ClassLoss=11.557 [Epoch 76] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.435 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.204 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.448 person=34.2 bicycle=15.8 car=24.7 motorcycle=27.1 airplane=40.8 bus=45.2 train=49.6 truck=22.2 boat=11.6 traffic light=10.1 fire hydrant=35.1 stop sign=38.9 parking meter=22.4 bench=12.0 bird=15.5 cat=43.9 dog=29.2 horse=33.7 sheep=25.8 cow=31.5 elephant=38.8 bear=48.3 zebra=46.7 giraffe=40.6 backpack=4.8 umbrella=20.4 handbag=4.5 tie=17.2 suitcase=17.1 frisbee=30.6 skis=9.2 snowboard=13.0 sports ball=23.0 kite=22.2 baseball bat=10.5 baseball glove=16.0 skateboard=26.5 surfboard=17.8 tennis racket=25.1 bottle=16.8 wine glass=19.6 cup=24.4 fork=9.9 knife=2.6 spoon=2.2 bowl=24.4 banana=12.1 apple=8.5 sandwich=18.9 orange=19.5 broccoli=9.0 carrot=6.6 hot dog=15.9 pizza=30.5 donut=29.6 cake=20.6 chair=13.6 couch=27.0 potted plant=12.5 bed=29.8 dining table=17.6 toilet=34.2 tv=33.6 laptop=34.9 mouse=31.0 remote=8.8 keyboard=28.6 cell phone=14.7 microwave=35.8 oven=19.4 toaster=0.0 sink=18.3 refrigerator=32.0 book=4.2 clock=32.1 vase=20.9 scissors=9.1 teddy bear=23.6 hair drier=0.0 toothbrush=4.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.0 [Epoch 77][Batch 99], LR: 1.00E-03, Speed: 6.585 samples/sec, ObjLoss=26.417, BoxCenterLoss=14.571, BoxScaleLoss=5.340, ClassLoss=11.557 [Epoch 77][Batch 199], LR: 1.00E-03, Speed: 12.307 samples/sec, ObjLoss=26.415, BoxCenterLoss=14.571, BoxScaleLoss=5.339, ClassLoss=11.555 [Epoch 77][Batch 299], LR: 1.00E-03, Speed: 92.404 samples/sec, ObjLoss=26.414, BoxCenterLoss=14.571, BoxScaleLoss=5.339, ClassLoss=11.553 [Epoch 77][Batch 399], LR: 1.00E-03, Speed: 11.612 samples/sec, ObjLoss=26.412, BoxCenterLoss=14.571, BoxScaleLoss=5.339, ClassLoss=11.552 [Epoch 77][Batch 499], LR: 1.00E-03, Speed: 122.044 samples/sec, ObjLoss=26.412, BoxCenterLoss=14.572, BoxScaleLoss=5.339, ClassLoss=11.550 [Epoch 77][Batch 599], LR: 1.00E-03, Speed: 13.689 samples/sec, ObjLoss=26.410, BoxCenterLoss=14.572, BoxScaleLoss=5.339, ClassLoss=11.549 [Epoch 77][Batch 699], LR: 1.00E-03, Speed: 8.495 samples/sec, ObjLoss=26.409, BoxCenterLoss=14.572, BoxScaleLoss=5.338, ClassLoss=11.548 [Epoch 77][Batch 799], LR: 1.00E-03, Speed: 9.339 samples/sec, ObjLoss=26.407, BoxCenterLoss=14.572, BoxScaleLoss=5.338, ClassLoss=11.546 [Epoch 77][Batch 899], LR: 1.00E-03, Speed: 10.013 samples/sec, ObjLoss=26.404, BoxCenterLoss=14.571, BoxScaleLoss=5.338, ClassLoss=11.544 [Epoch 77][Batch 999], LR: 1.00E-03, Speed: 9.339 samples/sec, ObjLoss=26.401, BoxCenterLoss=14.571, BoxScaleLoss=5.337, ClassLoss=11.543 [Epoch 77][Batch 1099], LR: 1.00E-03, Speed: 109.541 samples/sec, ObjLoss=26.400, BoxCenterLoss=14.571, BoxScaleLoss=5.337, ClassLoss=11.541 [Epoch 77][Batch 1199], LR: 1.00E-03, Speed: 8.625 samples/sec, ObjLoss=26.398, BoxCenterLoss=14.571, BoxScaleLoss=5.337, ClassLoss=11.540 [Epoch 77][Batch 1299], LR: 1.00E-03, Speed: 8.852 samples/sec, ObjLoss=26.396, BoxCenterLoss=14.571, BoxScaleLoss=5.337, ClassLoss=11.538 [Epoch 77][Batch 1399], LR: 1.00E-03, Speed: 11.186 samples/sec, ObjLoss=26.395, BoxCenterLoss=14.571, BoxScaleLoss=5.337, ClassLoss=11.537 [Epoch 77][Batch 1499], LR: 1.00E-03, Speed: 10.513 samples/sec, ObjLoss=26.394, BoxCenterLoss=14.571, BoxScaleLoss=5.336, ClassLoss=11.536 [Epoch 77][Batch 1599], LR: 1.00E-03, Speed: 8.997 samples/sec, ObjLoss=26.393, BoxCenterLoss=14.572, BoxScaleLoss=5.336, ClassLoss=11.535 [Epoch 77][Batch 1699], LR: 1.00E-03, Speed: 11.082 samples/sec, ObjLoss=26.391, BoxCenterLoss=14.572, BoxScaleLoss=5.336, ClassLoss=11.533 [Epoch 77][Batch 1799], LR: 1.00E-03, Speed: 10.468 samples/sec, ObjLoss=26.390, BoxCenterLoss=14.572, BoxScaleLoss=5.336, ClassLoss=11.532 [Epoch 77] Training cost: 2194.142, ObjLoss=26.389, BoxCenterLoss=14.572, BoxScaleLoss=5.336, ClassLoss=11.532 [Epoch 77] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.211 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.426 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 person=33.0 bicycle=16.7 car=23.2 motorcycle=27.2 airplane=42.6 bus=38.2 train=45.9 truck=18.5 boat=11.5 traffic light=10.2 fire hydrant=45.5 stop sign=37.0 parking meter=22.5 bench=11.0 bird=14.4 cat=43.3 dog=31.8 horse=33.6 sheep=32.4 cow=31.4 elephant=41.6 bear=42.7 zebra=43.2 giraffe=44.1 backpack=4.8 umbrella=19.8 handbag=3.9 tie=14.8 suitcase=15.3 frisbee=31.1 skis=9.7 snowboard=12.9 sports ball=19.0 kite=21.7 baseball bat=10.5 baseball glove=15.0 skateboard=28.6 surfboard=15.8 tennis racket=25.9 bottle=16.1 wine glass=16.9 cup=22.5 fork=9.6 knife=3.3 spoon=3.1 bowl=19.5 banana=11.5 apple=6.6 sandwich=15.3 orange=16.2 broccoli=10.4 carrot=7.3 hot dog=14.3 pizza=30.7 donut=25.1 cake=17.3 chair=13.6 couch=24.1 potted plant=10.3 bed=27.8 dining table=18.0 toilet=34.2 tv=27.4 laptop=32.0 mouse=33.1 remote=8.3 keyboard=28.8 cell phone=13.7 microwave=30.3 oven=13.1 toaster=0.0 sink=17.8 refrigerator=23.2 book=4.6 clock=28.2 vase=15.9 scissors=13.0 teddy bear=27.2 hair drier=0.0 toothbrush=4.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.1 [Epoch 78][Batch 99], LR: 1.00E-03, Speed: 9.801 samples/sec, ObjLoss=26.387, BoxCenterLoss=14.571, BoxScaleLoss=5.335, ClassLoss=11.530 [Epoch 78][Batch 199], LR: 1.00E-03, Speed: 123.286 samples/sec, ObjLoss=26.385, BoxCenterLoss=14.571, BoxScaleLoss=5.335, ClassLoss=11.529 [Epoch 78][Batch 299], LR: 1.00E-03, Speed: 114.172 samples/sec, ObjLoss=26.383, BoxCenterLoss=14.571, BoxScaleLoss=5.335, ClassLoss=11.527 [Epoch 78][Batch 399], LR: 1.00E-03, Speed: 11.078 samples/sec, ObjLoss=26.381, BoxCenterLoss=14.571, BoxScaleLoss=5.335, ClassLoss=11.526 [Epoch 78][Batch 499], LR: 1.00E-03, Speed: 13.990 samples/sec, ObjLoss=26.379, BoxCenterLoss=14.570, BoxScaleLoss=5.334, ClassLoss=11.524 [Epoch 78][Batch 599], LR: 1.00E-03, Speed: 12.303 samples/sec, ObjLoss=26.376, BoxCenterLoss=14.570, BoxScaleLoss=5.334, ClassLoss=11.522 [Epoch 78][Batch 699], LR: 1.00E-03, Speed: 9.187 samples/sec, ObjLoss=26.374, BoxCenterLoss=14.569, BoxScaleLoss=5.333, ClassLoss=11.520 [Epoch 78][Batch 799], LR: 1.00E-03, Speed: 10.287 samples/sec, ObjLoss=26.373, BoxCenterLoss=14.569, BoxScaleLoss=5.333, ClassLoss=11.519 [Epoch 78][Batch 899], LR: 1.00E-03, Speed: 9.124 samples/sec, ObjLoss=26.370, BoxCenterLoss=14.569, BoxScaleLoss=5.333, ClassLoss=11.517 [Epoch 78][Batch 999], LR: 1.00E-03, Speed: 12.841 samples/sec, ObjLoss=26.368, BoxCenterLoss=14.568, BoxScaleLoss=5.332, ClassLoss=11.516 [Epoch 78][Batch 1099], LR: 1.00E-03, Speed: 117.944 samples/sec, ObjLoss=26.365, BoxCenterLoss=14.567, BoxScaleLoss=5.332, ClassLoss=11.514 [Epoch 78][Batch 1199], LR: 1.00E-03, Speed: 10.632 samples/sec, ObjLoss=26.363, BoxCenterLoss=14.567, BoxScaleLoss=5.332, ClassLoss=11.512 [Epoch 78][Batch 1299], LR: 1.00E-03, Speed: 12.103 samples/sec, ObjLoss=26.360, BoxCenterLoss=14.567, BoxScaleLoss=5.331, ClassLoss=11.511 [Epoch 78][Batch 1399], LR: 1.00E-03, Speed: 9.866 samples/sec, ObjLoss=26.358, BoxCenterLoss=14.566, BoxScaleLoss=5.331, ClassLoss=11.509 [Epoch 78][Batch 1499], LR: 1.00E-03, Speed: 113.600 samples/sec, ObjLoss=26.355, BoxCenterLoss=14.566, BoxScaleLoss=5.330, ClassLoss=11.507 [Epoch 78][Batch 1599], LR: 1.00E-03, Speed: 9.049 samples/sec, ObjLoss=26.353, BoxCenterLoss=14.565, BoxScaleLoss=5.330, ClassLoss=11.506 [Epoch 78][Batch 1699], LR: 1.00E-03, Speed: 9.240 samples/sec, ObjLoss=26.352, BoxCenterLoss=14.565, BoxScaleLoss=5.330, ClassLoss=11.504 [Epoch 78][Batch 1799], LR: 1.00E-03, Speed: 12.751 samples/sec, ObjLoss=26.350, BoxCenterLoss=14.565, BoxScaleLoss=5.330, ClassLoss=11.503 [Epoch 78] Training cost: 2165.009, ObjLoss=26.350, BoxCenterLoss=14.565, BoxScaleLoss=5.329, ClassLoss=11.502 [Epoch 78] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.433 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.126 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429 person=32.7 bicycle=15.8 car=24.4 motorcycle=24.6 airplane=35.5 bus=38.4 train=37.5 truck=19.7 boat=12.3 traffic light=11.4 fire hydrant=39.0 stop sign=36.1 parking meter=26.2 bench=12.3 bird=16.4 cat=45.4 dog=35.8 horse=31.8 sheep=24.6 cow=33.6 elephant=41.7 bear=39.0 zebra=43.6 giraffe=43.1 backpack=4.7 umbrella=19.6 handbag=4.3 tie=16.6 suitcase=16.2 frisbee=32.0 skis=8.7 snowboard=11.3 sports ball=27.1 kite=23.5 baseball bat=12.1 baseball glove=17.0 skateboard=26.4 surfboard=19.3 tennis racket=23.5 bottle=17.4 wine glass=13.9 cup=23.1 fork=10.4 knife=3.8 spoon=3.8 bowl=18.4 banana=12.2 apple=5.0 sandwich=17.7 orange=14.0 broccoli=8.2 carrot=6.7 hot dog=18.0 pizza=30.0 donut=22.9 cake=19.1 chair=13.1 couch=26.3 potted plant=12.9 bed=26.9 dining table=15.9 toilet=27.7 tv=34.1 laptop=31.3 mouse=23.9 remote=8.1 keyboard=28.2 cell phone=16.2 microwave=26.4 oven=14.0 toaster=0.0 sink=17.7 refrigerator=27.0 book=3.7 clock=28.9 vase=22.7 scissors=14.0 teddy bear=26.0 hair drier=0.0 toothbrush=7.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.0 [Epoch 79][Batch 99], LR: 1.00E-03, Speed: 9.597 samples/sec, ObjLoss=26.348, BoxCenterLoss=14.565, BoxScaleLoss=5.329, ClassLoss=11.501 [Epoch 79][Batch 199], LR: 1.00E-03, Speed: 10.745 samples/sec, ObjLoss=26.347, BoxCenterLoss=14.565, BoxScaleLoss=5.329, ClassLoss=11.499 [Epoch 79][Batch 299], LR: 1.00E-03, Speed: 11.034 samples/sec, ObjLoss=26.346, BoxCenterLoss=14.566, BoxScaleLoss=5.329, ClassLoss=11.498 [Epoch 79][Batch 399], LR: 1.00E-03, Speed: 118.102 samples/sec, ObjLoss=26.345, BoxCenterLoss=14.566, BoxScaleLoss=5.329, ClassLoss=11.497 [Epoch 79][Batch 499], LR: 1.00E-03, Speed: 9.240 samples/sec, ObjLoss=26.343, BoxCenterLoss=14.566, BoxScaleLoss=5.329, ClassLoss=11.496 [Epoch 79][Batch 599], LR: 1.00E-03, Speed: 8.736 samples/sec, ObjLoss=26.342, BoxCenterLoss=14.567, BoxScaleLoss=5.329, ClassLoss=11.495 [Epoch 79][Batch 699], LR: 1.00E-03, Speed: 9.448 samples/sec, ObjLoss=26.340, BoxCenterLoss=14.566, BoxScaleLoss=5.329, ClassLoss=11.493 [Epoch 79][Batch 799], LR: 1.00E-03, Speed: 10.318 samples/sec, ObjLoss=26.338, BoxCenterLoss=14.567, BoxScaleLoss=5.329, ClassLoss=11.492 [Epoch 79][Batch 899], LR: 1.00E-03, Speed: 69.787 samples/sec, ObjLoss=26.337, BoxCenterLoss=14.567, BoxScaleLoss=5.328, ClassLoss=11.490 [Epoch 79][Batch 999], LR: 1.00E-03, Speed: 121.358 samples/sec, ObjLoss=26.335, BoxCenterLoss=14.567, BoxScaleLoss=5.328, ClassLoss=11.489 [Epoch 79][Batch 1099], LR: 1.00E-03, Speed: 9.121 samples/sec, ObjLoss=26.333, BoxCenterLoss=14.566, BoxScaleLoss=5.328, ClassLoss=11.487 [Epoch 79][Batch 1199], LR: 1.00E-03, Speed: 119.488 samples/sec, ObjLoss=26.331, BoxCenterLoss=14.566, BoxScaleLoss=5.328, ClassLoss=11.486 [Epoch 79][Batch 1299], LR: 1.00E-03, Speed: 10.494 samples/sec, ObjLoss=26.329, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.485 [Epoch 79][Batch 1399], LR: 1.00E-03, Speed: 10.467 samples/sec, ObjLoss=26.328, BoxCenterLoss=14.566, BoxScaleLoss=5.328, ClassLoss=11.484 [Epoch 79][Batch 1499], LR: 1.00E-03, Speed: 93.386 samples/sec, ObjLoss=26.327, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.482 [Epoch 79][Batch 1599], LR: 1.00E-03, Speed: 9.531 samples/sec, ObjLoss=26.325, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.481 [Epoch 79][Batch 1699], LR: 1.00E-03, Speed: 9.699 samples/sec, ObjLoss=26.323, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.480 [Epoch 79][Batch 1799], LR: 1.00E-03, Speed: 11.886 samples/sec, ObjLoss=26.321, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.479 [Epoch 79] Training cost: 2160.265, ObjLoss=26.320, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.478 [Epoch 79] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.426 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422 person=36.8 bicycle=15.3 car=25.3 motorcycle=26.2 airplane=34.9 bus=41.0 train=47.0 truck=20.5 boat=12.7 traffic light=13.3 fire hydrant=41.6 stop sign=39.5 parking meter=25.5 bench=11.2 bird=14.1 cat=41.9 dog=33.1 horse=34.0 sheep=28.3 cow=31.8 elephant=43.7 bear=41.4 zebra=46.4 giraffe=44.2 backpack=5.3 umbrella=22.2 handbag=4.6 tie=14.4 suitcase=15.8 frisbee=32.9 skis=8.6 snowboard=13.5 sports ball=24.2 kite=16.9 baseball bat=11.0 baseball glove=17.5 skateboard=28.0 surfboard=19.1 tennis racket=22.9 bottle=15.8 wine glass=15.0 cup=23.6 fork=9.2 knife=2.5 spoon=4.2 bowl=22.0 banana=9.6 apple=6.3 sandwich=16.9 orange=16.0 broccoli=8.6 carrot=5.5 hot dog=15.2 pizza=31.1 donut=28.2 cake=19.7 chair=13.5 couch=24.5 potted plant=10.8 bed=30.6 dining table=15.9 toilet=33.9 tv=34.6 laptop=32.1 mouse=37.0 remote=9.4 keyboard=20.1 cell phone=16.8 microwave=29.8 oven=16.6 toaster=0.0 sink=19.7 refrigerator=28.9 book=4.1 clock=29.7 vase=18.1 scissors=11.2 teddy bear=24.2 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.5 [Epoch 80][Batch 99], LR: 1.00E-03, Speed: 10.256 samples/sec, ObjLoss=26.319, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.477 [Epoch 80][Batch 199], LR: 1.00E-03, Speed: 9.084 samples/sec, ObjLoss=26.318, BoxCenterLoss=14.566, BoxScaleLoss=5.327, ClassLoss=11.476 [Epoch 80][Batch 299], LR: 1.00E-03, Speed: 10.172 samples/sec, ObjLoss=26.317, BoxCenterLoss=14.567, BoxScaleLoss=5.327, ClassLoss=11.475 [Epoch 80][Batch 399], LR: 1.00E-03, Speed: 10.505 samples/sec, ObjLoss=26.315, BoxCenterLoss=14.566, BoxScaleLoss=5.326, ClassLoss=11.474 [Epoch 80][Batch 499], LR: 1.00E-03, Speed: 9.613 samples/sec, ObjLoss=26.313, BoxCenterLoss=14.566, BoxScaleLoss=5.326, ClassLoss=11.472 [Epoch 80][Batch 599], LR: 1.00E-03, Speed: 9.794 samples/sec, ObjLoss=26.312, BoxCenterLoss=14.566, BoxScaleLoss=5.326, ClassLoss=11.471 [Epoch 80][Batch 699], LR: 1.00E-03, Speed: 10.851 samples/sec, ObjLoss=26.311, BoxCenterLoss=14.567, BoxScaleLoss=5.326, ClassLoss=11.470 [Epoch 80][Batch 799], LR: 1.00E-03, Speed: 8.735 samples/sec, ObjLoss=26.309, BoxCenterLoss=14.566, BoxScaleLoss=5.326, ClassLoss=11.468 [Epoch 80][Batch 899], LR: 1.00E-03, Speed: 121.592 samples/sec, ObjLoss=26.307, BoxCenterLoss=14.566, BoxScaleLoss=5.325, ClassLoss=11.467 [Epoch 80][Batch 999], LR: 1.00E-03, Speed: 98.859 samples/sec, ObjLoss=26.305, BoxCenterLoss=14.566, BoxScaleLoss=5.325, ClassLoss=11.465 [Epoch 80][Batch 1099], LR: 1.00E-03, Speed: 116.130 samples/sec, ObjLoss=26.303, BoxCenterLoss=14.565, BoxScaleLoss=5.325, ClassLoss=11.464 [Epoch 80][Batch 1199], LR: 1.00E-03, Speed: 9.477 samples/sec, ObjLoss=26.301, BoxCenterLoss=14.565, BoxScaleLoss=5.324, ClassLoss=11.463 [Epoch 80][Batch 1299], LR: 1.00E-03, Speed: 10.035 samples/sec, ObjLoss=26.299, BoxCenterLoss=14.564, BoxScaleLoss=5.324, ClassLoss=11.461 [Epoch 80][Batch 1399], LR: 1.00E-03, Speed: 8.809 samples/sec, ObjLoss=26.297, BoxCenterLoss=14.564, BoxScaleLoss=5.323, ClassLoss=11.459 [Epoch 80][Batch 1499], LR: 1.00E-03, Speed: 7.744 samples/sec, ObjLoss=26.294, BoxCenterLoss=14.564, BoxScaleLoss=5.323, ClassLoss=11.458 [Epoch 80][Batch 1599], LR: 1.00E-03, Speed: 11.519 samples/sec, ObjLoss=26.293, BoxCenterLoss=14.564, BoxScaleLoss=5.323, ClassLoss=11.456 [Epoch 80][Batch 1699], LR: 1.00E-03, Speed: 8.645 samples/sec, ObjLoss=26.292, BoxCenterLoss=14.564, BoxScaleLoss=5.323, ClassLoss=11.455 [Epoch 80][Batch 1799], LR: 1.00E-03, Speed: 12.672 samples/sec, ObjLoss=26.291, BoxCenterLoss=14.564, BoxScaleLoss=5.323, ClassLoss=11.454 [Epoch 80] Training cost: 2096.215, ObjLoss=26.290, BoxCenterLoss=14.564, BoxScaleLoss=5.322, ClassLoss=11.453 [Epoch 80] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.432 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.207 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.438 person=33.0 bicycle=15.5 car=23.8 motorcycle=25.7 airplane=39.5 bus=46.1 train=49.0 truck=20.5 boat=8.7 traffic light=13.4 fire hydrant=41.4 stop sign=35.0 parking meter=20.6 bench=12.6 bird=19.2 cat=46.0 dog=38.7 horse=31.3 sheep=29.2 cow=31.7 elephant=43.5 bear=46.7 zebra=44.9 giraffe=43.9 backpack=5.8 umbrella=21.5 handbag=4.8 tie=16.7 suitcase=15.7 frisbee=35.5 skis=10.2 snowboard=12.4 sports ball=24.9 kite=23.8 baseball bat=10.3 baseball glove=18.0 skateboard=24.4 surfboard=19.2 tennis racket=25.8 bottle=14.0 wine glass=16.6 cup=21.8 fork=12.0 knife=2.8 spoon=2.1 bowl=20.0 banana=11.5 apple=6.0 sandwich=17.4 orange=17.5 broccoli=8.6 carrot=6.6 hot dog=14.2 pizza=29.0 donut=27.3 cake=19.6 chair=12.8 couch=29.1 potted plant=10.6 bed=31.4 dining table=20.0 toilet=33.1 tv=36.8 laptop=38.3 mouse=30.8 remote=8.9 keyboard=32.1 cell phone=15.2 microwave=33.3 oven=19.3 toaster=0.0 sink=17.9 refrigerator=31.4 book=4.5 clock=32.2 vase=17.5 scissors=14.7 teddy bear=28.8 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.2 [Epoch 81][Batch 99], LR: 1.00E-03, Speed: 10.781 samples/sec, ObjLoss=26.288, BoxCenterLoss=14.564, BoxScaleLoss=5.322, ClassLoss=11.452 [Epoch 81][Batch 199], LR: 1.00E-03, Speed: 92.401 samples/sec, ObjLoss=26.287, BoxCenterLoss=14.563, BoxScaleLoss=5.322, ClassLoss=11.450 [Epoch 81][Batch 299], LR: 1.00E-03, Speed: 10.077 samples/sec, ObjLoss=26.284, BoxCenterLoss=14.563, BoxScaleLoss=5.322, ClassLoss=11.449 [Epoch 81][Batch 399], LR: 1.00E-03, Speed: 7.774 samples/sec, ObjLoss=26.282, BoxCenterLoss=14.562, BoxScaleLoss=5.321, ClassLoss=11.447 [Epoch 81][Batch 499], LR: 1.00E-03, Speed: 9.419 samples/sec, ObjLoss=26.280, BoxCenterLoss=14.562, BoxScaleLoss=5.320, ClassLoss=11.445 [Epoch 81][Batch 599], LR: 1.00E-03, Speed: 9.538 samples/sec, ObjLoss=26.278, BoxCenterLoss=14.562, BoxScaleLoss=5.320, ClassLoss=11.444 [Epoch 81][Batch 699], LR: 1.00E-03, Speed: 10.740 samples/sec, ObjLoss=26.276, BoxCenterLoss=14.562, BoxScaleLoss=5.320, ClassLoss=11.442 [Epoch 81][Batch 799], LR: 1.00E-03, Speed: 9.626 samples/sec, ObjLoss=26.275, BoxCenterLoss=14.561, BoxScaleLoss=5.320, ClassLoss=11.441 [Epoch 81][Batch 899], LR: 1.00E-03, Speed: 11.705 samples/sec, ObjLoss=26.273, BoxCenterLoss=14.561, BoxScaleLoss=5.320, ClassLoss=11.440 [Epoch 81][Batch 999], LR: 1.00E-03, Speed: 9.566 samples/sec, ObjLoss=26.270, BoxCenterLoss=14.561, BoxScaleLoss=5.320, ClassLoss=11.439 [Epoch 81][Batch 1099], LR: 1.00E-03, Speed: 12.262 samples/sec, ObjLoss=26.268, BoxCenterLoss=14.560, BoxScaleLoss=5.319, ClassLoss=11.437 [Epoch 81][Batch 1199], LR: 1.00E-03, Speed: 8.515 samples/sec, ObjLoss=26.266, BoxCenterLoss=14.560, BoxScaleLoss=5.319, ClassLoss=11.436 [Epoch 81][Batch 1299], LR: 1.00E-03, Speed: 10.997 samples/sec, ObjLoss=26.265, BoxCenterLoss=14.560, BoxScaleLoss=5.318, ClassLoss=11.434 [Epoch 81][Batch 1399], LR: 1.00E-03, Speed: 10.581 samples/sec, ObjLoss=26.263, BoxCenterLoss=14.560, BoxScaleLoss=5.318, ClassLoss=11.433 [Epoch 81][Batch 1499], LR: 1.00E-03, Speed: 9.755 samples/sec, ObjLoss=26.261, BoxCenterLoss=14.560, BoxScaleLoss=5.318, ClassLoss=11.431 [Epoch 81][Batch 1599], LR: 1.00E-03, Speed: 8.747 samples/sec, ObjLoss=26.260, BoxCenterLoss=14.560, BoxScaleLoss=5.318, ClassLoss=11.430 [Epoch 81][Batch 1699], LR: 1.00E-03, Speed: 8.374 samples/sec, ObjLoss=26.257, BoxCenterLoss=14.559, BoxScaleLoss=5.317, ClassLoss=11.428 [Epoch 81][Batch 1799], LR: 1.00E-03, Speed: 8.658 samples/sec, ObjLoss=26.255, BoxCenterLoss=14.559, BoxScaleLoss=5.317, ClassLoss=11.426 [Epoch 81] Training cost: 2205.684, ObjLoss=26.254, BoxCenterLoss=14.559, BoxScaleLoss=5.316, ClassLoss=11.426 [Epoch 81] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.428 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418 person=37.2 bicycle=14.9 car=24.6 motorcycle=27.0 airplane=38.7 bus=45.1 train=49.5 truck=21.5 boat=12.5 traffic light=11.8 fire hydrant=41.6 stop sign=41.9 parking meter=24.1 bench=10.9 bird=19.1 cat=40.5 dog=33.0 horse=32.4 sheep=28.2 cow=32.0 elephant=41.7 bear=43.8 zebra=43.5 giraffe=45.2 backpack=4.5 umbrella=19.7 handbag=5.5 tie=13.7 suitcase=16.1 frisbee=32.9 skis=8.2 snowboard=11.9 sports ball=25.1 kite=22.7 baseball bat=9.8 baseball glove=16.6 skateboard=22.5 surfboard=18.9 tennis racket=17.2 bottle=16.8 wine glass=16.2 cup=21.4 fork=9.6 knife=1.8 spoon=3.0 bowl=20.6 banana=12.9 apple=5.3 sandwich=17.5 orange=15.6 broccoli=8.4 carrot=8.1 hot dog=15.8 pizza=30.3 donut=22.6 cake=18.1 chair=13.2 couch=24.5 potted plant=10.2 bed=26.1 dining table=16.1 toilet=32.1 tv=27.4 laptop=37.1 mouse=37.1 remote=7.5 keyboard=30.0 cell phone=13.8 microwave=38.7 oven=15.3 toaster=0.0 sink=20.7 refrigerator=25.6 book=3.1 clock=28.3 vase=17.3 scissors=15.6 teddy bear=26.1 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.5 [Epoch 82][Batch 99], LR: 1.00E-03, Speed: 9.809 samples/sec, ObjLoss=26.253, BoxCenterLoss=14.559, BoxScaleLoss=5.316, ClassLoss=11.424 [Epoch 82][Batch 199], LR: 1.00E-03, Speed: 9.324 samples/sec, ObjLoss=26.251, BoxCenterLoss=14.558, BoxScaleLoss=5.316, ClassLoss=11.424 [Epoch 82][Batch 299], LR: 1.00E-03, Speed: 8.923 samples/sec, ObjLoss=26.249, BoxCenterLoss=14.558, BoxScaleLoss=5.316, ClassLoss=11.422 [Epoch 82][Batch 399], LR: 1.00E-03, Speed: 10.296 samples/sec, ObjLoss=26.246, BoxCenterLoss=14.558, BoxScaleLoss=5.315, ClassLoss=11.421 [Epoch 82][Batch 499], LR: 1.00E-03, Speed: 9.370 samples/sec, ObjLoss=26.245, BoxCenterLoss=14.557, BoxScaleLoss=5.315, ClassLoss=11.419 [Epoch 82][Batch 599], LR: 1.00E-03, Speed: 9.562 samples/sec, ObjLoss=26.244, BoxCenterLoss=14.558, BoxScaleLoss=5.315, ClassLoss=11.418 [Epoch 82][Batch 699], LR: 1.00E-03, Speed: 10.522 samples/sec, ObjLoss=26.242, BoxCenterLoss=14.558, BoxScaleLoss=5.315, ClassLoss=11.417 [Epoch 82][Batch 799], LR: 1.00E-03, Speed: 12.901 samples/sec, ObjLoss=26.240, BoxCenterLoss=14.557, BoxScaleLoss=5.314, ClassLoss=11.415 [Epoch 82][Batch 899], LR: 1.00E-03, Speed: 109.703 samples/sec, ObjLoss=26.238, BoxCenterLoss=14.557, BoxScaleLoss=5.314, ClassLoss=11.413 [Epoch 82][Batch 999], LR: 1.00E-03, Speed: 13.213 samples/sec, ObjLoss=26.236, BoxCenterLoss=14.556, BoxScaleLoss=5.313, ClassLoss=11.411 [Epoch 82][Batch 1099], LR: 1.00E-03, Speed: 132.028 samples/sec, ObjLoss=26.235, BoxCenterLoss=14.556, BoxScaleLoss=5.313, ClassLoss=11.410 [Epoch 82][Batch 1199], LR: 1.00E-03, Speed: 10.590 samples/sec, ObjLoss=26.233, BoxCenterLoss=14.556, BoxScaleLoss=5.313, ClassLoss=11.408 [Epoch 82][Batch 1299], LR: 1.00E-03, Speed: 10.089 samples/sec, ObjLoss=26.231, BoxCenterLoss=14.556, BoxScaleLoss=5.312, ClassLoss=11.407 [Epoch 82][Batch 1399], LR: 1.00E-03, Speed: 8.282 samples/sec, ObjLoss=26.231, BoxCenterLoss=14.556, BoxScaleLoss=5.312, ClassLoss=11.406 [Epoch 82][Batch 1499], LR: 1.00E-03, Speed: 10.895 samples/sec, ObjLoss=26.230, BoxCenterLoss=14.557, BoxScaleLoss=5.312, ClassLoss=11.405 [Epoch 82][Batch 1599], LR: 1.00E-03, Speed: 8.959 samples/sec, ObjLoss=26.229, BoxCenterLoss=14.557, BoxScaleLoss=5.312, ClassLoss=11.404 [Epoch 82][Batch 1699], LR: 1.00E-03, Speed: 9.231 samples/sec, ObjLoss=26.228, BoxCenterLoss=14.557, BoxScaleLoss=5.312, ClassLoss=11.402 [Epoch 82][Batch 1799], LR: 1.00E-03, Speed: 11.260 samples/sec, ObjLoss=26.226, BoxCenterLoss=14.556, BoxScaleLoss=5.311, ClassLoss=11.400 [Epoch 82] Training cost: 2184.493, ObjLoss=26.225, BoxCenterLoss=14.556, BoxScaleLoss=5.311, ClassLoss=11.400 [Epoch 82] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.428 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.177 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.228 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.197 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424 person=33.6 bicycle=14.9 car=24.0 motorcycle=25.1 airplane=33.6 bus=38.7 train=44.3 truck=19.7 boat=11.4 traffic light=11.8 fire hydrant=42.4 stop sign=41.0 parking meter=27.7 bench=10.5 bird=18.4 cat=44.1 dog=35.7 horse=33.3 sheep=26.1 cow=33.4 elephant=41.7 bear=44.0 zebra=41.2 giraffe=42.7 backpack=4.7 umbrella=19.6 handbag=4.5 tie=14.9 suitcase=16.9 frisbee=31.4 skis=7.8 snowboard=9.9 sports ball=14.2 kite=22.8 baseball bat=7.8 baseball glove=15.3 skateboard=23.2 surfboard=19.1 tennis racket=21.0 bottle=18.2 wine glass=18.9 cup=21.1 fork=11.0 knife=3.2 spoon=2.8 bowl=18.1 banana=12.2 apple=7.6 sandwich=17.3 orange=15.9 broccoli=8.3 carrot=7.1 hot dog=13.3 pizza=27.9 donut=23.4 cake=21.9 chair=14.5 couch=23.2 potted plant=11.1 bed=34.5 dining table=17.6 toilet=31.2 tv=33.0 laptop=31.3 mouse=32.1 remote=8.8 keyboard=20.4 cell phone=16.2 microwave=22.8 oven=13.6 toaster=0.0 sink=15.5 refrigerator=29.7 book=2.8 clock=27.9 vase=19.1 scissors=10.5 teddy bear=25.6 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 83][Batch 99], LR: 1.00E-03, Speed: 8.837 samples/sec, ObjLoss=26.223, BoxCenterLoss=14.556, BoxScaleLoss=5.311, ClassLoss=11.398 [Epoch 83][Batch 199], LR: 1.00E-03, Speed: 9.037 samples/sec, ObjLoss=26.223, BoxCenterLoss=14.556, BoxScaleLoss=5.311, ClassLoss=11.398 [Epoch 83][Batch 299], LR: 1.00E-03, Speed: 9.180 samples/sec, ObjLoss=26.220, BoxCenterLoss=14.556, BoxScaleLoss=5.311, ClassLoss=11.396 [Epoch 83][Batch 399], LR: 1.00E-03, Speed: 10.832 samples/sec, ObjLoss=26.218, BoxCenterLoss=14.555, BoxScaleLoss=5.310, ClassLoss=11.395 [Epoch 83][Batch 499], LR: 1.00E-03, Speed: 6.190 samples/sec, ObjLoss=26.216, BoxCenterLoss=14.555, BoxScaleLoss=5.310, ClassLoss=11.394 [Epoch 83][Batch 599], LR: 1.00E-03, Speed: 8.017 samples/sec, ObjLoss=26.215, BoxCenterLoss=14.555, BoxScaleLoss=5.310, ClassLoss=11.392 [Epoch 83][Batch 699], LR: 1.00E-03, Speed: 8.987 samples/sec, ObjLoss=26.214, BoxCenterLoss=14.555, BoxScaleLoss=5.310, ClassLoss=11.391 [Epoch 83][Batch 799], LR: 1.00E-03, Speed: 7.787 samples/sec, ObjLoss=26.212, BoxCenterLoss=14.555, BoxScaleLoss=5.309, ClassLoss=11.390 [Epoch 83][Batch 899], LR: 1.00E-03, Speed: 7.750 samples/sec, ObjLoss=26.209, BoxCenterLoss=14.554, BoxScaleLoss=5.309, ClassLoss=11.388 [Epoch 83][Batch 999], LR: 1.00E-03, Speed: 8.983 samples/sec, ObjLoss=26.208, BoxCenterLoss=14.555, BoxScaleLoss=5.309, ClassLoss=11.387 [Epoch 83][Batch 1099], LR: 1.00E-03, Speed: 9.659 samples/sec, ObjLoss=26.207, BoxCenterLoss=14.554, BoxScaleLoss=5.309, ClassLoss=11.386 [Epoch 83][Batch 1199], LR: 1.00E-03, Speed: 7.819 samples/sec, ObjLoss=26.204, BoxCenterLoss=14.554, BoxScaleLoss=5.308, ClassLoss=11.384 [Epoch 83][Batch 1299], LR: 1.00E-03, Speed: 127.900 samples/sec, ObjLoss=26.202, BoxCenterLoss=14.553, BoxScaleLoss=5.308, ClassLoss=11.383 [Epoch 83][Batch 1399], LR: 1.00E-03, Speed: 9.429 samples/sec, ObjLoss=26.200, BoxCenterLoss=14.553, BoxScaleLoss=5.308, ClassLoss=11.381 [Epoch 83][Batch 1499], LR: 1.00E-03, Speed: 7.432 samples/sec, ObjLoss=26.198, BoxCenterLoss=14.553, BoxScaleLoss=5.307, ClassLoss=11.380 [Epoch 83][Batch 1599], LR: 1.00E-03, Speed: 10.286 samples/sec, ObjLoss=26.197, BoxCenterLoss=14.553, BoxScaleLoss=5.307, ClassLoss=11.379 [Epoch 83][Batch 1699], LR: 1.00E-03, Speed: 9.024 samples/sec, ObjLoss=26.195, BoxCenterLoss=14.552, BoxScaleLoss=5.306, ClassLoss=11.377 [Epoch 83][Batch 1799], LR: 1.00E-03, Speed: 15.556 samples/sec, ObjLoss=26.193, BoxCenterLoss=14.552, BoxScaleLoss=5.306, ClassLoss=11.376 [Epoch 83] Training cost: 2248.280, ObjLoss=26.193, BoxCenterLoss=14.552, BoxScaleLoss=5.306, ClassLoss=11.375 [Epoch 83] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.437 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.448 person=35.4 bicycle=16.1 car=25.8 motorcycle=26.9 airplane=40.3 bus=47.4 train=47.2 truck=22.8 boat=13.7 traffic light=12.2 fire hydrant=45.3 stop sign=40.5 parking meter=22.1 bench=12.5 bird=16.5 cat=40.1 dog=39.3 horse=36.0 sheep=32.3 cow=34.7 elephant=40.5 bear=43.3 zebra=45.8 giraffe=46.7 backpack=4.9 umbrella=21.3 handbag=4.5 tie=16.7 suitcase=17.5 frisbee=32.7 skis=8.8 snowboard=10.9 sports ball=25.7 kite=22.4 baseball bat=11.9 baseball glove=15.6 skateboard=25.6 surfboard=19.9 tennis racket=25.7 bottle=18.8 wine glass=19.6 cup=25.3 fork=14.0 knife=4.2 spoon=3.8 bowl=22.2 banana=12.6 apple=6.0 sandwich=21.3 orange=19.5 broccoli=11.3 carrot=6.7 hot dog=16.3 pizza=31.6 donut=19.7 cake=18.1 chair=14.1 couch=28.4 potted plant=13.6 bed=26.5 dining table=16.5 toilet=35.9 tv=35.4 laptop=35.6 mouse=31.8 remote=7.7 keyboard=27.0 cell phone=15.4 microwave=30.0 oven=19.5 toaster=0.0 sink=17.0 refrigerator=27.8 book=4.1 clock=30.4 vase=19.2 scissors=14.6 teddy bear=27.6 hair drier=0.0 toothbrush=3.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.5 [Epoch 84][Batch 99], LR: 1.00E-03, Speed: 125.379 samples/sec, ObjLoss=26.190, BoxCenterLoss=14.552, BoxScaleLoss=5.306, ClassLoss=11.374 [Epoch 84][Batch 199], LR: 1.00E-03, Speed: 7.168 samples/sec, ObjLoss=26.188, BoxCenterLoss=14.552, BoxScaleLoss=5.306, ClassLoss=11.372 [Epoch 84][Batch 299], LR: 1.00E-03, Speed: 8.751 samples/sec, ObjLoss=26.187, BoxCenterLoss=14.552, BoxScaleLoss=5.306, ClassLoss=11.371 [Epoch 84][Batch 399], LR: 1.00E-03, Speed: 8.774 samples/sec, ObjLoss=26.186, BoxCenterLoss=14.552, BoxScaleLoss=5.306, ClassLoss=11.370 [Epoch 84][Batch 499], LR: 1.00E-03, Speed: 105.493 samples/sec, ObjLoss=26.184, BoxCenterLoss=14.551, BoxScaleLoss=5.305, ClassLoss=11.369 [Epoch 84][Batch 599], LR: 1.00E-03, Speed: 9.879 samples/sec, ObjLoss=26.182, BoxCenterLoss=14.551, BoxScaleLoss=5.305, ClassLoss=11.367 [Epoch 84][Batch 699], LR: 1.00E-03, Speed: 104.271 samples/sec, ObjLoss=26.181, BoxCenterLoss=14.551, BoxScaleLoss=5.305, ClassLoss=11.366 [Epoch 84][Batch 799], LR: 1.00E-03, Speed: 8.484 samples/sec, ObjLoss=26.179, BoxCenterLoss=14.551, BoxScaleLoss=5.304, ClassLoss=11.364 [Epoch 84][Batch 899], LR: 1.00E-03, Speed: 9.982 samples/sec, ObjLoss=26.176, BoxCenterLoss=14.550, BoxScaleLoss=5.304, ClassLoss=11.362 [Epoch 84][Batch 999], LR: 1.00E-03, Speed: 8.958 samples/sec, ObjLoss=26.175, BoxCenterLoss=14.550, BoxScaleLoss=5.304, ClassLoss=11.361 [Epoch 84][Batch 1099], LR: 1.00E-03, Speed: 9.523 samples/sec, ObjLoss=26.174, BoxCenterLoss=14.550, BoxScaleLoss=5.303, ClassLoss=11.360 [Epoch 84][Batch 1199], LR: 1.00E-03, Speed: 7.201 samples/sec, ObjLoss=26.171, BoxCenterLoss=14.550, BoxScaleLoss=5.303, ClassLoss=11.358 [Epoch 84][Batch 1299], LR: 1.00E-03, Speed: 8.983 samples/sec, ObjLoss=26.170, BoxCenterLoss=14.549, BoxScaleLoss=5.303, ClassLoss=11.357 [Epoch 84][Batch 1399], LR: 1.00E-03, Speed: 9.868 samples/sec, ObjLoss=26.169, BoxCenterLoss=14.550, BoxScaleLoss=5.303, ClassLoss=11.356 [Epoch 84][Batch 1499], LR: 1.00E-03, Speed: 8.068 samples/sec, ObjLoss=26.167, BoxCenterLoss=14.550, BoxScaleLoss=5.303, ClassLoss=11.354 [Epoch 84][Batch 1599], LR: 1.00E-03, Speed: 8.671 samples/sec, ObjLoss=26.165, BoxCenterLoss=14.550, BoxScaleLoss=5.303, ClassLoss=11.353 [Epoch 84][Batch 1699], LR: 1.00E-03, Speed: 9.547 samples/sec, ObjLoss=26.164, BoxCenterLoss=14.550, BoxScaleLoss=5.302, ClassLoss=11.352 [Epoch 84][Batch 1799], LR: 1.00E-03, Speed: 10.306 samples/sec, ObjLoss=26.162, BoxCenterLoss=14.549, BoxScaleLoss=5.302, ClassLoss=11.351 [Epoch 84] Training cost: 2180.717, ObjLoss=26.161, BoxCenterLoss=14.549, BoxScaleLoss=5.302, ClassLoss=11.350 [Epoch 84] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.428 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416 person=37.0 bicycle=13.4 car=21.4 motorcycle=25.9 airplane=35.5 bus=43.3 train=40.2 truck=18.5 boat=10.5 traffic light=9.5 fire hydrant=43.7 stop sign=41.2 parking meter=28.2 bench=11.6 bird=16.8 cat=34.6 dog=31.0 horse=31.2 sheep=27.1 cow=24.8 elephant=42.2 bear=41.9 zebra=42.2 giraffe=43.1 backpack=4.4 umbrella=21.3 handbag=4.1 tie=15.1 suitcase=15.8 frisbee=26.4 skis=10.3 snowboard=13.3 sports ball=20.8 kite=25.6 baseball bat=8.2 baseball glove=17.9 skateboard=23.9 surfboard=19.1 tennis racket=25.5 bottle=15.7 wine glass=18.5 cup=23.0 fork=7.7 knife=1.8 spoon=3.7 bowl=22.7 banana=8.2 apple=7.2 sandwich=13.4 orange=19.0 broccoli=8.7 carrot=8.8 hot dog=17.6 pizza=30.4 donut=24.4 cake=20.7 chair=13.1 couch=26.1 potted plant=11.0 bed=32.3 dining table=19.0 toilet=35.0 tv=35.1 laptop=36.0 mouse=33.2 remote=9.1 keyboard=26.2 cell phone=15.3 microwave=23.2 oven=13.7 toaster=0.0 sink=16.0 refrigerator=28.5 book=4.1 clock=30.1 vase=20.1 scissors=10.0 teddy bear=23.8 hair drier=0.0 toothbrush=4.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.0 [Epoch 85][Batch 99], LR: 1.00E-03, Speed: 8.178 samples/sec, ObjLoss=26.160, BoxCenterLoss=14.549, BoxScaleLoss=5.302, ClassLoss=11.349 [Epoch 85][Batch 199], LR: 1.00E-03, Speed: 9.803 samples/sec, ObjLoss=26.158, BoxCenterLoss=14.549, BoxScaleLoss=5.301, ClassLoss=11.347 [Epoch 85][Batch 299], LR: 1.00E-03, Speed: 8.642 samples/sec, ObjLoss=26.157, BoxCenterLoss=14.549, BoxScaleLoss=5.301, ClassLoss=11.346 [Epoch 85][Batch 399], LR: 1.00E-03, Speed: 8.776 samples/sec, ObjLoss=26.155, BoxCenterLoss=14.549, BoxScaleLoss=5.301, ClassLoss=11.344 [Epoch 85][Batch 499], LR: 1.00E-03, Speed: 8.407 samples/sec, ObjLoss=26.154, BoxCenterLoss=14.549, BoxScaleLoss=5.301, ClassLoss=11.343 [Epoch 85][Batch 599], LR: 1.00E-03, Speed: 103.516 samples/sec, ObjLoss=26.152, BoxCenterLoss=14.548, BoxScaleLoss=5.301, ClassLoss=11.342 [Epoch 85][Batch 699], LR: 1.00E-03, Speed: 7.406 samples/sec, ObjLoss=26.151, BoxCenterLoss=14.548, BoxScaleLoss=5.300, ClassLoss=11.341 [Epoch 85][Batch 799], LR: 1.00E-03, Speed: 10.693 samples/sec, ObjLoss=26.149, BoxCenterLoss=14.548, BoxScaleLoss=5.300, ClassLoss=11.340 [Epoch 85][Batch 899], LR: 1.00E-03, Speed: 8.566 samples/sec, ObjLoss=26.146, BoxCenterLoss=14.547, BoxScaleLoss=5.299, ClassLoss=11.338 [Epoch 85][Batch 999], LR: 1.00E-03, Speed: 11.199 samples/sec, ObjLoss=26.145, BoxCenterLoss=14.547, BoxScaleLoss=5.299, ClassLoss=11.336 [Epoch 85][Batch 1099], LR: 1.00E-03, Speed: 9.086 samples/sec, ObjLoss=26.144, BoxCenterLoss=14.547, BoxScaleLoss=5.299, ClassLoss=11.335 [Epoch 85][Batch 1199], LR: 1.00E-03, Speed: 10.263 samples/sec, ObjLoss=26.142, BoxCenterLoss=14.547, BoxScaleLoss=5.298, ClassLoss=11.334 [Epoch 85][Batch 1299], LR: 1.00E-03, Speed: 83.785 samples/sec, ObjLoss=26.141, BoxCenterLoss=14.547, BoxScaleLoss=5.298, ClassLoss=11.333 [Epoch 85][Batch 1399], LR: 1.00E-03, Speed: 110.487 samples/sec, ObjLoss=26.139, BoxCenterLoss=14.547, BoxScaleLoss=5.298, ClassLoss=11.331 [Epoch 85][Batch 1499], LR: 1.00E-03, Speed: 9.138 samples/sec, ObjLoss=26.137, BoxCenterLoss=14.547, BoxScaleLoss=5.297, ClassLoss=11.329 [Epoch 85][Batch 1599], LR: 1.00E-03, Speed: 9.524 samples/sec, ObjLoss=26.135, BoxCenterLoss=14.546, BoxScaleLoss=5.297, ClassLoss=11.328 [Epoch 85][Batch 1699], LR: 1.00E-03, Speed: 8.713 samples/sec, ObjLoss=26.133, BoxCenterLoss=14.546, BoxScaleLoss=5.297, ClassLoss=11.326 [Epoch 85][Batch 1799], LR: 1.00E-03, Speed: 12.690 samples/sec, ObjLoss=26.131, BoxCenterLoss=14.545, BoxScaleLoss=5.296, ClassLoss=11.325 [Epoch 85] Training cost: 2238.755, ObjLoss=26.130, BoxCenterLoss=14.546, BoxScaleLoss=5.296, ClassLoss=11.325 [Epoch 85] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.217 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.426 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.199 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419 person=35.1 bicycle=17.2 car=24.3 motorcycle=28.4 airplane=40.0 bus=45.0 train=43.8 truck=19.2 boat=12.2 traffic light=13.2 fire hydrant=42.2 stop sign=39.8 parking meter=21.8 bench=11.0 bird=17.6 cat=39.5 dog=36.3 horse=35.4 sheep=31.6 cow=36.3 elephant=41.5 bear=38.4 zebra=44.0 giraffe=47.8 backpack=4.9 umbrella=22.7 handbag=4.3 tie=15.3 suitcase=17.2 frisbee=35.3 skis=9.8 snowboard=10.3 sports ball=27.1 kite=22.3 baseball bat=14.1 baseball glove=19.4 skateboard=22.1 surfboard=18.0 tennis racket=22.6 bottle=16.1 wine glass=17.1 cup=20.3 fork=10.8 knife=3.6 spoon=2.3 bowl=20.1 banana=9.9 apple=6.3 sandwich=15.9 orange=14.1 broccoli=9.8 carrot=8.7 hot dog=15.8 pizza=28.8 donut=20.7 cake=18.8 chair=13.2 couch=25.0 potted plant=13.2 bed=29.8 dining table=18.3 toilet=33.7 tv=35.4 laptop=32.0 mouse=32.2 remote=7.3 keyboard=30.2 cell phone=14.6 microwave=29.6 oven=15.6 toaster=0.0 sink=19.2 refrigerator=29.3 book=4.6 clock=29.9 vase=19.9 scissors=12.8 teddy bear=20.9 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.7 [Epoch 86][Batch 99], LR: 1.00E-03, Speed: 9.233 samples/sec, ObjLoss=26.128, BoxCenterLoss=14.545, BoxScaleLoss=5.296, ClassLoss=11.323 [Epoch 86][Batch 199], LR: 1.00E-03, Speed: 84.650 samples/sec, ObjLoss=26.126, BoxCenterLoss=14.544, BoxScaleLoss=5.296, ClassLoss=11.322 [Epoch 86][Batch 299], LR: 1.00E-03, Speed: 9.579 samples/sec, ObjLoss=26.125, BoxCenterLoss=14.545, BoxScaleLoss=5.296, ClassLoss=11.321 [Epoch 86][Batch 399], LR: 1.00E-03, Speed: 10.401 samples/sec, ObjLoss=26.123, BoxCenterLoss=14.544, BoxScaleLoss=5.295, ClassLoss=11.320 [Epoch 86][Batch 499], LR: 1.00E-03, Speed: 8.423 samples/sec, ObjLoss=26.121, BoxCenterLoss=14.544, BoxScaleLoss=5.295, ClassLoss=11.318 [Epoch 86][Batch 599], LR: 1.00E-03, Speed: 7.595 samples/sec, ObjLoss=26.119, BoxCenterLoss=14.544, BoxScaleLoss=5.295, ClassLoss=11.317 [Epoch 86][Batch 699], LR: 1.00E-03, Speed: 7.858 samples/sec, ObjLoss=26.118, BoxCenterLoss=14.544, BoxScaleLoss=5.295, ClassLoss=11.316 [Epoch 86][Batch 799], LR: 1.00E-03, Speed: 75.984 samples/sec, ObjLoss=26.116, BoxCenterLoss=14.543, BoxScaleLoss=5.295, ClassLoss=11.315 [Epoch 86][Batch 899], LR: 1.00E-03, Speed: 8.137 samples/sec, ObjLoss=26.114, BoxCenterLoss=14.544, BoxScaleLoss=5.295, ClassLoss=11.313 [Epoch 86][Batch 999], LR: 1.00E-03, Speed: 9.949 samples/sec, ObjLoss=26.113, BoxCenterLoss=14.544, BoxScaleLoss=5.294, ClassLoss=11.312 [Epoch 86][Batch 1099], LR: 1.00E-03, Speed: 6.956 samples/sec, ObjLoss=26.112, BoxCenterLoss=14.544, BoxScaleLoss=5.294, ClassLoss=11.311 [Epoch 86][Batch 1199], LR: 1.00E-03, Speed: 9.659 samples/sec, ObjLoss=26.111, BoxCenterLoss=14.544, BoxScaleLoss=5.294, ClassLoss=11.310 [Epoch 86][Batch 1299], LR: 1.00E-03, Speed: 8.851 samples/sec, ObjLoss=26.109, BoxCenterLoss=14.544, BoxScaleLoss=5.294, ClassLoss=11.309 [Epoch 86][Batch 1399], LR: 1.00E-03, Speed: 8.993 samples/sec, ObjLoss=26.108, BoxCenterLoss=14.544, BoxScaleLoss=5.294, ClassLoss=11.308 [Epoch 86][Batch 1499], LR: 1.00E-03, Speed: 93.852 samples/sec, ObjLoss=26.106, BoxCenterLoss=14.544, BoxScaleLoss=5.294, ClassLoss=11.307 [Epoch 86][Batch 1599], LR: 1.00E-03, Speed: 7.352 samples/sec, ObjLoss=26.104, BoxCenterLoss=14.543, BoxScaleLoss=5.293, ClassLoss=11.305 [Epoch 86][Batch 1699], LR: 1.00E-03, Speed: 9.994 samples/sec, ObjLoss=26.104, BoxCenterLoss=14.543, BoxScaleLoss=5.293, ClassLoss=11.304 [Epoch 86][Batch 1799], LR: 1.00E-03, Speed: 15.575 samples/sec, ObjLoss=26.102, BoxCenterLoss=14.544, BoxScaleLoss=5.293, ClassLoss=11.302 [Epoch 86] Training cost: 2238.088, ObjLoss=26.102, BoxCenterLoss=14.544, BoxScaleLoss=5.293, ClassLoss=11.302 [Epoch 86] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.432 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.207 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.447 person=34.4 bicycle=16.7 car=26.3 motorcycle=27.7 airplane=42.3 bus=48.3 train=46.2 truck=21.0 boat=12.2 traffic light=12.1 fire hydrant=41.7 stop sign=30.4 parking meter=22.9 bench=12.3 bird=18.9 cat=44.2 dog=34.9 horse=37.1 sheep=30.9 cow=31.2 elephant=41.6 bear=35.0 zebra=46.8 giraffe=46.0 backpack=4.4 umbrella=22.8 handbag=4.3 tie=13.8 suitcase=16.5 frisbee=37.6 skis=8.3 snowboard=13.7 sports ball=25.5 kite=24.0 baseball bat=8.8 baseball glove=21.4 skateboard=28.5 surfboard=18.1 tennis racket=26.1 bottle=16.9 wine glass=19.3 cup=23.9 fork=11.3 knife=3.3 spoon=3.9 bowl=23.7 banana=11.6 apple=6.9 sandwich=18.1 orange=15.4 broccoli=10.7 carrot=8.8 hot dog=20.6 pizza=33.0 donut=23.0 cake=19.7 chair=15.8 couch=26.7 potted plant=11.4 bed=30.9 dining table=18.3 toilet=36.7 tv=32.9 laptop=38.3 mouse=30.5 remote=9.7 keyboard=28.2 cell phone=17.2 microwave=30.2 oven=19.5 toaster=0.0 sink=20.0 refrigerator=27.3 book=4.4 clock=35.2 vase=18.9 scissors=14.9 teddy bear=25.9 hair drier=0.0 toothbrush=4.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.5 [Epoch 87][Batch 99], LR: 1.00E-03, Speed: 80.792 samples/sec, ObjLoss=26.100, BoxCenterLoss=14.543, BoxScaleLoss=5.293, ClassLoss=11.300 [Epoch 87][Batch 199], LR: 1.00E-03, Speed: 9.615 samples/sec, ObjLoss=26.098, BoxCenterLoss=14.543, BoxScaleLoss=5.292, ClassLoss=11.299 [Epoch 87][Batch 299], LR: 1.00E-03, Speed: 8.795 samples/sec, ObjLoss=26.097, BoxCenterLoss=14.543, BoxScaleLoss=5.292, ClassLoss=11.298 [Epoch 87][Batch 399], LR: 1.00E-03, Speed: 10.136 samples/sec, ObjLoss=26.095, BoxCenterLoss=14.542, BoxScaleLoss=5.292, ClassLoss=11.296 [Epoch 87][Batch 499], LR: 1.00E-03, Speed: 8.365 samples/sec, ObjLoss=26.094, BoxCenterLoss=14.542, BoxScaleLoss=5.291, ClassLoss=11.295 [Epoch 87][Batch 599], LR: 1.00E-03, Speed: 108.569 samples/sec, ObjLoss=26.092, BoxCenterLoss=14.542, BoxScaleLoss=5.291, ClassLoss=11.294 [Epoch 87][Batch 699], LR: 1.00E-03, Speed: 9.429 samples/sec, ObjLoss=26.090, BoxCenterLoss=14.542, BoxScaleLoss=5.291, ClassLoss=11.292 [Epoch 87][Batch 799], LR: 1.00E-03, Speed: 8.555 samples/sec, ObjLoss=26.089, BoxCenterLoss=14.542, BoxScaleLoss=5.291, ClassLoss=11.291 [Epoch 87][Batch 899], LR: 1.00E-03, Speed: 92.131 samples/sec, ObjLoss=26.088, BoxCenterLoss=14.542, BoxScaleLoss=5.290, ClassLoss=11.289 [Epoch 87][Batch 999], LR: 1.00E-03, Speed: 12.861 samples/sec, ObjLoss=26.087, BoxCenterLoss=14.542, BoxScaleLoss=5.290, ClassLoss=11.288 [Epoch 87][Batch 1099], LR: 1.00E-03, Speed: 9.859 samples/sec, ObjLoss=26.084, BoxCenterLoss=14.541, BoxScaleLoss=5.290, ClassLoss=11.286 [Epoch 87][Batch 1199], LR: 1.00E-03, Speed: 12.328 samples/sec, ObjLoss=26.083, BoxCenterLoss=14.541, BoxScaleLoss=5.289, ClassLoss=11.285 [Epoch 87][Batch 1299], LR: 1.00E-03, Speed: 11.640 samples/sec, ObjLoss=26.081, BoxCenterLoss=14.541, BoxScaleLoss=5.289, ClassLoss=11.284 [Epoch 87][Batch 1399], LR: 1.00E-03, Speed: 7.974 samples/sec, ObjLoss=26.080, BoxCenterLoss=14.541, BoxScaleLoss=5.289, ClassLoss=11.282 [Epoch 87][Batch 1499], LR: 1.00E-03, Speed: 9.694 samples/sec, ObjLoss=26.077, BoxCenterLoss=14.541, BoxScaleLoss=5.288, ClassLoss=11.281 [Epoch 87][Batch 1599], LR: 1.00E-03, Speed: 11.073 samples/sec, ObjLoss=26.076, BoxCenterLoss=14.541, BoxScaleLoss=5.288, ClassLoss=11.280 [Epoch 87][Batch 1699], LR: 1.00E-03, Speed: 7.405 samples/sec, ObjLoss=26.074, BoxCenterLoss=14.540, BoxScaleLoss=5.288, ClassLoss=11.278 [Epoch 87][Batch 1799], LR: 1.00E-03, Speed: 12.422 samples/sec, ObjLoss=26.072, BoxCenterLoss=14.540, BoxScaleLoss=5.288, ClassLoss=11.277 [Epoch 87] Training cost: 2216.156, ObjLoss=26.071, BoxCenterLoss=14.540, BoxScaleLoss=5.288, ClassLoss=11.277 [Epoch 87] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.433 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.200 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.230 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.205 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.444 person=36.4 bicycle=17.2 car=24.8 motorcycle=25.8 airplane=37.2 bus=43.6 train=43.1 truck=21.3 boat=12.1 traffic light=12.0 fire hydrant=40.7 stop sign=39.7 parking meter=30.0 bench=10.9 bird=19.5 cat=39.1 dog=38.4 horse=33.8 sheep=29.8 cow=31.3 elephant=44.0 bear=47.3 zebra=39.9 giraffe=46.1 backpack=4.7 umbrella=21.5 handbag=4.7 tie=15.1 suitcase=19.6 frisbee=32.6 skis=9.3 snowboard=11.4 sports ball=23.2 kite=21.9 baseball bat=11.2 baseball glove=16.6 skateboard=27.4 surfboard=17.8 tennis racket=26.1 bottle=17.9 wine glass=18.8 cup=23.3 fork=9.6 knife=4.0 spoon=3.9 bowl=21.5 banana=10.0 apple=7.5 sandwich=18.2 orange=15.5 broccoli=9.2 carrot=5.7 hot dog=12.5 pizza=27.1 donut=23.0 cake=16.2 chair=13.6 couch=28.7 potted plant=12.1 bed=17.2 dining table=14.7 toilet=39.4 tv=38.8 laptop=35.8 mouse=37.2 remote=7.9 keyboard=31.4 cell phone=16.2 microwave=33.3 oven=18.1 toaster=0.0 sink=18.8 refrigerator=28.3 book=4.7 clock=29.8 vase=18.9 scissors=15.7 teddy bear=25.2 hair drier=0.0 toothbrush=5.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.0 [Epoch 88][Batch 99], LR: 1.00E-03, Speed: 12.213 samples/sec, ObjLoss=26.070, BoxCenterLoss=14.540, BoxScaleLoss=5.287, ClassLoss=11.276 [Epoch 88][Batch 199], LR: 1.00E-03, Speed: 10.285 samples/sec, ObjLoss=26.068, BoxCenterLoss=14.539, BoxScaleLoss=5.287, ClassLoss=11.274 [Epoch 88][Batch 299], LR: 1.00E-03, Speed: 7.911 samples/sec, ObjLoss=26.066, BoxCenterLoss=14.539, BoxScaleLoss=5.287, ClassLoss=11.273 [Epoch 88][Batch 399], LR: 1.00E-03, Speed: 7.700 samples/sec, ObjLoss=26.064, BoxCenterLoss=14.538, BoxScaleLoss=5.286, ClassLoss=11.271 [Epoch 88][Batch 499], LR: 1.00E-03, Speed: 8.284 samples/sec, ObjLoss=26.063, BoxCenterLoss=14.538, BoxScaleLoss=5.286, ClassLoss=11.270 [Epoch 88][Batch 599], LR: 1.00E-03, Speed: 92.428 samples/sec, ObjLoss=26.061, BoxCenterLoss=14.538, BoxScaleLoss=5.286, ClassLoss=11.269 [Epoch 88][Batch 699], LR: 1.00E-03, Speed: 12.751 samples/sec, ObjLoss=26.059, BoxCenterLoss=14.538, BoxScaleLoss=5.285, ClassLoss=11.268 [Epoch 88][Batch 799], LR: 1.00E-03, Speed: 8.429 samples/sec, ObjLoss=26.058, BoxCenterLoss=14.538, BoxScaleLoss=5.285, ClassLoss=11.266 [Epoch 88][Batch 899], LR: 1.00E-03, Speed: 92.398 samples/sec, ObjLoss=26.055, BoxCenterLoss=14.537, BoxScaleLoss=5.285, ClassLoss=11.265 [Epoch 88][Batch 999], LR: 1.00E-03, Speed: 9.472 samples/sec, ObjLoss=26.054, BoxCenterLoss=14.537, BoxScaleLoss=5.284, ClassLoss=11.263 [Epoch 88][Batch 1099], LR: 1.00E-03, Speed: 7.519 samples/sec, ObjLoss=26.052, BoxCenterLoss=14.537, BoxScaleLoss=5.284, ClassLoss=11.262 [Epoch 88][Batch 1199], LR: 1.00E-03, Speed: 11.933 samples/sec, ObjLoss=26.050, BoxCenterLoss=14.536, BoxScaleLoss=5.284, ClassLoss=11.261 [Epoch 88][Batch 1299], LR: 1.00E-03, Speed: 10.414 samples/sec, ObjLoss=26.049, BoxCenterLoss=14.536, BoxScaleLoss=5.284, ClassLoss=11.260 [Epoch 88][Batch 1399], LR: 1.00E-03, Speed: 8.308 samples/sec, ObjLoss=26.048, BoxCenterLoss=14.536, BoxScaleLoss=5.284, ClassLoss=11.259 [Epoch 88][Batch 1499], LR: 1.00E-03, Speed: 8.847 samples/sec, ObjLoss=26.047, BoxCenterLoss=14.537, BoxScaleLoss=5.284, ClassLoss=11.258 [Epoch 88][Batch 1599], LR: 1.00E-03, Speed: 7.804 samples/sec, ObjLoss=26.045, BoxCenterLoss=14.536, BoxScaleLoss=5.283, ClassLoss=11.256 [Epoch 88][Batch 1699], LR: 1.00E-03, Speed: 10.619 samples/sec, ObjLoss=26.044, BoxCenterLoss=14.536, BoxScaleLoss=5.283, ClassLoss=11.255 [Epoch 88][Batch 1799], LR: 1.00E-03, Speed: 12.028 samples/sec, ObjLoss=26.042, BoxCenterLoss=14.535, BoxScaleLoss=5.282, ClassLoss=11.253 [Epoch 88] Training cost: 2183.189, ObjLoss=26.041, BoxCenterLoss=14.535, BoxScaleLoss=5.282, ClassLoss=11.253 [Epoch 88] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.228 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.446 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.447 person=37.0 bicycle=15.5 car=25.0 motorcycle=28.2 airplane=38.7 bus=43.2 train=49.2 truck=19.8 boat=13.8 traffic light=13.5 fire hydrant=37.0 stop sign=39.9 parking meter=31.7 bench=11.0 bird=19.6 cat=43.0 dog=35.0 horse=37.6 sheep=33.3 cow=38.0 elephant=44.3 bear=44.7 zebra=44.1 giraffe=46.1 backpack=4.6 umbrella=21.7 handbag=4.7 tie=17.6 suitcase=16.3 frisbee=28.5 skis=8.5 snowboard=13.5 sports ball=27.5 kite=26.2 baseball bat=12.0 baseball glove=20.2 skateboard=29.2 surfboard=19.3 tennis racket=28.3 bottle=18.1 wine glass=20.5 cup=24.2 fork=11.4 knife=3.5 spoon=3.0 bowl=21.1 banana=12.1 apple=9.2 sandwich=18.4 orange=15.0 broccoli=8.0 carrot=8.4 hot dog=20.1 pizza=30.5 donut=24.1 cake=16.3 chair=14.7 couch=26.0 potted plant=11.2 bed=30.9 dining table=14.0 toilet=42.5 tv=36.0 laptop=29.8 mouse=33.6 remote=9.0 keyboard=26.1 cell phone=17.3 microwave=34.1 oven=15.5 toaster=0.0 sink=18.6 refrigerator=31.1 book=3.7 clock=28.6 vase=22.3 scissors=14.6 teddy bear=30.0 hair drier=0.0 toothbrush=6.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.8 [Epoch 89][Batch 99], LR: 1.00E-03, Speed: 8.336 samples/sec, ObjLoss=26.040, BoxCenterLoss=14.535, BoxScaleLoss=5.282, ClassLoss=11.252 [Epoch 89][Batch 199], LR: 1.00E-03, Speed: 11.680 samples/sec, ObjLoss=26.038, BoxCenterLoss=14.535, BoxScaleLoss=5.282, ClassLoss=11.251 [Epoch 89][Batch 299], LR: 1.00E-03, Speed: 7.511 samples/sec, ObjLoss=26.036, BoxCenterLoss=14.534, BoxScaleLoss=5.282, ClassLoss=11.249 [Epoch 89][Batch 399], LR: 1.00E-03, Speed: 9.368 samples/sec, ObjLoss=26.034, BoxCenterLoss=14.534, BoxScaleLoss=5.281, ClassLoss=11.248 [Epoch 89][Batch 499], LR: 1.00E-03, Speed: 9.927 samples/sec, ObjLoss=26.032, BoxCenterLoss=14.534, BoxScaleLoss=5.281, ClassLoss=11.247 [Epoch 89][Batch 599], LR: 1.00E-03, Speed: 9.277 samples/sec, ObjLoss=26.031, BoxCenterLoss=14.533, BoxScaleLoss=5.281, ClassLoss=11.245 [Epoch 89][Batch 699], LR: 1.00E-03, Speed: 9.009 samples/sec, ObjLoss=26.030, BoxCenterLoss=14.534, BoxScaleLoss=5.281, ClassLoss=11.245 [Epoch 89][Batch 799], LR: 1.00E-03, Speed: 8.920 samples/sec, ObjLoss=26.028, BoxCenterLoss=14.533, BoxScaleLoss=5.281, ClassLoss=11.244 [Epoch 89][Batch 899], LR: 1.00E-03, Speed: 8.811 samples/sec, ObjLoss=26.027, BoxCenterLoss=14.533, BoxScaleLoss=5.281, ClassLoss=11.242 [Epoch 89][Batch 999], LR: 1.00E-03, Speed: 10.921 samples/sec, ObjLoss=26.026, BoxCenterLoss=14.533, BoxScaleLoss=5.280, ClassLoss=11.241 [Epoch 89][Batch 1099], LR: 1.00E-03, Speed: 10.714 samples/sec, ObjLoss=26.023, BoxCenterLoss=14.533, BoxScaleLoss=5.280, ClassLoss=11.240 [Epoch 89][Batch 1199], LR: 1.00E-03, Speed: 79.129 samples/sec, ObjLoss=26.022, BoxCenterLoss=14.533, BoxScaleLoss=5.280, ClassLoss=11.239 [Epoch 89][Batch 1299], LR: 1.00E-03, Speed: 10.117 samples/sec, ObjLoss=26.020, BoxCenterLoss=14.533, BoxScaleLoss=5.280, ClassLoss=11.238 [Epoch 89][Batch 1399], LR: 1.00E-03, Speed: 7.304 samples/sec, ObjLoss=26.018, BoxCenterLoss=14.532, BoxScaleLoss=5.279, ClassLoss=11.236 [Epoch 89][Batch 1499], LR: 1.00E-03, Speed: 9.493 samples/sec, ObjLoss=26.016, BoxCenterLoss=14.531, BoxScaleLoss=5.279, ClassLoss=11.235 [Epoch 89][Batch 1599], LR: 1.00E-03, Speed: 8.233 samples/sec, ObjLoss=26.014, BoxCenterLoss=14.531, BoxScaleLoss=5.279, ClassLoss=11.233 [Epoch 89][Batch 1699], LR: 1.00E-03, Speed: 16.412 samples/sec, ObjLoss=26.013, BoxCenterLoss=14.531, BoxScaleLoss=5.278, ClassLoss=11.232 [Epoch 89][Batch 1799], LR: 1.00E-03, Speed: 10.849 samples/sec, ObjLoss=26.011, BoxCenterLoss=14.531, BoxScaleLoss=5.278, ClassLoss=11.231 [Epoch 89] Training cost: 2186.651, ObjLoss=26.011, BoxCenterLoss=14.531, BoxScaleLoss=5.278, ClassLoss=11.231 [Epoch 89] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.437 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.205 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418 person=34.6 bicycle=14.6 car=24.2 motorcycle=23.2 airplane=35.3 bus=43.5 train=43.5 truck=19.6 boat=11.8 traffic light=12.4 fire hydrant=41.6 stop sign=37.8 parking meter=25.3 bench=11.5 bird=19.9 cat=40.1 dog=32.5 horse=33.4 sheep=29.6 cow=30.3 elephant=40.4 bear=38.9 zebra=38.3 giraffe=48.2 backpack=4.8 umbrella=20.8 handbag=4.5 tie=16.4 suitcase=17.3 frisbee=33.4 skis=6.6 snowboard=12.5 sports ball=24.0 kite=22.3 baseball bat=11.8 baseball glove=18.1 skateboard=29.6 surfboard=17.7 tennis racket=26.4 bottle=17.7 wine glass=19.0 cup=20.3 fork=10.2 knife=3.5 spoon=3.5 bowl=17.2 banana=11.4 apple=7.1 sandwich=16.5 orange=15.9 broccoli=7.9 carrot=7.7 hot dog=19.5 pizza=29.7 donut=27.9 cake=18.5 chair=13.4 couch=25.5 potted plant=10.0 bed=36.1 dining table=18.5 toilet=40.0 tv=35.0 laptop=36.1 mouse=36.2 remote=9.8 keyboard=25.8 cell phone=15.8 microwave=33.7 oven=21.3 toaster=0.0 sink=17.1 refrigerator=30.6 book=4.8 clock=28.8 vase=17.8 scissors=16.5 teddy bear=29.5 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.0 [Epoch 90][Batch 99], LR: 1.00E-03, Speed: 8.598 samples/sec, ObjLoss=26.010, BoxCenterLoss=14.531, BoxScaleLoss=5.278, ClassLoss=11.230 [Epoch 90][Batch 199], LR: 1.00E-03, Speed: 9.069 samples/sec, ObjLoss=26.009, BoxCenterLoss=14.531, BoxScaleLoss=5.278, ClassLoss=11.228 [Epoch 90][Batch 299], LR: 1.00E-03, Speed: 10.434 samples/sec, ObjLoss=26.008, BoxCenterLoss=14.531, BoxScaleLoss=5.278, ClassLoss=11.227 [Epoch 90][Batch 399], LR: 1.00E-03, Speed: 126.459 samples/sec, ObjLoss=26.006, BoxCenterLoss=14.531, BoxScaleLoss=5.277, ClassLoss=11.226 [Epoch 90][Batch 499], LR: 1.00E-03, Speed: 89.371 samples/sec, ObjLoss=26.004, BoxCenterLoss=14.531, BoxScaleLoss=5.277, ClassLoss=11.224 [Epoch 90][Batch 599], LR: 1.00E-03, Speed: 9.514 samples/sec, ObjLoss=26.003, BoxCenterLoss=14.530, BoxScaleLoss=5.277, ClassLoss=11.223 [Epoch 90][Batch 699], LR: 1.00E-03, Speed: 10.664 samples/sec, ObjLoss=26.001, BoxCenterLoss=14.530, BoxScaleLoss=5.276, ClassLoss=11.221 [Epoch 90][Batch 799], LR: 1.00E-03, Speed: 88.986 samples/sec, ObjLoss=26.000, BoxCenterLoss=14.530, BoxScaleLoss=5.276, ClassLoss=11.220 [Epoch 90][Batch 899], LR: 1.00E-03, Speed: 18.126 samples/sec, ObjLoss=25.999, BoxCenterLoss=14.530, BoxScaleLoss=5.276, ClassLoss=11.219 [Epoch 90][Batch 999], LR: 1.00E-03, Speed: 8.582 samples/sec, ObjLoss=25.997, BoxCenterLoss=14.530, BoxScaleLoss=5.276, ClassLoss=11.218 [Epoch 90][Batch 1099], LR: 1.00E-03, Speed: 9.662 samples/sec, ObjLoss=25.996, BoxCenterLoss=14.530, BoxScaleLoss=5.276, ClassLoss=11.218 [Epoch 90][Batch 1199], LR: 1.00E-03, Speed: 10.770 samples/sec, ObjLoss=25.994, BoxCenterLoss=14.530, BoxScaleLoss=5.276, ClassLoss=11.216 [Epoch 90][Batch 1299], LR: 1.00E-03, Speed: 106.209 samples/sec, ObjLoss=25.992, BoxCenterLoss=14.530, BoxScaleLoss=5.275, ClassLoss=11.215 [Epoch 90][Batch 1399], LR: 1.00E-03, Speed: 99.051 samples/sec, ObjLoss=25.990, BoxCenterLoss=14.529, BoxScaleLoss=5.275, ClassLoss=11.213 [Epoch 90][Batch 1499], LR: 1.00E-03, Speed: 9.200 samples/sec, ObjLoss=25.989, BoxCenterLoss=14.529, BoxScaleLoss=5.275, ClassLoss=11.212 [Epoch 90][Batch 1599], LR: 1.00E-03, Speed: 9.093 samples/sec, ObjLoss=25.987, BoxCenterLoss=14.529, BoxScaleLoss=5.275, ClassLoss=11.211 [Epoch 90][Batch 1699], LR: 1.00E-03, Speed: 10.674 samples/sec, ObjLoss=25.986, BoxCenterLoss=14.529, BoxScaleLoss=5.274, ClassLoss=11.210 [Epoch 90][Batch 1799], LR: 1.00E-03, Speed: 9.823 samples/sec, ObjLoss=25.984, BoxCenterLoss=14.529, BoxScaleLoss=5.274, ClassLoss=11.209 [Epoch 90] Training cost: 2147.324, ObjLoss=25.983, BoxCenterLoss=14.529, BoxScaleLoss=5.274, ClassLoss=11.208 [Epoch 90] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.228 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.438 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.217 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.084 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.451 person=37.0 bicycle=15.6 car=25.6 motorcycle=27.1 airplane=42.1 bus=46.5 train=46.8 truck=21.6 boat=13.0 traffic light=14.2 fire hydrant=37.6 stop sign=37.8 parking meter=19.0 bench=14.0 bird=18.0 cat=40.5 dog=37.0 horse=35.3 sheep=30.0 cow=32.9 elephant=44.6 bear=38.7 zebra=44.0 giraffe=47.8 backpack=4.7 umbrella=21.9 handbag=3.8 tie=16.7 suitcase=13.6 frisbee=33.6 skis=8.5 snowboard=13.6 sports ball=24.1 kite=25.2 baseball bat=11.4 baseball glove=18.7 skateboard=25.7 surfboard=19.1 tennis racket=28.0 bottle=19.7 wine glass=17.5 cup=23.9 fork=10.5 knife=3.7 spoon=3.9 bowl=23.2 banana=11.3 apple=6.1 sandwich=18.9 orange=15.7 broccoli=11.8 carrot=6.7 hot dog=15.8 pizza=34.3 donut=20.7 cake=18.4 chair=15.1 couch=30.0 potted plant=12.3 bed=30.1 dining table=20.5 toilet=36.4 tv=36.2 laptop=38.0 mouse=38.7 remote=8.8 keyboard=28.0 cell phone=17.5 microwave=37.2 oven=20.4 toaster=0.0 sink=23.1 refrigerator=32.7 book=3.6 clock=31.1 vase=19.1 scissors=18.4 teddy bear=27.2 hair drier=0.0 toothbrush=6.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.8 [Epoch 91][Batch 99], LR: 1.00E-03, Speed: 10.251 samples/sec, ObjLoss=25.981, BoxCenterLoss=14.528, BoxScaleLoss=5.274, ClassLoss=11.207 [Epoch 91][Batch 199], LR: 1.00E-03, Speed: 10.002 samples/sec, ObjLoss=25.980, BoxCenterLoss=14.528, BoxScaleLoss=5.274, ClassLoss=11.206 [Epoch 91][Batch 299], LR: 1.00E-03, Speed: 7.900 samples/sec, ObjLoss=25.978, BoxCenterLoss=14.528, BoxScaleLoss=5.273, ClassLoss=11.204 [Epoch 91][Batch 399], LR: 1.00E-03, Speed: 8.797 samples/sec, ObjLoss=25.976, BoxCenterLoss=14.527, BoxScaleLoss=5.273, ClassLoss=11.203 [Epoch 91][Batch 499], LR: 1.00E-03, Speed: 7.195 samples/sec, ObjLoss=25.975, BoxCenterLoss=14.527, BoxScaleLoss=5.273, ClassLoss=11.202 [Epoch 91][Batch 599], LR: 1.00E-03, Speed: 9.328 samples/sec, ObjLoss=25.974, BoxCenterLoss=14.528, BoxScaleLoss=5.273, ClassLoss=11.201 [Epoch 91][Batch 699], LR: 1.00E-03, Speed: 12.584 samples/sec, ObjLoss=25.971, BoxCenterLoss=14.527, BoxScaleLoss=5.272, ClassLoss=11.200 [Epoch 91][Batch 799], LR: 1.00E-03, Speed: 9.383 samples/sec, ObjLoss=25.969, BoxCenterLoss=14.526, BoxScaleLoss=5.272, ClassLoss=11.198 [Epoch 91][Batch 899], LR: 1.00E-03, Speed: 11.140 samples/sec, ObjLoss=25.968, BoxCenterLoss=14.527, BoxScaleLoss=5.272, ClassLoss=11.197 [Epoch 91][Batch 999], LR: 1.00E-03, Speed: 9.764 samples/sec, ObjLoss=25.967, BoxCenterLoss=14.526, BoxScaleLoss=5.272, ClassLoss=11.196 [Epoch 91][Batch 1099], LR: 1.00E-03, Speed: 7.822 samples/sec, ObjLoss=25.966, BoxCenterLoss=14.527, BoxScaleLoss=5.272, ClassLoss=11.195 [Epoch 91][Batch 1199], LR: 1.00E-03, Speed: 105.925 samples/sec, ObjLoss=25.964, BoxCenterLoss=14.526, BoxScaleLoss=5.271, ClassLoss=11.193 [Epoch 91][Batch 1299], LR: 1.00E-03, Speed: 96.605 samples/sec, ObjLoss=25.963, BoxCenterLoss=14.526, BoxScaleLoss=5.271, ClassLoss=11.192 [Epoch 91][Batch 1399], LR: 1.00E-03, Speed: 11.588 samples/sec, ObjLoss=25.962, BoxCenterLoss=14.526, BoxScaleLoss=5.271, ClassLoss=11.191 [Epoch 91][Batch 1499], LR: 1.00E-03, Speed: 124.843 samples/sec, ObjLoss=25.960, BoxCenterLoss=14.526, BoxScaleLoss=5.270, ClassLoss=11.190 [Epoch 91][Batch 1599], LR: 1.00E-03, Speed: 10.633 samples/sec, ObjLoss=25.959, BoxCenterLoss=14.526, BoxScaleLoss=5.270, ClassLoss=11.189 [Epoch 91][Batch 1699], LR: 1.00E-03, Speed: 9.096 samples/sec, ObjLoss=25.957, BoxCenterLoss=14.526, BoxScaleLoss=5.270, ClassLoss=11.188 [Epoch 91][Batch 1799], LR: 1.00E-03, Speed: 11.386 samples/sec, ObjLoss=25.957, BoxCenterLoss=14.527, BoxScaleLoss=5.270, ClassLoss=11.187 [Epoch 91] Training cost: 2154.794, ObjLoss=25.957, BoxCenterLoss=14.527, BoxScaleLoss=5.270, ClassLoss=11.186 [Epoch 91] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.436 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.087 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.202 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 person=35.2 bicycle=15.5 car=22.8 motorcycle=24.9 airplane=36.2 bus=42.2 train=46.4 truck=20.0 boat=13.9 traffic light=11.9 fire hydrant=41.6 stop sign=38.4 parking meter=19.0 bench=10.5 bird=16.9 cat=43.2 dog=32.9 horse=31.7 sheep=30.0 cow=31.3 elephant=42.4 bear=42.1 zebra=41.6 giraffe=47.0 backpack=4.6 umbrella=17.9 handbag=5.1 tie=15.1 suitcase=17.5 frisbee=36.9 skis=9.8 snowboard=8.7 sports ball=23.4 kite=21.3 baseball bat=11.0 baseball glove=14.1 skateboard=25.0 surfboard=17.3 tennis racket=24.4 bottle=15.6 wine glass=19.5 cup=20.8 fork=11.2 knife=3.4 spoon=3.8 bowl=23.8 banana=9.8 apple=6.5 sandwich=16.3 orange=14.0 broccoli=9.6 carrot=5.1 hot dog=8.7 pizza=30.1 donut=23.8 cake=19.1 chair=14.6 couch=26.9 potted plant=11.6 bed=30.4 dining table=20.4 toilet=35.8 tv=28.9 laptop=33.8 mouse=35.2 remote=8.3 keyboard=24.7 cell phone=14.2 microwave=32.0 oven=21.4 toaster=0.0 sink=19.0 refrigerator=31.4 book=3.9 clock=30.8 vase=19.3 scissors=14.3 teddy bear=22.1 hair drier=0.0 toothbrush=2.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.4 [Epoch 92][Batch 99], LR: 1.00E-03, Speed: 87.355 samples/sec, ObjLoss=25.955, BoxCenterLoss=14.526, BoxScaleLoss=5.270, ClassLoss=11.185 [Epoch 92][Batch 199], LR: 1.00E-03, Speed: 9.618 samples/sec, ObjLoss=25.954, BoxCenterLoss=14.527, BoxScaleLoss=5.269, ClassLoss=11.183 [Epoch 92][Batch 299], LR: 1.00E-03, Speed: 10.531 samples/sec, ObjLoss=25.952, BoxCenterLoss=14.526, BoxScaleLoss=5.269, ClassLoss=11.182 [Epoch 92][Batch 399], LR: 1.00E-03, Speed: 9.245 samples/sec, ObjLoss=25.951, BoxCenterLoss=14.526, BoxScaleLoss=5.269, ClassLoss=11.181 [Epoch 92][Batch 499], LR: 1.00E-03, Speed: 9.075 samples/sec, ObjLoss=25.950, BoxCenterLoss=14.526, BoxScaleLoss=5.269, ClassLoss=11.180 [Epoch 92][Batch 599], LR: 1.00E-03, Speed: 8.875 samples/sec, ObjLoss=25.950, BoxCenterLoss=14.527, BoxScaleLoss=5.269, ClassLoss=11.179 [Epoch 92][Batch 699], LR: 1.00E-03, Speed: 10.687 samples/sec, ObjLoss=25.949, BoxCenterLoss=14.527, BoxScaleLoss=5.269, ClassLoss=11.179 [Epoch 92][Batch 799], LR: 1.00E-03, Speed: 9.104 samples/sec, ObjLoss=25.947, BoxCenterLoss=14.527, BoxScaleLoss=5.269, ClassLoss=11.178 [Epoch 92][Batch 899], LR: 1.00E-03, Speed: 10.019 samples/sec, ObjLoss=25.946, BoxCenterLoss=14.527, BoxScaleLoss=5.269, ClassLoss=11.176 [Epoch 92][Batch 999], LR: 1.00E-03, Speed: 9.872 samples/sec, ObjLoss=25.944, BoxCenterLoss=14.527, BoxScaleLoss=5.268, ClassLoss=11.175 [Epoch 92][Batch 1099], LR: 1.00E-03, Speed: 11.354 samples/sec, ObjLoss=25.944, BoxCenterLoss=14.527, BoxScaleLoss=5.268, ClassLoss=11.174 [Epoch 92][Batch 1199], LR: 1.00E-03, Speed: 112.571 samples/sec, ObjLoss=25.943, BoxCenterLoss=14.527, BoxScaleLoss=5.268, ClassLoss=11.173 [Epoch 92][Batch 1299], LR: 1.00E-03, Speed: 8.165 samples/sec, ObjLoss=25.940, BoxCenterLoss=14.527, BoxScaleLoss=5.268, ClassLoss=11.171 [Epoch 92][Batch 1399], LR: 1.00E-03, Speed: 10.839 samples/sec, ObjLoss=25.939, BoxCenterLoss=14.526, BoxScaleLoss=5.267, ClassLoss=11.170 [Epoch 92][Batch 1499], LR: 1.00E-03, Speed: 9.231 samples/sec, ObjLoss=25.938, BoxCenterLoss=14.527, BoxScaleLoss=5.267, ClassLoss=11.169 [Epoch 92][Batch 1599], LR: 1.00E-03, Speed: 10.686 samples/sec, ObjLoss=25.936, BoxCenterLoss=14.526, BoxScaleLoss=5.267, ClassLoss=11.168 [Epoch 92][Batch 1699], LR: 1.00E-03, Speed: 10.143 samples/sec, ObjLoss=25.935, BoxCenterLoss=14.526, BoxScaleLoss=5.267, ClassLoss=11.167 [Epoch 92][Batch 1799], LR: 1.00E-03, Speed: 11.268 samples/sec, ObjLoss=25.934, BoxCenterLoss=14.527, BoxScaleLoss=5.267, ClassLoss=11.166 [Epoch 92] Training cost: 2175.342, ObjLoss=25.934, BoxCenterLoss=14.527, BoxScaleLoss=5.267, ClassLoss=11.166 [Epoch 92] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.438 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.214 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.226 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.448 person=35.6 bicycle=16.9 car=24.2 motorcycle=28.3 airplane=42.2 bus=45.2 train=45.5 truck=21.2 boat=12.3 traffic light=11.0 fire hydrant=44.1 stop sign=42.1 parking meter=28.8 bench=13.3 bird=20.2 cat=42.7 dog=38.4 horse=37.5 sheep=31.5 cow=33.5 elephant=42.3 bear=44.6 zebra=47.9 giraffe=45.6 backpack=5.8 umbrella=21.5 handbag=5.2 tie=16.3 suitcase=16.5 frisbee=36.0 skis=8.3 snowboard=7.9 sports ball=24.9 kite=22.5 baseball bat=11.7 baseball glove=17.7 skateboard=27.3 surfboard=18.5 tennis racket=24.4 bottle=15.7 wine glass=17.6 cup=21.9 fork=11.0 knife=4.8 spoon=3.4 bowl=19.6 banana=12.5 apple=6.9 sandwich=18.2 orange=12.2 broccoli=9.2 carrot=4.7 hot dog=16.5 pizza=35.7 donut=23.2 cake=19.4 chair=13.5 couch=28.1 potted plant=12.1 bed=28.3 dining table=18.7 toilet=38.5 tv=32.9 laptop=36.9 mouse=32.2 remote=8.8 keyboard=28.5 cell phone=15.6 microwave=31.0 oven=21.2 toaster=0.0 sink=21.7 refrigerator=32.2 book=3.8 clock=28.4 vase=19.5 scissors=20.2 teddy bear=29.8 hair drier=0.0 toothbrush=5.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.7 [Epoch 93][Batch 99], LR: 1.00E-03, Speed: 9.561 samples/sec, ObjLoss=25.932, BoxCenterLoss=14.527, BoxScaleLoss=5.267, ClassLoss=11.164 [Epoch 93][Batch 199], LR: 1.00E-03, Speed: 9.836 samples/sec, ObjLoss=25.930, BoxCenterLoss=14.526, BoxScaleLoss=5.266, ClassLoss=11.163 [Epoch 93][Batch 299], LR: 1.00E-03, Speed: 9.640 samples/sec, ObjLoss=25.929, BoxCenterLoss=14.526, BoxScaleLoss=5.266, ClassLoss=11.162 [Epoch 93][Batch 399], LR: 1.00E-03, Speed: 11.523 samples/sec, ObjLoss=25.928, BoxCenterLoss=14.526, BoxScaleLoss=5.266, ClassLoss=11.161 [Epoch 93][Batch 499], LR: 1.00E-03, Speed: 10.843 samples/sec, ObjLoss=25.927, BoxCenterLoss=14.526, BoxScaleLoss=5.266, ClassLoss=11.159 [Epoch 93][Batch 599], LR: 1.00E-03, Speed: 11.621 samples/sec, ObjLoss=25.925, BoxCenterLoss=14.526, BoxScaleLoss=5.265, ClassLoss=11.158 [Epoch 93][Batch 699], LR: 1.00E-03, Speed: 123.425 samples/sec, ObjLoss=25.923, BoxCenterLoss=14.525, BoxScaleLoss=5.265, ClassLoss=11.157 [Epoch 93][Batch 799], LR: 1.00E-03, Speed: 83.068 samples/sec, ObjLoss=25.922, BoxCenterLoss=14.525, BoxScaleLoss=5.265, ClassLoss=11.156 [Epoch 93][Batch 899], LR: 1.00E-03, Speed: 10.303 samples/sec, ObjLoss=25.922, BoxCenterLoss=14.526, BoxScaleLoss=5.265, ClassLoss=11.155 [Epoch 93][Batch 999], LR: 1.00E-03, Speed: 12.111 samples/sec, ObjLoss=25.920, BoxCenterLoss=14.526, BoxScaleLoss=5.264, ClassLoss=11.153 [Epoch 93][Batch 1099], LR: 1.00E-03, Speed: 8.849 samples/sec, ObjLoss=25.919, BoxCenterLoss=14.525, BoxScaleLoss=5.264, ClassLoss=11.152 [Epoch 93][Batch 1199], LR: 1.00E-03, Speed: 112.098 samples/sec, ObjLoss=25.917, BoxCenterLoss=14.525, BoxScaleLoss=5.263, ClassLoss=11.150 [Epoch 93][Batch 1299], LR: 1.00E-03, Speed: 11.002 samples/sec, ObjLoss=25.916, BoxCenterLoss=14.525, BoxScaleLoss=5.263, ClassLoss=11.149 [Epoch 93][Batch 1399], LR: 1.00E-03, Speed: 11.071 samples/sec, ObjLoss=25.915, BoxCenterLoss=14.525, BoxScaleLoss=5.263, ClassLoss=11.148 [Epoch 93][Batch 1499], LR: 1.00E-03, Speed: 8.923 samples/sec, ObjLoss=25.914, BoxCenterLoss=14.525, BoxScaleLoss=5.263, ClassLoss=11.147 [Epoch 93][Batch 1599], LR: 1.00E-03, Speed: 10.541 samples/sec, ObjLoss=25.912, BoxCenterLoss=14.524, BoxScaleLoss=5.262, ClassLoss=11.146 [Epoch 93][Batch 1699], LR: 1.00E-03, Speed: 9.830 samples/sec, ObjLoss=25.910, BoxCenterLoss=14.524, BoxScaleLoss=5.262, ClassLoss=11.144 [Epoch 93][Batch 1799], LR: 1.00E-03, Speed: 12.692 samples/sec, ObjLoss=25.909, BoxCenterLoss=14.524, BoxScaleLoss=5.261, ClassLoss=11.143 [Epoch 93] Training cost: 2219.248, ObjLoss=25.908, BoxCenterLoss=14.524, BoxScaleLoss=5.261, ClassLoss=11.143 [Epoch 93] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.440 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.205 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436 person=35.4 bicycle=15.8 car=25.0 motorcycle=26.9 airplane=37.9 bus=40.4 train=40.5 truck=21.8 boat=13.2 traffic light=11.6 fire hydrant=41.6 stop sign=40.5 parking meter=29.5 bench=10.8 bird=18.9 cat=38.6 dog=37.1 horse=28.6 sheep=30.5 cow=31.0 elephant=40.8 bear=45.0 zebra=44.3 giraffe=47.5 backpack=6.2 umbrella=23.4 handbag=4.7 tie=16.4 suitcase=14.7 frisbee=37.2 skis=8.6 snowboard=15.3 sports ball=23.1 kite=22.0 baseball bat=10.1 baseball glove=18.2 skateboard=30.2 surfboard=18.0 tennis racket=25.3 bottle=16.5 wine glass=17.9 cup=24.2 fork=12.1 knife=4.9 spoon=2.7 bowl=22.6 banana=11.6 apple=6.3 sandwich=22.4 orange=15.5 broccoli=8.3 carrot=7.5 hot dog=14.7 pizza=33.9 donut=19.4 cake=22.7 chair=13.1 couch=21.3 potted plant=11.7 bed=28.5 dining table=18.5 toilet=36.3 tv=29.5 laptop=31.2 mouse=34.3 remote=10.4 keyboard=29.2 cell phone=16.3 microwave=31.9 oven=17.9 toaster=0.0 sink=19.4 refrigerator=26.4 book=5.1 clock=32.0 vase=19.8 scissors=12.3 teddy bear=26.0 hair drier=0.0 toothbrush=3.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.1 [Epoch 94][Batch 99], LR: 1.00E-03, Speed: 12.732 samples/sec, ObjLoss=25.907, BoxCenterLoss=14.523, BoxScaleLoss=5.261, ClassLoss=11.141 [Epoch 94][Batch 199], LR: 1.00E-03, Speed: 9.408 samples/sec, ObjLoss=25.907, BoxCenterLoss=14.524, BoxScaleLoss=5.261, ClassLoss=11.141 [Epoch 94][Batch 299], LR: 1.00E-03, Speed: 9.626 samples/sec, ObjLoss=25.906, BoxCenterLoss=14.524, BoxScaleLoss=5.261, ClassLoss=11.140 [Epoch 94][Batch 399], LR: 1.00E-03, Speed: 113.130 samples/sec, ObjLoss=25.905, BoxCenterLoss=14.524, BoxScaleLoss=5.261, ClassLoss=11.139 [Epoch 94][Batch 499], LR: 1.00E-03, Speed: 10.252 samples/sec, ObjLoss=25.903, BoxCenterLoss=14.524, BoxScaleLoss=5.261, ClassLoss=11.138 [Epoch 94][Batch 599], LR: 1.00E-03, Speed: 10.570 samples/sec, ObjLoss=25.901, BoxCenterLoss=14.523, BoxScaleLoss=5.260, ClassLoss=11.136 [Epoch 94][Batch 699], LR: 1.00E-03, Speed: 10.710 samples/sec, ObjLoss=25.899, BoxCenterLoss=14.523, BoxScaleLoss=5.260, ClassLoss=11.135 [Epoch 94][Batch 799], LR: 1.00E-03, Speed: 9.912 samples/sec, ObjLoss=25.898, BoxCenterLoss=14.523, BoxScaleLoss=5.260, ClassLoss=11.134 [Epoch 94][Batch 899], LR: 1.00E-03, Speed: 13.100 samples/sec, ObjLoss=25.897, BoxCenterLoss=14.523, BoxScaleLoss=5.260, ClassLoss=11.133 [Epoch 94][Batch 999], LR: 1.00E-03, Speed: 117.549 samples/sec, ObjLoss=25.896, BoxCenterLoss=14.523, BoxScaleLoss=5.259, ClassLoss=11.132 [Epoch 94][Batch 1099], LR: 1.00E-03, Speed: 7.847 samples/sec, ObjLoss=25.894, BoxCenterLoss=14.522, BoxScaleLoss=5.259, ClassLoss=11.131 [Epoch 94][Batch 1199], LR: 1.00E-03, Speed: 7.933 samples/sec, ObjLoss=25.892, BoxCenterLoss=14.523, BoxScaleLoss=5.259, ClassLoss=11.130 [Epoch 94][Batch 1299], LR: 1.00E-03, Speed: 9.026 samples/sec, ObjLoss=25.891, BoxCenterLoss=14.523, BoxScaleLoss=5.259, ClassLoss=11.129 [Epoch 94][Batch 1399], LR: 1.00E-03, Speed: 10.737 samples/sec, ObjLoss=25.890, BoxCenterLoss=14.523, BoxScaleLoss=5.259, ClassLoss=11.128 [Epoch 94][Batch 1499], LR: 1.00E-03, Speed: 86.835 samples/sec, ObjLoss=25.889, BoxCenterLoss=14.523, BoxScaleLoss=5.259, ClassLoss=11.126 [Epoch 94][Batch 1599], LR: 1.00E-03, Speed: 12.265 samples/sec, ObjLoss=25.888, BoxCenterLoss=14.523, BoxScaleLoss=5.258, ClassLoss=11.125 [Epoch 94][Batch 1699], LR: 1.00E-03, Speed: 8.752 samples/sec, ObjLoss=25.887, BoxCenterLoss=14.523, BoxScaleLoss=5.258, ClassLoss=11.124 [Epoch 94][Batch 1799], LR: 1.00E-03, Speed: 13.212 samples/sec, ObjLoss=25.886, BoxCenterLoss=14.523, BoxScaleLoss=5.258, ClassLoss=11.123 [Epoch 94] Training cost: 2199.120, ObjLoss=25.885, BoxCenterLoss=14.523, BoxScaleLoss=5.258, ClassLoss=11.122 [Epoch 94] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.234 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.465 person=36.9 bicycle=17.5 car=24.7 motorcycle=28.2 airplane=42.0 bus=46.1 train=46.1 truck=21.5 boat=13.2 traffic light=14.3 fire hydrant=45.6 stop sign=39.2 parking meter=28.7 bench=13.1 bird=20.4 cat=44.0 dog=35.7 horse=36.6 sheep=30.0 cow=29.8 elephant=43.1 bear=46.1 zebra=47.5 giraffe=47.7 backpack=4.5 umbrella=21.9 handbag=4.7 tie=13.0 suitcase=17.1 frisbee=35.4 skis=10.2 snowboard=11.2 sports ball=26.5 kite=26.5 baseball bat=12.0 baseball glove=16.7 skateboard=27.7 surfboard=19.0 tennis racket=25.8 bottle=16.9 wine glass=19.6 cup=24.6 fork=14.2 knife=3.4 spoon=2.9 bowl=24.1 banana=11.5 apple=5.8 sandwich=20.7 orange=16.2 broccoli=10.5 carrot=8.5 hot dog=15.3 pizza=33.6 donut=30.8 cake=23.2 chair=15.7 couch=25.7 potted plant=11.8 bed=33.9 dining table=19.7 toilet=37.6 tv=34.8 laptop=34.7 mouse=38.8 remote=11.0 keyboard=28.5 cell phone=17.7 microwave=30.0 oven=18.5 toaster=0.0 sink=20.1 refrigerator=26.9 book=5.3 clock=30.9 vase=23.6 scissors=20.8 teddy bear=27.4 hair drier=0.0 toothbrush=4.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.4 [Epoch 95][Batch 99], LR: 1.00E-03, Speed: 9.165 samples/sec, ObjLoss=25.884, BoxCenterLoss=14.523, BoxScaleLoss=5.258, ClassLoss=11.121 [Epoch 95][Batch 199], LR: 1.00E-03, Speed: 91.262 samples/sec, ObjLoss=25.883, BoxCenterLoss=14.523, BoxScaleLoss=5.257, ClassLoss=11.120 [Epoch 95][Batch 299], LR: 1.00E-03, Speed: 8.923 samples/sec, ObjLoss=25.881, BoxCenterLoss=14.522, BoxScaleLoss=5.257, ClassLoss=11.119 [Epoch 95][Batch 399], LR: 1.00E-03, Speed: 7.504 samples/sec, ObjLoss=25.879, BoxCenterLoss=14.522, BoxScaleLoss=5.257, ClassLoss=11.117 [Epoch 95][Batch 499], LR: 1.00E-03, Speed: 9.411 samples/sec, ObjLoss=25.878, BoxCenterLoss=14.522, BoxScaleLoss=5.257, ClassLoss=11.116 [Epoch 95][Batch 599], LR: 1.00E-03, Speed: 12.074 samples/sec, ObjLoss=25.877, BoxCenterLoss=14.522, BoxScaleLoss=5.257, ClassLoss=11.116 [Epoch 95][Batch 699], LR: 1.00E-03, Speed: 9.668 samples/sec, ObjLoss=25.875, BoxCenterLoss=14.522, BoxScaleLoss=5.256, ClassLoss=11.115 [Epoch 95][Batch 799], LR: 1.00E-03, Speed: 10.995 samples/sec, ObjLoss=25.874, BoxCenterLoss=14.522, BoxScaleLoss=5.256, ClassLoss=11.113 [Epoch 95][Batch 899], LR: 1.00E-03, Speed: 133.249 samples/sec, ObjLoss=25.872, BoxCenterLoss=14.521, BoxScaleLoss=5.256, ClassLoss=11.112 [Epoch 95][Batch 999], LR: 1.00E-03, Speed: 10.800 samples/sec, ObjLoss=25.871, BoxCenterLoss=14.522, BoxScaleLoss=5.256, ClassLoss=11.111 [Epoch 95][Batch 1099], LR: 1.00E-03, Speed: 131.464 samples/sec, ObjLoss=25.870, BoxCenterLoss=14.521, BoxScaleLoss=5.256, ClassLoss=11.110 [Epoch 95][Batch 1199], LR: 1.00E-03, Speed: 9.998 samples/sec, ObjLoss=25.868, BoxCenterLoss=14.521, BoxScaleLoss=5.255, ClassLoss=11.109 [Epoch 95][Batch 1299], LR: 1.00E-03, Speed: 9.625 samples/sec, ObjLoss=25.867, BoxCenterLoss=14.521, BoxScaleLoss=5.255, ClassLoss=11.108 [Epoch 95][Batch 1399], LR: 1.00E-03, Speed: 9.742 samples/sec, ObjLoss=25.866, BoxCenterLoss=14.521, BoxScaleLoss=5.255, ClassLoss=11.107 [Epoch 95][Batch 1499], LR: 1.00E-03, Speed: 9.520 samples/sec, ObjLoss=25.864, BoxCenterLoss=14.521, BoxScaleLoss=5.255, ClassLoss=11.106 [Epoch 95][Batch 1599], LR: 1.00E-03, Speed: 9.752 samples/sec, ObjLoss=25.863, BoxCenterLoss=14.521, BoxScaleLoss=5.254, ClassLoss=11.105 [Epoch 95][Batch 1699], LR: 1.00E-03, Speed: 8.789 samples/sec, ObjLoss=25.862, BoxCenterLoss=14.521, BoxScaleLoss=5.254, ClassLoss=11.103 [Epoch 95][Batch 1799], LR: 1.00E-03, Speed: 11.281 samples/sec, ObjLoss=25.862, BoxCenterLoss=14.521, BoxScaleLoss=5.254, ClassLoss=11.103 [Epoch 95] Training cost: 2145.818, ObjLoss=25.862, BoxCenterLoss=14.522, BoxScaleLoss=5.254, ClassLoss=11.103 [Epoch 95] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.441 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.199 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.205 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442 person=35.0 bicycle=15.5 car=24.9 motorcycle=28.1 airplane=38.7 bus=46.4 train=45.5 truck=20.6 boat=14.9 traffic light=12.7 fire hydrant=34.2 stop sign=31.6 parking meter=29.3 bench=13.9 bird=19.0 cat=38.2 dog=34.0 horse=31.0 sheep=26.7 cow=35.3 elephant=42.1 bear=41.6 zebra=41.6 giraffe=40.3 backpack=4.9 umbrella=21.4 handbag=4.6 tie=15.9 suitcase=16.9 frisbee=30.3 skis=10.9 snowboard=7.9 sports ball=8.8 kite=24.8 baseball bat=11.9 baseball glove=18.0 skateboard=28.3 surfboard=17.2 tennis racket=29.9 bottle=19.5 wine glass=19.9 cup=22.0 fork=11.8 knife=3.5 spoon=3.8 bowl=22.6 banana=10.5 apple=7.6 sandwich=18.1 orange=17.0 broccoli=11.0 carrot=9.2 hot dog=21.5 pizza=30.3 donut=27.2 cake=22.7 chair=14.2 couch=25.5 potted plant=12.1 bed=27.9 dining table=17.8 toilet=34.4 tv=34.7 laptop=36.3 mouse=38.4 remote=8.7 keyboard=29.7 cell phone=15.8 microwave=34.9 oven=16.2 toaster=5.9 sink=18.3 refrigerator=26.7 book=3.7 clock=30.0 vase=20.7 scissors=14.8 teddy bear=25.4 hair drier=0.0 toothbrush=7.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.1 [Epoch 96][Batch 99], LR: 1.00E-03, Speed: 6.456 samples/sec, ObjLoss=25.860, BoxCenterLoss=14.521, BoxScaleLoss=5.254, ClassLoss=11.101 [Epoch 96][Batch 199], LR: 1.00E-03, Speed: 8.561 samples/sec, ObjLoss=25.859, BoxCenterLoss=14.521, BoxScaleLoss=5.254, ClassLoss=11.100 [Epoch 96][Batch 299], LR: 1.00E-03, Speed: 10.487 samples/sec, ObjLoss=25.857, BoxCenterLoss=14.521, BoxScaleLoss=5.254, ClassLoss=11.099 [Epoch 96][Batch 399], LR: 1.00E-03, Speed: 9.999 samples/sec, ObjLoss=25.856, BoxCenterLoss=14.521, BoxScaleLoss=5.253, ClassLoss=11.098 [Epoch 96][Batch 499], LR: 1.00E-03, Speed: 8.632 samples/sec, ObjLoss=25.854, BoxCenterLoss=14.521, BoxScaleLoss=5.253, ClassLoss=11.096 [Epoch 96][Batch 599], LR: 1.00E-03, Speed: 10.464 samples/sec, ObjLoss=25.853, BoxCenterLoss=14.520, BoxScaleLoss=5.253, ClassLoss=11.095 [Epoch 96][Batch 699], LR: 1.00E-03, Speed: 9.245 samples/sec, ObjLoss=25.851, BoxCenterLoss=14.520, BoxScaleLoss=5.252, ClassLoss=11.094 [Epoch 96][Batch 799], LR: 1.00E-03, Speed: 106.822 samples/sec, ObjLoss=25.849, BoxCenterLoss=14.520, BoxScaleLoss=5.252, ClassLoss=11.093 [Epoch 96][Batch 899], LR: 1.00E-03, Speed: 10.820 samples/sec, ObjLoss=25.847, BoxCenterLoss=14.519, BoxScaleLoss=5.252, ClassLoss=11.092 [Epoch 96][Batch 999], LR: 1.00E-03, Speed: 9.213 samples/sec, ObjLoss=25.846, BoxCenterLoss=14.519, BoxScaleLoss=5.252, ClassLoss=11.091 [Epoch 96][Batch 1099], LR: 1.00E-03, Speed: 7.622 samples/sec, ObjLoss=25.845, BoxCenterLoss=14.519, BoxScaleLoss=5.252, ClassLoss=11.090 [Epoch 96][Batch 1199], LR: 1.00E-03, Speed: 7.701 samples/sec, ObjLoss=25.843, BoxCenterLoss=14.519, BoxScaleLoss=5.252, ClassLoss=11.089 [Epoch 96][Batch 1299], LR: 1.00E-03, Speed: 10.160 samples/sec, ObjLoss=25.842, BoxCenterLoss=14.519, BoxScaleLoss=5.251, ClassLoss=11.088 [Epoch 96][Batch 1399], LR: 1.00E-03, Speed: 8.465 samples/sec, ObjLoss=25.841, BoxCenterLoss=14.519, BoxScaleLoss=5.251, ClassLoss=11.087 [Epoch 96][Batch 1499], LR: 1.00E-03, Speed: 8.845 samples/sec, ObjLoss=25.840, BoxCenterLoss=14.519, BoxScaleLoss=5.251, ClassLoss=11.086 [Epoch 96][Batch 1599], LR: 1.00E-03, Speed: 10.032 samples/sec, ObjLoss=25.838, BoxCenterLoss=14.518, BoxScaleLoss=5.251, ClassLoss=11.085 [Epoch 96][Batch 1699], LR: 1.00E-03, Speed: 7.635 samples/sec, ObjLoss=25.837, BoxCenterLoss=14.518, BoxScaleLoss=5.251, ClassLoss=11.083 [Epoch 96][Batch 1799], LR: 1.00E-03, Speed: 10.974 samples/sec, ObjLoss=25.835, BoxCenterLoss=14.518, BoxScaleLoss=5.250, ClassLoss=11.082 [Epoch 96] Training cost: 2211.971, ObjLoss=25.835, BoxCenterLoss=14.518, BoxScaleLoss=5.250, ClassLoss=11.082 [Epoch 96] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.199 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.429 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.087 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.188 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.374 person=33.7 bicycle=16.5 car=25.2 motorcycle=27.2 airplane=34.6 bus=39.0 train=34.1 truck=16.6 boat=13.6 traffic light=10.9 fire hydrant=39.9 stop sign=29.1 parking meter=22.8 bench=11.0 bird=15.2 cat=38.7 dog=33.3 horse=28.0 sheep=27.4 cow=28.9 elephant=40.0 bear=35.0 zebra=37.3 giraffe=36.9 backpack=5.6 umbrella=20.0 handbag=4.3 tie=12.3 suitcase=16.1 frisbee=32.3 skis=9.3 snowboard=14.0 sports ball=22.3 kite=20.8 baseball bat=9.8 baseball glove=15.8 skateboard=22.2 surfboard=17.3 tennis racket=24.6 bottle=13.7 wine glass=16.1 cup=17.1 fork=11.6 knife=2.9 spoon=1.9 bowl=20.8 banana=9.9 apple=8.3 sandwich=14.0 orange=13.7 broccoli=9.4 carrot=7.4 hot dog=13.9 pizza=30.6 donut=20.8 cake=20.1 chair=13.5 couch=20.4 potted plant=12.5 bed=30.5 dining table=18.5 toilet=33.4 tv=26.5 laptop=31.4 mouse=28.3 remote=9.5 keyboard=30.4 cell phone=13.6 microwave=28.1 oven=17.7 toaster=0.0 sink=16.8 refrigerator=21.5 book=2.8 clock=26.9 vase=15.4 scissors=15.3 teddy bear=21.4 hair drier=0.0 toothbrush=5.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.9 [Epoch 97][Batch 99], LR: 1.00E-03, Speed: 97.536 samples/sec, ObjLoss=25.833, BoxCenterLoss=14.518, BoxScaleLoss=5.250, ClassLoss=11.081 [Epoch 97][Batch 199], LR: 1.00E-03, Speed: 11.742 samples/sec, ObjLoss=25.833, BoxCenterLoss=14.518, BoxScaleLoss=5.250, ClassLoss=11.080 [Epoch 97][Batch 299], LR: 1.00E-03, Speed: 8.824 samples/sec, ObjLoss=25.831, BoxCenterLoss=14.517, BoxScaleLoss=5.249, ClassLoss=11.079 [Epoch 97][Batch 399], LR: 1.00E-03, Speed: 9.344 samples/sec, ObjLoss=25.830, BoxCenterLoss=14.517, BoxScaleLoss=5.249, ClassLoss=11.078 [Epoch 97][Batch 499], LR: 1.00E-03, Speed: 9.957 samples/sec, ObjLoss=25.828, BoxCenterLoss=14.517, BoxScaleLoss=5.249, ClassLoss=11.076 [Epoch 97][Batch 599], LR: 1.00E-03, Speed: 10.589 samples/sec, ObjLoss=25.827, BoxCenterLoss=14.517, BoxScaleLoss=5.249, ClassLoss=11.076 [Epoch 97][Batch 699], LR: 1.00E-03, Speed: 12.518 samples/sec, ObjLoss=25.826, BoxCenterLoss=14.517, BoxScaleLoss=5.248, ClassLoss=11.074 [Epoch 97][Batch 799], LR: 1.00E-03, Speed: 10.334 samples/sec, ObjLoss=25.825, BoxCenterLoss=14.517, BoxScaleLoss=5.249, ClassLoss=11.074 [Epoch 97][Batch 899], LR: 1.00E-03, Speed: 9.710 samples/sec, ObjLoss=25.823, BoxCenterLoss=14.517, BoxScaleLoss=5.248, ClassLoss=11.073 [Epoch 97][Batch 999], LR: 1.00E-03, Speed: 10.072 samples/sec, ObjLoss=25.822, BoxCenterLoss=14.517, BoxScaleLoss=5.248, ClassLoss=11.072 [Epoch 97][Batch 1099], LR: 1.00E-03, Speed: 9.255 samples/sec, ObjLoss=25.820, BoxCenterLoss=14.517, BoxScaleLoss=5.248, ClassLoss=11.070 [Epoch 97][Batch 1199], LR: 1.00E-03, Speed: 7.402 samples/sec, ObjLoss=25.819, BoxCenterLoss=14.517, BoxScaleLoss=5.248, ClassLoss=11.069 [Epoch 97][Batch 1299], LR: 1.00E-03, Speed: 9.045 samples/sec, ObjLoss=25.818, BoxCenterLoss=14.516, BoxScaleLoss=5.247, ClassLoss=11.068 [Epoch 97][Batch 1399], LR: 1.00E-03, Speed: 9.981 samples/sec, ObjLoss=25.816, BoxCenterLoss=14.516, BoxScaleLoss=5.247, ClassLoss=11.067 [Epoch 97][Batch 1499], LR: 1.00E-03, Speed: 7.982 samples/sec, ObjLoss=25.815, BoxCenterLoss=14.516, BoxScaleLoss=5.247, ClassLoss=11.065 [Epoch 97][Batch 1599], LR: 1.00E-03, Speed: 10.516 samples/sec, ObjLoss=25.814, BoxCenterLoss=14.516, BoxScaleLoss=5.246, ClassLoss=11.064 [Epoch 97][Batch 1699], LR: 1.00E-03, Speed: 11.167 samples/sec, ObjLoss=25.813, BoxCenterLoss=14.517, BoxScaleLoss=5.246, ClassLoss=11.063 [Epoch 97][Batch 1799], LR: 1.00E-03, Speed: 11.404 samples/sec, ObjLoss=25.813, BoxCenterLoss=14.517, BoxScaleLoss=5.246, ClassLoss=11.062 [Epoch 97] Training cost: 2230.770, ObjLoss=25.812, BoxCenterLoss=14.517, BoxScaleLoss=5.246, ClassLoss=11.062 [Epoch 97] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.433 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.234 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.197 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.293 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404 person=31.9 bicycle=15.2 car=26.4 motorcycle=25.2 airplane=36.2 bus=39.9 train=40.2 truck=16.6 boat=12.5 traffic light=12.1 fire hydrant=33.8 stop sign=36.9 parking meter=24.5 bench=12.5 bird=18.2 cat=43.6 dog=34.0 horse=29.4 sheep=29.1 cow=29.0 elephant=31.3 bear=31.0 zebra=44.5 giraffe=36.6 backpack=5.2 umbrella=22.0 handbag=4.8 tie=16.6 suitcase=15.7 frisbee=30.9 skis=8.8 snowboard=11.4 sports ball=24.4 kite=21.8 baseball bat=11.7 baseball glove=17.0 skateboard=27.0 surfboard=16.1 tennis racket=22.7 bottle=18.8 wine glass=17.0 cup=22.8 fork=10.2 knife=3.6 spoon=5.3 bowl=22.8 banana=10.0 apple=6.5 sandwich=16.2 orange=14.8 broccoli=8.8 carrot=7.0 hot dog=17.2 pizza=33.6 donut=24.8 cake=18.2 chair=13.1 couch=23.9 potted plant=10.4 bed=28.1 dining table=16.3 toilet=36.1 tv=29.0 laptop=30.7 mouse=37.5 remote=11.3 keyboard=29.4 cell phone=13.7 microwave=33.7 oven=17.9 toaster=7.1 sink=20.0 refrigerator=26.7 book=3.5 clock=33.3 vase=17.8 scissors=11.0 teddy bear=22.1 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.0 [Epoch 98][Batch 99], LR: 1.00E-03, Speed: 9.186 samples/sec, ObjLoss=25.812, BoxCenterLoss=14.517, BoxScaleLoss=5.246, ClassLoss=11.061 [Epoch 98][Batch 199], LR: 1.00E-03, Speed: 9.813 samples/sec, ObjLoss=25.810, BoxCenterLoss=14.517, BoxScaleLoss=5.246, ClassLoss=11.059 [Epoch 98][Batch 299], LR: 1.00E-03, Speed: 8.516 samples/sec, ObjLoss=25.808, BoxCenterLoss=14.516, BoxScaleLoss=5.245, ClassLoss=11.058 [Epoch 98][Batch 399], LR: 1.00E-03, Speed: 9.225 samples/sec, ObjLoss=25.807, BoxCenterLoss=14.516, BoxScaleLoss=5.245, ClassLoss=11.057 [Epoch 98][Batch 499], LR: 1.00E-03, Speed: 9.638 samples/sec, ObjLoss=25.805, BoxCenterLoss=14.516, BoxScaleLoss=5.245, ClassLoss=11.056 [Epoch 98][Batch 599], LR: 1.00E-03, Speed: 8.969 samples/sec, ObjLoss=25.803, BoxCenterLoss=14.516, BoxScaleLoss=5.245, ClassLoss=11.055 [Epoch 98][Batch 699], LR: 1.00E-03, Speed: 10.036 samples/sec, ObjLoss=25.801, BoxCenterLoss=14.515, BoxScaleLoss=5.244, ClassLoss=11.054 [Epoch 98][Batch 799], LR: 1.00E-03, Speed: 9.884 samples/sec, ObjLoss=25.800, BoxCenterLoss=14.515, BoxScaleLoss=5.244, ClassLoss=11.052 [Epoch 98][Batch 899], LR: 1.00E-03, Speed: 7.648 samples/sec, ObjLoss=25.799, BoxCenterLoss=14.515, BoxScaleLoss=5.244, ClassLoss=11.052 [Epoch 98][Batch 999], LR: 1.00E-03, Speed: 111.038 samples/sec, ObjLoss=25.797, BoxCenterLoss=14.514, BoxScaleLoss=5.244, ClassLoss=11.050 [Epoch 98][Batch 1099], LR: 1.00E-03, Speed: 111.419 samples/sec, ObjLoss=25.796, BoxCenterLoss=14.514, BoxScaleLoss=5.243, ClassLoss=11.049 [Epoch 98][Batch 1199], LR: 1.00E-03, Speed: 9.955 samples/sec, ObjLoss=25.795, BoxCenterLoss=14.514, BoxScaleLoss=5.243, ClassLoss=11.048 [Epoch 98][Batch 1299], LR: 1.00E-03, Speed: 11.424 samples/sec, ObjLoss=25.794, BoxCenterLoss=14.514, BoxScaleLoss=5.243, ClassLoss=11.047 [Epoch 98][Batch 1399], LR: 1.00E-03, Speed: 7.612 samples/sec, ObjLoss=25.792, BoxCenterLoss=14.514, BoxScaleLoss=5.243, ClassLoss=11.046 [Epoch 98][Batch 1499], LR: 1.00E-03, Speed: 9.139 samples/sec, ObjLoss=25.791, BoxCenterLoss=14.514, BoxScaleLoss=5.243, ClassLoss=11.045 [Epoch 98][Batch 1599], LR: 1.00E-03, Speed: 120.201 samples/sec, ObjLoss=25.789, BoxCenterLoss=14.513, BoxScaleLoss=5.243, ClassLoss=11.044 [Epoch 98][Batch 1699], LR: 1.00E-03, Speed: 8.749 samples/sec, ObjLoss=25.788, BoxCenterLoss=14.513, BoxScaleLoss=5.243, ClassLoss=11.043 [Epoch 98][Batch 1799], LR: 1.00E-03, Speed: 9.734 samples/sec, ObjLoss=25.787, BoxCenterLoss=14.513, BoxScaleLoss=5.242, ClassLoss=11.042 [Epoch 98] Training cost: 2192.249, ObjLoss=25.786, BoxCenterLoss=14.513, BoxScaleLoss=5.242, ClassLoss=11.042 [Epoch 98] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.438 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.456 person=35.2 bicycle=17.0 car=23.8 motorcycle=27.1 airplane=41.2 bus=46.3 train=46.6 truck=19.6 boat=11.1 traffic light=13.7 fire hydrant=41.6 stop sign=38.3 parking meter=29.0 bench=11.7 bird=18.8 cat=44.6 dog=36.8 horse=35.1 sheep=28.6 cow=35.6 elephant=43.5 bear=51.1 zebra=43.6 giraffe=45.5 backpack=5.6 umbrella=21.6 handbag=4.4 tie=15.8 suitcase=14.2 frisbee=32.0 skis=11.3 snowboard=12.4 sports ball=24.6 kite=26.0 baseball bat=12.4 baseball glove=20.5 skateboard=32.0 surfboard=17.5 tennis racket=24.6 bottle=16.8 wine glass=17.5 cup=23.8 fork=12.5 knife=4.3 spoon=3.3 bowl=19.9 banana=10.5 apple=7.1 sandwich=20.9 orange=14.8 broccoli=9.7 carrot=7.8 hot dog=15.2 pizza=33.3 donut=21.4 cake=19.0 chair=12.7 couch=27.2 potted plant=10.9 bed=31.9 dining table=18.0 toilet=33.9 tv=32.3 laptop=37.5 mouse=36.5 remote=9.2 keyboard=29.8 cell phone=16.3 microwave=30.6 oven=18.5 toaster=0.0 sink=20.8 refrigerator=24.4 book=4.6 clock=32.0 vase=17.3 scissors=14.9 teddy bear=28.7 hair drier=0.0 toothbrush=7.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.7 [Epoch 99][Batch 99], LR: 1.00E-03, Speed: 8.861 samples/sec, ObjLoss=25.785, BoxCenterLoss=14.513, BoxScaleLoss=5.242, ClassLoss=11.041 [Epoch 99][Batch 199], LR: 1.00E-03, Speed: 9.654 samples/sec, ObjLoss=25.784, BoxCenterLoss=14.513, BoxScaleLoss=5.242, ClassLoss=11.040 [Epoch 99][Batch 299], LR: 1.00E-03, Speed: 9.087 samples/sec, ObjLoss=25.783, BoxCenterLoss=14.513, BoxScaleLoss=5.241, ClassLoss=11.038 [Epoch 99][Batch 399], LR: 1.00E-03, Speed: 11.942 samples/sec, ObjLoss=25.782, BoxCenterLoss=14.513, BoxScaleLoss=5.241, ClassLoss=11.037 [Epoch 99][Batch 499], LR: 1.00E-03, Speed: 8.002 samples/sec, ObjLoss=25.781, BoxCenterLoss=14.513, BoxScaleLoss=5.241, ClassLoss=11.036 [Epoch 99][Batch 599], LR: 1.00E-03, Speed: 112.681 samples/sec, ObjLoss=25.779, BoxCenterLoss=14.512, BoxScaleLoss=5.241, ClassLoss=11.035 [Epoch 99][Batch 699], LR: 1.00E-03, Speed: 11.754 samples/sec, ObjLoss=25.778, BoxCenterLoss=14.513, BoxScaleLoss=5.241, ClassLoss=11.034 [Epoch 99][Batch 799], LR: 1.00E-03, Speed: 7.532 samples/sec, ObjLoss=25.777, BoxCenterLoss=14.513, BoxScaleLoss=5.241, ClassLoss=11.033 [Epoch 99][Batch 899], LR: 1.00E-03, Speed: 13.134 samples/sec, ObjLoss=25.775, BoxCenterLoss=14.512, BoxScaleLoss=5.240, ClassLoss=11.032 [Epoch 99][Batch 999], LR: 1.00E-03, Speed: 8.523 samples/sec, ObjLoss=25.774, BoxCenterLoss=14.513, BoxScaleLoss=5.240, ClassLoss=11.031 [Epoch 99][Batch 1099], LR: 1.00E-03, Speed: 12.838 samples/sec, ObjLoss=25.774, BoxCenterLoss=14.513, BoxScaleLoss=5.240, ClassLoss=11.030 [Epoch 99][Batch 1199], LR: 1.00E-03, Speed: 10.467 samples/sec, ObjLoss=25.773, BoxCenterLoss=14.513, BoxScaleLoss=5.240, ClassLoss=11.029 [Epoch 99][Batch 1299], LR: 1.00E-03, Speed: 10.427 samples/sec, ObjLoss=25.772, BoxCenterLoss=14.513, BoxScaleLoss=5.240, ClassLoss=11.028 [Epoch 99][Batch 1399], LR: 1.00E-03, Speed: 8.661 samples/sec, ObjLoss=25.771, BoxCenterLoss=14.513, BoxScaleLoss=5.240, ClassLoss=11.027 [Epoch 99][Batch 1499], LR: 1.00E-03, Speed: 10.460 samples/sec, ObjLoss=25.769, BoxCenterLoss=14.513, BoxScaleLoss=5.239, ClassLoss=11.026 [Epoch 99][Batch 1599], LR: 1.00E-03, Speed: 10.474 samples/sec, ObjLoss=25.767, BoxCenterLoss=14.512, BoxScaleLoss=5.239, ClassLoss=11.025 [Epoch 99][Batch 1699], LR: 1.00E-03, Speed: 91.737 samples/sec, ObjLoss=25.765, BoxCenterLoss=14.512, BoxScaleLoss=5.239, ClassLoss=11.024 [Epoch 99][Batch 1799], LR: 1.00E-03, Speed: 135.010 samples/sec, ObjLoss=25.764, BoxCenterLoss=14.512, BoxScaleLoss=5.239, ClassLoss=11.023 [Epoch 99] Training cost: 2108.090, ObjLoss=25.764, BoxCenterLoss=14.512, BoxScaleLoss=5.239, ClassLoss=11.022 [Epoch 99] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.226 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.445 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.434 person=36.3 bicycle=16.6 car=24.4 motorcycle=26.7 airplane=34.9 bus=45.0 train=45.4 truck=19.7 boat=12.3 traffic light=12.9 fire hydrant=45.4 stop sign=43.1 parking meter=31.5 bench=10.4 bird=18.4 cat=39.0 dog=35.7 horse=33.0 sheep=28.6 cow=30.5 elephant=41.2 bear=42.2 zebra=47.9 giraffe=44.3 backpack=6.7 umbrella=21.6 handbag=5.7 tie=17.4 suitcase=16.7 frisbee=35.9 skis=9.3 snowboard=13.0 sports ball=23.0 kite=25.3 baseball bat=11.3 baseball glove=19.6 skateboard=28.9 surfboard=19.5 tennis racket=27.8 bottle=19.7 wine glass=17.5 cup=24.0 fork=13.1 knife=2.9 spoon=3.7 bowl=23.0 banana=12.2 apple=7.2 sandwich=17.0 orange=16.9 broccoli=8.6 carrot=7.6 hot dog=18.3 pizza=35.0 donut=23.4 cake=19.4 chair=12.0 couch=22.2 potted plant=14.2 bed=26.0 dining table=13.5 toilet=37.0 tv=34.0 laptop=35.1 mouse=37.2 remote=10.6 keyboard=27.0 cell phone=17.0 microwave=29.0 oven=16.5 toaster=8.3 sink=20.9 refrigerator=30.4 book=4.0 clock=30.2 vase=19.1 scissors=14.4 teddy bear=25.5 hair drier=0.0 toothbrush=7.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.6 [Epoch 100][Batch 99], LR: 1.00E-03, Speed: 7.865 samples/sec, ObjLoss=25.762, BoxCenterLoss=14.511, BoxScaleLoss=5.238, ClassLoss=11.021 [Epoch 100][Batch 199], LR: 1.00E-03, Speed: 10.854 samples/sec, ObjLoss=25.760, BoxCenterLoss=14.511, BoxScaleLoss=5.238, ClassLoss=11.020 [Epoch 100][Batch 299], LR: 1.00E-03, Speed: 9.716 samples/sec, ObjLoss=25.760, BoxCenterLoss=14.511, BoxScaleLoss=5.238, ClassLoss=11.019 [Epoch 100][Batch 399], LR: 1.00E-03, Speed: 10.055 samples/sec, ObjLoss=25.759, BoxCenterLoss=14.511, BoxScaleLoss=5.238, ClassLoss=11.019 [Epoch 100][Batch 499], LR: 1.00E-03, Speed: 112.354 samples/sec, ObjLoss=25.756, BoxCenterLoss=14.510, BoxScaleLoss=5.237, ClassLoss=11.017 [Epoch 100][Batch 599], LR: 1.00E-03, Speed: 8.995 samples/sec, ObjLoss=25.755, BoxCenterLoss=14.510, BoxScaleLoss=5.237, ClassLoss=11.016 [Epoch 100][Batch 699], LR: 1.00E-03, Speed: 10.165 samples/sec, ObjLoss=25.754, BoxCenterLoss=14.510, BoxScaleLoss=5.237, ClassLoss=11.015 [Epoch 100][Batch 799], LR: 1.00E-03, Speed: 122.855 samples/sec, ObjLoss=25.752, BoxCenterLoss=14.509, BoxScaleLoss=5.236, ClassLoss=11.014 [Epoch 100][Batch 899], LR: 1.00E-03, Speed: 121.774 samples/sec, ObjLoss=25.751, BoxCenterLoss=14.509, BoxScaleLoss=5.236, ClassLoss=11.013 [Epoch 100][Batch 999], LR: 1.00E-03, Speed: 8.479 samples/sec, ObjLoss=25.750, BoxCenterLoss=14.509, BoxScaleLoss=5.236, ClassLoss=11.012 [Epoch 100][Batch 1099], LR: 1.00E-03, Speed: 18.664 samples/sec, ObjLoss=25.748, BoxCenterLoss=14.509, BoxScaleLoss=5.236, ClassLoss=11.011 [Epoch 100][Batch 1199], LR: 1.00E-03, Speed: 9.576 samples/sec, ObjLoss=25.748, BoxCenterLoss=14.510, BoxScaleLoss=5.236, ClassLoss=11.010 [Epoch 100][Batch 1299], LR: 1.00E-03, Speed: 8.282 samples/sec, ObjLoss=25.747, BoxCenterLoss=14.510, BoxScaleLoss=5.236, ClassLoss=11.009 [Epoch 100][Batch 1399], LR: 1.00E-03, Speed: 108.375 samples/sec, ObjLoss=25.746, BoxCenterLoss=14.510, BoxScaleLoss=5.235, ClassLoss=11.008 [Epoch 100][Batch 1499], LR: 1.00E-03, Speed: 126.211 samples/sec, ObjLoss=25.745, BoxCenterLoss=14.510, BoxScaleLoss=5.235, ClassLoss=11.007 [Epoch 100][Batch 1599], LR: 1.00E-03, Speed: 89.795 samples/sec, ObjLoss=25.743, BoxCenterLoss=14.510, BoxScaleLoss=5.235, ClassLoss=11.006 [Epoch 100][Batch 1699], LR: 1.00E-03, Speed: 8.936 samples/sec, ObjLoss=25.743, BoxCenterLoss=14.510, BoxScaleLoss=5.235, ClassLoss=11.004 [Epoch 100][Batch 1799], LR: 1.00E-03, Speed: 10.309 samples/sec, ObjLoss=25.742, BoxCenterLoss=14.510, BoxScaleLoss=5.235, ClassLoss=11.003 [Epoch 100] Training cost: 2270.677, ObjLoss=25.742, BoxCenterLoss=14.510, BoxScaleLoss=5.234, ClassLoss=11.003 [Epoch 100] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.446 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.199 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.438 person=34.4 bicycle=16.1 car=24.5 motorcycle=26.2 airplane=37.8 bus=44.7 train=42.2 truck=22.1 boat=12.6 traffic light=13.0 fire hydrant=41.6 stop sign=39.6 parking meter=31.2 bench=12.4 bird=16.9 cat=33.2 dog=29.6 horse=30.7 sheep=26.9 cow=31.4 elephant=40.0 bear=43.7 zebra=45.7 giraffe=42.1 backpack=5.0 umbrella=22.8 handbag=4.7 tie=16.3 suitcase=17.6 frisbee=34.5 skis=10.0 snowboard=12.2 sports ball=24.7 kite=23.1 baseball bat=12.6 baseball glove=19.7 skateboard=28.5 surfboard=18.6 tennis racket=25.1 bottle=16.2 wine glass=17.1 cup=24.1 fork=13.3 knife=1.8 spoon=3.2 bowl=24.0 banana=12.2 apple=8.6 sandwich=18.9 orange=16.7 broccoli=10.0 carrot=8.5 hot dog=17.8 pizza=34.1 donut=26.1 cake=19.7 chair=14.4 couch=26.9 potted plant=12.8 bed=24.9 dining table=13.9 toilet=34.3 tv=34.0 laptop=37.4 mouse=34.2 remote=10.0 keyboard=30.4 cell phone=17.8 microwave=34.2 oven=17.2 toaster=7.1 sink=22.2 refrigerator=30.4 book=4.6 clock=31.1 vase=18.8 scissors=15.2 teddy bear=25.7 hair drier=0.0 toothbrush=4.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.4 [Epoch 101][Batch 99], LR: 1.00E-03, Speed: 9.838 samples/sec, ObjLoss=25.740, BoxCenterLoss=14.510, BoxScaleLoss=5.234, ClassLoss=11.002 [Epoch 101][Batch 199], LR: 1.00E-03, Speed: 8.857 samples/sec, ObjLoss=25.738, BoxCenterLoss=14.509, BoxScaleLoss=5.234, ClassLoss=11.001 [Epoch 101][Batch 299], LR: 1.00E-03, Speed: 9.992 samples/sec, ObjLoss=25.737, BoxCenterLoss=14.509, BoxScaleLoss=5.234, ClassLoss=11.000 [Epoch 101][Batch 399], LR: 1.00E-03, Speed: 8.329 samples/sec, ObjLoss=25.735, BoxCenterLoss=14.509, BoxScaleLoss=5.234, ClassLoss=10.999 [Epoch 101][Batch 499], LR: 1.00E-03, Speed: 10.401 samples/sec, ObjLoss=25.734, BoxCenterLoss=14.509, BoxScaleLoss=5.234, ClassLoss=10.997 [Epoch 101][Batch 599], LR: 1.00E-03, Speed: 8.588 samples/sec, ObjLoss=25.732, BoxCenterLoss=14.509, BoxScaleLoss=5.233, ClassLoss=10.997 [Epoch 101][Batch 699], LR: 1.00E-03, Speed: 9.905 samples/sec, ObjLoss=25.731, BoxCenterLoss=14.509, BoxScaleLoss=5.233, ClassLoss=10.996 [Epoch 101][Batch 799], LR: 1.00E-03, Speed: 107.482 samples/sec, ObjLoss=25.730, BoxCenterLoss=14.509, BoxScaleLoss=5.233, ClassLoss=10.995 [Epoch 101][Batch 899], LR: 1.00E-03, Speed: 9.563 samples/sec, ObjLoss=25.729, BoxCenterLoss=14.509, BoxScaleLoss=5.233, ClassLoss=10.994 [Epoch 101][Batch 999], LR: 1.00E-03, Speed: 12.188 samples/sec, ObjLoss=25.728, BoxCenterLoss=14.509, BoxScaleLoss=5.233, ClassLoss=10.993 [Epoch 101][Batch 1099], LR: 1.00E-03, Speed: 12.202 samples/sec, ObjLoss=25.727, BoxCenterLoss=14.509, BoxScaleLoss=5.233, ClassLoss=10.992 [Epoch 101][Batch 1199], LR: 1.00E-03, Speed: 9.309 samples/sec, ObjLoss=25.725, BoxCenterLoss=14.509, BoxScaleLoss=5.232, ClassLoss=10.991 [Epoch 101][Batch 1299], LR: 1.00E-03, Speed: 11.211 samples/sec, ObjLoss=25.724, BoxCenterLoss=14.508, BoxScaleLoss=5.232, ClassLoss=10.990 [Epoch 101][Batch 1399], LR: 1.00E-03, Speed: 9.338 samples/sec, ObjLoss=25.722, BoxCenterLoss=14.508, BoxScaleLoss=5.232, ClassLoss=10.989 [Epoch 101][Batch 1499], LR: 1.00E-03, Speed: 9.659 samples/sec, ObjLoss=25.721, BoxCenterLoss=14.508, BoxScaleLoss=5.232, ClassLoss=10.988 [Epoch 101][Batch 1599], LR: 1.00E-03, Speed: 10.297 samples/sec, ObjLoss=25.720, BoxCenterLoss=14.508, BoxScaleLoss=5.231, ClassLoss=10.987 [Epoch 101][Batch 1699], LR: 1.00E-03, Speed: 10.440 samples/sec, ObjLoss=25.719, BoxCenterLoss=14.508, BoxScaleLoss=5.231, ClassLoss=10.986 [Epoch 101][Batch 1799], LR: 1.00E-03, Speed: 13.253 samples/sec, ObjLoss=25.717, BoxCenterLoss=14.508, BoxScaleLoss=5.231, ClassLoss=10.985 [Epoch 101] Training cost: 2117.714, ObjLoss=25.717, BoxCenterLoss=14.507, BoxScaleLoss=5.231, ClassLoss=10.984 [Epoch 101] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.230 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.445 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.447 person=36.0 bicycle=15.9 car=23.7 motorcycle=28.0 airplane=39.8 bus=43.3 train=47.0 truck=19.9 boat=12.5 traffic light=12.3 fire hydrant=43.5 stop sign=40.7 parking meter=33.8 bench=12.6 bird=16.7 cat=41.1 dog=39.9 horse=37.7 sheep=30.1 cow=32.9 elephant=41.5 bear=46.3 zebra=44.7 giraffe=48.4 backpack=5.4 umbrella=20.4 handbag=4.6 tie=14.1 suitcase=16.4 frisbee=35.0 skis=7.0 snowboard=14.2 sports ball=26.4 kite=22.4 baseball bat=12.2 baseball glove=18.4 skateboard=26.1 surfboard=19.7 tennis racket=24.6 bottle=19.5 wine glass=21.8 cup=25.0 fork=10.7 knife=3.5 spoon=3.7 bowl=23.5 banana=12.5 apple=8.4 sandwich=21.2 orange=15.1 broccoli=10.3 carrot=8.7 hot dog=20.3 pizza=29.8 donut=25.7 cake=19.9 chair=13.1 couch=27.0 potted plant=12.7 bed=28.2 dining table=17.1 toilet=37.5 tv=36.3 laptop=35.9 mouse=37.5 remote=10.4 keyboard=24.5 cell phone=19.1 microwave=28.3 oven=18.8 toaster=8.3 sink=21.5 refrigerator=31.3 book=3.8 clock=30.8 vase=20.6 scissors=10.4 teddy bear=27.5 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.0 [Epoch 102][Batch 99], LR: 1.00E-03, Speed: 9.566 samples/sec, ObjLoss=25.715, BoxCenterLoss=14.507, BoxScaleLoss=5.231, ClassLoss=10.983 [Epoch 102][Batch 199], LR: 1.00E-03, Speed: 8.003 samples/sec, ObjLoss=25.713, BoxCenterLoss=14.506, BoxScaleLoss=5.230, ClassLoss=10.982 [Epoch 102][Batch 299], LR: 1.00E-03, Speed: 8.123 samples/sec, ObjLoss=25.711, BoxCenterLoss=14.506, BoxScaleLoss=5.230, ClassLoss=10.981 [Epoch 102][Batch 399], LR: 1.00E-03, Speed: 10.069 samples/sec, ObjLoss=25.711, BoxCenterLoss=14.506, BoxScaleLoss=5.230, ClassLoss=10.980 [Epoch 102][Batch 499], LR: 1.00E-03, Speed: 110.184 samples/sec, ObjLoss=25.709, BoxCenterLoss=14.506, BoxScaleLoss=5.230, ClassLoss=10.979 [Epoch 102][Batch 599], LR: 1.00E-03, Speed: 7.541 samples/sec, ObjLoss=25.708, BoxCenterLoss=14.506, BoxScaleLoss=5.230, ClassLoss=10.978 [Epoch 102][Batch 699], LR: 1.00E-03, Speed: 8.150 samples/sec, ObjLoss=25.707, BoxCenterLoss=14.506, BoxScaleLoss=5.229, ClassLoss=10.977 [Epoch 102][Batch 799], LR: 1.00E-03, Speed: 8.241 samples/sec, ObjLoss=25.706, BoxCenterLoss=14.506, BoxScaleLoss=5.229, ClassLoss=10.975 [Epoch 102][Batch 899], LR: 1.00E-03, Speed: 110.330 samples/sec, ObjLoss=25.704, BoxCenterLoss=14.506, BoxScaleLoss=5.229, ClassLoss=10.974 [Epoch 102][Batch 999], LR: 1.00E-03, Speed: 9.759 samples/sec, ObjLoss=25.704, BoxCenterLoss=14.506, BoxScaleLoss=5.229, ClassLoss=10.973 [Epoch 102][Batch 1099], LR: 1.00E-03, Speed: 11.114 samples/sec, ObjLoss=25.703, BoxCenterLoss=14.506, BoxScaleLoss=5.229, ClassLoss=10.972 [Epoch 102][Batch 1199], LR: 1.00E-03, Speed: 10.688 samples/sec, ObjLoss=25.702, BoxCenterLoss=14.506, BoxScaleLoss=5.228, ClassLoss=10.971 [Epoch 102][Batch 1299], LR: 1.00E-03, Speed: 8.860 samples/sec, ObjLoss=25.701, BoxCenterLoss=14.507, BoxScaleLoss=5.228, ClassLoss=10.971 [Epoch 102][Batch 1399], LR: 1.00E-03, Speed: 92.128 samples/sec, ObjLoss=25.700, BoxCenterLoss=14.506, BoxScaleLoss=5.228, ClassLoss=10.970 [Epoch 102][Batch 1499], LR: 1.00E-03, Speed: 11.136 samples/sec, ObjLoss=25.698, BoxCenterLoss=14.506, BoxScaleLoss=5.228, ClassLoss=10.969 [Epoch 102][Batch 1599], LR: 1.00E-03, Speed: 9.910 samples/sec, ObjLoss=25.696, BoxCenterLoss=14.505, BoxScaleLoss=5.228, ClassLoss=10.968 [Epoch 102][Batch 1699], LR: 1.00E-03, Speed: 9.069 samples/sec, ObjLoss=25.695, BoxCenterLoss=14.505, BoxScaleLoss=5.227, ClassLoss=10.966 [Epoch 102][Batch 1799], LR: 1.00E-03, Speed: 8.853 samples/sec, ObjLoss=25.693, BoxCenterLoss=14.504, BoxScaleLoss=5.227, ClassLoss=10.965 [Epoch 102] Training cost: 2144.791, ObjLoss=25.692, BoxCenterLoss=14.504, BoxScaleLoss=5.227, ClassLoss=10.965 [Epoch 102] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.447 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.463 person=36.4 bicycle=16.5 car=23.2 motorcycle=27.6 airplane=41.7 bus=46.7 train=45.1 truck=17.8 boat=13.6 traffic light=15.4 fire hydrant=46.0 stop sign=39.6 parking meter=27.2 bench=12.6 bird=20.0 cat=44.4 dog=39.6 horse=34.5 sheep=33.0 cow=34.3 elephant=43.6 bear=41.6 zebra=46.4 giraffe=50.0 backpack=5.1 umbrella=20.1 handbag=4.7 tie=14.3 suitcase=19.6 frisbee=30.7 skis=10.4 snowboard=13.0 sports ball=25.5 kite=23.1 baseball bat=12.6 baseball glove=17.8 skateboard=25.4 surfboard=17.6 tennis racket=26.6 bottle=19.2 wine glass=19.9 cup=23.3 fork=13.2 knife=4.0 spoon=4.1 bowl=21.8 banana=10.7 apple=9.5 sandwich=16.6 orange=18.4 broccoli=11.5 carrot=8.8 hot dog=17.2 pizza=31.5 donut=23.7 cake=21.7 chair=14.9 couch=27.5 potted plant=11.9 bed=33.1 dining table=21.0 toilet=38.1 tv=32.8 laptop=37.8 mouse=33.5 remote=11.5 keyboard=33.1 cell phone=18.1 microwave=25.4 oven=19.4 toaster=0.0 sink=20.6 refrigerator=28.4 book=4.7 clock=27.9 vase=21.3 scissors=14.5 teddy bear=30.3 hair drier=0.0 toothbrush=6.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.1 [Epoch 103][Batch 99], LR: 1.00E-03, Speed: 8.807 samples/sec, ObjLoss=25.691, BoxCenterLoss=14.504, BoxScaleLoss=5.227, ClassLoss=10.964 [Epoch 103][Batch 199], LR: 1.00E-03, Speed: 7.805 samples/sec, ObjLoss=25.690, BoxCenterLoss=14.504, BoxScaleLoss=5.227, ClassLoss=10.963 [Epoch 103][Batch 299], LR: 1.00E-03, Speed: 7.672 samples/sec, ObjLoss=25.688, BoxCenterLoss=14.503, BoxScaleLoss=5.226, ClassLoss=10.962 [Epoch 103][Batch 399], LR: 1.00E-03, Speed: 92.841 samples/sec, ObjLoss=25.686, BoxCenterLoss=14.503, BoxScaleLoss=5.226, ClassLoss=10.960 [Epoch 103][Batch 499], LR: 1.00E-03, Speed: 11.299 samples/sec, ObjLoss=25.685, BoxCenterLoss=14.502, BoxScaleLoss=5.226, ClassLoss=10.959 [Epoch 103][Batch 599], LR: 1.00E-03, Speed: 9.176 samples/sec, ObjLoss=25.683, BoxCenterLoss=14.502, BoxScaleLoss=5.225, ClassLoss=10.958 [Epoch 103][Batch 699], LR: 1.00E-03, Speed: 11.349 samples/sec, ObjLoss=25.682, BoxCenterLoss=14.502, BoxScaleLoss=5.225, ClassLoss=10.957 [Epoch 103][Batch 799], LR: 1.00E-03, Speed: 8.888 samples/sec, ObjLoss=25.680, BoxCenterLoss=14.502, BoxScaleLoss=5.225, ClassLoss=10.956 [Epoch 103][Batch 899], LR: 1.00E-03, Speed: 9.620 samples/sec, ObjLoss=25.679, BoxCenterLoss=14.502, BoxScaleLoss=5.225, ClassLoss=10.955 [Epoch 103][Batch 999], LR: 1.00E-03, Speed: 11.772 samples/sec, ObjLoss=25.678, BoxCenterLoss=14.502, BoxScaleLoss=5.224, ClassLoss=10.954 [Epoch 103][Batch 1099], LR: 1.00E-03, Speed: 9.351 samples/sec, ObjLoss=25.677, BoxCenterLoss=14.502, BoxScaleLoss=5.224, ClassLoss=10.953 [Epoch 103][Batch 1199], LR: 1.00E-03, Speed: 11.026 samples/sec, ObjLoss=25.675, BoxCenterLoss=14.501, BoxScaleLoss=5.224, ClassLoss=10.952 [Epoch 103][Batch 1299], LR: 1.00E-03, Speed: 10.804 samples/sec, ObjLoss=25.675, BoxCenterLoss=14.502, BoxScaleLoss=5.224, ClassLoss=10.951 [Epoch 103][Batch 1399], LR: 1.00E-03, Speed: 9.244 samples/sec, ObjLoss=25.674, BoxCenterLoss=14.502, BoxScaleLoss=5.224, ClassLoss=10.950 [Epoch 103][Batch 1499], LR: 1.00E-03, Speed: 11.382 samples/sec, ObjLoss=25.673, BoxCenterLoss=14.502, BoxScaleLoss=5.224, ClassLoss=10.949 [Epoch 103][Batch 1599], LR: 1.00E-03, Speed: 11.197 samples/sec, ObjLoss=25.671, BoxCenterLoss=14.501, BoxScaleLoss=5.223, ClassLoss=10.948 [Epoch 103][Batch 1699], LR: 1.00E-03, Speed: 127.931 samples/sec, ObjLoss=25.670, BoxCenterLoss=14.501, BoxScaleLoss=5.223, ClassLoss=10.947 [Epoch 103][Batch 1799], LR: 1.00E-03, Speed: 134.693 samples/sec, ObjLoss=25.668, BoxCenterLoss=14.501, BoxScaleLoss=5.223, ClassLoss=10.946 [Epoch 103] Training cost: 2140.139, ObjLoss=25.668, BoxCenterLoss=14.501, BoxScaleLoss=5.223, ClassLoss=10.946 [Epoch 103] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.226 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.445 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.206 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.451 person=37.7 bicycle=17.8 car=24.8 motorcycle=27.7 airplane=42.4 bus=41.3 train=42.4 truck=21.5 boat=12.6 traffic light=12.4 fire hydrant=48.4 stop sign=40.2 parking meter=30.2 bench=14.0 bird=19.8 cat=43.1 dog=35.3 horse=33.0 sheep=32.9 cow=28.7 elephant=41.8 bear=32.6 zebra=46.3 giraffe=50.3 backpack=7.5 umbrella=19.2 handbag=5.2 tie=14.2 suitcase=17.1 frisbee=29.7 skis=9.2 snowboard=13.6 sports ball=23.6 kite=23.5 baseball bat=15.6 baseball glove=19.2 skateboard=29.0 surfboard=20.2 tennis racket=27.1 bottle=18.1 wine glass=20.8 cup=23.1 fork=13.8 knife=3.9 spoon=3.3 bowl=19.0 banana=11.5 apple=6.2 sandwich=15.8 orange=14.3 broccoli=7.7 carrot=7.4 hot dog=16.4 pizza=31.5 donut=21.6 cake=18.8 chair=15.0 couch=26.8 potted plant=12.2 bed=22.7 dining table=16.6 toilet=41.4 tv=36.5 laptop=33.7 mouse=35.8 remote=10.2 keyboard=27.2 cell phone=17.7 microwave=27.5 oven=20.2 toaster=0.0 sink=22.6 refrigerator=29.7 book=4.7 clock=28.5 vase=23.3 scissors=21.2 teddy bear=26.9 hair drier=0.0 toothbrush=5.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.6 [Epoch 104][Batch 99], LR: 1.00E-03, Speed: 8.677 samples/sec, ObjLoss=25.667, BoxCenterLoss=14.501, BoxScaleLoss=5.223, ClassLoss=10.945 [Epoch 104][Batch 199], LR: 1.00E-03, Speed: 7.034 samples/sec, ObjLoss=25.666, BoxCenterLoss=14.501, BoxScaleLoss=5.222, ClassLoss=10.944 [Epoch 104][Batch 299], LR: 1.00E-03, Speed: 8.077 samples/sec, ObjLoss=25.665, BoxCenterLoss=14.501, BoxScaleLoss=5.222, ClassLoss=10.943 [Epoch 104][Batch 399], LR: 1.00E-03, Speed: 9.629 samples/sec, ObjLoss=25.663, BoxCenterLoss=14.500, BoxScaleLoss=5.222, ClassLoss=10.942 [Epoch 104][Batch 499], LR: 1.00E-03, Speed: 10.407 samples/sec, ObjLoss=25.662, BoxCenterLoss=14.500, BoxScaleLoss=5.222, ClassLoss=10.941 [Epoch 104][Batch 599], LR: 1.00E-03, Speed: 105.090 samples/sec, ObjLoss=25.660, BoxCenterLoss=14.500, BoxScaleLoss=5.222, ClassLoss=10.940 [Epoch 104][Batch 699], LR: 1.00E-03, Speed: 9.796 samples/sec, ObjLoss=25.658, BoxCenterLoss=14.500, BoxScaleLoss=5.221, ClassLoss=10.939 [Epoch 104][Batch 799], LR: 1.00E-03, Speed: 10.866 samples/sec, ObjLoss=25.656, BoxCenterLoss=14.499, BoxScaleLoss=5.221, ClassLoss=10.938 [Epoch 104][Batch 899], LR: 1.00E-03, Speed: 11.465 samples/sec, ObjLoss=25.655, BoxCenterLoss=14.499, BoxScaleLoss=5.221, ClassLoss=10.936 [Epoch 104][Batch 999], LR: 1.00E-03, Speed: 10.317 samples/sec, ObjLoss=25.654, BoxCenterLoss=14.499, BoxScaleLoss=5.221, ClassLoss=10.936 [Epoch 104][Batch 1099], LR: 1.00E-03, Speed: 11.414 samples/sec, ObjLoss=25.653, BoxCenterLoss=14.499, BoxScaleLoss=5.221, ClassLoss=10.935 [Epoch 104][Batch 1199], LR: 1.00E-03, Speed: 119.383 samples/sec, ObjLoss=25.651, BoxCenterLoss=14.499, BoxScaleLoss=5.220, ClassLoss=10.934 [Epoch 104][Batch 1299], LR: 1.00E-03, Speed: 10.076 samples/sec, ObjLoss=25.650, BoxCenterLoss=14.499, BoxScaleLoss=5.220, ClassLoss=10.933 [Epoch 104][Batch 1399], LR: 1.00E-03, Speed: 8.466 samples/sec, ObjLoss=25.649, BoxCenterLoss=14.499, BoxScaleLoss=5.220, ClassLoss=10.932 [Epoch 104][Batch 1499], LR: 1.00E-03, Speed: 10.367 samples/sec, ObjLoss=25.648, BoxCenterLoss=14.499, BoxScaleLoss=5.220, ClassLoss=10.931 [Epoch 104][Batch 1599], LR: 1.00E-03, Speed: 8.210 samples/sec, ObjLoss=25.647, BoxCenterLoss=14.499, BoxScaleLoss=5.220, ClassLoss=10.930 [Epoch 104][Batch 1699], LR: 1.00E-03, Speed: 8.829 samples/sec, ObjLoss=25.645, BoxCenterLoss=14.499, BoxScaleLoss=5.219, ClassLoss=10.929 [Epoch 104][Batch 1799], LR: 1.00E-03, Speed: 10.631 samples/sec, ObjLoss=25.644, BoxCenterLoss=14.498, BoxScaleLoss=5.219, ClassLoss=10.928 [Epoch 104] Training cost: 2154.315, ObjLoss=25.643, BoxCenterLoss=14.498, BoxScaleLoss=5.219, ClassLoss=10.928 [Epoch 104] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.220 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.246 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.136 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.450 person=36.5 bicycle=15.9 car=26.3 motorcycle=26.1 airplane=44.5 bus=47.9 train=48.8 truck=22.3 boat=14.0 traffic light=12.5 fire hydrant=44.4 stop sign=37.1 parking meter=26.1 bench=13.5 bird=17.4 cat=41.4 dog=37.9 horse=32.1 sheep=30.2 cow=38.1 elephant=41.9 bear=34.7 zebra=49.0 giraffe=45.7 backpack=6.2 umbrella=20.6 handbag=5.1 tie=18.1 suitcase=17.5 frisbee=30.3 skis=9.8 snowboard=10.4 sports ball=28.3 kite=25.1 baseball bat=13.3 baseball glove=19.1 skateboard=27.9 surfboard=20.4 tennis racket=24.9 bottle=17.5 wine glass=20.4 cup=22.9 fork=13.8 knife=4.1 spoon=4.8 bowl=26.9 banana=12.1 apple=6.9 sandwich=17.7 orange=15.4 broccoli=9.4 carrot=7.6 hot dog=15.5 pizza=33.4 donut=19.9 cake=23.1 chair=15.0 couch=28.4 potted plant=13.3 bed=34.4 dining table=18.4 toilet=37.9 tv=37.4 laptop=35.7 mouse=35.6 remote=9.6 keyboard=28.5 cell phone=16.3 microwave=38.0 oven=21.2 toaster=4.8 sink=20.7 refrigerator=30.3 book=4.4 clock=30.5 vase=22.5 scissors=14.9 teddy bear=23.2 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.2 [Epoch 105][Batch 99], LR: 1.00E-03, Speed: 31.441 samples/sec, ObjLoss=25.642, BoxCenterLoss=14.498, BoxScaleLoss=5.219, ClassLoss=10.927 [Epoch 105][Batch 199], LR: 1.00E-03, Speed: 8.074 samples/sec, ObjLoss=25.641, BoxCenterLoss=14.498, BoxScaleLoss=5.219, ClassLoss=10.926 [Epoch 105][Batch 299], LR: 1.00E-03, Speed: 8.466 samples/sec, ObjLoss=25.640, BoxCenterLoss=14.498, BoxScaleLoss=5.218, ClassLoss=10.925 [Epoch 105][Batch 399], LR: 1.00E-03, Speed: 9.664 samples/sec, ObjLoss=25.639, BoxCenterLoss=14.498, BoxScaleLoss=5.218, ClassLoss=10.924 [Epoch 105][Batch 499], LR: 1.00E-03, Speed: 7.761 samples/sec, ObjLoss=25.638, BoxCenterLoss=14.498, BoxScaleLoss=5.218, ClassLoss=10.924 [Epoch 105][Batch 599], LR: 1.00E-03, Speed: 9.923 samples/sec, ObjLoss=25.637, BoxCenterLoss=14.498, BoxScaleLoss=5.218, ClassLoss=10.923 [Epoch 105][Batch 699], LR: 1.00E-03, Speed: 10.098 samples/sec, ObjLoss=25.635, BoxCenterLoss=14.498, BoxScaleLoss=5.218, ClassLoss=10.922 [Epoch 105][Batch 799], LR: 1.00E-03, Speed: 9.400 samples/sec, ObjLoss=25.633, BoxCenterLoss=14.497, BoxScaleLoss=5.218, ClassLoss=10.920 [Epoch 105][Batch 899], LR: 1.00E-03, Speed: 6.197 samples/sec, ObjLoss=25.632, BoxCenterLoss=14.497, BoxScaleLoss=5.217, ClassLoss=10.919 [Epoch 105][Batch 999], LR: 1.00E-03, Speed: 11.866 samples/sec, ObjLoss=25.630, BoxCenterLoss=14.496, BoxScaleLoss=5.217, ClassLoss=10.918 [Epoch 105][Batch 1099], LR: 1.00E-03, Speed: 11.707 samples/sec, ObjLoss=25.629, BoxCenterLoss=14.496, BoxScaleLoss=5.217, ClassLoss=10.917 [Epoch 105][Batch 1199], LR: 1.00E-03, Speed: 10.424 samples/sec, ObjLoss=25.628, BoxCenterLoss=14.496, BoxScaleLoss=5.216, ClassLoss=10.916 [Epoch 105][Batch 1299], LR: 1.00E-03, Speed: 10.500 samples/sec, ObjLoss=25.626, BoxCenterLoss=14.495, BoxScaleLoss=5.216, ClassLoss=10.914 [Epoch 105][Batch 1399], LR: 1.00E-03, Speed: 11.152 samples/sec, ObjLoss=25.625, BoxCenterLoss=14.495, BoxScaleLoss=5.216, ClassLoss=10.913 [Epoch 105][Batch 1499], LR: 1.00E-03, Speed: 13.392 samples/sec, ObjLoss=25.623, BoxCenterLoss=14.494, BoxScaleLoss=5.215, ClassLoss=10.912 [Epoch 105][Batch 1599], LR: 1.00E-03, Speed: 9.639 samples/sec, ObjLoss=25.621, BoxCenterLoss=14.494, BoxScaleLoss=5.215, ClassLoss=10.911 [Epoch 105][Batch 1699], LR: 1.00E-03, Speed: 9.277 samples/sec, ObjLoss=25.620, BoxCenterLoss=14.494, BoxScaleLoss=5.215, ClassLoss=10.910 [Epoch 105][Batch 1799], LR: 1.00E-03, Speed: 9.073 samples/sec, ObjLoss=25.619, BoxCenterLoss=14.494, BoxScaleLoss=5.215, ClassLoss=10.909 [Epoch 105] Training cost: 2165.562, ObjLoss=25.618, BoxCenterLoss=14.494, BoxScaleLoss=5.215, ClassLoss=10.908 [Epoch 105] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.234 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467 person=38.0 bicycle=18.4 car=26.0 motorcycle=28.1 airplane=41.8 bus=50.5 train=49.2 truck=22.0 boat=13.6 traffic light=13.3 fire hydrant=36.3 stop sign=42.6 parking meter=31.8 bench=12.5 bird=21.8 cat=44.0 dog=37.1 horse=40.8 sheep=33.6 cow=37.0 elephant=44.4 bear=47.3 zebra=46.3 giraffe=49.0 backpack=5.7 umbrella=23.2 handbag=5.9 tie=17.2 suitcase=17.7 frisbee=33.1 skis=10.8 snowboard=12.4 sports ball=21.2 kite=24.2 baseball bat=12.2 baseball glove=19.4 skateboard=30.7 surfboard=18.1 tennis racket=24.3 bottle=17.4 wine glass=19.0 cup=23.5 fork=13.8 knife=3.4 spoon=4.6 bowl=20.0 banana=12.1 apple=5.2 sandwich=18.7 orange=15.3 broccoli=8.2 carrot=5.7 hot dog=18.5 pizza=31.7 donut=26.2 cake=20.9 chair=14.3 couch=29.1 potted plant=11.0 bed=31.2 dining table=18.5 toilet=39.1 tv=36.2 laptop=33.7 mouse=30.8 remote=10.7 keyboard=33.0 cell phone=19.1 microwave=33.1 oven=19.7 toaster=0.0 sink=23.0 refrigerator=26.6 book=5.1 clock=31.4 vase=20.1 scissors=10.3 teddy bear=25.0 hair drier=0.0 toothbrush=6.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.4 [Epoch 106][Batch 99], LR: 1.00E-03, Speed: 7.146 samples/sec, ObjLoss=25.617, BoxCenterLoss=14.494, BoxScaleLoss=5.214, ClassLoss=10.907 [Epoch 106][Batch 199], LR: 1.00E-03, Speed: 8.750 samples/sec, ObjLoss=25.617, BoxCenterLoss=14.494, BoxScaleLoss=5.214, ClassLoss=10.906 [Epoch 106][Batch 299], LR: 1.00E-03, Speed: 8.775 samples/sec, ObjLoss=25.615, BoxCenterLoss=14.494, BoxScaleLoss=5.214, ClassLoss=10.905 [Epoch 106][Batch 399], LR: 1.00E-03, Speed: 10.483 samples/sec, ObjLoss=25.615, BoxCenterLoss=14.495, BoxScaleLoss=5.214, ClassLoss=10.905 [Epoch 106][Batch 499], LR: 1.00E-03, Speed: 9.760 samples/sec, ObjLoss=25.614, BoxCenterLoss=14.495, BoxScaleLoss=5.214, ClassLoss=10.904 [Epoch 106][Batch 599], LR: 1.00E-03, Speed: 9.352 samples/sec, ObjLoss=25.614, BoxCenterLoss=14.495, BoxScaleLoss=5.214, ClassLoss=10.903 [Epoch 106][Batch 699], LR: 1.00E-03, Speed: 122.333 samples/sec, ObjLoss=25.612, BoxCenterLoss=14.495, BoxScaleLoss=5.214, ClassLoss=10.902 [Epoch 106][Batch 799], LR: 1.00E-03, Speed: 8.490 samples/sec, ObjLoss=25.611, BoxCenterLoss=14.494, BoxScaleLoss=5.214, ClassLoss=10.901 [Epoch 106][Batch 899], LR: 1.00E-03, Speed: 10.681 samples/sec, ObjLoss=25.609, BoxCenterLoss=14.494, BoxScaleLoss=5.214, ClassLoss=10.900 [Epoch 106][Batch 999], LR: 1.00E-03, Speed: 8.439 samples/sec, ObjLoss=25.608, BoxCenterLoss=14.494, BoxScaleLoss=5.213, ClassLoss=10.899 [Epoch 106][Batch 1099], LR: 1.00E-03, Speed: 11.067 samples/sec, ObjLoss=25.606, BoxCenterLoss=14.494, BoxScaleLoss=5.213, ClassLoss=10.898 [Epoch 106][Batch 1199], LR: 1.00E-03, Speed: 10.524 samples/sec, ObjLoss=25.604, BoxCenterLoss=14.493, BoxScaleLoss=5.213, ClassLoss=10.897 [Epoch 106][Batch 1299], LR: 1.00E-03, Speed: 9.084 samples/sec, ObjLoss=25.603, BoxCenterLoss=14.493, BoxScaleLoss=5.213, ClassLoss=10.896 [Epoch 106][Batch 1399], LR: 1.00E-03, Speed: 9.334 samples/sec, ObjLoss=25.602, BoxCenterLoss=14.493, BoxScaleLoss=5.213, ClassLoss=10.895 [Epoch 106][Batch 1499], LR: 1.00E-03, Speed: 10.185 samples/sec, ObjLoss=25.600, BoxCenterLoss=14.493, BoxScaleLoss=5.212, ClassLoss=10.893 [Epoch 106][Batch 1599], LR: 1.00E-03, Speed: 8.969 samples/sec, ObjLoss=25.598, BoxCenterLoss=14.493, BoxScaleLoss=5.212, ClassLoss=10.892 [Epoch 106][Batch 1699], LR: 1.00E-03, Speed: 10.141 samples/sec, ObjLoss=25.597, BoxCenterLoss=14.492, BoxScaleLoss=5.212, ClassLoss=10.891 [Epoch 106][Batch 1799], LR: 1.00E-03, Speed: 121.606 samples/sec, ObjLoss=25.596, BoxCenterLoss=14.492, BoxScaleLoss=5.212, ClassLoss=10.890 [Epoch 106] Training cost: 2167.593, ObjLoss=25.595, BoxCenterLoss=14.492, BoxScaleLoss=5.212, ClassLoss=10.890 [Epoch 106] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.440 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.224 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445 person=37.6 bicycle=16.5 car=23.9 motorcycle=27.0 airplane=38.7 bus=40.9 train=44.8 truck=22.9 boat=13.4 traffic light=15.1 fire hydrant=48.2 stop sign=40.1 parking meter=19.4 bench=13.7 bird=20.1 cat=43.2 dog=37.7 horse=37.5 sheep=33.5 cow=33.7 elephant=41.3 bear=52.6 zebra=46.9 giraffe=48.4 backpack=5.2 umbrella=21.7 handbag=5.2 tie=15.4 suitcase=17.9 frisbee=31.1 skis=10.0 snowboard=12.9 sports ball=21.0 kite=24.5 baseball bat=11.5 baseball glove=17.7 skateboard=27.3 surfboard=17.3 tennis racket=25.8 bottle=19.2 wine glass=20.1 cup=24.1 fork=14.0 knife=4.0 spoon=4.1 bowl=23.1 banana=11.5 apple=7.2 sandwich=14.7 orange=17.4 broccoli=12.2 carrot=9.4 hot dog=15.1 pizza=28.0 donut=26.1 cake=23.1 chair=14.6 couch=28.7 potted plant=13.1 bed=29.8 dining table=19.4 toilet=36.7 tv=35.7 laptop=37.5 mouse=37.9 remote=9.8 keyboard=27.6 cell phone=18.1 microwave=33.3 oven=22.0 toaster=7.1 sink=20.6 refrigerator=30.1 book=3.8 clock=30.2 vase=21.8 scissors=19.2 teddy bear=26.1 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.3 [Epoch 107][Batch 99], LR: 1.00E-03, Speed: 9.354 samples/sec, ObjLoss=25.594, BoxCenterLoss=14.492, BoxScaleLoss=5.212, ClassLoss=10.889 [Epoch 107][Batch 199], LR: 1.00E-03, Speed: 11.113 samples/sec, ObjLoss=25.593, BoxCenterLoss=14.492, BoxScaleLoss=5.212, ClassLoss=10.888 [Epoch 107][Batch 299], LR: 1.00E-03, Speed: 8.331 samples/sec, ObjLoss=25.592, BoxCenterLoss=14.493, BoxScaleLoss=5.212, ClassLoss=10.888 [Epoch 107][Batch 399], LR: 1.00E-03, Speed: 12.925 samples/sec, ObjLoss=25.591, BoxCenterLoss=14.492, BoxScaleLoss=5.211, ClassLoss=10.886 [Epoch 107][Batch 499], LR: 1.00E-03, Speed: 8.565 samples/sec, ObjLoss=25.589, BoxCenterLoss=14.492, BoxScaleLoss=5.211, ClassLoss=10.885 [Epoch 107][Batch 599], LR: 1.00E-03, Speed: 95.981 samples/sec, ObjLoss=25.589, BoxCenterLoss=14.492, BoxScaleLoss=5.211, ClassLoss=10.884 [Epoch 107][Batch 699], LR: 1.00E-03, Speed: 10.381 samples/sec, ObjLoss=25.588, BoxCenterLoss=14.492, BoxScaleLoss=5.210, ClassLoss=10.883 [Epoch 107][Batch 799], LR: 1.00E-03, Speed: 9.140 samples/sec, ObjLoss=25.586, BoxCenterLoss=14.491, BoxScaleLoss=5.210, ClassLoss=10.882 [Epoch 107][Batch 899], LR: 1.00E-03, Speed: 11.813 samples/sec, ObjLoss=25.585, BoxCenterLoss=14.491, BoxScaleLoss=5.210, ClassLoss=10.881 [Epoch 107][Batch 999], LR: 1.00E-03, Speed: 9.242 samples/sec, ObjLoss=25.583, BoxCenterLoss=14.491, BoxScaleLoss=5.210, ClassLoss=10.880 [Epoch 107][Batch 1099], LR: 1.00E-03, Speed: 10.657 samples/sec, ObjLoss=25.582, BoxCenterLoss=14.491, BoxScaleLoss=5.210, ClassLoss=10.879 [Epoch 107][Batch 1199], LR: 1.00E-03, Speed: 8.642 samples/sec, ObjLoss=25.582, BoxCenterLoss=14.491, BoxScaleLoss=5.209, ClassLoss=10.878 [Epoch 107][Batch 1299], LR: 1.00E-03, Speed: 8.925 samples/sec, ObjLoss=25.580, BoxCenterLoss=14.491, BoxScaleLoss=5.209, ClassLoss=10.877 [Epoch 107][Batch 1399], LR: 1.00E-03, Speed: 92.522 samples/sec, ObjLoss=25.578, BoxCenterLoss=14.490, BoxScaleLoss=5.209, ClassLoss=10.875 [Epoch 107][Batch 1499], LR: 1.00E-03, Speed: 108.464 samples/sec, ObjLoss=25.577, BoxCenterLoss=14.490, BoxScaleLoss=5.209, ClassLoss=10.875 [Epoch 107][Batch 1599], LR: 1.00E-03, Speed: 9.300 samples/sec, ObjLoss=25.576, BoxCenterLoss=14.490, BoxScaleLoss=5.209, ClassLoss=10.874 [Epoch 107][Batch 1699], LR: 1.00E-03, Speed: 10.882 samples/sec, ObjLoss=25.575, BoxCenterLoss=14.490, BoxScaleLoss=5.208, ClassLoss=10.873 [Epoch 107][Batch 1799], LR: 1.00E-03, Speed: 13.135 samples/sec, ObjLoss=25.573, BoxCenterLoss=14.490, BoxScaleLoss=5.208, ClassLoss=10.872 [Epoch 107] Training cost: 2190.695, ObjLoss=25.573, BoxCenterLoss=14.490, BoxScaleLoss=5.208, ClassLoss=10.871 [Epoch 107] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.447 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.460 person=38.2 bicycle=17.5 car=27.2 motorcycle=28.6 airplane=42.6 bus=44.9 train=44.9 truck=23.8 boat=12.2 traffic light=15.5 fire hydrant=44.0 stop sign=41.4 parking meter=25.2 bench=13.5 bird=19.7 cat=43.6 dog=40.5 horse=37.6 sheep=34.6 cow=35.1 elephant=45.9 bear=46.3 zebra=45.7 giraffe=43.5 backpack=5.2 umbrella=23.8 handbag=4.9 tie=15.0 suitcase=17.8 frisbee=37.0 skis=9.9 snowboard=13.9 sports ball=22.7 kite=25.3 baseball bat=11.8 baseball glove=18.2 skateboard=29.1 surfboard=19.0 tennis racket=26.0 bottle=18.0 wine glass=20.1 cup=24.5 fork=13.3 knife=3.4 spoon=3.8 bowl=24.0 banana=12.2 apple=7.2 sandwich=21.8 orange=14.1 broccoli=12.3 carrot=9.7 hot dog=19.2 pizza=33.2 donut=28.3 cake=24.7 chair=14.6 couch=26.3 potted plant=12.0 bed=30.7 dining table=18.2 toilet=35.3 tv=39.5 laptop=37.2 mouse=34.0 remote=10.1 keyboard=31.2 cell phone=16.6 microwave=34.4 oven=19.2 toaster=3.6 sink=22.5 refrigerator=32.2 book=4.2 clock=33.5 vase=21.4 scissors=16.1 teddy bear=29.7 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.9 [Epoch 108][Batch 99], LR: 1.00E-03, Speed: 9.382 samples/sec, ObjLoss=25.572, BoxCenterLoss=14.490, BoxScaleLoss=5.208, ClassLoss=10.871 [Epoch 108][Batch 199], LR: 1.00E-03, Speed: 11.803 samples/sec, ObjLoss=25.570, BoxCenterLoss=14.489, BoxScaleLoss=5.208, ClassLoss=10.870 [Epoch 108][Batch 299], LR: 1.00E-03, Speed: 80.430 samples/sec, ObjLoss=25.569, BoxCenterLoss=14.489, BoxScaleLoss=5.208, ClassLoss=10.869 [Epoch 108][Batch 399], LR: 1.00E-03, Speed: 10.570 samples/sec, ObjLoss=25.568, BoxCenterLoss=14.489, BoxScaleLoss=5.208, ClassLoss=10.868 [Epoch 108][Batch 499], LR: 1.00E-03, Speed: 9.256 samples/sec, ObjLoss=25.567, BoxCenterLoss=14.489, BoxScaleLoss=5.207, ClassLoss=10.867 [Epoch 108][Batch 599], LR: 1.00E-03, Speed: 12.440 samples/sec, ObjLoss=25.565, BoxCenterLoss=14.489, BoxScaleLoss=5.207, ClassLoss=10.866 [Epoch 108][Batch 699], LR: 1.00E-03, Speed: 9.523 samples/sec, ObjLoss=25.564, BoxCenterLoss=14.489, BoxScaleLoss=5.207, ClassLoss=10.865 [Epoch 108][Batch 799], LR: 1.00E-03, Speed: 8.635 samples/sec, ObjLoss=25.562, BoxCenterLoss=14.488, BoxScaleLoss=5.207, ClassLoss=10.864 [Epoch 108][Batch 899], LR: 1.00E-03, Speed: 10.276 samples/sec, ObjLoss=25.561, BoxCenterLoss=14.488, BoxScaleLoss=5.206, ClassLoss=10.863 [Epoch 108][Batch 999], LR: 1.00E-03, Speed: 7.868 samples/sec, ObjLoss=25.560, BoxCenterLoss=14.488, BoxScaleLoss=5.206, ClassLoss=10.862 [Epoch 108][Batch 1099], LR: 1.00E-03, Speed: 9.698 samples/sec, ObjLoss=25.559, BoxCenterLoss=14.488, BoxScaleLoss=5.206, ClassLoss=10.861 [Epoch 108][Batch 1199], LR: 1.00E-03, Speed: 9.339 samples/sec, ObjLoss=25.558, BoxCenterLoss=14.488, BoxScaleLoss=5.206, ClassLoss=10.860 [Epoch 108][Batch 1299], LR: 1.00E-03, Speed: 8.900 samples/sec, ObjLoss=25.557, BoxCenterLoss=14.488, BoxScaleLoss=5.206, ClassLoss=10.859 [Epoch 108][Batch 1399], LR: 1.00E-03, Speed: 8.636 samples/sec, ObjLoss=25.556, BoxCenterLoss=14.488, BoxScaleLoss=5.206, ClassLoss=10.859 [Epoch 108][Batch 1499], LR: 1.00E-03, Speed: 7.930 samples/sec, ObjLoss=25.555, BoxCenterLoss=14.489, BoxScaleLoss=5.206, ClassLoss=10.858 [Epoch 108][Batch 1599], LR: 1.00E-03, Speed: 13.391 samples/sec, ObjLoss=25.555, BoxCenterLoss=14.489, BoxScaleLoss=5.206, ClassLoss=10.857 [Epoch 108][Batch 1699], LR: 1.00E-03, Speed: 10.692 samples/sec, ObjLoss=25.554, BoxCenterLoss=14.489, BoxScaleLoss=5.206, ClassLoss=10.856 [Epoch 108][Batch 1799], LR: 1.00E-03, Speed: 10.568 samples/sec, ObjLoss=25.553, BoxCenterLoss=14.489, BoxScaleLoss=5.205, ClassLoss=10.855 [Epoch 108] Training cost: 2192.988, ObjLoss=25.552, BoxCenterLoss=14.489, BoxScaleLoss=5.205, ClassLoss=10.855 [Epoch 108] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.446 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.450 person=37.1 bicycle=15.4 car=25.2 motorcycle=25.9 airplane=40.2 bus=49.5 train=51.5 truck=22.4 boat=10.9 traffic light=12.1 fire hydrant=48.8 stop sign=45.6 parking meter=24.0 bench=11.5 bird=18.2 cat=40.4 dog=39.4 horse=35.6 sheep=31.6 cow=32.6 elephant=44.5 bear=45.2 zebra=47.5 giraffe=47.7 backpack=5.4 umbrella=23.1 handbag=5.0 tie=17.5 suitcase=15.8 frisbee=32.8 skis=9.9 snowboard=13.1 sports ball=22.7 kite=21.3 baseball bat=13.2 baseball glove=15.6 skateboard=27.4 surfboard=19.5 tennis racket=25.0 bottle=17.7 wine glass=19.5 cup=23.3 fork=13.8 knife=5.2 spoon=4.8 bowl=22.6 banana=11.4 apple=4.3 sandwich=20.9 orange=14.2 broccoli=10.0 carrot=6.1 hot dog=17.5 pizza=34.7 donut=30.5 cake=21.1 chair=15.5 couch=26.3 potted plant=13.9 bed=33.5 dining table=19.8 toilet=38.0 tv=39.3 laptop=39.1 mouse=40.6 remote=8.4 keyboard=26.6 cell phone=16.6 microwave=29.7 oven=20.0 toaster=0.0 sink=19.5 refrigerator=29.8 book=3.4 clock=32.0 vase=19.5 scissors=9.0 teddy bear=26.7 hair drier=0.0 toothbrush=6.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.3 [Epoch 109][Batch 99], LR: 1.00E-03, Speed: 9.056 samples/sec, ObjLoss=25.552, BoxCenterLoss=14.489, BoxScaleLoss=5.205, ClassLoss=10.854 [Epoch 109][Batch 199], LR: 1.00E-03, Speed: 9.747 samples/sec, ObjLoss=25.552, BoxCenterLoss=14.489, BoxScaleLoss=5.205, ClassLoss=10.853 [Epoch 109][Batch 299], LR: 1.00E-03, Speed: 9.805 samples/sec, ObjLoss=25.551, BoxCenterLoss=14.490, BoxScaleLoss=5.205, ClassLoss=10.852 [Epoch 109][Batch 399], LR: 1.00E-03, Speed: 11.585 samples/sec, ObjLoss=25.550, BoxCenterLoss=14.490, BoxScaleLoss=5.205, ClassLoss=10.852 [Epoch 109][Batch 499], LR: 1.00E-03, Speed: 9.161 samples/sec, ObjLoss=25.548, BoxCenterLoss=14.489, BoxScaleLoss=5.204, ClassLoss=10.850 [Epoch 109][Batch 599], LR: 1.00E-03, Speed: 11.204 samples/sec, ObjLoss=25.547, BoxCenterLoss=14.489, BoxScaleLoss=5.204, ClassLoss=10.849 [Epoch 109][Batch 699], LR: 1.00E-03, Speed: 11.407 samples/sec, ObjLoss=25.546, BoxCenterLoss=14.489, BoxScaleLoss=5.204, ClassLoss=10.848 [Epoch 109][Batch 799], LR: 1.00E-03, Speed: 8.502 samples/sec, ObjLoss=25.545, BoxCenterLoss=14.489, BoxScaleLoss=5.204, ClassLoss=10.848 [Epoch 109][Batch 899], LR: 1.00E-03, Speed: 9.806 samples/sec, ObjLoss=25.544, BoxCenterLoss=14.489, BoxScaleLoss=5.204, ClassLoss=10.847 [Epoch 109][Batch 999], LR: 1.00E-03, Speed: 9.153 samples/sec, ObjLoss=25.543, BoxCenterLoss=14.489, BoxScaleLoss=5.203, ClassLoss=10.846 [Epoch 109][Batch 1099], LR: 1.00E-03, Speed: 9.265 samples/sec, ObjLoss=25.542, BoxCenterLoss=14.488, BoxScaleLoss=5.203, ClassLoss=10.845 [Epoch 109][Batch 1199], LR: 1.00E-03, Speed: 100.278 samples/sec, ObjLoss=25.541, BoxCenterLoss=14.488, BoxScaleLoss=5.203, ClassLoss=10.845 [Epoch 109][Batch 1299], LR: 1.00E-03, Speed: 10.426 samples/sec, ObjLoss=25.540, BoxCenterLoss=14.489, BoxScaleLoss=5.203, ClassLoss=10.844 [Epoch 109][Batch 1399], LR: 1.00E-03, Speed: 127.297 samples/sec, ObjLoss=25.540, BoxCenterLoss=14.489, BoxScaleLoss=5.203, ClassLoss=10.843 [Epoch 109][Batch 1499], LR: 1.00E-03, Speed: 13.949 samples/sec, ObjLoss=25.538, BoxCenterLoss=14.489, BoxScaleLoss=5.203, ClassLoss=10.842 [Epoch 109][Batch 1599], LR: 1.00E-03, Speed: 9.726 samples/sec, ObjLoss=25.537, BoxCenterLoss=14.489, BoxScaleLoss=5.203, ClassLoss=10.841 [Epoch 109][Batch 1699], LR: 1.00E-03, Speed: 7.108 samples/sec, ObjLoss=25.536, BoxCenterLoss=14.489, BoxScaleLoss=5.203, ClassLoss=10.840 [Epoch 109][Batch 1799], LR: 1.00E-03, Speed: 9.908 samples/sec, ObjLoss=25.534, BoxCenterLoss=14.488, BoxScaleLoss=5.202, ClassLoss=10.839 [Epoch 109] Training cost: 2230.461, ObjLoss=25.534, BoxCenterLoss=14.488, BoxScaleLoss=5.202, ClassLoss=10.839 [Epoch 109] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.246 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461 person=35.7 bicycle=18.4 car=25.3 motorcycle=29.3 airplane=41.4 bus=49.0 train=46.8 truck=23.7 boat=13.6 traffic light=12.9 fire hydrant=39.4 stop sign=46.9 parking meter=24.2 bench=12.2 bird=20.1 cat=42.7 dog=35.3 horse=35.0 sheep=32.6 cow=36.5 elephant=41.3 bear=46.5 zebra=45.2 giraffe=49.0 backpack=5.8 umbrella=22.0 handbag=5.6 tie=14.9 suitcase=16.5 frisbee=35.4 skis=9.7 snowboard=14.9 sports ball=25.3 kite=23.0 baseball bat=12.2 baseball glove=19.3 skateboard=28.5 surfboard=18.0 tennis racket=24.3 bottle=19.3 wine glass=20.8 cup=25.0 fork=14.1 knife=4.1 spoon=3.2 bowl=22.9 banana=11.5 apple=5.9 sandwich=20.3 orange=17.3 broccoli=9.7 carrot=8.1 hot dog=18.6 pizza=34.6 donut=30.5 cake=19.0 chair=15.6 couch=26.8 potted plant=13.5 bed=31.5 dining table=20.6 toilet=34.1 tv=37.8 laptop=40.7 mouse=37.0 remote=10.6 keyboard=30.6 cell phone=18.2 microwave=36.4 oven=21.0 toaster=0.0 sink=23.6 refrigerator=27.2 book=4.2 clock=29.4 vase=21.2 scissors=16.0 teddy bear=27.9 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.7 [Epoch 110][Batch 99], LR: 1.00E-03, Speed: 9.604 samples/sec, ObjLoss=25.532, BoxCenterLoss=14.488, BoxScaleLoss=5.202, ClassLoss=10.838 [Epoch 110][Batch 199], LR: 1.00E-03, Speed: 9.445 samples/sec, ObjLoss=25.531, BoxCenterLoss=14.488, BoxScaleLoss=5.202, ClassLoss=10.837 [Epoch 110][Batch 299], LR: 1.00E-03, Speed: 9.197 samples/sec, ObjLoss=25.530, BoxCenterLoss=14.488, BoxScaleLoss=5.202, ClassLoss=10.836 [Epoch 110][Batch 399], LR: 1.00E-03, Speed: 10.717 samples/sec, ObjLoss=25.530, BoxCenterLoss=14.488, BoxScaleLoss=5.202, ClassLoss=10.835 [Epoch 110][Batch 499], LR: 1.00E-03, Speed: 10.232 samples/sec, ObjLoss=25.528, BoxCenterLoss=14.488, BoxScaleLoss=5.201, ClassLoss=10.834 [Epoch 110][Batch 599], LR: 1.00E-03, Speed: 8.946 samples/sec, ObjLoss=25.526, BoxCenterLoss=14.487, BoxScaleLoss=5.201, ClassLoss=10.833 [Epoch 110][Batch 699], LR: 1.00E-03, Speed: 12.051 samples/sec, ObjLoss=25.525, BoxCenterLoss=14.487, BoxScaleLoss=5.201, ClassLoss=10.832 [Epoch 110][Batch 799], LR: 1.00E-03, Speed: 8.732 samples/sec, ObjLoss=25.524, BoxCenterLoss=14.487, BoxScaleLoss=5.201, ClassLoss=10.831 [Epoch 110][Batch 899], LR: 1.00E-03, Speed: 8.300 samples/sec, ObjLoss=25.524, BoxCenterLoss=14.488, BoxScaleLoss=5.201, ClassLoss=10.830 [Epoch 110][Batch 999], LR: 1.00E-03, Speed: 109.330 samples/sec, ObjLoss=25.523, BoxCenterLoss=14.488, BoxScaleLoss=5.201, ClassLoss=10.829 [Epoch 110][Batch 1099], LR: 1.00E-03, Speed: 8.369 samples/sec, ObjLoss=25.521, BoxCenterLoss=14.488, BoxScaleLoss=5.200, ClassLoss=10.828 [Epoch 110][Batch 1199], LR: 1.00E-03, Speed: 125.153 samples/sec, ObjLoss=25.520, BoxCenterLoss=14.488, BoxScaleLoss=5.200, ClassLoss=10.828 [Epoch 110][Batch 1299], LR: 1.00E-03, Speed: 10.760 samples/sec, ObjLoss=25.519, BoxCenterLoss=14.487, BoxScaleLoss=5.200, ClassLoss=10.826 [Epoch 110][Batch 1399], LR: 1.00E-03, Speed: 9.487 samples/sec, ObjLoss=25.518, BoxCenterLoss=14.487, BoxScaleLoss=5.200, ClassLoss=10.826 [Epoch 110][Batch 1499], LR: 1.00E-03, Speed: 9.438 samples/sec, ObjLoss=25.517, BoxCenterLoss=14.487, BoxScaleLoss=5.200, ClassLoss=10.825 [Epoch 110][Batch 1599], LR: 1.00E-03, Speed: 108.571 samples/sec, ObjLoss=25.515, BoxCenterLoss=14.487, BoxScaleLoss=5.199, ClassLoss=10.824 [Epoch 110][Batch 1699], LR: 1.00E-03, Speed: 11.921 samples/sec, ObjLoss=25.514, BoxCenterLoss=14.487, BoxScaleLoss=5.199, ClassLoss=10.822 [Epoch 110][Batch 1799], LR: 1.00E-03, Speed: 135.915 samples/sec, ObjLoss=25.513, BoxCenterLoss=14.487, BoxScaleLoss=5.199, ClassLoss=10.821 [Epoch 110] Training cost: 2202.452, ObjLoss=25.513, BoxCenterLoss=14.487, BoxScaleLoss=5.199, ClassLoss=10.821 [Epoch 110] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.246 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.449 person=36.8 bicycle=16.4 car=25.0 motorcycle=26.0 airplane=40.5 bus=43.4 train=46.5 truck=21.9 boat=13.0 traffic light=11.7 fire hydrant=45.2 stop sign=37.3 parking meter=21.6 bench=12.8 bird=21.9 cat=43.8 dog=38.5 horse=38.1 sheep=32.8 cow=35.4 elephant=40.1 bear=50.1 zebra=48.9 giraffe=50.2 backpack=4.2 umbrella=22.9 handbag=6.2 tie=14.7 suitcase=12.5 frisbee=33.8 skis=8.9 snowboard=11.3 sports ball=26.4 kite=21.7 baseball bat=14.7 baseball glove=19.9 skateboard=27.8 surfboard=17.1 tennis racket=27.2 bottle=16.7 wine glass=20.2 cup=21.1 fork=13.1 knife=3.9 spoon=5.2 bowl=22.1 banana=13.2 apple=7.7 sandwich=22.7 orange=17.7 broccoli=8.9 carrot=7.9 hot dog=17.1 pizza=34.4 donut=26.6 cake=18.4 chair=14.2 couch=28.1 potted plant=13.5 bed=32.1 dining table=19.1 toilet=39.0 tv=37.0 laptop=37.4 mouse=39.7 remote=11.9 keyboard=31.8 cell phone=16.9 microwave=32.6 oven=19.6 toaster=4.8 sink=19.6 refrigerator=31.6 book=4.2 clock=31.7 vase=21.3 scissors=15.1 teddy bear=23.3 hair drier=0.0 toothbrush=8.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.5 [Epoch 111][Batch 99], LR: 1.00E-03, Speed: 9.213 samples/sec, ObjLoss=25.512, BoxCenterLoss=14.487, BoxScaleLoss=5.199, ClassLoss=10.820 [Epoch 111][Batch 199], LR: 1.00E-03, Speed: 115.262 samples/sec, ObjLoss=25.511, BoxCenterLoss=14.487, BoxScaleLoss=5.198, ClassLoss=10.819 [Epoch 111][Batch 299], LR: 1.00E-03, Speed: 110.060 samples/sec, ObjLoss=25.510, BoxCenterLoss=14.487, BoxScaleLoss=5.198, ClassLoss=10.818 [Epoch 111][Batch 399], LR: 1.00E-03, Speed: 11.250 samples/sec, ObjLoss=25.509, BoxCenterLoss=14.487, BoxScaleLoss=5.198, ClassLoss=10.817 [Epoch 111][Batch 499], LR: 1.00E-03, Speed: 7.518 samples/sec, ObjLoss=25.507, BoxCenterLoss=14.487, BoxScaleLoss=5.198, ClassLoss=10.815 [Epoch 111][Batch 599], LR: 1.00E-03, Speed: 10.030 samples/sec, ObjLoss=25.506, BoxCenterLoss=14.486, BoxScaleLoss=5.197, ClassLoss=10.814 [Epoch 111][Batch 699], LR: 1.00E-03, Speed: 131.557 samples/sec, ObjLoss=25.505, BoxCenterLoss=14.486, BoxScaleLoss=5.197, ClassLoss=10.814 [Epoch 111][Batch 799], LR: 1.00E-03, Speed: 8.731 samples/sec, ObjLoss=25.504, BoxCenterLoss=14.486, BoxScaleLoss=5.197, ClassLoss=10.813 [Epoch 111][Batch 899], LR: 1.00E-03, Speed: 9.654 samples/sec, ObjLoss=25.503, BoxCenterLoss=14.486, BoxScaleLoss=5.197, ClassLoss=10.812 [Epoch 111][Batch 999], LR: 1.00E-03, Speed: 93.374 samples/sec, ObjLoss=25.502, BoxCenterLoss=14.486, BoxScaleLoss=5.197, ClassLoss=10.811 [Epoch 111][Batch 1099], LR: 1.00E-03, Speed: 10.508 samples/sec, ObjLoss=25.501, BoxCenterLoss=14.486, BoxScaleLoss=5.197, ClassLoss=10.811 [Epoch 111][Batch 1199], LR: 1.00E-03, Speed: 10.309 samples/sec, ObjLoss=25.500, BoxCenterLoss=14.486, BoxScaleLoss=5.197, ClassLoss=10.810 [Epoch 111][Batch 1299], LR: 1.00E-03, Speed: 8.910 samples/sec, ObjLoss=25.498, BoxCenterLoss=14.486, BoxScaleLoss=5.196, ClassLoss=10.809 [Epoch 111][Batch 1399], LR: 1.00E-03, Speed: 12.249 samples/sec, ObjLoss=25.497, BoxCenterLoss=14.485, BoxScaleLoss=5.196, ClassLoss=10.808 [Epoch 111][Batch 1499], LR: 1.00E-03, Speed: 10.024 samples/sec, ObjLoss=25.496, BoxCenterLoss=14.485, BoxScaleLoss=5.196, ClassLoss=10.807 [Epoch 111][Batch 1599], LR: 1.00E-03, Speed: 10.629 samples/sec, ObjLoss=25.495, BoxCenterLoss=14.485, BoxScaleLoss=5.196, ClassLoss=10.806 [Epoch 111][Batch 1699], LR: 1.00E-03, Speed: 11.071 samples/sec, ObjLoss=25.494, BoxCenterLoss=14.485, BoxScaleLoss=5.196, ClassLoss=10.805 [Epoch 111][Batch 1799], LR: 1.00E-03, Speed: 9.506 samples/sec, ObjLoss=25.492, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.804 [Epoch 111] Training cost: 2158.359, ObjLoss=25.493, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.804 [Epoch 111] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.143 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.459 person=36.7 bicycle=15.4 car=27.0 motorcycle=27.2 airplane=41.6 bus=46.8 train=46.4 truck=22.8 boat=13.3 traffic light=14.0 fire hydrant=46.3 stop sign=37.7 parking meter=26.6 bench=12.6 bird=21.1 cat=41.8 dog=37.9 horse=35.3 sheep=32.8 cow=32.8 elephant=43.2 bear=48.0 zebra=44.1 giraffe=46.5 backpack=6.6 umbrella=23.6 handbag=6.0 tie=17.9 suitcase=15.2 frisbee=35.9 skis=8.4 snowboard=14.4 sports ball=20.0 kite=22.5 baseball bat=12.6 baseball glove=21.6 skateboard=23.9 surfboard=19.4 tennis racket=25.3 bottle=20.0 wine glass=20.7 cup=23.8 fork=14.4 knife=4.9 spoon=3.7 bowl=19.5 banana=12.6 apple=5.7 sandwich=18.3 orange=16.1 broccoli=10.5 carrot=6.8 hot dog=19.0 pizza=33.6 donut=28.9 cake=21.3 chair=15.2 couch=28.6 potted plant=15.4 bed=37.3 dining table=16.9 toilet=36.3 tv=36.7 laptop=40.0 mouse=39.0 remote=10.7 keyboard=27.3 cell phone=17.1 microwave=31.8 oven=18.9 toaster=4.8 sink=21.6 refrigerator=28.7 book=3.9 clock=33.8 vase=18.7 scissors=16.7 teddy bear=28.6 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.5 [Epoch 112][Batch 99], LR: 1.00E-03, Speed: 131.668 samples/sec, ObjLoss=25.492, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.803 [Epoch 112][Batch 199], LR: 1.00E-03, Speed: 9.677 samples/sec, ObjLoss=25.490, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.802 [Epoch 112][Batch 299], LR: 1.00E-03, Speed: 110.992 samples/sec, ObjLoss=25.489, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.802 [Epoch 112][Batch 399], LR: 1.00E-03, Speed: 9.793 samples/sec, ObjLoss=25.488, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.801 [Epoch 112][Batch 499], LR: 1.00E-03, Speed: 101.251 samples/sec, ObjLoss=25.487, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.800 [Epoch 112][Batch 599], LR: 1.00E-03, Speed: 10.518 samples/sec, ObjLoss=25.485, BoxCenterLoss=14.485, BoxScaleLoss=5.195, ClassLoss=10.799 [Epoch 112][Batch 699], LR: 1.00E-03, Speed: 59.910 samples/sec, ObjLoss=25.484, BoxCenterLoss=14.484, BoxScaleLoss=5.195, ClassLoss=10.798 [Epoch 112][Batch 799], LR: 1.00E-03, Speed: 9.899 samples/sec, ObjLoss=25.483, BoxCenterLoss=14.484, BoxScaleLoss=5.194, ClassLoss=10.797 [Epoch 112][Batch 899], LR: 1.00E-03, Speed: 9.406 samples/sec, ObjLoss=25.482, BoxCenterLoss=14.484, BoxScaleLoss=5.194, ClassLoss=10.796 [Epoch 112][Batch 999], LR: 1.00E-03, Speed: 101.349 samples/sec, ObjLoss=25.480, BoxCenterLoss=14.484, BoxScaleLoss=5.194, ClassLoss=10.795 [Epoch 112][Batch 1099], LR: 1.00E-03, Speed: 8.552 samples/sec, ObjLoss=25.479, BoxCenterLoss=14.484, BoxScaleLoss=5.194, ClassLoss=10.794 [Epoch 112][Batch 1199], LR: 1.00E-03, Speed: 9.484 samples/sec, ObjLoss=25.478, BoxCenterLoss=14.483, BoxScaleLoss=5.194, ClassLoss=10.793 [Epoch 112][Batch 1299], LR: 1.00E-03, Speed: 109.469 samples/sec, ObjLoss=25.476, BoxCenterLoss=14.483, BoxScaleLoss=5.193, ClassLoss=10.792 [Epoch 112][Batch 1399], LR: 1.00E-03, Speed: 7.633 samples/sec, ObjLoss=25.475, BoxCenterLoss=14.483, BoxScaleLoss=5.193, ClassLoss=10.791 [Epoch 112][Batch 1499], LR: 1.00E-03, Speed: 91.021 samples/sec, ObjLoss=25.474, BoxCenterLoss=14.483, BoxScaleLoss=5.193, ClassLoss=10.790 [Epoch 112][Batch 1599], LR: 1.00E-03, Speed: 9.971 samples/sec, ObjLoss=25.473, BoxCenterLoss=14.483, BoxScaleLoss=5.193, ClassLoss=10.789 [Epoch 112][Batch 1699], LR: 1.00E-03, Speed: 10.775 samples/sec, ObjLoss=25.471, BoxCenterLoss=14.482, BoxScaleLoss=5.192, ClassLoss=10.788 [Epoch 112][Batch 1799], LR: 1.00E-03, Speed: 11.333 samples/sec, ObjLoss=25.469, BoxCenterLoss=14.482, BoxScaleLoss=5.192, ClassLoss=10.787 [Epoch 112] Training cost: 2146.113, ObjLoss=25.469, BoxCenterLoss=14.482, BoxScaleLoss=5.192, ClassLoss=10.786 [Epoch 112] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.260 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.465 person=34.9 bicycle=17.9 car=25.8 motorcycle=30.0 airplane=37.7 bus=50.1 train=46.1 truck=23.5 boat=13.2 traffic light=10.7 fire hydrant=44.3 stop sign=43.5 parking meter=29.9 bench=13.3 bird=18.0 cat=48.7 dog=39.5 horse=36.8 sheep=33.2 cow=36.4 elephant=42.1 bear=54.2 zebra=49.4 giraffe=50.0 backpack=6.9 umbrella=22.0 handbag=5.7 tie=16.2 suitcase=18.0 frisbee=31.8 skis=10.4 snowboard=10.2 sports ball=25.3 kite=21.5 baseball bat=14.7 baseball glove=20.0 skateboard=28.2 surfboard=20.4 tennis racket=22.9 bottle=18.4 wine glass=18.3 cup=24.2 fork=14.2 knife=5.3 spoon=4.1 bowl=24.3 banana=15.2 apple=8.3 sandwich=22.8 orange=19.2 broccoli=10.7 carrot=8.3 hot dog=15.2 pizza=31.4 donut=24.1 cake=23.3 chair=15.0 couch=30.2 potted plant=13.6 bed=30.2 dining table=18.3 toilet=38.3 tv=37.8 laptop=39.4 mouse=35.1 remote=8.1 keyboard=30.7 cell phone=15.3 microwave=34.8 oven=22.3 toaster=0.0 sink=22.4 refrigerator=32.4 book=2.8 clock=34.5 vase=20.7 scissors=11.1 teddy bear=28.1 hair drier=0.0 toothbrush=6.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.0 [Epoch 113][Batch 99], LR: 1.00E-03, Speed: 8.124 samples/sec, ObjLoss=25.468, BoxCenterLoss=14.482, BoxScaleLoss=5.192, ClassLoss=10.785 [Epoch 113][Batch 199], LR: 1.00E-03, Speed: 8.493 samples/sec, ObjLoss=25.467, BoxCenterLoss=14.482, BoxScaleLoss=5.192, ClassLoss=10.785 [Epoch 113][Batch 299], LR: 1.00E-03, Speed: 7.570 samples/sec, ObjLoss=25.466, BoxCenterLoss=14.482, BoxScaleLoss=5.192, ClassLoss=10.783 [Epoch 113][Batch 399], LR: 1.00E-03, Speed: 8.614 samples/sec, ObjLoss=25.464, BoxCenterLoss=14.481, BoxScaleLoss=5.191, ClassLoss=10.783 [Epoch 113][Batch 499], LR: 1.00E-03, Speed: 7.621 samples/sec, ObjLoss=25.463, BoxCenterLoss=14.481, BoxScaleLoss=5.191, ClassLoss=10.782 [Epoch 113][Batch 599], LR: 1.00E-03, Speed: 9.511 samples/sec, ObjLoss=25.463, BoxCenterLoss=14.481, BoxScaleLoss=5.191, ClassLoss=10.781 [Epoch 113][Batch 699], LR: 1.00E-03, Speed: 10.396 samples/sec, ObjLoss=25.462, BoxCenterLoss=14.481, BoxScaleLoss=5.191, ClassLoss=10.780 [Epoch 113][Batch 799], LR: 1.00E-03, Speed: 119.437 samples/sec, ObjLoss=25.461, BoxCenterLoss=14.481, BoxScaleLoss=5.191, ClassLoss=10.779 [Epoch 113][Batch 899], LR: 1.00E-03, Speed: 9.933 samples/sec, ObjLoss=25.459, BoxCenterLoss=14.481, BoxScaleLoss=5.191, ClassLoss=10.778 [Epoch 113][Batch 999], LR: 1.00E-03, Speed: 8.760 samples/sec, ObjLoss=25.459, BoxCenterLoss=14.482, BoxScaleLoss=5.191, ClassLoss=10.778 [Epoch 113][Batch 1099], LR: 1.00E-03, Speed: 8.404 samples/sec, ObjLoss=25.458, BoxCenterLoss=14.482, BoxScaleLoss=5.191, ClassLoss=10.777 [Epoch 113][Batch 1199], LR: 1.00E-03, Speed: 9.145 samples/sec, ObjLoss=25.457, BoxCenterLoss=14.482, BoxScaleLoss=5.191, ClassLoss=10.776 [Epoch 113][Batch 1299], LR: 1.00E-03, Speed: 8.491 samples/sec, ObjLoss=25.456, BoxCenterLoss=14.482, BoxScaleLoss=5.190, ClassLoss=10.775 [Epoch 113][Batch 1399], LR: 1.00E-03, Speed: 8.837 samples/sec, ObjLoss=25.455, BoxCenterLoss=14.482, BoxScaleLoss=5.190, ClassLoss=10.774 [Epoch 113][Batch 1499], LR: 1.00E-03, Speed: 9.949 samples/sec, ObjLoss=25.454, BoxCenterLoss=14.482, BoxScaleLoss=5.190, ClassLoss=10.773 [Epoch 113][Batch 1599], LR: 1.00E-03, Speed: 9.505 samples/sec, ObjLoss=25.453, BoxCenterLoss=14.481, BoxScaleLoss=5.190, ClassLoss=10.772 [Epoch 113][Batch 1699], LR: 1.00E-03, Speed: 11.336 samples/sec, ObjLoss=25.451, BoxCenterLoss=14.481, BoxScaleLoss=5.190, ClassLoss=10.771 [Epoch 113][Batch 1799], LR: 1.00E-03, Speed: 8.507 samples/sec, ObjLoss=25.449, BoxCenterLoss=14.481, BoxScaleLoss=5.189, ClassLoss=10.770 [Epoch 113] Training cost: 2183.602, ObjLoss=25.448, BoxCenterLoss=14.481, BoxScaleLoss=5.189, ClassLoss=10.770 [Epoch 113] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.449 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 person=35.1 bicycle=17.6 car=24.4 motorcycle=28.3 airplane=43.7 bus=46.7 train=49.8 truck=24.1 boat=12.9 traffic light=13.3 fire hydrant=44.4 stop sign=44.7 parking meter=27.7 bench=12.8 bird=21.0 cat=44.5 dog=40.0 horse=36.2 sheep=33.4 cow=37.9 elephant=44.9 bear=52.6 zebra=49.7 giraffe=50.3 backpack=5.3 umbrella=25.2 handbag=5.0 tie=19.4 suitcase=17.6 frisbee=32.6 skis=8.3 snowboard=12.5 sports ball=25.3 kite=22.4 baseball bat=12.5 baseball glove=19.6 skateboard=27.1 surfboard=21.7 tennis racket=28.9 bottle=17.4 wine glass=20.2 cup=23.5 fork=14.0 knife=3.3 spoon=4.4 bowl=20.6 banana=12.3 apple=7.1 sandwich=21.7 orange=18.0 broccoli=7.7 carrot=7.1 hot dog=18.1 pizza=31.8 donut=29.0 cake=21.0 chair=14.8 couch=30.0 potted plant=13.7 bed=34.2 dining table=21.0 toilet=41.0 tv=37.9 laptop=39.7 mouse=33.0 remote=9.5 keyboard=32.4 cell phone=16.7 microwave=29.5 oven=20.5 toaster=1.7 sink=21.1 refrigerator=31.6 book=3.8 clock=33.4 vase=21.8 scissors=15.6 teddy bear=27.6 hair drier=0.0 toothbrush=4.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.1 [Epoch 114][Batch 99], LR: 1.00E-03, Speed: 9.246 samples/sec, ObjLoss=25.447, BoxCenterLoss=14.480, BoxScaleLoss=5.189, ClassLoss=10.769 [Epoch 114][Batch 199], LR: 1.00E-03, Speed: 7.286 samples/sec, ObjLoss=25.446, BoxCenterLoss=14.480, BoxScaleLoss=5.189, ClassLoss=10.768 [Epoch 114][Batch 299], LR: 1.00E-03, Speed: 9.920 samples/sec, ObjLoss=25.446, BoxCenterLoss=14.481, BoxScaleLoss=5.189, ClassLoss=10.767 [Epoch 114][Batch 399], LR: 1.00E-03, Speed: 12.376 samples/sec, ObjLoss=25.445, BoxCenterLoss=14.481, BoxScaleLoss=5.189, ClassLoss=10.766 [Epoch 114][Batch 499], LR: 1.00E-03, Speed: 10.748 samples/sec, ObjLoss=25.444, BoxCenterLoss=14.481, BoxScaleLoss=5.189, ClassLoss=10.765 [Epoch 114][Batch 599], LR: 1.00E-03, Speed: 9.330 samples/sec, ObjLoss=25.443, BoxCenterLoss=14.481, BoxScaleLoss=5.188, ClassLoss=10.764 [Epoch 114][Batch 699], LR: 1.00E-03, Speed: 108.847 samples/sec, ObjLoss=25.442, BoxCenterLoss=14.481, BoxScaleLoss=5.188, ClassLoss=10.763 [Epoch 114][Batch 799], LR: 1.00E-03, Speed: 6.767 samples/sec, ObjLoss=25.442, BoxCenterLoss=14.481, BoxScaleLoss=5.188, ClassLoss=10.763 [Epoch 114][Batch 899], LR: 1.00E-03, Speed: 10.017 samples/sec, ObjLoss=25.441, BoxCenterLoss=14.481, BoxScaleLoss=5.188, ClassLoss=10.762 [Epoch 114][Batch 999], LR: 1.00E-03, Speed: 11.691 samples/sec, ObjLoss=25.439, BoxCenterLoss=14.481, BoxScaleLoss=5.187, ClassLoss=10.761 [Epoch 114][Batch 1099], LR: 1.00E-03, Speed: 10.485 samples/sec, ObjLoss=25.438, BoxCenterLoss=14.481, BoxScaleLoss=5.187, ClassLoss=10.760 [Epoch 114][Batch 1199], LR: 1.00E-03, Speed: 14.350 samples/sec, ObjLoss=25.437, BoxCenterLoss=14.481, BoxScaleLoss=5.187, ClassLoss=10.759 [Epoch 114][Batch 1299], LR: 1.00E-03, Speed: 10.146 samples/sec, ObjLoss=25.437, BoxCenterLoss=14.481, BoxScaleLoss=5.187, ClassLoss=10.758 [Epoch 114][Batch 1399], LR: 1.00E-03, Speed: 10.503 samples/sec, ObjLoss=25.436, BoxCenterLoss=14.481, BoxScaleLoss=5.187, ClassLoss=10.757 [Epoch 114][Batch 1499], LR: 1.00E-03, Speed: 9.991 samples/sec, ObjLoss=25.435, BoxCenterLoss=14.481, BoxScaleLoss=5.187, ClassLoss=10.756 [Epoch 114][Batch 1599], LR: 1.00E-03, Speed: 105.350 samples/sec, ObjLoss=25.434, BoxCenterLoss=14.481, BoxScaleLoss=5.186, ClassLoss=10.755 [Epoch 114][Batch 1699], LR: 1.00E-03, Speed: 8.392 samples/sec, ObjLoss=25.432, BoxCenterLoss=14.480, BoxScaleLoss=5.186, ClassLoss=10.754 [Epoch 114][Batch 1799], LR: 1.00E-03, Speed: 10.266 samples/sec, ObjLoss=25.431, BoxCenterLoss=14.480, BoxScaleLoss=5.186, ClassLoss=10.753 [Epoch 114] Training cost: 2200.345, ObjLoss=25.430, BoxCenterLoss=14.480, BoxScaleLoss=5.186, ClassLoss=10.753 [Epoch 114] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.450 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.473 person=36.7 bicycle=16.6 car=24.4 motorcycle=28.5 airplane=41.4 bus=50.3 train=50.2 truck=21.9 boat=13.3 traffic light=13.9 fire hydrant=49.3 stop sign=43.0 parking meter=28.3 bench=12.9 bird=17.3 cat=45.6 dog=41.1 horse=35.5 sheep=34.1 cow=30.3 elephant=38.1 bear=54.5 zebra=46.8 giraffe=45.1 backpack=5.9 umbrella=23.4 handbag=5.5 tie=15.5 suitcase=16.1 frisbee=36.9 skis=9.8 snowboard=14.1 sports ball=26.9 kite=23.3 baseball bat=12.4 baseball glove=16.2 skateboard=28.4 surfboard=17.6 tennis racket=27.1 bottle=19.1 wine glass=16.7 cup=25.5 fork=12.1 knife=4.3 spoon=4.8 bowl=24.9 banana=11.4 apple=9.3 sandwich=19.3 orange=17.4 broccoli=8.3 carrot=9.1 hot dog=20.8 pizza=32.7 donut=27.4 cake=21.3 chair=15.4 couch=30.7 potted plant=14.2 bed=32.8 dining table=19.8 toilet=38.1 tv=38.5 laptop=42.2 mouse=33.9 remote=9.0 keyboard=33.0 cell phone=14.8 microwave=29.4 oven=20.4 toaster=0.0 sink=23.0 refrigerator=32.7 book=4.4 clock=33.6 vase=22.0 scissors=16.9 teddy bear=30.9 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.1 [Epoch 115][Batch 99], LR: 1.00E-03, Speed: 113.021 samples/sec, ObjLoss=25.429, BoxCenterLoss=14.480, BoxScaleLoss=5.186, ClassLoss=10.752 [Epoch 115][Batch 199], LR: 1.00E-03, Speed: 110.340 samples/sec, ObjLoss=25.428, BoxCenterLoss=14.479, BoxScaleLoss=5.186, ClassLoss=10.751 [Epoch 115][Batch 299], LR: 1.00E-03, Speed: 8.072 samples/sec, ObjLoss=25.426, BoxCenterLoss=14.479, BoxScaleLoss=5.185, ClassLoss=10.750 [Epoch 115][Batch 399], LR: 1.00E-03, Speed: 9.395 samples/sec, ObjLoss=25.425, BoxCenterLoss=14.479, BoxScaleLoss=5.185, ClassLoss=10.749 [Epoch 115][Batch 499], LR: 1.00E-03, Speed: 9.965 samples/sec, ObjLoss=25.424, BoxCenterLoss=14.479, BoxScaleLoss=5.185, ClassLoss=10.748 [Epoch 115][Batch 599], LR: 1.00E-03, Speed: 12.920 samples/sec, ObjLoss=25.423, BoxCenterLoss=14.479, BoxScaleLoss=5.185, ClassLoss=10.747 [Epoch 115][Batch 699], LR: 1.00E-03, Speed: 9.673 samples/sec, ObjLoss=25.422, BoxCenterLoss=14.479, BoxScaleLoss=5.185, ClassLoss=10.746 [Epoch 115][Batch 799], LR: 1.00E-03, Speed: 36.107 samples/sec, ObjLoss=25.421, BoxCenterLoss=14.479, BoxScaleLoss=5.184, ClassLoss=10.745 [Epoch 115][Batch 899], LR: 1.00E-03, Speed: 12.117 samples/sec, ObjLoss=25.421, BoxCenterLoss=14.479, BoxScaleLoss=5.184, ClassLoss=10.744 [Epoch 115][Batch 999], LR: 1.00E-03, Speed: 9.333 samples/sec, ObjLoss=25.420, BoxCenterLoss=14.479, BoxScaleLoss=5.184, ClassLoss=10.744 [Epoch 115][Batch 1099], LR: 1.00E-03, Speed: 9.460 samples/sec, ObjLoss=25.418, BoxCenterLoss=14.478, BoxScaleLoss=5.184, ClassLoss=10.743 [Epoch 115][Batch 1199], LR: 1.00E-03, Speed: 10.098 samples/sec, ObjLoss=25.417, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.742 [Epoch 115][Batch 1299], LR: 1.00E-03, Speed: 8.329 samples/sec, ObjLoss=25.416, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.741 [Epoch 115][Batch 1399], LR: 1.00E-03, Speed: 108.114 samples/sec, ObjLoss=25.415, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.740 [Epoch 115][Batch 1499], LR: 1.00E-03, Speed: 8.016 samples/sec, ObjLoss=25.415, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.740 [Epoch 115][Batch 1599], LR: 1.00E-03, Speed: 93.219 samples/sec, ObjLoss=25.414, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.739 [Epoch 115][Batch 1699], LR: 1.00E-03, Speed: 8.847 samples/sec, ObjLoss=25.413, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.739 [Epoch 115][Batch 1799], LR: 1.00E-03, Speed: 11.372 samples/sec, ObjLoss=25.412, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.738 [Epoch 115] Training cost: 2216.812, ObjLoss=25.412, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.737 [Epoch 115] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.449 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.318 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.456 person=36.9 bicycle=18.2 car=24.4 motorcycle=28.3 airplane=45.0 bus=48.2 train=51.6 truck=20.5 boat=13.3 traffic light=13.6 fire hydrant=44.6 stop sign=45.6 parking meter=31.5 bench=12.9 bird=18.2 cat=39.2 dog=37.9 horse=38.1 sheep=26.8 cow=36.0 elephant=41.5 bear=46.6 zebra=44.2 giraffe=47.6 backpack=4.6 umbrella=22.8 handbag=5.6 tie=16.1 suitcase=16.1 frisbee=33.5 skis=11.8 snowboard=15.0 sports ball=17.2 kite=25.9 baseball bat=11.3 baseball glove=15.8 skateboard=29.9 surfboard=19.4 tennis racket=27.2 bottle=15.5 wine glass=18.9 cup=24.2 fork=13.8 knife=4.9 spoon=3.9 bowl=23.6 banana=11.0 apple=7.3 sandwich=18.9 orange=17.5 broccoli=10.8 carrot=9.0 hot dog=19.3 pizza=25.6 donut=25.5 cake=19.2 chair=15.1 couch=28.7 potted plant=10.7 bed=33.7 dining table=19.8 toilet=36.8 tv=38.1 laptop=34.6 mouse=34.1 remote=9.2 keyboard=18.1 cell phone=17.3 microwave=24.3 oven=19.5 toaster=2.4 sink=19.5 refrigerator=32.5 book=5.0 clock=29.7 vase=20.4 scissors=13.0 teddy bear=26.5 hair drier=0.0 toothbrush=5.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.1 [Epoch 116][Batch 99], LR: 1.00E-03, Speed: 9.143 samples/sec, ObjLoss=25.410, BoxCenterLoss=14.478, BoxScaleLoss=5.183, ClassLoss=10.737 [Epoch 116][Batch 199], LR: 1.00E-03, Speed: 107.725 samples/sec, ObjLoss=25.409, BoxCenterLoss=14.478, BoxScaleLoss=5.182, ClassLoss=10.736 [Epoch 116][Batch 299], LR: 1.00E-03, Speed: 8.059 samples/sec, ObjLoss=25.407, BoxCenterLoss=14.477, BoxScaleLoss=5.182, ClassLoss=10.735 [Epoch 116][Batch 399], LR: 1.00E-03, Speed: 9.882 samples/sec, ObjLoss=25.406, BoxCenterLoss=14.477, BoxScaleLoss=5.182, ClassLoss=10.733 [Epoch 116][Batch 499], LR: 1.00E-03, Speed: 8.352 samples/sec, ObjLoss=25.404, BoxCenterLoss=14.476, BoxScaleLoss=5.181, ClassLoss=10.732 [Epoch 116][Batch 599], LR: 1.00E-03, Speed: 8.770 samples/sec, ObjLoss=25.402, BoxCenterLoss=14.476, BoxScaleLoss=5.181, ClassLoss=10.731 [Epoch 116][Batch 699], LR: 1.00E-03, Speed: 9.625 samples/sec, ObjLoss=25.401, BoxCenterLoss=14.476, BoxScaleLoss=5.181, ClassLoss=10.730 [Epoch 116][Batch 799], LR: 1.00E-03, Speed: 8.277 samples/sec, ObjLoss=25.400, BoxCenterLoss=14.476, BoxScaleLoss=5.181, ClassLoss=10.730 [Epoch 116][Batch 899], LR: 1.00E-03, Speed: 9.127 samples/sec, ObjLoss=25.399, BoxCenterLoss=14.476, BoxScaleLoss=5.181, ClassLoss=10.729 [Epoch 116][Batch 999], LR: 1.00E-03, Speed: 7.578 samples/sec, ObjLoss=25.398, BoxCenterLoss=14.475, BoxScaleLoss=5.181, ClassLoss=10.728 [Epoch 116][Batch 1099], LR: 1.00E-03, Speed: 9.885 samples/sec, ObjLoss=25.397, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.727 [Epoch 116][Batch 1199], LR: 1.00E-03, Speed: 9.638 samples/sec, ObjLoss=25.396, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.726 [Epoch 116][Batch 1299], LR: 1.00E-03, Speed: 11.506 samples/sec, ObjLoss=25.395, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.725 [Epoch 116][Batch 1399], LR: 1.00E-03, Speed: 12.519 samples/sec, ObjLoss=25.393, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.724 [Epoch 116][Batch 1499], LR: 1.00E-03, Speed: 10.749 samples/sec, ObjLoss=25.393, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.724 [Epoch 116][Batch 1599], LR: 1.00E-03, Speed: 100.829 samples/sec, ObjLoss=25.392, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.723 [Epoch 116][Batch 1699], LR: 1.00E-03, Speed: 8.526 samples/sec, ObjLoss=25.391, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.722 [Epoch 116][Batch 1799], LR: 1.00E-03, Speed: 12.710 samples/sec, ObjLoss=25.391, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.722 [Epoch 116] Training cost: 2161.696, ObjLoss=25.391, BoxCenterLoss=14.476, BoxScaleLoss=5.180, ClassLoss=10.722 [Epoch 116] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.204 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.260 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430 person=36.6 bicycle=15.9 car=27.1 motorcycle=24.5 airplane=32.2 bus=40.7 train=36.7 truck=20.0 boat=11.9 traffic light=10.9 fire hydrant=44.6 stop sign=39.6 parking meter=27.6 bench=11.8 bird=20.8 cat=40.4 dog=36.9 horse=32.1 sheep=32.9 cow=32.0 elephant=42.6 bear=40.1 zebra=43.3 giraffe=40.1 backpack=6.1 umbrella=22.1 handbag=5.2 tie=17.3 suitcase=17.2 frisbee=36.7 skis=10.7 snowboard=12.7 sports ball=27.7 kite=24.3 baseball bat=12.7 baseball glove=20.2 skateboard=29.9 surfboard=19.3 tennis racket=29.9 bottle=20.0 wine glass=18.1 cup=25.1 fork=14.4 knife=6.5 spoon=5.6 bowl=24.0 banana=14.0 apple=8.9 sandwich=18.7 orange=15.3 broccoli=9.3 carrot=10.2 hot dog=19.3 pizza=32.4 donut=30.6 cake=20.2 chair=13.3 couch=24.2 potted plant=13.5 bed=23.1 dining table=14.3 toilet=36.4 tv=35.0 laptop=34.3 mouse=28.4 remote=10.4 keyboard=33.2 cell phone=19.5 microwave=36.0 oven=19.5 toaster=5.9 sink=20.0 refrigerator=30.0 book=5.1 clock=34.0 vase=20.4 scissors=11.9 teddy bear=27.8 hair drier=0.0 toothbrush=6.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.9 [Epoch 117][Batch 99], LR: 1.00E-03, Speed: 104.796 samples/sec, ObjLoss=25.390, BoxCenterLoss=14.475, BoxScaleLoss=5.180, ClassLoss=10.720 [Epoch 117][Batch 199], LR: 1.00E-03, Speed: 8.450 samples/sec, ObjLoss=25.388, BoxCenterLoss=14.475, BoxScaleLoss=5.179, ClassLoss=10.720 [Epoch 117][Batch 299], LR: 1.00E-03, Speed: 12.979 samples/sec, ObjLoss=25.387, BoxCenterLoss=14.475, BoxScaleLoss=5.179, ClassLoss=10.719 [Epoch 117][Batch 399], LR: 1.00E-03, Speed: 9.627 samples/sec, ObjLoss=25.387, BoxCenterLoss=14.476, BoxScaleLoss=5.179, ClassLoss=10.718 [Epoch 117][Batch 499], LR: 1.00E-03, Speed: 9.176 samples/sec, ObjLoss=25.386, BoxCenterLoss=14.476, BoxScaleLoss=5.179, ClassLoss=10.717 [Epoch 117][Batch 599], LR: 1.00E-03, Speed: 7.000 samples/sec, ObjLoss=25.386, BoxCenterLoss=14.476, BoxScaleLoss=5.179, ClassLoss=10.717 [Epoch 117][Batch 699], LR: 1.00E-03, Speed: 8.158 samples/sec, ObjLoss=25.385, BoxCenterLoss=14.476, BoxScaleLoss=5.179, ClassLoss=10.716 [Epoch 117][Batch 799], LR: 1.00E-03, Speed: 11.722 samples/sec, ObjLoss=25.384, BoxCenterLoss=14.476, BoxScaleLoss=5.179, ClassLoss=10.715 [Epoch 117][Batch 899], LR: 1.00E-03, Speed: 10.333 samples/sec, ObjLoss=25.383, BoxCenterLoss=14.476, BoxScaleLoss=5.179, ClassLoss=10.714 [Epoch 117][Batch 999], LR: 1.00E-03, Speed: 8.190 samples/sec, ObjLoss=25.382, BoxCenterLoss=14.476, BoxScaleLoss=5.178, ClassLoss=10.713 [Epoch 117][Batch 1099], LR: 1.00E-03, Speed: 12.253 samples/sec, ObjLoss=25.380, BoxCenterLoss=14.475, BoxScaleLoss=5.178, ClassLoss=10.713 [Epoch 117][Batch 1199], LR: 1.00E-03, Speed: 8.498 samples/sec, ObjLoss=25.379, BoxCenterLoss=14.475, BoxScaleLoss=5.178, ClassLoss=10.712 [Epoch 117][Batch 1299], LR: 1.00E-03, Speed: 10.586 samples/sec, ObjLoss=25.378, BoxCenterLoss=14.475, BoxScaleLoss=5.178, ClassLoss=10.711 [Epoch 117][Batch 1399], LR: 1.00E-03, Speed: 9.520 samples/sec, ObjLoss=25.377, BoxCenterLoss=14.474, BoxScaleLoss=5.177, ClassLoss=10.710 [Epoch 117][Batch 1499], LR: 1.00E-03, Speed: 8.889 samples/sec, ObjLoss=25.375, BoxCenterLoss=14.474, BoxScaleLoss=5.177, ClassLoss=10.709 [Epoch 117][Batch 1599], LR: 1.00E-03, Speed: 93.611 samples/sec, ObjLoss=25.375, BoxCenterLoss=14.474, BoxScaleLoss=5.177, ClassLoss=10.708 [Epoch 117][Batch 1699], LR: 1.00E-03, Speed: 9.132 samples/sec, ObjLoss=25.373, BoxCenterLoss=14.474, BoxScaleLoss=5.177, ClassLoss=10.707 [Epoch 117][Batch 1799], LR: 1.00E-03, Speed: 99.115 samples/sec, ObjLoss=25.372, BoxCenterLoss=14.473, BoxScaleLoss=5.176, ClassLoss=10.706 [Epoch 117] Training cost: 2247.534, ObjLoss=25.371, BoxCenterLoss=14.473, BoxScaleLoss=5.176, ClassLoss=10.705 [Epoch 117] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.457 person=38.2 bicycle=18.5 car=26.6 motorcycle=28.7 airplane=36.3 bus=47.1 train=44.0 truck=21.7 boat=12.3 traffic light=11.0 fire hydrant=49.0 stop sign=44.6 parking meter=28.2 bench=12.9 bird=18.2 cat=40.9 dog=38.8 horse=35.0 sheep=33.7 cow=33.0 elephant=43.9 bear=45.5 zebra=46.6 giraffe=49.3 backpack=4.7 umbrella=23.5 handbag=4.8 tie=16.7 suitcase=15.9 frisbee=36.7 skis=12.0 snowboard=14.3 sports ball=28.3 kite=21.4 baseball bat=11.0 baseball glove=18.6 skateboard=31.3 surfboard=18.8 tennis racket=26.9 bottle=21.0 wine glass=20.2 cup=23.9 fork=13.3 knife=4.3 spoon=4.7 bowl=25.3 banana=12.1 apple=8.3 sandwich=18.2 orange=20.0 broccoli=11.2 carrot=9.7 hot dog=14.8 pizza=35.7 donut=26.4 cake=22.0 chair=14.6 couch=26.5 potted plant=12.3 bed=33.4 dining table=19.6 toilet=37.8 tv=38.0 laptop=37.1 mouse=33.7 remote=10.0 keyboard=26.3 cell phone=17.8 microwave=32.7 oven=19.3 toaster=0.0 sink=21.5 refrigerator=33.7 book=4.6 clock=32.7 vase=23.7 scissors=16.3 teddy bear=29.2 hair drier=0.0 toothbrush=5.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.8 [Epoch 118][Batch 99], LR: 1.00E-03, Speed: 16.350 samples/sec, ObjLoss=25.370, BoxCenterLoss=14.473, BoxScaleLoss=5.176, ClassLoss=10.705 [Epoch 118][Batch 199], LR: 1.00E-03, Speed: 10.851 samples/sec, ObjLoss=25.369, BoxCenterLoss=14.473, BoxScaleLoss=5.176, ClassLoss=10.704 [Epoch 118][Batch 299], LR: 1.00E-03, Speed: 111.863 samples/sec, ObjLoss=25.368, BoxCenterLoss=14.473, BoxScaleLoss=5.176, ClassLoss=10.703 [Epoch 118][Batch 399], LR: 1.00E-03, Speed: 107.212 samples/sec, ObjLoss=25.366, BoxCenterLoss=14.472, BoxScaleLoss=5.175, ClassLoss=10.702 [Epoch 118][Batch 499], LR: 1.00E-03, Speed: 8.999 samples/sec, ObjLoss=25.365, BoxCenterLoss=14.472, BoxScaleLoss=5.175, ClassLoss=10.701 [Epoch 118][Batch 599], LR: 1.00E-03, Speed: 12.154 samples/sec, ObjLoss=25.365, BoxCenterLoss=14.472, BoxScaleLoss=5.175, ClassLoss=10.700 [Epoch 118][Batch 699], LR: 1.00E-03, Speed: 9.957 samples/sec, ObjLoss=25.364, BoxCenterLoss=14.472, BoxScaleLoss=5.175, ClassLoss=10.699 [Epoch 118][Batch 799], LR: 1.00E-03, Speed: 8.762 samples/sec, ObjLoss=25.363, BoxCenterLoss=14.472, BoxScaleLoss=5.175, ClassLoss=10.698 [Epoch 118][Batch 899], LR: 1.00E-03, Speed: 12.205 samples/sec, ObjLoss=25.362, BoxCenterLoss=14.472, BoxScaleLoss=5.174, ClassLoss=10.698 [Epoch 118][Batch 999], LR: 1.00E-03, Speed: 96.538 samples/sec, ObjLoss=25.361, BoxCenterLoss=14.472, BoxScaleLoss=5.174, ClassLoss=10.697 [Epoch 118][Batch 1099], LR: 1.00E-03, Speed: 10.787 samples/sec, ObjLoss=25.360, BoxCenterLoss=14.472, BoxScaleLoss=5.174, ClassLoss=10.696 [Epoch 118][Batch 1199], LR: 1.00E-03, Speed: 9.060 samples/sec, ObjLoss=25.359, BoxCenterLoss=14.471, BoxScaleLoss=5.174, ClassLoss=10.695 [Epoch 118][Batch 1299], LR: 1.00E-03, Speed: 12.530 samples/sec, ObjLoss=25.357, BoxCenterLoss=14.471, BoxScaleLoss=5.174, ClassLoss=10.694 [Epoch 118][Batch 1399], LR: 1.00E-03, Speed: 8.997 samples/sec, ObjLoss=25.357, BoxCenterLoss=14.471, BoxScaleLoss=5.174, ClassLoss=10.693 [Epoch 118][Batch 1499], LR: 1.00E-03, Speed: 9.139 samples/sec, ObjLoss=25.356, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.692 [Epoch 118][Batch 1599], LR: 1.00E-03, Speed: 9.133 samples/sec, ObjLoss=25.355, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.691 [Epoch 118][Batch 1699], LR: 1.00E-03, Speed: 8.942 samples/sec, ObjLoss=25.354, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.691 [Epoch 118][Batch 1799], LR: 1.00E-03, Speed: 13.565 samples/sec, ObjLoss=25.353, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.690 [Epoch 118] Training cost: 2227.733, ObjLoss=25.352, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.689 [Epoch 118] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.453 person=37.4 bicycle=16.4 car=25.7 motorcycle=28.4 airplane=42.3 bus=44.9 train=47.9 truck=21.1 boat=12.6 traffic light=15.1 fire hydrant=45.7 stop sign=43.8 parking meter=29.8 bench=13.3 bird=16.7 cat=42.6 dog=37.5 horse=36.5 sheep=33.5 cow=32.4 elephant=44.7 bear=44.9 zebra=47.0 giraffe=47.4 backpack=5.3 umbrella=23.4 handbag=5.5 tie=16.5 suitcase=20.9 frisbee=35.9 skis=7.6 snowboard=12.9 sports ball=23.3 kite=27.9 baseball bat=12.1 baseball glove=18.3 skateboard=25.0 surfboard=21.0 tennis racket=26.5 bottle=19.7 wine glass=18.7 cup=23.4 fork=12.7 knife=4.2 spoon=3.5 bowl=22.4 banana=13.6 apple=8.1 sandwich=18.5 orange=17.6 broccoli=11.6 carrot=9.3 hot dog=16.5 pizza=34.5 donut=25.9 cake=16.5 chair=15.7 couch=29.5 potted plant=13.2 bed=30.5 dining table=17.8 toilet=37.5 tv=34.1 laptop=33.1 mouse=32.7 remote=10.4 keyboard=28.7 cell phone=18.5 microwave=31.7 oven=19.8 toaster=0.0 sink=19.3 refrigerator=32.7 book=4.7 clock=31.5 vase=21.5 scissors=18.3 teddy bear=28.0 hair drier=0.0 toothbrush=5.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.5 [Epoch 119][Batch 99], LR: 1.00E-03, Speed: 8.205 samples/sec, ObjLoss=25.352, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.689 [Epoch 119][Batch 199], LR: 1.00E-03, Speed: 10.815 samples/sec, ObjLoss=25.351, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.688 [Epoch 119][Batch 299], LR: 1.00E-03, Speed: 11.100 samples/sec, ObjLoss=25.349, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.687 [Epoch 119][Batch 399], LR: 1.00E-03, Speed: 10.269 samples/sec, ObjLoss=25.348, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.686 [Epoch 119][Batch 499], LR: 1.00E-03, Speed: 7.881 samples/sec, ObjLoss=25.347, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.685 [Epoch 119][Batch 599], LR: 1.00E-03, Speed: 113.193 samples/sec, ObjLoss=25.347, BoxCenterLoss=14.472, BoxScaleLoss=5.173, ClassLoss=10.685 [Epoch 119][Batch 699], LR: 1.00E-03, Speed: 9.781 samples/sec, ObjLoss=25.346, BoxCenterLoss=14.472, BoxScaleLoss=5.173, ClassLoss=10.684 [Epoch 119][Batch 799], LR: 1.00E-03, Speed: 12.664 samples/sec, ObjLoss=25.345, BoxCenterLoss=14.472, BoxScaleLoss=5.173, ClassLoss=10.684 [Epoch 119][Batch 899], LR: 1.00E-03, Speed: 12.048 samples/sec, ObjLoss=25.344, BoxCenterLoss=14.471, BoxScaleLoss=5.173, ClassLoss=10.683 [Epoch 119][Batch 999], LR: 1.00E-03, Speed: 92.076 samples/sec, ObjLoss=25.343, BoxCenterLoss=14.471, BoxScaleLoss=5.172, ClassLoss=10.682 [Epoch 119][Batch 1099], LR: 1.00E-03, Speed: 8.282 samples/sec, ObjLoss=25.342, BoxCenterLoss=14.471, BoxScaleLoss=5.172, ClassLoss=10.681 [Epoch 119][Batch 1199], LR: 1.00E-03, Speed: 9.863 samples/sec, ObjLoss=25.342, BoxCenterLoss=14.472, BoxScaleLoss=5.172, ClassLoss=10.680 [Epoch 119][Batch 1299], LR: 1.00E-03, Speed: 7.605 samples/sec, ObjLoss=25.341, BoxCenterLoss=14.472, BoxScaleLoss=5.172, ClassLoss=10.680 [Epoch 119][Batch 1399], LR: 1.00E-03, Speed: 73.059 samples/sec, ObjLoss=25.340, BoxCenterLoss=14.472, BoxScaleLoss=5.172, ClassLoss=10.679 [Epoch 119][Batch 1499], LR: 1.00E-03, Speed: 10.571 samples/sec, ObjLoss=25.340, BoxCenterLoss=14.472, BoxScaleLoss=5.172, ClassLoss=10.678 [Epoch 119][Batch 1599], LR: 1.00E-03, Speed: 9.600 samples/sec, ObjLoss=25.339, BoxCenterLoss=14.472, BoxScaleLoss=5.171, ClassLoss=10.677 [Epoch 119][Batch 1699], LR: 1.00E-03, Speed: 8.526 samples/sec, ObjLoss=25.338, BoxCenterLoss=14.472, BoxScaleLoss=5.171, ClassLoss=10.676 [Epoch 119][Batch 1799], LR: 1.00E-03, Speed: 12.937 samples/sec, ObjLoss=25.337, BoxCenterLoss=14.471, BoxScaleLoss=5.171, ClassLoss=10.675 [Epoch 119] Training cost: 2220.708, ObjLoss=25.337, BoxCenterLoss=14.471, BoxScaleLoss=5.171, ClassLoss=10.675 [Epoch 119] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.446 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.217 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.469 person=35.6 bicycle=16.1 car=28.4 motorcycle=25.9 airplane=39.6 bus=48.1 train=48.7 truck=23.5 boat=13.2 traffic light=13.9 fire hydrant=45.6 stop sign=42.4 parking meter=28.8 bench=13.4 bird=18.7 cat=43.7 dog=35.9 horse=35.5 sheep=31.1 cow=34.2 elephant=44.7 bear=50.8 zebra=47.1 giraffe=45.7 backpack=5.8 umbrella=22.7 handbag=5.0 tie=16.7 suitcase=18.7 frisbee=28.3 skis=10.4 snowboard=9.7 sports ball=14.1 kite=23.5 baseball bat=11.0 baseball glove=21.0 skateboard=27.4 surfboard=19.0 tennis racket=27.0 bottle=17.6 wine glass=18.4 cup=22.2 fork=11.7 knife=3.7 spoon=4.1 bowl=22.9 banana=12.0 apple=7.2 sandwich=20.7 orange=15.3 broccoli=10.1 carrot=8.9 hot dog=19.5 pizza=31.6 donut=23.4 cake=17.1 chair=14.2 couch=29.1 potted plant=13.1 bed=28.6 dining table=17.6 toilet=40.3 tv=36.8 laptop=40.6 mouse=24.5 remote=10.2 keyboard=31.7 cell phone=18.3 microwave=35.7 oven=18.2 toaster=4.8 sink=21.7 refrigerator=27.5 book=4.9 clock=31.2 vase=18.9 scissors=10.4 teddy bear=31.9 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.1 [Epoch 120][Batch 99], LR: 1.00E-03, Speed: 9.210 samples/sec, ObjLoss=25.335, BoxCenterLoss=14.471, BoxScaleLoss=5.170, ClassLoss=10.674 [Epoch 120][Batch 199], LR: 1.00E-03, Speed: 7.895 samples/sec, ObjLoss=25.334, BoxCenterLoss=14.471, BoxScaleLoss=5.170, ClassLoss=10.673 [Epoch 120][Batch 299], LR: 1.00E-03, Speed: 10.663 samples/sec, ObjLoss=25.334, BoxCenterLoss=14.471, BoxScaleLoss=5.170, ClassLoss=10.672 [Epoch 120][Batch 399], LR: 1.00E-03, Speed: 10.417 samples/sec, ObjLoss=25.333, BoxCenterLoss=14.471, BoxScaleLoss=5.170, ClassLoss=10.671 [Epoch 120][Batch 499], LR: 1.00E-03, Speed: 12.074 samples/sec, ObjLoss=25.331, BoxCenterLoss=14.471, BoxScaleLoss=5.170, ClassLoss=10.670 [Epoch 120][Batch 599], LR: 1.00E-03, Speed: 101.098 samples/sec, ObjLoss=25.331, BoxCenterLoss=14.471, BoxScaleLoss=5.170, ClassLoss=10.670 [Epoch 120][Batch 699], LR: 1.00E-03, Speed: 109.941 samples/sec, ObjLoss=25.330, BoxCenterLoss=14.472, BoxScaleLoss=5.170, ClassLoss=10.669 [Epoch 120][Batch 799], LR: 1.00E-03, Speed: 100.865 samples/sec, ObjLoss=25.330, BoxCenterLoss=14.472, BoxScaleLoss=5.170, ClassLoss=10.668 [Epoch 120][Batch 899], LR: 1.00E-03, Speed: 9.666 samples/sec, ObjLoss=25.328, BoxCenterLoss=14.472, BoxScaleLoss=5.169, ClassLoss=10.667 [Epoch 120][Batch 999], LR: 1.00E-03, Speed: 8.030 samples/sec, ObjLoss=25.327, BoxCenterLoss=14.471, BoxScaleLoss=5.169, ClassLoss=10.666 [Epoch 120][Batch 1099], LR: 1.00E-03, Speed: 9.024 samples/sec, ObjLoss=25.325, BoxCenterLoss=14.471, BoxScaleLoss=5.169, ClassLoss=10.665 [Epoch 120][Batch 1199], LR: 1.00E-03, Speed: 10.292 samples/sec, ObjLoss=25.324, BoxCenterLoss=14.471, BoxScaleLoss=5.169, ClassLoss=10.665 [Epoch 120][Batch 1299], LR: 1.00E-03, Speed: 11.066 samples/sec, ObjLoss=25.323, BoxCenterLoss=14.470, BoxScaleLoss=5.169, ClassLoss=10.664 [Epoch 120][Batch 1399], LR: 1.00E-03, Speed: 8.633 samples/sec, ObjLoss=25.322, BoxCenterLoss=14.470, BoxScaleLoss=5.168, ClassLoss=10.663 [Epoch 120][Batch 1499], LR: 1.00E-03, Speed: 10.528 samples/sec, ObjLoss=25.321, BoxCenterLoss=14.470, BoxScaleLoss=5.168, ClassLoss=10.662 [Epoch 120][Batch 1599], LR: 1.00E-03, Speed: 8.613 samples/sec, ObjLoss=25.320, BoxCenterLoss=14.470, BoxScaleLoss=5.168, ClassLoss=10.661 [Epoch 120][Batch 1699], LR: 1.00E-03, Speed: 9.639 samples/sec, ObjLoss=25.319, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.660 [Epoch 120][Batch 1799], LR: 1.00E-03, Speed: 8.911 samples/sec, ObjLoss=25.318, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.659 [Epoch 120] Training cost: 2217.090, ObjLoss=25.318, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.659 [Epoch 120] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.440 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.087 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436 person=35.7 bicycle=16.6 car=23.1 motorcycle=29.1 airplane=41.9 bus=40.4 train=47.1 truck=20.1 boat=9.4 traffic light=14.5 fire hydrant=45.1 stop sign=43.1 parking meter=26.4 bench=14.1 bird=18.0 cat=45.2 dog=40.1 horse=33.6 sheep=30.8 cow=29.8 elephant=41.6 bear=46.8 zebra=43.1 giraffe=50.0 backpack=4.9 umbrella=18.3 handbag=5.6 tie=14.9 suitcase=17.2 frisbee=32.2 skis=8.9 snowboard=16.9 sports ball=26.0 kite=26.9 baseball bat=11.4 baseball glove=15.5 skateboard=25.5 surfboard=19.3 tennis racket=27.9 bottle=19.3 wine glass=20.0 cup=23.7 fork=10.7 knife=4.2 spoon=3.4 bowl=18.9 banana=10.6 apple=5.1 sandwich=15.8 orange=12.6 broccoli=8.5 carrot=6.0 hot dog=15.9 pizza=29.4 donut=22.6 cake=21.5 chair=13.8 couch=29.9 potted plant=11.3 bed=33.9 dining table=20.7 toilet=37.9 tv=35.8 laptop=35.0 mouse=33.6 remote=9.8 keyboard=27.5 cell phone=18.2 microwave=25.9 oven=19.9 toaster=0.0 sink=18.5 refrigerator=33.4 book=4.4 clock=32.6 vase=19.8 scissors=16.4 teddy bear=26.3 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.7 [Epoch 121][Batch 99], LR: 1.00E-03, Speed: 6.868 samples/sec, ObjLoss=25.317, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.658 [Epoch 121][Batch 199], LR: 1.00E-03, Speed: 11.420 samples/sec, ObjLoss=25.317, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.657 [Epoch 121][Batch 299], LR: 1.00E-03, Speed: 11.900 samples/sec, ObjLoss=25.316, BoxCenterLoss=14.471, BoxScaleLoss=5.167, ClassLoss=10.657 [Epoch 121][Batch 399], LR: 1.00E-03, Speed: 10.091 samples/sec, ObjLoss=25.315, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.656 [Epoch 121][Batch 499], LR: 1.00E-03, Speed: 106.257 samples/sec, ObjLoss=25.315, BoxCenterLoss=14.471, BoxScaleLoss=5.167, ClassLoss=10.655 [Epoch 121][Batch 599], LR: 1.00E-03, Speed: 8.511 samples/sec, ObjLoss=25.314, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.654 [Epoch 121][Batch 699], LR: 1.00E-03, Speed: 7.068 samples/sec, ObjLoss=25.313, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.654 [Epoch 121][Batch 799], LR: 1.00E-03, Speed: 10.145 samples/sec, ObjLoss=25.312, BoxCenterLoss=14.470, BoxScaleLoss=5.167, ClassLoss=10.653 [Epoch 121][Batch 899], LR: 1.00E-03, Speed: 7.586 samples/sec, ObjLoss=25.311, BoxCenterLoss=14.470, BoxScaleLoss=5.166, ClassLoss=10.652 [Epoch 121][Batch 999], LR: 1.00E-03, Speed: 10.784 samples/sec, ObjLoss=25.309, BoxCenterLoss=14.470, BoxScaleLoss=5.166, ClassLoss=10.651 [Epoch 121][Batch 1099], LR: 1.00E-03, Speed: 123.869 samples/sec, ObjLoss=25.308, BoxCenterLoss=14.470, BoxScaleLoss=5.166, ClassLoss=10.650 [Epoch 121][Batch 1199], LR: 1.00E-03, Speed: 16.848 samples/sec, ObjLoss=25.307, BoxCenterLoss=14.469, BoxScaleLoss=5.166, ClassLoss=10.650 [Epoch 121][Batch 1299], LR: 1.00E-03, Speed: 8.915 samples/sec, ObjLoss=25.306, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.649 [Epoch 121][Batch 1399], LR: 1.00E-03, Speed: 103.852 samples/sec, ObjLoss=25.305, BoxCenterLoss=14.469, BoxScaleLoss=5.166, ClassLoss=10.648 [Epoch 121][Batch 1499], LR: 1.00E-03, Speed: 12.100 samples/sec, ObjLoss=25.304, BoxCenterLoss=14.469, BoxScaleLoss=5.166, ClassLoss=10.647 [Epoch 121][Batch 1599], LR: 1.00E-03, Speed: 9.584 samples/sec, ObjLoss=25.303, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.647 [Epoch 121][Batch 1699], LR: 1.00E-03, Speed: 10.696 samples/sec, ObjLoss=25.302, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.646 [Epoch 121][Batch 1799], LR: 1.00E-03, Speed: 11.174 samples/sec, ObjLoss=25.301, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.645 [Epoch 121] Training cost: 2078.573, ObjLoss=25.300, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.645 [Epoch 121] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.234 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.220 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461 person=35.7 bicycle=17.8 car=25.2 motorcycle=30.9 airplane=44.2 bus=46.6 train=50.7 truck=22.5 boat=11.8 traffic light=13.1 fire hydrant=42.1 stop sign=40.3 parking meter=24.9 bench=13.5 bird=19.7 cat=43.2 dog=36.5 horse=33.4 sheep=27.0 cow=33.4 elephant=41.2 bear=47.3 zebra=43.9 giraffe=51.0 backpack=5.6 umbrella=21.8 handbag=5.6 tie=17.1 suitcase=16.2 frisbee=28.4 skis=10.6 snowboard=15.6 sports ball=27.6 kite=23.1 baseball bat=13.6 baseball glove=16.6 skateboard=29.5 surfboard=20.0 tennis racket=25.7 bottle=20.7 wine glass=21.5 cup=24.0 fork=11.6 knife=4.7 spoon=5.4 bowl=23.7 banana=10.7 apple=6.6 sandwich=20.3 orange=16.8 broccoli=9.8 carrot=11.1 hot dog=18.8 pizza=35.7 donut=21.8 cake=22.9 chair=12.4 couch=28.1 potted plant=11.5 bed=33.6 dining table=18.8 toilet=36.6 tv=34.0 laptop=34.0 mouse=30.5 remote=12.9 keyboard=32.6 cell phone=16.9 microwave=33.3 oven=20.5 toaster=0.0 sink=17.4 refrigerator=29.7 book=4.2 clock=33.1 vase=19.6 scissors=23.2 teddy bear=29.4 hair drier=0.0 toothbrush=2.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.4 [Epoch 122][Batch 99], LR: 1.00E-03, Speed: 11.780 samples/sec, ObjLoss=25.299, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.644 [Epoch 122][Batch 199], LR: 1.00E-03, Speed: 8.603 samples/sec, ObjLoss=25.298, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.643 [Epoch 122][Batch 299], LR: 1.00E-03, Speed: 8.614 samples/sec, ObjLoss=25.297, BoxCenterLoss=14.469, BoxScaleLoss=5.165, ClassLoss=10.642 [Epoch 122][Batch 399], LR: 1.00E-03, Speed: 9.813 samples/sec, ObjLoss=25.296, BoxCenterLoss=14.468, BoxScaleLoss=5.164, ClassLoss=10.642 [Epoch 122][Batch 499], LR: 1.00E-03, Speed: 16.449 samples/sec, ObjLoss=25.295, BoxCenterLoss=14.468, BoxScaleLoss=5.164, ClassLoss=10.641 [Epoch 122][Batch 599], LR: 1.00E-03, Speed: 33.035 samples/sec, ObjLoss=25.294, BoxCenterLoss=14.468, BoxScaleLoss=5.164, ClassLoss=10.640 [Epoch 122][Batch 699], LR: 1.00E-03, Speed: 9.988 samples/sec, ObjLoss=25.293, BoxCenterLoss=14.468, BoxScaleLoss=5.164, ClassLoss=10.639 [Epoch 122][Batch 799], LR: 1.00E-03, Speed: 101.918 samples/sec, ObjLoss=25.292, BoxCenterLoss=14.468, BoxScaleLoss=5.164, ClassLoss=10.638 [Epoch 122][Batch 899], LR: 1.00E-03, Speed: 9.452 samples/sec, ObjLoss=25.291, BoxCenterLoss=14.468, BoxScaleLoss=5.164, ClassLoss=10.637 [Epoch 122][Batch 999], LR: 1.00E-03, Speed: 127.765 samples/sec, ObjLoss=25.290, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.636 [Epoch 122][Batch 1099], LR: 1.00E-03, Speed: 9.680 samples/sec, ObjLoss=25.289, BoxCenterLoss=14.467, BoxScaleLoss=5.163, ClassLoss=10.636 [Epoch 122][Batch 1199], LR: 1.00E-03, Speed: 10.552 samples/sec, ObjLoss=25.289, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.635 [Epoch 122][Batch 1299], LR: 1.00E-03, Speed: 63.582 samples/sec, ObjLoss=25.288, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.634 [Epoch 122][Batch 1399], LR: 1.00E-03, Speed: 7.600 samples/sec, ObjLoss=25.287, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.634 [Epoch 122][Batch 1499], LR: 1.00E-03, Speed: 8.038 samples/sec, ObjLoss=25.286, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.633 [Epoch 122][Batch 1599], LR: 1.00E-03, Speed: 9.085 samples/sec, ObjLoss=25.285, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.632 [Epoch 122][Batch 1699], LR: 1.00E-03, Speed: 8.531 samples/sec, ObjLoss=25.284, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.631 [Epoch 122][Batch 1799], LR: 1.00E-03, Speed: 10.724 samples/sec, ObjLoss=25.283, BoxCenterLoss=14.468, BoxScaleLoss=5.163, ClassLoss=10.630 [Epoch 122] Training cost: 2163.681, ObjLoss=25.283, BoxCenterLoss=14.468, BoxScaleLoss=5.162, ClassLoss=10.630 [Epoch 122] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.447 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.217 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441 person=36.2 bicycle=19.0 car=24.5 motorcycle=29.0 airplane=32.8 bus=46.1 train=45.5 truck=22.5 boat=10.7 traffic light=14.0 fire hydrant=51.4 stop sign=40.8 parking meter=26.7 bench=11.9 bird=17.8 cat=44.1 dog=39.6 horse=35.9 sheep=32.7 cow=35.7 elephant=45.2 bear=51.3 zebra=47.1 giraffe=49.5 backpack=5.9 umbrella=24.4 handbag=5.6 tie=15.2 suitcase=18.4 frisbee=30.9 skis=11.3 snowboard=12.2 sports ball=23.7 kite=24.5 baseball bat=10.4 baseball glove=19.1 skateboard=28.1 surfboard=18.1 tennis racket=26.3 bottle=18.2 wine glass=19.6 cup=24.1 fork=11.6 knife=4.1 spoon=4.4 bowl=20.8 banana=12.2 apple=7.1 sandwich=17.6 orange=17.2 broccoli=9.8 carrot=9.0 hot dog=15.5 pizza=33.4 donut=24.0 cake=19.1 chair=14.2 couch=26.6 potted plant=11.9 bed=31.7 dining table=19.4 toilet=37.0 tv=35.0 laptop=39.6 mouse=32.6 remote=9.4 keyboard=25.6 cell phone=17.2 microwave=24.9 oven=19.8 toaster=0.0 sink=20.9 refrigerator=32.5 book=5.2 clock=33.1 vase=20.8 scissors=14.7 teddy bear=29.8 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.2 [Epoch 123][Batch 99], LR: 1.00E-03, Speed: 9.840 samples/sec, ObjLoss=25.282, BoxCenterLoss=14.468, BoxScaleLoss=5.162, ClassLoss=10.629 [Epoch 123][Batch 199], LR: 1.00E-03, Speed: 8.849 samples/sec, ObjLoss=25.281, BoxCenterLoss=14.468, BoxScaleLoss=5.162, ClassLoss=10.629 [Epoch 123][Batch 299], LR: 1.00E-03, Speed: 6.833 samples/sec, ObjLoss=25.280, BoxCenterLoss=14.468, BoxScaleLoss=5.162, ClassLoss=10.628 [Epoch 123][Batch 399], LR: 1.00E-03, Speed: 9.945 samples/sec, ObjLoss=25.280, BoxCenterLoss=14.467, BoxScaleLoss=5.162, ClassLoss=10.627 [Epoch 123][Batch 499], LR: 1.00E-03, Speed: 9.538 samples/sec, ObjLoss=25.278, BoxCenterLoss=14.467, BoxScaleLoss=5.161, ClassLoss=10.626 [Epoch 123][Batch 599], LR: 1.00E-03, Speed: 8.541 samples/sec, ObjLoss=25.277, BoxCenterLoss=14.467, BoxScaleLoss=5.161, ClassLoss=10.625 [Epoch 123][Batch 699], LR: 1.00E-03, Speed: 8.962 samples/sec, ObjLoss=25.276, BoxCenterLoss=14.466, BoxScaleLoss=5.161, ClassLoss=10.624 [Epoch 123][Batch 799], LR: 1.00E-03, Speed: 10.173 samples/sec, ObjLoss=25.275, BoxCenterLoss=14.466, BoxScaleLoss=5.161, ClassLoss=10.624 [Epoch 123][Batch 899], LR: 1.00E-03, Speed: 38.688 samples/sec, ObjLoss=25.274, BoxCenterLoss=14.466, BoxScaleLoss=5.161, ClassLoss=10.623 [Epoch 123][Batch 999], LR: 1.00E-03, Speed: 11.639 samples/sec, ObjLoss=25.273, BoxCenterLoss=14.466, BoxScaleLoss=5.161, ClassLoss=10.622 [Epoch 123][Batch 1099], LR: 1.00E-03, Speed: 7.272 samples/sec, ObjLoss=25.272, BoxCenterLoss=14.467, BoxScaleLoss=5.161, ClassLoss=10.622 [Epoch 123][Batch 1199], LR: 1.00E-03, Speed: 83.451 samples/sec, ObjLoss=25.272, BoxCenterLoss=14.467, BoxScaleLoss=5.161, ClassLoss=10.621 [Epoch 123][Batch 1299], LR: 1.00E-03, Speed: 11.584 samples/sec, ObjLoss=25.271, BoxCenterLoss=14.467, BoxScaleLoss=5.161, ClassLoss=10.620 [Epoch 123][Batch 1399], LR: 1.00E-03, Speed: 9.727 samples/sec, ObjLoss=25.269, BoxCenterLoss=14.466, BoxScaleLoss=5.160, ClassLoss=10.619 [Epoch 123][Batch 1499], LR: 1.00E-03, Speed: 11.369 samples/sec, ObjLoss=25.269, BoxCenterLoss=14.467, BoxScaleLoss=5.160, ClassLoss=10.618 [Epoch 123][Batch 1599], LR: 1.00E-03, Speed: 103.378 samples/sec, ObjLoss=25.267, BoxCenterLoss=14.466, BoxScaleLoss=5.160, ClassLoss=10.617 [Epoch 123][Batch 1699], LR: 1.00E-03, Speed: 9.253 samples/sec, ObjLoss=25.266, BoxCenterLoss=14.466, BoxScaleLoss=5.160, ClassLoss=10.616 [Epoch 123][Batch 1799], LR: 1.00E-03, Speed: 10.384 samples/sec, ObjLoss=25.266, BoxCenterLoss=14.466, BoxScaleLoss=5.159, ClassLoss=10.615 [Epoch 123] Training cost: 2212.401, ObjLoss=25.265, BoxCenterLoss=14.466, BoxScaleLoss=5.159, ClassLoss=10.615 [Epoch 123] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.451 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.476 person=37.0 bicycle=16.7 car=26.2 motorcycle=32.1 airplane=41.1 bus=46.3 train=45.0 truck=24.1 boat=14.7 traffic light=14.6 fire hydrant=45.5 stop sign=40.5 parking meter=30.5 bench=14.2 bird=18.7 cat=44.3 dog=36.1 horse=34.7 sheep=31.3 cow=35.3 elephant=43.0 bear=52.6 zebra=48.7 giraffe=49.4 backpack=5.8 umbrella=21.7 handbag=5.2 tie=17.1 suitcase=16.8 frisbee=28.2 skis=12.4 snowboard=14.8 sports ball=25.3 kite=24.9 baseball bat=11.7 baseball glove=17.9 skateboard=26.7 surfboard=20.4 tennis racket=26.8 bottle=19.2 wine glass=21.8 cup=24.7 fork=13.2 knife=3.5 spoon=4.7 bowl=21.7 banana=10.5 apple=6.9 sandwich=15.1 orange=14.2 broccoli=10.9 carrot=10.5 hot dog=19.5 pizza=29.6 donut=25.6 cake=18.1 chair=14.8 couch=27.4 potted plant=12.4 bed=36.8 dining table=18.2 toilet=38.1 tv=36.7 laptop=38.1 mouse=38.9 remote=11.1 keyboard=30.5 cell phone=20.1 microwave=35.9 oven=21.3 toaster=0.0 sink=21.7 refrigerator=33.2 book=4.4 clock=32.4 vase=22.8 scissors=17.6 teddy bear=26.0 hair drier=0.0 toothbrush=7.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.9 [Epoch 124][Batch 99], LR: 1.00E-03, Speed: 10.394 samples/sec, ObjLoss=25.264, BoxCenterLoss=14.466, BoxScaleLoss=5.159, ClassLoss=10.614 [Epoch 124][Batch 199], LR: 1.00E-03, Speed: 8.759 samples/sec, ObjLoss=25.263, BoxCenterLoss=14.466, BoxScaleLoss=5.159, ClassLoss=10.613 [Epoch 124][Batch 299], LR: 1.00E-03, Speed: 6.937 samples/sec, ObjLoss=25.262, BoxCenterLoss=14.466, BoxScaleLoss=5.159, ClassLoss=10.612 [Epoch 124][Batch 399], LR: 1.00E-03, Speed: 8.443 samples/sec, ObjLoss=25.261, BoxCenterLoss=14.466, BoxScaleLoss=5.159, ClassLoss=10.611 [Epoch 124][Batch 499], LR: 1.00E-03, Speed: 9.422 samples/sec, ObjLoss=25.260, BoxCenterLoss=14.466, BoxScaleLoss=5.158, ClassLoss=10.611 [Epoch 124][Batch 599], LR: 1.00E-03, Speed: 6.994 samples/sec, ObjLoss=25.260, BoxCenterLoss=14.466, BoxScaleLoss=5.158, ClassLoss=10.610 [Epoch 124][Batch 699], LR: 1.00E-03, Speed: 10.736 samples/sec, ObjLoss=25.259, BoxCenterLoss=14.466, BoxScaleLoss=5.158, ClassLoss=10.609 [Epoch 124][Batch 799], LR: 1.00E-03, Speed: 9.669 samples/sec, ObjLoss=25.258, BoxCenterLoss=14.466, BoxScaleLoss=5.158, ClassLoss=10.608 [Epoch 124][Batch 899], LR: 1.00E-03, Speed: 10.448 samples/sec, ObjLoss=25.257, BoxCenterLoss=14.466, BoxScaleLoss=5.158, ClassLoss=10.608 [Epoch 124][Batch 999], LR: 1.00E-03, Speed: 10.440 samples/sec, ObjLoss=25.255, BoxCenterLoss=14.465, BoxScaleLoss=5.158, ClassLoss=10.607 [Epoch 124][Batch 1099], LR: 1.00E-03, Speed: 65.942 samples/sec, ObjLoss=25.254, BoxCenterLoss=14.465, BoxScaleLoss=5.158, ClassLoss=10.606 [Epoch 124][Batch 1199], LR: 1.00E-03, Speed: 11.177 samples/sec, ObjLoss=25.253, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.605 [Epoch 124][Batch 1299], LR: 1.00E-03, Speed: 11.524 samples/sec, ObjLoss=25.252, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.605 [Epoch 124][Batch 1399], LR: 1.00E-03, Speed: 8.817 samples/sec, ObjLoss=25.252, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.604 [Epoch 124][Batch 1499], LR: 1.00E-03, Speed: 10.206 samples/sec, ObjLoss=25.251, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.603 [Epoch 124][Batch 1599], LR: 1.00E-03, Speed: 11.524 samples/sec, ObjLoss=25.250, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.602 [Epoch 124][Batch 1699], LR: 1.00E-03, Speed: 10.356 samples/sec, ObjLoss=25.249, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.601 [Epoch 124][Batch 1799], LR: 1.00E-03, Speed: 13.320 samples/sec, ObjLoss=25.248, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.601 [Epoch 124] Training cost: 2155.047, ObjLoss=25.248, BoxCenterLoss=14.465, BoxScaleLoss=5.157, ClassLoss=10.600 [Epoch 124] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.228 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455 person=38.2 bicycle=18.7 car=26.6 motorcycle=28.8 airplane=40.1 bus=46.5 train=47.0 truck=24.3 boat=12.7 traffic light=14.6 fire hydrant=45.1 stop sign=42.7 parking meter=29.9 bench=13.2 bird=13.4 cat=46.1 dog=40.0 horse=37.2 sheep=31.4 cow=34.8 elephant=44.2 bear=53.9 zebra=43.4 giraffe=48.7 backpack=5.1 umbrella=22.6 handbag=6.6 tie=17.3 suitcase=22.5 frisbee=31.6 skis=11.9 snowboard=16.5 sports ball=21.4 kite=24.4 baseball bat=14.5 baseball glove=16.6 skateboard=28.7 surfboard=18.7 tennis racket=27.6 bottle=20.0 wine glass=19.7 cup=24.1 fork=13.5 knife=4.3 spoon=5.9 bowl=20.5 banana=12.1 apple=8.1 sandwich=20.2 orange=12.6 broccoli=10.9 carrot=6.9 hot dog=18.5 pizza=34.3 donut=26.4 cake=17.8 chair=15.3 couch=29.1 potted plant=14.4 bed=33.1 dining table=14.0 toilet=35.2 tv=37.7 laptop=38.0 mouse=28.5 remote=7.6 keyboard=30.7 cell phone=18.4 microwave=32.8 oven=21.7 toaster=0.0 sink=19.8 refrigerator=31.3 book=4.7 clock=35.1 vase=20.9 scissors=17.6 teddy bear=31.2 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.8 [Epoch 125][Batch 99], LR: 1.00E-03, Speed: 7.737 samples/sec, ObjLoss=25.246, BoxCenterLoss=14.464, BoxScaleLoss=5.156, ClassLoss=10.599 [Epoch 125][Batch 199], LR: 1.00E-03, Speed: 12.073 samples/sec, ObjLoss=25.246, BoxCenterLoss=14.464, BoxScaleLoss=5.156, ClassLoss=10.598 [Epoch 125][Batch 299], LR: 1.00E-03, Speed: 11.144 samples/sec, ObjLoss=25.244, BoxCenterLoss=14.464, BoxScaleLoss=5.156, ClassLoss=10.598 [Epoch 125][Batch 399], LR: 1.00E-03, Speed: 11.640 samples/sec, ObjLoss=25.243, BoxCenterLoss=14.463, BoxScaleLoss=5.155, ClassLoss=10.596 [Epoch 125][Batch 499], LR: 1.00E-03, Speed: 8.297 samples/sec, ObjLoss=25.242, BoxCenterLoss=14.463, BoxScaleLoss=5.155, ClassLoss=10.596 [Epoch 125][Batch 599], LR: 1.00E-03, Speed: 8.243 samples/sec, ObjLoss=25.241, BoxCenterLoss=14.463, BoxScaleLoss=5.155, ClassLoss=10.594 [Epoch 125][Batch 699], LR: 1.00E-03, Speed: 10.642 samples/sec, ObjLoss=25.240, BoxCenterLoss=14.463, BoxScaleLoss=5.155, ClassLoss=10.594 [Epoch 125][Batch 799], LR: 1.00E-03, Speed: 10.438 samples/sec, ObjLoss=25.239, BoxCenterLoss=14.463, BoxScaleLoss=5.155, ClassLoss=10.593 [Epoch 125][Batch 899], LR: 1.00E-03, Speed: 9.235 samples/sec, ObjLoss=25.238, BoxCenterLoss=14.463, BoxScaleLoss=5.154, ClassLoss=10.592 [Epoch 125][Batch 999], LR: 1.00E-03, Speed: 12.247 samples/sec, ObjLoss=25.237, BoxCenterLoss=14.463, BoxScaleLoss=5.154, ClassLoss=10.592 [Epoch 125][Batch 1099], LR: 1.00E-03, Speed: 10.137 samples/sec, ObjLoss=25.236, BoxCenterLoss=14.463, BoxScaleLoss=5.154, ClassLoss=10.591 [Epoch 125][Batch 1199], LR: 1.00E-03, Speed: 9.088 samples/sec, ObjLoss=25.234, BoxCenterLoss=14.462, BoxScaleLoss=5.154, ClassLoss=10.590 [Epoch 125][Batch 1299], LR: 1.00E-03, Speed: 9.702 samples/sec, ObjLoss=25.233, BoxCenterLoss=14.462, BoxScaleLoss=5.154, ClassLoss=10.589 [Epoch 125][Batch 1399], LR: 1.00E-03, Speed: 10.022 samples/sec, ObjLoss=25.232, BoxCenterLoss=14.461, BoxScaleLoss=5.153, ClassLoss=10.588 [Epoch 125][Batch 1499], LR: 1.00E-03, Speed: 13.287 samples/sec, ObjLoss=25.231, BoxCenterLoss=14.462, BoxScaleLoss=5.153, ClassLoss=10.587 [Epoch 125][Batch 1599], LR: 1.00E-03, Speed: 112.798 samples/sec, ObjLoss=25.231, BoxCenterLoss=14.461, BoxScaleLoss=5.153, ClassLoss=10.587 [Epoch 125][Batch 1699], LR: 1.00E-03, Speed: 7.417 samples/sec, ObjLoss=25.230, BoxCenterLoss=14.462, BoxScaleLoss=5.153, ClassLoss=10.586 [Epoch 125][Batch 1799], LR: 1.00E-03, Speed: 97.288 samples/sec, ObjLoss=25.229, BoxCenterLoss=14.462, BoxScaleLoss=5.153, ClassLoss=10.585 [Epoch 125] Training cost: 2262.659, ObjLoss=25.229, BoxCenterLoss=14.462, BoxScaleLoss=5.153, ClassLoss=10.585 [Epoch 125] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.224 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.158 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.466 person=37.9 bicycle=18.1 car=27.5 motorcycle=30.7 airplane=46.5 bus=50.2 train=50.0 truck=25.3 boat=13.5 traffic light=14.2 fire hydrant=44.0 stop sign=44.4 parking meter=33.2 bench=12.5 bird=19.3 cat=41.6 dog=40.8 horse=39.6 sheep=30.1 cow=35.8 elephant=44.0 bear=50.0 zebra=49.0 giraffe=51.7 backpack=5.7 umbrella=24.1 handbag=6.7 tie=17.9 suitcase=20.0 frisbee=34.6 skis=11.2 snowboard=17.5 sports ball=27.3 kite=25.1 baseball bat=13.8 baseball glove=19.6 skateboard=29.5 surfboard=19.1 tennis racket=26.6 bottle=18.8 wine glass=21.2 cup=26.5 fork=12.4 knife=3.5 spoon=4.1 bowl=24.8 banana=10.0 apple=8.8 sandwich=19.3 orange=17.3 broccoli=11.6 carrot=10.2 hot dog=13.7 pizza=35.1 donut=26.2 cake=21.0 chair=17.0 couch=29.1 potted plant=13.2 bed=33.6 dining table=19.7 toilet=37.8 tv=39.1 laptop=42.5 mouse=37.3 remote=11.5 keyboard=33.5 cell phone=17.7 microwave=33.7 oven=18.8 toaster=0.0 sink=21.3 refrigerator=28.6 book=5.0 clock=36.8 vase=22.0 scissors=11.4 teddy bear=29.4 hair drier=0.0 toothbrush=8.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.8 [Epoch 126][Batch 99], LR: 1.00E-03, Speed: 6.978 samples/sec, ObjLoss=25.228, BoxCenterLoss=14.462, BoxScaleLoss=5.153, ClassLoss=10.585 [Epoch 126][Batch 199], LR: 1.00E-03, Speed: 9.788 samples/sec, ObjLoss=25.228, BoxCenterLoss=14.462, BoxScaleLoss=5.153, ClassLoss=10.584 [Epoch 126][Batch 299], LR: 1.00E-03, Speed: 7.910 samples/sec, ObjLoss=25.227, BoxCenterLoss=14.462, BoxScaleLoss=5.152, ClassLoss=10.583 [Epoch 126][Batch 399], LR: 1.00E-03, Speed: 9.869 samples/sec, ObjLoss=25.226, BoxCenterLoss=14.462, BoxScaleLoss=5.152, ClassLoss=10.582 [Epoch 126][Batch 499], LR: 1.00E-03, Speed: 10.376 samples/sec, ObjLoss=25.226, BoxCenterLoss=14.462, BoxScaleLoss=5.152, ClassLoss=10.582 [Epoch 126][Batch 599], LR: 1.00E-03, Speed: 112.762 samples/sec, ObjLoss=25.225, BoxCenterLoss=14.462, BoxScaleLoss=5.152, ClassLoss=10.581 [Epoch 126][Batch 699], LR: 1.00E-03, Speed: 7.803 samples/sec, ObjLoss=25.224, BoxCenterLoss=14.462, BoxScaleLoss=5.152, ClassLoss=10.581 [Epoch 126][Batch 799], LR: 1.00E-03, Speed: 10.053 samples/sec, ObjLoss=25.224, BoxCenterLoss=14.462, BoxScaleLoss=5.152, ClassLoss=10.580 [Epoch 126][Batch 899], LR: 1.00E-03, Speed: 10.856 samples/sec, ObjLoss=25.223, BoxCenterLoss=14.462, BoxScaleLoss=5.152, ClassLoss=10.579 [Epoch 126][Batch 999], LR: 1.00E-03, Speed: 11.677 samples/sec, ObjLoss=25.223, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.579 [Epoch 126][Batch 1099], LR: 1.00E-03, Speed: 106.670 samples/sec, ObjLoss=25.222, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.578 [Epoch 126][Batch 1199], LR: 1.00E-03, Speed: 12.642 samples/sec, ObjLoss=25.221, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.578 [Epoch 126][Batch 1299], LR: 1.00E-03, Speed: 121.126 samples/sec, ObjLoss=25.220, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.577 [Epoch 126][Batch 1399], LR: 1.00E-03, Speed: 12.204 samples/sec, ObjLoss=25.219, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.576 [Epoch 126][Batch 1499], LR: 1.00E-03, Speed: 118.452 samples/sec, ObjLoss=25.219, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.576 [Epoch 126][Batch 1599], LR: 1.00E-03, Speed: 7.229 samples/sec, ObjLoss=25.218, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.575 [Epoch 126][Batch 1699], LR: 1.00E-03, Speed: 11.412 samples/sec, ObjLoss=25.217, BoxCenterLoss=14.463, BoxScaleLoss=5.152, ClassLoss=10.574 [Epoch 126][Batch 1799], LR: 1.00E-03, Speed: 11.409 samples/sec, ObjLoss=25.216, BoxCenterLoss=14.462, BoxScaleLoss=5.151, ClassLoss=10.573 [Epoch 126] Training cost: 2184.317, ObjLoss=25.215, BoxCenterLoss=14.462, BoxScaleLoss=5.151, ClassLoss=10.573 [Epoch 126] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.200 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.447 person=36.0 bicycle=18.3 car=25.2 motorcycle=27.5 airplane=36.2 bus=45.8 train=48.8 truck=21.8 boat=12.7 traffic light=14.6 fire hydrant=27.9 stop sign=32.4 parking meter=26.0 bench=12.8 bird=17.0 cat=40.8 dog=35.0 horse=33.8 sheep=31.9 cow=33.9 elephant=44.1 bear=48.1 zebra=44.2 giraffe=47.4 backpack=6.3 umbrella=21.9 handbag=6.3 tie=17.2 suitcase=17.8 frisbee=34.3 skis=7.7 snowboard=13.2 sports ball=20.4 kite=23.1 baseball bat=11.3 baseball glove=16.0 skateboard=31.1 surfboard=20.4 tennis racket=26.2 bottle=15.6 wine glass=20.3 cup=20.1 fork=14.6 knife=4.3 spoon=4.1 bowl=23.6 banana=11.7 apple=6.4 sandwich=17.8 orange=12.2 broccoli=10.0 carrot=6.5 hot dog=16.2 pizza=32.2 donut=29.5 cake=19.9 chair=15.5 couch=28.3 potted plant=15.2 bed=26.2 dining table=17.6 toilet=38.2 tv=36.5 laptop=35.9 mouse=35.9 remote=10.1 keyboard=32.1 cell phone=17.0 microwave=27.8 oven=19.6 toaster=0.0 sink=21.3 refrigerator=30.6 book=3.5 clock=32.2 vase=18.7 scissors=16.9 teddy bear=27.6 hair drier=0.0 toothbrush=6.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.7 [Epoch 127][Batch 99], LR: 1.00E-03, Speed: 10.866 samples/sec, ObjLoss=25.215, BoxCenterLoss=14.462, BoxScaleLoss=5.151, ClassLoss=10.572 [Epoch 127][Batch 199], LR: 1.00E-03, Speed: 9.844 samples/sec, ObjLoss=25.214, BoxCenterLoss=14.462, BoxScaleLoss=5.151, ClassLoss=10.571 [Epoch 127][Batch 299], LR: 1.00E-03, Speed: 8.529 samples/sec, ObjLoss=25.213, BoxCenterLoss=14.462, BoxScaleLoss=5.151, ClassLoss=10.571 [Epoch 127][Batch 399], LR: 1.00E-03, Speed: 8.399 samples/sec, ObjLoss=25.212, BoxCenterLoss=14.462, BoxScaleLoss=5.151, ClassLoss=10.570 [Epoch 127][Batch 499], LR: 1.00E-03, Speed: 10.230 samples/sec, ObjLoss=25.211, BoxCenterLoss=14.462, BoxScaleLoss=5.151, ClassLoss=10.569 [Epoch 127][Batch 599], LR: 1.00E-03, Speed: 8.647 samples/sec, ObjLoss=25.210, BoxCenterLoss=14.462, BoxScaleLoss=5.150, ClassLoss=10.568 [Epoch 127][Batch 699], LR: 1.00E-03, Speed: 9.047 samples/sec, ObjLoss=25.210, BoxCenterLoss=14.462, BoxScaleLoss=5.150, ClassLoss=10.568 [Epoch 127][Batch 799], LR: 1.00E-03, Speed: 10.855 samples/sec, ObjLoss=25.209, BoxCenterLoss=14.462, BoxScaleLoss=5.150, ClassLoss=10.567 [Epoch 127][Batch 899], LR: 1.00E-03, Speed: 96.483 samples/sec, ObjLoss=25.208, BoxCenterLoss=14.462, BoxScaleLoss=5.150, ClassLoss=10.566 [Epoch 127][Batch 999], LR: 1.00E-03, Speed: 9.749 samples/sec, ObjLoss=25.207, BoxCenterLoss=14.462, BoxScaleLoss=5.150, ClassLoss=10.565 [Epoch 127][Batch 1099], LR: 1.00E-03, Speed: 8.806 samples/sec, ObjLoss=25.206, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.564 [Epoch 127][Batch 1199], LR: 1.00E-03, Speed: 27.874 samples/sec, ObjLoss=25.205, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.563 [Epoch 127][Batch 1299], LR: 1.00E-03, Speed: 9.461 samples/sec, ObjLoss=25.204, BoxCenterLoss=14.461, BoxScaleLoss=5.149, ClassLoss=10.562 [Epoch 127][Batch 1399], LR: 1.00E-03, Speed: 10.446 samples/sec, ObjLoss=25.204, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.562 [Epoch 127][Batch 1499], LR: 1.00E-03, Speed: 11.742 samples/sec, ObjLoss=25.203, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.561 [Epoch 127][Batch 1599], LR: 1.00E-03, Speed: 12.769 samples/sec, ObjLoss=25.203, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.561 [Epoch 127][Batch 1699], LR: 1.00E-03, Speed: 7.867 samples/sec, ObjLoss=25.202, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.560 [Epoch 127][Batch 1799], LR: 1.00E-03, Speed: 10.229 samples/sec, ObjLoss=25.201, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.560 [Epoch 127] Training cost: 2248.456, ObjLoss=25.201, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.559 [Epoch 127] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.339 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.456 person=37.5 bicycle=18.5 car=26.9 motorcycle=28.5 airplane=41.2 bus=45.8 train=48.1 truck=21.7 boat=12.9 traffic light=12.6 fire hydrant=41.3 stop sign=43.8 parking meter=29.3 bench=14.3 bird=19.2 cat=44.8 dog=38.6 horse=37.2 sheep=34.1 cow=35.2 elephant=45.7 bear=48.3 zebra=44.3 giraffe=45.6 backpack=6.0 umbrella=23.3 handbag=5.1 tie=16.2 suitcase=17.4 frisbee=33.0 skis=9.3 snowboard=13.4 sports ball=25.9 kite=24.8 baseball bat=13.6 baseball glove=21.3 skateboard=33.1 surfboard=20.4 tennis racket=27.6 bottle=20.1 wine glass=20.5 cup=25.0 fork=14.0 knife=4.4 spoon=5.2 bowl=22.4 banana=13.7 apple=6.8 sandwich=18.6 orange=17.1 broccoli=11.7 carrot=9.8 hot dog=18.6 pizza=32.1 donut=31.0 cake=18.7 chair=15.6 couch=27.9 potted plant=13.7 bed=31.6 dining table=19.1 toilet=37.6 tv=37.8 laptop=34.8 mouse=37.9 remote=10.1 keyboard=30.5 cell phone=17.7 microwave=29.8 oven=20.0 toaster=0.0 sink=20.2 refrigerator=32.6 book=4.8 clock=27.1 vase=21.0 scissors=13.0 teddy bear=29.4 hair drier=0.0 toothbrush=7.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.9 [Epoch 128][Batch 99], LR: 1.00E-03, Speed: 8.399 samples/sec, ObjLoss=25.200, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.559 [Epoch 128][Batch 199], LR: 1.00E-03, Speed: 9.757 samples/sec, ObjLoss=25.200, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.558 [Epoch 128][Batch 299], LR: 1.00E-03, Speed: 10.454 samples/sec, ObjLoss=25.199, BoxCenterLoss=14.462, BoxScaleLoss=5.149, ClassLoss=10.557 [Epoch 128][Batch 399], LR: 1.00E-03, Speed: 7.826 samples/sec, ObjLoss=25.199, BoxCenterLoss=14.463, BoxScaleLoss=5.149, ClassLoss=10.557 [Epoch 128][Batch 499], LR: 1.00E-03, Speed: 9.855 samples/sec, ObjLoss=25.198, BoxCenterLoss=14.463, BoxScaleLoss=5.148, ClassLoss=10.556 [Epoch 128][Batch 599], LR: 1.00E-03, Speed: 87.713 samples/sec, ObjLoss=25.196, BoxCenterLoss=14.462, BoxScaleLoss=5.148, ClassLoss=10.555 [Epoch 128][Batch 699], LR: 1.00E-03, Speed: 8.515 samples/sec, ObjLoss=25.196, BoxCenterLoss=14.462, BoxScaleLoss=5.148, ClassLoss=10.555 [Epoch 128][Batch 799], LR: 1.00E-03, Speed: 125.225 samples/sec, ObjLoss=25.195, BoxCenterLoss=14.462, BoxScaleLoss=5.148, ClassLoss=10.554 [Epoch 128][Batch 899], LR: 1.00E-03, Speed: 11.535 samples/sec, ObjLoss=25.194, BoxCenterLoss=14.463, BoxScaleLoss=5.148, ClassLoss=10.553 [Epoch 128][Batch 999], LR: 1.00E-03, Speed: 10.206 samples/sec, ObjLoss=25.194, BoxCenterLoss=14.463, BoxScaleLoss=5.148, ClassLoss=10.553 [Epoch 128][Batch 1099], LR: 1.00E-03, Speed: 10.101 samples/sec, ObjLoss=25.193, BoxCenterLoss=14.462, BoxScaleLoss=5.148, ClassLoss=10.552 [Epoch 128][Batch 1199], LR: 1.00E-03, Speed: 117.227 samples/sec, ObjLoss=25.191, BoxCenterLoss=14.462, BoxScaleLoss=5.148, ClassLoss=10.551 [Epoch 128][Batch 1299], LR: 1.00E-03, Speed: 11.107 samples/sec, ObjLoss=25.190, BoxCenterLoss=14.462, BoxScaleLoss=5.148, ClassLoss=10.550 [Epoch 128][Batch 1399], LR: 1.00E-03, Speed: 9.313 samples/sec, ObjLoss=25.190, BoxCenterLoss=14.462, BoxScaleLoss=5.147, ClassLoss=10.550 [Epoch 128][Batch 1499], LR: 1.00E-03, Speed: 11.188 samples/sec, ObjLoss=25.189, BoxCenterLoss=14.462, BoxScaleLoss=5.147, ClassLoss=10.549 [Epoch 128][Batch 1599], LR: 1.00E-03, Speed: 9.810 samples/sec, ObjLoss=25.188, BoxCenterLoss=14.462, BoxScaleLoss=5.147, ClassLoss=10.548 [Epoch 128][Batch 1699], LR: 1.00E-03, Speed: 8.479 samples/sec, ObjLoss=25.187, BoxCenterLoss=14.462, BoxScaleLoss=5.147, ClassLoss=10.547 [Epoch 128][Batch 1799], LR: 1.00E-03, Speed: 117.252 samples/sec, ObjLoss=25.186, BoxCenterLoss=14.461, BoxScaleLoss=5.147, ClassLoss=10.546 [Epoch 128] Training cost: 2180.281, ObjLoss=25.185, BoxCenterLoss=14.461, BoxScaleLoss=5.147, ClassLoss=10.546 [Epoch 128] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.463 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.464 person=35.9 bicycle=18.8 car=26.1 motorcycle=29.3 airplane=44.5 bus=43.3 train=46.0 truck=24.6 boat=14.8 traffic light=14.3 fire hydrant=44.9 stop sign=43.1 parking meter=27.1 bench=13.4 bird=20.8 cat=43.9 dog=35.1 horse=34.7 sheep=31.2 cow=33.0 elephant=45.9 bear=48.9 zebra=47.7 giraffe=47.3 backpack=7.0 umbrella=24.7 handbag=6.8 tie=17.1 suitcase=17.9 frisbee=35.7 skis=10.7 snowboard=15.6 sports ball=29.8 kite=25.8 baseball bat=14.9 baseball glove=14.0 skateboard=27.8 surfboard=19.5 tennis racket=25.1 bottle=18.5 wine glass=18.3 cup=24.4 fork=13.2 knife=3.4 spoon=3.6 bowl=25.8 banana=11.9 apple=9.9 sandwich=21.2 orange=17.3 broccoli=11.1 carrot=7.6 hot dog=18.4 pizza=32.7 donut=24.6 cake=21.2 chair=15.6 couch=28.6 potted plant=15.4 bed=35.6 dining table=21.1 toilet=40.8 tv=34.8 laptop=38.6 mouse=33.6 remote=10.7 keyboard=33.4 cell phone=14.8 microwave=34.8 oven=22.8 toaster=0.0 sink=22.5 refrigerator=31.8 book=4.9 clock=33.8 vase=22.9 scissors=15.3 teddy bear=29.1 hair drier=0.0 toothbrush=6.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.2 [Epoch 129][Batch 99], LR: 1.00E-03, Speed: 13.752 samples/sec, ObjLoss=25.184, BoxCenterLoss=14.461, BoxScaleLoss=5.146, ClassLoss=10.545 [Epoch 129][Batch 199], LR: 1.00E-03, Speed: 8.554 samples/sec, ObjLoss=25.183, BoxCenterLoss=14.461, BoxScaleLoss=5.146, ClassLoss=10.545 [Epoch 129][Batch 299], LR: 1.00E-03, Speed: 107.993 samples/sec, ObjLoss=25.183, BoxCenterLoss=14.461, BoxScaleLoss=5.146, ClassLoss=10.544 [Epoch 129][Batch 399], LR: 1.00E-03, Speed: 10.025 samples/sec, ObjLoss=25.181, BoxCenterLoss=14.461, BoxScaleLoss=5.146, ClassLoss=10.543 [Epoch 129][Batch 499], LR: 1.00E-03, Speed: 10.451 samples/sec, ObjLoss=25.180, BoxCenterLoss=14.460, BoxScaleLoss=5.146, ClassLoss=10.542 [Epoch 129][Batch 599], LR: 1.00E-03, Speed: 120.651 samples/sec, ObjLoss=25.179, BoxCenterLoss=14.460, BoxScaleLoss=5.146, ClassLoss=10.542 [Epoch 129][Batch 699], LR: 1.00E-03, Speed: 10.780 samples/sec, ObjLoss=25.178, BoxCenterLoss=14.460, BoxScaleLoss=5.146, ClassLoss=10.541 [Epoch 129][Batch 799], LR: 1.00E-03, Speed: 93.572 samples/sec, ObjLoss=25.177, BoxCenterLoss=14.460, BoxScaleLoss=5.145, ClassLoss=10.541 [Epoch 129][Batch 899], LR: 1.00E-03, Speed: 11.593 samples/sec, ObjLoss=25.176, BoxCenterLoss=14.460, BoxScaleLoss=5.145, ClassLoss=10.540 [Epoch 129][Batch 999], LR: 1.00E-03, Speed: 9.460 samples/sec, ObjLoss=25.175, BoxCenterLoss=14.460, BoxScaleLoss=5.145, ClassLoss=10.539 [Epoch 129][Batch 1099], LR: 1.00E-03, Speed: 119.989 samples/sec, ObjLoss=25.175, BoxCenterLoss=14.460, BoxScaleLoss=5.145, ClassLoss=10.539 [Epoch 129][Batch 1199], LR: 1.00E-03, Speed: 10.450 samples/sec, ObjLoss=25.174, BoxCenterLoss=14.459, BoxScaleLoss=5.145, ClassLoss=10.538 [Epoch 129][Batch 1299], LR: 1.00E-03, Speed: 7.422 samples/sec, ObjLoss=25.173, BoxCenterLoss=14.459, BoxScaleLoss=5.145, ClassLoss=10.537 [Epoch 129][Batch 1399], LR: 1.00E-03, Speed: 10.043 samples/sec, ObjLoss=25.172, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.536 [Epoch 129][Batch 1499], LR: 1.00E-03, Speed: 10.165 samples/sec, ObjLoss=25.171, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.535 [Epoch 129][Batch 1599], LR: 1.00E-03, Speed: 10.258 samples/sec, ObjLoss=25.170, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.535 [Epoch 129][Batch 1699], LR: 1.00E-03, Speed: 12.522 samples/sec, ObjLoss=25.169, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.534 [Epoch 129][Batch 1799], LR: 1.00E-03, Speed: 12.533 samples/sec, ObjLoss=25.168, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.533 [Epoch 129] Training cost: 2192.951, ObjLoss=25.168, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.533 [Epoch 129] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.217 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458 person=38.0 bicycle=19.5 car=27.1 motorcycle=29.4 airplane=38.0 bus=48.6 train=47.6 truck=21.9 boat=12.8 traffic light=13.2 fire hydrant=43.1 stop sign=33.4 parking meter=25.7 bench=13.0 bird=19.7 cat=47.8 dog=37.2 horse=34.9 sheep=32.2 cow=32.8 elephant=45.4 bear=44.6 zebra=47.4 giraffe=48.4 backpack=5.2 umbrella=23.2 handbag=5.2 tie=16.1 suitcase=17.6 frisbee=31.8 skis=11.1 snowboard=11.5 sports ball=22.3 kite=23.2 baseball bat=13.1 baseball glove=16.9 skateboard=30.2 surfboard=17.9 tennis racket=24.2 bottle=18.0 wine glass=19.3 cup=25.4 fork=13.3 knife=4.7 spoon=3.5 bowl=23.2 banana=12.8 apple=8.7 sandwich=19.9 orange=15.7 broccoli=9.7 carrot=10.6 hot dog=17.3 pizza=33.4 donut=28.2 cake=25.8 chair=15.5 couch=25.4 potted plant=15.2 bed=31.4 dining table=19.9 toilet=41.4 tv=30.5 laptop=33.2 mouse=34.1 remote=9.8 keyboard=25.6 cell phone=19.1 microwave=35.5 oven=18.3 toaster=0.0 sink=16.9 refrigerator=26.8 book=3.7 clock=27.9 vase=19.4 scissors=14.5 teddy bear=28.2 hair drier=0.0 toothbrush=4.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.2 [Epoch 130][Batch 99], LR: 1.00E-03, Speed: 8.653 samples/sec, ObjLoss=25.167, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.532 [Epoch 130][Batch 199], LR: 1.00E-03, Speed: 102.792 samples/sec, ObjLoss=25.167, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.532 [Epoch 130][Batch 299], LR: 1.00E-03, Speed: 19.659 samples/sec, ObjLoss=25.167, BoxCenterLoss=14.460, BoxScaleLoss=5.144, ClassLoss=10.531 [Epoch 130][Batch 399], LR: 1.00E-03, Speed: 9.369 samples/sec, ObjLoss=25.166, BoxCenterLoss=14.459, BoxScaleLoss=5.144, ClassLoss=10.531 [Epoch 130][Batch 499], LR: 1.00E-03, Speed: 7.141 samples/sec, ObjLoss=25.165, BoxCenterLoss=14.459, BoxScaleLoss=5.143, ClassLoss=10.530 [Epoch 130][Batch 599], LR: 1.00E-03, Speed: 87.495 samples/sec, ObjLoss=25.164, BoxCenterLoss=14.459, BoxScaleLoss=5.143, ClassLoss=10.529 [Epoch 130][Batch 699], LR: 1.00E-03, Speed: 6.863 samples/sec, ObjLoss=25.162, BoxCenterLoss=14.459, BoxScaleLoss=5.143, ClassLoss=10.528 [Epoch 130][Batch 799], LR: 1.00E-03, Speed: 8.872 samples/sec, ObjLoss=25.161, BoxCenterLoss=14.458, BoxScaleLoss=5.143, ClassLoss=10.527 [Epoch 130][Batch 899], LR: 1.00E-03, Speed: 8.144 samples/sec, ObjLoss=25.160, BoxCenterLoss=14.458, BoxScaleLoss=5.143, ClassLoss=10.527 [Epoch 130][Batch 999], LR: 1.00E-03, Speed: 9.389 samples/sec, ObjLoss=25.160, BoxCenterLoss=14.458, BoxScaleLoss=5.143, ClassLoss=10.526 [Epoch 130][Batch 1099], LR: 1.00E-03, Speed: 9.259 samples/sec, ObjLoss=25.159, BoxCenterLoss=14.458, BoxScaleLoss=5.143, ClassLoss=10.525 [Epoch 130][Batch 1199], LR: 1.00E-03, Speed: 9.426 samples/sec, ObjLoss=25.158, BoxCenterLoss=14.459, BoxScaleLoss=5.143, ClassLoss=10.525 [Epoch 130][Batch 1299], LR: 1.00E-03, Speed: 11.789 samples/sec, ObjLoss=25.158, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.524 [Epoch 130][Batch 1399], LR: 1.00E-03, Speed: 10.356 samples/sec, ObjLoss=25.157, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.523 [Epoch 130][Batch 1499], LR: 1.00E-03, Speed: 9.215 samples/sec, ObjLoss=25.156, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.522 [Epoch 130][Batch 1599], LR: 1.00E-03, Speed: 11.003 samples/sec, ObjLoss=25.155, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.522 [Epoch 130][Batch 1699], LR: 1.00E-03, Speed: 101.292 samples/sec, ObjLoss=25.154, BoxCenterLoss=14.458, BoxScaleLoss=5.142, ClassLoss=10.521 [Epoch 130][Batch 1799], LR: 1.00E-03, Speed: 10.659 samples/sec, ObjLoss=25.154, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.520 [Epoch 130] Training cost: 2216.144, ObjLoss=25.154, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.520 [Epoch 130] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.441 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442 person=36.4 bicycle=17.2 car=21.6 motorcycle=25.4 airplane=47.2 bus=43.4 train=41.6 truck=21.1 boat=10.6 traffic light=11.2 fire hydrant=38.4 stop sign=29.6 parking meter=19.1 bench=12.7 bird=21.4 cat=43.1 dog=36.6 horse=32.6 sheep=31.0 cow=33.6 elephant=42.4 bear=38.5 zebra=45.2 giraffe=48.3 backpack=4.0 umbrella=23.4 handbag=5.1 tie=16.3 suitcase=18.4 frisbee=36.2 skis=10.1 snowboard=14.2 sports ball=9.8 kite=22.4 baseball bat=15.6 baseball glove=19.1 skateboard=28.2 surfboard=21.5 tennis racket=25.8 bottle=15.0 wine glass=14.7 cup=16.5 fork=12.3 knife=4.5 spoon=4.7 bowl=21.2 banana=12.4 apple=6.9 sandwich=20.2 orange=15.4 broccoli=9.9 carrot=7.4 hot dog=18.1 pizza=31.5 donut=25.7 cake=23.1 chair=12.4 couch=24.5 potted plant=12.1 bed=29.9 dining table=20.4 toilet=33.7 tv=31.6 laptop=34.7 mouse=31.9 remote=11.3 keyboard=31.6 cell phone=15.4 microwave=28.0 oven=18.6 toaster=0.0 sink=18.8 refrigerator=31.3 book=3.5 clock=31.5 vase=18.3 scissors=17.7 teddy bear=27.1 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.1 [Epoch 131][Batch 99], LR: 1.00E-03, Speed: 8.928 samples/sec, ObjLoss=25.152, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.519 [Epoch 131][Batch 199], LR: 1.00E-03, Speed: 11.683 samples/sec, ObjLoss=25.151, BoxCenterLoss=14.459, BoxScaleLoss=5.142, ClassLoss=10.519 [Epoch 131][Batch 299], LR: 1.00E-03, Speed: 11.270 samples/sec, ObjLoss=25.150, BoxCenterLoss=14.458, BoxScaleLoss=5.141, ClassLoss=10.518 [Epoch 131][Batch 399], LR: 1.00E-03, Speed: 9.494 samples/sec, ObjLoss=25.150, BoxCenterLoss=14.458, BoxScaleLoss=5.141, ClassLoss=10.517 [Epoch 131][Batch 499], LR: 1.00E-03, Speed: 7.772 samples/sec, ObjLoss=25.149, BoxCenterLoss=14.458, BoxScaleLoss=5.141, ClassLoss=10.517 [Epoch 131][Batch 599], LR: 1.00E-03, Speed: 8.430 samples/sec, ObjLoss=25.148, BoxCenterLoss=14.458, BoxScaleLoss=5.141, ClassLoss=10.516 [Epoch 131][Batch 699], LR: 1.00E-03, Speed: 12.396 samples/sec, ObjLoss=25.147, BoxCenterLoss=14.458, BoxScaleLoss=5.141, ClassLoss=10.515 [Epoch 131][Batch 799], LR: 1.00E-03, Speed: 11.571 samples/sec, ObjLoss=25.146, BoxCenterLoss=14.458, BoxScaleLoss=5.141, ClassLoss=10.514 [Epoch 131][Batch 899], LR: 1.00E-03, Speed: 9.421 samples/sec, ObjLoss=25.145, BoxCenterLoss=14.458, BoxScaleLoss=5.140, ClassLoss=10.514 [Epoch 131][Batch 999], LR: 1.00E-03, Speed: 9.014 samples/sec, ObjLoss=25.144, BoxCenterLoss=14.458, BoxScaleLoss=5.140, ClassLoss=10.513 [Epoch 131][Batch 1099], LR: 1.00E-03, Speed: 9.068 samples/sec, ObjLoss=25.143, BoxCenterLoss=14.458, BoxScaleLoss=5.140, ClassLoss=10.512 [Epoch 131][Batch 1199], LR: 1.00E-03, Speed: 8.831 samples/sec, ObjLoss=25.142, BoxCenterLoss=14.457, BoxScaleLoss=5.140, ClassLoss=10.511 [Epoch 131][Batch 1299], LR: 1.00E-03, Speed: 11.147 samples/sec, ObjLoss=25.141, BoxCenterLoss=14.458, BoxScaleLoss=5.140, ClassLoss=10.511 [Epoch 131][Batch 1399], LR: 1.00E-03, Speed: 94.086 samples/sec, ObjLoss=25.140, BoxCenterLoss=14.457, BoxScaleLoss=5.140, ClassLoss=10.510 [Epoch 131][Batch 1499], LR: 1.00E-03, Speed: 10.737 samples/sec, ObjLoss=25.139, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.509 [Epoch 131][Batch 1599], LR: 1.00E-03, Speed: 7.731 samples/sec, ObjLoss=25.138, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.508 [Epoch 131][Batch 1699], LR: 1.00E-03, Speed: 11.178 samples/sec, ObjLoss=25.137, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.507 [Epoch 131][Batch 1799], LR: 1.00E-03, Speed: 11.346 samples/sec, ObjLoss=25.136, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.506 [Epoch 131] Training cost: 2142.212, ObjLoss=25.136, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.506 [Epoch 131] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.223 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.449 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.431 person=37.3 bicycle=16.2 car=27.8 motorcycle=25.8 airplane=30.3 bus=40.1 train=36.6 truck=20.4 boat=12.2 traffic light=12.3 fire hydrant=41.8 stop sign=39.7 parking meter=30.6 bench=13.4 bird=17.6 cat=37.7 dog=34.6 horse=29.6 sheep=32.1 cow=32.3 elephant=40.7 bear=35.1 zebra=43.4 giraffe=41.1 backpack=5.8 umbrella=23.0 handbag=5.2 tie=18.1 suitcase=20.6 frisbee=36.1 skis=11.1 snowboard=15.7 sports ball=23.9 kite=22.1 baseball bat=12.3 baseball glove=19.9 skateboard=30.0 surfboard=19.8 tennis racket=28.0 bottle=15.8 wine glass=19.5 cup=23.2 fork=12.7 knife=2.6 spoon=5.6 bowl=21.5 banana=9.7 apple=5.7 sandwich=13.5 orange=13.9 broccoli=8.0 carrot=9.7 hot dog=13.4 pizza=24.7 donut=27.0 cake=20.0 chair=15.7 couch=31.3 potted plant=13.8 bed=26.4 dining table=13.8 toilet=37.9 tv=38.6 laptop=35.3 mouse=39.1 remote=11.4 keyboard=25.0 cell phone=16.5 microwave=38.5 oven=17.8 toaster=0.0 sink=20.8 refrigerator=33.6 book=5.0 clock=33.0 vase=18.1 scissors=12.6 teddy bear=27.4 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.3 [Epoch 132][Batch 99], LR: 1.00E-03, Speed: 7.682 samples/sec, ObjLoss=25.135, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.506 [Epoch 132][Batch 199], LR: 1.00E-03, Speed: 8.223 samples/sec, ObjLoss=25.134, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.505 [Epoch 132][Batch 299], LR: 1.00E-03, Speed: 9.389 samples/sec, ObjLoss=25.133, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.504 [Epoch 132][Batch 399], LR: 1.00E-03, Speed: 9.338 samples/sec, ObjLoss=25.133, BoxCenterLoss=14.457, BoxScaleLoss=5.139, ClassLoss=10.504 [Epoch 132][Batch 499], LR: 1.00E-03, Speed: 11.326 samples/sec, ObjLoss=25.132, BoxCenterLoss=14.457, BoxScaleLoss=5.138, ClassLoss=10.503 [Epoch 132][Batch 599], LR: 1.00E-03, Speed: 8.635 samples/sec, ObjLoss=25.131, BoxCenterLoss=14.456, BoxScaleLoss=5.138, ClassLoss=10.502 [Epoch 132][Batch 699], LR: 1.00E-03, Speed: 9.420 samples/sec, ObjLoss=25.130, BoxCenterLoss=14.457, BoxScaleLoss=5.138, ClassLoss=10.501 [Epoch 132][Batch 799], LR: 1.00E-03, Speed: 10.554 samples/sec, ObjLoss=25.130, BoxCenterLoss=14.457, BoxScaleLoss=5.138, ClassLoss=10.501 [Epoch 132][Batch 899], LR: 1.00E-03, Speed: 8.824 samples/sec, ObjLoss=25.129, BoxCenterLoss=14.457, BoxScaleLoss=5.138, ClassLoss=10.500 [Epoch 132][Batch 999], LR: 1.00E-03, Speed: 7.208 samples/sec, ObjLoss=25.128, BoxCenterLoss=14.456, BoxScaleLoss=5.137, ClassLoss=10.499 [Epoch 132][Batch 1099], LR: 1.00E-03, Speed: 9.092 samples/sec, ObjLoss=25.127, BoxCenterLoss=14.456, BoxScaleLoss=5.137, ClassLoss=10.498 [Epoch 132][Batch 1199], LR: 1.00E-03, Speed: 9.318 samples/sec, ObjLoss=25.126, BoxCenterLoss=14.456, BoxScaleLoss=5.137, ClassLoss=10.498 [Epoch 132][Batch 1299], LR: 1.00E-03, Speed: 9.072 samples/sec, ObjLoss=25.125, BoxCenterLoss=14.456, BoxScaleLoss=5.137, ClassLoss=10.497 [Epoch 132][Batch 1399], LR: 1.00E-03, Speed: 95.837 samples/sec, ObjLoss=25.124, BoxCenterLoss=14.456, BoxScaleLoss=5.137, ClassLoss=10.496 [Epoch 132][Batch 1499], LR: 1.00E-03, Speed: 7.847 samples/sec, ObjLoss=25.123, BoxCenterLoss=14.456, BoxScaleLoss=5.137, ClassLoss=10.496 [Epoch 132][Batch 1599], LR: 1.00E-03, Speed: 8.556 samples/sec, ObjLoss=25.123, BoxCenterLoss=14.456, BoxScaleLoss=5.137, ClassLoss=10.495 [Epoch 132][Batch 1699], LR: 1.00E-03, Speed: 8.550 samples/sec, ObjLoss=25.122, BoxCenterLoss=14.456, BoxScaleLoss=5.136, ClassLoss=10.494 [Epoch 132][Batch 1799], LR: 1.00E-03, Speed: 87.530 samples/sec, ObjLoss=25.121, BoxCenterLoss=14.456, BoxScaleLoss=5.136, ClassLoss=10.494 [Epoch 132] Training cost: 2185.274, ObjLoss=25.121, BoxCenterLoss=14.456, BoxScaleLoss=5.136, ClassLoss=10.493 [Epoch 132] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.449 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.220 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.462 person=37.0 bicycle=16.8 car=26.3 motorcycle=25.9 airplane=36.2 bus=49.3 train=45.9 truck=22.9 boat=12.3 traffic light=13.7 fire hydrant=42.8 stop sign=39.3 parking meter=26.0 bench=11.9 bird=17.3 cat=47.8 dog=37.4 horse=32.5 sheep=29.9 cow=32.6 elephant=42.7 bear=48.2 zebra=44.6 giraffe=43.5 backpack=6.0 umbrella=21.7 handbag=5.1 tie=18.5 suitcase=18.2 frisbee=28.3 skis=9.2 snowboard=13.0 sports ball=23.8 kite=24.1 baseball bat=12.8 baseball glove=20.4 skateboard=30.0 surfboard=16.8 tennis racket=23.7 bottle=17.8 wine glass=15.6 cup=24.5 fork=12.8 knife=3.6 spoon=4.8 bowl=20.6 banana=12.7 apple=7.1 sandwich=18.8 orange=17.8 broccoli=9.3 carrot=8.5 hot dog=19.7 pizza=31.5 donut=26.3 cake=19.4 chair=16.1 couch=24.6 potted plant=11.6 bed=29.3 dining table=17.5 toilet=41.5 tv=37.5 laptop=35.9 mouse=37.2 remote=9.4 keyboard=29.5 cell phone=16.5 microwave=37.4 oven=21.5 toaster=3.6 sink=18.6 refrigerator=31.2 book=4.7 clock=33.6 vase=21.0 scissors=18.4 teddy bear=26.7 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.2 [Epoch 133][Batch 99], LR: 1.00E-03, Speed: 10.859 samples/sec, ObjLoss=25.121, BoxCenterLoss=14.456, BoxScaleLoss=5.136, ClassLoss=10.493 [Epoch 133][Batch 199], LR: 1.00E-03, Speed: 9.034 samples/sec, ObjLoss=25.120, BoxCenterLoss=14.456, BoxScaleLoss=5.136, ClassLoss=10.492 [Epoch 133][Batch 299], LR: 1.00E-03, Speed: 7.749 samples/sec, ObjLoss=25.119, BoxCenterLoss=14.456, BoxScaleLoss=5.136, ClassLoss=10.491 [Epoch 133][Batch 399], LR: 1.00E-03, Speed: 125.334 samples/sec, ObjLoss=25.118, BoxCenterLoss=14.456, BoxScaleLoss=5.136, ClassLoss=10.491 [Epoch 133][Batch 499], LR: 1.00E-03, Speed: 59.335 samples/sec, ObjLoss=25.117, BoxCenterLoss=14.455, BoxScaleLoss=5.135, ClassLoss=10.490 [Epoch 133][Batch 599], LR: 1.00E-03, Speed: 8.080 samples/sec, ObjLoss=25.115, BoxCenterLoss=14.455, BoxScaleLoss=5.135, ClassLoss=10.489 [Epoch 133][Batch 699], LR: 1.00E-03, Speed: 94.423 samples/sec, ObjLoss=25.114, BoxCenterLoss=14.455, BoxScaleLoss=5.135, ClassLoss=10.488 [Epoch 133][Batch 799], LR: 1.00E-03, Speed: 7.977 samples/sec, ObjLoss=25.114, BoxCenterLoss=14.455, BoxScaleLoss=5.135, ClassLoss=10.488 [Epoch 133][Batch 899], LR: 1.00E-03, Speed: 8.733 samples/sec, ObjLoss=25.112, BoxCenterLoss=14.455, BoxScaleLoss=5.135, ClassLoss=10.487 [Epoch 133][Batch 999], LR: 1.00E-03, Speed: 9.943 samples/sec, ObjLoss=25.112, BoxCenterLoss=14.454, BoxScaleLoss=5.134, ClassLoss=10.486 [Epoch 133][Batch 1099], LR: 1.00E-03, Speed: 11.904 samples/sec, ObjLoss=25.111, BoxCenterLoss=14.454, BoxScaleLoss=5.134, ClassLoss=10.485 [Epoch 133][Batch 1199], LR: 1.00E-03, Speed: 9.224 samples/sec, ObjLoss=25.110, BoxCenterLoss=14.454, BoxScaleLoss=5.134, ClassLoss=10.485 [Epoch 133][Batch 1299], LR: 1.00E-03, Speed: 95.283 samples/sec, ObjLoss=25.109, BoxCenterLoss=14.454, BoxScaleLoss=5.134, ClassLoss=10.484 [Epoch 133][Batch 1399], LR: 1.00E-03, Speed: 9.596 samples/sec, ObjLoss=25.107, BoxCenterLoss=14.453, BoxScaleLoss=5.133, ClassLoss=10.483 [Epoch 133][Batch 1499], LR: 1.00E-03, Speed: 9.783 samples/sec, ObjLoss=25.107, BoxCenterLoss=14.454, BoxScaleLoss=5.133, ClassLoss=10.482 [Epoch 133][Batch 1599], LR: 1.00E-03, Speed: 8.968 samples/sec, ObjLoss=25.106, BoxCenterLoss=14.454, BoxScaleLoss=5.133, ClassLoss=10.482 [Epoch 133][Batch 1699], LR: 1.00E-03, Speed: 10.361 samples/sec, ObjLoss=25.106, BoxCenterLoss=14.454, BoxScaleLoss=5.133, ClassLoss=10.481 [Epoch 133][Batch 1799], LR: 1.00E-03, Speed: 10.968 samples/sec, ObjLoss=25.105, BoxCenterLoss=14.454, BoxScaleLoss=5.133, ClassLoss=10.480 [Epoch 133] Training cost: 2154.420, ObjLoss=25.104, BoxCenterLoss=14.454, BoxScaleLoss=5.133, ClassLoss=10.480 [Epoch 133] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.143 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455 person=37.9 bicycle=17.7 car=28.7 motorcycle=30.3 airplane=41.0 bus=46.9 train=51.5 truck=23.3 boat=12.1 traffic light=12.4 fire hydrant=43.4 stop sign=41.5 parking meter=28.6 bench=14.8 bird=20.0 cat=43.3 dog=37.5 horse=39.5 sheep=34.3 cow=31.5 elephant=44.6 bear=45.6 zebra=45.3 giraffe=48.6 backpack=5.5 umbrella=22.2 handbag=4.3 tie=16.0 suitcase=18.0 frisbee=31.8 skis=10.2 snowboard=15.8 sports ball=20.3 kite=26.6 baseball bat=11.4 baseball glove=20.8 skateboard=25.4 surfboard=21.4 tennis racket=28.1 bottle=16.2 wine glass=19.7 cup=23.8 fork=15.1 knife=4.7 spoon=4.4 bowl=21.4 banana=14.1 apple=9.1 sandwich=20.0 orange=13.5 broccoli=7.2 carrot=8.0 hot dog=18.2 pizza=33.1 donut=25.7 cake=22.3 chair=14.3 couch=27.3 potted plant=12.6 bed=33.2 dining table=20.5 toilet=36.6 tv=33.4 laptop=35.6 mouse=40.7 remote=12.1 keyboard=29.7 cell phone=19.6 microwave=30.3 oven=16.9 toaster=0.0 sink=19.1 refrigerator=30.5 book=4.4 clock=35.1 vase=20.7 scissors=14.4 teddy bear=30.1 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.7 [Epoch 134][Batch 99], LR: 1.00E-03, Speed: 7.835 samples/sec, ObjLoss=25.103, BoxCenterLoss=14.453, BoxScaleLoss=5.133, ClassLoss=10.479 [Epoch 134][Batch 199], LR: 1.00E-03, Speed: 7.786 samples/sec, ObjLoss=25.102, BoxCenterLoss=14.453, BoxScaleLoss=5.133, ClassLoss=10.479 [Epoch 134][Batch 299], LR: 1.00E-03, Speed: 128.793 samples/sec, ObjLoss=25.101, BoxCenterLoss=14.453, BoxScaleLoss=5.133, ClassLoss=10.478 [Epoch 134][Batch 399], LR: 1.00E-03, Speed: 123.543 samples/sec, ObjLoss=25.100, BoxCenterLoss=14.453, BoxScaleLoss=5.133, ClassLoss=10.477 [Epoch 134][Batch 499], LR: 1.00E-03, Speed: 10.658 samples/sec, ObjLoss=25.099, BoxCenterLoss=14.453, BoxScaleLoss=5.133, ClassLoss=10.477 [Epoch 134][Batch 599], LR: 1.00E-03, Speed: 10.476 samples/sec, ObjLoss=25.098, BoxCenterLoss=14.453, BoxScaleLoss=5.132, ClassLoss=10.476 [Epoch 134][Batch 699], LR: 1.00E-03, Speed: 8.933 samples/sec, ObjLoss=25.097, BoxCenterLoss=14.453, BoxScaleLoss=5.132, ClassLoss=10.476 [Epoch 134][Batch 799], LR: 1.00E-03, Speed: 8.870 samples/sec, ObjLoss=25.096, BoxCenterLoss=14.453, BoxScaleLoss=5.132, ClassLoss=10.475 [Epoch 134][Batch 899], LR: 1.00E-03, Speed: 113.906 samples/sec, ObjLoss=25.095, BoxCenterLoss=14.453, BoxScaleLoss=5.132, ClassLoss=10.474 [Epoch 134][Batch 999], LR: 1.00E-03, Speed: 8.036 samples/sec, ObjLoss=25.095, BoxCenterLoss=14.452, BoxScaleLoss=5.132, ClassLoss=10.473 [Epoch 134][Batch 1099], LR: 1.00E-03, Speed: 7.932 samples/sec, ObjLoss=25.094, BoxCenterLoss=14.453, BoxScaleLoss=5.132, ClassLoss=10.473 [Epoch 134][Batch 1199], LR: 1.00E-03, Speed: 8.426 samples/sec, ObjLoss=25.093, BoxCenterLoss=14.452, BoxScaleLoss=5.132, ClassLoss=10.472 [Epoch 134][Batch 1299], LR: 1.00E-03, Speed: 92.502 samples/sec, ObjLoss=25.092, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.471 [Epoch 134][Batch 1399], LR: 1.00E-03, Speed: 12.205 samples/sec, ObjLoss=25.091, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.471 [Epoch 134][Batch 1499], LR: 1.00E-03, Speed: 8.782 samples/sec, ObjLoss=25.090, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.470 [Epoch 134][Batch 1599], LR: 1.00E-03, Speed: 56.338 samples/sec, ObjLoss=25.089, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.469 [Epoch 134][Batch 1699], LR: 1.00E-03, Speed: 9.935 samples/sec, ObjLoss=25.088, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.469 [Epoch 134][Batch 1799], LR: 1.00E-03, Speed: 130.746 samples/sec, ObjLoss=25.087, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.468 [Epoch 134] Training cost: 2158.770, ObjLoss=25.087, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.468 [Epoch 134] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.143 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.462 person=36.0 bicycle=19.3 car=25.5 motorcycle=29.4 airplane=40.0 bus=43.4 train=46.4 truck=20.7 boat=11.6 traffic light=12.8 fire hydrant=45.0 stop sign=46.3 parking meter=24.4 bench=14.8 bird=21.6 cat=44.0 dog=37.0 horse=33.6 sheep=31.6 cow=35.0 elephant=45.6 bear=41.2 zebra=47.4 giraffe=50.4 backpack=5.7 umbrella=23.3 handbag=6.5 tie=16.7 suitcase=19.3 frisbee=36.3 skis=10.0 snowboard=15.3 sports ball=25.5 kite=24.4 baseball bat=15.7 baseball glove=20.6 skateboard=31.8 surfboard=18.6 tennis racket=28.0 bottle=15.2 wine glass=18.0 cup=22.8 fork=13.8 knife=5.2 spoon=4.1 bowl=22.9 banana=13.4 apple=6.4 sandwich=21.1 orange=18.5 broccoli=8.3 carrot=8.5 hot dog=16.2 pizza=36.1 donut=29.8 cake=19.9 chair=16.1 couch=27.0 potted plant=12.6 bed=26.7 dining table=17.2 toilet=34.1 tv=33.4 laptop=40.2 mouse=38.7 remote=10.5 keyboard=31.8 cell phone=14.5 microwave=32.5 oven=19.7 toaster=0.0 sink=20.3 refrigerator=31.0 book=4.9 clock=32.5 vase=19.5 scissors=15.2 teddy bear=29.3 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.7 [Epoch 135][Batch 99], LR: 1.00E-03, Speed: 11.308 samples/sec, ObjLoss=25.086, BoxCenterLoss=14.452, BoxScaleLoss=5.131, ClassLoss=10.467 [Epoch 135][Batch 199], LR: 1.00E-03, Speed: 9.071 samples/sec, ObjLoss=25.085, BoxCenterLoss=14.452, BoxScaleLoss=5.130, ClassLoss=10.467 [Epoch 135][Batch 299], LR: 1.00E-03, Speed: 9.518 samples/sec, ObjLoss=25.085, BoxCenterLoss=14.451, BoxScaleLoss=5.130, ClassLoss=10.466 [Epoch 135][Batch 399], LR: 1.00E-03, Speed: 9.256 samples/sec, ObjLoss=25.084, BoxCenterLoss=14.451, BoxScaleLoss=5.130, ClassLoss=10.465 [Epoch 135][Batch 499], LR: 1.00E-03, Speed: 8.818 samples/sec, ObjLoss=25.082, BoxCenterLoss=14.451, BoxScaleLoss=5.130, ClassLoss=10.464 [Epoch 135][Batch 599], LR: 1.00E-03, Speed: 10.253 samples/sec, ObjLoss=25.081, BoxCenterLoss=14.451, BoxScaleLoss=5.130, ClassLoss=10.463 [Epoch 135][Batch 699], LR: 1.00E-03, Speed: 8.283 samples/sec, ObjLoss=25.081, BoxCenterLoss=14.451, BoxScaleLoss=5.130, ClassLoss=10.463 [Epoch 135][Batch 799], LR: 1.00E-03, Speed: 8.894 samples/sec, ObjLoss=25.080, BoxCenterLoss=14.451, BoxScaleLoss=5.129, ClassLoss=10.462 [Epoch 135][Batch 899], LR: 1.00E-03, Speed: 86.804 samples/sec, ObjLoss=25.078, BoxCenterLoss=14.450, BoxScaleLoss=5.129, ClassLoss=10.461 [Epoch 135][Batch 999], LR: 1.00E-03, Speed: 8.338 samples/sec, ObjLoss=25.077, BoxCenterLoss=14.450, BoxScaleLoss=5.129, ClassLoss=10.461 [Epoch 135][Batch 1099], LR: 1.00E-03, Speed: 8.326 samples/sec, ObjLoss=25.076, BoxCenterLoss=14.450, BoxScaleLoss=5.129, ClassLoss=10.460 [Epoch 135][Batch 1199], LR: 1.00E-03, Speed: 10.708 samples/sec, ObjLoss=25.075, BoxCenterLoss=14.450, BoxScaleLoss=5.129, ClassLoss=10.459 [Epoch 135][Batch 1299], LR: 1.00E-03, Speed: 113.123 samples/sec, ObjLoss=25.074, BoxCenterLoss=14.450, BoxScaleLoss=5.129, ClassLoss=10.459 [Epoch 135][Batch 1399], LR: 1.00E-03, Speed: 9.385 samples/sec, ObjLoss=25.074, BoxCenterLoss=14.450, BoxScaleLoss=5.129, ClassLoss=10.458 [Epoch 135][Batch 1499], LR: 1.00E-03, Speed: 108.576 samples/sec, ObjLoss=25.073, BoxCenterLoss=14.449, BoxScaleLoss=5.128, ClassLoss=10.457 [Epoch 135][Batch 1599], LR: 1.00E-03, Speed: 93.375 samples/sec, ObjLoss=25.072, BoxCenterLoss=14.450, BoxScaleLoss=5.128, ClassLoss=10.456 [Epoch 135][Batch 1699], LR: 1.00E-03, Speed: 9.654 samples/sec, ObjLoss=25.071, BoxCenterLoss=14.449, BoxScaleLoss=5.128, ClassLoss=10.456 [Epoch 135][Batch 1799], LR: 1.00E-03, Speed: 10.057 samples/sec, ObjLoss=25.070, BoxCenterLoss=14.449, BoxScaleLoss=5.128, ClassLoss=10.455 [Epoch 135] Training cost: 2249.832, ObjLoss=25.070, BoxCenterLoss=14.449, BoxScaleLoss=5.128, ClassLoss=10.455 [Epoch 135] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.226 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.199 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.207 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.452 person=33.8 bicycle=16.0 car=25.7 motorcycle=27.4 airplane=37.7 bus=44.0 train=48.5 truck=24.1 boat=13.4 traffic light=13.9 fire hydrant=41.2 stop sign=36.4 parking meter=26.3 bench=13.3 bird=16.8 cat=42.2 dog=32.9 horse=31.6 sheep=32.3 cow=32.4 elephant=44.4 bear=45.0 zebra=38.7 giraffe=43.9 backpack=5.7 umbrella=22.3 handbag=5.8 tie=14.0 suitcase=20.1 frisbee=31.1 skis=10.4 snowboard=14.5 sports ball=23.7 kite=22.4 baseball bat=12.4 baseball glove=17.4 skateboard=28.0 surfboard=18.6 tennis racket=28.8 bottle=15.2 wine glass=19.0 cup=20.4 fork=11.9 knife=4.6 spoon=3.1 bowl=24.0 banana=11.8 apple=3.6 sandwich=21.3 orange=16.6 broccoli=8.4 carrot=9.5 hot dog=16.5 pizza=30.7 donut=27.0 cake=18.4 chair=15.1 couch=30.1 potted plant=12.6 bed=31.2 dining table=20.1 toilet=40.8 tv=37.4 laptop=37.8 mouse=29.4 remote=9.0 keyboard=30.0 cell phone=16.4 microwave=31.6 oven=19.4 toaster=0.0 sink=20.9 refrigerator=29.7 book=4.5 clock=30.2 vase=18.7 scissors=14.0 teddy bear=26.3 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.6 [Epoch 136][Batch 99], LR: 1.00E-03, Speed: 11.786 samples/sec, ObjLoss=25.070, BoxCenterLoss=14.450, BoxScaleLoss=5.128, ClassLoss=10.454 [Epoch 136][Batch 199], LR: 1.00E-03, Speed: 8.615 samples/sec, ObjLoss=25.069, BoxCenterLoss=14.449, BoxScaleLoss=5.128, ClassLoss=10.454 [Epoch 136][Batch 299], LR: 1.00E-03, Speed: 8.334 samples/sec, ObjLoss=25.068, BoxCenterLoss=14.449, BoxScaleLoss=5.128, ClassLoss=10.453 [Epoch 136][Batch 399], LR: 1.00E-03, Speed: 10.223 samples/sec, ObjLoss=25.067, BoxCenterLoss=14.449, BoxScaleLoss=5.127, ClassLoss=10.452 [Epoch 136][Batch 499], LR: 1.00E-03, Speed: 9.806 samples/sec, ObjLoss=25.066, BoxCenterLoss=14.449, BoxScaleLoss=5.127, ClassLoss=10.451 [Epoch 136][Batch 599], LR: 1.00E-03, Speed: 13.741 samples/sec, ObjLoss=25.065, BoxCenterLoss=14.449, BoxScaleLoss=5.127, ClassLoss=10.451 [Epoch 136][Batch 699], LR: 1.00E-03, Speed: 9.155 samples/sec, ObjLoss=25.064, BoxCenterLoss=14.449, BoxScaleLoss=5.127, ClassLoss=10.450 [Epoch 136][Batch 799], LR: 1.00E-03, Speed: 108.035 samples/sec, ObjLoss=25.063, BoxCenterLoss=14.449, BoxScaleLoss=5.127, ClassLoss=10.449 [Epoch 136][Batch 899], LR: 1.00E-03, Speed: 60.683 samples/sec, ObjLoss=25.063, BoxCenterLoss=14.449, BoxScaleLoss=5.127, ClassLoss=10.449 [Epoch 136][Batch 999], LR: 1.00E-03, Speed: 11.001 samples/sec, ObjLoss=25.062, BoxCenterLoss=14.448, BoxScaleLoss=5.126, ClassLoss=10.448 [Epoch 136][Batch 1099], LR: 1.00E-03, Speed: 10.063 samples/sec, ObjLoss=25.061, BoxCenterLoss=14.448, BoxScaleLoss=5.126, ClassLoss=10.447 [Epoch 136][Batch 1199], LR: 1.00E-03, Speed: 11.008 samples/sec, ObjLoss=25.059, BoxCenterLoss=14.448, BoxScaleLoss=5.126, ClassLoss=10.446 [Epoch 136][Batch 1299], LR: 1.00E-03, Speed: 8.811 samples/sec, ObjLoss=25.059, BoxCenterLoss=14.448, BoxScaleLoss=5.126, ClassLoss=10.446 [Epoch 136][Batch 1399], LR: 1.00E-03, Speed: 134.635 samples/sec, ObjLoss=25.058, BoxCenterLoss=14.448, BoxScaleLoss=5.126, ClassLoss=10.445 [Epoch 136][Batch 1499], LR: 1.00E-03, Speed: 99.478 samples/sec, ObjLoss=25.057, BoxCenterLoss=14.448, BoxScaleLoss=5.126, ClassLoss=10.444 [Epoch 136][Batch 1599], LR: 1.00E-03, Speed: 13.167 samples/sec, ObjLoss=25.056, BoxCenterLoss=14.448, BoxScaleLoss=5.125, ClassLoss=10.444 [Epoch 136][Batch 1699], LR: 1.00E-03, Speed: 106.201 samples/sec, ObjLoss=25.055, BoxCenterLoss=14.448, BoxScaleLoss=5.125, ClassLoss=10.443 [Epoch 136][Batch 1799], LR: 1.00E-03, Speed: 12.068 samples/sec, ObjLoss=25.054, BoxCenterLoss=14.448, BoxScaleLoss=5.125, ClassLoss=10.443 [Epoch 136] Training cost: 2182.302, ObjLoss=25.054, BoxCenterLoss=14.448, BoxScaleLoss=5.125, ClassLoss=10.442 [Epoch 136] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.464 person=36.2 bicycle=15.6 car=25.4 motorcycle=28.9 airplane=42.2 bus=43.1 train=49.6 truck=22.4 boat=11.8 traffic light=13.3 fire hydrant=34.9 stop sign=33.6 parking meter=23.4 bench=12.0 bird=15.3 cat=43.1 dog=27.7 horse=35.6 sheep=31.1 cow=33.5 elephant=43.7 bear=42.9 zebra=44.1 giraffe=41.4 backpack=5.0 umbrella=21.2 handbag=6.2 tie=15.4 suitcase=16.4 frisbee=33.1 skis=11.8 snowboard=11.5 sports ball=29.3 kite=21.5 baseball bat=12.1 baseball glove=18.1 skateboard=28.4 surfboard=19.3 tennis racket=26.3 bottle=16.6 wine glass=17.7 cup=23.0 fork=15.3 knife=4.8 spoon=6.6 bowl=22.3 banana=12.7 apple=8.4 sandwich=23.1 orange=13.7 broccoli=9.7 carrot=9.4 hot dog=15.9 pizza=31.2 donut=30.5 cake=21.0 chair=13.4 couch=28.6 potted plant=12.3 bed=33.2 dining table=18.1 toilet=38.8 tv=35.7 laptop=37.1 mouse=38.2 remote=10.7 keyboard=28.5 cell phone=15.9 microwave=35.4 oven=20.7 toaster=0.0 sink=21.0 refrigerator=27.5 book=5.6 clock=32.0 vase=19.7 scissors=24.4 teddy bear=22.5 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.9 [Epoch 137][Batch 99], LR: 1.00E-03, Speed: 8.909 samples/sec, ObjLoss=25.053, BoxCenterLoss=14.447, BoxScaleLoss=5.125, ClassLoss=10.442 [Epoch 137][Batch 199], LR: 1.00E-03, Speed: 10.098 samples/sec, ObjLoss=25.052, BoxCenterLoss=14.447, BoxScaleLoss=5.125, ClassLoss=10.441 [Epoch 137][Batch 299], LR: 1.00E-03, Speed: 11.662 samples/sec, ObjLoss=25.052, BoxCenterLoss=14.448, BoxScaleLoss=5.125, ClassLoss=10.440 [Epoch 137][Batch 399], LR: 1.00E-03, Speed: 114.097 samples/sec, ObjLoss=25.051, BoxCenterLoss=14.448, BoxScaleLoss=5.125, ClassLoss=10.440 [Epoch 137][Batch 499], LR: 1.00E-03, Speed: 9.406 samples/sec, ObjLoss=25.050, BoxCenterLoss=14.447, BoxScaleLoss=5.125, ClassLoss=10.439 [Epoch 137][Batch 599], LR: 1.00E-03, Speed: 10.491 samples/sec, ObjLoss=25.050, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.438 [Epoch 137][Batch 699], LR: 1.00E-03, Speed: 98.099 samples/sec, ObjLoss=25.049, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.437 [Epoch 137][Batch 799], LR: 1.00E-03, Speed: 9.522 samples/sec, ObjLoss=25.048, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.436 [Epoch 137][Batch 899], LR: 1.00E-03, Speed: 6.744 samples/sec, ObjLoss=25.047, BoxCenterLoss=14.448, BoxScaleLoss=5.124, ClassLoss=10.436 [Epoch 137][Batch 999], LR: 1.00E-03, Speed: 9.267 samples/sec, ObjLoss=25.047, BoxCenterLoss=14.448, BoxScaleLoss=5.124, ClassLoss=10.435 [Epoch 137][Batch 1099], LR: 1.00E-03, Speed: 8.190 samples/sec, ObjLoss=25.046, BoxCenterLoss=14.448, BoxScaleLoss=5.124, ClassLoss=10.435 [Epoch 137][Batch 1199], LR: 1.00E-03, Speed: 101.052 samples/sec, ObjLoss=25.045, BoxCenterLoss=14.448, BoxScaleLoss=5.124, ClassLoss=10.434 [Epoch 137][Batch 1299], LR: 1.00E-03, Speed: 9.894 samples/sec, ObjLoss=25.044, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.433 [Epoch 137][Batch 1399], LR: 1.00E-03, Speed: 7.048 samples/sec, ObjLoss=25.043, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.432 [Epoch 137][Batch 1499], LR: 1.00E-03, Speed: 9.940 samples/sec, ObjLoss=25.042, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.432 [Epoch 137][Batch 1599], LR: 1.00E-03, Speed: 9.688 samples/sec, ObjLoss=25.041, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.431 [Epoch 137][Batch 1699], LR: 1.00E-03, Speed: 10.692 samples/sec, ObjLoss=25.041, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.431 [Epoch 137][Batch 1799], LR: 1.00E-03, Speed: 16.054 samples/sec, ObjLoss=25.040, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.430 [Epoch 137] Training cost: 2226.588, ObjLoss=25.039, BoxCenterLoss=14.447, BoxScaleLoss=5.124, ClassLoss=10.430 [Epoch 137] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.470 person=38.4 bicycle=16.2 car=28.3 motorcycle=29.7 airplane=42.4 bus=50.3 train=47.4 truck=23.4 boat=13.3 traffic light=13.7 fire hydrant=47.5 stop sign=39.3 parking meter=33.2 bench=14.8 bird=20.6 cat=45.8 dog=42.2 horse=35.6 sheep=30.9 cow=36.2 elephant=46.3 bear=45.2 zebra=48.5 giraffe=51.3 backpack=5.4 umbrella=23.9 handbag=6.3 tie=18.5 suitcase=20.5 frisbee=40.5 skis=8.2 snowboard=16.9 sports ball=29.3 kite=24.0 baseball bat=12.7 baseball glove=18.8 skateboard=30.1 surfboard=21.3 tennis racket=27.5 bottle=19.7 wine glass=20.5 cup=24.9 fork=15.2 knife=5.6 spoon=4.8 bowl=23.0 banana=11.9 apple=8.0 sandwich=19.0 orange=17.0 broccoli=9.3 carrot=9.6 hot dog=22.4 pizza=33.0 donut=30.9 cake=19.2 chair=16.1 couch=27.0 potted plant=12.6 bed=29.2 dining table=19.9 toilet=38.8 tv=33.8 laptop=38.5 mouse=38.5 remote=11.9 keyboard=31.5 cell phone=17.7 microwave=35.3 oven=21.4 toaster=0.0 sink=19.6 refrigerator=32.7 book=6.2 clock=35.1 vase=22.7 scissors=14.0 teddy bear=28.6 hair drier=0.0 toothbrush=6.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.7 [Epoch 138][Batch 99], LR: 1.00E-03, Speed: 9.015 samples/sec, ObjLoss=25.038, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.429 [Epoch 138][Batch 199], LR: 1.00E-03, Speed: 129.170 samples/sec, ObjLoss=25.038, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.429 [Epoch 138][Batch 299], LR: 1.00E-03, Speed: 11.342 samples/sec, ObjLoss=25.037, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.428 [Epoch 138][Batch 399], LR: 1.00E-03, Speed: 11.333 samples/sec, ObjLoss=25.036, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.427 [Epoch 138][Batch 499], LR: 1.00E-03, Speed: 119.456 samples/sec, ObjLoss=25.036, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.426 [Epoch 138][Batch 599], LR: 1.00E-03, Speed: 12.414 samples/sec, ObjLoss=25.035, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.426 [Epoch 138][Batch 699], LR: 1.00E-03, Speed: 7.873 samples/sec, ObjLoss=25.034, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.425 [Epoch 138][Batch 799], LR: 1.00E-03, Speed: 10.863 samples/sec, ObjLoss=25.033, BoxCenterLoss=14.447, BoxScaleLoss=5.123, ClassLoss=10.425 [Epoch 138][Batch 899], LR: 1.00E-03, Speed: 129.943 samples/sec, ObjLoss=25.032, BoxCenterLoss=14.446, BoxScaleLoss=5.122, ClassLoss=10.424 [Epoch 138][Batch 999], LR: 1.00E-03, Speed: 8.228 samples/sec, ObjLoss=25.032, BoxCenterLoss=14.447, BoxScaleLoss=5.122, ClassLoss=10.423 [Epoch 138][Batch 1099], LR: 1.00E-03, Speed: 9.150 samples/sec, ObjLoss=25.031, BoxCenterLoss=14.447, BoxScaleLoss=5.122, ClassLoss=10.422 [Epoch 138][Batch 1199], LR: 1.00E-03, Speed: 8.401 samples/sec, ObjLoss=25.030, BoxCenterLoss=14.446, BoxScaleLoss=5.122, ClassLoss=10.422 [Epoch 138][Batch 1299], LR: 1.00E-03, Speed: 9.195 samples/sec, ObjLoss=25.029, BoxCenterLoss=14.446, BoxScaleLoss=5.122, ClassLoss=10.421 [Epoch 138][Batch 1399], LR: 1.00E-03, Speed: 10.423 samples/sec, ObjLoss=25.028, BoxCenterLoss=14.447, BoxScaleLoss=5.122, ClassLoss=10.420 [Epoch 138][Batch 1499], LR: 1.00E-03, Speed: 10.343 samples/sec, ObjLoss=25.028, BoxCenterLoss=14.447, BoxScaleLoss=5.122, ClassLoss=10.420 [Epoch 138][Batch 1599], LR: 1.00E-03, Speed: 9.318 samples/sec, ObjLoss=25.027, BoxCenterLoss=14.447, BoxScaleLoss=5.122, ClassLoss=10.419 [Epoch 138][Batch 1699], LR: 1.00E-03, Speed: 9.347 samples/sec, ObjLoss=25.027, BoxCenterLoss=14.447, BoxScaleLoss=5.122, ClassLoss=10.419 [Epoch 138][Batch 1799], LR: 1.00E-03, Speed: 10.119 samples/sec, ObjLoss=25.026, BoxCenterLoss=14.446, BoxScaleLoss=5.121, ClassLoss=10.418 [Epoch 138] Training cost: 2220.406, ObjLoss=25.025, BoxCenterLoss=14.446, BoxScaleLoss=5.121, ClassLoss=10.417 [Epoch 138] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.211 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.318 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.443 person=36.1 bicycle=16.6 car=24.2 motorcycle=28.9 airplane=39.9 bus=43.9 train=48.9 truck=20.3 boat=11.8 traffic light=14.9 fire hydrant=39.0 stop sign=38.5 parking meter=26.7 bench=12.1 bird=17.6 cat=43.1 dog=33.9 horse=37.6 sheep=31.7 cow=33.8 elephant=44.7 bear=45.7 zebra=46.7 giraffe=47.0 backpack=5.1 umbrella=19.8 handbag=5.4 tie=17.7 suitcase=18.0 frisbee=32.0 skis=9.3 snowboard=15.0 sports ball=21.2 kite=24.9 baseball bat=12.7 baseball glove=19.0 skateboard=30.7 surfboard=17.5 tennis racket=27.4 bottle=19.1 wine glass=20.4 cup=23.4 fork=15.1 knife=5.9 spoon=4.1 bowl=21.4 banana=11.5 apple=7.5 sandwich=14.9 orange=13.5 broccoli=9.3 carrot=10.4 hot dog=15.0 pizza=31.0 donut=26.4 cake=21.0 chair=14.4 couch=27.6 potted plant=13.6 bed=29.6 dining table=20.4 toilet=38.8 tv=32.4 laptop=35.2 mouse=32.7 remote=12.0 keyboard=26.0 cell phone=17.6 microwave=39.7 oven=19.5 toaster=0.0 sink=22.8 refrigerator=30.8 book=5.4 clock=30.4 vase=20.4 scissors=20.0 teddy bear=28.0 hair drier=0.0 toothbrush=6.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.2 [Epoch 139][Batch 99], LR: 1.00E-03, Speed: 9.238 samples/sec, ObjLoss=25.025, BoxCenterLoss=14.446, BoxScaleLoss=5.121, ClassLoss=10.417 [Epoch 139][Batch 199], LR: 1.00E-03, Speed: 8.068 samples/sec, ObjLoss=25.024, BoxCenterLoss=14.446, BoxScaleLoss=5.121, ClassLoss=10.416 [Epoch 139][Batch 299], LR: 1.00E-03, Speed: 8.619 samples/sec, ObjLoss=25.024, BoxCenterLoss=14.446, BoxScaleLoss=5.120, ClassLoss=10.415 [Epoch 139][Batch 399], LR: 1.00E-03, Speed: 121.469 samples/sec, ObjLoss=25.023, BoxCenterLoss=14.446, BoxScaleLoss=5.120, ClassLoss=10.415 [Epoch 139][Batch 499], LR: 1.00E-03, Speed: 10.147 samples/sec, ObjLoss=25.023, BoxCenterLoss=14.446, BoxScaleLoss=5.120, ClassLoss=10.414 [Epoch 139][Batch 599], LR: 1.00E-03, Speed: 110.061 samples/sec, ObjLoss=25.022, BoxCenterLoss=14.446, BoxScaleLoss=5.120, ClassLoss=10.413 [Epoch 139][Batch 699], LR: 1.00E-03, Speed: 105.048 samples/sec, ObjLoss=25.021, BoxCenterLoss=14.446, BoxScaleLoss=5.120, ClassLoss=10.412 [Epoch 139][Batch 799], LR: 1.00E-03, Speed: 10.411 samples/sec, ObjLoss=25.020, BoxCenterLoss=14.447, BoxScaleLoss=5.120, ClassLoss=10.412 [Epoch 139][Batch 899], LR: 1.00E-03, Speed: 11.121 samples/sec, ObjLoss=25.020, BoxCenterLoss=14.447, BoxScaleLoss=5.120, ClassLoss=10.411 [Epoch 139][Batch 999], LR: 1.00E-03, Speed: 11.478 samples/sec, ObjLoss=25.019, BoxCenterLoss=14.447, BoxScaleLoss=5.120, ClassLoss=10.411 [Epoch 139][Batch 1099], LR: 1.00E-03, Speed: 110.885 samples/sec, ObjLoss=25.018, BoxCenterLoss=14.446, BoxScaleLoss=5.120, ClassLoss=10.410 [Epoch 139][Batch 1199], LR: 1.00E-03, Speed: 10.368 samples/sec, ObjLoss=25.018, BoxCenterLoss=14.447, BoxScaleLoss=5.120, ClassLoss=10.410 [Epoch 139][Batch 1299], LR: 1.00E-03, Speed: 11.188 samples/sec, ObjLoss=25.017, BoxCenterLoss=14.446, BoxScaleLoss=5.120, ClassLoss=10.409 [Epoch 139][Batch 1399], LR: 1.00E-03, Speed: 10.705 samples/sec, ObjLoss=25.016, BoxCenterLoss=14.446, BoxScaleLoss=5.119, ClassLoss=10.408 [Epoch 139][Batch 1499], LR: 1.00E-03, Speed: 96.743 samples/sec, ObjLoss=25.015, BoxCenterLoss=14.446, BoxScaleLoss=5.119, ClassLoss=10.407 [Epoch 139][Batch 1599], LR: 1.00E-03, Speed: 88.129 samples/sec, ObjLoss=25.013, BoxCenterLoss=14.445, BoxScaleLoss=5.119, ClassLoss=10.407 [Epoch 139][Batch 1699], LR: 1.00E-03, Speed: 8.414 samples/sec, ObjLoss=25.013, BoxCenterLoss=14.445, BoxScaleLoss=5.119, ClassLoss=10.406 [Epoch 139][Batch 1799], LR: 1.00E-03, Speed: 11.298 samples/sec, ObjLoss=25.012, BoxCenterLoss=14.445, BoxScaleLoss=5.119, ClassLoss=10.406 [Epoch 139] Training cost: 2181.525, ObjLoss=25.012, BoxCenterLoss=14.445, BoxScaleLoss=5.119, ClassLoss=10.405 [Epoch 139] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.224 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.453 person=38.7 bicycle=15.3 car=26.8 motorcycle=30.7 airplane=43.8 bus=49.8 train=46.3 truck=22.5 boat=13.2 traffic light=14.3 fire hydrant=40.7 stop sign=40.3 parking meter=24.3 bench=13.9 bird=21.0 cat=43.9 dog=39.8 horse=34.1 sheep=28.8 cow=32.8 elephant=35.9 bear=45.8 zebra=45.7 giraffe=47.0 backpack=6.0 umbrella=22.2 handbag=5.4 tie=12.5 suitcase=15.4 frisbee=31.9 skis=6.7 snowboard=14.1 sports ball=28.1 kite=19.1 baseball bat=14.0 baseball glove=18.6 skateboard=27.8 surfboard=21.0 tennis racket=24.4 bottle=19.3 wine glass=18.5 cup=24.5 fork=14.8 knife=3.5 spoon=4.3 bowl=23.1 banana=12.5 apple=6.7 sandwich=19.9 orange=18.0 broccoli=10.8 carrot=8.1 hot dog=19.6 pizza=36.3 donut=29.7 cake=21.6 chair=15.1 couch=32.6 potted plant=14.1 bed=32.7 dining table=20.1 toilet=31.0 tv=37.6 laptop=40.4 mouse=36.1 remote=11.2 keyboard=29.2 cell phone=17.4 microwave=29.7 oven=18.7 toaster=5.9 sink=19.5 refrigerator=32.3 book=4.3 clock=35.9 vase=20.5 scissors=21.7 teddy bear=25.0 hair drier=0.0 toothbrush=7.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.6 [Epoch 140][Batch 99], LR: 1.00E-03, Speed: 10.467 samples/sec, ObjLoss=25.011, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.405 [Epoch 140][Batch 199], LR: 1.00E-03, Speed: 10.881 samples/sec, ObjLoss=25.011, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.404 [Epoch 140][Batch 299], LR: 1.00E-03, Speed: 9.735 samples/sec, ObjLoss=25.010, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.403 [Epoch 140][Batch 399], LR: 1.00E-03, Speed: 10.954 samples/sec, ObjLoss=25.009, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.403 [Epoch 140][Batch 499], LR: 1.00E-03, Speed: 115.390 samples/sec, ObjLoss=25.008, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.402 [Epoch 140][Batch 599], LR: 1.00E-03, Speed: 10.863 samples/sec, ObjLoss=25.007, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.401 [Epoch 140][Batch 699], LR: 1.00E-03, Speed: 8.072 samples/sec, ObjLoss=25.006, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.401 [Epoch 140][Batch 799], LR: 1.00E-03, Speed: 10.803 samples/sec, ObjLoss=25.006, BoxCenterLoss=14.445, BoxScaleLoss=5.118, ClassLoss=10.400 [Epoch 140][Batch 899], LR: 1.00E-03, Speed: 117.655 samples/sec, ObjLoss=25.004, BoxCenterLoss=14.444, BoxScaleLoss=5.118, ClassLoss=10.400 [Epoch 140][Batch 999], LR: 1.00E-03, Speed: 10.471 samples/sec, ObjLoss=25.003, BoxCenterLoss=14.444, BoxScaleLoss=5.117, ClassLoss=10.399 [Epoch 140][Batch 1099], LR: 1.00E-03, Speed: 117.421 samples/sec, ObjLoss=25.002, BoxCenterLoss=14.444, BoxScaleLoss=5.117, ClassLoss=10.398 [Epoch 140][Batch 1199], LR: 1.00E-03, Speed: 8.052 samples/sec, ObjLoss=25.001, BoxCenterLoss=14.444, BoxScaleLoss=5.117, ClassLoss=10.397 [Epoch 140][Batch 1299], LR: 1.00E-03, Speed: 10.441 samples/sec, ObjLoss=25.001, BoxCenterLoss=14.443, BoxScaleLoss=5.117, ClassLoss=10.397 [Epoch 140][Batch 1399], LR: 1.00E-03, Speed: 10.438 samples/sec, ObjLoss=24.999, BoxCenterLoss=14.443, BoxScaleLoss=5.117, ClassLoss=10.396 [Epoch 140][Batch 1499], LR: 1.00E-03, Speed: 99.526 samples/sec, ObjLoss=24.998, BoxCenterLoss=14.443, BoxScaleLoss=5.116, ClassLoss=10.395 [Epoch 140][Batch 1599], LR: 1.00E-03, Speed: 10.402 samples/sec, ObjLoss=24.997, BoxCenterLoss=14.442, BoxScaleLoss=5.116, ClassLoss=10.394 [Epoch 140][Batch 1699], LR: 1.00E-03, Speed: 9.319 samples/sec, ObjLoss=24.996, BoxCenterLoss=14.443, BoxScaleLoss=5.116, ClassLoss=10.394 [Epoch 140][Batch 1799], LR: 1.00E-03, Speed: 9.940 samples/sec, ObjLoss=24.996, BoxCenterLoss=14.443, BoxScaleLoss=5.116, ClassLoss=10.393 [Epoch 140] Training cost: 2180.623, ObjLoss=24.996, BoxCenterLoss=14.443, BoxScaleLoss=5.116, ClassLoss=10.393 [Epoch 140] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.463 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.158 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472 person=38.5 bicycle=16.1 car=26.5 motorcycle=27.8 airplane=43.4 bus=50.1 train=49.1 truck=22.1 boat=14.0 traffic light=12.6 fire hydrant=42.9 stop sign=43.6 parking meter=28.1 bench=13.8 bird=20.7 cat=43.4 dog=39.9 horse=36.4 sheep=31.9 cow=34.6 elephant=47.2 bear=41.2 zebra=47.4 giraffe=50.6 backpack=6.6 umbrella=22.3 handbag=6.1 tie=17.4 suitcase=18.3 frisbee=34.2 skis=9.2 snowboard=17.9 sports ball=22.4 kite=26.6 baseball bat=16.3 baseball glove=16.0 skateboard=31.0 surfboard=20.3 tennis racket=28.2 bottle=20.5 wine glass=20.6 cup=24.7 fork=13.3 knife=5.0 spoon=5.3 bowl=24.0 banana=12.2 apple=9.8 sandwich=23.1 orange=16.0 broccoli=10.7 carrot=9.9 hot dog=17.0 pizza=29.5 donut=27.1 cake=21.1 chair=15.5 couch=27.3 potted plant=12.8 bed=30.2 dining table=20.9 toilet=42.9 tv=34.6 laptop=39.0 mouse=38.4 remote=10.3 keyboard=29.5 cell phone=17.2 microwave=30.8 oven=23.9 toaster=0.0 sink=23.6 refrigerator=32.2 book=5.9 clock=32.9 vase=22.9 scissors=17.3 teddy bear=28.8 hair drier=0.0 toothbrush=6.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.3 [Epoch 141][Batch 99], LR: 1.00E-03, Speed: 10.445 samples/sec, ObjLoss=24.995, BoxCenterLoss=14.443, BoxScaleLoss=5.116, ClassLoss=10.392 [Epoch 141][Batch 199], LR: 1.00E-03, Speed: 8.978 samples/sec, ObjLoss=24.995, BoxCenterLoss=14.443, BoxScaleLoss=5.116, ClassLoss=10.392 [Epoch 141][Batch 299], LR: 1.00E-03, Speed: 9.915 samples/sec, ObjLoss=24.994, BoxCenterLoss=14.443, BoxScaleLoss=5.116, ClassLoss=10.391 [Epoch 141][Batch 399], LR: 1.00E-03, Speed: 11.440 samples/sec, ObjLoss=24.993, BoxCenterLoss=14.443, BoxScaleLoss=5.115, ClassLoss=10.390 [Epoch 141][Batch 499], LR: 1.00E-03, Speed: 109.221 samples/sec, ObjLoss=24.992, BoxCenterLoss=14.443, BoxScaleLoss=5.115, ClassLoss=10.389 [Epoch 141][Batch 599], LR: 1.00E-03, Speed: 98.391 samples/sec, ObjLoss=24.991, BoxCenterLoss=14.442, BoxScaleLoss=5.115, ClassLoss=10.389 [Epoch 141][Batch 699], LR: 1.00E-03, Speed: 9.146 samples/sec, ObjLoss=24.990, BoxCenterLoss=14.442, BoxScaleLoss=5.115, ClassLoss=10.388 [Epoch 141][Batch 799], LR: 1.00E-03, Speed: 26.015 samples/sec, ObjLoss=24.989, BoxCenterLoss=14.442, BoxScaleLoss=5.115, ClassLoss=10.387 [Epoch 141][Batch 899], LR: 1.00E-03, Speed: 9.199 samples/sec, ObjLoss=24.989, BoxCenterLoss=14.442, BoxScaleLoss=5.115, ClassLoss=10.387 [Epoch 141][Batch 999], LR: 1.00E-03, Speed: 8.577 samples/sec, ObjLoss=24.989, BoxCenterLoss=14.442, BoxScaleLoss=5.115, ClassLoss=10.387 [Epoch 141][Batch 1099], LR: 1.00E-03, Speed: 8.159 samples/sec, ObjLoss=24.988, BoxCenterLoss=14.442, BoxScaleLoss=5.115, ClassLoss=10.386 [Epoch 141][Batch 1199], LR: 1.00E-03, Speed: 11.459 samples/sec, ObjLoss=24.987, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.385 [Epoch 141][Batch 1299], LR: 1.00E-03, Speed: 8.794 samples/sec, ObjLoss=24.987, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.384 [Epoch 141][Batch 1399], LR: 1.00E-03, Speed: 8.969 samples/sec, ObjLoss=24.986, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.384 [Epoch 141][Batch 1499], LR: 1.00E-03, Speed: 113.352 samples/sec, ObjLoss=24.985, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.383 [Epoch 141][Batch 1599], LR: 1.00E-03, Speed: 11.149 samples/sec, ObjLoss=24.985, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.383 [Epoch 141][Batch 1699], LR: 1.00E-03, Speed: 8.487 samples/sec, ObjLoss=24.984, BoxCenterLoss=14.443, BoxScaleLoss=5.114, ClassLoss=10.382 [Epoch 141][Batch 1799], LR: 1.00E-03, Speed: 96.072 samples/sec, ObjLoss=24.983, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.381 [Epoch 141] Training cost: 2263.568, ObjLoss=24.983, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.381 [Epoch 141] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.268 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.477 person=38.9 bicycle=14.4 car=26.4 motorcycle=26.0 airplane=44.2 bus=49.4 train=53.0 truck=24.2 boat=13.8 traffic light=15.2 fire hydrant=41.9 stop sign=42.1 parking meter=26.9 bench=13.0 bird=20.4 cat=43.5 dog=37.3 horse=36.0 sheep=33.2 cow=35.4 elephant=43.2 bear=49.2 zebra=46.8 giraffe=49.2 backpack=6.4 umbrella=24.0 handbag=6.6 tie=17.0 suitcase=16.4 frisbee=37.2 skis=11.1 snowboard=17.9 sports ball=28.2 kite=24.8 baseball bat=11.5 baseball glove=18.9 skateboard=29.9 surfboard=22.1 tennis racket=24.8 bottle=19.0 wine glass=20.9 cup=26.0 fork=16.8 knife=5.6 spoon=4.0 bowl=25.0 banana=12.9 apple=9.0 sandwich=25.1 orange=19.5 broccoli=11.6 carrot=8.4 hot dog=16.6 pizza=36.2 donut=30.2 cake=24.4 chair=16.4 couch=25.9 potted plant=13.6 bed=31.5 dining table=18.3 toilet=42.1 tv=36.3 laptop=36.9 mouse=33.7 remote=8.7 keyboard=31.8 cell phone=19.8 microwave=33.5 oven=20.7 toaster=0.0 sink=23.4 refrigerator=36.1 book=5.0 clock=34.5 vase=21.3 scissors=19.2 teddy bear=29.0 hair drier=0.0 toothbrush=5.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.7 [Epoch 142][Batch 99], LR: 1.00E-03, Speed: 10.524 samples/sec, ObjLoss=24.982, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.380 [Epoch 142][Batch 199], LR: 1.00E-03, Speed: 119.156 samples/sec, ObjLoss=24.981, BoxCenterLoss=14.442, BoxScaleLoss=5.114, ClassLoss=10.380 [Epoch 142][Batch 299], LR: 1.00E-03, Speed: 10.264 samples/sec, ObjLoss=24.981, BoxCenterLoss=14.442, BoxScaleLoss=5.113, ClassLoss=10.379 [Epoch 142][Batch 399], LR: 1.00E-03, Speed: 11.026 samples/sec, ObjLoss=24.980, BoxCenterLoss=14.443, BoxScaleLoss=5.113, ClassLoss=10.378 [Epoch 142][Batch 499], LR: 1.00E-03, Speed: 7.163 samples/sec, ObjLoss=24.980, BoxCenterLoss=14.443, BoxScaleLoss=5.113, ClassLoss=10.378 [Epoch 142][Batch 599], LR: 1.00E-03, Speed: 9.178 samples/sec, ObjLoss=24.978, BoxCenterLoss=14.442, BoxScaleLoss=5.113, ClassLoss=10.377 [Epoch 142][Batch 699], LR: 1.00E-03, Speed: 11.827 samples/sec, ObjLoss=24.977, BoxCenterLoss=14.442, BoxScaleLoss=5.113, ClassLoss=10.376 [Epoch 142][Batch 799], LR: 1.00E-03, Speed: 8.270 samples/sec, ObjLoss=24.976, BoxCenterLoss=14.442, BoxScaleLoss=5.113, ClassLoss=10.376 [Epoch 142][Batch 899], LR: 1.00E-03, Speed: 11.285 samples/sec, ObjLoss=24.976, BoxCenterLoss=14.442, BoxScaleLoss=5.113, ClassLoss=10.375 [Epoch 142][Batch 999], LR: 1.00E-03, Speed: 10.589 samples/sec, ObjLoss=24.975, BoxCenterLoss=14.442, BoxScaleLoss=5.113, ClassLoss=10.375 [Epoch 142][Batch 1099], LR: 1.00E-03, Speed: 8.000 samples/sec, ObjLoss=24.975, BoxCenterLoss=14.442, BoxScaleLoss=5.113, ClassLoss=10.374 [Epoch 142][Batch 1199], LR: 1.00E-03, Speed: 119.086 samples/sec, ObjLoss=24.973, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.374 [Epoch 142][Batch 1299], LR: 1.00E-03, Speed: 6.831 samples/sec, ObjLoss=24.973, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.373 [Epoch 142][Batch 1399], LR: 1.00E-03, Speed: 11.166 samples/sec, ObjLoss=24.972, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.373 [Epoch 142][Batch 1499], LR: 1.00E-03, Speed: 111.912 samples/sec, ObjLoss=24.971, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.372 [Epoch 142][Batch 1599], LR: 1.00E-03, Speed: 46.113 samples/sec, ObjLoss=24.970, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.371 [Epoch 142][Batch 1699], LR: 1.00E-03, Speed: 8.501 samples/sec, ObjLoss=24.970, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.371 [Epoch 142][Batch 1799], LR: 1.00E-03, Speed: 11.928 samples/sec, ObjLoss=24.969, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.370 [Epoch 142] Training cost: 2177.163, ObjLoss=24.969, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.370 [Epoch 142] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.202 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.244 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.456 person=36.1 bicycle=14.9 car=26.3 motorcycle=27.0 airplane=38.2 bus=44.8 train=45.7 truck=21.8 boat=11.6 traffic light=14.3 fire hydrant=48.4 stop sign=35.6 parking meter=33.8 bench=11.5 bird=20.6 cat=40.9 dog=36.6 horse=39.1 sheep=30.4 cow=32.6 elephant=43.9 bear=42.1 zebra=46.3 giraffe=47.7 backpack=5.5 umbrella=24.3 handbag=6.3 tie=16.8 suitcase=19.6 frisbee=27.8 skis=9.0 snowboard=16.7 sports ball=24.9 kite=27.3 baseball bat=10.7 baseball glove=20.1 skateboard=28.4 surfboard=20.3 tennis racket=24.9 bottle=20.6 wine glass=19.1 cup=22.6 fork=14.4 knife=4.4 spoon=5.1 bowl=26.2 banana=13.7 apple=8.0 sandwich=20.1 orange=19.1 broccoli=10.5 carrot=10.1 hot dog=11.9 pizza=31.4 donut=23.9 cake=19.9 chair=15.7 couch=27.6 potted plant=14.1 bed=33.9 dining table=19.9 toilet=36.7 tv=31.1 laptop=34.0 mouse=36.0 remote=9.4 keyboard=22.5 cell phone=12.8 microwave=29.7 oven=18.2 toaster=0.0 sink=20.8 refrigerator=33.3 book=5.1 clock=27.7 vase=21.6 scissors=12.6 teddy bear=28.6 hair drier=0.0 toothbrush=7.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.1 [Epoch 143][Batch 99], LR: 1.00E-03, Speed: 9.131 samples/sec, ObjLoss=24.968, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.369 [Epoch 143][Batch 199], LR: 1.00E-03, Speed: 10.237 samples/sec, ObjLoss=24.968, BoxCenterLoss=14.442, BoxScaleLoss=5.112, ClassLoss=10.369 [Epoch 143][Batch 299], LR: 1.00E-03, Speed: 9.088 samples/sec, ObjLoss=24.967, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.368 [Epoch 143][Batch 399], LR: 1.00E-03, Speed: 7.999 samples/sec, ObjLoss=24.966, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.367 [Epoch 143][Batch 499], LR: 1.00E-03, Speed: 8.777 samples/sec, ObjLoss=24.966, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.367 [Epoch 143][Batch 599], LR: 1.00E-03, Speed: 10.971 samples/sec, ObjLoss=24.965, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.366 [Epoch 143][Batch 699], LR: 1.00E-03, Speed: 8.335 samples/sec, ObjLoss=24.964, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.365 [Epoch 143][Batch 799], LR: 1.00E-03, Speed: 99.836 samples/sec, ObjLoss=24.963, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.365 [Epoch 143][Batch 899], LR: 1.00E-03, Speed: 11.028 samples/sec, ObjLoss=24.963, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.364 [Epoch 143][Batch 999], LR: 1.00E-03, Speed: 119.821 samples/sec, ObjLoss=24.962, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.363 [Epoch 143][Batch 1099], LR: 1.00E-03, Speed: 12.481 samples/sec, ObjLoss=24.962, BoxCenterLoss=14.442, BoxScaleLoss=5.111, ClassLoss=10.363 [Epoch 143][Batch 1199], LR: 1.00E-03, Speed: 8.622 samples/sec, ObjLoss=24.961, BoxCenterLoss=14.442, BoxScaleLoss=5.110, ClassLoss=10.362 [Epoch 143][Batch 1299], LR: 1.00E-03, Speed: 116.090 samples/sec, ObjLoss=24.960, BoxCenterLoss=14.441, BoxScaleLoss=5.110, ClassLoss=10.361 [Epoch 143][Batch 1399], LR: 1.00E-03, Speed: 10.719 samples/sec, ObjLoss=24.958, BoxCenterLoss=14.441, BoxScaleLoss=5.110, ClassLoss=10.361 [Epoch 143][Batch 1499], LR: 1.00E-03, Speed: 9.431 samples/sec, ObjLoss=24.958, BoxCenterLoss=14.441, BoxScaleLoss=5.110, ClassLoss=10.360 [Epoch 143][Batch 1599], LR: 1.00E-03, Speed: 8.484 samples/sec, ObjLoss=24.956, BoxCenterLoss=14.441, BoxScaleLoss=5.110, ClassLoss=10.359 [Epoch 143][Batch 1699], LR: 1.00E-03, Speed: 99.537 samples/sec, ObjLoss=24.956, BoxCenterLoss=14.441, BoxScaleLoss=5.109, ClassLoss=10.358 [Epoch 143][Batch 1799], LR: 1.00E-03, Speed: 11.681 samples/sec, ObjLoss=24.955, BoxCenterLoss=14.441, BoxScaleLoss=5.109, ClassLoss=10.358 [Epoch 143] Training cost: 2154.838, ObjLoss=24.955, BoxCenterLoss=14.441, BoxScaleLoss=5.109, ClassLoss=10.358 [Epoch 143] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.271 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491 person=37.8 bicycle=17.2 car=28.4 motorcycle=27.6 airplane=40.9 bus=46.3 train=46.3 truck=23.6 boat=13.8 traffic light=16.4 fire hydrant=43.1 stop sign=39.1 parking meter=31.9 bench=14.6 bird=20.2 cat=46.5 dog=43.9 horse=37.4 sheep=34.1 cow=37.2 elephant=46.0 bear=50.0 zebra=47.6 giraffe=49.8 backpack=6.6 umbrella=24.4 handbag=6.7 tie=19.0 suitcase=18.6 frisbee=37.7 skis=12.3 snowboard=14.9 sports ball=28.9 kite=25.8 baseball bat=13.9 baseball glove=18.8 skateboard=30.7 surfboard=20.7 tennis racket=25.8 bottle=20.2 wine glass=19.2 cup=26.8 fork=14.9 knife=4.1 spoon=3.7 bowl=24.2 banana=11.3 apple=8.0 sandwich=16.8 orange=18.9 broccoli=10.7 carrot=11.8 hot dog=21.1 pizza=32.7 donut=29.7 cake=20.7 chair=16.4 couch=27.9 potted plant=14.2 bed=36.6 dining table=21.5 toilet=41.4 tv=37.6 laptop=38.0 mouse=34.4 remote=11.5 keyboard=22.9 cell phone=17.5 microwave=34.1 oven=22.5 toaster=10.0 sink=22.3 refrigerator=32.3 book=4.5 clock=31.4 vase=23.1 scissors=18.8 teddy bear=30.4 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.9 [Epoch 144][Batch 99], LR: 1.00E-03, Speed: 9.030 samples/sec, ObjLoss=24.954, BoxCenterLoss=14.440, BoxScaleLoss=5.109, ClassLoss=10.357 [Epoch 144][Batch 199], LR: 1.00E-03, Speed: 7.979 samples/sec, ObjLoss=24.954, BoxCenterLoss=14.441, BoxScaleLoss=5.109, ClassLoss=10.356 [Epoch 144][Batch 299], LR: 1.00E-03, Speed: 8.303 samples/sec, ObjLoss=24.952, BoxCenterLoss=14.440, BoxScaleLoss=5.109, ClassLoss=10.356 [Epoch 144][Batch 399], LR: 1.00E-03, Speed: 8.309 samples/sec, ObjLoss=24.952, BoxCenterLoss=14.441, BoxScaleLoss=5.109, ClassLoss=10.355 [Epoch 144][Batch 499], LR: 1.00E-03, Speed: 101.632 samples/sec, ObjLoss=24.951, BoxCenterLoss=14.440, BoxScaleLoss=5.109, ClassLoss=10.354 [Epoch 144][Batch 599], LR: 1.00E-03, Speed: 11.246 samples/sec, ObjLoss=24.950, BoxCenterLoss=14.440, BoxScaleLoss=5.109, ClassLoss=10.354 [Epoch 144][Batch 699], LR: 1.00E-03, Speed: 9.726 samples/sec, ObjLoss=24.949, BoxCenterLoss=14.440, BoxScaleLoss=5.108, ClassLoss=10.353 [Epoch 144][Batch 799], LR: 1.00E-03, Speed: 8.820 samples/sec, ObjLoss=24.949, BoxCenterLoss=14.440, BoxScaleLoss=5.108, ClassLoss=10.353 [Epoch 144][Batch 899], LR: 1.00E-03, Speed: 10.428 samples/sec, ObjLoss=24.948, BoxCenterLoss=14.440, BoxScaleLoss=5.108, ClassLoss=10.352 [Epoch 144][Batch 999], LR: 1.00E-03, Speed: 10.386 samples/sec, ObjLoss=24.948, BoxCenterLoss=14.440, BoxScaleLoss=5.108, ClassLoss=10.352 [Epoch 144][Batch 1099], LR: 1.00E-03, Speed: 10.386 samples/sec, ObjLoss=24.947, BoxCenterLoss=14.441, BoxScaleLoss=5.108, ClassLoss=10.351 [Epoch 144][Batch 1199], LR: 1.00E-03, Speed: 10.601 samples/sec, ObjLoss=24.947, BoxCenterLoss=14.441, BoxScaleLoss=5.108, ClassLoss=10.351 [Epoch 144][Batch 1299], LR: 1.00E-03, Speed: 11.450 samples/sec, ObjLoss=24.946, BoxCenterLoss=14.441, BoxScaleLoss=5.108, ClassLoss=10.350 [Epoch 144][Batch 1399], LR: 1.00E-03, Speed: 10.623 samples/sec, ObjLoss=24.945, BoxCenterLoss=14.440, BoxScaleLoss=5.108, ClassLoss=10.349 [Epoch 144][Batch 1499], LR: 1.00E-03, Speed: 99.313 samples/sec, ObjLoss=24.944, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.348 [Epoch 144][Batch 1599], LR: 1.00E-03, Speed: 10.109 samples/sec, ObjLoss=24.943, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.348 [Epoch 144][Batch 1699], LR: 1.00E-03, Speed: 13.596 samples/sec, ObjLoss=24.943, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.347 [Epoch 144][Batch 1799], LR: 1.00E-03, Speed: 16.723 samples/sec, ObjLoss=24.942, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.347 [Epoch 144] Training cost: 2156.571, ObjLoss=24.941, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.346 [Epoch 144] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.211 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445 person=35.7 bicycle=18.3 car=25.6 motorcycle=27.6 airplane=38.8 bus=44.7 train=38.6 truck=21.0 boat=12.8 traffic light=14.3 fire hydrant=38.7 stop sign=38.6 parking meter=28.0 bench=14.6 bird=18.3 cat=42.7 dog=34.7 horse=32.8 sheep=32.5 cow=29.7 elephant=37.1 bear=41.9 zebra=45.8 giraffe=49.2 backpack=6.9 umbrella=22.5 handbag=5.3 tie=15.9 suitcase=14.4 frisbee=25.5 skis=9.7 snowboard=12.4 sports ball=24.8 kite=26.5 baseball bat=10.6 baseball glove=19.7 skateboard=28.6 surfboard=16.7 tennis racket=29.5 bottle=18.1 wine glass=20.2 cup=24.2 fork=15.8 knife=4.7 spoon=5.0 bowl=25.3 banana=11.7 apple=8.2 sandwich=15.3 orange=14.4 broccoli=12.1 carrot=9.7 hot dog=15.8 pizza=33.7 donut=25.2 cake=20.9 chair=17.5 couch=30.1 potted plant=11.6 bed=32.5 dining table=19.7 toilet=39.3 tv=38.4 laptop=35.3 mouse=35.0 remote=9.7 keyboard=29.9 cell phone=18.3 microwave=32.2 oven=22.0 toaster=0.0 sink=22.2 refrigerator=29.7 book=3.9 clock=29.1 vase=21.4 scissors=24.7 teddy bear=30.2 hair drier=0.0 toothbrush=4.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.1 [Epoch 145][Batch 99], LR: 1.00E-03, Speed: 88.478 samples/sec, ObjLoss=24.941, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.346 [Epoch 145][Batch 199], LR: 1.00E-03, Speed: 8.756 samples/sec, ObjLoss=24.941, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.345 [Epoch 145][Batch 299], LR: 1.00E-03, Speed: 74.504 samples/sec, ObjLoss=24.940, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.345 [Epoch 145][Batch 399], LR: 1.00E-03, Speed: 7.125 samples/sec, ObjLoss=24.940, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.344 [Epoch 145][Batch 499], LR: 1.00E-03, Speed: 118.420 samples/sec, ObjLoss=24.938, BoxCenterLoss=14.440, BoxScaleLoss=5.106, ClassLoss=10.343 [Epoch 145][Batch 599], LR: 1.00E-03, Speed: 9.457 samples/sec, ObjLoss=24.938, BoxCenterLoss=14.440, BoxScaleLoss=5.107, ClassLoss=10.343 [Epoch 145][Batch 699], LR: 1.00E-03, Speed: 8.938 samples/sec, ObjLoss=24.937, BoxCenterLoss=14.440, BoxScaleLoss=5.106, ClassLoss=10.343 [Epoch 145][Batch 799], LR: 1.00E-03, Speed: 10.280 samples/sec, ObjLoss=24.937, BoxCenterLoss=14.440, BoxScaleLoss=5.106, ClassLoss=10.342 [Epoch 145][Batch 899], LR: 1.00E-03, Speed: 8.730 samples/sec, ObjLoss=24.936, BoxCenterLoss=14.440, BoxScaleLoss=5.106, ClassLoss=10.341 [Epoch 145][Batch 999], LR: 1.00E-03, Speed: 10.432 samples/sec, ObjLoss=24.935, BoxCenterLoss=14.440, BoxScaleLoss=5.106, ClassLoss=10.341 [Epoch 145][Batch 1099], LR: 1.00E-03, Speed: 10.382 samples/sec, ObjLoss=24.934, BoxCenterLoss=14.440, BoxScaleLoss=5.106, ClassLoss=10.340 [Epoch 145][Batch 1199], LR: 1.00E-03, Speed: 120.160 samples/sec, ObjLoss=24.933, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.339 [Epoch 145][Batch 1299], LR: 1.00E-03, Speed: 9.461 samples/sec, ObjLoss=24.933, BoxCenterLoss=14.440, BoxScaleLoss=5.106, ClassLoss=10.339 [Epoch 145][Batch 1399], LR: 1.00E-03, Speed: 9.889 samples/sec, ObjLoss=24.933, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.338 [Epoch 145][Batch 1499], LR: 1.00E-03, Speed: 9.603 samples/sec, ObjLoss=24.932, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.338 [Epoch 145][Batch 1599], LR: 1.00E-03, Speed: 7.727 samples/sec, ObjLoss=24.931, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.337 [Epoch 145][Batch 1699], LR: 1.00E-03, Speed: 98.625 samples/sec, ObjLoss=24.931, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.337 [Epoch 145][Batch 1799], LR: 1.00E-03, Speed: 10.510 samples/sec, ObjLoss=24.930, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.336 [Epoch 145] Training cost: 2232.867, ObjLoss=24.930, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.336 [Epoch 145] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.223 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.346 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.459 person=36.7 bicycle=17.9 car=28.0 motorcycle=28.8 airplane=37.9 bus=45.7 train=45.5 truck=21.2 boat=13.4 traffic light=13.8 fire hydrant=43.2 stop sign=38.9 parking meter=27.8 bench=12.8 bird=22.4 cat=40.5 dog=37.5 horse=37.5 sheep=32.4 cow=34.7 elephant=44.8 bear=41.8 zebra=42.4 giraffe=44.2 backpack=5.2 umbrella=23.9 handbag=6.4 tie=18.6 suitcase=16.8 frisbee=38.2 skis=11.3 snowboard=13.9 sports ball=24.2 kite=23.4 baseball bat=15.3 baseball glove=17.7 skateboard=30.0 surfboard=20.2 tennis racket=29.8 bottle=22.2 wine glass=20.7 cup=25.4 fork=15.4 knife=5.8 spoon=6.5 bowl=22.7 banana=14.2 apple=7.9 sandwich=15.7 orange=14.6 broccoli=10.1 carrot=7.7 hot dog=14.1 pizza=33.0 donut=25.0 cake=21.2 chair=16.6 couch=29.1 potted plant=12.4 bed=33.6 dining table=21.4 toilet=40.3 tv=35.5 laptop=33.9 mouse=36.1 remote=9.2 keyboard=30.1 cell phone=18.1 microwave=37.2 oven=22.2 toaster=0.0 sink=23.8 refrigerator=34.9 book=5.2 clock=34.1 vase=23.1 scissors=17.7 teddy bear=27.3 hair drier=0.0 toothbrush=6.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.9 [Epoch 146][Batch 99], LR: 1.00E-03, Speed: 9.518 samples/sec, ObjLoss=24.929, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.335 [Epoch 146][Batch 199], LR: 1.00E-03, Speed: 112.247 samples/sec, ObjLoss=24.929, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.334 [Epoch 146][Batch 299], LR: 1.00E-03, Speed: 10.221 samples/sec, ObjLoss=24.929, BoxCenterLoss=14.440, BoxScaleLoss=5.105, ClassLoss=10.334 [Epoch 146][Batch 399], LR: 1.00E-03, Speed: 29.205 samples/sec, ObjLoss=24.927, BoxCenterLoss=14.440, BoxScaleLoss=5.104, ClassLoss=10.333 [Epoch 146][Batch 499], LR: 1.00E-03, Speed: 8.692 samples/sec, ObjLoss=24.927, BoxCenterLoss=14.440, BoxScaleLoss=5.104, ClassLoss=10.332 [Epoch 146][Batch 599], LR: 1.00E-03, Speed: 9.791 samples/sec, ObjLoss=24.926, BoxCenterLoss=14.440, BoxScaleLoss=5.104, ClassLoss=10.332 [Epoch 146][Batch 699], LR: 1.00E-03, Speed: 12.647 samples/sec, ObjLoss=24.925, BoxCenterLoss=14.440, BoxScaleLoss=5.104, ClassLoss=10.332 [Epoch 146][Batch 799], LR: 1.00E-03, Speed: 9.114 samples/sec, ObjLoss=24.924, BoxCenterLoss=14.440, BoxScaleLoss=5.104, ClassLoss=10.331 [Epoch 146][Batch 899], LR: 1.00E-03, Speed: 10.666 samples/sec, ObjLoss=24.923, BoxCenterLoss=14.440, BoxScaleLoss=5.104, ClassLoss=10.330 [Epoch 146][Batch 999], LR: 1.00E-03, Speed: 8.545 samples/sec, ObjLoss=24.922, BoxCenterLoss=14.440, BoxScaleLoss=5.104, ClassLoss=10.330 [Epoch 146][Batch 1099], LR: 1.00E-03, Speed: 8.743 samples/sec, ObjLoss=24.922, BoxCenterLoss=14.439, BoxScaleLoss=5.104, ClassLoss=10.329 [Epoch 146][Batch 1199], LR: 1.00E-03, Speed: 6.369 samples/sec, ObjLoss=24.920, BoxCenterLoss=14.439, BoxScaleLoss=5.104, ClassLoss=10.328 [Epoch 146][Batch 1299], LR: 1.00E-03, Speed: 8.584 samples/sec, ObjLoss=24.920, BoxCenterLoss=14.439, BoxScaleLoss=5.104, ClassLoss=10.328 [Epoch 146][Batch 1399], LR: 1.00E-03, Speed: 8.028 samples/sec, ObjLoss=24.919, BoxCenterLoss=14.439, BoxScaleLoss=5.103, ClassLoss=10.327 [Epoch 146][Batch 1499], LR: 1.00E-03, Speed: 9.083 samples/sec, ObjLoss=24.918, BoxCenterLoss=14.439, BoxScaleLoss=5.103, ClassLoss=10.326 [Epoch 146][Batch 1599], LR: 1.00E-03, Speed: 8.523 samples/sec, ObjLoss=24.917, BoxCenterLoss=14.439, BoxScaleLoss=5.103, ClassLoss=10.326 [Epoch 146][Batch 1699], LR: 1.00E-03, Speed: 9.360 samples/sec, ObjLoss=24.916, BoxCenterLoss=14.438, BoxScaleLoss=5.103, ClassLoss=10.325 [Epoch 146][Batch 1799], LR: 1.00E-03, Speed: 10.521 samples/sec, ObjLoss=24.915, BoxCenterLoss=14.438, BoxScaleLoss=5.103, ClassLoss=10.325 [Epoch 146] Training cost: 2218.331, ObjLoss=24.915, BoxCenterLoss=14.438, BoxScaleLoss=5.103, ClassLoss=10.325 [Epoch 146] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.449 person=36.6 bicycle=17.7 car=26.3 motorcycle=25.7 airplane=41.3 bus=47.1 train=45.0 truck=21.0 boat=13.0 traffic light=14.0 fire hydrant=46.4 stop sign=36.7 parking meter=23.4 bench=13.6 bird=21.6 cat=45.6 dog=39.5 horse=39.6 sheep=35.9 cow=36.9 elephant=46.4 bear=48.1 zebra=43.3 giraffe=47.6 backpack=7.3 umbrella=23.7 handbag=5.9 tie=16.1 suitcase=16.0 frisbee=36.9 skis=6.6 snowboard=10.6 sports ball=26.4 kite=24.9 baseball bat=14.2 baseball glove=19.8 skateboard=28.5 surfboard=18.6 tennis racket=28.2 bottle=19.5 wine glass=21.4 cup=24.0 fork=13.1 knife=5.0 spoon=5.0 bowl=21.8 banana=12.8 apple=7.3 sandwich=19.7 orange=14.5 broccoli=8.0 carrot=6.9 hot dog=17.5 pizza=34.7 donut=25.8 cake=21.1 chair=15.2 couch=28.5 potted plant=15.0 bed=29.9 dining table=15.6 toilet=39.6 tv=37.7 laptop=38.7 mouse=26.1 remote=8.7 keyboard=33.6 cell phone=14.2 microwave=29.0 oven=20.1 toaster=0.0 sink=15.7 refrigerator=37.4 book=3.6 clock=25.5 vase=21.8 scissors=12.4 teddy bear=31.5 hair drier=0.0 toothbrush=5.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.5 [Epoch 147][Batch 99], LR: 1.00E-03, Speed: 9.571 samples/sec, ObjLoss=24.915, BoxCenterLoss=14.438, BoxScaleLoss=5.103, ClassLoss=10.324 [Epoch 147][Batch 199], LR: 1.00E-03, Speed: 93.137 samples/sec, ObjLoss=24.914, BoxCenterLoss=14.438, BoxScaleLoss=5.103, ClassLoss=10.323 [Epoch 147][Batch 299], LR: 1.00E-03, Speed: 105.345 samples/sec, ObjLoss=24.914, BoxCenterLoss=14.439, BoxScaleLoss=5.103, ClassLoss=10.323 [Epoch 147][Batch 399], LR: 1.00E-03, Speed: 9.398 samples/sec, ObjLoss=24.913, BoxCenterLoss=14.438, BoxScaleLoss=5.102, ClassLoss=10.322 [Epoch 147][Batch 499], LR: 1.00E-03, Speed: 9.011 samples/sec, ObjLoss=24.911, BoxCenterLoss=14.438, BoxScaleLoss=5.102, ClassLoss=10.321 [Epoch 147][Batch 599], LR: 1.00E-03, Speed: 9.566 samples/sec, ObjLoss=24.911, BoxCenterLoss=14.438, BoxScaleLoss=5.102, ClassLoss=10.320 [Epoch 147][Batch 699], LR: 1.00E-03, Speed: 10.309 samples/sec, ObjLoss=24.910, BoxCenterLoss=14.437, BoxScaleLoss=5.102, ClassLoss=10.320 [Epoch 147][Batch 799], LR: 1.00E-03, Speed: 10.801 samples/sec, ObjLoss=24.909, BoxCenterLoss=14.438, BoxScaleLoss=5.102, ClassLoss=10.319 [Epoch 147][Batch 899], LR: 1.00E-03, Speed: 132.496 samples/sec, ObjLoss=24.909, BoxCenterLoss=14.438, BoxScaleLoss=5.102, ClassLoss=10.319 [Epoch 147][Batch 999], LR: 1.00E-03, Speed: 12.757 samples/sec, ObjLoss=24.908, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.318 [Epoch 147][Batch 1099], LR: 1.00E-03, Speed: 122.750 samples/sec, ObjLoss=24.907, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.317 [Epoch 147][Batch 1199], LR: 1.00E-03, Speed: 11.200 samples/sec, ObjLoss=24.907, BoxCenterLoss=14.438, BoxScaleLoss=5.101, ClassLoss=10.317 [Epoch 147][Batch 1299], LR: 1.00E-03, Speed: 9.432 samples/sec, ObjLoss=24.906, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.316 [Epoch 147][Batch 1399], LR: 1.00E-03, Speed: 10.751 samples/sec, ObjLoss=24.905, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.316 [Epoch 147][Batch 1499], LR: 1.00E-03, Speed: 12.110 samples/sec, ObjLoss=24.905, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.315 [Epoch 147][Batch 1599], LR: 1.00E-03, Speed: 9.718 samples/sec, ObjLoss=24.904, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.314 [Epoch 147][Batch 1699], LR: 1.00E-03, Speed: 9.369 samples/sec, ObjLoss=24.904, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.314 [Epoch 147][Batch 1799], LR: 1.00E-03, Speed: 124.875 samples/sec, ObjLoss=24.903, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.313 [Epoch 147] Training cost: 2118.126, ObjLoss=24.903, BoxCenterLoss=14.437, BoxScaleLoss=5.100, ClassLoss=10.313 [Epoch 147] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467 person=38.1 bicycle=16.7 car=26.4 motorcycle=28.9 airplane=37.3 bus=46.5 train=43.8 truck=23.3 boat=12.4 traffic light=15.1 fire hydrant=43.9 stop sign=44.6 parking meter=28.2 bench=12.9 bird=19.7 cat=44.1 dog=41.3 horse=41.7 sheep=32.4 cow=33.1 elephant=45.0 bear=53.8 zebra=48.6 giraffe=48.2 backpack=6.3 umbrella=22.9 handbag=5.5 tie=17.8 suitcase=19.2 frisbee=36.0 skis=7.3 snowboard=21.5 sports ball=26.6 kite=22.4 baseball bat=12.6 baseball glove=19.9 skateboard=27.9 surfboard=22.0 tennis racket=27.1 bottle=18.9 wine glass=20.7 cup=25.7 fork=12.1 knife=4.5 spoon=5.3 bowl=22.4 banana=11.5 apple=7.5 sandwich=23.4 orange=14.8 broccoli=8.4 carrot=7.0 hot dog=19.7 pizza=34.4 donut=28.6 cake=23.2 chair=15.8 couch=27.6 potted plant=15.1 bed=30.1 dining table=18.5 toilet=35.9 tv=36.4 laptop=37.4 mouse=37.2 remote=11.6 keyboard=29.8 cell phone=18.9 microwave=32.6 oven=20.7 toaster=0.0 sink=22.8 refrigerator=30.1 book=4.0 clock=30.6 vase=22.8 scissors=17.6 teddy bear=30.0 hair drier=0.0 toothbrush=6.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.3 [Epoch 148][Batch 99], LR: 1.00E-03, Speed: 128.294 samples/sec, ObjLoss=24.902, BoxCenterLoss=14.437, BoxScaleLoss=5.101, ClassLoss=10.312 [Epoch 148][Batch 199], LR: 1.00E-03, Speed: 9.902 samples/sec, ObjLoss=24.901, BoxCenterLoss=14.437, BoxScaleLoss=5.100, ClassLoss=10.312 [Epoch 148][Batch 299], LR: 1.00E-03, Speed: 118.345 samples/sec, ObjLoss=24.900, BoxCenterLoss=14.437, BoxScaleLoss=5.100, ClassLoss=10.311 [Epoch 148][Batch 399], LR: 1.00E-03, Speed: 12.265 samples/sec, ObjLoss=24.899, BoxCenterLoss=14.437, BoxScaleLoss=5.100, ClassLoss=10.310 [Epoch 148][Batch 499], LR: 1.00E-03, Speed: 9.088 samples/sec, ObjLoss=24.899, BoxCenterLoss=14.436, BoxScaleLoss=5.100, ClassLoss=10.310 [Epoch 148][Batch 599], LR: 1.00E-03, Speed: 11.487 samples/sec, ObjLoss=24.898, BoxCenterLoss=14.437, BoxScaleLoss=5.100, ClassLoss=10.309 [Epoch 148][Batch 699], LR: 1.00E-03, Speed: 12.018 samples/sec, ObjLoss=24.897, BoxCenterLoss=14.437, BoxScaleLoss=5.100, ClassLoss=10.309 [Epoch 148][Batch 799], LR: 1.00E-03, Speed: 9.126 samples/sec, ObjLoss=24.896, BoxCenterLoss=14.437, BoxScaleLoss=5.100, ClassLoss=10.308 [Epoch 148][Batch 899], LR: 1.00E-03, Speed: 7.616 samples/sec, ObjLoss=24.895, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.307 [Epoch 148][Batch 999], LR: 1.00E-03, Speed: 9.102 samples/sec, ObjLoss=24.895, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.307 [Epoch 148][Batch 1099], LR: 1.00E-03, Speed: 25.696 samples/sec, ObjLoss=24.894, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.306 [Epoch 148][Batch 1199], LR: 1.00E-03, Speed: 10.846 samples/sec, ObjLoss=24.893, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.306 [Epoch 148][Batch 1299], LR: 1.00E-03, Speed: 91.026 samples/sec, ObjLoss=24.892, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.305 [Epoch 148][Batch 1399], LR: 1.00E-03, Speed: 8.761 samples/sec, ObjLoss=24.892, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.304 [Epoch 148][Batch 1499], LR: 1.00E-03, Speed: 10.665 samples/sec, ObjLoss=24.891, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.304 [Epoch 148][Batch 1599], LR: 1.00E-03, Speed: 7.318 samples/sec, ObjLoss=24.890, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.303 [Epoch 148][Batch 1699], LR: 1.00E-03, Speed: 9.654 samples/sec, ObjLoss=24.890, BoxCenterLoss=14.437, BoxScaleLoss=5.099, ClassLoss=10.303 [Epoch 148][Batch 1799], LR: 1.00E-03, Speed: 13.064 samples/sec, ObjLoss=24.889, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.302 [Epoch 148] Training cost: 2088.575, ObjLoss=24.889, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.302 [Epoch 148] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.228 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.206 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.108 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.260 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.164 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.432 person=34.7 bicycle=15.2 car=22.8 motorcycle=25.7 airplane=39.8 bus=45.7 train=50.7 truck=20.1 boat=11.0 traffic light=11.5 fire hydrant=42.5 stop sign=32.8 parking meter=28.9 bench=14.0 bird=16.2 cat=44.3 dog=32.7 horse=35.3 sheep=27.8 cow=31.9 elephant=41.7 bear=43.5 zebra=39.4 giraffe=39.6 backpack=7.1 umbrella=24.0 handbag=6.0 tie=17.0 suitcase=19.7 frisbee=35.8 skis=9.9 snowboard=11.9 sports ball=21.6 kite=25.5 baseball bat=12.0 baseball glove=21.7 skateboard=30.4 surfboard=18.9 tennis racket=24.8 bottle=21.0 wine glass=17.9 cup=25.6 fork=10.9 knife=3.1 spoon=5.0 bowl=24.7 banana=11.8 apple=8.2 sandwich=16.0 orange=15.3 broccoli=10.2 carrot=11.2 hot dog=15.2 pizza=27.0 donut=19.9 cake=20.7 chair=15.0 couch=25.3 potted plant=15.1 bed=34.1 dining table=17.6 toilet=34.6 tv=32.3 laptop=33.3 mouse=38.9 remote=10.6 keyboard=26.5 cell phone=19.5 microwave=39.1 oven=19.2 toaster=4.2 sink=24.5 refrigerator=29.6 book=4.8 clock=32.3 vase=20.8 scissors=15.0 teddy bear=27.5 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.8 [Epoch 149][Batch 99], LR: 1.00E-03, Speed: 10.601 samples/sec, ObjLoss=24.889, BoxCenterLoss=14.437, BoxScaleLoss=5.099, ClassLoss=10.301 [Epoch 149][Batch 199], LR: 1.00E-03, Speed: 8.602 samples/sec, ObjLoss=24.888, BoxCenterLoss=14.436, BoxScaleLoss=5.099, ClassLoss=10.301 [Epoch 149][Batch 299], LR: 1.00E-03, Speed: 10.027 samples/sec, ObjLoss=24.887, BoxCenterLoss=14.437, BoxScaleLoss=5.099, ClassLoss=10.300 [Epoch 149][Batch 399], LR: 1.00E-03, Speed: 10.847 samples/sec, ObjLoss=24.887, BoxCenterLoss=14.436, BoxScaleLoss=5.098, ClassLoss=10.300 [Epoch 149][Batch 499], LR: 1.00E-03, Speed: 11.353 samples/sec, ObjLoss=24.886, BoxCenterLoss=14.436, BoxScaleLoss=5.098, ClassLoss=10.299 [Epoch 149][Batch 599], LR: 1.00E-03, Speed: 12.422 samples/sec, ObjLoss=24.885, BoxCenterLoss=14.436, BoxScaleLoss=5.098, ClassLoss=10.298 [Epoch 149][Batch 699], LR: 1.00E-03, Speed: 115.278 samples/sec, ObjLoss=24.883, BoxCenterLoss=14.436, BoxScaleLoss=5.098, ClassLoss=10.298 [Epoch 149][Batch 799], LR: 1.00E-03, Speed: 9.217 samples/sec, ObjLoss=24.883, BoxCenterLoss=14.436, BoxScaleLoss=5.098, ClassLoss=10.297 [Epoch 149][Batch 899], LR: 1.00E-03, Speed: 9.933 samples/sec, ObjLoss=24.882, BoxCenterLoss=14.435, BoxScaleLoss=5.098, ClassLoss=10.296 [Epoch 149][Batch 999], LR: 1.00E-03, Speed: 8.123 samples/sec, ObjLoss=24.881, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.296 [Epoch 149][Batch 1099], LR: 1.00E-03, Speed: 8.849 samples/sec, ObjLoss=24.880, BoxCenterLoss=14.435, BoxScaleLoss=5.098, ClassLoss=10.295 [Epoch 149][Batch 1199], LR: 1.00E-03, Speed: 8.534 samples/sec, ObjLoss=24.880, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.295 [Epoch 149][Batch 1299], LR: 1.00E-03, Speed: 8.365 samples/sec, ObjLoss=24.879, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.294 [Epoch 149][Batch 1399], LR: 1.00E-03, Speed: 9.367 samples/sec, ObjLoss=24.878, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.294 [Epoch 149][Batch 1499], LR: 1.00E-03, Speed: 10.944 samples/sec, ObjLoss=24.877, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.293 [Epoch 149][Batch 1599], LR: 1.00E-03, Speed: 8.164 samples/sec, ObjLoss=24.876, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.292 [Epoch 149][Batch 1699], LR: 1.00E-03, Speed: 7.563 samples/sec, ObjLoss=24.875, BoxCenterLoss=14.434, BoxScaleLoss=5.097, ClassLoss=10.292 [Epoch 149][Batch 1799], LR: 1.00E-03, Speed: 12.523 samples/sec, ObjLoss=24.875, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.291 [Epoch 149] Training cost: 2194.320, ObjLoss=24.875, BoxCenterLoss=14.435, BoxScaleLoss=5.097, ClassLoss=10.291 [Epoch 149] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.092 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.469 person=34.8 bicycle=15.9 car=24.5 motorcycle=28.1 airplane=41.4 bus=43.6 train=48.4 truck=20.6 boat=12.4 traffic light=14.4 fire hydrant=47.9 stop sign=39.3 parking meter=28.7 bench=14.0 bird=18.0 cat=48.3 dog=38.7 horse=31.5 sheep=31.6 cow=30.4 elephant=44.9 bear=50.9 zebra=43.3 giraffe=50.6 backpack=4.5 umbrella=21.8 handbag=5.4 tie=17.9 suitcase=17.6 frisbee=32.0 skis=7.5 snowboard=14.4 sports ball=24.0 kite=23.1 baseball bat=15.0 baseball glove=18.1 skateboard=29.8 surfboard=19.0 tennis racket=26.1 bottle=20.1 wine glass=19.0 cup=22.2 fork=15.4 knife=3.5 spoon=4.3 bowl=20.2 banana=11.5 apple=8.2 sandwich=21.4 orange=14.2 broccoli=11.6 carrot=10.8 hot dog=18.2 pizza=35.5 donut=23.4 cake=20.7 chair=13.8 couch=27.6 potted plant=12.8 bed=29.6 dining table=20.0 toilet=39.4 tv=34.8 laptop=33.8 mouse=36.3 remote=10.8 keyboard=35.6 cell phone=17.1 microwave=34.6 oven=19.8 toaster=0.0 sink=20.8 refrigerator=31.6 book=3.5 clock=31.5 vase=22.2 scissors=15.1 teddy bear=27.7 hair drier=0.0 toothbrush=6.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.5 [Epoch 150][Batch 99], LR: 1.00E-03, Speed: 9.718 samples/sec, ObjLoss=24.874, BoxCenterLoss=14.434, BoxScaleLoss=5.096, ClassLoss=10.290 [Epoch 150][Batch 199], LR: 1.00E-03, Speed: 9.284 samples/sec, ObjLoss=24.873, BoxCenterLoss=14.434, BoxScaleLoss=5.096, ClassLoss=10.290 [Epoch 150][Batch 299], LR: 1.00E-03, Speed: 10.688 samples/sec, ObjLoss=24.872, BoxCenterLoss=14.434, BoxScaleLoss=5.096, ClassLoss=10.289 [Epoch 150][Batch 399], LR: 1.00E-03, Speed: 8.165 samples/sec, ObjLoss=24.871, BoxCenterLoss=14.434, BoxScaleLoss=5.096, ClassLoss=10.288 [Epoch 150][Batch 499], LR: 1.00E-03, Speed: 9.916 samples/sec, ObjLoss=24.871, BoxCenterLoss=14.434, BoxScaleLoss=5.096, ClassLoss=10.287 [Epoch 150][Batch 599], LR: 1.00E-03, Speed: 8.270 samples/sec, ObjLoss=24.870, BoxCenterLoss=14.434, BoxScaleLoss=5.095, ClassLoss=10.287 [Epoch 150][Batch 699], LR: 1.00E-03, Speed: 8.346 samples/sec, ObjLoss=24.870, BoxCenterLoss=14.434, BoxScaleLoss=5.096, ClassLoss=10.286 [Epoch 150][Batch 799], LR: 1.00E-03, Speed: 10.914 samples/sec, ObjLoss=24.869, BoxCenterLoss=14.434, BoxScaleLoss=5.095, ClassLoss=10.286 [Epoch 150][Batch 899], LR: 1.00E-03, Speed: 8.361 samples/sec, ObjLoss=24.868, BoxCenterLoss=14.434, BoxScaleLoss=5.095, ClassLoss=10.285 [Epoch 150][Batch 999], LR: 1.00E-03, Speed: 8.159 samples/sec, ObjLoss=24.867, BoxCenterLoss=14.434, BoxScaleLoss=5.095, ClassLoss=10.284 [Epoch 150][Batch 1099], LR: 1.00E-03, Speed: 8.092 samples/sec, ObjLoss=24.866, BoxCenterLoss=14.434, BoxScaleLoss=5.095, ClassLoss=10.283 [Epoch 150][Batch 1199], LR: 1.00E-03, Speed: 9.598 samples/sec, ObjLoss=24.866, BoxCenterLoss=14.434, BoxScaleLoss=5.094, ClassLoss=10.283 [Epoch 150][Batch 1299], LR: 1.00E-03, Speed: 8.780 samples/sec, ObjLoss=24.865, BoxCenterLoss=14.434, BoxScaleLoss=5.094, ClassLoss=10.282 [Epoch 150][Batch 1399], LR: 1.00E-03, Speed: 7.825 samples/sec, ObjLoss=24.864, BoxCenterLoss=14.433, BoxScaleLoss=5.094, ClassLoss=10.281 [Epoch 150][Batch 1499], LR: 1.00E-03, Speed: 10.388 samples/sec, ObjLoss=24.864, BoxCenterLoss=14.433, BoxScaleLoss=5.094, ClassLoss=10.281 [Epoch 150][Batch 1599], LR: 1.00E-03, Speed: 92.693 samples/sec, ObjLoss=24.863, BoxCenterLoss=14.433, BoxScaleLoss=5.094, ClassLoss=10.280 [Epoch 150][Batch 1699], LR: 1.00E-03, Speed: 9.874 samples/sec, ObjLoss=24.862, BoxCenterLoss=14.433, BoxScaleLoss=5.094, ClassLoss=10.279 [Epoch 150][Batch 1799], LR: 1.00E-03, Speed: 9.947 samples/sec, ObjLoss=24.861, BoxCenterLoss=14.433, BoxScaleLoss=5.094, ClassLoss=10.279 [Epoch 150] Training cost: 2279.439, ObjLoss=24.861, BoxCenterLoss=14.433, BoxScaleLoss=5.094, ClassLoss=10.279 [Epoch 150] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.468 person=38.9 bicycle=20.3 car=23.4 motorcycle=30.4 airplane=44.3 bus=47.0 train=48.9 truck=21.4 boat=11.4 traffic light=13.7 fire hydrant=50.1 stop sign=43.2 parking meter=34.3 bench=14.2 bird=21.9 cat=47.5 dog=42.0 horse=39.9 sheep=35.2 cow=34.2 elephant=46.0 bear=53.9 zebra=48.3 giraffe=45.7 backpack=6.4 umbrella=22.6 handbag=5.4 tie=14.5 suitcase=17.3 frisbee=33.1 skis=9.6 snowboard=14.0 sports ball=25.7 kite=27.2 baseball bat=14.0 baseball glove=18.4 skateboard=27.0 surfboard=21.2 tennis racket=26.7 bottle=20.0 wine glass=21.0 cup=26.6 fork=15.2 knife=3.4 spoon=5.1 bowl=23.9 banana=11.9 apple=8.8 sandwich=22.3 orange=18.1 broccoli=10.7 carrot=11.1 hot dog=21.6 pizza=28.5 donut=24.3 cake=22.3 chair=16.5 couch=29.8 potted plant=14.7 bed=31.6 dining table=19.1 toilet=40.4 tv=37.2 laptop=38.6 mouse=33.6 remote=11.4 keyboard=33.0 cell phone=17.4 microwave=36.6 oven=21.0 toaster=0.0 sink=21.3 refrigerator=29.5 book=4.2 clock=30.8 vase=22.8 scissors=14.3 teddy bear=30.1 hair drier=0.0 toothbrush=5.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.7 [Epoch 151][Batch 99], LR: 1.00E-03, Speed: 10.378 samples/sec, ObjLoss=24.860, BoxCenterLoss=14.433, BoxScaleLoss=5.094, ClassLoss=10.278 [Epoch 151][Batch 199], LR: 1.00E-03, Speed: 7.843 samples/sec, ObjLoss=24.859, BoxCenterLoss=14.433, BoxScaleLoss=5.093, ClassLoss=10.277 [Epoch 151][Batch 299], LR: 1.00E-03, Speed: 9.152 samples/sec, ObjLoss=24.858, BoxCenterLoss=14.432, BoxScaleLoss=5.093, ClassLoss=10.277 [Epoch 151][Batch 399], LR: 1.00E-03, Speed: 11.040 samples/sec, ObjLoss=24.858, BoxCenterLoss=14.433, BoxScaleLoss=5.093, ClassLoss=10.276 [Epoch 151][Batch 499], LR: 1.00E-03, Speed: 12.535 samples/sec, ObjLoss=24.857, BoxCenterLoss=14.432, BoxScaleLoss=5.093, ClassLoss=10.276 [Epoch 151][Batch 599], LR: 1.00E-03, Speed: 9.963 samples/sec, ObjLoss=24.856, BoxCenterLoss=14.432, BoxScaleLoss=5.093, ClassLoss=10.275 [Epoch 151][Batch 699], LR: 1.00E-03, Speed: 9.906 samples/sec, ObjLoss=24.856, BoxCenterLoss=14.432, BoxScaleLoss=5.093, ClassLoss=10.274 [Epoch 151][Batch 799], LR: 1.00E-03, Speed: 106.334 samples/sec, ObjLoss=24.854, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.274 [Epoch 151][Batch 899], LR: 1.00E-03, Speed: 8.218 samples/sec, ObjLoss=24.854, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.273 [Epoch 151][Batch 999], LR: 1.00E-03, Speed: 9.771 samples/sec, ObjLoss=24.853, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.273 [Epoch 151][Batch 1099], LR: 1.00E-03, Speed: 94.742 samples/sec, ObjLoss=24.853, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.272 [Epoch 151][Batch 1199], LR: 1.00E-03, Speed: 13.016 samples/sec, ObjLoss=24.852, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.272 [Epoch 151][Batch 1299], LR: 1.00E-03, Speed: 9.270 samples/sec, ObjLoss=24.851, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.271 [Epoch 151][Batch 1399], LR: 1.00E-03, Speed: 9.182 samples/sec, ObjLoss=24.849, BoxCenterLoss=14.431, BoxScaleLoss=5.092, ClassLoss=10.270 [Epoch 151][Batch 1499], LR: 1.00E-03, Speed: 8.260 samples/sec, ObjLoss=24.849, BoxCenterLoss=14.431, BoxScaleLoss=5.092, ClassLoss=10.270 [Epoch 151][Batch 1599], LR: 1.00E-03, Speed: 9.675 samples/sec, ObjLoss=24.849, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.269 [Epoch 151][Batch 1699], LR: 1.00E-03, Speed: 10.763 samples/sec, ObjLoss=24.848, BoxCenterLoss=14.432, BoxScaleLoss=5.092, ClassLoss=10.269 [Epoch 151][Batch 1799], LR: 1.00E-03, Speed: 93.633 samples/sec, ObjLoss=24.847, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.268 [Epoch 151] Training cost: 2233.843, ObjLoss=24.846, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.268 [Epoch 151] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.339 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481 person=37.4 bicycle=15.8 car=24.7 motorcycle=29.1 airplane=40.6 bus=50.2 train=52.1 truck=22.6 boat=13.6 traffic light=11.4 fire hydrant=49.4 stop sign=44.7 parking meter=27.9 bench=11.7 bird=20.6 cat=46.1 dog=39.4 horse=33.4 sheep=31.8 cow=35.3 elephant=47.2 bear=51.2 zebra=44.9 giraffe=45.4 backpack=5.8 umbrella=23.2 handbag=5.7 tie=15.0 suitcase=18.9 frisbee=32.5 skis=11.6 snowboard=17.4 sports ball=26.4 kite=22.6 baseball bat=15.3 baseball glove=20.6 skateboard=31.5 surfboard=19.9 tennis racket=26.1 bottle=19.7 wine glass=21.4 cup=25.9 fork=13.1 knife=4.4 spoon=4.4 bowl=24.2 banana=12.3 apple=7.1 sandwich=24.7 orange=14.9 broccoli=11.5 carrot=11.2 hot dog=20.2 pizza=35.6 donut=26.0 cake=23.9 chair=15.8 couch=26.5 potted plant=13.6 bed=34.2 dining table=17.6 toilet=39.4 tv=33.1 laptop=38.0 mouse=37.6 remote=11.6 keyboard=28.6 cell phone=16.4 microwave=34.1 oven=18.1 toaster=0.0 sink=20.9 refrigerator=31.5 book=4.3 clock=33.2 vase=20.9 scissors=17.3 teddy bear=29.5 hair drier=0.0 toothbrush=5.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.3 [Epoch 152][Batch 99], LR: 1.00E-03, Speed: 9.868 samples/sec, ObjLoss=24.845, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.267 [Epoch 152][Batch 199], LR: 1.00E-03, Speed: 110.129 samples/sec, ObjLoss=24.845, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.266 [Epoch 152][Batch 299], LR: 1.00E-03, Speed: 10.404 samples/sec, ObjLoss=24.844, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.266 [Epoch 152][Batch 399], LR: 1.00E-03, Speed: 10.034 samples/sec, ObjLoss=24.843, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.265 [Epoch 152][Batch 499], LR: 1.00E-03, Speed: 7.742 samples/sec, ObjLoss=24.842, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.264 [Epoch 152][Batch 599], LR: 1.00E-03, Speed: 91.542 samples/sec, ObjLoss=24.842, BoxCenterLoss=14.431, BoxScaleLoss=5.091, ClassLoss=10.264 [Epoch 152][Batch 699], LR: 1.00E-03, Speed: 7.281 samples/sec, ObjLoss=24.841, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.263 [Epoch 152][Batch 799], LR: 1.00E-03, Speed: 9.320 samples/sec, ObjLoss=24.841, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.262 [Epoch 152][Batch 899], LR: 1.00E-03, Speed: 104.827 samples/sec, ObjLoss=24.840, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.262 [Epoch 152][Batch 999], LR: 1.00E-03, Speed: 10.010 samples/sec, ObjLoss=24.840, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.261 [Epoch 152][Batch 1099], LR: 1.00E-03, Speed: 107.328 samples/sec, ObjLoss=24.839, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.261 [Epoch 152][Batch 1199], LR: 1.00E-03, Speed: 12.018 samples/sec, ObjLoss=24.839, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.260 [Epoch 152][Batch 1299], LR: 1.00E-03, Speed: 11.855 samples/sec, ObjLoss=24.838, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.259 [Epoch 152][Batch 1399], LR: 1.00E-03, Speed: 8.846 samples/sec, ObjLoss=24.838, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.259 [Epoch 152][Batch 1499], LR: 1.00E-03, Speed: 9.545 samples/sec, ObjLoss=24.837, BoxCenterLoss=14.431, BoxScaleLoss=5.090, ClassLoss=10.258 [Epoch 152][Batch 1599], LR: 1.00E-03, Speed: 7.670 samples/sec, ObjLoss=24.836, BoxCenterLoss=14.431, BoxScaleLoss=5.089, ClassLoss=10.258 [Epoch 152][Batch 1699], LR: 1.00E-03, Speed: 10.723 samples/sec, ObjLoss=24.836, BoxCenterLoss=14.431, BoxScaleLoss=5.089, ClassLoss=10.257 [Epoch 152][Batch 1799], LR: 1.00E-03, Speed: 10.583 samples/sec, ObjLoss=24.835, BoxCenterLoss=14.431, BoxScaleLoss=5.089, ClassLoss=10.257 [Epoch 152] Training cost: 2230.895, ObjLoss=24.835, BoxCenterLoss=14.431, BoxScaleLoss=5.089, ClassLoss=10.256 [Epoch 152] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.457 person=37.2 bicycle=17.9 car=26.6 motorcycle=28.1 airplane=38.3 bus=45.7 train=50.0 truck=23.0 boat=11.6 traffic light=14.8 fire hydrant=47.6 stop sign=41.8 parking meter=23.7 bench=12.1 bird=18.8 cat=42.6 dog=36.1 horse=36.6 sheep=27.9 cow=33.0 elephant=41.5 bear=44.7 zebra=45.8 giraffe=49.4 backpack=6.6 umbrella=23.8 handbag=5.5 tie=16.7 suitcase=21.1 frisbee=38.1 skis=9.5 snowboard=14.3 sports ball=24.7 kite=22.0 baseball bat=13.5 baseball glove=17.8 skateboard=28.0 surfboard=20.0 tennis racket=26.3 bottle=19.7 wine glass=19.5 cup=25.6 fork=14.1 knife=5.0 spoon=3.4 bowl=23.8 banana=12.8 apple=9.6 sandwich=18.3 orange=15.9 broccoli=10.6 carrot=10.0 hot dog=17.3 pizza=29.8 donut=26.6 cake=19.8 chair=15.9 couch=27.8 potted plant=14.6 bed=33.0 dining table=22.0 toilet=38.8 tv=39.1 laptop=32.6 mouse=41.7 remote=11.6 keyboard=28.0 cell phone=19.4 microwave=32.1 oven=19.4 toaster=1.4 sink=17.9 refrigerator=34.1 book=4.5 clock=36.4 vase=22.3 scissors=17.3 teddy bear=28.7 hair drier=0.0 toothbrush=6.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.9 [Epoch 153][Batch 99], LR: 1.00E-03, Speed: 9.125 samples/sec, ObjLoss=24.834, BoxCenterLoss=14.431, BoxScaleLoss=5.089, ClassLoss=10.256 [Epoch 153][Batch 199], LR: 1.00E-03, Speed: 7.718 samples/sec, ObjLoss=24.833, BoxCenterLoss=14.431, BoxScaleLoss=5.089, ClassLoss=10.255 [Epoch 153][Batch 299], LR: 1.00E-03, Speed: 7.791 samples/sec, ObjLoss=24.833, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.254 [Epoch 153][Batch 399], LR: 1.00E-03, Speed: 11.050 samples/sec, ObjLoss=24.832, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.254 [Epoch 153][Batch 499], LR: 1.00E-03, Speed: 10.844 samples/sec, ObjLoss=24.831, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.253 [Epoch 153][Batch 599], LR: 1.00E-03, Speed: 11.878 samples/sec, ObjLoss=24.830, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.253 [Epoch 153][Batch 699], LR: 1.00E-03, Speed: 7.750 samples/sec, ObjLoss=24.830, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.252 [Epoch 153][Batch 799], LR: 1.00E-03, Speed: 119.298 samples/sec, ObjLoss=24.829, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.252 [Epoch 153][Batch 899], LR: 1.00E-03, Speed: 9.580 samples/sec, ObjLoss=24.828, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.251 [Epoch 153][Batch 999], LR: 1.00E-03, Speed: 11.209 samples/sec, ObjLoss=24.827, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.250 [Epoch 153][Batch 1099], LR: 1.00E-03, Speed: 9.708 samples/sec, ObjLoss=24.827, BoxCenterLoss=14.430, BoxScaleLoss=5.088, ClassLoss=10.250 [Epoch 153][Batch 1199], LR: 1.00E-03, Speed: 10.631 samples/sec, ObjLoss=24.826, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.250 [Epoch 153][Batch 1299], LR: 1.00E-03, Speed: 10.807 samples/sec, ObjLoss=24.825, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.249 [Epoch 153][Batch 1399], LR: 1.00E-03, Speed: 10.362 samples/sec, ObjLoss=24.825, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.249 [Epoch 153][Batch 1499], LR: 1.00E-03, Speed: 9.929 samples/sec, ObjLoss=24.824, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.248 [Epoch 153][Batch 1599], LR: 1.00E-03, Speed: 8.936 samples/sec, ObjLoss=24.823, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.248 [Epoch 153][Batch 1699], LR: 1.00E-03, Speed: 12.199 samples/sec, ObjLoss=24.823, BoxCenterLoss=14.429, BoxScaleLoss=5.087, ClassLoss=10.247 [Epoch 153][Batch 1799], LR: 1.00E-03, Speed: 137.528 samples/sec, ObjLoss=24.822, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.247 [Epoch 153] Training cost: 2235.925, ObjLoss=24.822, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.246 [Epoch 153] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445 person=37.0 bicycle=17.2 car=27.0 motorcycle=26.8 airplane=38.5 bus=36.8 train=41.8 truck=19.7 boat=12.5 traffic light=15.3 fire hydrant=41.3 stop sign=37.0 parking meter=26.6 bench=11.5 bird=20.4 cat=42.3 dog=31.2 horse=32.4 sheep=34.4 cow=35.1 elephant=42.0 bear=42.5 zebra=42.0 giraffe=45.8 backpack=8.8 umbrella=22.7 handbag=5.9 tie=18.4 suitcase=19.2 frisbee=31.3 skis=12.2 snowboard=17.0 sports ball=21.3 kite=26.8 baseball bat=12.4 baseball glove=17.2 skateboard=33.5 surfboard=20.6 tennis racket=29.2 bottle=21.6 wine glass=20.8 cup=22.9 fork=15.3 knife=6.2 spoon=3.8 bowl=21.3 banana=12.8 apple=7.8 sandwich=14.8 orange=18.1 broccoli=10.7 carrot=7.4 hot dog=18.9 pizza=31.2 donut=27.6 cake=19.8 chair=16.7 couch=22.2 potted plant=13.1 bed=25.8 dining table=12.3 toilet=36.4 tv=38.9 laptop=36.6 mouse=39.2 remote=12.3 keyboard=32.7 cell phone=20.3 microwave=36.0 oven=17.7 toaster=0.0 sink=22.1 refrigerator=28.9 book=4.4 clock=33.6 vase=23.0 scissors=14.2 teddy bear=27.7 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.2 [Epoch 154][Batch 99], LR: 1.00E-03, Speed: 8.142 samples/sec, ObjLoss=24.821, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.246 [Epoch 154][Batch 199], LR: 1.00E-03, Speed: 17.812 samples/sec, ObjLoss=24.821, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.245 [Epoch 154][Batch 299], LR: 1.00E-03, Speed: 8.885 samples/sec, ObjLoss=24.820, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.245 [Epoch 154][Batch 399], LR: 1.00E-03, Speed: 114.185 samples/sec, ObjLoss=24.820, BoxCenterLoss=14.430, BoxScaleLoss=5.087, ClassLoss=10.244 [Epoch 154][Batch 499], LR: 1.00E-03, Speed: 119.542 samples/sec, ObjLoss=24.819, BoxCenterLoss=14.430, BoxScaleLoss=5.086, ClassLoss=10.244 [Epoch 154][Batch 599], LR: 1.00E-03, Speed: 9.565 samples/sec, ObjLoss=24.818, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.243 [Epoch 154][Batch 699], LR: 1.00E-03, Speed: 9.302 samples/sec, ObjLoss=24.817, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.242 [Epoch 154][Batch 799], LR: 1.00E-03, Speed: 9.785 samples/sec, ObjLoss=24.816, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.242 [Epoch 154][Batch 899], LR: 1.00E-03, Speed: 114.014 samples/sec, ObjLoss=24.816, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.241 [Epoch 154][Batch 999], LR: 1.00E-03, Speed: 10.128 samples/sec, ObjLoss=24.815, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.241 [Epoch 154][Batch 1099], LR: 1.00E-03, Speed: 10.066 samples/sec, ObjLoss=24.815, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.240 [Epoch 154][Batch 1199], LR: 1.00E-03, Speed: 9.151 samples/sec, ObjLoss=24.814, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.240 [Epoch 154][Batch 1299], LR: 1.00E-03, Speed: 10.208 samples/sec, ObjLoss=24.813, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.239 [Epoch 154][Batch 1399], LR: 1.00E-03, Speed: 9.673 samples/sec, ObjLoss=24.813, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.239 [Epoch 154][Batch 1499], LR: 1.00E-03, Speed: 10.695 samples/sec, ObjLoss=24.813, BoxCenterLoss=14.429, BoxScaleLoss=5.086, ClassLoss=10.238 [Epoch 154][Batch 1599], LR: 1.00E-03, Speed: 10.500 samples/sec, ObjLoss=24.811, BoxCenterLoss=14.429, BoxScaleLoss=5.085, ClassLoss=10.238 [Epoch 154][Batch 1699], LR: 1.00E-03, Speed: 8.073 samples/sec, ObjLoss=24.811, BoxCenterLoss=14.429, BoxScaleLoss=5.085, ClassLoss=10.237 [Epoch 154][Batch 1799], LR: 1.00E-03, Speed: 12.710 samples/sec, ObjLoss=24.810, BoxCenterLoss=14.429, BoxScaleLoss=5.085, ClassLoss=10.237 [Epoch 154] Training cost: 2146.039, ObjLoss=24.811, BoxCenterLoss=14.429, BoxScaleLoss=5.085, ClassLoss=10.236 [Epoch 154] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 person=39.2 bicycle=18.3 car=27.1 motorcycle=28.8 airplane=43.0 bus=48.4 train=51.4 truck=21.2 boat=13.4 traffic light=15.5 fire hydrant=44.6 stop sign=44.0 parking meter=28.5 bench=14.0 bird=19.3 cat=46.6 dog=37.9 horse=33.8 sheep=31.0 cow=32.5 elephant=47.3 bear=49.0 zebra=49.6 giraffe=49.4 backpack=6.9 umbrella=24.5 handbag=6.5 tie=15.9 suitcase=20.7 frisbee=36.9 skis=12.0 snowboard=15.2 sports ball=26.7 kite=25.5 baseball bat=14.0 baseball glove=20.2 skateboard=31.3 surfboard=21.5 tennis racket=33.4 bottle=20.8 wine glass=19.0 cup=23.1 fork=15.4 knife=6.4 spoon=5.2 bowl=23.2 banana=13.8 apple=8.9 sandwich=19.6 orange=17.5 broccoli=9.3 carrot=10.8 hot dog=18.2 pizza=29.7 donut=30.8 cake=20.9 chair=17.3 couch=29.8 potted plant=12.4 bed=30.4 dining table=20.1 toilet=39.4 tv=34.7 laptop=39.9 mouse=37.5 remote=12.3 keyboard=30.3 cell phone=20.0 microwave=34.1 oven=17.5 toaster=0.0 sink=21.0 refrigerator=36.3 book=4.9 clock=33.3 vase=20.0 scissors=16.6 teddy bear=29.0 hair drier=0.0 toothbrush=6.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.8 [Epoch 155][Batch 99], LR: 1.00E-03, Speed: 131.524 samples/sec, ObjLoss=24.810, BoxCenterLoss=14.429, BoxScaleLoss=5.085, ClassLoss=10.236 [Epoch 155][Batch 199], LR: 1.00E-03, Speed: 8.052 samples/sec, ObjLoss=24.809, BoxCenterLoss=14.430, BoxScaleLoss=5.085, ClassLoss=10.236 [Epoch 155][Batch 299], LR: 1.00E-03, Speed: 10.416 samples/sec, ObjLoss=24.809, BoxCenterLoss=14.430, BoxScaleLoss=5.085, ClassLoss=10.235 [Epoch 155][Batch 399], LR: 1.00E-03, Speed: 11.489 samples/sec, ObjLoss=24.808, BoxCenterLoss=14.430, BoxScaleLoss=5.085, ClassLoss=10.235 [Epoch 155][Batch 499], LR: 1.00E-03, Speed: 9.012 samples/sec, ObjLoss=24.807, BoxCenterLoss=14.429, BoxScaleLoss=5.085, ClassLoss=10.234 [Epoch 155][Batch 599], LR: 1.00E-03, Speed: 101.777 samples/sec, ObjLoss=24.807, BoxCenterLoss=14.430, BoxScaleLoss=5.085, ClassLoss=10.233 [Epoch 155][Batch 699], LR: 1.00E-03, Speed: 122.437 samples/sec, ObjLoss=24.806, BoxCenterLoss=14.430, BoxScaleLoss=5.085, ClassLoss=10.233 [Epoch 155][Batch 799], LR: 1.00E-03, Speed: 8.478 samples/sec, ObjLoss=24.805, BoxCenterLoss=14.430, BoxScaleLoss=5.085, ClassLoss=10.232 [Epoch 155][Batch 899], LR: 1.00E-03, Speed: 11.240 samples/sec, ObjLoss=24.805, BoxCenterLoss=14.429, BoxScaleLoss=5.085, ClassLoss=10.232 [Epoch 155][Batch 999], LR: 1.00E-03, Speed: 11.678 samples/sec, ObjLoss=24.804, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.231 [Epoch 155][Batch 1099], LR: 1.00E-03, Speed: 10.839 samples/sec, ObjLoss=24.803, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.231 [Epoch 155][Batch 1199], LR: 1.00E-03, Speed: 9.226 samples/sec, ObjLoss=24.802, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.230 [Epoch 155][Batch 1299], LR: 1.00E-03, Speed: 11.704 samples/sec, ObjLoss=24.802, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.230 [Epoch 155][Batch 1399], LR: 1.00E-03, Speed: 10.275 samples/sec, ObjLoss=24.802, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.229 [Epoch 155][Batch 1499], LR: 1.00E-03, Speed: 8.729 samples/sec, ObjLoss=24.801, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.229 [Epoch 155][Batch 1599], LR: 1.00E-03, Speed: 11.950 samples/sec, ObjLoss=24.800, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.228 [Epoch 155][Batch 1699], LR: 1.00E-03, Speed: 11.710 samples/sec, ObjLoss=24.799, BoxCenterLoss=14.429, BoxScaleLoss=5.084, ClassLoss=10.227 [Epoch 155][Batch 1799], LR: 1.00E-03, Speed: 10.549 samples/sec, ObjLoss=24.798, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.227 [Epoch 155] Training cost: 2133.510, ObjLoss=24.798, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.226 [Epoch 155] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.459 person=36.4 bicycle=17.0 car=25.9 motorcycle=27.1 airplane=34.9 bus=43.5 train=47.5 truck=20.5 boat=12.3 traffic light=12.7 fire hydrant=40.6 stop sign=41.3 parking meter=29.9 bench=12.8 bird=20.0 cat=42.3 dog=37.6 horse=34.4 sheep=32.6 cow=31.1 elephant=42.9 bear=45.2 zebra=45.0 giraffe=45.9 backpack=5.9 umbrella=20.8 handbag=5.0 tie=16.0 suitcase=15.2 frisbee=34.4 skis=13.6 snowboard=17.6 sports ball=27.4 kite=25.4 baseball bat=14.4 baseball glove=20.0 skateboard=29.7 surfboard=22.1 tennis racket=27.8 bottle=19.1 wine glass=17.7 cup=23.5 fork=12.9 knife=4.7 spoon=6.1 bowl=19.5 banana=10.5 apple=8.3 sandwich=18.0 orange=18.2 broccoli=9.1 carrot=8.6 hot dog=17.0 pizza=31.8 donut=26.6 cake=20.8 chair=14.3 couch=26.5 potted plant=12.9 bed=32.7 dining table=19.2 toilet=38.2 tv=35.9 laptop=39.3 mouse=37.4 remote=10.7 keyboard=30.7 cell phone=19.1 microwave=25.2 oven=18.7 toaster=1.2 sink=23.4 refrigerator=26.8 book=3.7 clock=35.6 vase=20.7 scissors=11.9 teddy bear=26.9 hair drier=0.0 toothbrush=5.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.3 [Epoch 156][Batch 99], LR: 1.00E-03, Speed: 10.800 samples/sec, ObjLoss=24.798, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.226 [Epoch 156][Batch 199], LR: 1.00E-03, Speed: 9.128 samples/sec, ObjLoss=24.797, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.226 [Epoch 156][Batch 299], LR: 1.00E-03, Speed: 9.421 samples/sec, ObjLoss=24.796, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.225 [Epoch 156][Batch 399], LR: 1.00E-03, Speed: 11.259 samples/sec, ObjLoss=24.795, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.224 [Epoch 156][Batch 499], LR: 1.00E-03, Speed: 10.591 samples/sec, ObjLoss=24.795, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.224 [Epoch 156][Batch 599], LR: 1.00E-03, Speed: 9.519 samples/sec, ObjLoss=24.794, BoxCenterLoss=14.429, BoxScaleLoss=5.083, ClassLoss=10.223 [Epoch 156][Batch 699], LR: 1.00E-03, Speed: 9.389 samples/sec, ObjLoss=24.793, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.223 [Epoch 156][Batch 799], LR: 1.00E-03, Speed: 107.315 samples/sec, ObjLoss=24.792, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.222 [Epoch 156][Batch 899], LR: 1.00E-03, Speed: 7.662 samples/sec, ObjLoss=24.791, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.222 [Epoch 156][Batch 999], LR: 1.00E-03, Speed: 9.588 samples/sec, ObjLoss=24.791, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.221 [Epoch 156][Batch 1099], LR: 1.00E-03, Speed: 10.335 samples/sec, ObjLoss=24.790, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.221 [Epoch 156][Batch 1199], LR: 1.00E-03, Speed: 8.203 samples/sec, ObjLoss=24.790, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.220 [Epoch 156][Batch 1299], LR: 1.00E-03, Speed: 10.900 samples/sec, ObjLoss=24.789, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.220 [Epoch 156][Batch 1399], LR: 1.00E-03, Speed: 9.369 samples/sec, ObjLoss=24.788, BoxCenterLoss=14.428, BoxScaleLoss=5.083, ClassLoss=10.219 [Epoch 156][Batch 1499], LR: 1.00E-03, Speed: 10.623 samples/sec, ObjLoss=24.787, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.218 [Epoch 156][Batch 1599], LR: 1.00E-03, Speed: 10.189 samples/sec, ObjLoss=24.787, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.218 [Epoch 156][Batch 1699], LR: 1.00E-03, Speed: 9.973 samples/sec, ObjLoss=24.786, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.217 [Epoch 156][Batch 1799], LR: 1.00E-03, Speed: 12.189 samples/sec, ObjLoss=24.786, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.216 [Epoch 156] Training cost: 2190.420, ObjLoss=24.785, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.216 [Epoch 156] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.227 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.463 person=36.0 bicycle=16.4 car=25.4 motorcycle=27.3 airplane=39.6 bus=44.8 train=47.9 truck=21.0 boat=13.1 traffic light=13.2 fire hydrant=44.0 stop sign=41.7 parking meter=29.2 bench=12.3 bird=20.1 cat=45.7 dog=38.8 horse=36.3 sheep=32.9 cow=35.6 elephant=45.8 bear=41.0 zebra=46.5 giraffe=44.0 backpack=6.6 umbrella=23.8 handbag=5.6 tie=16.8 suitcase=19.9 frisbee=37.1 skis=12.3 snowboard=17.0 sports ball=19.0 kite=26.9 baseball bat=14.8 baseball glove=19.0 skateboard=26.5 surfboard=19.3 tennis racket=26.0 bottle=18.5 wine glass=19.1 cup=24.5 fork=14.3 knife=5.7 spoon=6.1 bowl=23.5 banana=12.3 apple=6.1 sandwich=20.8 orange=16.8 broccoli=10.1 carrot=9.3 hot dog=18.9 pizza=31.2 donut=29.6 cake=21.7 chair=15.1 couch=29.9 potted plant=15.0 bed=30.1 dining table=19.5 toilet=40.2 tv=38.4 laptop=35.8 mouse=30.2 remote=10.1 keyboard=34.4 cell phone=16.3 microwave=32.2 oven=21.8 toaster=0.0 sink=23.2 refrigerator=31.9 book=4.3 clock=30.9 vase=20.6 scissors=16.8 teddy bear=26.1 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.8 [Epoch 157][Batch 99], LR: 1.00E-03, Speed: 9.941 samples/sec, ObjLoss=24.785, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.216 [Epoch 157][Batch 199], LR: 1.00E-03, Speed: 10.542 samples/sec, ObjLoss=24.784, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.215 [Epoch 157][Batch 299], LR: 1.00E-03, Speed: 6.971 samples/sec, ObjLoss=24.784, BoxCenterLoss=14.428, BoxScaleLoss=5.082, ClassLoss=10.215 [Epoch 157][Batch 399], LR: 1.00E-03, Speed: 8.030 samples/sec, ObjLoss=24.783, BoxCenterLoss=14.428, BoxScaleLoss=5.081, ClassLoss=10.214 [Epoch 157][Batch 499], LR: 1.00E-03, Speed: 9.613 samples/sec, ObjLoss=24.783, BoxCenterLoss=14.428, BoxScaleLoss=5.081, ClassLoss=10.214 [Epoch 157][Batch 599], LR: 1.00E-03, Speed: 104.235 samples/sec, ObjLoss=24.782, BoxCenterLoss=14.428, BoxScaleLoss=5.081, ClassLoss=10.213 [Epoch 157][Batch 699], LR: 1.00E-03, Speed: 9.744 samples/sec, ObjLoss=24.781, BoxCenterLoss=14.428, BoxScaleLoss=5.081, ClassLoss=10.212 [Epoch 157][Batch 799], LR: 1.00E-03, Speed: 110.677 samples/sec, ObjLoss=24.780, BoxCenterLoss=14.428, BoxScaleLoss=5.081, ClassLoss=10.212 [Epoch 157][Batch 899], LR: 1.00E-03, Speed: 9.524 samples/sec, ObjLoss=24.779, BoxCenterLoss=14.427, BoxScaleLoss=5.081, ClassLoss=10.211 [Epoch 157][Batch 999], LR: 1.00E-03, Speed: 110.449 samples/sec, ObjLoss=24.779, BoxCenterLoss=14.427, BoxScaleLoss=5.081, ClassLoss=10.211 [Epoch 157][Batch 1099], LR: 1.00E-03, Speed: 12.315 samples/sec, ObjLoss=24.778, BoxCenterLoss=14.427, BoxScaleLoss=5.081, ClassLoss=10.210 [Epoch 157][Batch 1199], LR: 1.00E-03, Speed: 8.726 samples/sec, ObjLoss=24.777, BoxCenterLoss=14.427, BoxScaleLoss=5.081, ClassLoss=10.209 [Epoch 157][Batch 1299], LR: 1.00E-03, Speed: 8.915 samples/sec, ObjLoss=24.777, BoxCenterLoss=14.428, BoxScaleLoss=5.081, ClassLoss=10.209 [Epoch 157][Batch 1399], LR: 1.00E-03, Speed: 11.593 samples/sec, ObjLoss=24.776, BoxCenterLoss=14.427, BoxScaleLoss=5.081, ClassLoss=10.208 [Epoch 157][Batch 1499], LR: 1.00E-03, Speed: 9.516 samples/sec, ObjLoss=24.775, BoxCenterLoss=14.427, BoxScaleLoss=5.080, ClassLoss=10.208 [Epoch 157][Batch 1599], LR: 1.00E-03, Speed: 9.640 samples/sec, ObjLoss=24.774, BoxCenterLoss=14.427, BoxScaleLoss=5.080, ClassLoss=10.207 [Epoch 157][Batch 1699], LR: 1.00E-03, Speed: 10.645 samples/sec, ObjLoss=24.774, BoxCenterLoss=14.427, BoxScaleLoss=5.080, ClassLoss=10.207 [Epoch 157][Batch 1799], LR: 1.00E-03, Speed: 9.359 samples/sec, ObjLoss=24.773, BoxCenterLoss=14.427, BoxScaleLoss=5.080, ClassLoss=10.206 [Epoch 157] Training cost: 2196.014, ObjLoss=24.773, BoxCenterLoss=14.427, BoxScaleLoss=5.080, ClassLoss=10.206 [Epoch 157] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.211 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.456 person=36.2 bicycle=18.4 car=27.3 motorcycle=28.5 airplane=40.1 bus=44.0 train=46.9 truck=21.9 boat=13.7 traffic light=13.4 fire hydrant=43.4 stop sign=38.3 parking meter=26.3 bench=12.1 bird=19.1 cat=42.3 dog=36.3 horse=33.0 sheep=34.8 cow=34.0 elephant=42.2 bear=38.5 zebra=38.9 giraffe=47.1 backpack=6.9 umbrella=23.4 handbag=5.7 tie=14.7 suitcase=17.4 frisbee=37.4 skis=9.1 snowboard=12.1 sports ball=24.7 kite=27.9 baseball bat=15.1 baseball glove=17.1 skateboard=29.2 surfboard=19.6 tennis racket=23.9 bottle=20.5 wine glass=20.2 cup=23.4 fork=13.8 knife=4.6 spoon=4.8 bowl=23.5 banana=13.6 apple=9.9 sandwich=19.4 orange=14.0 broccoli=9.5 carrot=7.8 hot dog=19.4 pizza=34.0 donut=25.1 cake=23.2 chair=17.7 couch=25.8 potted plant=14.5 bed=31.5 dining table=21.3 toilet=37.9 tv=37.2 laptop=38.3 mouse=33.0 remote=10.3 keyboard=27.2 cell phone=17.4 microwave=33.7 oven=20.9 toaster=4.8 sink=19.4 refrigerator=32.6 book=5.5 clock=32.5 vase=23.3 scissors=16.3 teddy bear=29.3 hair drier=0.0 toothbrush=7.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.6 [Epoch 158][Batch 99], LR: 1.00E-03, Speed: 11.311 samples/sec, ObjLoss=24.771, BoxCenterLoss=14.426, BoxScaleLoss=5.080, ClassLoss=10.205 [Epoch 158][Batch 199], LR: 1.00E-03, Speed: 9.548 samples/sec, ObjLoss=24.771, BoxCenterLoss=14.426, BoxScaleLoss=5.080, ClassLoss=10.205 [Epoch 158][Batch 299], LR: 1.00E-03, Speed: 12.459 samples/sec, ObjLoss=24.770, BoxCenterLoss=14.426, BoxScaleLoss=5.079, ClassLoss=10.204 [Epoch 158][Batch 399], LR: 1.00E-03, Speed: 79.401 samples/sec, ObjLoss=24.769, BoxCenterLoss=14.426, BoxScaleLoss=5.079, ClassLoss=10.203 [Epoch 158][Batch 499], LR: 1.00E-03, Speed: 11.008 samples/sec, ObjLoss=24.768, BoxCenterLoss=14.425, BoxScaleLoss=5.079, ClassLoss=10.203 [Epoch 158][Batch 599], LR: 1.00E-03, Speed: 8.810 samples/sec, ObjLoss=24.767, BoxCenterLoss=14.425, BoxScaleLoss=5.079, ClassLoss=10.202 [Epoch 158][Batch 699], LR: 1.00E-03, Speed: 9.361 samples/sec, ObjLoss=24.766, BoxCenterLoss=14.425, BoxScaleLoss=5.079, ClassLoss=10.201 [Epoch 158][Batch 799], LR: 1.00E-03, Speed: 9.716 samples/sec, ObjLoss=24.766, BoxCenterLoss=14.425, BoxScaleLoss=5.079, ClassLoss=10.201 [Epoch 158][Batch 899], LR: 1.00E-03, Speed: 11.127 samples/sec, ObjLoss=24.765, BoxCenterLoss=14.425, BoxScaleLoss=5.079, ClassLoss=10.200 [Epoch 158][Batch 999], LR: 1.00E-03, Speed: 10.329 samples/sec, ObjLoss=24.764, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.200 [Epoch 158][Batch 1099], LR: 1.00E-03, Speed: 8.497 samples/sec, ObjLoss=24.764, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.199 [Epoch 158][Batch 1199], LR: 1.00E-03, Speed: 120.523 samples/sec, ObjLoss=24.763, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.199 [Epoch 158][Batch 1299], LR: 1.00E-03, Speed: 10.973 samples/sec, ObjLoss=24.762, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.198 [Epoch 158][Batch 1399], LR: 1.00E-03, Speed: 9.392 samples/sec, ObjLoss=24.761, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.197 [Epoch 158][Batch 1499], LR: 1.00E-03, Speed: 10.906 samples/sec, ObjLoss=24.761, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.197 [Epoch 158][Batch 1599], LR: 1.00E-03, Speed: 9.595 samples/sec, ObjLoss=24.760, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.196 [Epoch 158][Batch 1699], LR: 1.00E-03, Speed: 7.913 samples/sec, ObjLoss=24.760, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.196 [Epoch 158][Batch 1799], LR: 1.00E-03, Speed: 135.339 samples/sec, ObjLoss=24.759, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.195 [Epoch 158] Training cost: 2151.112, ObjLoss=24.759, BoxCenterLoss=14.425, BoxScaleLoss=5.078, ClassLoss=10.195 [Epoch 158] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485 person=37.3 bicycle=18.6 car=27.7 motorcycle=28.4 airplane=39.6 bus=51.2 train=48.1 truck=23.5 boat=14.4 traffic light=14.4 fire hydrant=43.1 stop sign=41.8 parking meter=31.2 bench=14.3 bird=21.4 cat=48.2 dog=40.7 horse=39.4 sheep=34.9 cow=39.2 elephant=46.3 bear=47.3 zebra=48.6 giraffe=50.6 backpack=6.4 umbrella=22.8 handbag=6.1 tie=17.6 suitcase=21.1 frisbee=35.5 skis=11.2 snowboard=17.4 sports ball=24.8 kite=22.0 baseball bat=11.8 baseball glove=21.0 skateboard=26.7 surfboard=20.8 tennis racket=27.2 bottle=21.9 wine glass=20.6 cup=25.0 fork=17.7 knife=4.4 spoon=5.2 bowl=23.2 banana=14.1 apple=7.5 sandwich=19.8 orange=18.7 broccoli=12.8 carrot=9.7 hot dog=18.9 pizza=36.3 donut=26.4 cake=22.2 chair=16.4 couch=29.2 potted plant=13.7 bed=33.1 dining table=19.3 toilet=40.3 tv=35.7 laptop=40.0 mouse=38.0 remote=12.0 keyboard=33.8 cell phone=18.3 microwave=34.6 oven=21.3 toaster=1.2 sink=22.3 refrigerator=30.2 book=5.0 clock=33.7 vase=23.5 scissors=23.1 teddy bear=29.7 hair drier=0.0 toothbrush=5.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.1 [Epoch 159][Batch 99], LR: 1.00E-03, Speed: 8.245 samples/sec, ObjLoss=24.758, BoxCenterLoss=14.425, BoxScaleLoss=5.077, ClassLoss=10.194 [Epoch 159][Batch 199], LR: 1.00E-03, Speed: 109.298 samples/sec, ObjLoss=24.758, BoxCenterLoss=14.425, BoxScaleLoss=5.077, ClassLoss=10.194 [Epoch 159][Batch 299], LR: 1.00E-03, Speed: 8.761 samples/sec, ObjLoss=24.757, BoxCenterLoss=14.425, BoxScaleLoss=5.077, ClassLoss=10.193 [Epoch 159][Batch 399], LR: 1.00E-03, Speed: 9.616 samples/sec, ObjLoss=24.756, BoxCenterLoss=14.424, BoxScaleLoss=5.077, ClassLoss=10.192 [Epoch 159][Batch 499], LR: 1.00E-03, Speed: 8.711 samples/sec, ObjLoss=24.756, BoxCenterLoss=14.424, BoxScaleLoss=5.077, ClassLoss=10.192 [Epoch 159][Batch 599], LR: 1.00E-03, Speed: 9.048 samples/sec, ObjLoss=24.755, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.191 [Epoch 159][Batch 699], LR: 1.00E-03, Speed: 12.142 samples/sec, ObjLoss=24.754, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.191 [Epoch 159][Batch 799], LR: 1.00E-03, Speed: 9.912 samples/sec, ObjLoss=24.753, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.190 [Epoch 159][Batch 899], LR: 1.00E-03, Speed: 88.297 samples/sec, ObjLoss=24.752, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.189 [Epoch 159][Batch 999], LR: 1.00E-03, Speed: 62.825 samples/sec, ObjLoss=24.752, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.189 [Epoch 159][Batch 1099], LR: 1.00E-03, Speed: 10.491 samples/sec, ObjLoss=24.751, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.188 [Epoch 159][Batch 1199], LR: 1.00E-03, Speed: 9.140 samples/sec, ObjLoss=24.750, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.188 [Epoch 159][Batch 1299], LR: 1.00E-03, Speed: 87.887 samples/sec, ObjLoss=24.750, BoxCenterLoss=14.424, BoxScaleLoss=5.076, ClassLoss=10.187 [Epoch 159][Batch 1399], LR: 1.00E-03, Speed: 8.448 samples/sec, ObjLoss=24.749, BoxCenterLoss=14.424, BoxScaleLoss=5.075, ClassLoss=10.187 [Epoch 159][Batch 1499], LR: 1.00E-03, Speed: 12.787 samples/sec, ObjLoss=24.748, BoxCenterLoss=14.424, BoxScaleLoss=5.075, ClassLoss=10.186 [Epoch 159][Batch 1599], LR: 1.00E-03, Speed: 9.535 samples/sec, ObjLoss=24.748, BoxCenterLoss=14.424, BoxScaleLoss=5.075, ClassLoss=10.186 [Epoch 159][Batch 1699], LR: 1.00E-03, Speed: 8.032 samples/sec, ObjLoss=24.747, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.185 [Epoch 159][Batch 1799], LR: 1.00E-03, Speed: 11.315 samples/sec, ObjLoss=24.746, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.184 [Epoch 159] Training cost: 2211.792, ObjLoss=24.746, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.184 [Epoch 159] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.107 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480 person=39.9 bicycle=20.0 car=26.7 motorcycle=31.7 airplane=45.6 bus=49.4 train=54.6 truck=23.5 boat=14.6 traffic light=13.9 fire hydrant=44.4 stop sign=43.4 parking meter=26.8 bench=15.3 bird=18.7 cat=42.3 dog=40.1 horse=40.6 sheep=32.6 cow=38.1 elephant=46.3 bear=47.7 zebra=47.0 giraffe=48.0 backpack=6.2 umbrella=24.4 handbag=6.4 tie=19.9 suitcase=20.0 frisbee=37.2 skis=12.2 snowboard=19.1 sports ball=25.6 kite=26.5 baseball bat=14.6 baseball glove=21.7 skateboard=32.4 surfboard=22.6 tennis racket=30.8 bottle=20.4 wine glass=22.4 cup=28.1 fork=17.7 knife=6.6 spoon=5.6 bowl=27.3 banana=12.7 apple=8.6 sandwich=18.1 orange=17.4 broccoli=11.3 carrot=11.9 hot dog=20.1 pizza=37.2 donut=25.3 cake=22.6 chair=16.0 couch=28.5 potted plant=15.0 bed=32.8 dining table=20.6 toilet=40.5 tv=36.4 laptop=40.0 mouse=35.2 remote=11.4 keyboard=31.9 cell phone=19.5 microwave=30.6 oven=22.2 toaster=7.1 sink=18.9 refrigerator=35.4 book=5.1 clock=31.9 vase=21.7 scissors=17.2 teddy bear=26.7 hair drier=0.0 toothbrush=7.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.5 [Epoch 160][Batch 99], LR: 1.00E-03, Speed: 130.438 samples/sec, ObjLoss=24.745, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.184 [Epoch 160][Batch 199], LR: 1.00E-03, Speed: 9.508 samples/sec, ObjLoss=24.744, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.183 [Epoch 160][Batch 299], LR: 1.00E-03, Speed: 91.689 samples/sec, ObjLoss=24.744, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.183 [Epoch 160][Batch 399], LR: 1.00E-03, Speed: 10.375 samples/sec, ObjLoss=24.743, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.182 [Epoch 160][Batch 499], LR: 1.00E-03, Speed: 49.835 samples/sec, ObjLoss=24.743, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.182 [Epoch 160][Batch 599], LR: 1.00E-03, Speed: 7.570 samples/sec, ObjLoss=24.742, BoxCenterLoss=14.423, BoxScaleLoss=5.074, ClassLoss=10.181 [Epoch 160][Batch 699], LR: 1.00E-03, Speed: 118.481 samples/sec, ObjLoss=24.741, BoxCenterLoss=14.423, BoxScaleLoss=5.075, ClassLoss=10.181 [Epoch 160][Batch 799], LR: 1.00E-03, Speed: 8.751 samples/sec, ObjLoss=24.741, BoxCenterLoss=14.423, BoxScaleLoss=5.074, ClassLoss=10.180 [Epoch 160][Batch 899], LR: 1.00E-03, Speed: 11.968 samples/sec, ObjLoss=24.741, BoxCenterLoss=14.424, BoxScaleLoss=5.075, ClassLoss=10.180 [Epoch 160][Batch 999], LR: 1.00E-03, Speed: 7.540 samples/sec, ObjLoss=24.741, BoxCenterLoss=14.424, BoxScaleLoss=5.075, ClassLoss=10.180 [Epoch 160][Batch 1099], LR: 1.00E-03, Speed: 8.775 samples/sec, ObjLoss=24.740, BoxCenterLoss=14.424, BoxScaleLoss=5.074, ClassLoss=10.179 [Epoch 160][Batch 1199], LR: 1.00E-03, Speed: 10.582 samples/sec, ObjLoss=24.740, BoxCenterLoss=14.424, BoxScaleLoss=5.074, ClassLoss=10.178 [Epoch 160][Batch 1299], LR: 1.00E-03, Speed: 122.497 samples/sec, ObjLoss=24.739, BoxCenterLoss=14.424, BoxScaleLoss=5.074, ClassLoss=10.178 [Epoch 160][Batch 1399], LR: 1.00E-03, Speed: 9.260 samples/sec, ObjLoss=24.738, BoxCenterLoss=14.423, BoxScaleLoss=5.074, ClassLoss=10.177 [Epoch 160][Batch 1499], LR: 1.00E-03, Speed: 7.852 samples/sec, ObjLoss=24.737, BoxCenterLoss=14.423, BoxScaleLoss=5.074, ClassLoss=10.176 [Epoch 160][Batch 1599], LR: 1.00E-03, Speed: 115.581 samples/sec, ObjLoss=24.737, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.176 [Epoch 160][Batch 1699], LR: 1.00E-03, Speed: 109.206 samples/sec, ObjLoss=24.736, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.175 [Epoch 160][Batch 1799], LR: 1.00E-03, Speed: 8.163 samples/sec, ObjLoss=24.735, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.174 [Epoch 160] Training cost: 2169.362, ObjLoss=24.735, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.174 [Epoch 160] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.211 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.449 person=34.7 bicycle=17.9 car=21.8 motorcycle=28.2 airplane=37.3 bus=42.0 train=49.6 truck=21.2 boat=11.9 traffic light=13.9 fire hydrant=39.9 stop sign=36.1 parking meter=29.2 bench=12.3 bird=21.6 cat=45.1 dog=35.3 horse=32.6 sheep=33.1 cow=27.9 elephant=43.3 bear=43.3 zebra=44.2 giraffe=43.3 backpack=5.8 umbrella=20.8 handbag=5.2 tie=15.6 suitcase=17.4 frisbee=27.6 skis=8.4 snowboard=18.5 sports ball=25.8 kite=24.3 baseball bat=9.4 baseball glove=20.0 skateboard=26.1 surfboard=16.5 tennis racket=26.8 bottle=20.0 wine glass=21.6 cup=25.8 fork=13.5 knife=4.1 spoon=4.9 bowl=22.9 banana=14.0 apple=5.2 sandwich=22.0 orange=15.1 broccoli=11.1 carrot=9.7 hot dog=15.1 pizza=34.9 donut=31.5 cake=20.6 chair=15.5 couch=24.3 potted plant=13.9 bed=28.4 dining table=14.8 toilet=36.7 tv=36.4 laptop=40.0 mouse=31.6 remote=11.8 keyboard=24.8 cell phone=16.6 microwave=33.0 oven=17.4 toaster=3.0 sink=21.1 refrigerator=26.5 book=3.2 clock=32.5 vase=21.3 scissors=22.0 teddy bear=30.1 hair drier=0.0 toothbrush=2.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.9 [Epoch 161][Batch 99], LR: 1.00E-03, Speed: 8.383 samples/sec, ObjLoss=24.734, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.174 [Epoch 161][Batch 199], LR: 1.00E-03, Speed: 7.799 samples/sec, ObjLoss=24.733, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.173 [Epoch 161][Batch 299], LR: 1.00E-03, Speed: 93.202 samples/sec, ObjLoss=24.733, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.172 [Epoch 161][Batch 399], LR: 1.00E-03, Speed: 8.088 samples/sec, ObjLoss=24.733, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.172 [Epoch 161][Batch 499], LR: 1.00E-03, Speed: 8.279 samples/sec, ObjLoss=24.732, BoxCenterLoss=14.423, BoxScaleLoss=5.073, ClassLoss=10.171 [Epoch 161][Batch 599], LR: 1.00E-03, Speed: 7.939 samples/sec, ObjLoss=24.731, BoxCenterLoss=14.423, BoxScaleLoss=5.072, ClassLoss=10.171 [Epoch 161][Batch 699], LR: 1.00E-03, Speed: 10.710 samples/sec, ObjLoss=24.731, BoxCenterLoss=14.423, BoxScaleLoss=5.072, ClassLoss=10.170 [Epoch 161][Batch 799], LR: 1.00E-03, Speed: 10.013 samples/sec, ObjLoss=24.730, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.170 [Epoch 161][Batch 899], LR: 1.00E-03, Speed: 9.423 samples/sec, ObjLoss=24.729, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.169 [Epoch 161][Batch 999], LR: 1.00E-03, Speed: 9.929 samples/sec, ObjLoss=24.728, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.169 [Epoch 161][Batch 1099], LR: 1.00E-03, Speed: 8.724 samples/sec, ObjLoss=24.727, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.168 [Epoch 161][Batch 1199], LR: 1.00E-03, Speed: 9.972 samples/sec, ObjLoss=24.727, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.168 [Epoch 161][Batch 1299], LR: 1.00E-03, Speed: 10.149 samples/sec, ObjLoss=24.726, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.167 [Epoch 161][Batch 1399], LR: 1.00E-03, Speed: 8.368 samples/sec, ObjLoss=24.726, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.167 [Epoch 161][Batch 1499], LR: 1.00E-03, Speed: 114.551 samples/sec, ObjLoss=24.725, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.166 [Epoch 161][Batch 1599], LR: 1.00E-03, Speed: 9.791 samples/sec, ObjLoss=24.724, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.166 [Epoch 161][Batch 1699], LR: 1.00E-03, Speed: 9.760 samples/sec, ObjLoss=24.724, BoxCenterLoss=14.422, BoxScaleLoss=5.072, ClassLoss=10.165 [Epoch 161][Batch 1799], LR: 1.00E-03, Speed: 10.908 samples/sec, ObjLoss=24.723, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.164 [Epoch 161] Training cost: 2139.485, ObjLoss=24.723, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.164 [Epoch 161] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467 person=37.7 bicycle=17.5 car=26.8 motorcycle=30.9 airplane=31.7 bus=51.6 train=51.3 truck=22.0 boat=14.1 traffic light=11.8 fire hydrant=45.0 stop sign=40.1 parking meter=28.5 bench=15.1 bird=19.9 cat=45.0 dog=42.8 horse=36.4 sheep=33.4 cow=36.4 elephant=47.1 bear=54.0 zebra=44.8 giraffe=49.6 backpack=5.5 umbrella=22.2 handbag=5.7 tie=17.8 suitcase=17.2 frisbee=40.4 skis=10.3 snowboard=15.0 sports ball=26.5 kite=26.7 baseball bat=13.2 baseball glove=20.8 skateboard=30.8 surfboard=18.0 tennis racket=26.9 bottle=17.2 wine glass=20.3 cup=24.7 fork=16.3 knife=6.3 spoon=5.6 bowl=26.2 banana=11.1 apple=7.4 sandwich=18.2 orange=18.0 broccoli=12.5 carrot=10.2 hot dog=16.8 pizza=28.7 donut=28.7 cake=21.8 chair=15.0 couch=30.4 potted plant=13.3 bed=33.0 dining table=20.1 toilet=40.6 tv=36.6 laptop=35.0 mouse=39.6 remote=10.8 keyboard=27.6 cell phone=18.0 microwave=34.6 oven=20.0 toaster=0.0 sink=21.6 refrigerator=32.4 book=4.8 clock=32.8 vase=21.7 scissors=14.5 teddy bear=29.1 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.4 [Epoch 162][Batch 99], LR: 1.00E-03, Speed: 11.268 samples/sec, ObjLoss=24.722, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.164 [Epoch 162][Batch 199], LR: 1.00E-03, Speed: 9.397 samples/sec, ObjLoss=24.721, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.163 [Epoch 162][Batch 299], LR: 1.00E-03, Speed: 106.597 samples/sec, ObjLoss=24.720, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.162 [Epoch 162][Batch 399], LR: 1.00E-03, Speed: 8.115 samples/sec, ObjLoss=24.720, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.162 [Epoch 162][Batch 499], LR: 1.00E-03, Speed: 7.919 samples/sec, ObjLoss=24.719, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.161 [Epoch 162][Batch 599], LR: 1.00E-03, Speed: 101.073 samples/sec, ObjLoss=24.719, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.161 [Epoch 162][Batch 699], LR: 1.00E-03, Speed: 7.112 samples/sec, ObjLoss=24.719, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.161 [Epoch 162][Batch 799], LR: 1.00E-03, Speed: 10.221 samples/sec, ObjLoss=24.718, BoxCenterLoss=14.422, BoxScaleLoss=5.071, ClassLoss=10.160 [Epoch 162][Batch 899], LR: 1.00E-03, Speed: 8.640 samples/sec, ObjLoss=24.717, BoxCenterLoss=14.422, BoxScaleLoss=5.070, ClassLoss=10.159 [Epoch 162][Batch 999], LR: 1.00E-03, Speed: 9.262 samples/sec, ObjLoss=24.716, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.159 [Epoch 162][Batch 1099], LR: 1.00E-03, Speed: 11.166 samples/sec, ObjLoss=24.716, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.158 [Epoch 162][Batch 1199], LR: 1.00E-03, Speed: 11.707 samples/sec, ObjLoss=24.715, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.158 [Epoch 162][Batch 1299], LR: 1.00E-03, Speed: 8.664 samples/sec, ObjLoss=24.714, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.157 [Epoch 162][Batch 1399], LR: 1.00E-03, Speed: 9.206 samples/sec, ObjLoss=24.714, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.157 [Epoch 162][Batch 1499], LR: 1.00E-03, Speed: 7.450 samples/sec, ObjLoss=24.714, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.156 [Epoch 162][Batch 1599], LR: 1.00E-03, Speed: 9.213 samples/sec, ObjLoss=24.713, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.155 [Epoch 162][Batch 1699], LR: 1.00E-03, Speed: 10.886 samples/sec, ObjLoss=24.712, BoxCenterLoss=14.421, BoxScaleLoss=5.070, ClassLoss=10.155 [Epoch 162][Batch 1799], LR: 1.00E-03, Speed: 12.151 samples/sec, ObjLoss=24.712, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.154 [Epoch 162] Training cost: 2188.269, ObjLoss=24.712, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.154 [Epoch 162] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.223 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471 person=37.7 bicycle=16.2 car=25.1 motorcycle=26.6 airplane=37.8 bus=43.6 train=45.6 truck=22.9 boat=15.0 traffic light=15.8 fire hydrant=42.7 stop sign=46.0 parking meter=27.0 bench=14.0 bird=18.4 cat=42.8 dog=36.1 horse=32.5 sheep=32.4 cow=29.7 elephant=46.5 bear=50.0 zebra=47.6 giraffe=47.0 backpack=5.3 umbrella=23.1 handbag=3.9 tie=15.7 suitcase=18.7 frisbee=28.2 skis=12.0 snowboard=18.6 sports ball=23.9 kite=23.3 baseball bat=14.3 baseball glove=12.8 skateboard=29.2 surfboard=20.7 tennis racket=26.3 bottle=18.1 wine glass=19.1 cup=21.1 fork=14.6 knife=5.5 spoon=5.7 bowl=25.3 banana=12.6 apple=8.4 sandwich=21.0 orange=17.0 broccoli=9.0 carrot=7.8 hot dog=19.2 pizza=39.0 donut=29.8 cake=21.3 chair=16.3 couch=32.9 potted plant=13.3 bed=32.9 dining table=17.3 toilet=42.5 tv=40.8 laptop=37.2 mouse=34.2 remote=12.2 keyboard=31.7 cell phone=14.9 microwave=30.9 oven=19.1 toaster=0.0 sink=21.6 refrigerator=36.1 book=4.2 clock=30.8 vase=22.2 scissors=16.5 teddy bear=28.0 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.9 [Epoch 163][Batch 99], LR: 1.00E-03, Speed: 8.575 samples/sec, ObjLoss=24.711, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.154 [Epoch 163][Batch 199], LR: 1.00E-03, Speed: 6.993 samples/sec, ObjLoss=24.710, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.153 [Epoch 163][Batch 299], LR: 1.00E-03, Speed: 9.191 samples/sec, ObjLoss=24.710, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.153 [Epoch 163][Batch 399], LR: 1.00E-03, Speed: 9.358 samples/sec, ObjLoss=24.709, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.153 [Epoch 163][Batch 499], LR: 1.00E-03, Speed: 9.125 samples/sec, ObjLoss=24.708, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.152 [Epoch 163][Batch 599], LR: 1.00E-03, Speed: 113.419 samples/sec, ObjLoss=24.707, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.152 [Epoch 163][Batch 699], LR: 1.00E-03, Speed: 7.069 samples/sec, ObjLoss=24.707, BoxCenterLoss=14.421, BoxScaleLoss=5.069, ClassLoss=10.151 [Epoch 163][Batch 799], LR: 1.00E-03, Speed: 12.659 samples/sec, ObjLoss=24.706, BoxCenterLoss=14.420, BoxScaleLoss=5.069, ClassLoss=10.150 [Epoch 163][Batch 899], LR: 1.00E-03, Speed: 9.645 samples/sec, ObjLoss=24.705, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.150 [Epoch 163][Batch 999], LR: 1.00E-03, Speed: 10.277 samples/sec, ObjLoss=24.705, BoxCenterLoss=14.421, BoxScaleLoss=5.068, ClassLoss=10.149 [Epoch 163][Batch 1099], LR: 1.00E-03, Speed: 125.342 samples/sec, ObjLoss=24.704, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.149 [Epoch 163][Batch 1199], LR: 1.00E-03, Speed: 10.966 samples/sec, ObjLoss=24.704, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.148 [Epoch 163][Batch 1299], LR: 1.00E-03, Speed: 111.735 samples/sec, ObjLoss=24.703, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.148 [Epoch 163][Batch 1399], LR: 1.00E-03, Speed: 10.743 samples/sec, ObjLoss=24.702, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.147 [Epoch 163][Batch 1499], LR: 1.00E-03, Speed: 8.523 samples/sec, ObjLoss=24.701, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.146 [Epoch 163][Batch 1599], LR: 1.00E-03, Speed: 11.622 samples/sec, ObjLoss=24.700, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.146 [Epoch 163][Batch 1699], LR: 1.00E-03, Speed: 11.469 samples/sec, ObjLoss=24.699, BoxCenterLoss=14.420, BoxScaleLoss=5.068, ClassLoss=10.145 [Epoch 163][Batch 1799], LR: 1.00E-03, Speed: 11.341 samples/sec, ObjLoss=24.698, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.145 [Epoch 163] Training cost: 2112.246, ObjLoss=24.698, BoxCenterLoss=14.419, BoxScaleLoss=5.067, ClassLoss=10.145 [Epoch 163] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455 person=38.5 bicycle=18.4 car=27.5 motorcycle=27.2 airplane=43.3 bus=41.5 train=44.7 truck=23.8 boat=16.0 traffic light=13.5 fire hydrant=46.3 stop sign=40.4 parking meter=26.6 bench=13.3 bird=19.4 cat=46.0 dog=37.1 horse=37.9 sheep=34.9 cow=34.7 elephant=43.4 bear=43.1 zebra=45.5 giraffe=40.8 backpack=4.9 umbrella=23.8 handbag=4.8 tie=15.3 suitcase=19.8 frisbee=35.7 skis=11.2 snowboard=12.9 sports ball=27.9 kite=23.9 baseball bat=16.0 baseball glove=17.3 skateboard=28.9 surfboard=20.7 tennis racket=23.8 bottle=21.4 wine glass=18.4 cup=25.6 fork=14.1 knife=5.2 spoon=5.4 bowl=24.1 banana=11.6 apple=7.6 sandwich=19.2 orange=14.8 broccoli=10.5 carrot=10.5 hot dog=17.8 pizza=31.3 donut=26.5 cake=23.0 chair=15.2 couch=25.4 potted plant=14.7 bed=31.0 dining table=20.1 toilet=36.0 tv=37.0 laptop=38.0 mouse=38.5 remote=9.5 keyboard=29.5 cell phone=14.8 microwave=27.6 oven=20.1 toaster=0.0 sink=22.8 refrigerator=34.9 book=4.8 clock=31.7 vase=20.8 scissors=14.7 teddy bear=25.8 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.7 [Epoch 164][Batch 99], LR: 1.00E-03, Speed: 10.334 samples/sec, ObjLoss=24.698, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.144 [Epoch 164][Batch 199], LR: 1.00E-03, Speed: 10.062 samples/sec, ObjLoss=24.697, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.144 [Epoch 164][Batch 299], LR: 1.00E-03, Speed: 90.508 samples/sec, ObjLoss=24.697, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.143 [Epoch 164][Batch 399], LR: 1.00E-03, Speed: 11.225 samples/sec, ObjLoss=24.697, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.143 [Epoch 164][Batch 499], LR: 1.00E-03, Speed: 87.414 samples/sec, ObjLoss=24.696, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.142 [Epoch 164][Batch 599], LR: 1.00E-03, Speed: 7.901 samples/sec, ObjLoss=24.696, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.142 [Epoch 164][Batch 699], LR: 1.00E-03, Speed: 7.699 samples/sec, ObjLoss=24.695, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.141 [Epoch 164][Batch 799], LR: 1.00E-03, Speed: 10.445 samples/sec, ObjLoss=24.694, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.141 [Epoch 164][Batch 899], LR: 1.00E-03, Speed: 9.036 samples/sec, ObjLoss=24.693, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.140 [Epoch 164][Batch 999], LR: 1.00E-03, Speed: 8.182 samples/sec, ObjLoss=24.693, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.140 [Epoch 164][Batch 1099], LR: 1.00E-03, Speed: 8.855 samples/sec, ObjLoss=24.692, BoxCenterLoss=14.420, BoxScaleLoss=5.067, ClassLoss=10.139 [Epoch 164][Batch 1199], LR: 1.00E-03, Speed: 10.812 samples/sec, ObjLoss=24.692, BoxCenterLoss=14.420, BoxScaleLoss=5.066, ClassLoss=10.139 [Epoch 164][Batch 1299], LR: 1.00E-03, Speed: 109.928 samples/sec, ObjLoss=24.691, BoxCenterLoss=14.420, BoxScaleLoss=5.066, ClassLoss=10.138 [Epoch 164][Batch 1399], LR: 1.00E-03, Speed: 10.351 samples/sec, ObjLoss=24.690, BoxCenterLoss=14.420, BoxScaleLoss=5.066, ClassLoss=10.138 [Epoch 164][Batch 1499], LR: 1.00E-03, Speed: 8.804 samples/sec, ObjLoss=24.690, BoxCenterLoss=14.420, BoxScaleLoss=5.066, ClassLoss=10.137 [Epoch 164][Batch 1599], LR: 1.00E-03, Speed: 7.746 samples/sec, ObjLoss=24.689, BoxCenterLoss=14.420, BoxScaleLoss=5.066, ClassLoss=10.137 [Epoch 164][Batch 1699], LR: 1.00E-03, Speed: 107.527 samples/sec, ObjLoss=24.688, BoxCenterLoss=14.420, BoxScaleLoss=5.066, ClassLoss=10.136 [Epoch 164][Batch 1799], LR: 1.00E-03, Speed: 12.042 samples/sec, ObjLoss=24.687, BoxCenterLoss=14.419, BoxScaleLoss=5.066, ClassLoss=10.135 [Epoch 164] Training cost: 2166.596, ObjLoss=24.687, BoxCenterLoss=14.419, BoxScaleLoss=5.066, ClassLoss=10.135 [Epoch 164] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461 person=38.2 bicycle=17.7 car=26.2 motorcycle=25.6 airplane=39.1 bus=40.4 train=44.5 truck=20.5 boat=11.8 traffic light=14.4 fire hydrant=46.0 stop sign=39.2 parking meter=27.9 bench=13.7 bird=19.5 cat=44.9 dog=40.9 horse=39.5 sheep=30.9 cow=37.3 elephant=43.7 bear=41.2 zebra=46.0 giraffe=48.6 backpack=5.6 umbrella=25.0 handbag=6.4 tie=17.3 suitcase=17.0 frisbee=32.8 skis=10.7 snowboard=16.6 sports ball=27.4 kite=25.4 baseball bat=13.0 baseball glove=18.5 skateboard=30.2 surfboard=22.4 tennis racket=24.3 bottle=22.3 wine glass=21.5 cup=26.1 fork=14.0 knife=5.2 spoon=4.6 bowl=20.0 banana=12.6 apple=10.6 sandwich=17.6 orange=17.6 broccoli=7.7 carrot=9.9 hot dog=19.8 pizza=32.9 donut=25.4 cake=19.5 chair=16.8 couch=26.5 potted plant=13.8 bed=33.9 dining table=22.0 toilet=37.1 tv=38.4 laptop=36.3 mouse=28.9 remote=10.4 keyboard=27.3 cell phone=16.8 microwave=28.2 oven=15.5 toaster=8.3 sink=21.3 refrigerator=31.7 book=5.6 clock=28.7 vase=21.1 scissors=13.3 teddy bear=30.4 hair drier=0.0 toothbrush=3.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.7 [Epoch 165][Batch 99], LR: 1.00E-03, Speed: 9.233 samples/sec, ObjLoss=24.686, BoxCenterLoss=14.419, BoxScaleLoss=5.066, ClassLoss=10.135 [Epoch 165][Batch 199], LR: 1.00E-03, Speed: 11.881 samples/sec, ObjLoss=24.686, BoxCenterLoss=14.419, BoxScaleLoss=5.066, ClassLoss=10.134 [Epoch 165][Batch 299], LR: 1.00E-03, Speed: 12.443 samples/sec, ObjLoss=24.685, BoxCenterLoss=14.419, BoxScaleLoss=5.066, ClassLoss=10.134 [Epoch 165][Batch 399], LR: 1.00E-03, Speed: 8.077 samples/sec, ObjLoss=24.684, BoxCenterLoss=14.419, BoxScaleLoss=5.065, ClassLoss=10.133 [Epoch 165][Batch 499], LR: 1.00E-03, Speed: 9.106 samples/sec, ObjLoss=24.684, BoxCenterLoss=14.419, BoxScaleLoss=5.065, ClassLoss=10.133 [Epoch 165][Batch 599], LR: 1.00E-03, Speed: 111.659 samples/sec, ObjLoss=24.683, BoxCenterLoss=14.419, BoxScaleLoss=5.065, ClassLoss=10.132 [Epoch 165][Batch 699], LR: 1.00E-03, Speed: 10.606 samples/sec, ObjLoss=24.682, BoxCenterLoss=14.419, BoxScaleLoss=5.065, ClassLoss=10.132 [Epoch 165][Batch 799], LR: 1.00E-03, Speed: 8.058 samples/sec, ObjLoss=24.681, BoxCenterLoss=14.419, BoxScaleLoss=5.065, ClassLoss=10.131 [Epoch 165][Batch 899], LR: 1.00E-03, Speed: 11.419 samples/sec, ObjLoss=24.680, BoxCenterLoss=14.419, BoxScaleLoss=5.065, ClassLoss=10.130 [Epoch 165][Batch 999], LR: 1.00E-03, Speed: 8.303 samples/sec, ObjLoss=24.680, BoxCenterLoss=14.418, BoxScaleLoss=5.065, ClassLoss=10.130 [Epoch 165][Batch 1099], LR: 1.00E-03, Speed: 10.468 samples/sec, ObjLoss=24.679, BoxCenterLoss=14.418, BoxScaleLoss=5.064, ClassLoss=10.129 [Epoch 165][Batch 1199], LR: 1.00E-03, Speed: 114.141 samples/sec, ObjLoss=24.678, BoxCenterLoss=14.418, BoxScaleLoss=5.064, ClassLoss=10.129 [Epoch 165][Batch 1299], LR: 1.00E-03, Speed: 9.494 samples/sec, ObjLoss=24.678, BoxCenterLoss=14.418, BoxScaleLoss=5.064, ClassLoss=10.128 [Epoch 165][Batch 1399], LR: 1.00E-03, Speed: 8.453 samples/sec, ObjLoss=24.677, BoxCenterLoss=14.418, BoxScaleLoss=5.064, ClassLoss=10.128 [Epoch 165][Batch 1499], LR: 1.00E-03, Speed: 9.281 samples/sec, ObjLoss=24.676, BoxCenterLoss=14.418, BoxScaleLoss=5.064, ClassLoss=10.127 [Epoch 165][Batch 1599], LR: 1.00E-03, Speed: 13.034 samples/sec, ObjLoss=24.676, BoxCenterLoss=14.418, BoxScaleLoss=5.064, ClassLoss=10.127 [Epoch 165][Batch 1699], LR: 1.00E-03, Speed: 10.390 samples/sec, ObjLoss=24.674, BoxCenterLoss=14.417, BoxScaleLoss=5.064, ClassLoss=10.126 [Epoch 165][Batch 1799], LR: 1.00E-03, Speed: 10.110 samples/sec, ObjLoss=24.673, BoxCenterLoss=14.417, BoxScaleLoss=5.063, ClassLoss=10.125 [Epoch 165] Training cost: 2149.253, ObjLoss=24.673, BoxCenterLoss=14.417, BoxScaleLoss=5.063, ClassLoss=10.125 [Epoch 165] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.269 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.229 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493 person=38.9 bicycle=19.5 car=28.7 motorcycle=29.3 airplane=43.3 bus=49.7 train=49.2 truck=23.0 boat=12.9 traffic light=12.8 fire hydrant=42.5 stop sign=40.4 parking meter=32.2 bench=12.3 bird=21.1 cat=44.3 dog=38.9 horse=39.3 sheep=35.9 cow=35.0 elephant=49.9 bear=49.5 zebra=51.6 giraffe=46.9 backpack=7.2 umbrella=22.5 handbag=5.7 tie=17.7 suitcase=19.3 frisbee=40.7 skis=9.5 snowboard=15.9 sports ball=27.1 kite=26.5 baseball bat=15.7 baseball glove=20.8 skateboard=31.2 surfboard=21.8 tennis racket=31.9 bottle=19.3 wine glass=20.5 cup=24.2 fork=12.2 knife=5.8 spoon=5.2 bowl=24.0 banana=13.8 apple=9.2 sandwich=23.3 orange=16.1 broccoli=11.6 carrot=11.9 hot dog=19.6 pizza=36.9 donut=24.4 cake=23.2 chair=18.1 couch=32.3 potted plant=13.9 bed=35.3 dining table=20.1 toilet=42.9 tv=38.6 laptop=36.3 mouse=39.5 remote=14.0 keyboard=30.1 cell phone=18.8 microwave=32.9 oven=22.3 toaster=0.0 sink=25.3 refrigerator=33.6 book=5.6 clock=34.7 vase=20.4 scissors=17.3 teddy bear=30.9 hair drier=0.0 toothbrush=7.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.4 [Epoch 166][Batch 99], LR: 1.00E-03, Speed: 8.954 samples/sec, ObjLoss=24.673, BoxCenterLoss=14.418, BoxScaleLoss=5.063, ClassLoss=10.125 [Epoch 166][Batch 199], LR: 1.00E-03, Speed: 9.679 samples/sec, ObjLoss=24.673, BoxCenterLoss=14.418, BoxScaleLoss=5.063, ClassLoss=10.124 [Epoch 166][Batch 299], LR: 1.00E-03, Speed: 9.124 samples/sec, ObjLoss=24.672, BoxCenterLoss=14.418, BoxScaleLoss=5.063, ClassLoss=10.124 [Epoch 166][Batch 399], LR: 1.00E-03, Speed: 9.882 samples/sec, ObjLoss=24.672, BoxCenterLoss=14.417, BoxScaleLoss=5.063, ClassLoss=10.123 [Epoch 166][Batch 499], LR: 1.00E-03, Speed: 8.781 samples/sec, ObjLoss=24.671, BoxCenterLoss=14.417, BoxScaleLoss=5.063, ClassLoss=10.122 [Epoch 166][Batch 599], LR: 1.00E-03, Speed: 8.806 samples/sec, ObjLoss=24.670, BoxCenterLoss=14.417, BoxScaleLoss=5.063, ClassLoss=10.122 [Epoch 166][Batch 699], LR: 1.00E-03, Speed: 9.046 samples/sec, ObjLoss=24.669, BoxCenterLoss=14.417, BoxScaleLoss=5.063, ClassLoss=10.121 [Epoch 166][Batch 799], LR: 1.00E-03, Speed: 107.753 samples/sec, ObjLoss=24.668, BoxCenterLoss=14.417, BoxScaleLoss=5.062, ClassLoss=10.121 [Epoch 166][Batch 899], LR: 1.00E-03, Speed: 7.678 samples/sec, ObjLoss=24.668, BoxCenterLoss=14.417, BoxScaleLoss=5.062, ClassLoss=10.120 [Epoch 166][Batch 999], LR: 1.00E-03, Speed: 7.887 samples/sec, ObjLoss=24.667, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.120 [Epoch 166][Batch 1099], LR: 1.00E-03, Speed: 8.488 samples/sec, ObjLoss=24.666, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.119 [Epoch 166][Batch 1199], LR: 1.00E-03, Speed: 9.019 samples/sec, ObjLoss=24.665, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.119 [Epoch 166][Batch 1299], LR: 1.00E-03, Speed: 9.208 samples/sec, ObjLoss=24.665, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.118 [Epoch 166][Batch 1399], LR: 1.00E-03, Speed: 9.498 samples/sec, ObjLoss=24.664, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.118 [Epoch 166][Batch 1499], LR: 1.00E-03, Speed: 9.909 samples/sec, ObjLoss=24.663, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.117 [Epoch 166][Batch 1599], LR: 1.00E-03, Speed: 10.748 samples/sec, ObjLoss=24.662, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.117 [Epoch 166][Batch 1699], LR: 1.00E-03, Speed: 106.556 samples/sec, ObjLoss=24.662, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.116 [Epoch 166][Batch 1799], LR: 1.00E-03, Speed: 13.385 samples/sec, ObjLoss=24.661, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.116 [Epoch 166] Training cost: 2210.982, ObjLoss=24.661, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.116 [Epoch 166] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.217 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.211 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441 person=35.1 bicycle=17.0 car=24.8 motorcycle=26.5 airplane=40.5 bus=39.7 train=45.2 truck=20.6 boat=11.7 traffic light=11.7 fire hydrant=42.2 stop sign=35.2 parking meter=23.1 bench=14.9 bird=21.0 cat=46.8 dog=39.9 horse=36.7 sheep=33.6 cow=32.1 elephant=44.8 bear=41.5 zebra=44.5 giraffe=50.9 backpack=5.6 umbrella=22.7 handbag=5.6 tie=15.7 suitcase=17.3 frisbee=34.5 skis=8.9 snowboard=13.7 sports ball=28.4 kite=25.6 baseball bat=15.9 baseball glove=19.7 skateboard=31.5 surfboard=20.3 tennis racket=27.6 bottle=18.8 wine glass=20.4 cup=21.9 fork=12.4 knife=4.3 spoon=5.0 bowl=22.4 banana=10.9 apple=6.4 sandwich=19.6 orange=14.4 broccoli=12.4 carrot=10.0 hot dog=20.5 pizza=31.0 donut=22.1 cake=19.8 chair=14.2 couch=29.6 potted plant=11.2 bed=31.9 dining table=17.7 toilet=34.9 tv=35.8 laptop=36.7 mouse=37.4 remote=12.1 keyboard=31.5 cell phone=18.0 microwave=31.4 oven=17.8 toaster=0.0 sink=18.5 refrigerator=33.2 book=3.9 clock=35.0 vase=18.9 scissors=15.2 teddy bear=28.3 hair drier=0.0 toothbrush=8.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.3 [Epoch 167][Batch 99], LR: 1.00E-03, Speed: 11.339 samples/sec, ObjLoss=24.661, BoxCenterLoss=14.416, BoxScaleLoss=5.062, ClassLoss=10.115 [Epoch 167][Batch 199], LR: 1.00E-03, Speed: 9.411 samples/sec, ObjLoss=24.659, BoxCenterLoss=14.415, BoxScaleLoss=5.061, ClassLoss=10.115 [Epoch 167][Batch 299], LR: 1.00E-03, Speed: 10.078 samples/sec, ObjLoss=24.659, BoxCenterLoss=14.415, BoxScaleLoss=5.061, ClassLoss=10.114 [Epoch 167][Batch 399], LR: 1.00E-03, Speed: 7.783 samples/sec, ObjLoss=24.658, BoxCenterLoss=14.415, BoxScaleLoss=5.061, ClassLoss=10.113 [Epoch 167][Batch 499], LR: 1.00E-03, Speed: 11.569 samples/sec, ObjLoss=24.657, BoxCenterLoss=14.415, BoxScaleLoss=5.061, ClassLoss=10.113 [Epoch 167][Batch 599], LR: 1.00E-03, Speed: 9.658 samples/sec, ObjLoss=24.657, BoxCenterLoss=14.415, BoxScaleLoss=5.061, ClassLoss=10.112 [Epoch 167][Batch 699], LR: 1.00E-03, Speed: 8.066 samples/sec, ObjLoss=24.656, BoxCenterLoss=14.414, BoxScaleLoss=5.060, ClassLoss=10.112 [Epoch 167][Batch 799], LR: 1.00E-03, Speed: 10.608 samples/sec, ObjLoss=24.655, BoxCenterLoss=14.414, BoxScaleLoss=5.060, ClassLoss=10.111 [Epoch 167][Batch 899], LR: 1.00E-03, Speed: 9.214 samples/sec, ObjLoss=24.655, BoxCenterLoss=14.414, BoxScaleLoss=5.060, ClassLoss=10.111 [Epoch 167][Batch 999], LR: 1.00E-03, Speed: 8.988 samples/sec, ObjLoss=24.654, BoxCenterLoss=14.414, BoxScaleLoss=5.060, ClassLoss=10.110 [Epoch 167][Batch 1099], LR: 1.00E-03, Speed: 9.990 samples/sec, ObjLoss=24.653, BoxCenterLoss=14.414, BoxScaleLoss=5.060, ClassLoss=10.110 [Epoch 167][Batch 1199], LR: 1.00E-03, Speed: 110.081 samples/sec, ObjLoss=24.653, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.109 [Epoch 167][Batch 1299], LR: 1.00E-03, Speed: 8.067 samples/sec, ObjLoss=24.652, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.109 [Epoch 167][Batch 1399], LR: 1.00E-03, Speed: 8.423 samples/sec, ObjLoss=24.652, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.108 [Epoch 167][Batch 1499], LR: 1.00E-03, Speed: 9.397 samples/sec, ObjLoss=24.651, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.108 [Epoch 167][Batch 1599], LR: 1.00E-03, Speed: 7.260 samples/sec, ObjLoss=24.651, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.107 [Epoch 167][Batch 1699], LR: 1.00E-03, Speed: 10.330 samples/sec, ObjLoss=24.650, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.107 [Epoch 167][Batch 1799], LR: 1.00E-03, Speed: 12.605 samples/sec, ObjLoss=24.649, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.106 [Epoch 167] Training cost: 2225.538, ObjLoss=24.649, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.106 [Epoch 167] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.473 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.366 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479 person=37.2 bicycle=17.5 car=28.5 motorcycle=29.8 airplane=40.5 bus=51.6 train=50.5 truck=25.1 boat=12.9 traffic light=14.6 fire hydrant=45.3 stop sign=42.7 parking meter=32.9 bench=15.7 bird=20.2 cat=44.4 dog=41.5 horse=37.5 sheep=33.4 cow=35.2 elephant=41.0 bear=44.7 zebra=44.8 giraffe=47.5 backpack=5.7 umbrella=25.4 handbag=5.5 tie=17.4 suitcase=19.1 frisbee=39.7 skis=11.4 snowboard=16.7 sports ball=27.4 kite=30.4 baseball bat=15.7 baseball glove=21.3 skateboard=29.7 surfboard=20.8 tennis racket=29.1 bottle=20.3 wine glass=20.1 cup=25.3 fork=13.0 knife=5.0 spoon=5.7 bowl=26.6 banana=14.4 apple=8.2 sandwich=24.8 orange=17.9 broccoli=13.3 carrot=11.2 hot dog=20.2 pizza=35.2 donut=28.5 cake=22.0 chair=17.0 couch=29.7 potted plant=15.3 bed=32.7 dining table=17.0 toilet=43.3 tv=38.4 laptop=39.9 mouse=37.2 remote=12.1 keyboard=33.1 cell phone=18.8 microwave=33.8 oven=23.3 toaster=7.1 sink=24.6 refrigerator=36.1 book=4.5 clock=34.6 vase=22.7 scissors=17.6 teddy bear=29.6 hair drier=0.0 toothbrush=6.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.5 [Epoch 168][Batch 99], LR: 1.00E-03, Speed: 9.580 samples/sec, ObjLoss=24.649, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.106 [Epoch 168][Batch 199], LR: 1.00E-03, Speed: 12.402 samples/sec, ObjLoss=24.649, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.105 [Epoch 168][Batch 299], LR: 1.00E-03, Speed: 8.975 samples/sec, ObjLoss=24.648, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.105 [Epoch 168][Batch 399], LR: 1.00E-03, Speed: 9.273 samples/sec, ObjLoss=24.647, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.104 [Epoch 168][Batch 499], LR: 1.00E-03, Speed: 9.792 samples/sec, ObjLoss=24.646, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.104 [Epoch 168][Batch 599], LR: 1.00E-03, Speed: 8.137 samples/sec, ObjLoss=24.646, BoxCenterLoss=14.415, BoxScaleLoss=5.060, ClassLoss=10.103 [Epoch 168][Batch 699], LR: 1.00E-03, Speed: 7.350 samples/sec, ObjLoss=24.646, BoxCenterLoss=14.415, BoxScaleLoss=5.059, ClassLoss=10.103 [Epoch 168][Batch 799], LR: 1.00E-03, Speed: 10.822 samples/sec, ObjLoss=24.645, BoxCenterLoss=14.415, BoxScaleLoss=5.059, ClassLoss=10.102 [Epoch 168][Batch 899], LR: 1.00E-03, Speed: 9.274 samples/sec, ObjLoss=24.644, BoxCenterLoss=14.414, BoxScaleLoss=5.059, ClassLoss=10.102 [Epoch 168][Batch 999], LR: 1.00E-03, Speed: 7.560 samples/sec, ObjLoss=24.643, BoxCenterLoss=14.414, BoxScaleLoss=5.059, ClassLoss=10.101 [Epoch 168][Batch 1099], LR: 1.00E-03, Speed: 10.355 samples/sec, ObjLoss=24.643, BoxCenterLoss=14.414, BoxScaleLoss=5.059, ClassLoss=10.100 [Epoch 168][Batch 1199], LR: 1.00E-03, Speed: 109.354 samples/sec, ObjLoss=24.642, BoxCenterLoss=14.414, BoxScaleLoss=5.059, ClassLoss=10.100 [Epoch 168][Batch 1299], LR: 1.00E-03, Speed: 9.633 samples/sec, ObjLoss=24.642, BoxCenterLoss=14.414, BoxScaleLoss=5.059, ClassLoss=10.099 [Epoch 168][Batch 1399], LR: 1.00E-03, Speed: 10.223 samples/sec, ObjLoss=24.641, BoxCenterLoss=14.414, BoxScaleLoss=5.059, ClassLoss=10.099 [Epoch 168][Batch 1499], LR: 1.00E-03, Speed: 9.335 samples/sec, ObjLoss=24.641, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.098 [Epoch 168][Batch 1599], LR: 1.00E-03, Speed: 11.397 samples/sec, ObjLoss=24.640, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.098 [Epoch 168][Batch 1699], LR: 1.00E-03, Speed: 8.648 samples/sec, ObjLoss=24.640, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.097 [Epoch 168][Batch 1799], LR: 1.00E-03, Speed: 10.754 samples/sec, ObjLoss=24.639, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.097 [Epoch 168] Training cost: 2244.719, ObjLoss=24.639, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.096 [Epoch 168] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.379 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.143 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 person=38.1 bicycle=19.7 car=27.9 motorcycle=29.3 airplane=40.7 bus=50.3 train=51.2 truck=23.4 boat=14.0 traffic light=14.6 fire hydrant=47.3 stop sign=43.8 parking meter=33.2 bench=14.8 bird=19.1 cat=47.5 dog=41.7 horse=39.5 sheep=34.6 cow=35.1 elephant=46.5 bear=47.7 zebra=47.1 giraffe=49.5 backpack=5.8 umbrella=24.5 handbag=6.1 tie=15.5 suitcase=19.7 frisbee=36.2 skis=12.6 snowboard=14.5 sports ball=28.8 kite=25.5 baseball bat=18.9 baseball glove=18.8 skateboard=28.5 surfboard=20.9 tennis racket=30.4 bottle=18.6 wine glass=21.6 cup=26.1 fork=14.9 knife=4.1 spoon=5.6 bowl=22.7 banana=12.4 apple=10.2 sandwich=19.9 orange=19.8 broccoli=13.6 carrot=9.9 hot dog=17.1 pizza=31.4 donut=31.2 cake=22.8 chair=15.8 couch=30.9 potted plant=12.6 bed=33.2 dining table=21.0 toilet=43.8 tv=41.6 laptop=41.9 mouse=36.2 remote=11.8 keyboard=28.3 cell phone=17.8 microwave=32.4 oven=22.6 toaster=2.1 sink=22.4 refrigerator=36.1 book=4.7 clock=32.2 vase=23.7 scissors=21.0 teddy bear=28.7 hair drier=0.0 toothbrush=6.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.4 [Epoch 169][Batch 99], LR: 1.00E-03, Speed: 13.288 samples/sec, ObjLoss=24.638, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.096 [Epoch 169][Batch 199], LR: 1.00E-03, Speed: 8.899 samples/sec, ObjLoss=24.637, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.096 [Epoch 169][Batch 299], LR: 1.00E-03, Speed: 11.295 samples/sec, ObjLoss=24.637, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.095 [Epoch 169][Batch 399], LR: 1.00E-03, Speed: 7.925 samples/sec, ObjLoss=24.636, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.094 [Epoch 169][Batch 499], LR: 1.00E-03, Speed: 8.695 samples/sec, ObjLoss=24.636, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.094 [Epoch 169][Batch 599], LR: 1.00E-03, Speed: 8.556 samples/sec, ObjLoss=24.636, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.093 [Epoch 169][Batch 699], LR: 1.00E-03, Speed: 7.985 samples/sec, ObjLoss=24.634, BoxCenterLoss=14.414, BoxScaleLoss=5.058, ClassLoss=10.093 [Epoch 169][Batch 799], LR: 1.00E-03, Speed: 10.594 samples/sec, ObjLoss=24.634, BoxCenterLoss=14.414, BoxScaleLoss=5.057, ClassLoss=10.092 [Epoch 169][Batch 899], LR: 1.00E-03, Speed: 10.025 samples/sec, ObjLoss=24.633, BoxCenterLoss=14.414, BoxScaleLoss=5.057, ClassLoss=10.092 [Epoch 169][Batch 999], LR: 1.00E-03, Speed: 10.071 samples/sec, ObjLoss=24.633, BoxCenterLoss=14.414, BoxScaleLoss=5.057, ClassLoss=10.091 [Epoch 169][Batch 1099], LR: 1.00E-03, Speed: 112.544 samples/sec, ObjLoss=24.632, BoxCenterLoss=14.414, BoxScaleLoss=5.057, ClassLoss=10.091 [Epoch 169][Batch 1199], LR: 1.00E-03, Speed: 9.175 samples/sec, ObjLoss=24.631, BoxCenterLoss=14.414, BoxScaleLoss=5.057, ClassLoss=10.090 [Epoch 169][Batch 1299], LR: 1.00E-03, Speed: 9.520 samples/sec, ObjLoss=24.631, BoxCenterLoss=14.413, BoxScaleLoss=5.057, ClassLoss=10.090 [Epoch 169][Batch 1399], LR: 1.00E-03, Speed: 13.206 samples/sec, ObjLoss=24.630, BoxCenterLoss=14.413, BoxScaleLoss=5.057, ClassLoss=10.089 [Epoch 169][Batch 1499], LR: 1.00E-03, Speed: 10.108 samples/sec, ObjLoss=24.629, BoxCenterLoss=14.413, BoxScaleLoss=5.057, ClassLoss=10.089 [Epoch 169][Batch 1599], LR: 1.00E-03, Speed: 10.064 samples/sec, ObjLoss=24.629, BoxCenterLoss=14.413, BoxScaleLoss=5.057, ClassLoss=10.088 [Epoch 169][Batch 1699], LR: 1.00E-03, Speed: 9.823 samples/sec, ObjLoss=24.627, BoxCenterLoss=14.413, BoxScaleLoss=5.056, ClassLoss=10.087 [Epoch 169][Batch 1799], LR: 1.00E-03, Speed: 12.912 samples/sec, ObjLoss=24.627, BoxCenterLoss=14.413, BoxScaleLoss=5.056, ClassLoss=10.087 [Epoch 169] Training cost: 2234.036, ObjLoss=24.627, BoxCenterLoss=14.413, BoxScaleLoss=5.056, ClassLoss=10.087 [Epoch 169] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479 person=38.1 bicycle=14.9 car=26.7 motorcycle=28.9 airplane=41.7 bus=44.7 train=49.8 truck=24.7 boat=11.9 traffic light=14.7 fire hydrant=43.8 stop sign=35.6 parking meter=30.1 bench=14.3 bird=18.6 cat=48.2 dog=38.3 horse=37.2 sheep=30.5 cow=33.2 elephant=45.1 bear=48.0 zebra=45.0 giraffe=49.1 backpack=7.1 umbrella=22.6 handbag=5.3 tie=18.2 suitcase=17.4 frisbee=33.7 skis=11.4 snowboard=16.0 sports ball=28.1 kite=20.1 baseball bat=14.1 baseball glove=18.8 skateboard=32.2 surfboard=19.7 tennis racket=26.1 bottle=18.8 wine glass=22.0 cup=26.0 fork=15.4 knife=4.3 spoon=6.2 bowl=22.2 banana=13.0 apple=10.5 sandwich=23.0 orange=17.5 broccoli=10.4 carrot=8.1 hot dog=20.2 pizza=36.1 donut=24.4 cake=23.1 chair=16.5 couch=28.6 potted plant=16.8 bed=31.2 dining table=15.3 toilet=42.5 tv=39.7 laptop=38.8 mouse=36.4 remote=10.0 keyboard=31.5 cell phone=21.6 microwave=34.5 oven=21.4 toaster=2.4 sink=22.6 refrigerator=36.1 book=5.1 clock=31.2 vase=21.5 scissors=22.2 teddy bear=28.1 hair drier=0.0 toothbrush=5.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.6 [Epoch 170][Batch 99], LR: 1.00E-03, Speed: 9.681 samples/sec, ObjLoss=24.626, BoxCenterLoss=14.413, BoxScaleLoss=5.056, ClassLoss=10.086 [Epoch 170][Batch 199], LR: 1.00E-03, Speed: 7.310 samples/sec, ObjLoss=24.626, BoxCenterLoss=14.413, BoxScaleLoss=5.056, ClassLoss=10.086 [Epoch 170][Batch 299], LR: 1.00E-03, Speed: 8.462 samples/sec, ObjLoss=24.625, BoxCenterLoss=14.413, BoxScaleLoss=5.056, ClassLoss=10.085 [Epoch 170][Batch 399], LR: 1.00E-03, Speed: 10.249 samples/sec, ObjLoss=24.624, BoxCenterLoss=14.413, BoxScaleLoss=5.056, ClassLoss=10.085 [Epoch 170][Batch 499], LR: 1.00E-03, Speed: 9.587 samples/sec, ObjLoss=24.623, BoxCenterLoss=14.412, BoxScaleLoss=5.056, ClassLoss=10.084 [Epoch 170][Batch 599], LR: 1.00E-03, Speed: 88.056 samples/sec, ObjLoss=24.623, BoxCenterLoss=14.412, BoxScaleLoss=5.056, ClassLoss=10.084 [Epoch 170][Batch 699], LR: 1.00E-03, Speed: 8.967 samples/sec, ObjLoss=24.622, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.083 [Epoch 170][Batch 799], LR: 1.00E-03, Speed: 101.625 samples/sec, ObjLoss=24.621, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.083 [Epoch 170][Batch 899], LR: 1.00E-03, Speed: 93.336 samples/sec, ObjLoss=24.621, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.082 [Epoch 170][Batch 999], LR: 1.00E-03, Speed: 8.028 samples/sec, ObjLoss=24.620, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.082 [Epoch 170][Batch 1099], LR: 1.00E-03, Speed: 8.946 samples/sec, ObjLoss=24.619, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.082 [Epoch 170][Batch 1199], LR: 1.00E-03, Speed: 9.569 samples/sec, ObjLoss=24.619, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.081 [Epoch 170][Batch 1299], LR: 1.00E-03, Speed: 110.052 samples/sec, ObjLoss=24.618, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.080 [Epoch 170][Batch 1399], LR: 1.00E-03, Speed: 11.426 samples/sec, ObjLoss=24.617, BoxCenterLoss=14.412, BoxScaleLoss=5.055, ClassLoss=10.080 [Epoch 170][Batch 1499], LR: 1.00E-03, Speed: 8.891 samples/sec, ObjLoss=24.616, BoxCenterLoss=14.411, BoxScaleLoss=5.055, ClassLoss=10.079 [Epoch 170][Batch 1599], LR: 1.00E-03, Speed: 7.827 samples/sec, ObjLoss=24.616, BoxCenterLoss=14.411, BoxScaleLoss=5.055, ClassLoss=10.079 [Epoch 170][Batch 1699], LR: 1.00E-03, Speed: 9.536 samples/sec, ObjLoss=24.615, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.078 [Epoch 170][Batch 1799], LR: 1.00E-03, Speed: 10.543 samples/sec, ObjLoss=24.614, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.078 [Epoch 170] Training cost: 2245.026, ObjLoss=24.614, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.077 [Epoch 170] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.269 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.490 person=38.2 bicycle=18.9 car=25.3 motorcycle=29.4 airplane=38.9 bus=51.0 train=50.4 truck=23.7 boat=13.3 traffic light=14.3 fire hydrant=43.5 stop sign=42.4 parking meter=30.6 bench=14.6 bird=19.8 cat=47.5 dog=42.2 horse=39.4 sheep=35.4 cow=36.9 elephant=48.2 bear=45.5 zebra=50.4 giraffe=48.2 backpack=5.2 umbrella=23.9 handbag=5.8 tie=17.9 suitcase=15.7 frisbee=44.4 skis=10.8 snowboard=16.6 sports ball=26.2 kite=25.5 baseball bat=12.6 baseball glove=19.6 skateboard=30.1 surfboard=19.4 tennis racket=31.2 bottle=20.4 wine glass=19.8 cup=27.8 fork=15.3 knife=4.6 spoon=3.9 bowl=23.9 banana=12.1 apple=7.5 sandwich=21.5 orange=15.3 broccoli=10.9 carrot=7.2 hot dog=16.4 pizza=36.2 donut=26.2 cake=20.3 chair=17.3 couch=30.5 potted plant=13.8 bed=32.1 dining table=19.7 toilet=41.1 tv=40.1 laptop=40.5 mouse=39.5 remote=9.4 keyboard=33.4 cell phone=17.8 microwave=32.7 oven=20.6 toaster=7.1 sink=23.6 refrigerator=34.7 book=4.5 clock=36.7 vase=22.2 scissors=24.1 teddy bear=29.5 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.2 [Epoch 171][Batch 99], LR: 1.00E-03, Speed: 8.663 samples/sec, ObjLoss=24.614, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.077 [Epoch 171][Batch 199], LR: 1.00E-03, Speed: 9.053 samples/sec, ObjLoss=24.613, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.076 [Epoch 171][Batch 299], LR: 1.00E-03, Speed: 8.249 samples/sec, ObjLoss=24.612, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.076 [Epoch 171][Batch 399], LR: 1.00E-03, Speed: 8.256 samples/sec, ObjLoss=24.612, BoxCenterLoss=14.412, BoxScaleLoss=5.054, ClassLoss=10.075 [Epoch 171][Batch 499], LR: 1.00E-03, Speed: 10.099 samples/sec, ObjLoss=24.612, BoxCenterLoss=14.412, BoxScaleLoss=5.054, ClassLoss=10.075 [Epoch 171][Batch 599], LR: 1.00E-03, Speed: 8.599 samples/sec, ObjLoss=24.611, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.074 [Epoch 171][Batch 699], LR: 1.00E-03, Speed: 8.843 samples/sec, ObjLoss=24.611, BoxCenterLoss=14.411, BoxScaleLoss=5.054, ClassLoss=10.074 [Epoch 171][Batch 799], LR: 1.00E-03, Speed: 7.654 samples/sec, ObjLoss=24.610, BoxCenterLoss=14.412, BoxScaleLoss=5.054, ClassLoss=10.074 [Epoch 171][Batch 899], LR: 1.00E-03, Speed: 12.907 samples/sec, ObjLoss=24.610, BoxCenterLoss=14.412, BoxScaleLoss=5.054, ClassLoss=10.073 [Epoch 171][Batch 999], LR: 1.00E-03, Speed: 10.398 samples/sec, ObjLoss=24.609, BoxCenterLoss=14.412, BoxScaleLoss=5.054, ClassLoss=10.073 [Epoch 171][Batch 1099], LR: 1.00E-03, Speed: 10.881 samples/sec, ObjLoss=24.608, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.072 [Epoch 171][Batch 1199], LR: 1.00E-03, Speed: 10.253 samples/sec, ObjLoss=24.608, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.072 [Epoch 171][Batch 1299], LR: 1.00E-03, Speed: 12.605 samples/sec, ObjLoss=24.607, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.071 [Epoch 171][Batch 1399], LR: 1.00E-03, Speed: 14.281 samples/sec, ObjLoss=24.607, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.071 [Epoch 171][Batch 1499], LR: 1.00E-03, Speed: 10.804 samples/sec, ObjLoss=24.606, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.070 [Epoch 171][Batch 1599], LR: 1.00E-03, Speed: 106.364 samples/sec, ObjLoss=24.605, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.070 [Epoch 171][Batch 1699], LR: 1.00E-03, Speed: 8.450 samples/sec, ObjLoss=24.605, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.069 [Epoch 171][Batch 1799], LR: 1.00E-03, Speed: 11.957 samples/sec, ObjLoss=24.604, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.068 [Epoch 171] Training cost: 2215.095, ObjLoss=24.604, BoxCenterLoss=14.411, BoxScaleLoss=5.053, ClassLoss=10.068 [Epoch 171] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472 person=38.1 bicycle=18.4 car=27.2 motorcycle=30.6 airplane=42.6 bus=50.7 train=42.8 truck=23.1 boat=13.4 traffic light=13.7 fire hydrant=43.0 stop sign=37.0 parking meter=32.8 bench=15.7 bird=20.3 cat=43.8 dog=38.1 horse=38.4 sheep=33.4 cow=37.4 elephant=45.5 bear=41.1 zebra=49.1 giraffe=50.2 backpack=6.1 umbrella=23.1 handbag=5.5 tie=17.8 suitcase=16.7 frisbee=35.7 skis=9.7 snowboard=17.3 sports ball=26.5 kite=26.3 baseball bat=16.9 baseball glove=20.1 skateboard=30.8 surfboard=22.5 tennis racket=27.8 bottle=19.9 wine glass=18.6 cup=24.3 fork=17.2 knife=5.1 spoon=5.0 bowl=23.5 banana=11.6 apple=9.8 sandwich=20.4 orange=15.2 broccoli=12.2 carrot=9.8 hot dog=15.7 pizza=37.1 donut=28.8 cake=23.3 chair=16.3 couch=27.9 potted plant=15.2 bed=32.8 dining table=19.2 toilet=41.1 tv=37.3 laptop=37.1 mouse=40.8 remote=11.0 keyboard=33.1 cell phone=16.4 microwave=35.0 oven=19.9 toaster=7.1 sink=21.3 refrigerator=34.7 book=5.2 clock=34.8 vase=21.0 scissors=16.8 teddy bear=27.5 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.8 [Epoch 172][Batch 99], LR: 1.00E-03, Speed: 7.744 samples/sec, ObjLoss=24.603, BoxCenterLoss=14.411, BoxScaleLoss=5.052, ClassLoss=10.068 [Epoch 172][Batch 199], LR: 1.00E-03, Speed: 11.151 samples/sec, ObjLoss=24.603, BoxCenterLoss=14.411, BoxScaleLoss=5.052, ClassLoss=10.067 [Epoch 172][Batch 299], LR: 1.00E-03, Speed: 9.432 samples/sec, ObjLoss=24.602, BoxCenterLoss=14.411, BoxScaleLoss=5.052, ClassLoss=10.067 [Epoch 172][Batch 399], LR: 1.00E-03, Speed: 9.723 samples/sec, ObjLoss=24.601, BoxCenterLoss=14.411, BoxScaleLoss=5.052, ClassLoss=10.066 [Epoch 172][Batch 499], LR: 1.00E-03, Speed: 10.065 samples/sec, ObjLoss=24.601, BoxCenterLoss=14.411, BoxScaleLoss=5.052, ClassLoss=10.066 [Epoch 172][Batch 599], LR: 1.00E-03, Speed: 10.281 samples/sec, ObjLoss=24.600, BoxCenterLoss=14.410, BoxScaleLoss=5.052, ClassLoss=10.065 [Epoch 172][Batch 699], LR: 1.00E-03, Speed: 10.256 samples/sec, ObjLoss=24.599, BoxCenterLoss=14.410, BoxScaleLoss=5.052, ClassLoss=10.065 [Epoch 172][Batch 799], LR: 1.00E-03, Speed: 7.726 samples/sec, ObjLoss=24.598, BoxCenterLoss=14.410, BoxScaleLoss=5.052, ClassLoss=10.064 [Epoch 172][Batch 899], LR: 1.00E-03, Speed: 91.845 samples/sec, ObjLoss=24.598, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.064 [Epoch 172][Batch 999], LR: 1.00E-03, Speed: 11.509 samples/sec, ObjLoss=24.597, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.063 [Epoch 172][Batch 1099], LR: 1.00E-03, Speed: 10.172 samples/sec, ObjLoss=24.596, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.062 [Epoch 172][Batch 1199], LR: 1.00E-03, Speed: 9.901 samples/sec, ObjLoss=24.596, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.062 [Epoch 172][Batch 1299], LR: 1.00E-03, Speed: 11.004 samples/sec, ObjLoss=24.596, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.061 [Epoch 172][Batch 1399], LR: 1.00E-03, Speed: 9.222 samples/sec, ObjLoss=24.595, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.061 [Epoch 172][Batch 1499], LR: 1.00E-03, Speed: 10.074 samples/sec, ObjLoss=24.594, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.061 [Epoch 172][Batch 1599], LR: 1.00E-03, Speed: 9.021 samples/sec, ObjLoss=24.594, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.060 [Epoch 172][Batch 1699], LR: 1.00E-03, Speed: 9.039 samples/sec, ObjLoss=24.594, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.060 [Epoch 172][Batch 1799], LR: 1.00E-03, Speed: 9.945 samples/sec, ObjLoss=24.593, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.059 [Epoch 172] Training cost: 2191.825, ObjLoss=24.593, BoxCenterLoss=14.410, BoxScaleLoss=5.051, ClassLoss=10.059 [Epoch 172] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.475 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.276 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.339 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.365 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 person=38.4 bicycle=20.0 car=27.4 motorcycle=31.1 airplane=43.3 bus=52.7 train=51.4 truck=24.6 boat=15.8 traffic light=15.2 fire hydrant=48.6 stop sign=39.5 parking meter=29.5 bench=14.7 bird=22.2 cat=48.2 dog=39.4 horse=38.4 sheep=37.7 cow=38.1 elephant=48.3 bear=47.9 zebra=51.5 giraffe=52.0 backpack=6.1 umbrella=25.8 handbag=6.0 tie=18.1 suitcase=19.4 frisbee=39.1 skis=12.6 snowboard=16.1 sports ball=26.4 kite=27.2 baseball bat=17.3 baseball glove=23.1 skateboard=30.7 surfboard=18.3 tennis racket=30.7 bottle=22.1 wine glass=23.6 cup=26.1 fork=17.2 knife=5.1 spoon=6.8 bowl=25.2 banana=12.2 apple=8.8 sandwich=22.1 orange=18.7 broccoli=13.5 carrot=10.6 hot dog=21.5 pizza=31.7 donut=30.1 cake=24.8 chair=16.7 couch=30.5 potted plant=15.7 bed=35.2 dining table=20.7 toilet=42.6 tv=40.6 laptop=37.2 mouse=34.0 remote=11.3 keyboard=33.1 cell phone=20.6 microwave=34.9 oven=23.6 toaster=0.0 sink=24.9 refrigerator=34.4 book=4.6 clock=33.3 vase=21.4 scissors=18.4 teddy bear=34.7 hair drier=0.0 toothbrush=6.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =26.1 [Epoch 173][Batch 99], LR: 1.00E-03, Speed: 11.232 samples/sec, ObjLoss=24.592, BoxCenterLoss=14.410, BoxScaleLoss=5.050, ClassLoss=10.059 [Epoch 173][Batch 199], LR: 1.00E-03, Speed: 9.246 samples/sec, ObjLoss=24.592, BoxCenterLoss=14.410, BoxScaleLoss=5.050, ClassLoss=10.058 [Epoch 173][Batch 299], LR: 1.00E-03, Speed: 12.207 samples/sec, ObjLoss=24.591, BoxCenterLoss=14.410, BoxScaleLoss=5.050, ClassLoss=10.057 [Epoch 173][Batch 399], LR: 1.00E-03, Speed: 89.860 samples/sec, ObjLoss=24.590, BoxCenterLoss=14.409, BoxScaleLoss=5.050, ClassLoss=10.057 [Epoch 173][Batch 499], LR: 1.00E-03, Speed: 85.616 samples/sec, ObjLoss=24.589, BoxCenterLoss=14.409, BoxScaleLoss=5.050, ClassLoss=10.056 [Epoch 173][Batch 599], LR: 1.00E-03, Speed: 90.784 samples/sec, ObjLoss=24.589, BoxCenterLoss=14.409, BoxScaleLoss=5.050, ClassLoss=10.056 [Epoch 173][Batch 699], LR: 1.00E-03, Speed: 113.974 samples/sec, ObjLoss=24.588, BoxCenterLoss=14.409, BoxScaleLoss=5.050, ClassLoss=10.055 [Epoch 173][Batch 799], LR: 1.00E-03, Speed: 13.916 samples/sec, ObjLoss=24.587, BoxCenterLoss=14.409, BoxScaleLoss=5.049, ClassLoss=10.054 [Epoch 173][Batch 899], LR: 1.00E-03, Speed: 117.682 samples/sec, ObjLoss=24.587, BoxCenterLoss=14.409, BoxScaleLoss=5.049, ClassLoss=10.054 [Epoch 173][Batch 999], LR: 1.00E-03, Speed: 93.013 samples/sec, ObjLoss=24.586, BoxCenterLoss=14.409, BoxScaleLoss=5.049, ClassLoss=10.053 [Epoch 173][Batch 1099], LR: 1.00E-03, Speed: 10.088 samples/sec, ObjLoss=24.585, BoxCenterLoss=14.409, BoxScaleLoss=5.049, ClassLoss=10.053 [Epoch 173][Batch 1199], LR: 1.00E-03, Speed: 107.250 samples/sec, ObjLoss=24.585, BoxCenterLoss=14.409, BoxScaleLoss=5.049, ClassLoss=10.052 [Epoch 173][Batch 1299], LR: 1.00E-03, Speed: 8.997 samples/sec, ObjLoss=24.584, BoxCenterLoss=14.409, BoxScaleLoss=5.049, ClassLoss=10.052 [Epoch 173][Batch 1399], LR: 1.00E-03, Speed: 7.005 samples/sec, ObjLoss=24.583, BoxCenterLoss=14.409, BoxScaleLoss=5.049, ClassLoss=10.051 [Epoch 173][Batch 1499], LR: 1.00E-03, Speed: 9.121 samples/sec, ObjLoss=24.582, BoxCenterLoss=14.408, BoxScaleLoss=5.048, ClassLoss=10.050 [Epoch 173][Batch 1599], LR: 1.00E-03, Speed: 9.470 samples/sec, ObjLoss=24.582, BoxCenterLoss=14.408, BoxScaleLoss=5.048, ClassLoss=10.050 [Epoch 173][Batch 1699], LR: 1.00E-03, Speed: 9.091 samples/sec, ObjLoss=24.581, BoxCenterLoss=14.408, BoxScaleLoss=5.048, ClassLoss=10.050 [Epoch 173][Batch 1799], LR: 1.00E-03, Speed: 10.921 samples/sec, ObjLoss=24.581, BoxCenterLoss=14.409, BoxScaleLoss=5.048, ClassLoss=10.049 [Epoch 173] Training cost: 2201.680, ObjLoss=24.581, BoxCenterLoss=14.409, BoxScaleLoss=5.048, ClassLoss=10.049 [Epoch 173] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494 person=36.7 bicycle=19.9 car=26.6 motorcycle=32.1 airplane=46.9 bus=48.3 train=51.5 truck=22.3 boat=13.9 traffic light=13.1 fire hydrant=48.6 stop sign=41.1 parking meter=27.6 bench=16.1 bird=19.6 cat=45.0 dog=38.2 horse=38.5 sheep=34.3 cow=36.4 elephant=47.7 bear=47.5 zebra=50.2 giraffe=51.0 backpack=4.4 umbrella=25.2 handbag=4.5 tie=19.4 suitcase=16.5 frisbee=33.1 skis=9.9 snowboard=15.3 sports ball=27.4 kite=24.8 baseball bat=17.0 baseball glove=21.7 skateboard=28.9 surfboard=21.8 tennis racket=31.4 bottle=18.2 wine glass=21.8 cup=24.2 fork=15.0 knife=4.6 spoon=4.7 bowl=22.9 banana=12.1 apple=8.7 sandwich=22.1 orange=17.0 broccoli=12.7 carrot=8.5 hot dog=19.9 pizza=32.7 donut=28.1 cake=24.1 chair=16.5 couch=32.5 potted plant=14.8 bed=35.2 dining table=20.1 toilet=40.2 tv=36.8 laptop=37.4 mouse=30.8 remote=10.4 keyboard=33.3 cell phone=16.3 microwave=32.3 oven=21.1 toaster=0.0 sink=23.1 refrigerator=36.4 book=3.7 clock=31.5 vase=20.5 scissors=15.5 teddy bear=30.9 hair drier=0.0 toothbrush=6.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.0 [Epoch 174][Batch 99], LR: 1.00E-03, Speed: 114.733 samples/sec, ObjLoss=24.580, BoxCenterLoss=14.408, BoxScaleLoss=5.048, ClassLoss=10.048 [Epoch 174][Batch 199], LR: 1.00E-03, Speed: 8.742 samples/sec, ObjLoss=24.579, BoxCenterLoss=14.408, BoxScaleLoss=5.048, ClassLoss=10.048 [Epoch 174][Batch 299], LR: 1.00E-03, Speed: 7.591 samples/sec, ObjLoss=24.579, BoxCenterLoss=14.408, BoxScaleLoss=5.048, ClassLoss=10.047 [Epoch 174][Batch 399], LR: 1.00E-03, Speed: 8.659 samples/sec, ObjLoss=24.578, BoxCenterLoss=14.408, BoxScaleLoss=5.047, ClassLoss=10.047 [Epoch 174][Batch 499], LR: 1.00E-03, Speed: 9.188 samples/sec, ObjLoss=24.578, BoxCenterLoss=14.408, BoxScaleLoss=5.047, ClassLoss=10.046 [Epoch 174][Batch 599], LR: 1.00E-03, Speed: 8.617 samples/sec, ObjLoss=24.577, BoxCenterLoss=14.408, BoxScaleLoss=5.047, ClassLoss=10.046 [Epoch 174][Batch 699], LR: 1.00E-03, Speed: 10.545 samples/sec, ObjLoss=24.576, BoxCenterLoss=14.407, BoxScaleLoss=5.047, ClassLoss=10.045 [Epoch 174][Batch 799], LR: 1.00E-03, Speed: 11.755 samples/sec, ObjLoss=24.575, BoxCenterLoss=14.407, BoxScaleLoss=5.047, ClassLoss=10.045 [Epoch 174][Batch 899], LR: 1.00E-03, Speed: 10.600 samples/sec, ObjLoss=24.574, BoxCenterLoss=14.407, BoxScaleLoss=5.047, ClassLoss=10.044 [Epoch 174][Batch 999], LR: 1.00E-03, Speed: 13.091 samples/sec, ObjLoss=24.573, BoxCenterLoss=14.407, BoxScaleLoss=5.047, ClassLoss=10.044 [Epoch 174][Batch 1099], LR: 1.00E-03, Speed: 9.236 samples/sec, ObjLoss=24.573, BoxCenterLoss=14.407, BoxScaleLoss=5.047, ClassLoss=10.043 [Epoch 174][Batch 1199], LR: 1.00E-03, Speed: 11.752 samples/sec, ObjLoss=24.572, BoxCenterLoss=14.407, BoxScaleLoss=5.047, ClassLoss=10.043 [Epoch 174][Batch 1299], LR: 1.00E-03, Speed: 11.180 samples/sec, ObjLoss=24.572, BoxCenterLoss=14.407, BoxScaleLoss=5.047, ClassLoss=10.042 [Epoch 174][Batch 1399], LR: 1.00E-03, Speed: 11.140 samples/sec, ObjLoss=24.571, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.042 [Epoch 174][Batch 1499], LR: 1.00E-03, Speed: 10.338 samples/sec, ObjLoss=24.571, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.041 [Epoch 174][Batch 1599], LR: 1.00E-03, Speed: 8.330 samples/sec, ObjLoss=24.570, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.041 [Epoch 174][Batch 1699], LR: 1.00E-03, Speed: 10.326 samples/sec, ObjLoss=24.570, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.040 [Epoch 174][Batch 1799], LR: 1.00E-03, Speed: 132.623 samples/sec, ObjLoss=24.569, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.040 [Epoch 174] Training cost: 2141.342, ObjLoss=24.569, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.040 [Epoch 174] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.488 person=39.7 bicycle=19.4 car=26.5 motorcycle=30.5 airplane=41.7 bus=48.1 train=50.7 truck=23.2 boat=14.1 traffic light=15.6 fire hydrant=47.4 stop sign=47.2 parking meter=28.0 bench=14.0 bird=20.4 cat=46.2 dog=40.8 horse=38.2 sheep=30.9 cow=32.6 elephant=39.6 bear=50.4 zebra=49.0 giraffe=49.1 backpack=5.7 umbrella=26.9 handbag=5.8 tie=15.2 suitcase=18.7 frisbee=36.1 skis=11.1 snowboard=19.8 sports ball=23.7 kite=27.2 baseball bat=14.5 baseball glove=17.5 skateboard=30.8 surfboard=22.1 tennis racket=27.7 bottle=21.3 wine glass=21.1 cup=26.6 fork=14.4 knife=3.9 spoon=4.8 bowl=20.9 banana=13.6 apple=9.5 sandwich=16.5 orange=17.1 broccoli=13.4 carrot=12.3 hot dog=20.1 pizza=33.2 donut=29.9 cake=23.1 chair=17.5 couch=30.1 potted plant=15.3 bed=30.2 dining table=19.1 toilet=42.7 tv=40.3 laptop=42.0 mouse=38.9 remote=13.2 keyboard=32.1 cell phone=19.0 microwave=29.6 oven=21.8 toaster=0.0 sink=20.9 refrigerator=33.4 book=4.6 clock=33.0 vase=21.0 scissors=15.4 teddy bear=30.0 hair drier=0.0 toothbrush=8.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.1 [Epoch 175][Batch 99], LR: 1.00E-03, Speed: 8.697 samples/sec, ObjLoss=24.568, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.039 [Epoch 175][Batch 199], LR: 1.00E-03, Speed: 9.711 samples/sec, ObjLoss=24.568, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.039 [Epoch 175][Batch 299], LR: 1.00E-03, Speed: 7.766 samples/sec, ObjLoss=24.568, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.039 [Epoch 175][Batch 399], LR: 1.00E-03, Speed: 7.334 samples/sec, ObjLoss=24.567, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.038 [Epoch 175][Batch 499], LR: 1.00E-03, Speed: 13.706 samples/sec, ObjLoss=24.567, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.038 [Epoch 175][Batch 599], LR: 1.00E-03, Speed: 8.251 samples/sec, ObjLoss=24.567, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.037 [Epoch 175][Batch 699], LR: 1.00E-03, Speed: 108.780 samples/sec, ObjLoss=24.566, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.037 [Epoch 175][Batch 799], LR: 1.00E-03, Speed: 11.104 samples/sec, ObjLoss=24.565, BoxCenterLoss=14.407, BoxScaleLoss=5.046, ClassLoss=10.036 [Epoch 175][Batch 899], LR: 1.00E-03, Speed: 10.448 samples/sec, ObjLoss=24.565, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.036 [Epoch 175][Batch 999], LR: 1.00E-03, Speed: 9.180 samples/sec, ObjLoss=24.565, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.036 [Epoch 175][Batch 1099], LR: 1.00E-03, Speed: 9.697 samples/sec, ObjLoss=24.564, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.035 [Epoch 175][Batch 1199], LR: 1.00E-03, Speed: 94.451 samples/sec, ObjLoss=24.564, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.035 [Epoch 175][Batch 1299], LR: 1.00E-03, Speed: 10.348 samples/sec, ObjLoss=24.563, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.034 [Epoch 175][Batch 1399], LR: 1.00E-03, Speed: 8.650 samples/sec, ObjLoss=24.563, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.034 [Epoch 175][Batch 1499], LR: 1.00E-03, Speed: 9.399 samples/sec, ObjLoss=24.562, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.033 [Epoch 175][Batch 1599], LR: 1.00E-03, Speed: 8.116 samples/sec, ObjLoss=24.562, BoxCenterLoss=14.408, BoxScaleLoss=5.046, ClassLoss=10.033 [Epoch 175][Batch 1699], LR: 1.00E-03, Speed: 7.589 samples/sec, ObjLoss=24.561, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.033 [Epoch 175][Batch 1799], LR: 1.00E-03, Speed: 11.626 samples/sec, ObjLoss=24.560, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.032 [Epoch 175] Training cost: 2208.489, ObjLoss=24.560, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.032 [Epoch 175] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.474 person=37.5 bicycle=16.1 car=27.0 motorcycle=28.5 airplane=42.4 bus=44.6 train=47.9 truck=24.3 boat=15.2 traffic light=14.5 fire hydrant=42.3 stop sign=43.5 parking meter=26.3 bench=14.7 bird=20.4 cat=46.1 dog=38.6 horse=33.5 sheep=30.1 cow=31.0 elephant=42.9 bear=50.1 zebra=46.3 giraffe=49.1 backpack=6.1 umbrella=25.8 handbag=5.9 tie=12.9 suitcase=18.4 frisbee=36.3 skis=9.3 snowboard=15.5 sports ball=17.3 kite=26.3 baseball bat=13.3 baseball glove=18.7 skateboard=32.3 surfboard=21.7 tennis racket=28.1 bottle=20.3 wine glass=21.5 cup=24.9 fork=14.1 knife=5.3 spoon=5.8 bowl=22.8 banana=13.4 apple=8.3 sandwich=20.9 orange=15.9 broccoli=11.9 carrot=11.1 hot dog=16.7 pizza=32.8 donut=25.9 cake=23.1 chair=17.1 couch=31.3 potted plant=15.1 bed=35.6 dining table=19.7 toilet=39.5 tv=35.8 laptop=43.1 mouse=36.6 remote=11.0 keyboard=27.4 cell phone=19.5 microwave=35.4 oven=19.8 toaster=7.1 sink=17.7 refrigerator=33.1 book=4.4 clock=32.3 vase=19.8 scissors=19.6 teddy bear=27.9 hair drier=0.0 toothbrush=9.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.4 [Epoch 176][Batch 99], LR: 1.00E-03, Speed: 11.678 samples/sec, ObjLoss=24.560, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.031 [Epoch 176][Batch 199], LR: 1.00E-03, Speed: 7.447 samples/sec, ObjLoss=24.559, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.031 [Epoch 176][Batch 299], LR: 1.00E-03, Speed: 8.594 samples/sec, ObjLoss=24.559, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.031 [Epoch 176][Batch 399], LR: 1.00E-03, Speed: 97.379 samples/sec, ObjLoss=24.558, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.030 [Epoch 176][Batch 499], LR: 1.00E-03, Speed: 113.601 samples/sec, ObjLoss=24.557, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.029 [Epoch 176][Batch 599], LR: 1.00E-03, Speed: 8.059 samples/sec, ObjLoss=24.556, BoxCenterLoss=14.407, BoxScaleLoss=5.045, ClassLoss=10.029 [Epoch 176][Batch 699], LR: 1.00E-03, Speed: 8.328 samples/sec, ObjLoss=24.555, BoxCenterLoss=14.406, BoxScaleLoss=5.045, ClassLoss=10.028 [Epoch 176][Batch 799], LR: 1.00E-03, Speed: 10.568 samples/sec, ObjLoss=24.555, BoxCenterLoss=14.406, BoxScaleLoss=5.044, ClassLoss=10.028 [Epoch 176][Batch 899], LR: 1.00E-03, Speed: 9.512 samples/sec, ObjLoss=24.554, BoxCenterLoss=14.406, BoxScaleLoss=5.044, ClassLoss=10.027 [Epoch 176][Batch 999], LR: 1.00E-03, Speed: 11.633 samples/sec, ObjLoss=24.553, BoxCenterLoss=14.406, BoxScaleLoss=5.044, ClassLoss=10.027 [Epoch 176][Batch 1099], LR: 1.00E-03, Speed: 9.658 samples/sec, ObjLoss=24.553, BoxCenterLoss=14.406, BoxScaleLoss=5.044, ClassLoss=10.026 [Epoch 176][Batch 1199], LR: 1.00E-03, Speed: 10.697 samples/sec, ObjLoss=24.551, BoxCenterLoss=14.406, BoxScaleLoss=5.044, ClassLoss=10.025 [Epoch 176][Batch 1299], LR: 1.00E-03, Speed: 9.002 samples/sec, ObjLoss=24.551, BoxCenterLoss=14.406, BoxScaleLoss=5.044, ClassLoss=10.025 [Epoch 176][Batch 1399], LR: 1.00E-03, Speed: 113.935 samples/sec, ObjLoss=24.551, BoxCenterLoss=14.406, BoxScaleLoss=5.044, ClassLoss=10.024 [Epoch 176][Batch 1499], LR: 1.00E-03, Speed: 9.759 samples/sec, ObjLoss=24.550, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.024 [Epoch 176][Batch 1599], LR: 1.00E-03, Speed: 9.073 samples/sec, ObjLoss=24.549, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.023 [Epoch 176][Batch 1699], LR: 1.00E-03, Speed: 10.579 samples/sec, ObjLoss=24.549, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.023 [Epoch 176][Batch 1799], LR: 1.00E-03, Speed: 18.527 samples/sec, ObjLoss=24.548, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.022 [Epoch 176] Training cost: 2166.972, ObjLoss=24.548, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.022 [Epoch 176] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.096 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478 person=38.6 bicycle=19.7 car=27.0 motorcycle=31.0 airplane=43.0 bus=49.4 train=50.7 truck=22.7 boat=13.9 traffic light=15.0 fire hydrant=40.8 stop sign=45.8 parking meter=32.2 bench=15.1 bird=21.1 cat=49.1 dog=40.6 horse=39.1 sheep=33.5 cow=34.2 elephant=42.9 bear=51.4 zebra=50.4 giraffe=53.1 backpack=4.9 umbrella=22.6 handbag=5.6 tie=16.6 suitcase=18.8 frisbee=36.9 skis=9.8 snowboard=12.1 sports ball=23.9 kite=25.3 baseball bat=15.6 baseball glove=19.7 skateboard=28.5 surfboard=22.1 tennis racket=29.4 bottle=20.3 wine glass=21.0 cup=25.7 fork=16.0 knife=5.5 spoon=4.8 bowl=23.8 banana=11.9 apple=7.2 sandwich=21.4 orange=16.9 broccoli=13.0 carrot=7.5 hot dog=16.7 pizza=35.7 donut=23.4 cake=23.8 chair=16.9 couch=30.2 potted plant=15.8 bed=33.8 dining table=19.2 toilet=40.7 tv=36.9 laptop=40.2 mouse=38.4 remote=9.9 keyboard=32.8 cell phone=19.3 microwave=32.7 oven=20.2 toaster=3.0 sink=21.1 refrigerator=36.6 book=4.4 clock=32.4 vase=23.6 scissors=15.9 teddy bear=30.0 hair drier=0.0 toothbrush=8.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.1 [Epoch 177][Batch 99], LR: 1.00E-03, Speed: 9.002 samples/sec, ObjLoss=24.547, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.022 [Epoch 177][Batch 199], LR: 1.00E-03, Speed: 107.696 samples/sec, ObjLoss=24.547, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.021 [Epoch 177][Batch 299], LR: 1.00E-03, Speed: 89.395 samples/sec, ObjLoss=24.547, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.021 [Epoch 177][Batch 399], LR: 1.00E-03, Speed: 115.611 samples/sec, ObjLoss=24.546, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.020 [Epoch 177][Batch 499], LR: 1.00E-03, Speed: 7.179 samples/sec, ObjLoss=24.546, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.020 [Epoch 177][Batch 599], LR: 1.00E-03, Speed: 7.223 samples/sec, ObjLoss=24.545, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.020 [Epoch 177][Batch 699], LR: 1.00E-03, Speed: 9.015 samples/sec, ObjLoss=24.545, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.019 [Epoch 177][Batch 799], LR: 1.00E-03, Speed: 9.485 samples/sec, ObjLoss=24.544, BoxCenterLoss=14.406, BoxScaleLoss=5.043, ClassLoss=10.019 [Epoch 177][Batch 899], LR: 1.00E-03, Speed: 7.859 samples/sec, ObjLoss=24.543, BoxCenterLoss=14.406, BoxScaleLoss=5.042, ClassLoss=10.018 [Epoch 177][Batch 999], LR: 1.00E-03, Speed: 52.468 samples/sec, ObjLoss=24.542, BoxCenterLoss=14.406, BoxScaleLoss=5.042, ClassLoss=10.018 [Epoch 177][Batch 1099], LR: 1.00E-03, Speed: 10.055 samples/sec, ObjLoss=24.542, BoxCenterLoss=14.406, BoxScaleLoss=5.042, ClassLoss=10.017 [Epoch 177][Batch 1199], LR: 1.00E-03, Speed: 8.535 samples/sec, ObjLoss=24.541, BoxCenterLoss=14.406, BoxScaleLoss=5.042, ClassLoss=10.017 [Epoch 177][Batch 1299], LR: 1.00E-03, Speed: 9.382 samples/sec, ObjLoss=24.541, BoxCenterLoss=14.405, BoxScaleLoss=5.042, ClassLoss=10.016 [Epoch 177][Batch 1399], LR: 1.00E-03, Speed: 8.808 samples/sec, ObjLoss=24.540, BoxCenterLoss=14.405, BoxScaleLoss=5.042, ClassLoss=10.016 [Epoch 177][Batch 1499], LR: 1.00E-03, Speed: 8.347 samples/sec, ObjLoss=24.539, BoxCenterLoss=14.405, BoxScaleLoss=5.042, ClassLoss=10.015 [Epoch 177][Batch 1599], LR: 1.00E-03, Speed: 9.262 samples/sec, ObjLoss=24.539, BoxCenterLoss=14.405, BoxScaleLoss=5.042, ClassLoss=10.015 [Epoch 177][Batch 1699], LR: 1.00E-03, Speed: 9.934 samples/sec, ObjLoss=24.538, BoxCenterLoss=14.405, BoxScaleLoss=5.042, ClassLoss=10.014 [Epoch 177][Batch 1799], LR: 1.00E-03, Speed: 9.677 samples/sec, ObjLoss=24.538, BoxCenterLoss=14.405, BoxScaleLoss=5.041, ClassLoss=10.014 [Epoch 177] Training cost: 2202.653, ObjLoss=24.537, BoxCenterLoss=14.405, BoxScaleLoss=5.041, ClassLoss=10.014 [Epoch 177] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.476 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.280 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.162 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.496 person=39.0 bicycle=18.2 car=28.8 motorcycle=30.9 airplane=45.4 bus=53.1 train=50.1 truck=23.9 boat=14.1 traffic light=16.1 fire hydrant=43.3 stop sign=41.6 parking meter=31.5 bench=15.4 bird=21.3 cat=44.6 dog=40.5 horse=41.2 sheep=33.3 cow=40.1 elephant=46.9 bear=49.1 zebra=49.9 giraffe=51.4 backpack=6.3 umbrella=25.0 handbag=7.1 tie=19.7 suitcase=19.4 frisbee=39.6 skis=11.2 snowboard=18.6 sports ball=27.3 kite=27.3 baseball bat=14.1 baseball glove=21.3 skateboard=33.5 surfboard=22.3 tennis racket=28.7 bottle=22.5 wine glass=22.9 cup=27.8 fork=16.9 knife=5.9 spoon=7.2 bowl=24.9 banana=13.3 apple=11.0 sandwich=24.5 orange=18.6 broccoli=12.3 carrot=11.6 hot dog=20.7 pizza=34.0 donut=30.3 cake=24.7 chair=18.7 couch=30.3 potted plant=15.4 bed=35.2 dining table=19.5 toilet=44.4 tv=38.2 laptop=40.3 mouse=36.0 remote=13.6 keyboard=31.2 cell phone=19.4 microwave=31.1 oven=23.8 toaster=5.9 sink=23.2 refrigerator=36.4 book=5.8 clock=33.3 vase=22.7 scissors=18.7 teddy bear=30.0 hair drier=0.0 toothbrush=8.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =26.3 [Epoch 178][Batch 99], LR: 1.00E-03, Speed: 10.734 samples/sec, ObjLoss=24.537, BoxCenterLoss=14.405, BoxScaleLoss=5.041, ClassLoss=10.013 [Epoch 178][Batch 199], LR: 1.00E-03, Speed: 8.367 samples/sec, ObjLoss=24.536, BoxCenterLoss=14.405, BoxScaleLoss=5.041, ClassLoss=10.013 [Epoch 178][Batch 299], LR: 1.00E-03, Speed: 9.551 samples/sec, ObjLoss=24.535, BoxCenterLoss=14.405, BoxScaleLoss=5.041, ClassLoss=10.012 [Epoch 178][Batch 399], LR: 1.00E-03, Speed: 109.743 samples/sec, ObjLoss=24.534, BoxCenterLoss=14.404, BoxScaleLoss=5.041, ClassLoss=10.011 [Epoch 178][Batch 499], LR: 1.00E-03, Speed: 76.153 samples/sec, ObjLoss=24.534, BoxCenterLoss=14.404, BoxScaleLoss=5.041, ClassLoss=10.011 [Epoch 178][Batch 599], LR: 1.00E-03, Speed: 10.065 samples/sec, ObjLoss=24.533, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.010 [Epoch 178][Batch 699], LR: 1.00E-03, Speed: 8.179 samples/sec, ObjLoss=24.533, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.010 [Epoch 178][Batch 799], LR: 1.00E-03, Speed: 96.146 samples/sec, ObjLoss=24.532, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.009 [Epoch 178][Batch 899], LR: 1.00E-03, Speed: 10.031 samples/sec, ObjLoss=24.531, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.009 [Epoch 178][Batch 999], LR: 1.00E-03, Speed: 11.225 samples/sec, ObjLoss=24.530, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.008 [Epoch 178][Batch 1099], LR: 1.00E-03, Speed: 11.147 samples/sec, ObjLoss=24.530, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.008 [Epoch 178][Batch 1199], LR: 1.00E-03, Speed: 9.711 samples/sec, ObjLoss=24.529, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.007 [Epoch 178][Batch 1299], LR: 1.00E-03, Speed: 7.093 samples/sec, ObjLoss=24.529, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.007 [Epoch 178][Batch 1399], LR: 1.00E-03, Speed: 9.525 samples/sec, ObjLoss=24.528, BoxCenterLoss=14.404, BoxScaleLoss=5.040, ClassLoss=10.006 [Epoch 178][Batch 1499], LR: 1.00E-03, Speed: 10.614 samples/sec, ObjLoss=24.527, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.006 [Epoch 178][Batch 1599], LR: 1.00E-03, Speed: 9.334 samples/sec, ObjLoss=24.527, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.005 [Epoch 178][Batch 1699], LR: 1.00E-03, Speed: 8.402 samples/sec, ObjLoss=24.526, BoxCenterLoss=14.404, BoxScaleLoss=5.039, ClassLoss=10.005 [Epoch 178][Batch 1799], LR: 1.00E-03, Speed: 13.575 samples/sec, ObjLoss=24.525, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.004 [Epoch 178] Training cost: 2125.812, ObjLoss=24.525, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.004 [Epoch 178] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467 person=38.1 bicycle=18.4 car=25.4 motorcycle=25.7 airplane=44.1 bus=46.2 train=49.0 truck=18.9 boat=14.7 traffic light=12.4 fire hydrant=47.8 stop sign=35.9 parking meter=28.2 bench=14.1 bird=20.2 cat=44.5 dog=41.6 horse=38.5 sheep=33.9 cow=36.5 elephant=41.4 bear=50.4 zebra=46.9 giraffe=46.3 backpack=6.5 umbrella=24.4 handbag=5.3 tie=16.7 suitcase=17.3 frisbee=38.9 skis=11.1 snowboard=15.2 sports ball=26.7 kite=27.8 baseball bat=17.8 baseball glove=19.2 skateboard=30.9 surfboard=21.5 tennis racket=30.2 bottle=20.8 wine glass=19.3 cup=26.2 fork=16.4 knife=4.6 spoon=5.2 bowl=23.8 banana=11.8 apple=7.6 sandwich=21.0 orange=14.3 broccoli=10.7 carrot=9.8 hot dog=19.6 pizza=34.0 donut=26.8 cake=21.7 chair=16.4 couch=28.3 potted plant=11.8 bed=30.4 dining table=18.9 toilet=41.3 tv=39.4 laptop=40.6 mouse=33.3 remote=10.6 keyboard=30.9 cell phone=19.8 microwave=29.1 oven=22.3 toaster=4.8 sink=22.3 refrigerator=36.4 book=3.2 clock=31.8 vase=22.1 scissors=17.4 teddy bear=29.5 hair drier=0.0 toothbrush=8.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.6 [Epoch 179][Batch 99], LR: 1.00E-03, Speed: 113.342 samples/sec, ObjLoss=24.524, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.004 [Epoch 179][Batch 199], LR: 1.00E-03, Speed: 8.984 samples/sec, ObjLoss=24.524, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.003 [Epoch 179][Batch 299], LR: 1.00E-03, Speed: 87.285 samples/sec, ObjLoss=24.523, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.002 [Epoch 179][Batch 399], LR: 1.00E-03, Speed: 11.674 samples/sec, ObjLoss=24.522, BoxCenterLoss=14.403, BoxScaleLoss=5.039, ClassLoss=10.002 [Epoch 179][Batch 499], LR: 1.00E-03, Speed: 7.831 samples/sec, ObjLoss=24.521, BoxCenterLoss=14.403, BoxScaleLoss=5.038, ClassLoss=10.001 [Epoch 179][Batch 599], LR: 1.00E-03, Speed: 7.637 samples/sec, ObjLoss=24.521, BoxCenterLoss=14.403, BoxScaleLoss=5.038, ClassLoss=10.001 [Epoch 179][Batch 699], LR: 1.00E-03, Speed: 8.306 samples/sec, ObjLoss=24.520, BoxCenterLoss=14.403, BoxScaleLoss=5.038, ClassLoss=10.001 [Epoch 179][Batch 799], LR: 1.00E-03, Speed: 92.711 samples/sec, ObjLoss=24.520, BoxCenterLoss=14.403, BoxScaleLoss=5.038, ClassLoss=10.000 [Epoch 179][Batch 899], LR: 1.00E-03, Speed: 9.242 samples/sec, ObjLoss=24.519, BoxCenterLoss=14.403, BoxScaleLoss=5.038, ClassLoss=10.000 [Epoch 179][Batch 999], LR: 1.00E-03, Speed: 9.724 samples/sec, ObjLoss=24.518, BoxCenterLoss=14.402, BoxScaleLoss=5.038, ClassLoss=9.999 [Epoch 179][Batch 1099], LR: 1.00E-03, Speed: 9.121 samples/sec, ObjLoss=24.517, BoxCenterLoss=14.402, BoxScaleLoss=5.038, ClassLoss=9.999 [Epoch 179][Batch 1199], LR: 1.00E-03, Speed: 9.228 samples/sec, ObjLoss=24.517, BoxCenterLoss=14.402, BoxScaleLoss=5.038, ClassLoss=9.998 [Epoch 179][Batch 1299], LR: 1.00E-03, Speed: 94.690 samples/sec, ObjLoss=24.516, BoxCenterLoss=14.402, BoxScaleLoss=5.038, ClassLoss=9.997 [Epoch 179][Batch 1399], LR: 1.00E-03, Speed: 98.266 samples/sec, ObjLoss=24.515, BoxCenterLoss=14.402, BoxScaleLoss=5.037, ClassLoss=9.997 [Epoch 179][Batch 1499], LR: 1.00E-03, Speed: 120.696 samples/sec, ObjLoss=24.514, BoxCenterLoss=14.402, BoxScaleLoss=5.037, ClassLoss=9.996 [Epoch 179][Batch 1599], LR: 1.00E-03, Speed: 8.595 samples/sec, ObjLoss=24.514, BoxCenterLoss=14.402, BoxScaleLoss=5.037, ClassLoss=9.996 [Epoch 179][Batch 1699], LR: 1.00E-03, Speed: 105.639 samples/sec, ObjLoss=24.513, BoxCenterLoss=14.402, BoxScaleLoss=5.037, ClassLoss=9.996 [Epoch 179][Batch 1799], LR: 1.00E-03, Speed: 12.011 samples/sec, ObjLoss=24.513, BoxCenterLoss=14.402, BoxScaleLoss=5.037, ClassLoss=9.995 [Epoch 179] Training cost: 2146.488, ObjLoss=24.512, BoxCenterLoss=14.401, BoxScaleLoss=5.037, ClassLoss=9.995 [Epoch 179] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.106 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.224 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.158 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.358 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.463 person=38.3 bicycle=18.0 car=28.4 motorcycle=27.1 airplane=40.6 bus=44.1 train=50.0 truck=22.7 boat=11.4 traffic light=14.2 fire hydrant=40.5 stop sign=39.3 parking meter=26.0 bench=13.5 bird=19.9 cat=37.3 dog=39.1 horse=40.2 sheep=34.8 cow=37.6 elephant=41.9 bear=41.1 zebra=43.7 giraffe=46.4 backpack=6.7 umbrella=24.4 handbag=6.0 tie=18.2 suitcase=21.5 frisbee=38.3 skis=11.4 snowboard=18.0 sports ball=29.7 kite=26.2 baseball bat=14.9 baseball glove=20.9 skateboard=29.7 surfboard=19.8 tennis racket=26.0 bottle=19.6 wine glass=23.2 cup=26.7 fork=15.5 knife=5.6 spoon=5.4 bowl=27.1 banana=14.1 apple=8.8 sandwich=20.3 orange=16.9 broccoli=11.1 carrot=9.5 hot dog=20.4 pizza=37.2 donut=32.9 cake=19.6 chair=15.9 couch=30.7 potted plant=14.6 bed=33.4 dining table=21.5 toilet=38.2 tv=36.9 laptop=39.5 mouse=41.4 remote=13.1 keyboard=29.3 cell phone=18.6 microwave=40.8 oven=21.4 toaster=1.2 sink=25.3 refrigerator=36.4 book=5.2 clock=35.3 vase=21.9 scissors=21.8 teddy bear=27.0 hair drier=0.0 toothbrush=7.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.0 [Epoch 180][Batch 99], LR: 1.00E-03, Speed: 8.281 samples/sec, ObjLoss=24.512, BoxCenterLoss=14.401, BoxScaleLoss=5.037, ClassLoss=9.994 [Epoch 180][Batch 199], LR: 1.00E-03, Speed: 10.103 samples/sec, ObjLoss=24.511, BoxCenterLoss=14.401, BoxScaleLoss=5.037, ClassLoss=9.994 [Epoch 180][Batch 299], LR: 1.00E-03, Speed: 8.791 samples/sec, ObjLoss=24.510, BoxCenterLoss=14.401, BoxScaleLoss=5.037, ClassLoss=9.994 [Epoch 180][Batch 399], LR: 1.00E-03, Speed: 120.169 samples/sec, ObjLoss=24.509, BoxCenterLoss=14.401, BoxScaleLoss=5.037, ClassLoss=9.993 [Epoch 180][Batch 499], LR: 1.00E-03, Speed: 10.258 samples/sec, ObjLoss=24.509, BoxCenterLoss=14.401, BoxScaleLoss=5.037, ClassLoss=9.993 [Epoch 180][Batch 599], LR: 1.00E-03, Speed: 10.557 samples/sec, ObjLoss=24.508, BoxCenterLoss=14.401, BoxScaleLoss=5.037, ClassLoss=9.992 [Epoch 180][Batch 699], LR: 1.00E-03, Speed: 11.328 samples/sec, ObjLoss=24.507, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.992 [Epoch 180][Batch 799], LR: 1.00E-03, Speed: 16.382 samples/sec, ObjLoss=24.507, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.991 [Epoch 180][Batch 899], LR: 1.00E-03, Speed: 7.614 samples/sec, ObjLoss=24.506, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.991 [Epoch 180][Batch 999], LR: 1.00E-03, Speed: 10.537 samples/sec, ObjLoss=24.506, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.990 [Epoch 180][Batch 1099], LR: 1.00E-03, Speed: 9.319 samples/sec, ObjLoss=24.505, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.990 [Epoch 180][Batch 1199], LR: 1.00E-03, Speed: 84.016 samples/sec, ObjLoss=24.505, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.989 [Epoch 180][Batch 1299], LR: 1.00E-03, Speed: 9.277 samples/sec, ObjLoss=24.504, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.989 [Epoch 180][Batch 1399], LR: 1.00E-03, Speed: 104.911 samples/sec, ObjLoss=24.503, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.988 [Epoch 180][Batch 1499], LR: 1.00E-03, Speed: 10.742 samples/sec, ObjLoss=24.503, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.988 [Epoch 180][Batch 1599], LR: 1.00E-03, Speed: 8.538 samples/sec, ObjLoss=24.503, BoxCenterLoss=14.400, BoxScaleLoss=5.036, ClassLoss=9.988 [Epoch 180][Batch 1699], LR: 1.00E-03, Speed: 96.126 samples/sec, ObjLoss=24.502, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.987 [Epoch 180][Batch 1799], LR: 1.00E-03, Speed: 11.031 samples/sec, ObjLoss=24.502, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.987 [Epoch 180] Training cost: 2196.769, ObjLoss=24.501, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.987 [Epoch 180] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.111 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.158 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481 person=38.2 bicycle=16.5 car=28.4 motorcycle=28.0 airplane=45.0 bus=51.4 train=51.7 truck=24.5 boat=15.3 traffic light=15.5 fire hydrant=50.2 stop sign=39.5 parking meter=28.7 bench=14.7 bird=21.4 cat=49.3 dog=43.6 horse=38.9 sheep=36.4 cow=34.1 elephant=49.7 bear=47.9 zebra=46.6 giraffe=48.6 backpack=6.7 umbrella=25.6 handbag=5.7 tie=18.9 suitcase=20.7 frisbee=33.8 skis=9.7 snowboard=12.3 sports ball=26.2 kite=28.3 baseball bat=14.3 baseball glove=20.6 skateboard=30.3 surfboard=19.5 tennis racket=27.3 bottle=22.7 wine glass=19.6 cup=27.5 fork=15.1 knife=5.8 spoon=5.2 bowl=25.9 banana=12.8 apple=7.6 sandwich=19.1 orange=17.4 broccoli=13.7 carrot=12.5 hot dog=17.9 pizza=34.6 donut=29.9 cake=26.4 chair=15.5 couch=23.9 potted plant=12.3 bed=30.8 dining table=17.7 toilet=40.4 tv=43.6 laptop=37.7 mouse=32.3 remote=12.3 keyboard=37.9 cell phone=17.1 microwave=35.1 oven=21.7 toaster=7.1 sink=23.9 refrigerator=34.5 book=5.2 clock=34.4 vase=21.8 scissors=16.9 teddy bear=29.7 hair drier=0.0 toothbrush=5.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.4 [Epoch 181][Batch 99], LR: 1.00E-03, Speed: 7.777 samples/sec, ObjLoss=24.501, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.986 [Epoch 181][Batch 199], LR: 1.00E-03, Speed: 10.278 samples/sec, ObjLoss=24.500, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.986 [Epoch 181][Batch 299], LR: 1.00E-03, Speed: 7.502 samples/sec, ObjLoss=24.500, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.985 [Epoch 181][Batch 399], LR: 1.00E-03, Speed: 10.360 samples/sec, ObjLoss=24.499, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.985 [Epoch 181][Batch 499], LR: 1.00E-03, Speed: 9.980 samples/sec, ObjLoss=24.499, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.985 [Epoch 181][Batch 599], LR: 1.00E-03, Speed: 14.886 samples/sec, ObjLoss=24.499, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.985 [Epoch 181][Batch 699], LR: 1.00E-03, Speed: 8.350 samples/sec, ObjLoss=24.498, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.984 [Epoch 181][Batch 799], LR: 1.00E-03, Speed: 8.224 samples/sec, ObjLoss=24.497, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.984 [Epoch 181][Batch 899], LR: 1.00E-03, Speed: 11.231 samples/sec, ObjLoss=24.497, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.983 [Epoch 181][Batch 999], LR: 1.00E-03, Speed: 10.147 samples/sec, ObjLoss=24.496, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.983 [Epoch 181][Batch 1099], LR: 1.00E-03, Speed: 115.315 samples/sec, ObjLoss=24.496, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.982 [Epoch 181][Batch 1199], LR: 1.00E-03, Speed: 8.160 samples/sec, ObjLoss=24.496, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.982 [Epoch 181][Batch 1299], LR: 1.00E-03, Speed: 8.455 samples/sec, ObjLoss=24.495, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.982 [Epoch 181][Batch 1399], LR: 1.00E-03, Speed: 9.039 samples/sec, ObjLoss=24.494, BoxCenterLoss=14.400, BoxScaleLoss=5.035, ClassLoss=9.981 [Epoch 181][Batch 1499], LR: 1.00E-03, Speed: 15.184 samples/sec, ObjLoss=24.494, BoxCenterLoss=14.400, BoxScaleLoss=5.034, ClassLoss=9.981 [Epoch 181][Batch 1599], LR: 1.00E-03, Speed: 10.363 samples/sec, ObjLoss=24.493, BoxCenterLoss=14.400, BoxScaleLoss=5.034, ClassLoss=9.980 [Epoch 181][Batch 1699], LR: 1.00E-03, Speed: 11.378 samples/sec, ObjLoss=24.493, BoxCenterLoss=14.400, BoxScaleLoss=5.034, ClassLoss=9.980 [Epoch 181][Batch 1799], LR: 1.00E-03, Speed: 128.813 samples/sec, ObjLoss=24.492, BoxCenterLoss=14.400, BoxScaleLoss=5.034, ClassLoss=9.979 [Epoch 181] Training cost: 2201.481, ObjLoss=24.492, BoxCenterLoss=14.400, BoxScaleLoss=5.034, ClassLoss=9.979 [Epoch 181] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.464 person=39.3 bicycle=17.8 car=27.4 motorcycle=29.0 airplane=38.7 bus=48.1 train=44.5 truck=23.5 boat=14.3 traffic light=15.0 fire hydrant=34.5 stop sign=40.7 parking meter=22.7 bench=13.8 bird=19.1 cat=43.4 dog=41.4 horse=37.0 sheep=33.9 cow=36.0 elephant=46.2 bear=38.2 zebra=49.4 giraffe=49.1 backpack=7.0 umbrella=23.2 handbag=6.8 tie=17.1 suitcase=19.0 frisbee=37.9 skis=11.1 snowboard=13.6 sports ball=30.7 kite=27.8 baseball bat=13.4 baseball glove=23.6 skateboard=30.0 surfboard=21.0 tennis racket=32.2 bottle=21.2 wine glass=21.0 cup=27.0 fork=16.7 knife=6.0 spoon=5.2 bowl=25.5 banana=14.1 apple=9.4 sandwich=17.5 orange=19.9 broccoli=12.4 carrot=9.0 hot dog=17.8 pizza=36.4 donut=26.6 cake=25.3 chair=17.4 couch=29.1 potted plant=13.8 bed=31.3 dining table=18.8 toilet=43.7 tv=38.7 laptop=40.1 mouse=37.0 remote=9.8 keyboard=26.9 cell phone=18.1 microwave=32.9 oven=20.1 toaster=1.5 sink=23.7 refrigerator=34.8 book=5.3 clock=35.9 vase=20.8 scissors=21.1 teddy bear=27.5 hair drier=0.0 toothbrush=6.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.8 [Epoch 182][Batch 99], LR: 1.00E-03, Speed: 9.541 samples/sec, ObjLoss=24.491, BoxCenterLoss=14.400, BoxScaleLoss=5.034, ClassLoss=9.978 [Epoch 182][Batch 199], LR: 1.00E-03, Speed: 131.195 samples/sec, ObjLoss=24.490, BoxCenterLoss=14.399, BoxScaleLoss=5.034, ClassLoss=9.978 [Epoch 182][Batch 299], LR: 1.00E-03, Speed: 10.848 samples/sec, ObjLoss=24.490, BoxCenterLoss=14.399, BoxScaleLoss=5.034, ClassLoss=9.978 [Epoch 182][Batch 399], LR: 1.00E-03, Speed: 8.958 samples/sec, ObjLoss=24.489, BoxCenterLoss=14.399, BoxScaleLoss=5.034, ClassLoss=9.977 [Epoch 182][Batch 499], LR: 1.00E-03, Speed: 10.708 samples/sec, ObjLoss=24.488, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.976 [Epoch 182][Batch 599], LR: 1.00E-03, Speed: 10.622 samples/sec, ObjLoss=24.487, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.976 [Epoch 182][Batch 699], LR: 1.00E-03, Speed: 10.764 samples/sec, ObjLoss=24.487, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.975 [Epoch 182][Batch 799], LR: 1.00E-03, Speed: 8.056 samples/sec, ObjLoss=24.487, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.975 [Epoch 182][Batch 899], LR: 1.00E-03, Speed: 10.039 samples/sec, ObjLoss=24.486, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.974 [Epoch 182][Batch 999], LR: 1.00E-03, Speed: 8.826 samples/sec, ObjLoss=24.486, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.974 [Epoch 182][Batch 1099], LR: 1.00E-03, Speed: 10.361 samples/sec, ObjLoss=24.486, BoxCenterLoss=14.400, BoxScaleLoss=5.033, ClassLoss=9.973 [Epoch 182][Batch 1199], LR: 1.00E-03, Speed: 109.684 samples/sec, ObjLoss=24.485, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.973 [Epoch 182][Batch 1299], LR: 1.00E-03, Speed: 11.689 samples/sec, ObjLoss=24.484, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.972 [Epoch 182][Batch 1399], LR: 1.00E-03, Speed: 115.929 samples/sec, ObjLoss=24.483, BoxCenterLoss=14.399, BoxScaleLoss=5.033, ClassLoss=9.972 [Epoch 182][Batch 1499], LR: 1.00E-03, Speed: 8.792 samples/sec, ObjLoss=24.483, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.971 [Epoch 182][Batch 1599], LR: 1.00E-03, Speed: 10.688 samples/sec, ObjLoss=24.482, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.971 [Epoch 182][Batch 1699], LR: 1.00E-03, Speed: 11.073 samples/sec, ObjLoss=24.482, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.970 [Epoch 182][Batch 1799], LR: 1.00E-03, Speed: 12.310 samples/sec, ObjLoss=24.481, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.970 [Epoch 182] Training cost: 2169.682, ObjLoss=24.481, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.970 [Epoch 182] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.472 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.224 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.464 person=38.3 bicycle=18.7 car=25.6 motorcycle=30.8 airplane=44.2 bus=51.0 train=46.2 truck=23.6 boat=14.1 traffic light=14.4 fire hydrant=46.9 stop sign=37.7 parking meter=32.2 bench=14.7 bird=22.0 cat=39.0 dog=36.7 horse=36.7 sheep=32.0 cow=31.7 elephant=49.6 bear=42.7 zebra=46.0 giraffe=45.5 backpack=6.8 umbrella=24.6 handbag=6.5 tie=17.3 suitcase=16.8 frisbee=24.6 skis=11.5 snowboard=17.8 sports ball=28.8 kite=23.1 baseball bat=10.2 baseball glove=19.7 skateboard=26.3 surfboard=21.3 tennis racket=28.8 bottle=22.4 wine glass=22.6 cup=25.4 fork=15.3 knife=4.9 spoon=6.5 bowl=23.9 banana=13.3 apple=7.5 sandwich=17.6 orange=19.1 broccoli=10.1 carrot=10.2 hot dog=18.6 pizza=34.9 donut=30.9 cake=25.2 chair=15.8 couch=27.8 potted plant=14.7 bed=37.0 dining table=21.0 toilet=36.1 tv=31.0 laptop=36.1 mouse=40.3 remote=11.6 keyboard=29.6 cell phone=14.0 microwave=32.3 oven=21.0 toaster=0.0 sink=23.4 refrigerator=30.6 book=5.4 clock=26.9 vase=21.8 scissors=22.9 teddy bear=27.3 hair drier=0.0 toothbrush=5.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.3 [Epoch 183][Batch 99], LR: 1.00E-03, Speed: 8.665 samples/sec, ObjLoss=24.480, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.969 [Epoch 183][Batch 199], LR: 1.00E-03, Speed: 92.488 samples/sec, ObjLoss=24.480, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.969 [Epoch 183][Batch 299], LR: 1.00E-03, Speed: 9.221 samples/sec, ObjLoss=24.480, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.968 [Epoch 183][Batch 399], LR: 1.00E-03, Speed: 10.776 samples/sec, ObjLoss=24.479, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.968 [Epoch 183][Batch 499], LR: 1.00E-03, Speed: 99.740 samples/sec, ObjLoss=24.478, BoxCenterLoss=14.399, BoxScaleLoss=5.032, ClassLoss=9.967 [Epoch 183][Batch 599], LR: 1.00E-03, Speed: 7.487 samples/sec, ObjLoss=24.478, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.967 [Epoch 183][Batch 699], LR: 1.00E-03, Speed: 10.014 samples/sec, ObjLoss=24.477, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.966 [Epoch 183][Batch 799], LR: 1.00E-03, Speed: 11.278 samples/sec, ObjLoss=24.477, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.966 [Epoch 183][Batch 899], LR: 1.00E-03, Speed: 11.188 samples/sec, ObjLoss=24.476, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.965 [Epoch 183][Batch 999], LR: 1.00E-03, Speed: 8.944 samples/sec, ObjLoss=24.475, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.965 [Epoch 183][Batch 1099], LR: 1.00E-03, Speed: 121.443 samples/sec, ObjLoss=24.475, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.964 [Epoch 183][Batch 1199], LR: 1.00E-03, Speed: 10.278 samples/sec, ObjLoss=24.474, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.964 [Epoch 183][Batch 1299], LR: 1.00E-03, Speed: 11.911 samples/sec, ObjLoss=24.474, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.963 [Epoch 183][Batch 1399], LR: 1.00E-03, Speed: 10.228 samples/sec, ObjLoss=24.473, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.963 [Epoch 183][Batch 1499], LR: 1.00E-03, Speed: 11.298 samples/sec, ObjLoss=24.473, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.962 [Epoch 183][Batch 1599], LR: 1.00E-03, Speed: 129.364 samples/sec, ObjLoss=24.473, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.962 [Epoch 183][Batch 1699], LR: 1.00E-03, Speed: 9.730 samples/sec, ObjLoss=24.472, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.962 [Epoch 183][Batch 1799], LR: 1.00E-03, Speed: 14.558 samples/sec, ObjLoss=24.471, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.961 [Epoch 183] Training cost: 2150.843, ObjLoss=24.471, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.961 [Epoch 183] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.268 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481 person=38.0 bicycle=17.5 car=27.4 motorcycle=29.8 airplane=46.2 bus=47.2 train=45.8 truck=25.2 boat=15.5 traffic light=14.3 fire hydrant=37.4 stop sign=41.0 parking meter=32.6 bench=14.0 bird=21.5 cat=46.3 dog=37.3 horse=32.3 sheep=32.6 cow=36.6 elephant=44.9 bear=43.9 zebra=46.7 giraffe=45.9 backpack=6.9 umbrella=22.7 handbag=5.3 tie=19.0 suitcase=18.6 frisbee=31.9 skis=13.4 snowboard=20.0 sports ball=24.0 kite=25.4 baseball bat=15.2 baseball glove=20.8 skateboard=35.7 surfboard=20.6 tennis racket=29.3 bottle=19.6 wine glass=20.4 cup=25.7 fork=15.6 knife=6.6 spoon=6.2 bowl=25.7 banana=9.4 apple=9.1 sandwich=22.3 orange=18.9 broccoli=12.0 carrot=10.9 hot dog=17.7 pizza=34.3 donut=23.1 cake=22.7 chair=17.0 couch=26.9 potted plant=15.9 bed=33.3 dining table=19.8 toilet=40.1 tv=35.7 laptop=34.0 mouse=34.4 remote=10.5 keyboard=28.1 cell phone=17.3 microwave=31.2 oven=21.0 toaster=2.4 sink=20.1 refrigerator=29.8 book=4.8 clock=29.6 vase=19.2 scissors=23.1 teddy bear=25.6 hair drier=0.0 toothbrush=5.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.4 [Epoch 184][Batch 99], LR: 1.00E-03, Speed: 9.285 samples/sec, ObjLoss=24.471, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.961 [Epoch 184][Batch 199], LR: 1.00E-03, Speed: 10.427 samples/sec, ObjLoss=24.471, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.960 [Epoch 184][Batch 299], LR: 1.00E-03, Speed: 10.278 samples/sec, ObjLoss=24.471, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.960 [Epoch 184][Batch 399], LR: 1.00E-03, Speed: 7.498 samples/sec, ObjLoss=24.470, BoxCenterLoss=14.399, BoxScaleLoss=5.031, ClassLoss=9.959 [Epoch 184][Batch 499], LR: 1.00E-03, Speed: 8.939 samples/sec, ObjLoss=24.470, BoxCenterLoss=14.399, BoxScaleLoss=5.030, ClassLoss=9.959 [Epoch 184][Batch 599], LR: 1.00E-03, Speed: 10.277 samples/sec, ObjLoss=24.469, BoxCenterLoss=14.399, BoxScaleLoss=5.030, ClassLoss=9.958 [Epoch 184][Batch 699], LR: 1.00E-03, Speed: 6.406 samples/sec, ObjLoss=24.469, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.958 [Epoch 184][Batch 799], LR: 1.00E-03, Speed: 8.846 samples/sec, ObjLoss=24.468, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.958 [Epoch 184][Batch 899], LR: 1.00E-03, Speed: 10.614 samples/sec, ObjLoss=24.468, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.957 [Epoch 184][Batch 999], LR: 1.00E-03, Speed: 40.122 samples/sec, ObjLoss=24.467, BoxCenterLoss=14.399, BoxScaleLoss=5.030, ClassLoss=9.957 [Epoch 184][Batch 1099], LR: 1.00E-03, Speed: 9.707 samples/sec, ObjLoss=24.467, BoxCenterLoss=14.399, BoxScaleLoss=5.030, ClassLoss=9.956 [Epoch 184][Batch 1199], LR: 1.00E-03, Speed: 8.688 samples/sec, ObjLoss=24.466, BoxCenterLoss=14.399, BoxScaleLoss=5.030, ClassLoss=9.956 [Epoch 184][Batch 1299], LR: 1.00E-03, Speed: 9.204 samples/sec, ObjLoss=24.466, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.956 [Epoch 184][Batch 1399], LR: 1.00E-03, Speed: 11.947 samples/sec, ObjLoss=24.466, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.955 [Epoch 184][Batch 1499], LR: 1.00E-03, Speed: 10.033 samples/sec, ObjLoss=24.465, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.955 [Epoch 184][Batch 1599], LR: 1.00E-03, Speed: 84.494 samples/sec, ObjLoss=24.465, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.954 [Epoch 184][Batch 1699], LR: 1.00E-03, Speed: 8.993 samples/sec, ObjLoss=24.464, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.954 [Epoch 184][Batch 1799], LR: 1.00E-03, Speed: 9.960 samples/sec, ObjLoss=24.464, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.953 [Epoch 184] Training cost: 2242.424, ObjLoss=24.464, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.953 [Epoch 184] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.231 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.456 person=38.3 bicycle=17.2 car=26.9 motorcycle=30.6 airplane=40.9 bus=44.3 train=41.9 truck=24.2 boat=13.9 traffic light=15.4 fire hydrant=45.7 stop sign=41.6 parking meter=21.1 bench=13.6 bird=18.9 cat=45.5 dog=42.2 horse=32.4 sheep=30.1 cow=35.7 elephant=46.0 bear=40.4 zebra=44.2 giraffe=46.2 backpack=5.5 umbrella=21.7 handbag=5.1 tie=18.1 suitcase=18.6 frisbee=34.2 skis=10.1 snowboard=14.3 sports ball=27.2 kite=22.7 baseball bat=10.2 baseball glove=22.1 skateboard=31.0 surfboard=18.6 tennis racket=25.9 bottle=20.5 wine glass=21.1 cup=24.0 fork=16.4 knife=5.5 spoon=5.3 bowl=25.6 banana=12.4 apple=8.3 sandwich=19.1 orange=14.6 broccoli=11.9 carrot=7.8 hot dog=19.0 pizza=35.2 donut=29.0 cake=19.6 chair=16.6 couch=32.4 potted plant=14.3 bed=30.8 dining table=18.7 toilet=41.2 tv=36.4 laptop=37.0 mouse=34.9 remote=12.7 keyboard=26.9 cell phone=18.7 microwave=36.1 oven=20.3 toaster=0.0 sink=19.5 refrigerator=34.3 book=3.6 clock=35.9 vase=21.9 scissors=21.5 teddy bear=29.8 hair drier=0.0 toothbrush=7.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.1 [Epoch 185][Batch 99], LR: 1.00E-03, Speed: 8.463 samples/sec, ObjLoss=24.463, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.953 [Epoch 185][Batch 199], LR: 1.00E-03, Speed: 92.468 samples/sec, ObjLoss=24.463, BoxCenterLoss=14.400, BoxScaleLoss=5.030, ClassLoss=9.952 [Epoch 185][Batch 299], LR: 1.00E-03, Speed: 9.532 samples/sec, ObjLoss=24.462, BoxCenterLoss=14.400, BoxScaleLoss=5.029, ClassLoss=9.952 [Epoch 185][Batch 399], LR: 1.00E-03, Speed: 95.619 samples/sec, ObjLoss=24.462, BoxCenterLoss=14.400, BoxScaleLoss=5.029, ClassLoss=9.952 [Epoch 185][Batch 499], LR: 1.00E-03, Speed: 9.086 samples/sec, ObjLoss=24.461, BoxCenterLoss=14.400, BoxScaleLoss=5.029, ClassLoss=9.951 [Epoch 185][Batch 599], LR: 1.00E-03, Speed: 8.274 samples/sec, ObjLoss=24.461, BoxCenterLoss=14.400, BoxScaleLoss=5.029, ClassLoss=9.951 [Epoch 185][Batch 699], LR: 1.00E-03, Speed: 8.998 samples/sec, ObjLoss=24.460, BoxCenterLoss=14.399, BoxScaleLoss=5.029, ClassLoss=9.950 [Epoch 185][Batch 799], LR: 1.00E-03, Speed: 9.395 samples/sec, ObjLoss=24.459, BoxCenterLoss=14.399, BoxScaleLoss=5.029, ClassLoss=9.950 [Epoch 185][Batch 899], LR: 1.00E-03, Speed: 9.859 samples/sec, ObjLoss=24.458, BoxCenterLoss=14.399, BoxScaleLoss=5.029, ClassLoss=9.949 [Epoch 185][Batch 999], LR: 1.00E-03, Speed: 9.243 samples/sec, ObjLoss=24.458, BoxCenterLoss=14.399, BoxScaleLoss=5.029, ClassLoss=9.949 [Epoch 185][Batch 1099], LR: 1.00E-03, Speed: 18.493 samples/sec, ObjLoss=24.457, BoxCenterLoss=14.399, BoxScaleLoss=5.029, ClassLoss=9.948 [Epoch 185][Batch 1199], LR: 1.00E-03, Speed: 11.486 samples/sec, ObjLoss=24.457, BoxCenterLoss=14.399, BoxScaleLoss=5.029, ClassLoss=9.948 [Epoch 185][Batch 1299], LR: 1.00E-03, Speed: 8.424 samples/sec, ObjLoss=24.456, BoxCenterLoss=14.399, BoxScaleLoss=5.028, ClassLoss=9.947 [Epoch 185][Batch 1399], LR: 1.00E-03, Speed: 9.566 samples/sec, ObjLoss=24.455, BoxCenterLoss=14.399, BoxScaleLoss=5.028, ClassLoss=9.947 [Epoch 185][Batch 1499], LR: 1.00E-03, Speed: 95.731 samples/sec, ObjLoss=24.455, BoxCenterLoss=14.398, BoxScaleLoss=5.028, ClassLoss=9.946 [Epoch 185][Batch 1599], LR: 1.00E-03, Speed: 115.008 samples/sec, ObjLoss=24.454, BoxCenterLoss=14.398, BoxScaleLoss=5.028, ClassLoss=9.946 [Epoch 185][Batch 1699], LR: 1.00E-03, Speed: 9.687 samples/sec, ObjLoss=24.453, BoxCenterLoss=14.398, BoxScaleLoss=5.028, ClassLoss=9.945 [Epoch 185][Batch 1799], LR: 1.00E-03, Speed: 97.112 samples/sec, ObjLoss=24.453, BoxCenterLoss=14.398, BoxScaleLoss=5.028, ClassLoss=9.945 [Epoch 185] Training cost: 2252.010, ObjLoss=24.452, BoxCenterLoss=14.398, BoxScaleLoss=5.028, ClassLoss=9.944 [Epoch 185] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.459 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.469 person=38.6 bicycle=18.7 car=26.8 motorcycle=29.3 airplane=45.1 bus=45.0 train=47.0 truck=21.8 boat=12.0 traffic light=11.5 fire hydrant=43.6 stop sign=42.1 parking meter=30.1 bench=13.4 bird=19.9 cat=46.5 dog=41.0 horse=37.2 sheep=35.3 cow=34.9 elephant=45.8 bear=44.8 zebra=48.2 giraffe=50.1 backpack=6.8 umbrella=22.0 handbag=4.6 tie=17.2 suitcase=15.2 frisbee=37.6 skis=11.5 snowboard=15.9 sports ball=25.4 kite=21.6 baseball bat=12.6 baseball glove=19.3 skateboard=30.7 surfboard=21.2 tennis racket=27.3 bottle=19.7 wine glass=21.7 cup=26.8 fork=16.7 knife=5.9 spoon=6.0 bowl=24.5 banana=14.3 apple=5.4 sandwich=19.7 orange=15.4 broccoli=10.0 carrot=9.5 hot dog=16.8 pizza=35.6 donut=27.0 cake=21.8 chair=14.7 couch=29.6 potted plant=14.7 bed=30.3 dining table=18.9 toilet=40.2 tv=32.2 laptop=39.2 mouse=31.7 remote=10.2 keyboard=29.9 cell phone=12.9 microwave=27.8 oven=20.9 toaster=1.2 sink=21.9 refrigerator=30.6 book=5.5 clock=33.5 vase=24.2 scissors=15.5 teddy bear=28.3 hair drier=0.0 toothbrush=8.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.2 [Epoch 186][Batch 99], LR: 1.00E-03, Speed: 10.934 samples/sec, ObjLoss=24.452, BoxCenterLoss=14.398, BoxScaleLoss=5.027, ClassLoss=9.944 [Epoch 186][Batch 199], LR: 1.00E-03, Speed: 11.100 samples/sec, ObjLoss=24.451, BoxCenterLoss=14.398, BoxScaleLoss=5.027, ClassLoss=9.943 [Epoch 186][Batch 299], LR: 1.00E-03, Speed: 9.815 samples/sec, ObjLoss=24.451, BoxCenterLoss=14.398, BoxScaleLoss=5.027, ClassLoss=9.943 [Epoch 186][Batch 399], LR: 1.00E-03, Speed: 124.705 samples/sec, ObjLoss=24.450, BoxCenterLoss=14.398, BoxScaleLoss=5.027, ClassLoss=9.943 [Epoch 186][Batch 499], LR: 1.00E-03, Speed: 9.664 samples/sec, ObjLoss=24.450, BoxCenterLoss=14.398, BoxScaleLoss=5.027, ClassLoss=9.942 [Epoch 186][Batch 599], LR: 1.00E-03, Speed: 11.887 samples/sec, ObjLoss=24.449, BoxCenterLoss=14.397, BoxScaleLoss=5.027, ClassLoss=9.942 [Epoch 186][Batch 699], LR: 1.00E-03, Speed: 9.112 samples/sec, ObjLoss=24.449, BoxCenterLoss=14.397, BoxScaleLoss=5.027, ClassLoss=9.941 [Epoch 186][Batch 799], LR: 1.00E-03, Speed: 7.932 samples/sec, ObjLoss=24.448, BoxCenterLoss=14.397, BoxScaleLoss=5.027, ClassLoss=9.941 [Epoch 186][Batch 899], LR: 1.00E-03, Speed: 137.722 samples/sec, ObjLoss=24.447, BoxCenterLoss=14.397, BoxScaleLoss=5.027, ClassLoss=9.941 [Epoch 186][Batch 999], LR: 1.00E-03, Speed: 10.555 samples/sec, ObjLoss=24.447, BoxCenterLoss=14.397, BoxScaleLoss=5.027, ClassLoss=9.940 [Epoch 186][Batch 1099], LR: 1.00E-03, Speed: 10.775 samples/sec, ObjLoss=24.446, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.940 [Epoch 186][Batch 1199], LR: 1.00E-03, Speed: 11.398 samples/sec, ObjLoss=24.446, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.939 [Epoch 186][Batch 1299], LR: 1.00E-03, Speed: 128.067 samples/sec, ObjLoss=24.445, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.939 [Epoch 186][Batch 1399], LR: 1.00E-03, Speed: 8.912 samples/sec, ObjLoss=24.444, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.938 [Epoch 186][Batch 1499], LR: 1.00E-03, Speed: 8.069 samples/sec, ObjLoss=24.444, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.938 [Epoch 186][Batch 1599], LR: 1.00E-03, Speed: 9.379 samples/sec, ObjLoss=24.443, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.937 [Epoch 186][Batch 1699], LR: 1.00E-03, Speed: 11.137 samples/sec, ObjLoss=24.443, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.937 [Epoch 186][Batch 1799], LR: 1.00E-03, Speed: 11.878 samples/sec, ObjLoss=24.442, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.936 [Epoch 186] Training cost: 2287.401, ObjLoss=24.442, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.936 [Epoch 186] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.472 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.273 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.473 person=39.3 bicycle=17.3 car=27.8 motorcycle=26.5 airplane=41.7 bus=43.2 train=49.2 truck=22.0 boat=13.3 traffic light=14.4 fire hydrant=46.7 stop sign=42.7 parking meter=29.6 bench=13.9 bird=20.4 cat=46.4 dog=40.7 horse=35.8 sheep=33.4 cow=35.4 elephant=41.3 bear=48.3 zebra=44.4 giraffe=51.3 backpack=6.3 umbrella=24.9 handbag=6.3 tie=18.5 suitcase=17.4 frisbee=42.0 skis=10.9 snowboard=17.6 sports ball=25.6 kite=25.4 baseball bat=13.6 baseball glove=20.6 skateboard=30.6 surfboard=21.8 tennis racket=25.7 bottle=17.3 wine glass=21.0 cup=25.7 fork=15.1 knife=5.5 spoon=5.8 bowl=25.5 banana=10.5 apple=9.9 sandwich=21.7 orange=18.8 broccoli=12.5 carrot=12.2 hot dog=18.7 pizza=33.5 donut=28.9 cake=22.2 chair=17.1 couch=29.1 potted plant=15.1 bed=36.7 dining table=18.0 toilet=37.8 tv=39.1 laptop=35.5 mouse=42.0 remote=13.7 keyboard=31.9 cell phone=19.2 microwave=32.0 oven=22.1 toaster=4.8 sink=23.1 refrigerator=30.2 book=5.0 clock=33.8 vase=20.9 scissors=20.5 teddy bear=29.3 hair drier=0.0 toothbrush=9.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.0 [Epoch 187][Batch 99], LR: 1.00E-03, Speed: 9.893 samples/sec, ObjLoss=24.442, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.936 [Epoch 187][Batch 199], LR: 1.00E-03, Speed: 109.962 samples/sec, ObjLoss=24.441, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.936 [Epoch 187][Batch 299], LR: 1.00E-03, Speed: 7.352 samples/sec, ObjLoss=24.441, BoxCenterLoss=14.397, BoxScaleLoss=5.026, ClassLoss=9.935 [Epoch 187][Batch 399], LR: 1.00E-03, Speed: 110.140 samples/sec, ObjLoss=24.441, BoxCenterLoss=14.397, BoxScaleLoss=5.025, ClassLoss=9.935 [Epoch 187][Batch 499], LR: 1.00E-03, Speed: 8.614 samples/sec, ObjLoss=24.440, BoxCenterLoss=14.397, BoxScaleLoss=5.025, ClassLoss=9.934 [Epoch 187][Batch 599], LR: 1.00E-03, Speed: 10.662 samples/sec, ObjLoss=24.439, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.934 [Epoch 187][Batch 699], LR: 1.00E-03, Speed: 11.055 samples/sec, ObjLoss=24.439, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.933 [Epoch 187][Batch 799], LR: 1.00E-03, Speed: 102.397 samples/sec, ObjLoss=24.438, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.933 [Epoch 187][Batch 899], LR: 1.00E-03, Speed: 95.717 samples/sec, ObjLoss=24.437, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.932 [Epoch 187][Batch 999], LR: 1.00E-03, Speed: 8.239 samples/sec, ObjLoss=24.437, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.932 [Epoch 187][Batch 1099], LR: 1.00E-03, Speed: 9.399 samples/sec, ObjLoss=24.437, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.931 [Epoch 187][Batch 1199], LR: 1.00E-03, Speed: 8.728 samples/sec, ObjLoss=24.436, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.931 [Epoch 187][Batch 1299], LR: 1.00E-03, Speed: 9.740 samples/sec, ObjLoss=24.435, BoxCenterLoss=14.396, BoxScaleLoss=5.025, ClassLoss=9.930 [Epoch 187][Batch 1399], LR: 1.00E-03, Speed: 9.978 samples/sec, ObjLoss=24.435, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.930 [Epoch 187][Batch 1499], LR: 1.00E-03, Speed: 10.099 samples/sec, ObjLoss=24.434, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.929 [Epoch 187][Batch 1599], LR: 1.00E-03, Speed: 7.787 samples/sec, ObjLoss=24.434, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.929 [Epoch 187][Batch 1699], LR: 1.00E-03, Speed: 9.516 samples/sec, ObjLoss=24.433, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.929 [Epoch 187][Batch 1799], LR: 1.00E-03, Speed: 10.823 samples/sec, ObjLoss=24.433, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.928 [Epoch 187] Training cost: 2257.388, ObjLoss=24.433, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.928 [Epoch 187] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.463 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.470 person=38.1 bicycle=19.7 car=25.3 motorcycle=31.4 airplane=41.6 bus=41.6 train=47.3 truck=21.2 boat=12.6 traffic light=13.0 fire hydrant=40.3 stop sign=33.8 parking meter=24.7 bench=14.5 bird=21.0 cat=48.0 dog=42.6 horse=37.4 sheep=34.5 cow=35.2 elephant=39.0 bear=46.9 zebra=44.8 giraffe=48.0 backpack=6.5 umbrella=23.5 handbag=5.4 tie=16.1 suitcase=19.8 frisbee=33.8 skis=10.4 snowboard=18.2 sports ball=26.4 kite=25.6 baseball bat=16.7 baseball glove=20.3 skateboard=29.2 surfboard=22.8 tennis racket=27.7 bottle=18.8 wine glass=20.4 cup=24.2 fork=13.6 knife=4.3 spoon=5.4 bowl=23.7 banana=13.3 apple=7.8 sandwich=21.3 orange=15.1 broccoli=9.1 carrot=8.7 hot dog=20.1 pizza=33.9 donut=25.8 cake=19.2 chair=15.2 couch=32.4 potted plant=12.9 bed=31.9 dining table=19.7 toilet=40.6 tv=32.8 laptop=38.0 mouse=30.5 remote=10.3 keyboard=28.3 cell phone=12.3 microwave=29.7 oven=18.3 toaster=0.0 sink=19.0 refrigerator=31.6 book=3.5 clock=32.3 vase=20.7 scissors=18.0 teddy bear=28.4 hair drier=0.0 toothbrush=6.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =23.8 [Epoch 188][Batch 99], LR: 1.00E-03, Speed: 8.403 samples/sec, ObjLoss=24.432, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.928 [Epoch 188][Batch 199], LR: 1.00E-03, Speed: 9.296 samples/sec, ObjLoss=24.431, BoxCenterLoss=14.395, BoxScaleLoss=5.024, ClassLoss=9.927 [Epoch 188][Batch 299], LR: 1.00E-03, Speed: 93.536 samples/sec, ObjLoss=24.431, BoxCenterLoss=14.395, BoxScaleLoss=5.024, ClassLoss=9.926 [Epoch 188][Batch 399], LR: 1.00E-03, Speed: 9.954 samples/sec, ObjLoss=24.431, BoxCenterLoss=14.395, BoxScaleLoss=5.024, ClassLoss=9.926 [Epoch 188][Batch 499], LR: 1.00E-03, Speed: 9.911 samples/sec, ObjLoss=24.430, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.926 [Epoch 188][Batch 599], LR: 1.00E-03, Speed: 110.158 samples/sec, ObjLoss=24.430, BoxCenterLoss=14.396, BoxScaleLoss=5.024, ClassLoss=9.925 [Epoch 188][Batch 699], LR: 1.00E-03, Speed: 8.318 samples/sec, ObjLoss=24.429, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.925 [Epoch 188][Batch 799], LR: 1.00E-03, Speed: 9.047 samples/sec, ObjLoss=24.428, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.924 [Epoch 188][Batch 899], LR: 1.00E-03, Speed: 9.910 samples/sec, ObjLoss=24.428, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.924 [Epoch 188][Batch 999], LR: 1.00E-03, Speed: 10.137 samples/sec, ObjLoss=24.428, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.924 [Epoch 188][Batch 1099], LR: 1.00E-03, Speed: 110.100 samples/sec, ObjLoss=24.427, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.923 [Epoch 188][Batch 1199], LR: 1.00E-03, Speed: 10.310 samples/sec, ObjLoss=24.426, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.923 [Epoch 188][Batch 1299], LR: 1.00E-03, Speed: 10.144 samples/sec, ObjLoss=24.426, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.923 [Epoch 188][Batch 1399], LR: 1.00E-03, Speed: 11.494 samples/sec, ObjLoss=24.425, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.923 [Epoch 188][Batch 1499], LR: 1.00E-03, Speed: 10.621 samples/sec, ObjLoss=24.424, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.922 [Epoch 188][Batch 1599], LR: 1.00E-03, Speed: 9.339 samples/sec, ObjLoss=24.424, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.922 [Epoch 188][Batch 1699], LR: 1.00E-03, Speed: 9.347 samples/sec, ObjLoss=24.423, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.921 [Epoch 188][Batch 1799], LR: 1.00E-03, Speed: 119.959 samples/sec, ObjLoss=24.422, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.921 [Epoch 188] Training cost: 2080.120, ObjLoss=24.422, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.921 [Epoch 188] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.228 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429 person=36.0 bicycle=13.8 car=28.8 motorcycle=27.2 airplane=38.5 bus=40.8 train=47.7 truck=22.9 boat=11.1 traffic light=11.2 fire hydrant=31.8 stop sign=30.2 parking meter=21.1 bench=14.1 bird=19.5 cat=45.0 dog=38.0 horse=38.9 sheep=30.9 cow=35.8 elephant=40.5 bear=44.7 zebra=41.9 giraffe=32.5 backpack=6.6 umbrella=23.7 handbag=4.7 tie=15.9 suitcase=17.5 frisbee=38.0 skis=12.2 snowboard=15.3 sports ball=27.8 kite=25.0 baseball bat=14.0 baseball glove=22.6 skateboard=27.7 surfboard=19.7 tennis racket=27.5 bottle=19.0 wine glass=18.9 cup=24.5 fork=17.3 knife=4.9 spoon=6.9 bowl=23.7 banana=13.5 apple=6.6 sandwich=18.8 orange=12.1 broccoli=11.5 carrot=11.9 hot dog=16.3 pizza=31.8 donut=21.8 cake=21.2 chair=16.1 couch=29.9 potted plant=12.7 bed=34.1 dining table=19.6 toilet=24.4 tv=35.6 laptop=36.1 mouse=35.8 remote=13.0 keyboard=26.3 cell phone=18.5 microwave=26.8 oven=15.1 toaster=0.0 sink=20.4 refrigerator=26.7 book=4.5 clock=34.8 vase=18.3 scissors=17.7 teddy bear=25.9 hair drier=0.0 toothbrush=6.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.8 [Epoch 189][Batch 99], LR: 1.00E-03, Speed: 6.722 samples/sec, ObjLoss=24.422, BoxCenterLoss=14.395, BoxScaleLoss=5.023, ClassLoss=9.920 [Epoch 189][Batch 199], LR: 1.00E-03, Speed: 71.729 samples/sec, ObjLoss=24.421, BoxCenterLoss=14.395, BoxScaleLoss=5.022, ClassLoss=9.920 [Epoch 189][Batch 299], LR: 1.00E-03, Speed: 90.532 samples/sec, ObjLoss=24.421, BoxCenterLoss=14.395, BoxScaleLoss=5.022, ClassLoss=9.919 [Epoch 189][Batch 399], LR: 1.00E-03, Speed: 7.559 samples/sec, ObjLoss=24.420, BoxCenterLoss=14.395, BoxScaleLoss=5.022, ClassLoss=9.919 [Epoch 189][Batch 499], LR: 1.00E-03, Speed: 123.020 samples/sec, ObjLoss=24.419, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.918 [Epoch 189][Batch 599], LR: 1.00E-03, Speed: 8.670 samples/sec, ObjLoss=24.419, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.918 [Epoch 189][Batch 699], LR: 1.00E-03, Speed: 10.723 samples/sec, ObjLoss=24.418, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.917 [Epoch 189][Batch 799], LR: 1.00E-03, Speed: 8.856 samples/sec, ObjLoss=24.418, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.917 [Epoch 189][Batch 899], LR: 1.00E-03, Speed: 7.400 samples/sec, ObjLoss=24.417, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.916 [Epoch 189][Batch 999], LR: 1.00E-03, Speed: 85.029 samples/sec, ObjLoss=24.416, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.916 [Epoch 189][Batch 1099], LR: 1.00E-03, Speed: 9.560 samples/sec, ObjLoss=24.416, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.916 [Epoch 189][Batch 1199], LR: 1.00E-03, Speed: 11.708 samples/sec, ObjLoss=24.415, BoxCenterLoss=14.394, BoxScaleLoss=5.022, ClassLoss=9.915 [Epoch 189][Batch 1299], LR: 1.00E-03, Speed: 13.167 samples/sec, ObjLoss=24.414, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.915 [Epoch 189][Batch 1399], LR: 1.00E-03, Speed: 10.277 samples/sec, ObjLoss=24.413, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.914 [Epoch 189][Batch 1499], LR: 1.00E-03, Speed: 9.204 samples/sec, ObjLoss=24.413, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.913 [Epoch 189][Batch 1599], LR: 1.00E-03, Speed: 8.227 samples/sec, ObjLoss=24.412, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.913 [Epoch 189][Batch 1699], LR: 1.00E-03, Speed: 10.201 samples/sec, ObjLoss=24.412, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.912 [Epoch 189][Batch 1799], LR: 1.00E-03, Speed: 13.426 samples/sec, ObjLoss=24.411, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.912 [Epoch 189] Training cost: 2113.598, ObjLoss=24.411, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.912 [Epoch 189] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.496 person=37.6 bicycle=18.8 car=27.0 motorcycle=31.9 airplane=43.3 bus=48.1 train=50.0 truck=22.5 boat=14.9 traffic light=14.3 fire hydrant=47.1 stop sign=41.8 parking meter=31.3 bench=14.7 bird=20.7 cat=46.6 dog=42.6 horse=37.5 sheep=33.4 cow=33.9 elephant=48.2 bear=40.5 zebra=47.8 giraffe=48.5 backpack=6.3 umbrella=27.5 handbag=5.8 tie=18.1 suitcase=19.8 frisbee=39.6 skis=13.2 snowboard=18.1 sports ball=30.6 kite=25.1 baseball bat=16.4 baseball glove=21.9 skateboard=33.2 surfboard=22.1 tennis racket=30.9 bottle=21.5 wine glass=22.7 cup=26.4 fork=17.9 knife=5.4 spoon=6.1 bowl=25.2 banana=13.9 apple=7.1 sandwich=23.7 orange=20.2 broccoli=10.4 carrot=9.2 hot dog=17.4 pizza=38.4 donut=23.1 cake=23.5 chair=17.5 couch=27.3 potted plant=15.7 bed=31.3 dining table=20.1 toilet=43.0 tv=38.6 laptop=43.4 mouse=40.5 remote=12.0 keyboard=35.7 cell phone=19.4 microwave=37.1 oven=17.5 toaster=2.7 sink=23.4 refrigerator=30.7 book=5.1 clock=33.3 vase=21.8 scissors=21.9 teddy bear=28.5 hair drier=0.0 toothbrush=5.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.7 [Epoch 190][Batch 99], LR: 1.00E-03, Speed: 8.724 samples/sec, ObjLoss=24.411, BoxCenterLoss=14.393, BoxScaleLoss=5.021, ClassLoss=9.911 [Epoch 190][Batch 199], LR: 1.00E-03, Speed: 10.236 samples/sec, ObjLoss=24.410, BoxCenterLoss=14.392, BoxScaleLoss=5.020, ClassLoss=9.911 [Epoch 190][Batch 299], LR: 1.00E-03, Speed: 11.108 samples/sec, ObjLoss=24.409, BoxCenterLoss=14.392, BoxScaleLoss=5.020, ClassLoss=9.910 [Epoch 190][Batch 399], LR: 1.00E-03, Speed: 9.410 samples/sec, ObjLoss=24.408, BoxCenterLoss=14.392, BoxScaleLoss=5.020, ClassLoss=9.910 [Epoch 190][Batch 499], LR: 1.00E-03, Speed: 10.000 samples/sec, ObjLoss=24.408, BoxCenterLoss=14.392, BoxScaleLoss=5.020, ClassLoss=9.909 [Epoch 190][Batch 599], LR: 1.00E-03, Speed: 8.009 samples/sec, ObjLoss=24.407, BoxCenterLoss=14.392, BoxScaleLoss=5.020, ClassLoss=9.909 [Epoch 190][Batch 699], LR: 1.00E-03, Speed: 11.572 samples/sec, ObjLoss=24.406, BoxCenterLoss=14.392, BoxScaleLoss=5.020, ClassLoss=9.908 [Epoch 190][Batch 799], LR: 1.00E-03, Speed: 10.425 samples/sec, ObjLoss=24.406, BoxCenterLoss=14.392, BoxScaleLoss=5.020, ClassLoss=9.907 [Epoch 190][Batch 899], LR: 1.00E-03, Speed: 10.310 samples/sec, ObjLoss=24.405, BoxCenterLoss=14.392, BoxScaleLoss=5.019, ClassLoss=9.907 [Epoch 190][Batch 999], LR: 1.00E-03, Speed: 8.439 samples/sec, ObjLoss=24.404, BoxCenterLoss=14.392, BoxScaleLoss=5.019, ClassLoss=9.906 [Epoch 190][Batch 1099], LR: 1.00E-03, Speed: 83.383 samples/sec, ObjLoss=24.404, BoxCenterLoss=14.392, BoxScaleLoss=5.019, ClassLoss=9.906 [Epoch 190][Batch 1199], LR: 1.00E-03, Speed: 8.872 samples/sec, ObjLoss=24.404, BoxCenterLoss=14.392, BoxScaleLoss=5.019, ClassLoss=9.906 [Epoch 190][Batch 1299], LR: 1.00E-03, Speed: 10.051 samples/sec, ObjLoss=24.403, BoxCenterLoss=14.392, BoxScaleLoss=5.019, ClassLoss=9.905 [Epoch 190][Batch 1399], LR: 1.00E-03, Speed: 9.492 samples/sec, ObjLoss=24.402, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.905 [Epoch 190][Batch 1499], LR: 1.00E-03, Speed: 9.747 samples/sec, ObjLoss=24.401, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.904 [Epoch 190][Batch 1599], LR: 1.00E-03, Speed: 10.212 samples/sec, ObjLoss=24.400, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.904 [Epoch 190][Batch 1699], LR: 1.00E-03, Speed: 7.336 samples/sec, ObjLoss=24.400, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.903 [Epoch 190][Batch 1799], LR: 1.00E-03, Speed: 12.062 samples/sec, ObjLoss=24.400, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.903 [Epoch 190] Training cost: 2210.372, ObjLoss=24.399, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.903 [Epoch 190] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.234 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.225 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.339 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.159 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467 person=37.4 bicycle=18.9 car=27.3 motorcycle=29.9 airplane=42.0 bus=47.7 train=52.0 truck=22.3 boat=17.2 traffic light=14.0 fire hydrant=42.7 stop sign=42.4 parking meter=25.4 bench=14.2 bird=18.9 cat=46.2 dog=35.1 horse=33.6 sheep=29.6 cow=32.8 elephant=43.5 bear=48.2 zebra=41.2 giraffe=44.2 backpack=6.1 umbrella=23.9 handbag=6.1 tie=16.8 suitcase=20.7 frisbee=38.8 skis=12.3 snowboard=17.2 sports ball=20.5 kite=24.3 baseball bat=15.6 baseball glove=21.0 skateboard=31.6 surfboard=22.5 tennis racket=29.4 bottle=19.5 wine glass=18.7 cup=24.2 fork=17.0 knife=3.9 spoon=5.0 bowl=25.2 banana=12.8 apple=10.2 sandwich=21.7 orange=21.2 broccoli=10.5 carrot=10.5 hot dog=17.5 pizza=33.3 donut=32.2 cake=23.0 chair=15.6 couch=26.0 potted plant=14.5 bed=34.9 dining table=22.0 toilet=34.1 tv=40.7 laptop=41.1 mouse=34.2 remote=12.8 keyboard=32.3 cell phone=17.7 microwave=34.1 oven=20.0 toaster=4.2 sink=23.1 refrigerator=34.9 book=4.0 clock=30.4 vase=20.6 scissors=19.6 teddy bear=31.0 hair drier=0.0 toothbrush=6.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.7 [Epoch 191][Batch 99], LR: 1.00E-03, Speed: 8.761 samples/sec, ObjLoss=24.399, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.903 [Epoch 191][Batch 199], LR: 1.00E-03, Speed: 100.433 samples/sec, ObjLoss=24.399, BoxCenterLoss=14.391, BoxScaleLoss=5.019, ClassLoss=9.902 [Epoch 191][Batch 299], LR: 1.00E-03, Speed: 8.734 samples/sec, ObjLoss=24.398, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.902 [Epoch 191][Batch 399], LR: 1.00E-03, Speed: 10.938 samples/sec, ObjLoss=24.398, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.901 [Epoch 191][Batch 499], LR: 1.00E-03, Speed: 8.792 samples/sec, ObjLoss=24.398, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.901 [Epoch 191][Batch 599], LR: 1.00E-03, Speed: 10.469 samples/sec, ObjLoss=24.397, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.901 [Epoch 191][Batch 699], LR: 1.00E-03, Speed: 9.948 samples/sec, ObjLoss=24.396, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.900 [Epoch 191][Batch 799], LR: 1.00E-03, Speed: 10.396 samples/sec, ObjLoss=24.396, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.900 [Epoch 191][Batch 899], LR: 1.00E-03, Speed: 10.066 samples/sec, ObjLoss=24.395, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.899 [Epoch 191][Batch 999], LR: 1.00E-03, Speed: 11.218 samples/sec, ObjLoss=24.395, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.899 [Epoch 191][Batch 1099], LR: 1.00E-03, Speed: 126.078 samples/sec, ObjLoss=24.394, BoxCenterLoss=14.391, BoxScaleLoss=5.018, ClassLoss=9.898 [Epoch 191][Batch 1199], LR: 1.00E-03, Speed: 10.184 samples/sec, ObjLoss=24.394, BoxCenterLoss=14.390, BoxScaleLoss=5.018, ClassLoss=9.898 [Epoch 191][Batch 1299], LR: 1.00E-03, Speed: 10.195 samples/sec, ObjLoss=24.393, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.897 [Epoch 191][Batch 1399], LR: 1.00E-03, Speed: 10.463 samples/sec, ObjLoss=24.392, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.897 [Epoch 191][Batch 1499], LR: 1.00E-03, Speed: 110.453 samples/sec, ObjLoss=24.391, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.897 [Epoch 191][Batch 1599], LR: 1.00E-03, Speed: 7.544 samples/sec, ObjLoss=24.390, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.896 [Epoch 191][Batch 1699], LR: 1.00E-03, Speed: 7.381 samples/sec, ObjLoss=24.390, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.896 [Epoch 191][Batch 1799], LR: 1.00E-03, Speed: 9.068 samples/sec, ObjLoss=24.390, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.896 [Epoch 191] Training cost: 2153.274, ObjLoss=24.389, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.896 [Epoch 191] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.226 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471 person=37.2 bicycle=17.4 car=26.4 motorcycle=27.1 airplane=42.3 bus=47.6 train=46.7 truck=22.2 boat=12.9 traffic light=15.3 fire hydrant=37.3 stop sign=34.9 parking meter=32.3 bench=12.6 bird=20.7 cat=44.4 dog=37.8 horse=33.5 sheep=32.4 cow=34.0 elephant=42.5 bear=39.8 zebra=47.7 giraffe=46.5 backpack=7.4 umbrella=25.0 handbag=6.6 tie=17.1 suitcase=17.9 frisbee=38.5 skis=10.7 snowboard=17.2 sports ball=29.6 kite=23.9 baseball bat=16.6 baseball glove=22.8 skateboard=30.1 surfboard=21.2 tennis racket=25.3 bottle=21.6 wine glass=19.2 cup=25.6 fork=14.2 knife=3.7 spoon=4.3 bowl=23.6 banana=11.9 apple=8.2 sandwich=18.0 orange=15.7 broccoli=12.6 carrot=9.6 hot dog=17.8 pizza=31.2 donut=26.9 cake=21.5 chair=15.9 couch=29.5 potted plant=15.8 bed=30.5 dining table=21.6 toilet=38.6 tv=36.7 laptop=39.2 mouse=39.9 remote=12.9 keyboard=32.9 cell phone=20.9 microwave=36.7 oven=19.3 toaster=8.3 sink=25.3 refrigerator=29.4 book=6.6 clock=34.1 vase=20.8 scissors=15.1 teddy bear=31.4 hair drier=0.0 toothbrush=5.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.4 [Epoch 192][Batch 99], LR: 1.00E-03, Speed: 8.188 samples/sec, ObjLoss=24.389, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.895 [Epoch 192][Batch 199], LR: 1.00E-03, Speed: 9.292 samples/sec, ObjLoss=24.388, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.895 [Epoch 192][Batch 299], LR: 1.00E-03, Speed: 9.755 samples/sec, ObjLoss=24.388, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.894 [Epoch 192][Batch 399], LR: 1.00E-03, Speed: 122.905 samples/sec, ObjLoss=24.388, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.894 [Epoch 192][Batch 499], LR: 1.00E-03, Speed: 7.250 samples/sec, ObjLoss=24.387, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.893 [Epoch 192][Batch 599], LR: 1.00E-03, Speed: 8.852 samples/sec, ObjLoss=24.387, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.893 [Epoch 192][Batch 699], LR: 1.00E-03, Speed: 9.765 samples/sec, ObjLoss=24.386, BoxCenterLoss=14.389, BoxScaleLoss=5.017, ClassLoss=9.893 [Epoch 192][Batch 799], LR: 1.00E-03, Speed: 7.388 samples/sec, ObjLoss=24.385, BoxCenterLoss=14.389, BoxScaleLoss=5.017, ClassLoss=9.892 [Epoch 192][Batch 899], LR: 1.00E-03, Speed: 135.102 samples/sec, ObjLoss=24.385, BoxCenterLoss=14.389, BoxScaleLoss=5.017, ClassLoss=9.892 [Epoch 192][Batch 999], LR: 1.00E-03, Speed: 9.490 samples/sec, ObjLoss=24.384, BoxCenterLoss=14.389, BoxScaleLoss=5.017, ClassLoss=9.892 [Epoch 192][Batch 1099], LR: 1.00E-03, Speed: 124.870 samples/sec, ObjLoss=24.384, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.891 [Epoch 192][Batch 1199], LR: 1.00E-03, Speed: 9.654 samples/sec, ObjLoss=24.384, BoxCenterLoss=14.390, BoxScaleLoss=5.017, ClassLoss=9.891 [Epoch 192][Batch 1299], LR: 1.00E-03, Speed: 10.139 samples/sec, ObjLoss=24.384, BoxCenterLoss=14.390, BoxScaleLoss=5.016, ClassLoss=9.891 [Epoch 192][Batch 1399], LR: 1.00E-03, Speed: 8.411 samples/sec, ObjLoss=24.383, BoxCenterLoss=14.390, BoxScaleLoss=5.016, ClassLoss=9.890 [Epoch 192][Batch 1499], LR: 1.00E-03, Speed: 7.306 samples/sec, ObjLoss=24.383, BoxCenterLoss=14.390, BoxScaleLoss=5.016, ClassLoss=9.889 [Epoch 192][Batch 1599], LR: 1.00E-03, Speed: 9.581 samples/sec, ObjLoss=24.382, BoxCenterLoss=14.390, BoxScaleLoss=5.016, ClassLoss=9.889 [Epoch 192][Batch 1699], LR: 1.00E-03, Speed: 8.301 samples/sec, ObjLoss=24.381, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.888 [Epoch 192][Batch 1799], LR: 1.00E-03, Speed: 11.085 samples/sec, ObjLoss=24.380, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.888 [Epoch 192] Training cost: 2132.307, ObjLoss=24.380, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.888 [Epoch 192] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.271 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485 person=39.4 bicycle=19.1 car=27.0 motorcycle=29.9 airplane=41.5 bus=48.3 train=51.9 truck=24.7 boat=14.2 traffic light=15.4 fire hydrant=45.3 stop sign=44.0 parking meter=30.4 bench=13.6 bird=22.0 cat=44.0 dog=40.7 horse=40.3 sheep=36.5 cow=36.0 elephant=46.9 bear=51.8 zebra=48.5 giraffe=50.2 backpack=6.0 umbrella=22.9 handbag=5.8 tie=17.4 suitcase=19.5 frisbee=37.3 skis=13.0 snowboard=16.3 sports ball=28.6 kite=24.7 baseball bat=12.2 baseball glove=19.0 skateboard=27.9 surfboard=22.1 tennis racket=28.4 bottle=20.4 wine glass=22.1 cup=27.8 fork=17.9 knife=5.0 spoon=5.8 bowl=25.2 banana=13.0 apple=9.0 sandwich=21.8 orange=22.4 broccoli=13.9 carrot=9.7 hot dog=16.9 pizza=35.4 donut=28.4 cake=22.6 chair=17.0 couch=32.3 potted plant=12.8 bed=36.6 dining table=19.5 toilet=43.0 tv=39.4 laptop=42.1 mouse=31.8 remote=10.8 keyboard=32.0 cell phone=17.4 microwave=29.9 oven=20.3 toaster=7.1 sink=23.4 refrigerator=33.7 book=5.0 clock=32.5 vase=21.5 scissors=16.7 teddy bear=28.9 hair drier=0.0 toothbrush=5.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.5 [Epoch 193][Batch 99], LR: 1.00E-03, Speed: 121.361 samples/sec, ObjLoss=24.380, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.887 [Epoch 193][Batch 199], LR: 1.00E-03, Speed: 113.011 samples/sec, ObjLoss=24.379, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.887 [Epoch 193][Batch 299], LR: 1.00E-03, Speed: 9.367 samples/sec, ObjLoss=24.379, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.887 [Epoch 193][Batch 399], LR: 1.00E-03, Speed: 7.816 samples/sec, ObjLoss=24.378, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.886 [Epoch 193][Batch 499], LR: 1.00E-03, Speed: 108.748 samples/sec, ObjLoss=24.378, BoxCenterLoss=14.389, BoxScaleLoss=5.016, ClassLoss=9.886 [Epoch 193][Batch 599], LR: 1.00E-03, Speed: 10.347 samples/sec, ObjLoss=24.378, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.885 [Epoch 193][Batch 699], LR: 1.00E-03, Speed: 10.596 samples/sec, ObjLoss=24.377, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.885 [Epoch 193][Batch 799], LR: 1.00E-03, Speed: 95.400 samples/sec, ObjLoss=24.376, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.884 [Epoch 193][Batch 899], LR: 1.00E-03, Speed: 7.132 samples/sec, ObjLoss=24.375, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.884 [Epoch 193][Batch 999], LR: 1.00E-03, Speed: 9.119 samples/sec, ObjLoss=24.375, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.884 [Epoch 193][Batch 1099], LR: 1.00E-03, Speed: 8.187 samples/sec, ObjLoss=24.375, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.883 [Epoch 193][Batch 1199], LR: 1.00E-03, Speed: 10.335 samples/sec, ObjLoss=24.375, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.883 [Epoch 193][Batch 1299], LR: 1.00E-03, Speed: 87.117 samples/sec, ObjLoss=24.374, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.883 [Epoch 193][Batch 1399], LR: 1.00E-03, Speed: 8.586 samples/sec, ObjLoss=24.373, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.882 [Epoch 193][Batch 1499], LR: 1.00E-03, Speed: 8.475 samples/sec, ObjLoss=24.373, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.881 [Epoch 193][Batch 1599], LR: 1.00E-03, Speed: 105.884 samples/sec, ObjLoss=24.372, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.881 [Epoch 193][Batch 1699], LR: 1.00E-03, Speed: 7.659 samples/sec, ObjLoss=24.372, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.881 [Epoch 193][Batch 1799], LR: 1.00E-03, Speed: 9.984 samples/sec, ObjLoss=24.372, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.880 [Epoch 193] Training cost: 2222.239, ObjLoss=24.372, BoxCenterLoss=14.389, BoxScaleLoss=5.015, ClassLoss=9.880 [Epoch 193] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.236 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.470 person=37.5 bicycle=18.7 car=27.7 motorcycle=31.2 airplane=39.4 bus=45.8 train=44.1 truck=21.3 boat=15.0 traffic light=13.1 fire hydrant=47.8 stop sign=44.4 parking meter=28.8 bench=15.3 bird=19.8 cat=46.1 dog=41.3 horse=35.6 sheep=34.8 cow=32.9 elephant=46.6 bear=46.7 zebra=46.9 giraffe=51.5 backpack=6.3 umbrella=25.0 handbag=4.7 tie=17.5 suitcase=22.1 frisbee=37.3 skis=11.7 snowboard=16.9 sports ball=26.9 kite=17.6 baseball bat=14.7 baseball glove=21.2 skateboard=28.4 surfboard=17.4 tennis racket=28.2 bottle=21.3 wine glass=19.6 cup=22.0 fork=15.2 knife=4.0 spoon=5.0 bowl=23.1 banana=14.0 apple=8.1 sandwich=17.8 orange=15.8 broccoli=9.7 carrot=7.9 hot dog=18.8 pizza=34.6 donut=32.9 cake=22.9 chair=15.9 couch=27.2 potted plant=14.0 bed=32.4 dining table=20.6 toilet=35.9 tv=38.0 laptop=36.5 mouse=31.5 remote=11.9 keyboard=33.7 cell phone=17.7 microwave=29.3 oven=22.4 toaster=8.3 sink=21.8 refrigerator=30.3 book=5.3 clock=36.7 vase=17.9 scissors=16.5 teddy bear=29.6 hair drier=0.0 toothbrush=5.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.5 [Epoch 194][Batch 99], LR: 1.00E-03, Speed: 8.902 samples/sec, ObjLoss=24.371, BoxCenterLoss=14.390, BoxScaleLoss=5.014, ClassLoss=9.880 [Epoch 194][Batch 199], LR: 1.00E-03, Speed: 117.002 samples/sec, ObjLoss=24.370, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.879 [Epoch 194][Batch 299], LR: 1.00E-03, Speed: 8.984 samples/sec, ObjLoss=24.370, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.879 [Epoch 194][Batch 399], LR: 1.00E-03, Speed: 8.628 samples/sec, ObjLoss=24.370, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.878 [Epoch 194][Batch 499], LR: 1.00E-03, Speed: 10.445 samples/sec, ObjLoss=24.369, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.878 [Epoch 194][Batch 599], LR: 1.00E-03, Speed: 11.844 samples/sec, ObjLoss=24.368, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.877 [Epoch 194][Batch 699], LR: 1.00E-03, Speed: 7.487 samples/sec, ObjLoss=24.368, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.877 [Epoch 194][Batch 799], LR: 1.00E-03, Speed: 52.706 samples/sec, ObjLoss=24.368, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.877 [Epoch 194][Batch 899], LR: 1.00E-03, Speed: 9.494 samples/sec, ObjLoss=24.367, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.876 [Epoch 194][Batch 999], LR: 1.00E-03, Speed: 8.360 samples/sec, ObjLoss=24.366, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.876 [Epoch 194][Batch 1099], LR: 1.00E-03, Speed: 9.367 samples/sec, ObjLoss=24.366, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.875 [Epoch 194][Batch 1199], LR: 1.00E-03, Speed: 8.892 samples/sec, ObjLoss=24.365, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.875 [Epoch 194][Batch 1299], LR: 1.00E-03, Speed: 9.778 samples/sec, ObjLoss=24.365, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.875 [Epoch 194][Batch 1399], LR: 1.00E-03, Speed: 12.767 samples/sec, ObjLoss=24.365, BoxCenterLoss=14.389, BoxScaleLoss=5.014, ClassLoss=9.874 [Epoch 194][Batch 1499], LR: 1.00E-03, Speed: 8.676 samples/sec, ObjLoss=24.364, BoxCenterLoss=14.389, BoxScaleLoss=5.013, ClassLoss=9.874 [Epoch 194][Batch 1599], LR: 1.00E-03, Speed: 10.704 samples/sec, ObjLoss=24.363, BoxCenterLoss=14.389, BoxScaleLoss=5.013, ClassLoss=9.873 [Epoch 194][Batch 1699], LR: 1.00E-03, Speed: 8.641 samples/sec, ObjLoss=24.363, BoxCenterLoss=14.388, BoxScaleLoss=5.013, ClassLoss=9.873 [Epoch 194][Batch 1799], LR: 1.00E-03, Speed: 11.077 samples/sec, ObjLoss=24.363, BoxCenterLoss=14.389, BoxScaleLoss=5.013, ClassLoss=9.872 [Epoch 194] Training cost: 2219.852, ObjLoss=24.363, BoxCenterLoss=14.389, BoxScaleLoss=5.013, ClassLoss=9.872 [Epoch 194] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.153 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.346 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486 person=38.9 bicycle=16.7 car=27.1 motorcycle=30.2 airplane=44.6 bus=49.5 train=52.0 truck=25.1 boat=13.4 traffic light=14.9 fire hydrant=37.6 stop sign=44.6 parking meter=26.7 bench=15.2 bird=19.5 cat=48.7 dog=35.2 horse=37.2 sheep=32.3 cow=35.7 elephant=44.0 bear=44.0 zebra=48.2 giraffe=48.3 backpack=6.0 umbrella=22.9 handbag=6.1 tie=18.6 suitcase=18.7 frisbee=36.2 skis=8.0 snowboard=17.9 sports ball=25.9 kite=24.2 baseball bat=12.2 baseball glove=18.1 skateboard=29.2 surfboard=22.3 tennis racket=30.5 bottle=20.3 wine glass=20.3 cup=26.7 fork=16.3 knife=5.1 spoon=6.5 bowl=23.7 banana=14.7 apple=7.8 sandwich=16.7 orange=16.7 broccoli=11.4 carrot=10.5 hot dog=18.9 pizza=31.0 donut=25.9 cake=22.4 chair=17.0 couch=31.6 potted plant=12.6 bed=28.5 dining table=19.7 toilet=40.7 tv=43.6 laptop=39.6 mouse=43.2 remote=13.7 keyboard=34.5 cell phone=20.9 microwave=35.3 oven=17.9 toaster=1.9 sink=23.8 refrigerator=32.5 book=5.0 clock=35.7 vase=21.3 scissors=16.9 teddy bear=29.2 hair drier=0.0 toothbrush=8.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.9 [Epoch 195][Batch 99], LR: 1.00E-03, Speed: 7.970 samples/sec, ObjLoss=24.362, BoxCenterLoss=14.389, BoxScaleLoss=5.013, ClassLoss=9.872 [Epoch 195][Batch 199], LR: 1.00E-03, Speed: 11.766 samples/sec, ObjLoss=24.361, BoxCenterLoss=14.388, BoxScaleLoss=5.013, ClassLoss=9.871 [Epoch 195][Batch 299], LR: 1.00E-03, Speed: 7.938 samples/sec, ObjLoss=24.361, BoxCenterLoss=14.389, BoxScaleLoss=5.013, ClassLoss=9.871 [Epoch 195][Batch 399], LR: 1.00E-03, Speed: 87.516 samples/sec, ObjLoss=24.361, BoxCenterLoss=14.389, BoxScaleLoss=5.013, ClassLoss=9.871 [Epoch 195][Batch 499], LR: 1.00E-03, Speed: 8.310 samples/sec, ObjLoss=24.360, BoxCenterLoss=14.388, BoxScaleLoss=5.013, ClassLoss=9.870 [Epoch 195][Batch 599], LR: 1.00E-03, Speed: 12.369 samples/sec, ObjLoss=24.360, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.870 [Epoch 195][Batch 699], LR: 1.00E-03, Speed: 11.651 samples/sec, ObjLoss=24.360, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.870 [Epoch 195][Batch 799], LR: 1.00E-03, Speed: 10.883 samples/sec, ObjLoss=24.359, BoxCenterLoss=14.389, BoxScaleLoss=5.012, ClassLoss=9.869 [Epoch 195][Batch 899], LR: 1.00E-03, Speed: 7.565 samples/sec, ObjLoss=24.359, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.869 [Epoch 195][Batch 999], LR: 1.00E-03, Speed: 18.513 samples/sec, ObjLoss=24.358, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.868 [Epoch 195][Batch 1099], LR: 1.00E-03, Speed: 89.394 samples/sec, ObjLoss=24.357, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.868 [Epoch 195][Batch 1199], LR: 1.00E-03, Speed: 9.655 samples/sec, ObjLoss=24.357, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.868 [Epoch 195][Batch 1299], LR: 1.00E-03, Speed: 9.312 samples/sec, ObjLoss=24.357, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.867 [Epoch 195][Batch 1399], LR: 1.00E-03, Speed: 10.426 samples/sec, ObjLoss=24.356, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.867 [Epoch 195][Batch 1499], LR: 1.00E-03, Speed: 9.872 samples/sec, ObjLoss=24.356, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.866 [Epoch 195][Batch 1599], LR: 1.00E-03, Speed: 90.006 samples/sec, ObjLoss=24.356, BoxCenterLoss=14.389, BoxScaleLoss=5.012, ClassLoss=9.866 [Epoch 195][Batch 1699], LR: 1.00E-03, Speed: 94.574 samples/sec, ObjLoss=24.355, BoxCenterLoss=14.389, BoxScaleLoss=5.012, ClassLoss=9.866 [Epoch 195][Batch 1799], LR: 1.00E-03, Speed: 113.802 samples/sec, ObjLoss=24.355, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.865 [Epoch 195] Training cost: 2215.778, ObjLoss=24.354, BoxCenterLoss=14.388, BoxScaleLoss=5.012, ClassLoss=9.865 [Epoch 195] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.268 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472 person=39.2 bicycle=18.9 car=27.3 motorcycle=28.5 airplane=44.0 bus=49.4 train=43.1 truck=23.3 boat=14.6 traffic light=12.8 fire hydrant=44.7 stop sign=41.5 parking meter=32.8 bench=14.0 bird=20.2 cat=45.3 dog=39.5 horse=36.7 sheep=33.0 cow=33.5 elephant=45.6 bear=47.0 zebra=46.9 giraffe=46.8 backpack=6.9 umbrella=23.3 handbag=5.5 tie=20.8 suitcase=21.1 frisbee=36.8 skis=10.2 snowboard=16.6 sports ball=20.7 kite=29.1 baseball bat=13.3 baseball glove=19.3 skateboard=25.3 surfboard=17.0 tennis racket=29.9 bottle=22.3 wine glass=21.4 cup=25.7 fork=15.1 knife=5.3 spoon=7.0 bowl=25.0 banana=13.6 apple=8.3 sandwich=24.7 orange=17.4 broccoli=13.2 carrot=10.8 hot dog=13.9 pizza=37.1 donut=25.6 cake=23.4 chair=18.3 couch=27.5 potted plant=13.3 bed=32.3 dining table=16.5 toilet=42.6 tv=43.3 laptop=39.5 mouse=35.6 remote=10.2 keyboard=32.0 cell phone=18.9 microwave=37.6 oven=19.5 toaster=1.5 sink=23.6 refrigerator=35.1 book=4.9 clock=33.5 vase=20.0 scissors=20.9 teddy bear=29.1 hair drier=0.0 toothbrush=5.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.9 [Epoch 196][Batch 99], LR: 1.00E-03, Speed: 8.745 samples/sec, ObjLoss=24.354, BoxCenterLoss=14.388, BoxScaleLoss=5.011, ClassLoss=9.864 [Epoch 196][Batch 199], LR: 1.00E-03, Speed: 10.107 samples/sec, ObjLoss=24.353, BoxCenterLoss=14.388, BoxScaleLoss=5.011, ClassLoss=9.864 [Epoch 196][Batch 299], LR: 1.00E-03, Speed: 8.387 samples/sec, ObjLoss=24.352, BoxCenterLoss=14.388, BoxScaleLoss=5.011, ClassLoss=9.863 [Epoch 196][Batch 399], LR: 1.00E-03, Speed: 7.397 samples/sec, ObjLoss=24.351, BoxCenterLoss=14.387, BoxScaleLoss=5.011, ClassLoss=9.863 [Epoch 196][Batch 499], LR: 1.00E-03, Speed: 8.515 samples/sec, ObjLoss=24.350, BoxCenterLoss=14.387, BoxScaleLoss=5.011, ClassLoss=9.862 [Epoch 196][Batch 599], LR: 1.00E-03, Speed: 6.455 samples/sec, ObjLoss=24.350, BoxCenterLoss=14.387, BoxScaleLoss=5.011, ClassLoss=9.862 [Epoch 196][Batch 699], LR: 1.00E-03, Speed: 8.880 samples/sec, ObjLoss=24.349, BoxCenterLoss=14.387, BoxScaleLoss=5.011, ClassLoss=9.861 [Epoch 196][Batch 799], LR: 1.00E-03, Speed: 107.758 samples/sec, ObjLoss=24.349, BoxCenterLoss=14.387, BoxScaleLoss=5.011, ClassLoss=9.861 [Epoch 196][Batch 899], LR: 1.00E-03, Speed: 112.046 samples/sec, ObjLoss=24.348, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.861 [Epoch 196][Batch 999], LR: 1.00E-03, Speed: 7.669 samples/sec, ObjLoss=24.348, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.860 [Epoch 196][Batch 1099], LR: 1.00E-03, Speed: 9.485 samples/sec, ObjLoss=24.347, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.860 [Epoch 196][Batch 1199], LR: 1.00E-03, Speed: 11.219 samples/sec, ObjLoss=24.347, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.859 [Epoch 196][Batch 1299], LR: 1.00E-03, Speed: 9.818 samples/sec, ObjLoss=24.347, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.859 [Epoch 196][Batch 1399], LR: 1.00E-03, Speed: 9.324 samples/sec, ObjLoss=24.347, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.858 [Epoch 196][Batch 1499], LR: 1.00E-03, Speed: 8.541 samples/sec, ObjLoss=24.346, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.858 [Epoch 196][Batch 1599], LR: 1.00E-03, Speed: 9.651 samples/sec, ObjLoss=24.346, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.858 [Epoch 196][Batch 1699], LR: 1.00E-03, Speed: 9.709 samples/sec, ObjLoss=24.345, BoxCenterLoss=14.387, BoxScaleLoss=5.010, ClassLoss=9.857 [Epoch 196][Batch 1799], LR: 1.00E-03, Speed: 8.051 samples/sec, ObjLoss=24.345, BoxCenterLoss=14.387, BoxScaleLoss=5.009, ClassLoss=9.856 [Epoch 196] Training cost: 2269.042, ObjLoss=24.345, BoxCenterLoss=14.387, BoxScaleLoss=5.009, ClassLoss=9.856 [Epoch 196] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472 person=38.7 bicycle=16.6 car=23.8 motorcycle=29.4 airplane=40.0 bus=48.5 train=48.7 truck=24.5 boat=14.2 traffic light=14.5 fire hydrant=38.9 stop sign=43.3 parking meter=22.9 bench=15.6 bird=22.4 cat=46.5 dog=40.8 horse=39.3 sheep=32.3 cow=37.3 elephant=46.8 bear=48.4 zebra=49.5 giraffe=50.8 backpack=6.7 umbrella=20.3 handbag=6.2 tie=15.9 suitcase=20.8 frisbee=38.5 skis=12.7 snowboard=16.4 sports ball=24.7 kite=27.5 baseball bat=16.1 baseball glove=22.4 skateboard=30.5 surfboard=21.0 tennis racket=31.8 bottle=19.1 wine glass=22.3 cup=26.5 fork=15.9 knife=5.1 spoon=4.4 bowl=22.8 banana=10.8 apple=5.7 sandwich=24.2 orange=17.3 broccoli=10.5 carrot=8.4 hot dog=18.7 pizza=32.5 donut=28.5 cake=23.4 chair=16.4 couch=29.2 potted plant=14.5 bed=32.5 dining table=18.7 toilet=39.7 tv=38.4 laptop=40.5 mouse=37.3 remote=12.5 keyboard=29.4 cell phone=16.3 microwave=34.9 oven=20.0 toaster=0.0 sink=24.5 refrigerator=39.0 book=5.0 clock=33.8 vase=20.3 scissors=11.4 teddy bear=31.8 hair drier=0.0 toothbrush=9.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.0 [Epoch 197][Batch 99], LR: 1.00E-03, Speed: 81.076 samples/sec, ObjLoss=24.344, BoxCenterLoss=14.387, BoxScaleLoss=5.009, ClassLoss=9.856 [Epoch 197][Batch 199], LR: 1.00E-03, Speed: 114.090 samples/sec, ObjLoss=24.344, BoxCenterLoss=14.387, BoxScaleLoss=5.009, ClassLoss=9.856 [Epoch 197][Batch 299], LR: 1.00E-03, Speed: 11.111 samples/sec, ObjLoss=24.343, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.855 [Epoch 197][Batch 399], LR: 1.00E-03, Speed: 8.424 samples/sec, ObjLoss=24.342, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.855 [Epoch 197][Batch 499], LR: 1.00E-03, Speed: 10.489 samples/sec, ObjLoss=24.342, BoxCenterLoss=14.387, BoxScaleLoss=5.009, ClassLoss=9.855 [Epoch 197][Batch 599], LR: 1.00E-03, Speed: 8.457 samples/sec, ObjLoss=24.341, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.854 [Epoch 197][Batch 699], LR: 1.00E-03, Speed: 10.393 samples/sec, ObjLoss=24.341, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.854 [Epoch 197][Batch 799], LR: 1.00E-03, Speed: 11.355 samples/sec, ObjLoss=24.340, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.853 [Epoch 197][Batch 899], LR: 1.00E-03, Speed: 10.420 samples/sec, ObjLoss=24.340, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.853 [Epoch 197][Batch 999], LR: 1.00E-03, Speed: 9.979 samples/sec, ObjLoss=24.339, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.853 [Epoch 197][Batch 1099], LR: 1.00E-03, Speed: 10.940 samples/sec, ObjLoss=24.339, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.852 [Epoch 197][Batch 1199], LR: 1.00E-03, Speed: 10.106 samples/sec, ObjLoss=24.338, BoxCenterLoss=14.386, BoxScaleLoss=5.009, ClassLoss=9.852 [Epoch 197][Batch 1299], LR: 1.00E-03, Speed: 11.280 samples/sec, ObjLoss=24.338, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.851 [Epoch 197][Batch 1399], LR: 1.00E-03, Speed: 9.737 samples/sec, ObjLoss=24.337, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.851 [Epoch 197][Batch 1499], LR: 1.00E-03, Speed: 107.065 samples/sec, ObjLoss=24.337, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.851 [Epoch 197][Batch 1599], LR: 1.00E-03, Speed: 91.233 samples/sec, ObjLoss=24.336, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.850 [Epoch 197][Batch 1699], LR: 1.00E-03, Speed: 109.498 samples/sec, ObjLoss=24.336, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.850 [Epoch 197][Batch 1799], LR: 1.00E-03, Speed: 13.484 samples/sec, ObjLoss=24.335, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.849 [Epoch 197] Training cost: 2148.984, ObjLoss=24.335, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.849 [Epoch 197] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.335 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.172 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489 person=40.2 bicycle=18.8 car=28.2 motorcycle=29.9 airplane=43.5 bus=42.8 train=49.4 truck=24.4 boat=11.6 traffic light=15.8 fire hydrant=49.3 stop sign=47.2 parking meter=31.1 bench=15.2 bird=19.6 cat=42.9 dog=41.4 horse=38.3 sheep=30.5 cow=36.4 elephant=46.6 bear=49.4 zebra=49.7 giraffe=50.2 backpack=6.6 umbrella=24.2 handbag=6.0 tie=17.8 suitcase=21.8 frisbee=35.2 skis=10.2 snowboard=18.4 sports ball=26.3 kite=24.6 baseball bat=13.8 baseball glove=21.6 skateboard=30.8 surfboard=20.8 tennis racket=28.1 bottle=20.8 wine glass=20.2 cup=26.0 fork=16.4 knife=6.5 spoon=6.2 bowl=24.3 banana=15.2 apple=6.5 sandwich=21.5 orange=12.8 broccoli=9.9 carrot=7.8 hot dog=21.8 pizza=33.7 donut=26.9 cake=24.3 chair=16.7 couch=31.2 potted plant=14.7 bed=33.1 dining table=20.3 toilet=40.3 tv=39.6 laptop=36.6 mouse=35.4 remote=12.8 keyboard=33.8 cell phone=18.5 microwave=36.7 oven=18.1 toaster=0.0 sink=22.2 refrigerator=34.0 book=4.9 clock=32.5 vase=24.7 scissors=22.4 teddy bear=26.9 hair drier=0.0 toothbrush=8.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.3 [Epoch 198][Batch 99], LR: 1.00E-03, Speed: 8.161 samples/sec, ObjLoss=24.334, BoxCenterLoss=14.385, BoxScaleLoss=5.008, ClassLoss=9.849 [Epoch 198][Batch 199], LR: 1.00E-03, Speed: 10.354 samples/sec, ObjLoss=24.334, BoxCenterLoss=14.385, BoxScaleLoss=5.008, ClassLoss=9.848 [Epoch 198][Batch 299], LR: 1.00E-03, Speed: 9.282 samples/sec, ObjLoss=24.334, BoxCenterLoss=14.386, BoxScaleLoss=5.008, ClassLoss=9.848 [Epoch 198][Batch 399], LR: 1.00E-03, Speed: 9.290 samples/sec, ObjLoss=24.333, BoxCenterLoss=14.385, BoxScaleLoss=5.008, ClassLoss=9.848 [Epoch 198][Batch 499], LR: 1.00E-03, Speed: 8.424 samples/sec, ObjLoss=24.333, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.847 [Epoch 198][Batch 599], LR: 1.00E-03, Speed: 10.106 samples/sec, ObjLoss=24.332, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.847 [Epoch 198][Batch 699], LR: 1.00E-03, Speed: 9.526 samples/sec, ObjLoss=24.331, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.846 [Epoch 198][Batch 799], LR: 1.00E-03, Speed: 13.011 samples/sec, ObjLoss=24.331, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.846 [Epoch 198][Batch 899], LR: 1.00E-03, Speed: 11.268 samples/sec, ObjLoss=24.330, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.845 [Epoch 198][Batch 999], LR: 1.00E-03, Speed: 6.949 samples/sec, ObjLoss=24.330, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.845 [Epoch 198][Batch 1099], LR: 1.00E-03, Speed: 8.986 samples/sec, ObjLoss=24.329, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.845 [Epoch 198][Batch 1199], LR: 1.00E-03, Speed: 10.234 samples/sec, ObjLoss=24.329, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.844 [Epoch 198][Batch 1299], LR: 1.00E-03, Speed: 10.528 samples/sec, ObjLoss=24.329, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.844 [Epoch 198][Batch 1399], LR: 1.00E-03, Speed: 8.516 samples/sec, ObjLoss=24.328, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.844 [Epoch 198][Batch 1499], LR: 1.00E-03, Speed: 9.460 samples/sec, ObjLoss=24.328, BoxCenterLoss=14.386, BoxScaleLoss=5.007, ClassLoss=9.843 [Epoch 198][Batch 1599], LR: 1.00E-03, Speed: 9.207 samples/sec, ObjLoss=24.328, BoxCenterLoss=14.386, BoxScaleLoss=5.007, ClassLoss=9.843 [Epoch 198][Batch 1699], LR: 1.00E-03, Speed: 8.102 samples/sec, ObjLoss=24.327, BoxCenterLoss=14.386, BoxScaleLoss=5.007, ClassLoss=9.842 [Epoch 198][Batch 1799], LR: 1.00E-03, Speed: 12.683 samples/sec, ObjLoss=24.326, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.842 [Epoch 198] Training cost: 2183.224, ObjLoss=24.326, BoxCenterLoss=14.385, BoxScaleLoss=5.007, ClassLoss=9.842 [Epoch 198] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.467 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.111 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 person=39.5 bicycle=18.8 car=28.3 motorcycle=31.6 airplane=43.5 bus=51.6 train=55.1 truck=25.1 boat=14.8 traffic light=13.8 fire hydrant=46.9 stop sign=45.4 parking meter=29.3 bench=15.7 bird=21.9 cat=46.6 dog=42.2 horse=39.7 sheep=33.1 cow=35.2 elephant=45.4 bear=52.5 zebra=49.7 giraffe=48.6 backpack=7.7 umbrella=27.0 handbag=5.7 tie=18.2 suitcase=18.2 frisbee=41.6 skis=10.4 snowboard=16.4 sports ball=30.4 kite=28.9 baseball bat=14.5 baseball glove=23.1 skateboard=30.2 surfboard=23.1 tennis racket=32.3 bottle=19.9 wine glass=21.5 cup=27.3 fork=17.5 knife=4.4 spoon=5.7 bowl=26.5 banana=15.3 apple=8.2 sandwich=16.8 orange=20.3 broccoli=9.7 carrot=10.3 hot dog=19.4 pizza=31.9 donut=28.3 cake=19.0 chair=15.7 couch=32.0 potted plant=15.0 bed=31.7 dining table=18.9 toilet=38.0 tv=42.6 laptop=43.9 mouse=36.1 remote=10.8 keyboard=30.5 cell phone=18.7 microwave=37.4 oven=22.7 toaster=5.9 sink=23.1 refrigerator=33.5 book=5.6 clock=31.6 vase=20.5 scissors=16.6 teddy bear=30.4 hair drier=0.0 toothbrush=4.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.9 [Epoch 199][Batch 99], LR: 1.00E-03, Speed: 11.465 samples/sec, ObjLoss=24.325, BoxCenterLoss=14.385, BoxScaleLoss=5.006, ClassLoss=9.841 [Epoch 199][Batch 199], LR: 1.00E-03, Speed: 7.951 samples/sec, ObjLoss=24.325, BoxCenterLoss=14.385, BoxScaleLoss=5.006, ClassLoss=9.841 [Epoch 199][Batch 299], LR: 1.00E-03, Speed: 10.283 samples/sec, ObjLoss=24.324, BoxCenterLoss=14.385, BoxScaleLoss=5.006, ClassLoss=9.840 [Epoch 199][Batch 399], LR: 1.00E-03, Speed: 7.681 samples/sec, ObjLoss=24.323, BoxCenterLoss=14.384, BoxScaleLoss=5.006, ClassLoss=9.840 [Epoch 199][Batch 499], LR: 1.00E-03, Speed: 9.976 samples/sec, ObjLoss=24.323, BoxCenterLoss=14.384, BoxScaleLoss=5.006, ClassLoss=9.839 [Epoch 199][Batch 599], LR: 1.00E-03, Speed: 8.521 samples/sec, ObjLoss=24.322, BoxCenterLoss=14.384, BoxScaleLoss=5.006, ClassLoss=9.839 [Epoch 199][Batch 699], LR: 1.00E-03, Speed: 8.325 samples/sec, ObjLoss=24.322, BoxCenterLoss=14.385, BoxScaleLoss=5.006, ClassLoss=9.839 [Epoch 199][Batch 799], LR: 1.00E-03, Speed: 8.081 samples/sec, ObjLoss=24.321, BoxCenterLoss=14.385, BoxScaleLoss=5.006, ClassLoss=9.838 [Epoch 199][Batch 899], LR: 1.00E-03, Speed: 10.218 samples/sec, ObjLoss=24.321, BoxCenterLoss=14.384, BoxScaleLoss=5.006, ClassLoss=9.838 [Epoch 199][Batch 999], LR: 1.00E-03, Speed: 8.841 samples/sec, ObjLoss=24.320, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.837 [Epoch 199][Batch 1099], LR: 1.00E-03, Speed: 10.803 samples/sec, ObjLoss=24.320, BoxCenterLoss=14.384, BoxScaleLoss=5.006, ClassLoss=9.837 [Epoch 199][Batch 1199], LR: 1.00E-03, Speed: 8.837 samples/sec, ObjLoss=24.319, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.837 [Epoch 199][Batch 1299], LR: 1.00E-03, Speed: 8.821 samples/sec, ObjLoss=24.319, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.836 [Epoch 199][Batch 1399], LR: 1.00E-03, Speed: 9.606 samples/sec, ObjLoss=24.318, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.836 [Epoch 199][Batch 1499], LR: 1.00E-03, Speed: 9.365 samples/sec, ObjLoss=24.318, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.836 [Epoch 199][Batch 1599], LR: 1.00E-03, Speed: 10.945 samples/sec, ObjLoss=24.318, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.835 [Epoch 199][Batch 1699], LR: 1.00E-03, Speed: 8.452 samples/sec, ObjLoss=24.317, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.835 [Epoch 199][Batch 1799], LR: 1.00E-03, Speed: 16.029 samples/sec, ObjLoss=24.316, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.834 [Epoch 199] Training cost: 2206.827, ObjLoss=24.316, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.834 [Epoch 199] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.470 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.235 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.108 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.161 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479 person=38.2 bicycle=17.1 car=24.9 motorcycle=29.5 airplane=42.8 bus=42.4 train=44.5 truck=21.6 boat=13.6 traffic light=9.9 fire hydrant=46.9 stop sign=43.1 parking meter=29.5 bench=16.0 bird=20.0 cat=45.5 dog=38.5 horse=40.7 sheep=31.8 cow=38.0 elephant=41.4 bear=44.5 zebra=45.8 giraffe=46.3 backpack=5.2 umbrella=23.1 handbag=5.4 tie=15.5 suitcase=18.2 frisbee=37.2 skis=11.7 snowboard=15.0 sports ball=27.6 kite=27.2 baseball bat=12.6 baseball glove=19.2 skateboard=30.0 surfboard=18.3 tennis racket=27.0 bottle=16.8 wine glass=22.2 cup=24.4 fork=17.1 knife=5.6 spoon=6.8 bowl=25.8 banana=13.2 apple=7.5 sandwich=21.6 orange=14.6 broccoli=12.1 carrot=9.4 hot dog=19.1 pizza=34.6 donut=30.2 cake=22.3 chair=15.2 couch=29.6 potted plant=13.0 bed=29.1 dining table=19.7 toilet=40.7 tv=38.6 laptop=41.2 mouse=37.0 remote=13.0 keyboard=26.2 cell phone=19.8 microwave=34.7 oven=25.5 toaster=1.6 sink=23.3 refrigerator=37.4 book=4.3 clock=34.9 vase=22.9 scissors=13.8 teddy bear=27.5 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.5 [Epoch 200][Batch 99], LR: 1.00E-03, Speed: 8.607 samples/sec, ObjLoss=24.316, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.834 [Epoch 200][Batch 199], LR: 1.00E-03, Speed: 11.456 samples/sec, ObjLoss=24.316, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.834 [Epoch 200][Batch 299], LR: 1.00E-03, Speed: 112.139 samples/sec, ObjLoss=24.315, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.833 [Epoch 200][Batch 399], LR: 1.00E-03, Speed: 9.064 samples/sec, ObjLoss=24.314, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.833 [Epoch 200][Batch 499], LR: 1.00E-03, Speed: 7.037 samples/sec, ObjLoss=24.314, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.832 [Epoch 200][Batch 599], LR: 1.00E-03, Speed: 9.520 samples/sec, ObjLoss=24.314, BoxCenterLoss=14.384, BoxScaleLoss=5.005, ClassLoss=9.832 [Epoch 200][Batch 699], LR: 1.00E-03, Speed: 7.505 samples/sec, ObjLoss=24.313, BoxCenterLoss=14.384, BoxScaleLoss=5.004, ClassLoss=9.831 [Epoch 200][Batch 799], LR: 1.00E-03, Speed: 7.195 samples/sec, ObjLoss=24.313, BoxCenterLoss=14.384, BoxScaleLoss=5.004, ClassLoss=9.831 [Epoch 200][Batch 899], LR: 1.00E-03, Speed: 9.923 samples/sec, ObjLoss=24.312, BoxCenterLoss=14.384, BoxScaleLoss=5.004, ClassLoss=9.831 [Epoch 200][Batch 999], LR: 1.00E-03, Speed: 8.144 samples/sec, ObjLoss=24.312, BoxCenterLoss=14.384, BoxScaleLoss=5.004, ClassLoss=9.830 [Epoch 200][Batch 1099], LR: 1.00E-03, Speed: 11.303 samples/sec, ObjLoss=24.311, BoxCenterLoss=14.384, BoxScaleLoss=5.004, ClassLoss=9.830 [Epoch 200][Batch 1199], LR: 1.00E-03, Speed: 9.645 samples/sec, ObjLoss=24.310, BoxCenterLoss=14.383, BoxScaleLoss=5.004, ClassLoss=9.829 [Epoch 200][Batch 1299], LR: 1.00E-03, Speed: 12.622 samples/sec, ObjLoss=24.309, BoxCenterLoss=14.383, BoxScaleLoss=5.004, ClassLoss=9.829 [Epoch 200][Batch 1399], LR: 1.00E-03, Speed: 9.850 samples/sec, ObjLoss=24.309, BoxCenterLoss=14.383, BoxScaleLoss=5.004, ClassLoss=9.829 [Epoch 200][Batch 1499], LR: 1.00E-03, Speed: 11.419 samples/sec, ObjLoss=24.308, BoxCenterLoss=14.383, BoxScaleLoss=5.004, ClassLoss=9.828 [Epoch 200][Batch 1599], LR: 1.00E-03, Speed: 123.119 samples/sec, ObjLoss=24.308, BoxCenterLoss=14.383, BoxScaleLoss=5.004, ClassLoss=9.828 [Epoch 200][Batch 1699], LR: 1.00E-03, Speed: 116.342 samples/sec, ObjLoss=24.307, BoxCenterLoss=14.383, BoxScaleLoss=5.004, ClassLoss=9.827 [Epoch 200][Batch 1799], LR: 1.00E-03, Speed: 8.800 samples/sec, ObjLoss=24.307, BoxCenterLoss=14.383, BoxScaleLoss=5.003, ClassLoss=9.827 [Epoch 200] Training cost: 2215.062, ObjLoss=24.306, BoxCenterLoss=14.383, BoxScaleLoss=5.003, ClassLoss=9.827 [Epoch 200] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.470 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.113 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.229 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.334 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478 person=38.9 bicycle=20.1 car=27.5 motorcycle=29.7 airplane=44.4 bus=48.6 train=48.4 truck=25.1 boat=12.9 traffic light=15.8 fire hydrant=47.5 stop sign=48.4 parking meter=34.8 bench=13.8 bird=21.7 cat=46.6 dog=42.0 horse=40.2 sheep=35.1 cow=37.4 elephant=47.6 bear=48.7 zebra=49.1 giraffe=52.0 backpack=7.4 umbrella=26.5 handbag=6.6 tie=20.0 suitcase=17.5 frisbee=39.5 skis=12.5 snowboard=17.0 sports ball=27.1 kite=21.5 baseball bat=13.7 baseball glove=23.2 skateboard=31.4 surfboard=22.3 tennis racket=31.8 bottle=21.0 wine glass=22.1 cup=23.7 fork=16.5 knife=5.8 spoon=3.8 bowl=21.7 banana=11.8 apple=8.5 sandwich=21.7 orange=18.9 broccoli=11.8 carrot=11.4 hot dog=18.9 pizza=34.4 donut=29.5 cake=22.2 chair=15.8 couch=30.0 potted plant=14.2 bed=31.0 dining table=18.0 toilet=41.8 tv=39.0 laptop=42.1 mouse=38.8 remote=12.0 keyboard=28.3 cell phone=19.4 microwave=31.3 oven=22.1 toaster=2.4 sink=24.3 refrigerator=35.0 book=5.5 clock=33.9 vase=22.8 scissors=15.7 teddy bear=31.7 hair drier=0.0 toothbrush=9.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.8 [Epoch 201][Batch 99], LR: 1.00E-03, Speed: 9.692 samples/sec, ObjLoss=24.306, BoxCenterLoss=14.383, BoxScaleLoss=5.003, ClassLoss=9.826 [Epoch 201][Batch 199], LR: 1.00E-03, Speed: 94.972 samples/sec, ObjLoss=24.305, BoxCenterLoss=14.382, BoxScaleLoss=5.003, ClassLoss=9.826 [Epoch 201][Batch 299], LR: 1.00E-03, Speed: 93.809 samples/sec, ObjLoss=24.305, BoxCenterLoss=14.383, BoxScaleLoss=5.003, ClassLoss=9.825 [Epoch 201][Batch 399], LR: 1.00E-03, Speed: 17.992 samples/sec, ObjLoss=24.305, BoxCenterLoss=14.383, BoxScaleLoss=5.003, ClassLoss=9.825 [Epoch 201][Batch 499], LR: 1.00E-03, Speed: 118.160 samples/sec, ObjLoss=24.304, BoxCenterLoss=14.382, BoxScaleLoss=5.003, ClassLoss=9.825 [Epoch 201][Batch 599], LR: 1.00E-03, Speed: 102.236 samples/sec, ObjLoss=24.303, BoxCenterLoss=14.382, BoxScaleLoss=5.003, ClassLoss=9.824 [Epoch 201][Batch 699], LR: 1.00E-03, Speed: 8.472 samples/sec, ObjLoss=24.302, BoxCenterLoss=14.382, BoxScaleLoss=5.003, ClassLoss=9.824 [Epoch 201][Batch 799], LR: 1.00E-03, Speed: 9.125 samples/sec, ObjLoss=24.302, BoxCenterLoss=14.382, BoxScaleLoss=5.003, ClassLoss=9.823 [Epoch 201][Batch 899], LR: 1.00E-03, Speed: 92.743 samples/sec, ObjLoss=24.301, BoxCenterLoss=14.382, BoxScaleLoss=5.003, ClassLoss=9.823 [Epoch 201][Batch 999], LR: 1.00E-03, Speed: 12.118 samples/sec, ObjLoss=24.301, BoxCenterLoss=14.382, BoxScaleLoss=5.003, ClassLoss=9.822 [Epoch 201][Batch 1099], LR: 1.00E-03, Speed: 10.381 samples/sec, ObjLoss=24.300, BoxCenterLoss=14.382, BoxScaleLoss=5.002, ClassLoss=9.822 [Epoch 201][Batch 1199], LR: 1.00E-03, Speed: 9.023 samples/sec, ObjLoss=24.300, BoxCenterLoss=14.382, BoxScaleLoss=5.002, ClassLoss=9.822 [Epoch 201][Batch 1299], LR: 1.00E-03, Speed: 8.453 samples/sec, ObjLoss=24.299, BoxCenterLoss=14.382, BoxScaleLoss=5.002, ClassLoss=9.821 [Epoch 201][Batch 1399], LR: 1.00E-03, Speed: 8.516 samples/sec, ObjLoss=24.299, BoxCenterLoss=14.382, BoxScaleLoss=5.002, ClassLoss=9.821 [Epoch 201][Batch 1499], LR: 1.00E-03, Speed: 11.448 samples/sec, ObjLoss=24.299, BoxCenterLoss=14.382, BoxScaleLoss=5.002, ClassLoss=9.821 [Epoch 201][Batch 1599], LR: 1.00E-03, Speed: 9.475 samples/sec, ObjLoss=24.298, BoxCenterLoss=14.382, BoxScaleLoss=5.002, ClassLoss=9.820 [Epoch 201][Batch 1699], LR: 1.00E-03, Speed: 8.440 samples/sec, ObjLoss=24.297, BoxCenterLoss=14.382, BoxScaleLoss=5.002, ClassLoss=9.820 [Epoch 201][Batch 1799], LR: 1.00E-03, Speed: 13.536 samples/sec, ObjLoss=24.296, BoxCenterLoss=14.381, BoxScaleLoss=5.002, ClassLoss=9.819 [Epoch 201] Training cost: 2140.833, ObjLoss=24.296, BoxCenterLoss=14.381, BoxScaleLoss=5.002, ClassLoss=9.819 [Epoch 201] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 person=38.6 bicycle=20.9 car=25.9 motorcycle=30.4 airplane=42.4 bus=48.2 train=51.6 truck=24.3 boat=14.7 traffic light=14.7 fire hydrant=44.7 stop sign=42.3 parking meter=27.5 bench=14.9 bird=20.9 cat=46.1 dog=40.8 horse=41.9 sheep=34.3 cow=37.5 elephant=45.3 bear=49.2 zebra=46.4 giraffe=48.9 backpack=6.3 umbrella=25.2 handbag=5.6 tie=19.7 suitcase=19.2 frisbee=41.0 skis=12.0 snowboard=11.4 sports ball=23.7 kite=24.2 baseball bat=14.1 baseball glove=17.3 skateboard=29.1 surfboard=21.4 tennis racket=27.9 bottle=19.1 wine glass=24.5 cup=25.4 fork=18.2 knife=4.5 spoon=6.0 bowl=22.3 banana=11.8 apple=8.6 sandwich=21.3 orange=18.2 broccoli=10.9 carrot=9.4 hot dog=20.0 pizza=33.3 donut=26.6 cake=21.6 chair=17.5 couch=30.5 potted plant=14.9 bed=34.6 dining table=18.2 toilet=40.0 tv=39.8 laptop=41.7 mouse=33.0 remote=11.9 keyboard=30.8 cell phone=18.0 microwave=33.2 oven=24.3 toaster=7.1 sink=22.0 refrigerator=34.5 book=4.8 clock=37.2 vase=24.3 scissors=18.9 teddy bear=31.7 hair drier=0.0 toothbrush=5.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.3 [Epoch 202][Batch 99], LR: 1.00E-03, Speed: 9.525 samples/sec, ObjLoss=24.296, BoxCenterLoss=14.381, BoxScaleLoss=5.002, ClassLoss=9.818 [Epoch 202][Batch 199], LR: 1.00E-03, Speed: 9.230 samples/sec, ObjLoss=24.296, BoxCenterLoss=14.381, BoxScaleLoss=5.002, ClassLoss=9.818 [Epoch 202][Batch 299], LR: 1.00E-03, Speed: 113.865 samples/sec, ObjLoss=24.295, BoxCenterLoss=14.381, BoxScaleLoss=5.002, ClassLoss=9.818 [Epoch 202][Batch 399], LR: 1.00E-03, Speed: 11.210 samples/sec, ObjLoss=24.295, BoxCenterLoss=14.382, BoxScaleLoss=5.001, ClassLoss=9.818 [Epoch 202][Batch 499], LR: 1.00E-03, Speed: 91.286 samples/sec, ObjLoss=24.295, BoxCenterLoss=14.382, BoxScaleLoss=5.001, ClassLoss=9.817 [Epoch 202][Batch 599], LR: 1.00E-03, Speed: 95.570 samples/sec, ObjLoss=24.294, BoxCenterLoss=14.382, BoxScaleLoss=5.001, ClassLoss=9.817 [Epoch 202][Batch 699], LR: 1.00E-03, Speed: 10.722 samples/sec, ObjLoss=24.294, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.816 [Epoch 202][Batch 799], LR: 1.00E-03, Speed: 8.544 samples/sec, ObjLoss=24.293, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.816 [Epoch 202][Batch 899], LR: 1.00E-03, Speed: 10.156 samples/sec, ObjLoss=24.292, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.816 [Epoch 202][Batch 999], LR: 1.00E-03, Speed: 111.612 samples/sec, ObjLoss=24.292, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.815 [Epoch 202][Batch 1099], LR: 1.00E-03, Speed: 10.486 samples/sec, ObjLoss=24.291, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.815 [Epoch 202][Batch 1199], LR: 1.00E-03, Speed: 7.168 samples/sec, ObjLoss=24.291, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.814 [Epoch 202][Batch 1299], LR: 1.00E-03, Speed: 6.867 samples/sec, ObjLoss=24.291, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.814 [Epoch 202][Batch 1399], LR: 1.00E-03, Speed: 107.835 samples/sec, ObjLoss=24.290, BoxCenterLoss=14.381, BoxScaleLoss=5.001, ClassLoss=9.813 [Epoch 202][Batch 1499], LR: 1.00E-03, Speed: 9.635 samples/sec, ObjLoss=24.289, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.813 [Epoch 202][Batch 1599], LR: 1.00E-03, Speed: 10.435 samples/sec, ObjLoss=24.289, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.813 [Epoch 202][Batch 1699], LR: 1.00E-03, Speed: 110.422 samples/sec, ObjLoss=24.289, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.812 [Epoch 202][Batch 1799], LR: 1.00E-03, Speed: 11.379 samples/sec, ObjLoss=24.288, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.812 [Epoch 202] Training cost: 2191.130, ObjLoss=24.288, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.812 [Epoch 202] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.477 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.123 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.179 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.369 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475 person=38.4 bicycle=18.2 car=28.6 motorcycle=29.0 airplane=39.8 bus=47.5 train=47.6 truck=23.1 boat=15.0 traffic light=13.0 fire hydrant=41.1 stop sign=43.9 parking meter=28.3 bench=15.3 bird=22.3 cat=46.9 dog=40.7 horse=39.5 sheep=38.3 cow=38.6 elephant=44.8 bear=49.0 zebra=49.1 giraffe=48.6 backpack=7.0 umbrella=24.1 handbag=6.1 tie=18.5 suitcase=22.1 frisbee=39.6 skis=11.0 snowboard=20.4 sports ball=21.8 kite=26.2 baseball bat=13.5 baseball glove=21.5 skateboard=30.9 surfboard=21.2 tennis racket=29.9 bottle=20.0 wine glass=22.1 cup=26.5 fork=17.3 knife=5.1 spoon=5.6 bowl=26.4 banana=13.6 apple=9.0 sandwich=18.9 orange=18.9 broccoli=13.1 carrot=11.8 hot dog=17.4 pizza=36.6 donut=25.1 cake=23.1 chair=16.4 couch=25.8 potted plant=14.3 bed=33.3 dining table=20.6 toilet=32.9 tv=39.7 laptop=39.1 mouse=35.1 remote=12.7 keyboard=32.3 cell phone=19.6 microwave=37.8 oven=24.4 toaster=0.0 sink=22.6 refrigerator=34.8 book=6.5 clock=36.9 vase=22.5 scissors=17.2 teddy bear=26.5 hair drier=0.0 toothbrush=8.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.4 [Epoch 203][Batch 99], LR: 1.00E-03, Speed: 8.313 samples/sec, ObjLoss=24.288, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.811 [Epoch 203][Batch 199], LR: 1.00E-03, Speed: 110.656 samples/sec, ObjLoss=24.287, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.811 [Epoch 203][Batch 299], LR: 1.00E-03, Speed: 107.762 samples/sec, ObjLoss=24.287, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.811 [Epoch 203][Batch 399], LR: 1.00E-03, Speed: 8.893 samples/sec, ObjLoss=24.286, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.810 [Epoch 203][Batch 499], LR: 1.00E-03, Speed: 104.304 samples/sec, ObjLoss=24.286, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.810 [Epoch 203][Batch 599], LR: 1.00E-03, Speed: 11.929 samples/sec, ObjLoss=24.286, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.809 [Epoch 203][Batch 699], LR: 1.00E-03, Speed: 10.023 samples/sec, ObjLoss=24.285, BoxCenterLoss=14.382, BoxScaleLoss=5.000, ClassLoss=9.809 [Epoch 203][Batch 799], LR: 1.00E-03, Speed: 7.204 samples/sec, ObjLoss=24.285, BoxCenterLoss=14.382, BoxScaleLoss=5.000, ClassLoss=9.809 [Epoch 203][Batch 899], LR: 1.00E-03, Speed: 9.040 samples/sec, ObjLoss=24.284, BoxCenterLoss=14.382, BoxScaleLoss=5.000, ClassLoss=9.808 [Epoch 203][Batch 999], LR: 1.00E-03, Speed: 103.529 samples/sec, ObjLoss=24.284, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.808 [Epoch 203][Batch 1099], LR: 1.00E-03, Speed: 10.199 samples/sec, ObjLoss=24.283, BoxCenterLoss=14.382, BoxScaleLoss=5.000, ClassLoss=9.808 [Epoch 203][Batch 1199], LR: 1.00E-03, Speed: 10.717 samples/sec, ObjLoss=24.283, BoxCenterLoss=14.382, BoxScaleLoss=5.000, ClassLoss=9.807 [Epoch 203][Batch 1299], LR: 1.00E-03, Speed: 106.865 samples/sec, ObjLoss=24.282, BoxCenterLoss=14.382, BoxScaleLoss=5.000, ClassLoss=9.807 [Epoch 203][Batch 1399], LR: 1.00E-03, Speed: 9.368 samples/sec, ObjLoss=24.282, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.806 [Epoch 203][Batch 1499], LR: 1.00E-03, Speed: 17.270 samples/sec, ObjLoss=24.281, BoxCenterLoss=14.381, BoxScaleLoss=5.000, ClassLoss=9.806 [Epoch 203][Batch 1599], LR: 1.00E-03, Speed: 9.359 samples/sec, ObjLoss=24.280, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.805 [Epoch 203][Batch 1699], LR: 1.00E-03, Speed: 9.910 samples/sec, ObjLoss=24.280, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.805 [Epoch 203][Batch 1799], LR: 1.00E-03, Speed: 9.541 samples/sec, ObjLoss=24.279, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.804 [Epoch 203] Training cost: 2149.495, ObjLoss=24.279, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.804 [Epoch 203] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.473 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.239 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.107 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486 person=36.9 bicycle=17.0 car=27.7 motorcycle=29.6 airplane=47.6 bus=48.5 train=42.2 truck=23.5 boat=13.8 traffic light=10.5 fire hydrant=43.9 stop sign=41.0 parking meter=29.3 bench=14.1 bird=20.8 cat=48.3 dog=43.2 horse=40.0 sheep=34.6 cow=36.4 elephant=43.4 bear=50.5 zebra=46.3 giraffe=48.1 backpack=6.7 umbrella=24.5 handbag=7.3 tie=16.8 suitcase=16.7 frisbee=35.8 skis=11.8 snowboard=19.1 sports ball=25.4 kite=27.4 baseball bat=15.3 baseball glove=23.0 skateboard=31.5 surfboard=22.7 tennis racket=25.6 bottle=21.7 wine glass=23.4 cup=27.6 fork=16.3 knife=3.7 spoon=6.3 bowl=24.4 banana=11.8 apple=7.8 sandwich=22.5 orange=20.6 broccoli=11.0 carrot=11.9 hot dog=17.9 pizza=36.6 donut=28.0 cake=21.4 chair=16.8 couch=26.5 potted plant=15.0 bed=30.9 dining table=18.7 toilet=35.9 tv=39.3 laptop=37.6 mouse=42.5 remote=11.4 keyboard=30.6 cell phone=18.4 microwave=28.6 oven=17.1 toaster=0.0 sink=22.0 refrigerator=31.7 book=5.9 clock=34.6 vase=24.1 scissors=19.5 teddy bear=29.8 hair drier=0.0 toothbrush=6.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.0 [Epoch 204][Batch 99], LR: 1.00E-03, Speed: 102.092 samples/sec, ObjLoss=24.279, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.804 [Epoch 204][Batch 199], LR: 1.00E-03, Speed: 9.885 samples/sec, ObjLoss=24.279, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.804 [Epoch 204][Batch 299], LR: 1.00E-03, Speed: 9.358 samples/sec, ObjLoss=24.278, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.803 [Epoch 204][Batch 399], LR: 1.00E-03, Speed: 10.044 samples/sec, ObjLoss=24.278, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.803 [Epoch 204][Batch 499], LR: 1.00E-03, Speed: 84.354 samples/sec, ObjLoss=24.277, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.802 [Epoch 204][Batch 599], LR: 1.00E-03, Speed: 9.369 samples/sec, ObjLoss=24.277, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.802 [Epoch 204][Batch 699], LR: 1.00E-03, Speed: 101.609 samples/sec, ObjLoss=24.276, BoxCenterLoss=14.381, BoxScaleLoss=4.999, ClassLoss=9.801 [Epoch 204][Batch 799], LR: 1.00E-03, Speed: 116.141 samples/sec, ObjLoss=24.276, BoxCenterLoss=14.381, BoxScaleLoss=4.998, ClassLoss=9.801 [Epoch 204][Batch 899], LR: 1.00E-03, Speed: 94.482 samples/sec, ObjLoss=24.276, BoxCenterLoss=14.381, BoxScaleLoss=4.998, ClassLoss=9.801 [Epoch 204][Batch 999], LR: 1.00E-03, Speed: 10.126 samples/sec, ObjLoss=24.275, BoxCenterLoss=14.381, BoxScaleLoss=4.998, ClassLoss=9.800 [Epoch 204][Batch 1099], LR: 1.00E-03, Speed: 11.549 samples/sec, ObjLoss=24.275, BoxCenterLoss=14.381, BoxScaleLoss=4.998, ClassLoss=9.800 [Epoch 204][Batch 1199], LR: 1.00E-03, Speed: 9.666 samples/sec, ObjLoss=24.274, BoxCenterLoss=14.381, BoxScaleLoss=4.998, ClassLoss=9.799 [Epoch 204][Batch 1299], LR: 1.00E-03, Speed: 8.130 samples/sec, ObjLoss=24.274, BoxCenterLoss=14.381, BoxScaleLoss=4.998, ClassLoss=9.799 [Epoch 204][Batch 1399], LR: 1.00E-03, Speed: 10.549 samples/sec, ObjLoss=24.273, BoxCenterLoss=14.380, BoxScaleLoss=4.998, ClassLoss=9.799 [Epoch 204][Batch 1499], LR: 1.00E-03, Speed: 127.025 samples/sec, ObjLoss=24.273, BoxCenterLoss=14.380, BoxScaleLoss=4.998, ClassLoss=9.798 [Epoch 204][Batch 1599], LR: 1.00E-03, Speed: 10.783 samples/sec, ObjLoss=24.272, BoxCenterLoss=14.380, BoxScaleLoss=4.998, ClassLoss=9.798 [Epoch 204][Batch 1699], LR: 1.00E-03, Speed: 12.302 samples/sec, ObjLoss=24.272, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.798 [Epoch 204][Batch 1799], LR: 1.00E-03, Speed: 11.490 samples/sec, ObjLoss=24.271, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.797 [Epoch 204] Training cost: 2171.640, ObjLoss=24.271, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.797 [Epoch 204] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.248 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.225 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.482 person=37.0 bicycle=19.3 car=27.8 motorcycle=27.9 airplane=43.4 bus=46.2 train=47.6 truck=23.8 boat=14.4 traffic light=15.4 fire hydrant=41.4 stop sign=40.1 parking meter=25.7 bench=16.0 bird=19.9 cat=47.2 dog=41.8 horse=34.7 sheep=35.6 cow=38.6 elephant=43.9 bear=44.2 zebra=49.3 giraffe=46.5 backpack=6.2 umbrella=24.4 handbag=5.0 tie=16.2 suitcase=16.8 frisbee=35.4 skis=10.4 snowboard=17.2 sports ball=30.8 kite=26.2 baseball bat=14.2 baseball glove=18.9 skateboard=29.7 surfboard=20.0 tennis racket=28.1 bottle=19.2 wine glass=23.8 cup=25.5 fork=16.7 knife=5.1 spoon=6.1 bowl=25.1 banana=10.6 apple=8.0 sandwich=21.0 orange=17.8 broccoli=9.4 carrot=10.3 hot dog=17.7 pizza=37.0 donut=28.1 cake=23.0 chair=16.1 couch=28.1 potted plant=15.2 bed=32.9 dining table=20.1 toilet=35.6 tv=39.3 laptop=40.3 mouse=41.8 remote=14.1 keyboard=33.9 cell phone=19.5 microwave=34.2 oven=18.5 toaster=8.3 sink=19.5 refrigerator=34.0 book=4.6 clock=30.1 vase=21.3 scissors=19.9 teddy bear=29.9 hair drier=0.0 toothbrush=6.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.9 [Epoch 205][Batch 99], LR: 1.00E-03, Speed: 10.601 samples/sec, ObjLoss=24.270, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.797 [Epoch 205][Batch 199], LR: 1.00E-03, Speed: 9.751 samples/sec, ObjLoss=24.270, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.796 [Epoch 205][Batch 299], LR: 1.00E-03, Speed: 7.067 samples/sec, ObjLoss=24.270, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.796 [Epoch 205][Batch 399], LR: 1.00E-03, Speed: 8.408 samples/sec, ObjLoss=24.269, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.796 [Epoch 205][Batch 499], LR: 1.00E-03, Speed: 10.305 samples/sec, ObjLoss=24.269, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.795 [Epoch 205][Batch 599], LR: 1.00E-03, Speed: 9.807 samples/sec, ObjLoss=24.268, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.795 [Epoch 205][Batch 699], LR: 1.00E-03, Speed: 112.206 samples/sec, ObjLoss=24.268, BoxCenterLoss=14.380, BoxScaleLoss=4.997, ClassLoss=9.794 [Epoch 205][Batch 799], LR: 1.00E-03, Speed: 9.241 samples/sec, ObjLoss=24.267, BoxCenterLoss=14.379, BoxScaleLoss=4.997, ClassLoss=9.794 [Epoch 205][Batch 899], LR: 1.00E-03, Speed: 7.644 samples/sec, ObjLoss=24.267, BoxCenterLoss=14.379, BoxScaleLoss=4.997, ClassLoss=9.794 [Epoch 205][Batch 999], LR: 1.00E-03, Speed: 11.495 samples/sec, ObjLoss=24.266, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.793 [Epoch 205][Batch 1099], LR: 1.00E-03, Speed: 112.144 samples/sec, ObjLoss=24.266, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.793 [Epoch 205][Batch 1199], LR: 1.00E-03, Speed: 8.941 samples/sec, ObjLoss=24.265, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.793 [Epoch 205][Batch 1299], LR: 1.00E-03, Speed: 10.407 samples/sec, ObjLoss=24.264, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.792 [Epoch 205][Batch 1399], LR: 1.00E-03, Speed: 9.363 samples/sec, ObjLoss=24.264, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.792 [Epoch 205][Batch 1499], LR: 1.00E-03, Speed: 8.513 samples/sec, ObjLoss=24.263, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.791 [Epoch 205][Batch 1599], LR: 1.00E-03, Speed: 10.227 samples/sec, ObjLoss=24.262, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.791 [Epoch 205][Batch 1699], LR: 1.00E-03, Speed: 9.581 samples/sec, ObjLoss=24.262, BoxCenterLoss=14.378, BoxScaleLoss=4.996, ClassLoss=9.791 [Epoch 205][Batch 1799], LR: 1.00E-03, Speed: 10.513 samples/sec, ObjLoss=24.262, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.790 [Epoch 205] Training cost: 2152.734, ObjLoss=24.261, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.790 [Epoch 205] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.472 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481 person=37.4 bicycle=19.7 car=28.0 motorcycle=29.4 airplane=44.2 bus=48.2 train=50.8 truck=24.6 boat=12.6 traffic light=13.8 fire hydrant=47.4 stop sign=41.4 parking meter=29.0 bench=15.3 bird=19.9 cat=45.4 dog=38.5 horse=39.7 sheep=33.7 cow=37.1 elephant=51.0 bear=49.4 zebra=49.3 giraffe=45.2 backpack=5.5 umbrella=25.3 handbag=5.6 tie=15.7 suitcase=18.9 frisbee=41.3 skis=11.7 snowboard=18.4 sports ball=25.8 kite=28.3 baseball bat=14.9 baseball glove=19.4 skateboard=33.4 surfboard=18.2 tennis racket=27.7 bottle=21.5 wine glass=21.7 cup=25.1 fork=17.6 knife=6.3 spoon=4.8 bowl=25.0 banana=12.6 apple=7.7 sandwich=20.9 orange=17.9 broccoli=12.1 carrot=11.2 hot dog=18.6 pizza=34.5 donut=31.5 cake=22.4 chair=16.4 couch=27.6 potted plant=16.1 bed=33.2 dining table=20.9 toilet=39.4 tv=36.7 laptop=36.9 mouse=32.4 remote=13.0 keyboard=24.4 cell phone=18.7 microwave=33.2 oven=20.8 toaster=2.4 sink=23.3 refrigerator=35.3 book=5.1 clock=30.4 vase=23.8 scissors=24.1 teddy bear=29.5 hair drier=0.0 toothbrush=6.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.3 [Epoch 206][Batch 99], LR: 1.00E-03, Speed: 10.715 samples/sec, ObjLoss=24.261, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.790 [Epoch 206][Batch 199], LR: 1.00E-03, Speed: 10.109 samples/sec, ObjLoss=24.261, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.790 [Epoch 206][Batch 299], LR: 1.00E-03, Speed: 106.283 samples/sec, ObjLoss=24.261, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.789 [Epoch 206][Batch 399], LR: 1.00E-03, Speed: 9.420 samples/sec, ObjLoss=24.260, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.789 [Epoch 206][Batch 499], LR: 1.00E-03, Speed: 11.287 samples/sec, ObjLoss=24.260, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.789 [Epoch 206][Batch 599], LR: 1.00E-03, Speed: 8.783 samples/sec, ObjLoss=24.259, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.789 [Epoch 206][Batch 699], LR: 1.00E-03, Speed: 105.513 samples/sec, ObjLoss=24.259, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.788 [Epoch 206][Batch 799], LR: 1.00E-03, Speed: 9.440 samples/sec, ObjLoss=24.258, BoxCenterLoss=14.379, BoxScaleLoss=4.996, ClassLoss=9.788 [Epoch 206][Batch 899], LR: 1.00E-03, Speed: 9.351 samples/sec, ObjLoss=24.258, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.787 [Epoch 206][Batch 999], LR: 1.00E-03, Speed: 14.954 samples/sec, ObjLoss=24.257, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.787 [Epoch 206][Batch 1099], LR: 1.00E-03, Speed: 9.752 samples/sec, ObjLoss=24.257, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.787 [Epoch 206][Batch 1199], LR: 1.00E-03, Speed: 7.588 samples/sec, ObjLoss=24.256, BoxCenterLoss=14.378, BoxScaleLoss=4.995, ClassLoss=9.786 [Epoch 206][Batch 1299], LR: 1.00E-03, Speed: 11.653 samples/sec, ObjLoss=24.256, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.786 [Epoch 206][Batch 1399], LR: 1.00E-03, Speed: 9.471 samples/sec, ObjLoss=24.255, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.785 [Epoch 206][Batch 1499], LR: 1.00E-03, Speed: 118.997 samples/sec, ObjLoss=24.255, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.785 [Epoch 206][Batch 1599], LR: 1.00E-03, Speed: 8.235 samples/sec, ObjLoss=24.255, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.785 [Epoch 206][Batch 1699], LR: 1.00E-03, Speed: 8.374 samples/sec, ObjLoss=24.254, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.784 [Epoch 206][Batch 1799], LR: 1.00E-03, Speed: 11.311 samples/sec, ObjLoss=24.254, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.784 [Epoch 206] Training cost: 2170.794, ObjLoss=24.253, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.784 [Epoch 206] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.243 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.329 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481 person=38.0 bicycle=18.1 car=27.5 motorcycle=28.7 airplane=37.5 bus=51.1 train=50.5 truck=22.8 boat=13.0 traffic light=13.7 fire hydrant=49.8 stop sign=48.0 parking meter=31.7 bench=15.1 bird=20.3 cat=46.6 dog=40.6 horse=37.3 sheep=32.3 cow=34.2 elephant=47.3 bear=49.3 zebra=45.8 giraffe=47.4 backpack=6.1 umbrella=23.5 handbag=5.6 tie=16.8 suitcase=20.4 frisbee=33.8 skis=12.8 snowboard=16.2 sports ball=27.4 kite=24.5 baseball bat=13.6 baseball glove=19.2 skateboard=29.3 surfboard=20.0 tennis racket=27.7 bottle=20.8 wine glass=22.8 cup=26.8 fork=15.3 knife=4.2 spoon=5.6 bowl=24.8 banana=11.0 apple=6.1 sandwich=21.8 orange=18.4 broccoli=13.2 carrot=11.6 hot dog=19.5 pizza=34.7 donut=25.6 cake=19.9 chair=18.3 couch=30.7 potted plant=15.8 bed=31.0 dining table=19.7 toilet=42.6 tv=38.2 laptop=43.5 mouse=37.2 remote=12.1 keyboard=26.1 cell phone=19.6 microwave=32.8 oven=24.0 toaster=0.0 sink=22.2 refrigerator=32.3 book=5.2 clock=34.0 vase=21.8 scissors=19.2 teddy bear=29.4 hair drier=0.0 toothbrush=5.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.1 [Epoch 207][Batch 99], LR: 1.00E-03, Speed: 9.608 samples/sec, ObjLoss=24.253, BoxCenterLoss=14.379, BoxScaleLoss=4.995, ClassLoss=9.783 [Epoch 207][Batch 199], LR: 1.00E-03, Speed: 9.280 samples/sec, ObjLoss=24.252, BoxCenterLoss=14.378, BoxScaleLoss=4.995, ClassLoss=9.783 [Epoch 207][Batch 299], LR: 1.00E-03, Speed: 8.978 samples/sec, ObjLoss=24.251, BoxCenterLoss=14.378, BoxScaleLoss=4.994, ClassLoss=9.782 [Epoch 207][Batch 399], LR: 1.00E-03, Speed: 9.037 samples/sec, ObjLoss=24.251, BoxCenterLoss=14.378, BoxScaleLoss=4.994, ClassLoss=9.782 [Epoch 207][Batch 499], LR: 1.00E-03, Speed: 12.665 samples/sec, ObjLoss=24.251, BoxCenterLoss=14.378, BoxScaleLoss=4.994, ClassLoss=9.781 [Epoch 207][Batch 599], LR: 1.00E-03, Speed: 10.603 samples/sec, ObjLoss=24.250, BoxCenterLoss=14.378, BoxScaleLoss=4.994, ClassLoss=9.781 [Epoch 207][Batch 699], LR: 1.00E-03, Speed: 8.419 samples/sec, ObjLoss=24.250, BoxCenterLoss=14.378, BoxScaleLoss=4.994, ClassLoss=9.781 [Epoch 207][Batch 799], LR: 1.00E-03, Speed: 7.948 samples/sec, ObjLoss=24.249, BoxCenterLoss=14.377, BoxScaleLoss=4.994, ClassLoss=9.780 [Epoch 207][Batch 899], LR: 1.00E-03, Speed: 115.819 samples/sec, ObjLoss=24.248, BoxCenterLoss=14.377, BoxScaleLoss=4.994, ClassLoss=9.780 [Epoch 207][Batch 999], LR: 1.00E-03, Speed: 136.815 samples/sec, ObjLoss=24.247, BoxCenterLoss=14.377, BoxScaleLoss=4.993, ClassLoss=9.779 [Epoch 207][Batch 1099], LR: 1.00E-03, Speed: 10.728 samples/sec, ObjLoss=24.247, BoxCenterLoss=14.377, BoxScaleLoss=4.993, ClassLoss=9.779 [Epoch 207][Batch 1199], LR: 1.00E-03, Speed: 11.258 samples/sec, ObjLoss=24.246, BoxCenterLoss=14.377, BoxScaleLoss=4.993, ClassLoss=9.778 [Epoch 207][Batch 1299], LR: 1.00E-03, Speed: 10.355 samples/sec, ObjLoss=24.246, BoxCenterLoss=14.377, BoxScaleLoss=4.993, ClassLoss=9.778 [Epoch 207][Batch 1399], LR: 1.00E-03, Speed: 9.075 samples/sec, ObjLoss=24.246, BoxCenterLoss=14.377, BoxScaleLoss=4.993, ClassLoss=9.777 [Epoch 207][Batch 1499], LR: 1.00E-03, Speed: 8.353 samples/sec, ObjLoss=24.245, BoxCenterLoss=14.376, BoxScaleLoss=4.993, ClassLoss=9.777 [Epoch 207][Batch 1599], LR: 1.00E-03, Speed: 10.839 samples/sec, ObjLoss=24.245, BoxCenterLoss=14.376, BoxScaleLoss=4.993, ClassLoss=9.777 [Epoch 207][Batch 1699], LR: 1.00E-03, Speed: 8.392 samples/sec, ObjLoss=24.244, BoxCenterLoss=14.376, BoxScaleLoss=4.993, ClassLoss=9.776 [Epoch 207][Batch 1799], LR: 1.00E-03, Speed: 10.840 samples/sec, ObjLoss=24.244, BoxCenterLoss=14.376, BoxScaleLoss=4.993, ClassLoss=9.776 [Epoch 207] Training cost: 2232.467, ObjLoss=24.243, BoxCenterLoss=14.376, BoxScaleLoss=4.993, ClassLoss=9.776 [Epoch 207] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.260 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.325 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.335 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.497 person=37.2 bicycle=18.9 car=24.8 motorcycle=29.5 airplane=44.7 bus=49.1 train=54.5 truck=22.7 boat=15.9 traffic light=13.8 fire hydrant=41.2 stop sign=39.8 parking meter=34.9 bench=15.2 bird=21.0 cat=47.5 dog=38.3 horse=37.6 sheep=30.7 cow=35.2 elephant=46.3 bear=51.0 zebra=44.8 giraffe=49.1 backpack=5.7 umbrella=27.8 handbag=5.5 tie=16.6 suitcase=17.7 frisbee=34.5 skis=7.6 snowboard=17.9 sports ball=24.8 kite=24.0 baseball bat=13.9 baseball glove=17.4 skateboard=30.7 surfboard=21.8 tennis racket=29.0 bottle=21.7 wine glass=22.9 cup=24.8 fork=17.0 knife=5.5 spoon=6.1 bowl=24.9 banana=12.5 apple=5.1 sandwich=21.8 orange=17.1 broccoli=10.8 carrot=8.7 hot dog=18.8 pizza=34.2 donut=24.5 cake=22.3 chair=16.4 couch=32.2 potted plant=13.2 bed=36.2 dining table=20.5 toilet=39.4 tv=39.4 laptop=41.7 mouse=34.9 remote=10.3 keyboard=31.6 cell phone=19.0 microwave=39.8 oven=21.9 toaster=7.1 sink=22.2 refrigerator=32.7 book=3.9 clock=34.6 vase=25.8 scissors=16.5 teddy bear=30.9 hair drier=0.0 toothbrush=5.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.2 [Epoch 208][Batch 99], LR: 1.00E-03, Speed: 9.963 samples/sec, ObjLoss=24.243, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.775 [Epoch 208][Batch 199], LR: 1.00E-03, Speed: 8.462 samples/sec, ObjLoss=24.243, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.775 [Epoch 208][Batch 299], LR: 1.00E-03, Speed: 94.048 samples/sec, ObjLoss=24.242, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.775 [Epoch 208][Batch 399], LR: 1.00E-03, Speed: 10.609 samples/sec, ObjLoss=24.242, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.774 [Epoch 208][Batch 499], LR: 1.00E-03, Speed: 9.503 samples/sec, ObjLoss=24.241, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.774 [Epoch 208][Batch 599], LR: 1.00E-03, Speed: 88.764 samples/sec, ObjLoss=24.241, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.773 [Epoch 208][Batch 699], LR: 1.00E-03, Speed: 9.027 samples/sec, ObjLoss=24.241, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.773 [Epoch 208][Batch 799], LR: 1.00E-03, Speed: 8.424 samples/sec, ObjLoss=24.240, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.773 [Epoch 208][Batch 899], LR: 1.00E-03, Speed: 10.366 samples/sec, ObjLoss=24.240, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.772 [Epoch 208][Batch 999], LR: 1.00E-03, Speed: 110.653 samples/sec, ObjLoss=24.239, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.772 [Epoch 208][Batch 1099], LR: 1.00E-03, Speed: 9.214 samples/sec, ObjLoss=24.239, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.772 [Epoch 208][Batch 1199], LR: 1.00E-03, Speed: 7.536 samples/sec, ObjLoss=24.238, BoxCenterLoss=14.376, BoxScaleLoss=4.992, ClassLoss=9.771 [Epoch 208][Batch 1299], LR: 1.00E-03, Speed: 107.237 samples/sec, ObjLoss=24.238, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.771 [Epoch 208][Batch 1399], LR: 1.00E-03, Speed: 9.662 samples/sec, ObjLoss=24.237, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.770 [Epoch 208][Batch 1499], LR: 1.00E-03, Speed: 8.320 samples/sec, ObjLoss=24.237, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.770 [Epoch 208][Batch 1599], LR: 1.00E-03, Speed: 8.263 samples/sec, ObjLoss=24.236, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.769 [Epoch 208][Batch 1699], LR: 1.00E-03, Speed: 8.143 samples/sec, ObjLoss=24.236, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.769 [Epoch 208][Batch 1799], LR: 1.00E-03, Speed: 11.334 samples/sec, ObjLoss=24.236, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.769 [Epoch 208] Training cost: 2262.431, ObjLoss=24.235, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.768 [Epoch 208] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.273 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.159 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 person=36.1 bicycle=20.9 car=27.3 motorcycle=28.3 airplane=45.9 bus=49.5 train=50.9 truck=23.6 boat=14.6 traffic light=11.9 fire hydrant=48.1 stop sign=38.3 parking meter=34.6 bench=15.7 bird=20.2 cat=47.4 dog=41.9 horse=36.2 sheep=30.5 cow=34.0 elephant=47.1 bear=52.5 zebra=50.1 giraffe=47.9 backpack=6.0 umbrella=26.7 handbag=6.5 tie=20.0 suitcase=21.4 frisbee=36.8 skis=13.3 snowboard=17.8 sports ball=22.7 kite=25.2 baseball bat=17.2 baseball glove=21.9 skateboard=31.5 surfboard=20.5 tennis racket=30.2 bottle=19.1 wine glass=23.4 cup=25.0 fork=17.5 knife=5.0 spoon=6.0 bowl=24.8 banana=12.4 apple=8.6 sandwich=20.0 orange=18.1 broccoli=12.9 carrot=11.8 hot dog=16.9 pizza=36.6 donut=26.6 cake=20.8 chair=18.4 couch=28.3 potted plant=16.8 bed=34.9 dining table=19.5 toilet=41.8 tv=37.4 laptop=39.8 mouse=39.6 remote=12.3 keyboard=35.9 cell phone=18.1 microwave=36.9 oven=20.2 toaster=5.9 sink=23.1 refrigerator=34.9 book=5.0 clock=32.7 vase=23.1 scissors=21.8 teddy bear=30.7 hair drier=0.0 toothbrush=5.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.7 [Epoch 209][Batch 99], LR: 1.00E-03, Speed: 10.400 samples/sec, ObjLoss=24.235, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.768 [Epoch 209][Batch 199], LR: 1.00E-03, Speed: 9.408 samples/sec, ObjLoss=24.234, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.768 [Epoch 209][Batch 299], LR: 1.00E-03, Speed: 10.453 samples/sec, ObjLoss=24.234, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.767 [Epoch 209][Batch 399], LR: 1.00E-03, Speed: 125.867 samples/sec, ObjLoss=24.233, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.767 [Epoch 209][Batch 499], LR: 1.00E-03, Speed: 12.224 samples/sec, ObjLoss=24.233, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.767 [Epoch 209][Batch 599], LR: 1.00E-03, Speed: 7.746 samples/sec, ObjLoss=24.232, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.766 [Epoch 209][Batch 699], LR: 1.00E-03, Speed: 93.709 samples/sec, ObjLoss=24.232, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.766 [Epoch 209][Batch 799], LR: 1.00E-03, Speed: 67.218 samples/sec, ObjLoss=24.231, BoxCenterLoss=14.375, BoxScaleLoss=4.991, ClassLoss=9.765 [Epoch 209][Batch 899], LR: 1.00E-03, Speed: 10.541 samples/sec, ObjLoss=24.231, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.765 [Epoch 209][Batch 999], LR: 1.00E-03, Speed: 101.366 samples/sec, ObjLoss=24.231, BoxCenterLoss=14.376, BoxScaleLoss=4.991, ClassLoss=9.765 [Epoch 209][Batch 1099], LR: 1.00E-03, Speed: 122.464 samples/sec, ObjLoss=24.230, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.764 [Epoch 209][Batch 1199], LR: 1.00E-03, Speed: 9.221 samples/sec, ObjLoss=24.229, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.764 [Epoch 209][Batch 1299], LR: 1.00E-03, Speed: 9.155 samples/sec, ObjLoss=24.229, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.763 [Epoch 209][Batch 1399], LR: 1.00E-03, Speed: 15.193 samples/sec, ObjLoss=24.228, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.763 [Epoch 209][Batch 1499], LR: 1.00E-03, Speed: 10.425 samples/sec, ObjLoss=24.228, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.763 [Epoch 209][Batch 1599], LR: 1.00E-03, Speed: 8.931 samples/sec, ObjLoss=24.227, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.762 [Epoch 209][Batch 1699], LR: 1.00E-03, Speed: 75.475 samples/sec, ObjLoss=24.227, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.762 [Epoch 209][Batch 1799], LR: 1.00E-03, Speed: 13.603 samples/sec, ObjLoss=24.227, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.762 [Epoch 209] Training cost: 2141.137, ObjLoss=24.226, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.761 [Epoch 209] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.246 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.380 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.337 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.164 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491 person=39.5 bicycle=19.7 car=27.8 motorcycle=33.1 airplane=39.7 bus=48.6 train=48.2 truck=22.0 boat=14.7 traffic light=15.4 fire hydrant=49.5 stop sign=47.5 parking meter=35.7 bench=14.1 bird=20.6 cat=49.4 dog=42.6 horse=40.8 sheep=34.2 cow=33.4 elephant=45.1 bear=49.0 zebra=47.7 giraffe=48.6 backpack=7.0 umbrella=25.9 handbag=6.9 tie=19.3 suitcase=20.9 frisbee=37.2 skis=10.9 snowboard=16.0 sports ball=26.2 kite=25.4 baseball bat=16.0 baseball glove=19.7 skateboard=28.4 surfboard=22.6 tennis racket=31.5 bottle=22.4 wine glass=23.4 cup=26.4 fork=17.3 knife=6.4 spoon=5.1 bowl=25.3 banana=11.3 apple=7.1 sandwich=22.6 orange=19.2 broccoli=12.5 carrot=10.2 hot dog=18.8 pizza=36.2 donut=31.2 cake=26.0 chair=17.2 couch=26.0 potted plant=13.3 bed=29.1 dining table=20.0 toilet=40.1 tv=34.3 laptop=40.6 mouse=40.0 remote=13.3 keyboard=34.3 cell phone=20.0 microwave=32.4 oven=21.0 toaster=2.0 sink=23.2 refrigerator=35.1 book=6.1 clock=35.6 vase=22.7 scissors=17.4 teddy bear=31.7 hair drier=0.0 toothbrush=6.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.8 [Epoch 210][Batch 99], LR: 1.00E-03, Speed: 7.672 samples/sec, ObjLoss=24.226, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.761 [Epoch 210][Batch 199], LR: 1.00E-03, Speed: 8.837 samples/sec, ObjLoss=24.225, BoxCenterLoss=14.375, BoxScaleLoss=4.990, ClassLoss=9.761 [Epoch 210][Batch 299], LR: 1.00E-03, Speed: 82.678 samples/sec, ObjLoss=24.225, BoxCenterLoss=14.375, BoxScaleLoss=4.989, ClassLoss=9.760 [Epoch 210][Batch 399], LR: 1.00E-03, Speed: 7.974 samples/sec, ObjLoss=24.224, BoxCenterLoss=14.375, BoxScaleLoss=4.989, ClassLoss=9.760 [Epoch 210][Batch 499], LR: 1.00E-03, Speed: 7.411 samples/sec, ObjLoss=24.224, BoxCenterLoss=14.374, BoxScaleLoss=4.989, ClassLoss=9.759 [Epoch 210][Batch 599], LR: 1.00E-03, Speed: 9.072 samples/sec, ObjLoss=24.223, BoxCenterLoss=14.374, BoxScaleLoss=4.989, ClassLoss=9.759 [Epoch 210][Batch 699], LR: 1.00E-03, Speed: 10.380 samples/sec, ObjLoss=24.222, BoxCenterLoss=14.374, BoxScaleLoss=4.989, ClassLoss=9.758 [Epoch 210][Batch 799], LR: 1.00E-03, Speed: 12.077 samples/sec, ObjLoss=24.222, BoxCenterLoss=14.374, BoxScaleLoss=4.989, ClassLoss=9.758 [Epoch 210][Batch 899], LR: 1.00E-03, Speed: 7.331 samples/sec, ObjLoss=24.221, BoxCenterLoss=14.374, BoxScaleLoss=4.989, ClassLoss=9.758 [Epoch 210][Batch 999], LR: 1.00E-03, Speed: 97.638 samples/sec, ObjLoss=24.220, BoxCenterLoss=14.373, BoxScaleLoss=4.989, ClassLoss=9.757 [Epoch 210][Batch 1099], LR: 1.00E-03, Speed: 10.966 samples/sec, ObjLoss=24.220, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.757 [Epoch 210][Batch 1199], LR: 1.00E-03, Speed: 131.926 samples/sec, ObjLoss=24.220, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.756 [Epoch 210][Batch 1299], LR: 1.00E-03, Speed: 10.343 samples/sec, ObjLoss=24.219, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.756 [Epoch 210][Batch 1399], LR: 1.00E-03, Speed: 10.737 samples/sec, ObjLoss=24.219, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.756 [Epoch 210][Batch 1499], LR: 1.00E-03, Speed: 9.764 samples/sec, ObjLoss=24.218, BoxCenterLoss=14.374, BoxScaleLoss=4.988, ClassLoss=9.755 [Epoch 210][Batch 1599], LR: 1.00E-03, Speed: 116.246 samples/sec, ObjLoss=24.218, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.755 [Epoch 210][Batch 1699], LR: 1.00E-03, Speed: 113.511 samples/sec, ObjLoss=24.218, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.754 [Epoch 210][Batch 1799], LR: 1.00E-03, Speed: 9.395 samples/sec, ObjLoss=24.217, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.754 [Epoch 210] Training cost: 2186.398, ObjLoss=24.217, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.754 [Epoch 210] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.476 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.116 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.260 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.335 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.169 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 person=36.2 bicycle=18.7 car=27.0 motorcycle=29.0 airplane=41.6 bus=44.6 train=48.3 truck=23.1 boat=12.3 traffic light=15.0 fire hydrant=47.3 stop sign=42.3 parking meter=28.8 bench=13.1 bird=22.0 cat=50.8 dog=40.8 horse=42.8 sheep=34.0 cow=35.3 elephant=45.6 bear=48.9 zebra=46.7 giraffe=50.6 backpack=7.2 umbrella=24.1 handbag=6.5 tie=17.7 suitcase=21.2 frisbee=32.7 skis=11.3 snowboard=15.6 sports ball=26.9 kite=24.6 baseball bat=15.9 baseball glove=23.1 skateboard=32.2 surfboard=17.3 tennis racket=28.1 bottle=20.1 wine glass=22.2 cup=25.7 fork=16.4 knife=6.5 spoon=5.7 bowl=24.3 banana=14.1 apple=6.2 sandwich=22.3 orange=19.4 broccoli=13.1 carrot=10.2 hot dog=18.0 pizza=33.3 donut=30.1 cake=24.9 chair=17.4 couch=29.2 potted plant=15.1 bed=33.2 dining table=18.9 toilet=42.0 tv=37.4 laptop=39.7 mouse=42.2 remote=11.4 keyboard=29.0 cell phone=17.6 microwave=34.1 oven=15.5 toaster=2.4 sink=20.7 refrigerator=29.9 book=5.7 clock=31.9 vase=23.1 scissors=20.8 teddy bear=31.5 hair drier=0.0 toothbrush=7.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.2 [Epoch 211][Batch 99], LR: 1.00E-03, Speed: 8.437 samples/sec, ObjLoss=24.216, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.753 [Epoch 211][Batch 199], LR: 1.00E-03, Speed: 111.692 samples/sec, ObjLoss=24.216, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.753 [Epoch 211][Batch 299], LR: 1.00E-03, Speed: 107.454 samples/sec, ObjLoss=24.215, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.753 [Epoch 211][Batch 399], LR: 1.00E-03, Speed: 7.891 samples/sec, ObjLoss=24.215, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.752 [Epoch 211][Batch 499], LR: 1.00E-03, Speed: 9.895 samples/sec, ObjLoss=24.214, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.752 [Epoch 211][Batch 599], LR: 1.00E-03, Speed: 10.453 samples/sec, ObjLoss=24.214, BoxCenterLoss=14.373, BoxScaleLoss=4.988, ClassLoss=9.752 [Epoch 211][Batch 699], LR: 1.00E-03, Speed: 15.180 samples/sec, ObjLoss=24.213, BoxCenterLoss=14.373, BoxScaleLoss=4.987, ClassLoss=9.751 [Epoch 211][Batch 799], LR: 1.00E-03, Speed: 7.047 samples/sec, ObjLoss=24.212, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.751 [Epoch 211][Batch 899], LR: 1.00E-03, Speed: 94.467 samples/sec, ObjLoss=24.212, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.750 [Epoch 211][Batch 999], LR: 1.00E-03, Speed: 79.453 samples/sec, ObjLoss=24.212, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.750 [Epoch 211][Batch 1099], LR: 1.00E-03, Speed: 11.154 samples/sec, ObjLoss=24.211, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.750 [Epoch 211][Batch 1199], LR: 1.00E-03, Speed: 10.763 samples/sec, ObjLoss=24.211, BoxCenterLoss=14.373, BoxScaleLoss=4.987, ClassLoss=9.749 [Epoch 211][Batch 1299], LR: 1.00E-03, Speed: 86.964 samples/sec, ObjLoss=24.210, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.749 [Epoch 211][Batch 1399], LR: 1.00E-03, Speed: 10.199 samples/sec, ObjLoss=24.210, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.749 [Epoch 211][Batch 1499], LR: 1.00E-03, Speed: 10.711 samples/sec, ObjLoss=24.209, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.748 [Epoch 211][Batch 1599], LR: 1.00E-03, Speed: 9.865 samples/sec, ObjLoss=24.209, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.748 [Epoch 211][Batch 1699], LR: 1.00E-03, Speed: 10.267 samples/sec, ObjLoss=24.209, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.747 [Epoch 211][Batch 1799], LR: 1.00E-03, Speed: 102.472 samples/sec, ObjLoss=24.208, BoxCenterLoss=14.372, BoxScaleLoss=4.987, ClassLoss=9.747 [Epoch 211] Training cost: 2188.020, ObjLoss=24.208, BoxCenterLoss=14.372, BoxScaleLoss=4.986, ClassLoss=9.747 [Epoch 211] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.467 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.322 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479 person=37.2 bicycle=18.7 car=25.7 motorcycle=28.9 airplane=43.7 bus=47.1 train=49.3 truck=22.8 boat=14.5 traffic light=11.5 fire hydrant=46.1 stop sign=46.7 parking meter=29.0 bench=14.0 bird=21.3 cat=45.6 dog=40.8 horse=36.4 sheep=31.1 cow=38.5 elephant=46.8 bear=49.0 zebra=47.3 giraffe=50.9 backpack=5.5 umbrella=25.6 handbag=6.2 tie=15.2 suitcase=18.0 frisbee=37.0 skis=9.4 snowboard=19.3 sports ball=29.3 kite=26.4 baseball bat=14.6 baseball glove=20.7 skateboard=29.2 surfboard=20.6 tennis racket=27.9 bottle=19.4 wine glass=21.9 cup=26.4 fork=17.1 knife=6.7 spoon=6.0 bowl=24.7 banana=12.1 apple=7.4 sandwich=19.3 orange=16.5 broccoli=8.0 carrot=8.7 hot dog=17.8 pizza=33.4 donut=27.8 cake=21.8 chair=16.1 couch=30.1 potted plant=11.7 bed=31.9 dining table=18.6 toilet=40.8 tv=38.9 laptop=40.3 mouse=34.6 remote=11.9 keyboard=32.6 cell phone=19.1 microwave=35.9 oven=20.3 toaster=4.8 sink=21.7 refrigerator=33.5 book=5.7 clock=33.2 vase=23.3 scissors=15.4 teddy bear=29.4 hair drier=0.0 toothbrush=7.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.0 [Epoch 212][Batch 99], LR: 1.00E-03, Speed: 8.513 samples/sec, ObjLoss=24.207, BoxCenterLoss=14.372, BoxScaleLoss=4.986, ClassLoss=9.746 [Epoch 212][Batch 199], LR: 1.00E-03, Speed: 98.205 samples/sec, ObjLoss=24.207, BoxCenterLoss=14.372, BoxScaleLoss=4.986, ClassLoss=9.746 [Epoch 212][Batch 299], LR: 1.00E-03, Speed: 10.348 samples/sec, ObjLoss=24.206, BoxCenterLoss=14.372, BoxScaleLoss=4.986, ClassLoss=9.746 [Epoch 212][Batch 399], LR: 1.00E-03, Speed: 12.692 samples/sec, ObjLoss=24.206, BoxCenterLoss=14.372, BoxScaleLoss=4.986, ClassLoss=9.745 [Epoch 212][Batch 499], LR: 1.00E-03, Speed: 10.373 samples/sec, ObjLoss=24.206, BoxCenterLoss=14.372, BoxScaleLoss=4.986, ClassLoss=9.745 [Epoch 212][Batch 599], LR: 1.00E-03, Speed: 10.005 samples/sec, ObjLoss=24.205, BoxCenterLoss=14.371, BoxScaleLoss=4.986, ClassLoss=9.744 [Epoch 212][Batch 699], LR: 1.00E-03, Speed: 114.845 samples/sec, ObjLoss=24.204, BoxCenterLoss=14.371, BoxScaleLoss=4.986, ClassLoss=9.744 [Epoch 212][Batch 799], LR: 1.00E-03, Speed: 11.545 samples/sec, ObjLoss=24.204, BoxCenterLoss=14.371, BoxScaleLoss=4.986, ClassLoss=9.743 [Epoch 212][Batch 899], LR: 1.00E-03, Speed: 8.354 samples/sec, ObjLoss=24.204, BoxCenterLoss=14.371, BoxScaleLoss=4.986, ClassLoss=9.743 [Epoch 212][Batch 999], LR: 1.00E-03, Speed: 9.587 samples/sec, ObjLoss=24.203, BoxCenterLoss=14.371, BoxScaleLoss=4.986, ClassLoss=9.743 [Epoch 212][Batch 1099], LR: 1.00E-03, Speed: 7.799 samples/sec, ObjLoss=24.203, BoxCenterLoss=14.371, BoxScaleLoss=4.986, ClassLoss=9.743 [Epoch 212][Batch 1199], LR: 1.00E-03, Speed: 9.280 samples/sec, ObjLoss=24.202, BoxCenterLoss=14.371, BoxScaleLoss=4.986, ClassLoss=9.742 [Epoch 212][Batch 1299], LR: 1.00E-03, Speed: 10.405 samples/sec, ObjLoss=24.202, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.742 [Epoch 212][Batch 1399], LR: 1.00E-03, Speed: 10.176 samples/sec, ObjLoss=24.201, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.741 [Epoch 212][Batch 1499], LR: 1.00E-03, Speed: 8.953 samples/sec, ObjLoss=24.201, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.741 [Epoch 212][Batch 1599], LR: 1.00E-03, Speed: 9.425 samples/sec, ObjLoss=24.200, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.740 [Epoch 212][Batch 1699], LR: 1.00E-03, Speed: 7.923 samples/sec, ObjLoss=24.199, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.740 [Epoch 212][Batch 1799], LR: 1.00E-03, Speed: 11.656 samples/sec, ObjLoss=24.199, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.739 [Epoch 212] Training cost: 2265.929, ObjLoss=24.199, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.739 [Epoch 212] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.473 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.332 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 person=38.0 bicycle=19.1 car=28.5 motorcycle=29.2 airplane=45.9 bus=47.3 train=49.7 truck=25.1 boat=14.3 traffic light=16.3 fire hydrant=49.1 stop sign=44.2 parking meter=29.1 bench=15.8 bird=22.0 cat=45.9 dog=38.9 horse=40.5 sheep=35.2 cow=35.5 elephant=47.5 bear=52.0 zebra=47.7 giraffe=49.3 backpack=5.6 umbrella=25.6 handbag=6.2 tie=15.5 suitcase=20.6 frisbee=38.1 skis=13.4 snowboard=16.9 sports ball=27.8 kite=28.1 baseball bat=16.1 baseball glove=20.0 skateboard=27.9 surfboard=21.1 tennis racket=28.4 bottle=20.4 wine glass=19.5 cup=26.3 fork=14.5 knife=5.8 spoon=6.0 bowl=21.7 banana=13.3 apple=9.4 sandwich=20.1 orange=20.0 broccoli=10.9 carrot=12.8 hot dog=15.6 pizza=32.8 donut=31.5 cake=22.3 chair=17.4 couch=31.2 potted plant=14.6 bed=30.4 dining table=20.5 toilet=39.7 tv=37.7 laptop=38.7 mouse=37.1 remote=9.8 keyboard=34.1 cell phone=20.2 microwave=37.4 oven=20.3 toaster=3.6 sink=21.1 refrigerator=32.4 book=5.0 clock=33.6 vase=23.8 scissors=18.8 teddy bear=29.9 hair drier=0.0 toothbrush=6.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.6 [Epoch 213][Batch 99], LR: 1.00E-03, Speed: 8.681 samples/sec, ObjLoss=24.199, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.739 [Epoch 213][Batch 199], LR: 1.00E-03, Speed: 9.009 samples/sec, ObjLoss=24.198, BoxCenterLoss=14.371, BoxScaleLoss=4.985, ClassLoss=9.739 [Epoch 213][Batch 299], LR: 1.00E-03, Speed: 84.405 samples/sec, ObjLoss=24.197, BoxCenterLoss=14.370, BoxScaleLoss=4.985, ClassLoss=9.738 [Epoch 213][Batch 399], LR: 1.00E-03, Speed: 9.006 samples/sec, ObjLoss=24.197, BoxCenterLoss=14.370, BoxScaleLoss=4.985, ClassLoss=9.738 [Epoch 213][Batch 499], LR: 1.00E-03, Speed: 9.591 samples/sec, ObjLoss=24.196, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.737 [Epoch 213][Batch 599], LR: 1.00E-03, Speed: 8.258 samples/sec, ObjLoss=24.196, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.737 [Epoch 213][Batch 699], LR: 1.00E-03, Speed: 7.031 samples/sec, ObjLoss=24.196, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.737 [Epoch 213][Batch 799], LR: 1.00E-03, Speed: 7.595 samples/sec, ObjLoss=24.195, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.736 [Epoch 213][Batch 899], LR: 1.00E-03, Speed: 9.600 samples/sec, ObjLoss=24.195, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.736 [Epoch 213][Batch 999], LR: 1.00E-03, Speed: 11.366 samples/sec, ObjLoss=24.194, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.735 [Epoch 213][Batch 1099], LR: 1.00E-03, Speed: 9.532 samples/sec, ObjLoss=24.194, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.735 [Epoch 213][Batch 1199], LR: 1.00E-03, Speed: 9.445 samples/sec, ObjLoss=24.194, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.735 [Epoch 213][Batch 1299], LR: 1.00E-03, Speed: 10.496 samples/sec, ObjLoss=24.193, BoxCenterLoss=14.370, BoxScaleLoss=4.984, ClassLoss=9.734 [Epoch 213][Batch 1399], LR: 1.00E-03, Speed: 98.068 samples/sec, ObjLoss=24.192, BoxCenterLoss=14.370, BoxScaleLoss=4.983, ClassLoss=9.734 [Epoch 213][Batch 1499], LR: 1.00E-03, Speed: 11.066 samples/sec, ObjLoss=24.192, BoxCenterLoss=14.370, BoxScaleLoss=4.983, ClassLoss=9.733 [Epoch 213][Batch 1599], LR: 1.00E-03, Speed: 95.408 samples/sec, ObjLoss=24.192, BoxCenterLoss=14.370, BoxScaleLoss=4.983, ClassLoss=9.733 [Epoch 213][Batch 1699], LR: 1.00E-03, Speed: 8.430 samples/sec, ObjLoss=24.191, BoxCenterLoss=14.370, BoxScaleLoss=4.983, ClassLoss=9.733 [Epoch 213][Batch 1799], LR: 1.00E-03, Speed: 10.414 samples/sec, ObjLoss=24.191, BoxCenterLoss=14.370, BoxScaleLoss=4.983, ClassLoss=9.732 [Epoch 213] Training cost: 2189.571, ObjLoss=24.190, BoxCenterLoss=14.370, BoxScaleLoss=4.983, ClassLoss=9.732 [Epoch 213] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.232 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.459 person=38.0 bicycle=17.8 car=28.8 motorcycle=28.1 airplane=39.5 bus=44.0 train=45.4 truck=23.7 boat=13.1 traffic light=14.2 fire hydrant=41.9 stop sign=40.6 parking meter=27.8 bench=15.0 bird=22.2 cat=48.9 dog=38.1 horse=40.3 sheep=32.9 cow=33.9 elephant=43.4 bear=43.1 zebra=44.7 giraffe=47.1 backpack=6.8 umbrella=26.2 handbag=5.6 tie=18.8 suitcase=13.9 frisbee=32.0 skis=10.7 snowboard=20.9 sports ball=27.3 kite=25.1 baseball bat=11.7 baseball glove=18.6 skateboard=30.7 surfboard=21.5 tennis racket=27.5 bottle=21.1 wine glass=21.1 cup=25.3 fork=16.2 knife=4.9 spoon=4.9 bowl=18.3 banana=12.9 apple=7.9 sandwich=18.8 orange=19.1 broccoli=9.0 carrot=9.7 hot dog=15.1 pizza=28.7 donut=29.7 cake=19.5 chair=17.0 couch=25.7 potted plant=12.5 bed=32.9 dining table=17.4 toilet=41.2 tv=38.1 laptop=36.6 mouse=34.7 remote=12.8 keyboard=30.1 cell phone=15.8 microwave=35.9 oven=19.3 toaster=0.0 sink=22.4 refrigerator=31.6 book=4.4 clock=32.7 vase=22.9 scissors=18.6 teddy bear=29.6 hair drier=0.0 toothbrush=8.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.2 [Epoch 214][Batch 99], LR: 1.00E-03, Speed: 10.679 samples/sec, ObjLoss=24.190, BoxCenterLoss=14.370, BoxScaleLoss=4.983, ClassLoss=9.732 [Epoch 214][Batch 199], LR: 1.00E-03, Speed: 12.222 samples/sec, ObjLoss=24.189, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.731 [Epoch 214][Batch 299], LR: 1.00E-03, Speed: 8.789 samples/sec, ObjLoss=24.189, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.731 [Epoch 214][Batch 399], LR: 1.00E-03, Speed: 9.555 samples/sec, ObjLoss=24.188, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.731 [Epoch 214][Batch 499], LR: 1.00E-03, Speed: 8.817 samples/sec, ObjLoss=24.188, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.730 [Epoch 214][Batch 599], LR: 1.00E-03, Speed: 8.745 samples/sec, ObjLoss=24.187, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.730 [Epoch 214][Batch 699], LR: 1.00E-03, Speed: 9.475 samples/sec, ObjLoss=24.186, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.729 [Epoch 214][Batch 799], LR: 1.00E-03, Speed: 9.053 samples/sec, ObjLoss=24.186, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.729 [Epoch 214][Batch 899], LR: 1.00E-03, Speed: 10.035 samples/sec, ObjLoss=24.185, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.729 [Epoch 214][Batch 999], LR: 1.00E-03, Speed: 9.683 samples/sec, ObjLoss=24.185, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.729 [Epoch 214][Batch 1099], LR: 1.00E-03, Speed: 9.377 samples/sec, ObjLoss=24.185, BoxCenterLoss=14.369, BoxScaleLoss=4.983, ClassLoss=9.728 [Epoch 214][Batch 1199], LR: 1.00E-03, Speed: 98.897 samples/sec, ObjLoss=24.184, BoxCenterLoss=14.369, BoxScaleLoss=4.982, ClassLoss=9.728 [Epoch 214][Batch 1299], LR: 1.00E-03, Speed: 10.678 samples/sec, ObjLoss=24.184, BoxCenterLoss=14.369, BoxScaleLoss=4.982, ClassLoss=9.727 [Epoch 214][Batch 1399], LR: 1.00E-03, Speed: 9.660 samples/sec, ObjLoss=24.184, BoxCenterLoss=14.369, BoxScaleLoss=4.982, ClassLoss=9.727 [Epoch 214][Batch 1499], LR: 1.00E-03, Speed: 11.850 samples/sec, ObjLoss=24.184, BoxCenterLoss=14.369, BoxScaleLoss=4.982, ClassLoss=9.727 [Epoch 214][Batch 1599], LR: 1.00E-03, Speed: 110.944 samples/sec, ObjLoss=24.183, BoxCenterLoss=14.369, BoxScaleLoss=4.982, ClassLoss=9.727 [Epoch 214][Batch 1699], LR: 1.00E-03, Speed: 9.180 samples/sec, ObjLoss=24.183, BoxCenterLoss=14.369, BoxScaleLoss=4.982, ClassLoss=9.726 [Epoch 214][Batch 1799], LR: 1.00E-03, Speed: 10.311 samples/sec, ObjLoss=24.183, BoxCenterLoss=14.369, BoxScaleLoss=4.982, ClassLoss=9.726 [Epoch 214] Training cost: 2155.313, ObjLoss=24.183, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.726 [Epoch 214] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.234 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455 person=37.7 bicycle=17.2 car=26.9 motorcycle=27.7 airplane=43.1 bus=46.3 train=46.9 truck=22.8 boat=12.9 traffic light=12.0 fire hydrant=44.4 stop sign=40.6 parking meter=25.3 bench=12.3 bird=17.0 cat=46.7 dog=33.3 horse=37.5 sheep=27.7 cow=30.6 elephant=43.0 bear=47.9 zebra=46.5 giraffe=49.8 backpack=5.5 umbrella=26.2 handbag=5.1 tie=18.0 suitcase=18.6 frisbee=39.1 skis=11.7 snowboard=17.7 sports ball=23.4 kite=21.5 baseball bat=12.7 baseball glove=18.3 skateboard=31.2 surfboard=18.7 tennis racket=29.1 bottle=19.6 wine glass=20.7 cup=23.6 fork=17.9 knife=6.0 spoon=6.4 bowl=24.0 banana=11.3 apple=8.3 sandwich=20.4 orange=18.3 broccoli=13.0 carrot=10.7 hot dog=18.7 pizza=35.4 donut=23.4 cake=22.8 chair=16.3 couch=30.4 potted plant=11.7 bed=27.0 dining table=14.4 toilet=39.7 tv=37.7 laptop=40.7 mouse=40.1 remote=10.5 keyboard=32.7 cell phone=15.1 microwave=26.6 oven=18.9 toaster=0.0 sink=23.8 refrigerator=30.4 book=3.6 clock=34.6 vase=20.8 scissors=19.1 teddy bear=23.9 hair drier=0.0 toothbrush=5.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.0 [Epoch 215][Batch 99], LR: 1.00E-03, Speed: 80.273 samples/sec, ObjLoss=24.182, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.726 [Epoch 215][Batch 199], LR: 1.00E-03, Speed: 105.209 samples/sec, ObjLoss=24.182, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.725 [Epoch 215][Batch 299], LR: 1.00E-03, Speed: 102.970 samples/sec, ObjLoss=24.181, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.725 [Epoch 215][Batch 399], LR: 1.00E-03, Speed: 11.359 samples/sec, ObjLoss=24.181, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.725 [Epoch 215][Batch 499], LR: 1.00E-03, Speed: 7.853 samples/sec, ObjLoss=24.181, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.724 [Epoch 215][Batch 599], LR: 1.00E-03, Speed: 9.146 samples/sec, ObjLoss=24.181, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.724 [Epoch 215][Batch 699], LR: 1.00E-03, Speed: 10.278 samples/sec, ObjLoss=24.181, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.724 [Epoch 215][Batch 799], LR: 1.00E-03, Speed: 9.112 samples/sec, ObjLoss=24.180, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.723 [Epoch 215][Batch 899], LR: 1.00E-03, Speed: 124.856 samples/sec, ObjLoss=24.179, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.723 [Epoch 215][Batch 999], LR: 1.00E-03, Speed: 8.804 samples/sec, ObjLoss=24.179, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.722 [Epoch 215][Batch 1099], LR: 1.00E-03, Speed: 12.049 samples/sec, ObjLoss=24.179, BoxCenterLoss=14.370, BoxScaleLoss=4.982, ClassLoss=9.722 [Epoch 215][Batch 1199], LR: 1.00E-03, Speed: 11.026 samples/sec, ObjLoss=24.178, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.722 [Epoch 215][Batch 1299], LR: 1.00E-03, Speed: 13.158 samples/sec, ObjLoss=24.178, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.722 [Epoch 215][Batch 1399], LR: 1.00E-03, Speed: 6.371 samples/sec, ObjLoss=24.177, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.721 [Epoch 215][Batch 1499], LR: 1.00E-03, Speed: 11.800 samples/sec, ObjLoss=24.177, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.721 [Epoch 215][Batch 1599], LR: 1.00E-03, Speed: 120.761 samples/sec, ObjLoss=24.177, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.721 [Epoch 215][Batch 1699], LR: 1.00E-03, Speed: 102.422 samples/sec, ObjLoss=24.176, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.721 [Epoch 215][Batch 1799], LR: 1.00E-03, Speed: 11.745 samples/sec, ObjLoss=24.176, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.720 [Epoch 215] Training cost: 2156.345, ObjLoss=24.176, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.720 [Epoch 215] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.094 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472 person=36.0 bicycle=18.4 car=27.4 motorcycle=31.0 airplane=41.6 bus=48.2 train=48.9 truck=25.7 boat=11.8 traffic light=14.5 fire hydrant=36.5 stop sign=37.8 parking meter=29.6 bench=13.3 bird=21.5 cat=46.5 dog=39.0 horse=37.3 sheep=32.7 cow=34.1 elephant=47.3 bear=49.0 zebra=50.4 giraffe=45.7 backpack=6.6 umbrella=24.3 handbag=6.8 tie=13.4 suitcase=20.3 frisbee=28.7 skis=9.4 snowboard=16.6 sports ball=18.1 kite=22.5 baseball bat=15.9 baseball glove=21.4 skateboard=30.4 surfboard=20.8 tennis racket=31.1 bottle=20.6 wine glass=20.3 cup=24.9 fork=16.7 knife=5.7 spoon=5.2 bowl=23.9 banana=12.8 apple=7.5 sandwich=20.1 orange=16.1 broccoli=10.9 carrot=7.7 hot dog=17.5 pizza=34.8 donut=25.0 cake=18.3 chair=17.1 couch=24.2 potted plant=15.1 bed=31.2 dining table=21.9 toilet=38.3 tv=36.1 laptop=36.9 mouse=26.1 remote=8.9 keyboard=26.7 cell phone=17.3 microwave=31.2 oven=18.3 toaster=5.9 sink=21.3 refrigerator=34.3 book=5.7 clock=29.9 vase=21.1 scissors=18.9 teddy bear=30.7 hair drier=0.0 toothbrush=6.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.0 [Epoch 216][Batch 99], LR: 1.00E-03, Speed: 10.595 samples/sec, ObjLoss=24.175, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.720 [Epoch 216][Batch 199], LR: 1.00E-03, Speed: 8.074 samples/sec, ObjLoss=24.175, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.720 [Epoch 216][Batch 299], LR: 1.00E-03, Speed: 9.259 samples/sec, ObjLoss=24.175, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.719 [Epoch 216][Batch 399], LR: 1.00E-03, Speed: 10.501 samples/sec, ObjLoss=24.174, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.719 [Epoch 216][Batch 499], LR: 1.00E-03, Speed: 9.358 samples/sec, ObjLoss=24.173, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.718 [Epoch 216][Batch 599], LR: 1.00E-03, Speed: 13.493 samples/sec, ObjLoss=24.173, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.718 [Epoch 216][Batch 699], LR: 1.00E-03, Speed: 116.755 samples/sec, ObjLoss=24.172, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.718 [Epoch 216][Batch 799], LR: 1.00E-03, Speed: 7.270 samples/sec, ObjLoss=24.172, BoxCenterLoss=14.370, BoxScaleLoss=4.981, ClassLoss=9.717 [Epoch 216][Batch 899], LR: 1.00E-03, Speed: 9.828 samples/sec, ObjLoss=24.171, BoxCenterLoss=14.369, BoxScaleLoss=4.981, ClassLoss=9.717 [Epoch 216][Batch 999], LR: 1.00E-03, Speed: 10.255 samples/sec, ObjLoss=24.171, BoxCenterLoss=14.369, BoxScaleLoss=4.981, ClassLoss=9.717 [Epoch 216][Batch 1099], LR: 1.00E-03, Speed: 11.968 samples/sec, ObjLoss=24.170, BoxCenterLoss=14.369, BoxScaleLoss=4.981, ClassLoss=9.716 [Epoch 216][Batch 1199], LR: 1.00E-03, Speed: 8.226 samples/sec, ObjLoss=24.170, BoxCenterLoss=14.369, BoxScaleLoss=4.981, ClassLoss=9.716 [Epoch 216][Batch 1299], LR: 1.00E-03, Speed: 102.199 samples/sec, ObjLoss=24.169, BoxCenterLoss=14.369, BoxScaleLoss=4.981, ClassLoss=9.716 [Epoch 216][Batch 1399], LR: 1.00E-03, Speed: 64.271 samples/sec, ObjLoss=24.169, BoxCenterLoss=14.369, BoxScaleLoss=4.981, ClassLoss=9.715 [Epoch 216][Batch 1499], LR: 1.00E-03, Speed: 10.364 samples/sec, ObjLoss=24.168, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.715 [Epoch 216][Batch 1599], LR: 1.00E-03, Speed: 12.375 samples/sec, ObjLoss=24.168, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.714 [Epoch 216][Batch 1699], LR: 1.00E-03, Speed: 12.265 samples/sec, ObjLoss=24.167, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.714 [Epoch 216][Batch 1799], LR: 1.00E-03, Speed: 9.815 samples/sec, ObjLoss=24.167, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.714 [Epoch 216] Training cost: 2140.340, ObjLoss=24.166, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.713 [Epoch 216] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.326 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.159 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.456 person=39.2 bicycle=20.4 car=28.1 motorcycle=25.8 airplane=41.0 bus=49.8 train=50.2 truck=26.0 boat=14.6 traffic light=15.2 fire hydrant=44.6 stop sign=43.0 parking meter=33.0 bench=14.2 bird=19.9 cat=42.4 dog=32.9 horse=34.9 sheep=33.8 cow=35.0 elephant=43.8 bear=43.5 zebra=45.2 giraffe=48.3 backpack=6.3 umbrella=25.6 handbag=5.8 tie=18.4 suitcase=15.7 frisbee=38.1 skis=11.7 snowboard=16.3 sports ball=27.0 kite=27.0 baseball bat=14.7 baseball glove=17.8 skateboard=33.1 surfboard=19.6 tennis racket=31.5 bottle=20.0 wine glass=21.1 cup=24.7 fork=17.1 knife=4.4 spoon=4.6 bowl=24.0 banana=12.1 apple=6.4 sandwich=20.0 orange=17.7 broccoli=11.7 carrot=11.2 hot dog=17.7 pizza=33.9 donut=24.8 cake=19.7 chair=16.6 couch=30.6 potted plant=15.3 bed=32.5 dining table=20.7 toilet=37.4 tv=36.7 laptop=37.4 mouse=38.9 remote=13.8 keyboard=26.6 cell phone=18.4 microwave=34.4 oven=18.9 toaster=3.6 sink=20.5 refrigerator=35.1 book=5.3 clock=34.7 vase=20.4 scissors=26.6 teddy bear=21.3 hair drier=0.0 toothbrush=6.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =24.7 [Epoch 217][Batch 99], LR: 1.00E-03, Speed: 8.753 samples/sec, ObjLoss=24.166, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.713 [Epoch 217][Batch 199], LR: 1.00E-03, Speed: 9.005 samples/sec, ObjLoss=24.165, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.713 [Epoch 217][Batch 299], LR: 1.00E-03, Speed: 106.886 samples/sec, ObjLoss=24.165, BoxCenterLoss=14.368, BoxScaleLoss=4.980, ClassLoss=9.712 [Epoch 217][Batch 399], LR: 1.00E-03, Speed: 8.224 samples/sec, ObjLoss=24.164, BoxCenterLoss=14.368, BoxScaleLoss=4.980, ClassLoss=9.712 [Epoch 217][Batch 499], LR: 1.00E-03, Speed: 9.570 samples/sec, ObjLoss=24.164, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.711 [Epoch 217][Batch 599], LR: 1.00E-03, Speed: 10.547 samples/sec, ObjLoss=24.164, BoxCenterLoss=14.369, BoxScaleLoss=4.980, ClassLoss=9.711 [Epoch 217][Batch 699], LR: 1.00E-03, Speed: 133.526 samples/sec, ObjLoss=24.164, BoxCenterLoss=14.369, BoxScaleLoss=4.979, ClassLoss=9.711 [Epoch 217][Batch 799], LR: 1.00E-03, Speed: 7.925 samples/sec, ObjLoss=24.163, BoxCenterLoss=14.369, BoxScaleLoss=4.979, ClassLoss=9.711 [Epoch 217][Batch 899], LR: 1.00E-03, Speed: 10.402 samples/sec, ObjLoss=24.163, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.710 [Epoch 217][Batch 999], LR: 1.00E-03, Speed: 90.297 samples/sec, ObjLoss=24.162, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.710 [Epoch 217][Batch 1099], LR: 1.00E-03, Speed: 121.290 samples/sec, ObjLoss=24.162, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.709 [Epoch 217][Batch 1199], LR: 1.00E-03, Speed: 8.609 samples/sec, ObjLoss=24.161, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.709 [Epoch 217][Batch 1299], LR: 1.00E-03, Speed: 11.223 samples/sec, ObjLoss=24.161, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.709 [Epoch 217][Batch 1399], LR: 1.00E-03, Speed: 89.204 samples/sec, ObjLoss=24.161, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.708 [Epoch 217][Batch 1499], LR: 1.00E-03, Speed: 11.155 samples/sec, ObjLoss=24.160, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.708 [Epoch 217][Batch 1599], LR: 1.00E-03, Speed: 84.419 samples/sec, ObjLoss=24.160, BoxCenterLoss=14.368, BoxScaleLoss=4.979, ClassLoss=9.707 [Epoch 217][Batch 1699], LR: 1.00E-03, Speed: 8.248 samples/sec, ObjLoss=24.159, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.707 [Epoch 217][Batch 1799], LR: 1.00E-03, Speed: 10.395 samples/sec, ObjLoss=24.159, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.707 [Epoch 217] Training cost: 2224.900, ObjLoss=24.159, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.706 [Epoch 217] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.246 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.369 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.162 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 person=37.4 bicycle=18.4 car=27.6 motorcycle=32.1 airplane=48.7 bus=44.0 train=45.8 truck=24.5 boat=15.0 traffic light=13.6 fire hydrant=48.0 stop sign=37.5 parking meter=30.7 bench=13.5 bird=20.7 cat=45.8 dog=39.4 horse=36.3 sheep=33.3 cow=33.6 elephant=47.6 bear=51.7 zebra=44.9 giraffe=43.6 backpack=6.4 umbrella=23.6 handbag=6.4 tie=19.9 suitcase=21.2 frisbee=37.6 skis=11.5 snowboard=19.7 sports ball=25.3 kite=27.0 baseball bat=11.7 baseball glove=18.4 skateboard=30.7 surfboard=20.7 tennis racket=30.7 bottle=21.4 wine glass=21.7 cup=26.4 fork=18.0 knife=4.8 spoon=5.1 bowl=23.5 banana=11.0 apple=11.4 sandwich=20.0 orange=20.5 broccoli=12.5 carrot=10.7 hot dog=18.2 pizza=34.6 donut=30.5 cake=23.6 chair=17.1 couch=28.6 potted plant=14.1 bed=35.5 dining table=22.5 toilet=39.4 tv=38.7 laptop=39.5 mouse=37.2 remote=11.5 keyboard=29.7 cell phone=20.1 microwave=35.9 oven=23.7 toaster=5.9 sink=22.3 refrigerator=35.9 book=4.1 clock=30.9 vase=22.6 scissors=21.0 teddy bear=29.2 hair drier=0.0 toothbrush=5.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.4 [Epoch 218][Batch 99], LR: 1.00E-03, Speed: 10.489 samples/sec, ObjLoss=24.158, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.706 [Epoch 218][Batch 199], LR: 1.00E-03, Speed: 8.731 samples/sec, ObjLoss=24.158, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.706 [Epoch 218][Batch 299], LR: 1.00E-03, Speed: 9.738 samples/sec, ObjLoss=24.157, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.705 [Epoch 218][Batch 399], LR: 1.00E-03, Speed: 8.711 samples/sec, ObjLoss=24.157, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.705 [Epoch 218][Batch 499], LR: 1.00E-03, Speed: 10.104 samples/sec, ObjLoss=24.157, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.705 [Epoch 218][Batch 599], LR: 1.00E-03, Speed: 9.684 samples/sec, ObjLoss=24.157, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.704 [Epoch 218][Batch 699], LR: 1.00E-03, Speed: 99.941 samples/sec, ObjLoss=24.156, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.704 [Epoch 218][Batch 799], LR: 1.00E-03, Speed: 7.917 samples/sec, ObjLoss=24.155, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.704 [Epoch 218][Batch 899], LR: 1.00E-03, Speed: 9.809 samples/sec, ObjLoss=24.155, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.703 [Epoch 218][Batch 999], LR: 1.00E-03, Speed: 10.418 samples/sec, ObjLoss=24.154, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.703 [Epoch 218][Batch 1099], LR: 1.00E-03, Speed: 10.555 samples/sec, ObjLoss=24.154, BoxCenterLoss=14.368, BoxScaleLoss=4.978, ClassLoss=9.703 [Epoch 218][Batch 1199], LR: 1.00E-03, Speed: 9.739 samples/sec, ObjLoss=24.153, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.702 [Epoch 218][Batch 1299], LR: 1.00E-03, Speed: 9.476 samples/sec, ObjLoss=24.153, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.702 [Epoch 218][Batch 1399], LR: 1.00E-03, Speed: 8.849 samples/sec, ObjLoss=24.152, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.701 [Epoch 218][Batch 1499], LR: 1.00E-03, Speed: 96.609 samples/sec, ObjLoss=24.152, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.701 [Epoch 218][Batch 1599], LR: 1.00E-03, Speed: 9.685 samples/sec, ObjLoss=24.151, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.701 [Epoch 218][Batch 1699], LR: 1.00E-03, Speed: 9.252 samples/sec, ObjLoss=24.150, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.700 [Epoch 218][Batch 1799], LR: 1.00E-03, Speed: 9.689 samples/sec, ObjLoss=24.150, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.700 [Epoch 218] Training cost: 2190.930, ObjLoss=24.150, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.700 [Epoch 218] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.338 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.366 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.477 person=39.5 bicycle=17.1 car=27.7 motorcycle=29.7 airplane=45.6 bus=49.4 train=50.2 truck=23.4 boat=15.7 traffic light=15.0 fire hydrant=45.7 stop sign=37.3 parking meter=30.9 bench=15.1 bird=20.1 cat=50.9 dog=43.5 horse=40.3 sheep=35.3 cow=35.8 elephant=47.2 bear=47.4 zebra=47.4 giraffe=46.6 backpack=7.1 umbrella=22.4 handbag=6.7 tie=18.9 suitcase=18.1 frisbee=34.9 skis=14.7 snowboard=19.5 sports ball=29.1 kite=24.9 baseball bat=14.9 baseball glove=21.5 skateboard=32.7 surfboard=21.6 tennis racket=28.8 bottle=23.0 wine glass=22.8 cup=26.4 fork=15.3 knife=5.7 spoon=5.4 bowl=26.3 banana=14.7 apple=10.8 sandwich=21.7 orange=19.5 broccoli=12.3 carrot=11.2 hot dog=19.2 pizza=32.1 donut=33.2 cake=24.8 chair=17.6 couch=30.2 potted plant=15.0 bed=36.2 dining table=21.0 toilet=45.8 tv=38.7 laptop=40.7 mouse=37.7 remote=10.4 keyboard=29.2 cell phone=17.8 microwave=34.5 oven=21.3 toaster=7.1 sink=22.7 refrigerator=37.0 book=5.4 clock=35.0 vase=24.3 scissors=13.8 teddy bear=29.4 hair drier=0.0 toothbrush=8.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.9 [Epoch 219][Batch 99], LR: 1.00E-03, Speed: 8.663 samples/sec, ObjLoss=24.150, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.700 [Epoch 219][Batch 199], LR: 1.00E-03, Speed: 9.653 samples/sec, ObjLoss=24.149, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.699 [Epoch 219][Batch 299], LR: 1.00E-03, Speed: 93.553 samples/sec, ObjLoss=24.149, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.699 [Epoch 219][Batch 399], LR: 1.00E-03, Speed: 9.801 samples/sec, ObjLoss=24.149, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.698 [Epoch 219][Batch 499], LR: 1.00E-03, Speed: 9.275 samples/sec, ObjLoss=24.148, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.698 [Epoch 219][Batch 599], LR: 1.00E-03, Speed: 10.991 samples/sec, ObjLoss=24.148, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.698 [Epoch 219][Batch 699], LR: 1.00E-03, Speed: 130.841 samples/sec, ObjLoss=24.148, BoxCenterLoss=14.367, BoxScaleLoss=4.977, ClassLoss=9.698 [Epoch 219][Batch 799], LR: 1.00E-03, Speed: 10.110 samples/sec, ObjLoss=24.147, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.697 [Epoch 219][Batch 899], LR: 1.00E-03, Speed: 10.502 samples/sec, ObjLoss=24.147, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.697 [Epoch 219][Batch 999], LR: 1.00E-03, Speed: 11.619 samples/sec, ObjLoss=24.147, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.697 [Epoch 219][Batch 1099], LR: 1.00E-03, Speed: 10.054 samples/sec, ObjLoss=24.146, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.696 [Epoch 219][Batch 1199], LR: 1.00E-03, Speed: 8.833 samples/sec, ObjLoss=24.145, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.696 [Epoch 219][Batch 1299], LR: 1.00E-03, Speed: 8.846 samples/sec, ObjLoss=24.145, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.695 [Epoch 219][Batch 1399], LR: 1.00E-03, Speed: 8.308 samples/sec, ObjLoss=24.145, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.695 [Epoch 219][Batch 1499], LR: 1.00E-03, Speed: 13.240 samples/sec, ObjLoss=24.144, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.695 [Epoch 219][Batch 1599], LR: 1.00E-03, Speed: 8.662 samples/sec, ObjLoss=24.144, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.694 [Epoch 219][Batch 1699], LR: 1.00E-03, Speed: 11.132 samples/sec, ObjLoss=24.143, BoxCenterLoss=14.367, BoxScaleLoss=4.976, ClassLoss=9.694 [Epoch 219][Batch 1799], LR: 1.00E-03, Speed: 11.063 samples/sec, ObjLoss=24.142, BoxCenterLoss=14.366, BoxScaleLoss=4.976, ClassLoss=9.693 [Epoch 219] Training cost: 2132.613, ObjLoss=24.142, BoxCenterLoss=14.366, BoxScaleLoss=4.976, ClassLoss=9.693 [Epoch 219] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.247 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.116 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.330 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.339 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.341 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489 person=38.8 bicycle=19.1 car=27.2 motorcycle=30.3 airplane=44.2 bus=50.6 train=49.5 truck=24.0 boat=15.9 traffic light=16.2 fire hydrant=50.6 stop sign=45.7 parking meter=26.8 bench=13.3 bird=21.4 cat=46.5 dog=37.6 horse=40.8 sheep=31.7 cow=38.9 elephant=45.6 bear=37.6 zebra=50.3 giraffe=48.9 backpack=7.3 umbrella=25.8 handbag=5.4 tie=19.4 suitcase=19.4 frisbee=31.9 skis=11.1 snowboard=16.6 sports ball=19.2 kite=24.2 baseball bat=14.1 baseball glove=19.8 skateboard=31.0 surfboard=19.6 tennis racket=28.8 bottle=21.0 wine glass=22.4 cup=26.6 fork=18.1 knife=6.1 spoon=5.4 bowl=25.9 banana=13.9 apple=9.6 sandwich=20.9 orange=20.1 broccoli=10.2 carrot=11.6 hot dog=17.8 pizza=34.2 donut=29.2 cake=23.6 chair=15.5 couch=30.0 potted plant=13.2 bed=33.9 dining table=19.1 toilet=42.0 tv=37.3 laptop=39.2 mouse=37.4 remote=9.2 keyboard=23.9 cell phone=17.6 microwave=38.4 oven=18.2 toaster=5.9 sink=21.2 refrigerator=36.8 book=5.6 clock=32.5 vase=19.9 scissors=20.6 teddy bear=27.8 hair drier=0.0 toothbrush=5.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =25.2 [Epoch 220][Batch 99], LR: 1.00E-04, Speed: 10.735 samples/sec, ObjLoss=24.142, BoxCenterLoss=14.366, BoxScaleLoss=4.976, ClassLoss=9.693 [Epoch 220][Batch 199], LR: 1.00E-04, Speed: 7.061 samples/sec, ObjLoss=24.141, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.693 [Epoch 220][Batch 299], LR: 1.00E-04, Speed: 130.712 samples/sec, ObjLoss=24.141, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.693 [Epoch 220][Batch 399], LR: 1.00E-04, Speed: 9.468 samples/sec, ObjLoss=24.141, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.692 [Epoch 220][Batch 499], LR: 1.00E-04, Speed: 82.159 samples/sec, ObjLoss=24.140, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.692 [Epoch 220][Batch 599], LR: 1.00E-04, Speed: 12.419 samples/sec, ObjLoss=24.140, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.692 [Epoch 220][Batch 699], LR: 1.00E-04, Speed: 118.123 samples/sec, ObjLoss=24.140, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.692 [Epoch 220][Batch 799], LR: 1.00E-04, Speed: 11.141 samples/sec, ObjLoss=24.140, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.691 [Epoch 220][Batch 899], LR: 1.00E-04, Speed: 106.061 samples/sec, ObjLoss=24.139, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.691 [Epoch 220][Batch 999], LR: 1.00E-04, Speed: 8.711 samples/sec, ObjLoss=24.139, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.691 [Epoch 220][Batch 1099], LR: 1.00E-04, Speed: 87.748 samples/sec, ObjLoss=24.139, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.690 [Epoch 220][Batch 1199], LR: 1.00E-04, Speed: 8.129 samples/sec, ObjLoss=24.138, BoxCenterLoss=14.366, BoxScaleLoss=4.975, ClassLoss=9.690 [Epoch 220][Batch 1299], LR: 1.00E-04, Speed: 9.969 samples/sec, ObjLoss=24.138, BoxCenterLoss=14.366, BoxScaleLoss=4.974, ClassLoss=9.690 [Epoch 220][Batch 1399], LR: 1.00E-04, Speed: 8.705 samples/sec, ObjLoss=24.137, BoxCenterLoss=14.366, BoxScaleLoss=4.974, ClassLoss=9.689 [Epoch 220][Batch 1499], LR: 1.00E-04, Speed: 7.928 samples/sec, ObjLoss=24.137, BoxCenterLoss=14.366, BoxScaleLoss=4.974, ClassLoss=9.689 [Epoch 220][Batch 1599], LR: 1.00E-04, Speed: 9.069 samples/sec, ObjLoss=24.137, BoxCenterLoss=14.366, BoxScaleLoss=4.974, ClassLoss=9.689 [Epoch 220][Batch 1699], LR: 1.00E-04, Speed: 9.367 samples/sec, ObjLoss=24.137, BoxCenterLoss=14.367, BoxScaleLoss=4.974, ClassLoss=9.689 [Epoch 220][Batch 1799], LR: 1.00E-04, Speed: 10.133 samples/sec, ObjLoss=24.136, BoxCenterLoss=14.367, BoxScaleLoss=4.974, ClassLoss=9.688 [Epoch 220] Training cost: 2165.203, ObjLoss=24.136, BoxCenterLoss=14.367, BoxScaleLoss=4.974, ClassLoss=9.688 [Epoch 220] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.519 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.334 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.333 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.460 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.184 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.568 person=43.8 bicycle=23.8 car=31.7 motorcycle=36.3 airplane=52.3 bus=59.5 train=57.8 truck=28.1 boat=18.5 traffic light=17.9 fire hydrant=54.9 stop sign=51.6 parking meter=38.5 bench=17.6 bird=27.0 cat=56.6 dog=49.3 horse=44.9 sheep=40.6 cow=44.0 elephant=54.3 bear=57.8 zebra=54.9 giraffe=55.7 backpack=8.4 umbrella=30.7 handbag=8.2 tie=21.8 suitcase=24.7 frisbee=46.3 skis=14.7 snowboard=25.2 sports ball=33.5 kite=30.1 baseball bat=19.8 baseball glove=25.4 skateboard=40.0 surfboard=25.5 tennis racket=35.4 bottle=25.3 wine glass=26.6 cup=32.1 fork=22.7 knife=8.0 spoon=7.7 bowl=31.2 banana=17.7 apple=14.3 sandwich=27.7 orange=25.9 broccoli=13.2 carrot=14.2 hot dog=23.3 pizza=41.1 donut=35.5 cake=28.7 chair=21.1 couch=37.7 potted plant=18.4 bed=37.7 dining table=24.1 toilet=48.8 tv=46.9 laptop=46.6 mouse=49.1 remote=19.2 keyboard=40.8 cell phone=24.5 microwave=43.7 oven=24.0 toaster=5.9 sink=27.6 refrigerator=42.1 book=5.9 clock=41.2 vase=27.0 scissors=25.8 teddy bear=35.2 hair drier=0.0 toothbrush=8.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =31.3 [Epoch 221][Batch 99], LR: 1.00E-04, Speed: 8.811 samples/sec, ObjLoss=24.136, BoxCenterLoss=14.367, BoxScaleLoss=4.974, ClassLoss=9.688 [Epoch 221][Batch 199], LR: 1.00E-04, Speed: 9.222 samples/sec, ObjLoss=24.135, BoxCenterLoss=14.367, BoxScaleLoss=4.974, ClassLoss=9.688 [Epoch 221][Batch 299], LR: 1.00E-04, Speed: 8.827 samples/sec, ObjLoss=24.135, BoxCenterLoss=14.367, BoxScaleLoss=4.974, ClassLoss=9.687 [Epoch 221][Batch 399], LR: 1.00E-04, Speed: 8.035 samples/sec, ObjLoss=24.134, BoxCenterLoss=14.366, BoxScaleLoss=4.974, ClassLoss=9.687 [Epoch 221][Batch 499], LR: 1.00E-04, Speed: 8.027 samples/sec, ObjLoss=24.134, BoxCenterLoss=14.367, BoxScaleLoss=4.974, ClassLoss=9.687 [Epoch 221][Batch 599], LR: 1.00E-04, Speed: 9.664 samples/sec, ObjLoss=24.134, BoxCenterLoss=14.367, BoxScaleLoss=4.973, ClassLoss=9.686 [Epoch 221][Batch 699], LR: 1.00E-04, Speed: 8.706 samples/sec, ObjLoss=24.134, BoxCenterLoss=14.367, BoxScaleLoss=4.973, ClassLoss=9.686 [Epoch 221][Batch 799], LR: 1.00E-04, Speed: 12.344 samples/sec, ObjLoss=24.133, BoxCenterLoss=14.367, BoxScaleLoss=4.973, ClassLoss=9.685 [Epoch 221][Batch 899], LR: 1.00E-04, Speed: 11.047 samples/sec, ObjLoss=24.133, BoxCenterLoss=14.367, BoxScaleLoss=4.973, ClassLoss=9.685 [Epoch 221][Batch 999], LR: 1.00E-04, Speed: 44.909 samples/sec, ObjLoss=24.132, BoxCenterLoss=14.366, BoxScaleLoss=4.973, ClassLoss=9.685 [Epoch 221][Batch 1099], LR: 1.00E-04, Speed: 14.462 samples/sec, ObjLoss=24.132, BoxCenterLoss=14.366, BoxScaleLoss=4.973, ClassLoss=9.684 [Epoch 221][Batch 1199], LR: 1.00E-04, Speed: 11.029 samples/sec, ObjLoss=24.131, BoxCenterLoss=14.367, BoxScaleLoss=4.972, ClassLoss=9.684 [Epoch 221][Batch 1299], LR: 1.00E-04, Speed: 9.907 samples/sec, ObjLoss=24.131, BoxCenterLoss=14.367, BoxScaleLoss=4.972, ClassLoss=9.684 [Epoch 221][Batch 1399], LR: 1.00E-04, Speed: 10.470 samples/sec, ObjLoss=24.130, BoxCenterLoss=14.367, BoxScaleLoss=4.972, ClassLoss=9.683 [Epoch 221][Batch 1499], LR: 1.00E-04, Speed: 11.281 samples/sec, ObjLoss=24.130, BoxCenterLoss=14.366, BoxScaleLoss=4.972, ClassLoss=9.683 [Epoch 221][Batch 1599], LR: 1.00E-04, Speed: 8.379 samples/sec, ObjLoss=24.129, BoxCenterLoss=14.366, BoxScaleLoss=4.972, ClassLoss=9.682 [Epoch 221][Batch 1699], LR: 1.00E-04, Speed: 9.569 samples/sec, ObjLoss=24.129, BoxCenterLoss=14.366, BoxScaleLoss=4.972, ClassLoss=9.682 [Epoch 221][Batch 1799], LR: 1.00E-04, Speed: 13.938 samples/sec, ObjLoss=24.128, BoxCenterLoss=14.366, BoxScaleLoss=4.972, ClassLoss=9.682 [Epoch 221] Training cost: 2170.940, ObjLoss=24.128, BoxCenterLoss=14.366, BoxScaleLoss=4.972, ClassLoss=9.682 [Epoch 221] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.318 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.522 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.341 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.135 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.342 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.465 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.399 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.188 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.571 person=44.0 bicycle=25.3 car=32.7 motorcycle=37.8 airplane=53.4 bus=59.8 train=56.0 truck=29.0 boat=19.7 traffic light=19.8 fire hydrant=56.5 stop sign=53.6 parking meter=40.2 bench=18.7 bird=27.1 cat=56.8 dog=49.6 horse=44.7 sheep=42.8 cow=45.2 elephant=55.2 bear=58.0 zebra=55.1 giraffe=55.7 backpack=8.9 umbrella=31.1 handbag=8.2 tie=22.0 suitcase=24.2 frisbee=45.3 skis=16.5 snowboard=25.5 sports ball=31.3 kite=32.6 baseball bat=20.1 baseball glove=26.4 skateboard=38.0 surfboard=27.3 tennis racket=36.0 bottle=26.4 wine glass=27.4 cup=32.1 fork=23.1 knife=8.7 spoon=8.1 bowl=32.4 banana=17.4 apple=12.5 sandwich=25.9 orange=23.6 broccoli=15.4 carrot=14.9 hot dog=25.0 pizza=41.7 donut=35.8 cake=29.4 chair=21.6 couch=37.7 potted plant=19.1 bed=36.0 dining table=23.5 toilet=50.8 tv=47.5 laptop=46.8 mouse=48.3 remote=19.0 keyboard=42.7 cell phone=25.6 microwave=42.5 oven=26.1 toaster=7.1 sink=28.7 refrigerator=44.3 book=6.7 clock=41.3 vase=27.2 scissors=28.3 teddy bear=34.2 hair drier=0.0 toothbrush=10.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =31.8 [Epoch 222][Batch 99], LR: 1.00E-04, Speed: 9.050 samples/sec, ObjLoss=24.127, BoxCenterLoss=14.366, BoxScaleLoss=4.972, ClassLoss=9.681 [Epoch 222][Batch 199], LR: 1.00E-04, Speed: 9.076 samples/sec, ObjLoss=24.127, BoxCenterLoss=14.366, BoxScaleLoss=4.971, ClassLoss=9.681 [Epoch 222][Batch 299], LR: 1.00E-04, Speed: 10.253 samples/sec, ObjLoss=24.126, BoxCenterLoss=14.366, BoxScaleLoss=4.971, ClassLoss=9.680 [Epoch 222][Batch 399], LR: 1.00E-04, Speed: 9.339 samples/sec, ObjLoss=24.126, BoxCenterLoss=14.366, BoxScaleLoss=4.971, ClassLoss=9.680 [Epoch 222][Batch 499], LR: 1.00E-04, Speed: 9.166 samples/sec, ObjLoss=24.126, BoxCenterLoss=14.366, BoxScaleLoss=4.971, ClassLoss=9.679 [Epoch 222][Batch 599], LR: 1.00E-04, Speed: 11.602 samples/sec, ObjLoss=24.125, BoxCenterLoss=14.366, BoxScaleLoss=4.971, ClassLoss=9.679 [Epoch 222][Batch 699], LR: 1.00E-04, Speed: 9.286 samples/sec, ObjLoss=24.124, BoxCenterLoss=14.366, BoxScaleLoss=4.971, ClassLoss=9.678 [Epoch 222][Batch 799], LR: 1.00E-04, Speed: 51.622 samples/sec, ObjLoss=24.124, BoxCenterLoss=14.366, BoxScaleLoss=4.971, ClassLoss=9.678 [Epoch 222][Batch 899], LR: 1.00E-04, Speed: 9.632 samples/sec, ObjLoss=24.124, BoxCenterLoss=14.366, BoxScaleLoss=4.970, ClassLoss=9.678 [Epoch 222][Batch 999], LR: 1.00E-04, Speed: 84.268 samples/sec, ObjLoss=24.123, BoxCenterLoss=14.366, BoxScaleLoss=4.970, ClassLoss=9.677 [Epoch 222][Batch 1099], LR: 1.00E-04, Speed: 12.157 samples/sec, ObjLoss=24.123, BoxCenterLoss=14.366, BoxScaleLoss=4.970, ClassLoss=9.677 [Epoch 222][Batch 1199], LR: 1.00E-04, Speed: 118.433 samples/sec, ObjLoss=24.122, BoxCenterLoss=14.366, BoxScaleLoss=4.970, ClassLoss=9.677 [Epoch 222][Batch 1299], LR: 1.00E-04, Speed: 114.852 samples/sec, ObjLoss=24.122, BoxCenterLoss=14.366, BoxScaleLoss=4.970, ClassLoss=9.676 [Epoch 222][Batch 1399], LR: 1.00E-04, Speed: 10.466 samples/sec, ObjLoss=24.121, BoxCenterLoss=14.366, BoxScaleLoss=4.970, ClassLoss=9.676 [Epoch 222][Batch 1499], LR: 1.00E-04, Speed: 9.360 samples/sec, ObjLoss=24.120, BoxCenterLoss=14.366, BoxScaleLoss=4.970, ClassLoss=9.675 [Epoch 222][Batch 1599], LR: 1.00E-04, Speed: 11.566 samples/sec, ObjLoss=24.120, BoxCenterLoss=14.366, BoxScaleLoss=4.969, ClassLoss=9.675 [Epoch 222][Batch 1699], LR: 1.00E-04, Speed: 8.262 samples/sec, ObjLoss=24.120, BoxCenterLoss=14.366, BoxScaleLoss=4.969, ClassLoss=9.674 [Epoch 222][Batch 1799], LR: 1.00E-04, Speed: 10.530 samples/sec, ObjLoss=24.119, BoxCenterLoss=14.366, BoxScaleLoss=4.969, ClassLoss=9.674 [Epoch 222] Training cost: 2182.467, ObjLoss=24.119, BoxCenterLoss=14.365, BoxScaleLoss=4.969, ClassLoss=9.674 [Epoch 222] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.320 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.524 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.469 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.392 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.401 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.568 person=43.7 bicycle=24.3 car=32.6 motorcycle=38.1 airplane=52.4 bus=58.7 train=57.6 truck=29.7 boat=19.0 traffic light=18.7 fire hydrant=56.5 stop sign=53.7 parking meter=40.9 bench=18.5 bird=26.8 cat=57.9 dog=51.4 horse=46.1 sheep=42.4 cow=46.9 elephant=55.7 bear=57.0 zebra=55.7 giraffe=55.7 backpack=8.9 umbrella=32.0 handbag=8.6 tie=22.3 suitcase=26.0 frisbee=47.9 skis=16.1 snowboard=27.5 sports ball=33.2 kite=34.1 baseball bat=19.3 baseball glove=25.5 skateboard=40.8 surfboard=26.1 tennis racket=35.3 bottle=25.4 wine glass=26.4 cup=32.2 fork=22.8 knife=7.4 spoon=8.7 bowl=32.1 banana=18.2 apple=14.7 sandwich=28.8 orange=23.5 broccoli=15.5 carrot=15.5 hot dog=23.1 pizza=43.6 donut=37.8 cake=29.5 chair=21.3 couch=38.0 potted plant=19.5 bed=38.7 dining table=22.6 toilet=49.4 tv=45.6 laptop=47.2 mouse=49.6 remote=18.9 keyboard=42.3 cell phone=26.1 microwave=39.4 oven=24.0 toaster=7.1 sink=29.0 refrigerator=43.4 book=6.3 clock=40.9 vase=27.2 scissors=29.6 teddy bear=35.7 hair drier=0.0 toothbrush=9.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.0 [Epoch 223][Batch 99], LR: 1.00E-04, Speed: 10.156 samples/sec, ObjLoss=24.118, BoxCenterLoss=14.366, BoxScaleLoss=4.969, ClassLoss=9.673 [Epoch 223][Batch 199], LR: 1.00E-04, Speed: 8.504 samples/sec, ObjLoss=24.118, BoxCenterLoss=14.366, BoxScaleLoss=4.969, ClassLoss=9.673 [Epoch 223][Batch 299], LR: 1.00E-04, Speed: 7.903 samples/sec, ObjLoss=24.117, BoxCenterLoss=14.366, BoxScaleLoss=4.968, ClassLoss=9.673 [Epoch 223][Batch 399], LR: 1.00E-04, Speed: 10.402 samples/sec, ObjLoss=24.117, BoxCenterLoss=14.366, BoxScaleLoss=4.968, ClassLoss=9.672 [Epoch 223][Batch 499], LR: 1.00E-04, Speed: 11.753 samples/sec, ObjLoss=24.117, BoxCenterLoss=14.366, BoxScaleLoss=4.968, ClassLoss=9.672 [Epoch 223][Batch 599], LR: 1.00E-04, Speed: 9.661 samples/sec, ObjLoss=24.116, BoxCenterLoss=14.366, BoxScaleLoss=4.968, ClassLoss=9.671 [Epoch 223][Batch 699], LR: 1.00E-04, Speed: 12.155 samples/sec, ObjLoss=24.116, BoxCenterLoss=14.366, BoxScaleLoss=4.968, ClassLoss=9.671 [Epoch 223][Batch 799], LR: 1.00E-04, Speed: 10.113 samples/sec, ObjLoss=24.116, BoxCenterLoss=14.366, BoxScaleLoss=4.968, ClassLoss=9.671 [Epoch 223][Batch 899], LR: 1.00E-04, Speed: 9.586 samples/sec, ObjLoss=24.115, BoxCenterLoss=14.366, BoxScaleLoss=4.968, ClassLoss=9.670 [Epoch 223][Batch 999], LR: 1.00E-04, Speed: 9.316 samples/sec, ObjLoss=24.114, BoxCenterLoss=14.365, BoxScaleLoss=4.968, ClassLoss=9.670 [Epoch 223][Batch 1099], LR: 1.00E-04, Speed: 10.707 samples/sec, ObjLoss=24.114, BoxCenterLoss=14.365, BoxScaleLoss=4.967, ClassLoss=9.669 [Epoch 223][Batch 1199], LR: 1.00E-04, Speed: 10.438 samples/sec, ObjLoss=24.113, BoxCenterLoss=14.365, BoxScaleLoss=4.967, ClassLoss=9.669 [Epoch 223][Batch 1299], LR: 1.00E-04, Speed: 10.822 samples/sec, ObjLoss=24.112, BoxCenterLoss=14.365, BoxScaleLoss=4.967, ClassLoss=9.668 [Epoch 223][Batch 1399], LR: 1.00E-04, Speed: 12.093 samples/sec, ObjLoss=24.111, BoxCenterLoss=14.365, BoxScaleLoss=4.967, ClassLoss=9.668 [Epoch 223][Batch 1499], LR: 1.00E-04, Speed: 103.706 samples/sec, ObjLoss=24.111, BoxCenterLoss=14.365, BoxScaleLoss=4.967, ClassLoss=9.667 [Epoch 223][Batch 1599], LR: 1.00E-04, Speed: 9.160 samples/sec, ObjLoss=24.110, BoxCenterLoss=14.365, BoxScaleLoss=4.967, ClassLoss=9.667 [Epoch 223][Batch 1699], LR: 1.00E-04, Speed: 7.849 samples/sec, ObjLoss=24.110, BoxCenterLoss=14.365, BoxScaleLoss=4.966, ClassLoss=9.666 [Epoch 223][Batch 1799], LR: 1.00E-04, Speed: 9.979 samples/sec, ObjLoss=24.109, BoxCenterLoss=14.365, BoxScaleLoss=4.966, ClassLoss=9.666 [Epoch 223] Training cost: 2123.171, ObjLoss=24.109, BoxCenterLoss=14.365, BoxScaleLoss=4.966, ClassLoss=9.666 [Epoch 223] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.320 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.527 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.468 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.394 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.578 person=43.8 bicycle=24.4 car=32.8 motorcycle=37.5 airplane=51.2 bus=58.6 train=58.8 truck=27.7 boat=18.0 traffic light=19.4 fire hydrant=54.6 stop sign=51.8 parking meter=40.5 bench=18.7 bird=28.1 cat=58.6 dog=49.4 horse=45.0 sheep=42.4 cow=45.3 elephant=55.7 bear=55.8 zebra=55.4 giraffe=55.8 backpack=9.0 umbrella=31.5 handbag=8.5 tie=22.9 suitcase=25.4 frisbee=46.5 skis=16.8 snowboard=25.7 sports ball=32.9 kite=33.2 baseball bat=19.7 baseball glove=25.9 skateboard=40.1 surfboard=25.5 tennis racket=36.6 bottle=26.0 wine glass=27.0 cup=33.4 fork=22.4 knife=8.9 spoon=8.2 bowl=33.2 banana=17.1 apple=14.2 sandwich=27.3 orange=22.8 broccoli=14.8 carrot=15.6 hot dog=23.9 pizza=42.6 donut=37.8 cake=28.6 chair=21.8 couch=37.6 potted plant=20.5 bed=39.3 dining table=23.9 toilet=50.4 tv=47.5 laptop=47.0 mouse=49.2 remote=18.7 keyboard=43.6 cell phone=25.5 microwave=43.7 oven=27.3 toaster=7.1 sink=28.8 refrigerator=43.8 book=6.8 clock=41.7 vase=28.9 scissors=27.6 teddy bear=36.0 hair drier=0.0 toothbrush=10.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.0 [Epoch 224][Batch 99], LR: 1.00E-04, Speed: 9.136 samples/sec, ObjLoss=24.108, BoxCenterLoss=14.365, BoxScaleLoss=4.966, ClassLoss=9.665 [Epoch 224][Batch 199], LR: 1.00E-04, Speed: 8.697 samples/sec, ObjLoss=24.108, BoxCenterLoss=14.364, BoxScaleLoss=4.966, ClassLoss=9.665 [Epoch 224][Batch 299], LR: 1.00E-04, Speed: 10.445 samples/sec, ObjLoss=24.107, BoxCenterLoss=14.364, BoxScaleLoss=4.966, ClassLoss=9.664 [Epoch 224][Batch 399], LR: 1.00E-04, Speed: 10.389 samples/sec, ObjLoss=24.106, BoxCenterLoss=14.364, BoxScaleLoss=4.966, ClassLoss=9.664 [Epoch 224][Batch 499], LR: 1.00E-04, Speed: 10.151 samples/sec, ObjLoss=24.106, BoxCenterLoss=14.364, BoxScaleLoss=4.965, ClassLoss=9.663 [Epoch 224][Batch 599], LR: 1.00E-04, Speed: 130.886 samples/sec, ObjLoss=24.105, BoxCenterLoss=14.364, BoxScaleLoss=4.965, ClassLoss=9.663 [Epoch 224][Batch 699], LR: 1.00E-04, Speed: 10.076 samples/sec, ObjLoss=24.105, BoxCenterLoss=14.364, BoxScaleLoss=4.965, ClassLoss=9.662 [Epoch 224][Batch 799], LR: 1.00E-04, Speed: 10.534 samples/sec, ObjLoss=24.104, BoxCenterLoss=14.364, BoxScaleLoss=4.965, ClassLoss=9.662 [Epoch 224][Batch 899], LR: 1.00E-04, Speed: 9.525 samples/sec, ObjLoss=24.104, BoxCenterLoss=14.364, BoxScaleLoss=4.965, ClassLoss=9.661 [Epoch 224][Batch 999], LR: 1.00E-04, Speed: 7.647 samples/sec, ObjLoss=24.103, BoxCenterLoss=14.364, BoxScaleLoss=4.965, ClassLoss=9.661 [Epoch 224][Batch 1099], LR: 1.00E-04, Speed: 11.679 samples/sec, ObjLoss=24.103, BoxCenterLoss=14.364, BoxScaleLoss=4.964, ClassLoss=9.661 [Epoch 224][Batch 1199], LR: 1.00E-04, Speed: 9.328 samples/sec, ObjLoss=24.102, BoxCenterLoss=14.364, BoxScaleLoss=4.964, ClassLoss=9.660 [Epoch 224][Batch 1299], LR: 1.00E-04, Speed: 86.632 samples/sec, ObjLoss=24.101, BoxCenterLoss=14.363, BoxScaleLoss=4.964, ClassLoss=9.660 [Epoch 224][Batch 1399], LR: 1.00E-04, Speed: 11.423 samples/sec, ObjLoss=24.101, BoxCenterLoss=14.363, BoxScaleLoss=4.964, ClassLoss=9.659 [Epoch 224][Batch 1499], LR: 1.00E-04, Speed: 9.956 samples/sec, ObjLoss=24.100, BoxCenterLoss=14.363, BoxScaleLoss=4.964, ClassLoss=9.659 [Epoch 224][Batch 1599], LR: 1.00E-04, Speed: 9.454 samples/sec, ObjLoss=24.099, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.658 [Epoch 224][Batch 1699], LR: 1.00E-04, Speed: 8.695 samples/sec, ObjLoss=24.099, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.658 [Epoch 224][Batch 1799], LR: 1.00E-04, Speed: 9.457 samples/sec, ObjLoss=24.099, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.657 [Epoch 224] Training cost: 2168.009, ObjLoss=24.099, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.657 [Epoch 224] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.527 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.340 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.338 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.468 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.393 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.571 person=43.7 bicycle=24.8 car=33.0 motorcycle=37.6 airplane=55.1 bus=59.9 train=58.2 truck=28.9 boat=19.2 traffic light=19.6 fire hydrant=57.5 stop sign=51.0 parking meter=39.8 bench=18.2 bird=26.5 cat=59.1 dog=50.6 horse=46.0 sheep=40.8 cow=45.2 elephant=55.6 bear=60.0 zebra=55.3 giraffe=56.9 backpack=8.9 umbrella=31.1 handbag=8.3 tie=22.3 suitcase=26.8 frisbee=47.2 skis=16.5 snowboard=26.1 sports ball=34.4 kite=33.7 baseball bat=19.6 baseball glove=25.8 skateboard=39.9 surfboard=25.7 tennis racket=36.1 bottle=26.0 wine glass=26.9 cup=32.1 fork=23.3 knife=8.3 spoon=8.6 bowl=31.7 banana=17.2 apple=13.9 sandwich=27.7 orange=23.5 broccoli=15.1 carrot=15.2 hot dog=21.8 pizza=40.5 donut=35.0 cake=28.0 chair=21.3 couch=37.3 potted plant=18.3 bed=35.8 dining table=21.3 toilet=48.6 tv=47.0 laptop=49.0 mouse=47.5 remote=19.4 keyboard=40.0 cell phone=25.8 microwave=43.0 oven=26.3 toaster=5.9 sink=28.9 refrigerator=44.1 book=6.8 clock=41.9 vase=28.1 scissors=27.7 teddy bear=36.8 hair drier=0.0 toothbrush=7.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =31.9 [Epoch 225][Batch 99], LR: 1.00E-04, Speed: 8.029 samples/sec, ObjLoss=24.098, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.657 [Epoch 225][Batch 199], LR: 1.00E-04, Speed: 8.775 samples/sec, ObjLoss=24.098, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.656 [Epoch 225][Batch 299], LR: 1.00E-04, Speed: 93.736 samples/sec, ObjLoss=24.097, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.656 [Epoch 225][Batch 399], LR: 1.00E-04, Speed: 8.862 samples/sec, ObjLoss=24.097, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.656 [Epoch 225][Batch 499], LR: 1.00E-04, Speed: 102.805 samples/sec, ObjLoss=24.096, BoxCenterLoss=14.363, BoxScaleLoss=4.963, ClassLoss=9.655 [Epoch 225][Batch 599], LR: 1.00E-04, Speed: 13.879 samples/sec, ObjLoss=24.096, BoxCenterLoss=14.363, BoxScaleLoss=4.962, ClassLoss=9.655 [Epoch 225][Batch 699], LR: 1.00E-04, Speed: 10.531 samples/sec, ObjLoss=24.095, BoxCenterLoss=14.363, BoxScaleLoss=4.962, ClassLoss=9.654 [Epoch 225][Batch 799], LR: 1.00E-04, Speed: 103.040 samples/sec, ObjLoss=24.094, BoxCenterLoss=14.363, BoxScaleLoss=4.962, ClassLoss=9.654 [Epoch 225][Batch 899], LR: 1.00E-04, Speed: 10.485 samples/sec, ObjLoss=24.094, BoxCenterLoss=14.363, BoxScaleLoss=4.962, ClassLoss=9.653 [Epoch 225][Batch 999], LR: 1.00E-04, Speed: 7.915 samples/sec, ObjLoss=24.093, BoxCenterLoss=14.362, BoxScaleLoss=4.962, ClassLoss=9.653 [Epoch 225][Batch 1099], LR: 1.00E-04, Speed: 10.669 samples/sec, ObjLoss=24.092, BoxCenterLoss=14.362, BoxScaleLoss=4.962, ClassLoss=9.652 [Epoch 225][Batch 1199], LR: 1.00E-04, Speed: 8.052 samples/sec, ObjLoss=24.092, BoxCenterLoss=14.363, BoxScaleLoss=4.961, ClassLoss=9.652 [Epoch 225][Batch 1299], LR: 1.00E-04, Speed: 9.470 samples/sec, ObjLoss=24.092, BoxCenterLoss=14.363, BoxScaleLoss=4.961, ClassLoss=9.651 [Epoch 225][Batch 1399], LR: 1.00E-04, Speed: 16.756 samples/sec, ObjLoss=24.091, BoxCenterLoss=14.363, BoxScaleLoss=4.961, ClassLoss=9.651 [Epoch 225][Batch 1499], LR: 1.00E-04, Speed: 11.568 samples/sec, ObjLoss=24.091, BoxCenterLoss=14.363, BoxScaleLoss=4.961, ClassLoss=9.651 [Epoch 225][Batch 1599], LR: 1.00E-04, Speed: 8.480 samples/sec, ObjLoss=24.090, BoxCenterLoss=14.363, BoxScaleLoss=4.961, ClassLoss=9.650 [Epoch 225][Batch 1699], LR: 1.00E-04, Speed: 90.739 samples/sec, ObjLoss=24.090, BoxCenterLoss=14.362, BoxScaleLoss=4.961, ClassLoss=9.650 [Epoch 225][Batch 1799], LR: 1.00E-04, Speed: 10.550 samples/sec, ObjLoss=24.089, BoxCenterLoss=14.362, BoxScaleLoss=4.961, ClassLoss=9.649 [Epoch 225] Training cost: 2202.045, ObjLoss=24.089, BoxCenterLoss=14.362, BoxScaleLoss=4.961, ClassLoss=9.649 [Epoch 225] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.323 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.348 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.348 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.474 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.406 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.435 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.576 person=44.0 bicycle=24.9 car=33.2 motorcycle=37.9 airplane=53.7 bus=58.0 train=59.4 truck=29.8 boat=18.9 traffic light=17.4 fire hydrant=58.0 stop sign=55.3 parking meter=39.9 bench=18.5 bird=26.6 cat=56.2 dog=50.5 horse=45.8 sheep=40.5 cow=46.1 elephant=55.5 bear=55.6 zebra=54.6 giraffe=56.4 backpack=10.3 umbrella=31.7 handbag=8.6 tie=23.3 suitcase=25.4 frisbee=47.3 skis=15.7 snowboard=26.2 sports ball=31.4 kite=34.1 baseball bat=19.9 baseball glove=28.3 skateboard=40.5 surfboard=26.6 tennis racket=37.0 bottle=26.3 wine glass=27.5 cup=32.3 fork=24.3 knife=8.4 spoon=9.0 bowl=32.6 banana=18.3 apple=14.0 sandwich=27.0 orange=23.5 broccoli=15.4 carrot=14.9 hot dog=23.7 pizza=43.7 donut=38.2 cake=30.2 chair=22.0 couch=37.7 potted plant=18.5 bed=39.5 dining table=24.6 toilet=48.1 tv=49.3 laptop=49.1 mouse=49.2 remote=19.2 keyboard=43.1 cell phone=25.5 microwave=45.9 oven=27.7 toaster=7.1 sink=29.6 refrigerator=43.5 book=6.2 clock=41.4 vase=29.0 scissors=27.8 teddy bear=36.1 hair drier=0.0 toothbrush=9.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.3 [Epoch 226][Batch 99], LR: 1.00E-04, Speed: 9.642 samples/sec, ObjLoss=24.088, BoxCenterLoss=14.362, BoxScaleLoss=4.960, ClassLoss=9.649 [Epoch 226][Batch 199], LR: 1.00E-04, Speed: 50.250 samples/sec, ObjLoss=24.087, BoxCenterLoss=14.362, BoxScaleLoss=4.960, ClassLoss=9.648 [Epoch 226][Batch 299], LR: 1.00E-04, Speed: 10.152 samples/sec, ObjLoss=24.087, BoxCenterLoss=14.362, BoxScaleLoss=4.960, ClassLoss=9.647 [Epoch 226][Batch 399], LR: 1.00E-04, Speed: 10.313 samples/sec, ObjLoss=24.086, BoxCenterLoss=14.362, BoxScaleLoss=4.960, ClassLoss=9.647 [Epoch 226][Batch 499], LR: 1.00E-04, Speed: 8.201 samples/sec, ObjLoss=24.086, BoxCenterLoss=14.362, BoxScaleLoss=4.960, ClassLoss=9.647 [Epoch 226][Batch 599], LR: 1.00E-04, Speed: 8.490 samples/sec, ObjLoss=24.085, BoxCenterLoss=14.362, BoxScaleLoss=4.959, ClassLoss=9.646 [Epoch 226][Batch 699], LR: 1.00E-04, Speed: 8.601 samples/sec, ObjLoss=24.085, BoxCenterLoss=14.362, BoxScaleLoss=4.959, ClassLoss=9.645 [Epoch 226][Batch 799], LR: 1.00E-04, Speed: 8.704 samples/sec, ObjLoss=24.084, BoxCenterLoss=14.362, BoxScaleLoss=4.959, ClassLoss=9.645 [Epoch 226][Batch 899], LR: 1.00E-04, Speed: 10.387 samples/sec, ObjLoss=24.084, BoxCenterLoss=14.362, BoxScaleLoss=4.959, ClassLoss=9.644 [Epoch 226][Batch 999], LR: 1.00E-04, Speed: 9.941 samples/sec, ObjLoss=24.083, BoxCenterLoss=14.362, BoxScaleLoss=4.959, ClassLoss=9.644 [Epoch 226][Batch 1099], LR: 1.00E-04, Speed: 12.249 samples/sec, ObjLoss=24.082, BoxCenterLoss=14.361, BoxScaleLoss=4.959, ClassLoss=9.643 [Epoch 226][Batch 1199], LR: 1.00E-04, Speed: 109.163 samples/sec, ObjLoss=24.082, BoxCenterLoss=14.361, BoxScaleLoss=4.958, ClassLoss=9.643 [Epoch 226][Batch 1299], LR: 1.00E-04, Speed: 11.625 samples/sec, ObjLoss=24.081, BoxCenterLoss=14.361, BoxScaleLoss=4.958, ClassLoss=9.642 [Epoch 226][Batch 1399], LR: 1.00E-04, Speed: 127.237 samples/sec, ObjLoss=24.081, BoxCenterLoss=14.361, BoxScaleLoss=4.958, ClassLoss=9.642 [Epoch 226][Batch 1499], LR: 1.00E-04, Speed: 9.374 samples/sec, ObjLoss=24.080, BoxCenterLoss=14.361, BoxScaleLoss=4.958, ClassLoss=9.641 [Epoch 226][Batch 1599], LR: 1.00E-04, Speed: 8.554 samples/sec, ObjLoss=24.080, BoxCenterLoss=14.361, BoxScaleLoss=4.958, ClassLoss=9.641 [Epoch 226][Batch 1699], LR: 1.00E-04, Speed: 9.137 samples/sec, ObjLoss=24.079, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.641 [Epoch 226][Batch 1799], LR: 1.00E-04, Speed: 10.989 samples/sec, ObjLoss=24.079, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.640 [Epoch 226] Training cost: 2219.976, ObjLoss=24.079, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.640 [Epoch 226] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.530 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.349 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.396 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.199 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.578 person=44.4 bicycle=24.4 car=33.1 motorcycle=37.6 airplane=53.5 bus=60.0 train=58.4 truck=28.4 boat=19.3 traffic light=19.8 fire hydrant=57.0 stop sign=53.8 parking meter=39.9 bench=19.4 bird=27.2 cat=58.5 dog=51.6 horse=48.7 sheep=42.7 cow=46.9 elephant=56.7 bear=57.7 zebra=56.0 giraffe=56.8 backpack=10.1 umbrella=30.9 handbag=8.1 tie=22.9 suitcase=25.7 frisbee=47.9 skis=16.3 snowboard=25.8 sports ball=34.0 kite=33.3 baseball bat=19.9 baseball glove=26.5 skateboard=40.1 surfboard=27.5 tennis racket=36.2 bottle=26.3 wine glass=26.7 cup=32.3 fork=24.1 knife=8.7 spoon=8.6 bowl=31.8 banana=18.4 apple=12.2 sandwich=27.4 orange=21.4 broccoli=14.3 carrot=14.4 hot dog=24.0 pizza=43.7 donut=40.1 cake=30.3 chair=22.3 couch=37.5 potted plant=19.6 bed=40.4 dining table=24.1 toilet=51.8 tv=47.6 laptop=47.5 mouse=49.8 remote=20.8 keyboard=42.6 cell phone=26.4 microwave=42.4 oven=27.4 toaster=5.9 sink=29.5 refrigerator=45.8 book=7.5 clock=42.1 vase=29.2 scissors=27.9 teddy bear=36.1 hair drier=0.0 toothbrush=9.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.4 [Epoch 227][Batch 99], LR: 1.00E-04, Speed: 8.041 samples/sec, ObjLoss=24.078, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.640 [Epoch 227][Batch 199], LR: 1.00E-04, Speed: 11.331 samples/sec, ObjLoss=24.077, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.639 [Epoch 227][Batch 299], LR: 1.00E-04, Speed: 8.392 samples/sec, ObjLoss=24.077, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.639 [Epoch 227][Batch 399], LR: 1.00E-04, Speed: 8.633 samples/sec, ObjLoss=24.076, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.638 [Epoch 227][Batch 499], LR: 1.00E-04, Speed: 10.243 samples/sec, ObjLoss=24.076, BoxCenterLoss=14.361, BoxScaleLoss=4.957, ClassLoss=9.638 [Epoch 227][Batch 599], LR: 1.00E-04, Speed: 10.571 samples/sec, ObjLoss=24.075, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.637 [Epoch 227][Batch 699], LR: 1.00E-04, Speed: 8.747 samples/sec, ObjLoss=24.075, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.637 [Epoch 227][Batch 799], LR: 1.00E-04, Speed: 101.725 samples/sec, ObjLoss=24.074, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.637 [Epoch 227][Batch 899], LR: 1.00E-04, Speed: 10.108 samples/sec, ObjLoss=24.074, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.636 [Epoch 227][Batch 999], LR: 1.00E-04, Speed: 75.950 samples/sec, ObjLoss=24.073, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.636 [Epoch 227][Batch 1099], LR: 1.00E-04, Speed: 11.036 samples/sec, ObjLoss=24.073, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.635 [Epoch 227][Batch 1199], LR: 1.00E-04, Speed: 14.113 samples/sec, ObjLoss=24.072, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.635 [Epoch 227][Batch 1299], LR: 1.00E-04, Speed: 10.270 samples/sec, ObjLoss=24.071, BoxCenterLoss=14.361, BoxScaleLoss=4.956, ClassLoss=9.635 [Epoch 227][Batch 1399], LR: 1.00E-04, Speed: 115.607 samples/sec, ObjLoss=24.071, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.634 [Epoch 227][Batch 1499], LR: 1.00E-04, Speed: 9.783 samples/sec, ObjLoss=24.070, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.634 [Epoch 227][Batch 1599], LR: 1.00E-04, Speed: 116.730 samples/sec, ObjLoss=24.070, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.633 [Epoch 227][Batch 1699], LR: 1.00E-04, Speed: 8.673 samples/sec, ObjLoss=24.069, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.633 [Epoch 227][Batch 1799], LR: 1.00E-04, Speed: 9.397 samples/sec, ObjLoss=24.069, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.632 [Epoch 227] Training cost: 2185.309, ObjLoss=24.069, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.632 [Epoch 227] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.535 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.351 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.143 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.399 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.410 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.436 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.583 person=44.2 bicycle=23.3 car=33.2 motorcycle=37.6 airplane=53.7 bus=60.1 train=57.6 truck=29.9 boat=19.4 traffic light=20.3 fire hydrant=58.6 stop sign=53.4 parking meter=43.0 bench=19.4 bird=27.9 cat=59.0 dog=51.9 horse=48.4 sheep=43.4 cow=47.5 elephant=56.6 bear=58.4 zebra=56.3 giraffe=57.8 backpack=10.3 umbrella=31.8 handbag=8.8 tie=22.7 suitcase=25.7 frisbee=50.2 skis=15.8 snowboard=26.6 sports ball=35.5 kite=32.9 baseball bat=20.0 baseball glove=27.2 skateboard=40.6 surfboard=27.0 tennis racket=36.7 bottle=26.2 wine glass=26.1 cup=31.6 fork=23.7 knife=8.4 spoon=8.9 bowl=33.2 banana=18.1 apple=13.0 sandwich=28.7 orange=24.2 broccoli=14.9 carrot=15.0 hot dog=23.4 pizza=43.4 donut=38.3 cake=31.2 chair=22.3 couch=37.5 potted plant=19.2 bed=38.6 dining table=24.1 toilet=49.0 tv=47.2 laptop=47.6 mouse=50.2 remote=19.9 keyboard=44.3 cell phone=26.3 microwave=45.6 oven=27.8 toaster=5.9 sink=29.0 refrigerator=45.4 book=6.6 clock=41.8 vase=29.5 scissors=27.8 teddy bear=35.8 hair drier=0.0 toothbrush=10.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.7 [Epoch 228][Batch 99], LR: 1.00E-04, Speed: 9.334 samples/sec, ObjLoss=24.068, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.632 [Epoch 228][Batch 199], LR: 1.00E-04, Speed: 104.090 samples/sec, ObjLoss=24.068, BoxCenterLoss=14.361, BoxScaleLoss=4.955, ClassLoss=9.631 [Epoch 228][Batch 299], LR: 1.00E-04, Speed: 8.423 samples/sec, ObjLoss=24.067, BoxCenterLoss=14.361, BoxScaleLoss=4.954, ClassLoss=9.631 [Epoch 228][Batch 399], LR: 1.00E-04, Speed: 8.972 samples/sec, ObjLoss=24.067, BoxCenterLoss=14.361, BoxScaleLoss=4.954, ClassLoss=9.630 [Epoch 228][Batch 499], LR: 1.00E-04, Speed: 9.327 samples/sec, ObjLoss=24.066, BoxCenterLoss=14.361, BoxScaleLoss=4.954, ClassLoss=9.630 [Epoch 228][Batch 599], LR: 1.00E-04, Speed: 8.218 samples/sec, ObjLoss=24.066, BoxCenterLoss=14.361, BoxScaleLoss=4.954, ClassLoss=9.630 [Epoch 228][Batch 699], LR: 1.00E-04, Speed: 13.164 samples/sec, ObjLoss=24.065, BoxCenterLoss=14.361, BoxScaleLoss=4.954, ClassLoss=9.629 [Epoch 228][Batch 799], LR: 1.00E-04, Speed: 8.613 samples/sec, ObjLoss=24.064, BoxCenterLoss=14.361, BoxScaleLoss=4.954, ClassLoss=9.629 [Epoch 228][Batch 899], LR: 1.00E-04, Speed: 10.046 samples/sec, ObjLoss=24.064, BoxCenterLoss=14.360, BoxScaleLoss=4.954, ClassLoss=9.628 [Epoch 228][Batch 999], LR: 1.00E-04, Speed: 84.537 samples/sec, ObjLoss=24.063, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.628 [Epoch 228][Batch 1099], LR: 1.00E-04, Speed: 11.023 samples/sec, ObjLoss=24.063, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.627 [Epoch 228][Batch 1199], LR: 1.00E-04, Speed: 9.742 samples/sec, ObjLoss=24.062, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.627 [Epoch 228][Batch 1299], LR: 1.00E-04, Speed: 8.691 samples/sec, ObjLoss=24.062, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.626 [Epoch 228][Batch 1399], LR: 1.00E-04, Speed: 10.965 samples/sec, ObjLoss=24.061, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.626 [Epoch 228][Batch 1499], LR: 1.00E-04, Speed: 10.016 samples/sec, ObjLoss=24.061, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.625 [Epoch 228][Batch 1599], LR: 1.00E-04, Speed: 7.958 samples/sec, ObjLoss=24.060, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.625 [Epoch 228][Batch 1699], LR: 1.00E-04, Speed: 10.207 samples/sec, ObjLoss=24.060, BoxCenterLoss=14.360, BoxScaleLoss=4.953, ClassLoss=9.625 [Epoch 228][Batch 1799], LR: 1.00E-04, Speed: 9.002 samples/sec, ObjLoss=24.059, BoxCenterLoss=14.360, BoxScaleLoss=4.952, ClassLoss=9.624 [Epoch 228] Training cost: 2215.626, ObjLoss=24.059, BoxCenterLoss=14.360, BoxScaleLoss=4.952, ClassLoss=9.624 [Epoch 228] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.325 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.534 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.348 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.401 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.411 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.209 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.430 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.587 person=44.0 bicycle=23.9 car=33.1 motorcycle=38.3 airplane=55.8 bus=59.8 train=59.3 truck=29.4 boat=19.4 traffic light=18.6 fire hydrant=58.8 stop sign=54.9 parking meter=41.4 bench=20.6 bird=27.3 cat=58.2 dog=51.5 horse=46.8 sheep=43.6 cow=44.2 elephant=56.5 bear=60.9 zebra=56.8 giraffe=56.0 backpack=9.9 umbrella=32.5 handbag=8.5 tie=22.7 suitcase=26.4 frisbee=46.1 skis=17.1 snowboard=24.5 sports ball=35.6 kite=33.1 baseball bat=20.3 baseball glove=27.6 skateboard=40.6 surfboard=25.4 tennis racket=35.2 bottle=26.3 wine glass=26.6 cup=31.9 fork=24.1 knife=8.3 spoon=8.7 bowl=33.1 banana=18.3 apple=11.4 sandwich=27.8 orange=22.7 broccoli=15.5 carrot=14.6 hot dog=22.4 pizza=41.4 donut=36.1 cake=29.7 chair=22.4 couch=37.6 potted plant=19.1 bed=40.1 dining table=25.0 toilet=49.9 tv=47.9 laptop=48.0 mouse=47.6 remote=18.1 keyboard=42.4 cell phone=26.1 microwave=43.4 oven=27.0 toaster=6.2 sink=29.9 refrigerator=45.4 book=7.2 clock=41.7 vase=30.0 scissors=31.0 teddy bear=39.3 hair drier=0.0 toothbrush=10.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.5 [Epoch 229][Batch 99], LR: 1.00E-04, Speed: 9.180 samples/sec, ObjLoss=24.059, BoxCenterLoss=14.360, BoxScaleLoss=4.952, ClassLoss=9.623 [Epoch 229][Batch 199], LR: 1.00E-04, Speed: 104.262 samples/sec, ObjLoss=24.059, BoxCenterLoss=14.361, BoxScaleLoss=4.952, ClassLoss=9.623 [Epoch 229][Batch 299], LR: 1.00E-04, Speed: 90.748 samples/sec, ObjLoss=24.058, BoxCenterLoss=14.360, BoxScaleLoss=4.952, ClassLoss=9.623 [Epoch 229][Batch 399], LR: 1.00E-04, Speed: 8.360 samples/sec, ObjLoss=24.057, BoxCenterLoss=14.360, BoxScaleLoss=4.952, ClassLoss=9.622 [Epoch 229][Batch 499], LR: 1.00E-04, Speed: 9.405 samples/sec, ObjLoss=24.056, BoxCenterLoss=14.360, BoxScaleLoss=4.952, ClassLoss=9.622 [Epoch 229][Batch 599], LR: 1.00E-04, Speed: 113.032 samples/sec, ObjLoss=24.056, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.621 [Epoch 229][Batch 699], LR: 1.00E-04, Speed: 131.693 samples/sec, ObjLoss=24.055, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.621 [Epoch 229][Batch 799], LR: 1.00E-04, Speed: 9.752 samples/sec, ObjLoss=24.055, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.620 [Epoch 229][Batch 899], LR: 1.00E-04, Speed: 9.535 samples/sec, ObjLoss=24.054, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.620 [Epoch 229][Batch 999], LR: 1.00E-04, Speed: 97.234 samples/sec, ObjLoss=24.054, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.619 [Epoch 229][Batch 1099], LR: 1.00E-04, Speed: 8.429 samples/sec, ObjLoss=24.053, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.619 [Epoch 229][Batch 1199], LR: 1.00E-04, Speed: 114.736 samples/sec, ObjLoss=24.053, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.618 [Epoch 229][Batch 1299], LR: 1.00E-04, Speed: 9.537 samples/sec, ObjLoss=24.052, BoxCenterLoss=14.360, BoxScaleLoss=4.951, ClassLoss=9.618 [Epoch 229][Batch 1399], LR: 1.00E-04, Speed: 9.555 samples/sec, ObjLoss=24.052, BoxCenterLoss=14.360, BoxScaleLoss=4.950, ClassLoss=9.618 [Epoch 229][Batch 1499], LR: 1.00E-04, Speed: 9.441 samples/sec, ObjLoss=24.051, BoxCenterLoss=14.360, BoxScaleLoss=4.950, ClassLoss=9.617 [Epoch 229][Batch 1599], LR: 1.00E-04, Speed: 10.320 samples/sec, ObjLoss=24.051, BoxCenterLoss=14.360, BoxScaleLoss=4.950, ClassLoss=9.617 [Epoch 229][Batch 1699], LR: 1.00E-04, Speed: 10.000 samples/sec, ObjLoss=24.050, BoxCenterLoss=14.360, BoxScaleLoss=4.950, ClassLoss=9.616 [Epoch 229][Batch 1799], LR: 1.00E-04, Speed: 13.384 samples/sec, ObjLoss=24.049, BoxCenterLoss=14.360, BoxScaleLoss=4.950, ClassLoss=9.616 [Epoch 229] Training cost: 2078.160, ObjLoss=24.049, BoxCenterLoss=14.359, BoxScaleLoss=4.950, ClassLoss=9.616 [Epoch 229] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.326 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.532 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.351 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.346 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.399 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.410 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.436 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.580 person=44.1 bicycle=25.0 car=33.7 motorcycle=37.0 airplane=53.5 bus=59.9 train=60.8 truck=29.8 boat=19.1 traffic light=18.4 fire hydrant=57.8 stop sign=52.5 parking meter=41.1 bench=19.4 bird=27.3 cat=59.0 dog=52.2 horse=47.2 sheep=42.6 cow=45.1 elephant=56.8 bear=57.7 zebra=56.1 giraffe=57.2 backpack=10.6 umbrella=32.2 handbag=9.3 tie=23.0 suitcase=25.9 frisbee=47.8 skis=16.9 snowboard=26.9 sports ball=35.0 kite=32.2 baseball bat=19.8 baseball glove=25.0 skateboard=41.9 surfboard=27.3 tennis racket=36.8 bottle=25.1 wine glass=26.3 cup=32.8 fork=23.6 knife=7.2 spoon=8.3 bowl=31.7 banana=18.3 apple=13.6 sandwich=27.1 orange=23.7 broccoli=15.8 carrot=15.1 hot dog=24.7 pizza=45.6 donut=38.0 cake=31.0 chair=23.1 couch=37.8 potted plant=19.3 bed=40.6 dining table=24.7 toilet=51.8 tv=47.9 laptop=48.7 mouse=48.9 remote=19.7 keyboard=43.6 cell phone=25.6 microwave=45.1 oven=27.4 toaster=5.9 sink=29.2 refrigerator=44.6 book=7.1 clock=41.2 vase=29.5 scissors=30.6 teddy bear=35.9 hair drier=0.0 toothbrush=11.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.6 [Epoch 230][Batch 99], LR: 1.00E-04, Speed: 8.815 samples/sec, ObjLoss=24.049, BoxCenterLoss=14.359, BoxScaleLoss=4.950, ClassLoss=9.615 [Epoch 230][Batch 199], LR: 1.00E-04, Speed: 8.816 samples/sec, ObjLoss=24.048, BoxCenterLoss=14.359, BoxScaleLoss=4.949, ClassLoss=9.615 [Epoch 230][Batch 299], LR: 1.00E-04, Speed: 9.859 samples/sec, ObjLoss=24.047, BoxCenterLoss=14.359, BoxScaleLoss=4.949, ClassLoss=9.614 [Epoch 230][Batch 399], LR: 1.00E-04, Speed: 115.081 samples/sec, ObjLoss=24.047, BoxCenterLoss=14.359, BoxScaleLoss=4.949, ClassLoss=9.614 [Epoch 230][Batch 499], LR: 1.00E-04, Speed: 9.146 samples/sec, ObjLoss=24.046, BoxCenterLoss=14.359, BoxScaleLoss=4.949, ClassLoss=9.613 [Epoch 230][Batch 599], LR: 1.00E-04, Speed: 9.832 samples/sec, ObjLoss=24.046, BoxCenterLoss=14.359, BoxScaleLoss=4.949, ClassLoss=9.613 [Epoch 230][Batch 699], LR: 1.00E-04, Speed: 109.428 samples/sec, ObjLoss=24.045, BoxCenterLoss=14.359, BoxScaleLoss=4.949, ClassLoss=9.612 [Epoch 230][Batch 799], LR: 1.00E-04, Speed: 9.692 samples/sec, ObjLoss=24.045, BoxCenterLoss=14.359, BoxScaleLoss=4.949, ClassLoss=9.612 [Epoch 230][Batch 899], LR: 1.00E-04, Speed: 9.759 samples/sec, ObjLoss=24.044, BoxCenterLoss=14.359, BoxScaleLoss=4.948, ClassLoss=9.611 [Epoch 230][Batch 999], LR: 1.00E-04, Speed: 10.625 samples/sec, ObjLoss=24.044, BoxCenterLoss=14.359, BoxScaleLoss=4.948, ClassLoss=9.611 [Epoch 230][Batch 1099], LR: 1.00E-04, Speed: 10.907 samples/sec, ObjLoss=24.043, BoxCenterLoss=14.359, BoxScaleLoss=4.948, ClassLoss=9.611 [Epoch 230][Batch 1199], LR: 1.00E-04, Speed: 9.096 samples/sec, ObjLoss=24.042, BoxCenterLoss=14.359, BoxScaleLoss=4.948, ClassLoss=9.610 [Epoch 230][Batch 1299], LR: 1.00E-04, Speed: 11.458 samples/sec, ObjLoss=24.042, BoxCenterLoss=14.359, BoxScaleLoss=4.948, ClassLoss=9.610 [Epoch 230][Batch 1399], LR: 1.00E-04, Speed: 9.272 samples/sec, ObjLoss=24.041, BoxCenterLoss=14.358, BoxScaleLoss=4.948, ClassLoss=9.609 [Epoch 230][Batch 1499], LR: 1.00E-04, Speed: 113.010 samples/sec, ObjLoss=24.040, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.609 [Epoch 230][Batch 1599], LR: 1.00E-04, Speed: 86.741 samples/sec, ObjLoss=24.040, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.608 [Epoch 230][Batch 1699], LR: 1.00E-04, Speed: 89.536 samples/sec, ObjLoss=24.039, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.608 [Epoch 230][Batch 1799], LR: 1.00E-04, Speed: 8.882 samples/sec, ObjLoss=24.038, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.607 [Epoch 230] Training cost: 2204.355, ObjLoss=24.038, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.607 [Epoch 230] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.535 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.351 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.402 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.412 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.203 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.584 person=44.4 bicycle=24.2 car=33.6 motorcycle=37.4 airplane=54.9 bus=60.1 train=60.5 truck=30.8 boat=19.4 traffic light=18.9 fire hydrant=58.9 stop sign=53.6 parking meter=40.5 bench=18.9 bird=28.0 cat=58.0 dog=50.8 horse=48.2 sheep=42.7 cow=47.1 elephant=56.7 bear=60.1 zebra=55.8 giraffe=57.6 backpack=10.7 umbrella=32.3 handbag=9.4 tie=23.6 suitcase=25.3 frisbee=48.7 skis=16.6 snowboard=26.5 sports ball=34.4 kite=33.7 baseball bat=20.0 baseball glove=28.2 skateboard=42.0 surfboard=27.6 tennis racket=36.7 bottle=27.1 wine glass=26.3 cup=32.5 fork=24.9 knife=8.3 spoon=8.6 bowl=34.1 banana=17.8 apple=11.9 sandwich=27.8 orange=24.5 broccoli=15.3 carrot=15.4 hot dog=26.4 pizza=44.3 donut=39.7 cake=31.6 chair=22.7 couch=38.7 potted plant=18.8 bed=39.6 dining table=24.7 toilet=50.8 tv=50.1 laptop=49.0 mouse=48.4 remote=20.5 keyboard=40.6 cell phone=26.3 microwave=44.9 oven=27.2 toaster=5.9 sink=29.1 refrigerator=44.9 book=7.4 clock=41.6 vase=29.7 scissors=29.4 teddy bear=37.8 hair drier=0.0 toothbrush=9.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.9 [Epoch 231][Batch 99], LR: 1.00E-04, Speed: 10.056 samples/sec, ObjLoss=24.037, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.607 [Epoch 231][Batch 199], LR: 1.00E-04, Speed: 9.789 samples/sec, ObjLoss=24.037, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.606 [Epoch 231][Batch 299], LR: 1.00E-04, Speed: 10.497 samples/sec, ObjLoss=24.037, BoxCenterLoss=14.358, BoxScaleLoss=4.947, ClassLoss=9.606 [Epoch 231][Batch 399], LR: 1.00E-04, Speed: 7.470 samples/sec, ObjLoss=24.036, BoxCenterLoss=14.358, BoxScaleLoss=4.946, ClassLoss=9.605 [Epoch 231][Batch 499], LR: 1.00E-04, Speed: 9.739 samples/sec, ObjLoss=24.036, BoxCenterLoss=14.358, BoxScaleLoss=4.946, ClassLoss=9.605 [Epoch 231][Batch 599], LR: 1.00E-04, Speed: 10.038 samples/sec, ObjLoss=24.035, BoxCenterLoss=14.358, BoxScaleLoss=4.946, ClassLoss=9.604 [Epoch 231][Batch 699], LR: 1.00E-04, Speed: 10.544 samples/sec, ObjLoss=24.035, BoxCenterLoss=14.358, BoxScaleLoss=4.946, ClassLoss=9.604 [Epoch 231][Batch 799], LR: 1.00E-04, Speed: 8.221 samples/sec, ObjLoss=24.034, BoxCenterLoss=14.358, BoxScaleLoss=4.946, ClassLoss=9.604 [Epoch 231][Batch 899], LR: 1.00E-04, Speed: 11.207 samples/sec, ObjLoss=24.034, BoxCenterLoss=14.358, BoxScaleLoss=4.946, ClassLoss=9.603 [Epoch 231][Batch 999], LR: 1.00E-04, Speed: 12.901 samples/sec, ObjLoss=24.033, BoxCenterLoss=14.358, BoxScaleLoss=4.946, ClassLoss=9.603 [Epoch 231][Batch 1099], LR: 1.00E-04, Speed: 9.238 samples/sec, ObjLoss=24.032, BoxCenterLoss=14.358, BoxScaleLoss=4.945, ClassLoss=9.602 [Epoch 231][Batch 1199], LR: 1.00E-04, Speed: 11.531 samples/sec, ObjLoss=24.032, BoxCenterLoss=14.358, BoxScaleLoss=4.945, ClassLoss=9.602 [Epoch 231][Batch 1299], LR: 1.00E-04, Speed: 105.850 samples/sec, ObjLoss=24.031, BoxCenterLoss=14.357, BoxScaleLoss=4.945, ClassLoss=9.601 [Epoch 231][Batch 1399], LR: 1.00E-04, Speed: 11.564 samples/sec, ObjLoss=24.030, BoxCenterLoss=14.357, BoxScaleLoss=4.945, ClassLoss=9.601 [Epoch 231][Batch 1499], LR: 1.00E-04, Speed: 9.135 samples/sec, ObjLoss=24.030, BoxCenterLoss=14.357, BoxScaleLoss=4.945, ClassLoss=9.600 [Epoch 231][Batch 1599], LR: 1.00E-04, Speed: 10.727 samples/sec, ObjLoss=24.030, BoxCenterLoss=14.357, BoxScaleLoss=4.945, ClassLoss=9.600 [Epoch 231][Batch 1699], LR: 1.00E-04, Speed: 10.649 samples/sec, ObjLoss=24.029, BoxCenterLoss=14.357, BoxScaleLoss=4.944, ClassLoss=9.599 [Epoch 231][Batch 1799], LR: 1.00E-04, Speed: 10.469 samples/sec, ObjLoss=24.029, BoxCenterLoss=14.357, BoxScaleLoss=4.944, ClassLoss=9.599 [Epoch 231] Training cost: 2198.906, ObjLoss=24.028, BoxCenterLoss=14.357, BoxScaleLoss=4.944, ClassLoss=9.599 [Epoch 231] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.536 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.349 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.399 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.410 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.202 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.579 person=44.6 bicycle=23.5 car=33.7 motorcycle=36.9 airplane=55.0 bus=59.8 train=59.4 truck=31.1 boat=19.5 traffic light=19.4 fire hydrant=59.2 stop sign=55.7 parking meter=36.4 bench=19.6 bird=26.7 cat=59.6 dog=52.5 horse=47.8 sheep=42.5 cow=46.6 elephant=55.4 bear=57.5 zebra=56.9 giraffe=56.5 backpack=10.5 umbrella=30.7 handbag=9.0 tie=22.5 suitcase=25.0 frisbee=48.0 skis=16.6 snowboard=28.1 sports ball=35.5 kite=32.6 baseball bat=19.4 baseball glove=25.6 skateboard=41.3 surfboard=27.4 tennis racket=37.8 bottle=26.3 wine glass=26.8 cup=32.6 fork=24.5 knife=8.1 spoon=9.3 bowl=33.7 banana=18.3 apple=13.2 sandwich=27.2 orange=24.9 broccoli=16.1 carrot=14.8 hot dog=23.1 pizza=44.2 donut=37.8 cake=30.9 chair=23.1 couch=38.5 potted plant=19.4 bed=40.5 dining table=26.0 toilet=48.7 tv=49.1 laptop=48.6 mouse=50.2 remote=19.8 keyboard=42.7 cell phone=26.4 microwave=41.7 oven=28.1 toaster=7.1 sink=30.1 refrigerator=45.1 book=6.8 clock=40.8 vase=30.1 scissors=27.2 teddy bear=37.1 hair drier=0.0 toothbrush=9.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.7 [Epoch 232][Batch 99], LR: 1.00E-04, Speed: 7.729 samples/sec, ObjLoss=24.028, BoxCenterLoss=14.357, BoxScaleLoss=4.944, ClassLoss=9.598 [Epoch 232][Batch 199], LR: 1.00E-04, Speed: 108.658 samples/sec, ObjLoss=24.027, BoxCenterLoss=14.357, BoxScaleLoss=4.944, ClassLoss=9.598 [Epoch 232][Batch 299], LR: 1.00E-04, Speed: 8.190 samples/sec, ObjLoss=24.027, BoxCenterLoss=14.357, BoxScaleLoss=4.944, ClassLoss=9.597 [Epoch 232][Batch 399], LR: 1.00E-04, Speed: 111.721 samples/sec, ObjLoss=24.027, BoxCenterLoss=14.357, BoxScaleLoss=4.944, ClassLoss=9.597 [Epoch 232][Batch 499], LR: 1.00E-04, Speed: 7.624 samples/sec, ObjLoss=24.026, BoxCenterLoss=14.357, BoxScaleLoss=4.943, ClassLoss=9.596 [Epoch 232][Batch 599], LR: 1.00E-04, Speed: 11.334 samples/sec, ObjLoss=24.026, BoxCenterLoss=14.357, BoxScaleLoss=4.943, ClassLoss=9.596 [Epoch 232][Batch 699], LR: 1.00E-04, Speed: 10.114 samples/sec, ObjLoss=24.025, BoxCenterLoss=14.357, BoxScaleLoss=4.943, ClassLoss=9.596 [Epoch 232][Batch 799], LR: 1.00E-04, Speed: 8.805 samples/sec, ObjLoss=24.025, BoxCenterLoss=14.357, BoxScaleLoss=4.943, ClassLoss=9.595 [Epoch 232][Batch 899], LR: 1.00E-04, Speed: 10.300 samples/sec, ObjLoss=24.024, BoxCenterLoss=14.357, BoxScaleLoss=4.943, ClassLoss=9.595 [Epoch 232][Batch 999], LR: 1.00E-04, Speed: 9.035 samples/sec, ObjLoss=24.024, BoxCenterLoss=14.357, BoxScaleLoss=4.943, ClassLoss=9.594 [Epoch 232][Batch 1099], LR: 1.00E-04, Speed: 8.354 samples/sec, ObjLoss=24.023, BoxCenterLoss=14.357, BoxScaleLoss=4.943, ClassLoss=9.594 [Epoch 232][Batch 1199], LR: 1.00E-04, Speed: 12.103 samples/sec, ObjLoss=24.023, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.593 [Epoch 232][Batch 1299], LR: 1.00E-04, Speed: 14.694 samples/sec, ObjLoss=24.022, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.593 [Epoch 232][Batch 1399], LR: 1.00E-04, Speed: 97.058 samples/sec, ObjLoss=24.021, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.592 [Epoch 232][Batch 1499], LR: 1.00E-04, Speed: 132.426 samples/sec, ObjLoss=24.021, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.592 [Epoch 232][Batch 1599], LR: 1.00E-04, Speed: 36.787 samples/sec, ObjLoss=24.020, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.592 [Epoch 232][Batch 1699], LR: 1.00E-04, Speed: 9.196 samples/sec, ObjLoss=24.020, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.591 [Epoch 232][Batch 1799], LR: 1.00E-04, Speed: 11.810 samples/sec, ObjLoss=24.019, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.591 [Epoch 232] Training cost: 2122.256, ObjLoss=24.019, BoxCenterLoss=14.357, BoxScaleLoss=4.942, ClassLoss=9.591 [Epoch 232] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.325 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.537 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.348 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.408 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.435 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.573 person=43.8 bicycle=23.8 car=33.6 motorcycle=37.8 airplane=53.0 bus=58.6 train=58.6 truck=30.3 boat=19.3 traffic light=19.0 fire hydrant=56.9 stop sign=51.0 parking meter=38.8 bench=19.1 bird=27.9 cat=57.5 dog=50.2 horse=46.5 sheep=42.3 cow=46.0 elephant=53.8 bear=59.0 zebra=54.8 giraffe=55.8 backpack=10.6 umbrella=32.1 handbag=9.0 tie=21.9 suitcase=26.5 frisbee=48.5 skis=17.1 snowboard=27.2 sports ball=33.6 kite=31.6 baseball bat=21.0 baseball glove=27.6 skateboard=42.0 surfboard=27.3 tennis racket=37.1 bottle=27.4 wine glass=26.7 cup=32.8 fork=23.8 knife=9.4 spoon=8.5 bowl=33.7 banana=18.4 apple=10.5 sandwich=27.0 orange=23.9 broccoli=14.1 carrot=15.9 hot dog=24.2 pizza=44.0 donut=38.6 cake=32.6 chair=22.8 couch=38.0 potted plant=20.2 bed=40.4 dining table=25.0 toilet=49.5 tv=47.7 laptop=47.7 mouse=48.4 remote=19.2 keyboard=45.2 cell phone=25.9 microwave=44.9 oven=25.8 toaster=7.1 sink=30.6 refrigerator=43.4 book=6.7 clock=40.4 vase=29.4 scissors=28.6 teddy bear=36.3 hair drier=0.0 toothbrush=12.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.5 [Epoch 233][Batch 99], LR: 1.00E-04, Speed: 9.550 samples/sec, ObjLoss=24.018, BoxCenterLoss=14.356, BoxScaleLoss=4.941, ClassLoss=9.590 [Epoch 233][Batch 199], LR: 1.00E-04, Speed: 11.127 samples/sec, ObjLoss=24.017, BoxCenterLoss=14.356, BoxScaleLoss=4.941, ClassLoss=9.589 [Epoch 233][Batch 299], LR: 1.00E-04, Speed: 10.380 samples/sec, ObjLoss=24.016, BoxCenterLoss=14.356, BoxScaleLoss=4.941, ClassLoss=9.589 [Epoch 233][Batch 399], LR: 1.00E-04, Speed: 9.012 samples/sec, ObjLoss=24.016, BoxCenterLoss=14.356, BoxScaleLoss=4.941, ClassLoss=9.589 [Epoch 233][Batch 499], LR: 1.00E-04, Speed: 99.333 samples/sec, ObjLoss=24.015, BoxCenterLoss=14.356, BoxScaleLoss=4.941, ClassLoss=9.588 [Epoch 233][Batch 599], LR: 1.00E-04, Speed: 11.400 samples/sec, ObjLoss=24.015, BoxCenterLoss=14.356, BoxScaleLoss=4.940, ClassLoss=9.587 [Epoch 233][Batch 699], LR: 1.00E-04, Speed: 9.157 samples/sec, ObjLoss=24.014, BoxCenterLoss=14.356, BoxScaleLoss=4.940, ClassLoss=9.587 [Epoch 233][Batch 799], LR: 1.00E-04, Speed: 10.651 samples/sec, ObjLoss=24.014, BoxCenterLoss=14.356, BoxScaleLoss=4.940, ClassLoss=9.587 [Epoch 233][Batch 899], LR: 1.00E-04, Speed: 88.468 samples/sec, ObjLoss=24.013, BoxCenterLoss=14.356, BoxScaleLoss=4.940, ClassLoss=9.586 [Epoch 233][Batch 999], LR: 1.00E-04, Speed: 7.610 samples/sec, ObjLoss=24.012, BoxCenterLoss=14.355, BoxScaleLoss=4.940, ClassLoss=9.586 [Epoch 233][Batch 1099], LR: 1.00E-04, Speed: 9.368 samples/sec, ObjLoss=24.012, BoxCenterLoss=14.355, BoxScaleLoss=4.940, ClassLoss=9.585 [Epoch 233][Batch 1199], LR: 1.00E-04, Speed: 10.489 samples/sec, ObjLoss=24.011, BoxCenterLoss=14.355, BoxScaleLoss=4.940, ClassLoss=9.585 [Epoch 233][Batch 1299], LR: 1.00E-04, Speed: 10.692 samples/sec, ObjLoss=24.011, BoxCenterLoss=14.355, BoxScaleLoss=4.940, ClassLoss=9.584 [Epoch 233][Batch 1399], LR: 1.00E-04, Speed: 107.164 samples/sec, ObjLoss=24.011, BoxCenterLoss=14.356, BoxScaleLoss=4.940, ClassLoss=9.584 [Epoch 233][Batch 1499], LR: 1.00E-04, Speed: 9.799 samples/sec, ObjLoss=24.010, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.584 [Epoch 233][Batch 1599], LR: 1.00E-04, Speed: 109.822 samples/sec, ObjLoss=24.009, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.583 [Epoch 233][Batch 1699], LR: 1.00E-04, Speed: 10.046 samples/sec, ObjLoss=24.009, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.583 [Epoch 233][Batch 1799], LR: 1.00E-04, Speed: 12.728 samples/sec, ObjLoss=24.008, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.582 [Epoch 233] Training cost: 2161.093, ObjLoss=24.008, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.582 [Epoch 233] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.330 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.539 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.404 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.414 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.588 person=44.7 bicycle=23.9 car=33.8 motorcycle=36.9 airplane=54.2 bus=60.9 train=60.7 truck=31.0 boat=19.5 traffic light=20.2 fire hydrant=59.3 stop sign=53.8 parking meter=41.8 bench=19.9 bird=28.8 cat=57.9 dog=53.8 horse=46.1 sheep=43.1 cow=43.9 elephant=54.9 bear=63.5 zebra=57.8 giraffe=56.9 backpack=10.6 umbrella=32.7 handbag=9.6 tie=24.3 suitcase=26.8 frisbee=48.5 skis=16.1 snowboard=26.2 sports ball=32.2 kite=34.9 baseball bat=19.4 baseball glove=28.0 skateboard=41.7 surfboard=28.1 tennis racket=37.8 bottle=27.5 wine glass=28.1 cup=33.9 fork=23.5 knife=8.7 spoon=8.7 bowl=34.0 banana=18.4 apple=11.0 sandwich=29.8 orange=24.8 broccoli=14.1 carrot=14.8 hot dog=25.2 pizza=45.6 donut=41.1 cake=30.3 chair=21.9 couch=36.5 potted plant=20.4 bed=39.5 dining table=25.4 toilet=49.1 tv=48.7 laptop=49.8 mouse=48.4 remote=18.9 keyboard=42.3 cell phone=26.5 microwave=44.9 oven=27.4 toaster=7.1 sink=30.0 refrigerator=43.4 book=7.3 clock=42.5 vase=29.7 scissors=28.6 teddy bear=36.4 hair drier=0.0 toothbrush=11.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.0 [Epoch 234][Batch 99], LR: 1.00E-04, Speed: 9.177 samples/sec, ObjLoss=24.007, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.582 [Epoch 234][Batch 199], LR: 1.00E-04, Speed: 10.088 samples/sec, ObjLoss=24.007, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.581 [Epoch 234][Batch 299], LR: 1.00E-04, Speed: 126.499 samples/sec, ObjLoss=24.006, BoxCenterLoss=14.355, BoxScaleLoss=4.939, ClassLoss=9.581 [Epoch 234][Batch 399], LR: 1.00E-04, Speed: 86.146 samples/sec, ObjLoss=24.005, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.580 [Epoch 234][Batch 499], LR: 1.00E-04, Speed: 8.466 samples/sec, ObjLoss=24.005, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.580 [Epoch 234][Batch 599], LR: 1.00E-04, Speed: 12.400 samples/sec, ObjLoss=24.004, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.579 [Epoch 234][Batch 699], LR: 1.00E-04, Speed: 90.015 samples/sec, ObjLoss=24.004, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.579 [Epoch 234][Batch 799], LR: 1.00E-04, Speed: 10.117 samples/sec, ObjLoss=24.003, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.579 [Epoch 234][Batch 899], LR: 1.00E-04, Speed: 10.121 samples/sec, ObjLoss=24.003, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.578 [Epoch 234][Batch 999], LR: 1.00E-04, Speed: 91.957 samples/sec, ObjLoss=24.002, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.578 [Epoch 234][Batch 1099], LR: 1.00E-04, Speed: 9.156 samples/sec, ObjLoss=24.002, BoxCenterLoss=14.355, BoxScaleLoss=4.938, ClassLoss=9.578 [Epoch 234][Batch 1199], LR: 1.00E-04, Speed: 9.384 samples/sec, ObjLoss=24.001, BoxCenterLoss=14.355, BoxScaleLoss=4.937, ClassLoss=9.577 [Epoch 234][Batch 1299], LR: 1.00E-04, Speed: 10.143 samples/sec, ObjLoss=24.000, BoxCenterLoss=14.354, BoxScaleLoss=4.937, ClassLoss=9.576 [Epoch 234][Batch 1399], LR: 1.00E-04, Speed: 8.167 samples/sec, ObjLoss=24.000, BoxCenterLoss=14.354, BoxScaleLoss=4.937, ClassLoss=9.576 [Epoch 234][Batch 1499], LR: 1.00E-04, Speed: 8.376 samples/sec, ObjLoss=23.999, BoxCenterLoss=14.354, BoxScaleLoss=4.937, ClassLoss=9.575 [Epoch 234][Batch 1599], LR: 1.00E-04, Speed: 12.177 samples/sec, ObjLoss=23.999, BoxCenterLoss=14.354, BoxScaleLoss=4.937, ClassLoss=9.575 [Epoch 234][Batch 1699], LR: 1.00E-04, Speed: 6.799 samples/sec, ObjLoss=23.998, BoxCenterLoss=14.354, BoxScaleLoss=4.937, ClassLoss=9.574 [Epoch 234][Batch 1799], LR: 1.00E-04, Speed: 10.866 samples/sec, ObjLoss=23.998, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.574 [Epoch 234] Training cost: 2114.775, ObjLoss=23.997, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.574 [Epoch 234] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.542 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.359 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.497 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.407 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.205 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.443 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.599 person=44.9 bicycle=25.2 car=33.9 motorcycle=38.9 airplane=54.8 bus=61.3 train=60.8 truck=31.1 boat=19.1 traffic light=19.6 fire hydrant=58.1 stop sign=56.2 parking meter=39.5 bench=19.9 bird=27.2 cat=58.6 dog=53.4 horse=46.5 sheep=43.5 cow=46.3 elephant=55.7 bear=61.4 zebra=56.6 giraffe=57.7 backpack=10.5 umbrella=32.7 handbag=9.4 tie=23.7 suitcase=28.9 frisbee=49.5 skis=17.1 snowboard=27.8 sports ball=34.0 kite=32.8 baseball bat=19.1 baseball glove=27.5 skateboard=41.6 surfboard=29.0 tennis racket=37.1 bottle=27.7 wine glass=27.6 cup=33.4 fork=24.7 knife=9.5 spoon=9.8 bowl=34.2 banana=17.0 apple=12.7 sandwich=27.0 orange=24.2 broccoli=15.5 carrot=14.5 hot dog=25.3 pizza=45.5 donut=38.5 cake=31.5 chair=23.1 couch=38.5 potted plant=20.4 bed=40.6 dining table=25.0 toilet=52.0 tv=49.7 laptop=49.4 mouse=49.9 remote=19.8 keyboard=42.9 cell phone=27.1 microwave=45.4 oven=27.4 toaster=5.9 sink=31.4 refrigerator=45.5 book=7.4 clock=42.6 vase=30.2 scissors=28.7 teddy bear=38.0 hair drier=0.0 toothbrush=13.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.3 [Epoch 235][Batch 99], LR: 1.00E-04, Speed: 8.752 samples/sec, ObjLoss=23.997, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.573 [Epoch 235][Batch 199], LR: 1.00E-04, Speed: 9.533 samples/sec, ObjLoss=23.996, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.573 [Epoch 235][Batch 299], LR: 1.00E-04, Speed: 13.410 samples/sec, ObjLoss=23.996, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.573 [Epoch 235][Batch 399], LR: 1.00E-04, Speed: 10.026 samples/sec, ObjLoss=23.995, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.572 [Epoch 235][Batch 499], LR: 1.00E-04, Speed: 8.818 samples/sec, ObjLoss=23.995, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.572 [Epoch 235][Batch 599], LR: 1.00E-04, Speed: 12.864 samples/sec, ObjLoss=23.994, BoxCenterLoss=14.354, BoxScaleLoss=4.936, ClassLoss=9.571 [Epoch 235][Batch 699], LR: 1.00E-04, Speed: 9.851 samples/sec, ObjLoss=23.993, BoxCenterLoss=14.354, BoxScaleLoss=4.935, ClassLoss=9.570 [Epoch 235][Batch 799], LR: 1.00E-04, Speed: 12.670 samples/sec, ObjLoss=23.993, BoxCenterLoss=14.354, BoxScaleLoss=4.935, ClassLoss=9.570 [Epoch 235][Batch 899], LR: 1.00E-04, Speed: 10.816 samples/sec, ObjLoss=23.992, BoxCenterLoss=14.354, BoxScaleLoss=4.935, ClassLoss=9.569 [Epoch 235][Batch 999], LR: 1.00E-04, Speed: 123.499 samples/sec, ObjLoss=23.991, BoxCenterLoss=14.353, BoxScaleLoss=4.935, ClassLoss=9.569 [Epoch 235][Batch 1099], LR: 1.00E-04, Speed: 7.464 samples/sec, ObjLoss=23.991, BoxCenterLoss=14.354, BoxScaleLoss=4.935, ClassLoss=9.569 [Epoch 235][Batch 1199], LR: 1.00E-04, Speed: 8.888 samples/sec, ObjLoss=23.990, BoxCenterLoss=14.354, BoxScaleLoss=4.935, ClassLoss=9.568 [Epoch 235][Batch 1299], LR: 1.00E-04, Speed: 12.703 samples/sec, ObjLoss=23.990, BoxCenterLoss=14.353, BoxScaleLoss=4.935, ClassLoss=9.568 [Epoch 235][Batch 1399], LR: 1.00E-04, Speed: 9.319 samples/sec, ObjLoss=23.989, BoxCenterLoss=14.353, BoxScaleLoss=4.934, ClassLoss=9.567 [Epoch 235][Batch 1499], LR: 1.00E-04, Speed: 9.669 samples/sec, ObjLoss=23.989, BoxCenterLoss=14.353, BoxScaleLoss=4.934, ClassLoss=9.567 [Epoch 235][Batch 1599], LR: 1.00E-04, Speed: 9.835 samples/sec, ObjLoss=23.988, BoxCenterLoss=14.353, BoxScaleLoss=4.934, ClassLoss=9.566 [Epoch 235][Batch 1699], LR: 1.00E-04, Speed: 112.268 samples/sec, ObjLoss=23.987, BoxCenterLoss=14.353, BoxScaleLoss=4.934, ClassLoss=9.566 [Epoch 235][Batch 1799], LR: 1.00E-04, Speed: 131.698 samples/sec, ObjLoss=23.987, BoxCenterLoss=14.353, BoxScaleLoss=4.934, ClassLoss=9.565 [Epoch 235] Training cost: 2149.374, ObjLoss=23.987, BoxCenterLoss=14.353, BoxScaleLoss=4.934, ClassLoss=9.565 [Epoch 235] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.535 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.142 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.488 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.401 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.410 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.195 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.592 person=44.5 bicycle=23.8 car=33.3 motorcycle=37.3 airplane=54.0 bus=60.7 train=60.8 truck=29.4 boat=19.9 traffic light=19.4 fire hydrant=58.0 stop sign=55.7 parking meter=36.6 bench=19.3 bird=27.7 cat=58.1 dog=52.4 horse=46.5 sheep=43.9 cow=45.7 elephant=56.7 bear=59.5 zebra=55.3 giraffe=57.6 backpack=10.1 umbrella=32.1 handbag=9.2 tie=23.9 suitcase=28.2 frisbee=48.1 skis=16.7 snowboard=26.2 sports ball=35.1 kite=33.7 baseball bat=20.7 baseball glove=28.3 skateboard=41.2 surfboard=28.3 tennis racket=37.1 bottle=27.4 wine glass=26.3 cup=32.6 fork=24.4 knife=9.1 spoon=9.1 bowl=32.1 banana=17.8 apple=13.2 sandwich=27.8 orange=23.7 broccoli=16.0 carrot=15.6 hot dog=24.4 pizza=43.5 donut=37.1 cake=31.8 chair=22.1 couch=38.0 potted plant=18.4 bed=40.8 dining table=24.7 toilet=51.6 tv=48.3 laptop=48.3 mouse=50.7 remote=20.1 keyboard=43.9 cell phone=25.6 microwave=41.7 oven=28.4 toaster=5.9 sink=30.2 refrigerator=46.0 book=7.3 clock=42.7 vase=29.4 scissors=30.2 teddy bear=37.4 hair drier=0.0 toothbrush=15.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.9 [Epoch 236][Batch 99], LR: 1.00E-04, Speed: 8.160 samples/sec, ObjLoss=23.986, BoxCenterLoss=14.353, BoxScaleLoss=4.934, ClassLoss=9.565 [Epoch 236][Batch 199], LR: 1.00E-04, Speed: 8.632 samples/sec, ObjLoss=23.985, BoxCenterLoss=14.353, BoxScaleLoss=4.933, ClassLoss=9.564 [Epoch 236][Batch 299], LR: 1.00E-04, Speed: 9.588 samples/sec, ObjLoss=23.985, BoxCenterLoss=14.353, BoxScaleLoss=4.933, ClassLoss=9.564 [Epoch 236][Batch 399], LR: 1.00E-04, Speed: 8.882 samples/sec, ObjLoss=23.984, BoxCenterLoss=14.353, BoxScaleLoss=4.933, ClassLoss=9.563 [Epoch 236][Batch 499], LR: 1.00E-04, Speed: 124.454 samples/sec, ObjLoss=23.984, BoxCenterLoss=14.353, BoxScaleLoss=4.933, ClassLoss=9.563 [Epoch 236][Batch 599], LR: 1.00E-04, Speed: 9.486 samples/sec, ObjLoss=23.983, BoxCenterLoss=14.352, BoxScaleLoss=4.933, ClassLoss=9.562 [Epoch 236][Batch 699], LR: 1.00E-04, Speed: 8.473 samples/sec, ObjLoss=23.982, BoxCenterLoss=14.352, BoxScaleLoss=4.933, ClassLoss=9.562 [Epoch 236][Batch 799], LR: 1.00E-04, Speed: 10.047 samples/sec, ObjLoss=23.981, BoxCenterLoss=14.352, BoxScaleLoss=4.933, ClassLoss=9.562 [Epoch 236][Batch 899], LR: 1.00E-04, Speed: 11.776 samples/sec, ObjLoss=23.981, BoxCenterLoss=14.352, BoxScaleLoss=4.932, ClassLoss=9.561 [Epoch 236][Batch 999], LR: 1.00E-04, Speed: 8.181 samples/sec, ObjLoss=23.980, BoxCenterLoss=14.352, BoxScaleLoss=4.932, ClassLoss=9.561 [Epoch 236][Batch 1099], LR: 1.00E-04, Speed: 9.637 samples/sec, ObjLoss=23.980, BoxCenterLoss=14.352, BoxScaleLoss=4.932, ClassLoss=9.560 [Epoch 236][Batch 1199], LR: 1.00E-04, Speed: 10.019 samples/sec, ObjLoss=23.979, BoxCenterLoss=14.352, BoxScaleLoss=4.932, ClassLoss=9.560 [Epoch 236][Batch 1299], LR: 1.00E-04, Speed: 10.163 samples/sec, ObjLoss=23.978, BoxCenterLoss=14.352, BoxScaleLoss=4.932, ClassLoss=9.559 [Epoch 236][Batch 1399], LR: 1.00E-04, Speed: 7.412 samples/sec, ObjLoss=23.978, BoxCenterLoss=14.352, BoxScaleLoss=4.932, ClassLoss=9.559 [Epoch 236][Batch 1499], LR: 1.00E-04, Speed: 10.093 samples/sec, ObjLoss=23.977, BoxCenterLoss=14.352, BoxScaleLoss=4.931, ClassLoss=9.558 [Epoch 236][Batch 1599], LR: 1.00E-04, Speed: 6.857 samples/sec, ObjLoss=23.977, BoxCenterLoss=14.351, BoxScaleLoss=4.931, ClassLoss=9.558 [Epoch 236][Batch 1699], LR: 1.00E-04, Speed: 10.805 samples/sec, ObjLoss=23.976, BoxCenterLoss=14.351, BoxScaleLoss=4.931, ClassLoss=9.557 [Epoch 236][Batch 1799], LR: 1.00E-04, Speed: 106.218 samples/sec, ObjLoss=23.976, BoxCenterLoss=14.351, BoxScaleLoss=4.931, ClassLoss=9.557 [Epoch 236] Training cost: 2132.085, ObjLoss=23.975, BoxCenterLoss=14.351, BoxScaleLoss=4.931, ClassLoss=9.557 [Epoch 236] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.537 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.402 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.413 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.204 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.586 person=44.3 bicycle=24.2 car=34.0 motorcycle=38.0 airplane=53.3 bus=60.4 train=58.9 truck=30.1 boat=20.8 traffic light=19.4 fire hydrant=57.9 stop sign=54.9 parking meter=40.3 bench=19.5 bird=27.0 cat=57.8 dog=53.7 horse=48.4 sheep=43.7 cow=46.4 elephant=56.7 bear=64.6 zebra=55.4 giraffe=56.8 backpack=10.1 umbrella=31.5 handbag=8.7 tie=23.5 suitcase=28.4 frisbee=48.0 skis=16.3 snowboard=27.9 sports ball=33.0 kite=33.7 baseball bat=20.5 baseball glove=27.0 skateboard=41.2 surfboard=28.7 tennis racket=38.5 bottle=25.8 wine glass=26.6 cup=33.2 fork=24.3 knife=9.1 spoon=9.3 bowl=33.3 banana=17.8 apple=12.8 sandwich=26.1 orange=25.3 broccoli=14.3 carrot=16.4 hot dog=26.1 pizza=44.3 donut=38.8 cake=30.7 chair=23.0 couch=38.7 potted plant=19.6 bed=40.1 dining table=25.3 toilet=50.8 tv=46.7 laptop=47.4 mouse=50.2 remote=17.5 keyboard=43.8 cell phone=25.7 microwave=44.3 oven=28.0 toaster=5.9 sink=29.0 refrigerator=43.2 book=7.2 clock=42.1 vase=28.1 scissors=28.0 teddy bear=34.9 hair drier=0.0 toothbrush=11.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.9 [Epoch 237][Batch 99], LR: 1.00E-04, Speed: 9.074 samples/sec, ObjLoss=23.975, BoxCenterLoss=14.351, BoxScaleLoss=4.931, ClassLoss=9.556 [Epoch 237][Batch 199], LR: 1.00E-04, Speed: 125.925 samples/sec, ObjLoss=23.975, BoxCenterLoss=14.351, BoxScaleLoss=4.931, ClassLoss=9.556 [Epoch 237][Batch 299], LR: 1.00E-04, Speed: 12.562 samples/sec, ObjLoss=23.974, BoxCenterLoss=14.351, BoxScaleLoss=4.931, ClassLoss=9.555 [Epoch 237][Batch 399], LR: 1.00E-04, Speed: 9.960 samples/sec, ObjLoss=23.973, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.555 [Epoch 237][Batch 499], LR: 1.00E-04, Speed: 10.824 samples/sec, ObjLoss=23.972, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.554 [Epoch 237][Batch 599], LR: 1.00E-04, Speed: 9.665 samples/sec, ObjLoss=23.972, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.554 [Epoch 237][Batch 699], LR: 1.00E-04, Speed: 10.368 samples/sec, ObjLoss=23.971, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.553 [Epoch 237][Batch 799], LR: 1.00E-04, Speed: 7.176 samples/sec, ObjLoss=23.971, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.553 [Epoch 237][Batch 899], LR: 1.00E-04, Speed: 110.603 samples/sec, ObjLoss=23.970, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.553 [Epoch 237][Batch 999], LR: 1.00E-04, Speed: 10.361 samples/sec, ObjLoss=23.970, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.552 [Epoch 237][Batch 1099], LR: 1.00E-04, Speed: 10.254 samples/sec, ObjLoss=23.969, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.552 [Epoch 237][Batch 1199], LR: 1.00E-04, Speed: 10.645 samples/sec, ObjLoss=23.969, BoxCenterLoss=14.351, BoxScaleLoss=4.930, ClassLoss=9.552 [Epoch 237][Batch 1299], LR: 1.00E-04, Speed: 7.485 samples/sec, ObjLoss=23.968, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.551 [Epoch 237][Batch 1399], LR: 1.00E-04, Speed: 8.739 samples/sec, ObjLoss=23.968, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.551 [Epoch 237][Batch 1499], LR: 1.00E-04, Speed: 8.913 samples/sec, ObjLoss=23.967, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.550 [Epoch 237][Batch 1599], LR: 1.00E-04, Speed: 9.807 samples/sec, ObjLoss=23.966, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.550 [Epoch 237][Batch 1699], LR: 1.00E-04, Speed: 9.748 samples/sec, ObjLoss=23.966, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.549 [Epoch 237][Batch 1799], LR: 1.00E-04, Speed: 11.543 samples/sec, ObjLoss=23.965, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.549 [Epoch 237] Training cost: 2158.287, ObjLoss=23.965, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.549 [Epoch 237] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.539 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.142 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.488 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.402 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.412 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.202 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.592 person=44.2 bicycle=24.3 car=32.6 motorcycle=38.2 airplane=54.3 bus=59.9 train=60.9 truck=30.6 boat=19.8 traffic light=19.3 fire hydrant=58.2 stop sign=51.7 parking meter=37.3 bench=19.3 bird=28.1 cat=59.7 dog=54.0 horse=47.4 sheep=43.6 cow=46.3 elephant=54.8 bear=57.6 zebra=56.5 giraffe=57.9 backpack=10.5 umbrella=32.2 handbag=8.9 tie=22.6 suitcase=28.1 frisbee=48.0 skis=15.9 snowboard=26.8 sports ball=32.7 kite=31.7 baseball bat=20.8 baseball glove=27.9 skateboard=41.3 surfboard=27.5 tennis racket=37.2 bottle=26.1 wine glass=27.7 cup=31.6 fork=24.8 knife=8.9 spoon=9.0 bowl=32.6 banana=17.6 apple=12.4 sandwich=28.0 orange=25.1 broccoli=14.3 carrot=15.0 hot dog=24.7 pizza=43.9 donut=38.2 cake=30.6 chair=22.3 couch=38.2 potted plant=20.4 bed=40.3 dining table=25.7 toilet=52.1 tv=48.0 laptop=48.7 mouse=50.0 remote=20.2 keyboard=42.9 cell phone=26.1 microwave=42.9 oven=29.1 toaster=7.1 sink=30.4 refrigerator=43.8 book=7.6 clock=42.1 vase=30.4 scissors=29.3 teddy bear=38.0 hair drier=0.0 toothbrush=13.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.8 [Epoch 238][Batch 99], LR: 1.00E-04, Speed: 9.846 samples/sec, ObjLoss=23.965, BoxCenterLoss=14.351, BoxScaleLoss=4.929, ClassLoss=9.548 [Epoch 238][Batch 199], LR: 1.00E-04, Speed: 121.597 samples/sec, ObjLoss=23.964, BoxCenterLoss=14.350, BoxScaleLoss=4.928, ClassLoss=9.548 [Epoch 238][Batch 299], LR: 1.00E-04, Speed: 10.357 samples/sec, ObjLoss=23.963, BoxCenterLoss=14.350, BoxScaleLoss=4.928, ClassLoss=9.547 [Epoch 238][Batch 399], LR: 1.00E-04, Speed: 112.015 samples/sec, ObjLoss=23.963, BoxCenterLoss=14.350, BoxScaleLoss=4.928, ClassLoss=9.547 [Epoch 238][Batch 499], LR: 1.00E-04, Speed: 9.842 samples/sec, ObjLoss=23.962, BoxCenterLoss=14.350, BoxScaleLoss=4.928, ClassLoss=9.546 [Epoch 238][Batch 599], LR: 1.00E-04, Speed: 9.975 samples/sec, ObjLoss=23.961, BoxCenterLoss=14.350, BoxScaleLoss=4.928, ClassLoss=9.546 [Epoch 238][Batch 699], LR: 1.00E-04, Speed: 12.607 samples/sec, ObjLoss=23.961, BoxCenterLoss=14.350, BoxScaleLoss=4.928, ClassLoss=9.545 [Epoch 238][Batch 799], LR: 1.00E-04, Speed: 11.436 samples/sec, ObjLoss=23.960, BoxCenterLoss=14.350, BoxScaleLoss=4.927, ClassLoss=9.545 [Epoch 238][Batch 899], LR: 1.00E-04, Speed: 10.165 samples/sec, ObjLoss=23.959, BoxCenterLoss=14.350, BoxScaleLoss=4.927, ClassLoss=9.544 [Epoch 238][Batch 999], LR: 1.00E-04, Speed: 11.424 samples/sec, ObjLoss=23.958, BoxCenterLoss=14.349, BoxScaleLoss=4.927, ClassLoss=9.544 [Epoch 238][Batch 1099], LR: 1.00E-04, Speed: 120.285 samples/sec, ObjLoss=23.958, BoxCenterLoss=14.350, BoxScaleLoss=4.927, ClassLoss=9.543 [Epoch 238][Batch 1199], LR: 1.00E-04, Speed: 12.005 samples/sec, ObjLoss=23.958, BoxCenterLoss=14.350, BoxScaleLoss=4.927, ClassLoss=9.543 [Epoch 238][Batch 1299], LR: 1.00E-04, Speed: 105.341 samples/sec, ObjLoss=23.957, BoxCenterLoss=14.349, BoxScaleLoss=4.927, ClassLoss=9.542 [Epoch 238][Batch 1399], LR: 1.00E-04, Speed: 9.460 samples/sec, ObjLoss=23.956, BoxCenterLoss=14.349, BoxScaleLoss=4.927, ClassLoss=9.542 [Epoch 238][Batch 1499], LR: 1.00E-04, Speed: 11.250 samples/sec, ObjLoss=23.956, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.541 [Epoch 238][Batch 1599], LR: 1.00E-04, Speed: 11.589 samples/sec, ObjLoss=23.955, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.541 [Epoch 238][Batch 1699], LR: 1.00E-04, Speed: 9.693 samples/sec, ObjLoss=23.955, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.540 [Epoch 238][Batch 1799], LR: 1.00E-04, Speed: 13.310 samples/sec, ObjLoss=23.954, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.540 [Epoch 238] Training cost: 2131.683, ObjLoss=23.954, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.540 [Epoch 238] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.537 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.351 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.402 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.413 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.202 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590 person=44.4 bicycle=24.4 car=34.0 motorcycle=38.4 airplane=52.7 bus=59.2 train=59.4 truck=30.0 boat=19.3 traffic light=20.0 fire hydrant=57.2 stop sign=55.5 parking meter=38.4 bench=19.0 bird=28.6 cat=58.4 dog=53.1 horse=47.1 sheep=43.0 cow=45.0 elephant=55.7 bear=61.8 zebra=55.8 giraffe=55.8 backpack=10.7 umbrella=31.0 handbag=9.3 tie=24.4 suitcase=28.1 frisbee=47.9 skis=18.1 snowboard=24.3 sports ball=31.6 kite=32.2 baseball bat=20.3 baseball glove=28.0 skateboard=41.7 surfboard=28.0 tennis racket=37.7 bottle=27.6 wine glass=27.9 cup=32.9 fork=25.6 knife=8.8 spoon=9.2 bowl=33.5 banana=17.9 apple=12.0 sandwich=27.5 orange=24.3 broccoli=14.8 carrot=15.3 hot dog=25.1 pizza=45.1 donut=37.8 cake=30.2 chair=23.0 couch=35.2 potted plant=20.4 bed=40.4 dining table=25.5 toilet=49.3 tv=47.4 laptop=48.7 mouse=48.9 remote=19.1 keyboard=41.1 cell phone=26.6 microwave=44.6 oven=28.4 toaster=7.1 sink=32.0 refrigerator=44.6 book=7.8 clock=42.8 vase=30.0 scissors=30.4 teddy bear=37.7 hair drier=0.0 toothbrush=12.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.9 [Epoch 239][Batch 99], LR: 1.00E-04, Speed: 71.327 samples/sec, ObjLoss=23.953, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.539 [Epoch 239][Batch 199], LR: 1.00E-04, Speed: 9.448 samples/sec, ObjLoss=23.953, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.539 [Epoch 239][Batch 299], LR: 1.00E-04, Speed: 8.788 samples/sec, ObjLoss=23.952, BoxCenterLoss=14.349, BoxScaleLoss=4.926, ClassLoss=9.538 [Epoch 239][Batch 399], LR: 1.00E-04, Speed: 104.565 samples/sec, ObjLoss=23.952, BoxCenterLoss=14.349, BoxScaleLoss=4.925, ClassLoss=9.538 [Epoch 239][Batch 499], LR: 1.00E-04, Speed: 9.026 samples/sec, ObjLoss=23.952, BoxCenterLoss=14.349, BoxScaleLoss=4.925, ClassLoss=9.537 [Epoch 239][Batch 599], LR: 1.00E-04, Speed: 109.484 samples/sec, ObjLoss=23.951, BoxCenterLoss=14.349, BoxScaleLoss=4.925, ClassLoss=9.537 [Epoch 239][Batch 699], LR: 1.00E-04, Speed: 8.971 samples/sec, ObjLoss=23.951, BoxCenterLoss=14.349, BoxScaleLoss=4.925, ClassLoss=9.536 [Epoch 239][Batch 799], LR: 1.00E-04, Speed: 10.802 samples/sec, ObjLoss=23.950, BoxCenterLoss=14.349, BoxScaleLoss=4.925, ClassLoss=9.536 [Epoch 239][Batch 899], LR: 1.00E-04, Speed: 10.723 samples/sec, ObjLoss=23.950, BoxCenterLoss=14.349, BoxScaleLoss=4.925, ClassLoss=9.535 [Epoch 239][Batch 999], LR: 1.00E-04, Speed: 9.980 samples/sec, ObjLoss=23.949, BoxCenterLoss=14.349, BoxScaleLoss=4.924, ClassLoss=9.535 [Epoch 239][Batch 1099], LR: 1.00E-04, Speed: 8.929 samples/sec, ObjLoss=23.948, BoxCenterLoss=14.349, BoxScaleLoss=4.924, ClassLoss=9.534 [Epoch 239][Batch 1199], LR: 1.00E-04, Speed: 9.058 samples/sec, ObjLoss=23.948, BoxCenterLoss=14.349, BoxScaleLoss=4.924, ClassLoss=9.534 [Epoch 239][Batch 1299], LR: 1.00E-04, Speed: 10.177 samples/sec, ObjLoss=23.947, BoxCenterLoss=14.349, BoxScaleLoss=4.924, ClassLoss=9.533 [Epoch 239][Batch 1399], LR: 1.00E-04, Speed: 99.226 samples/sec, ObjLoss=23.947, BoxCenterLoss=14.349, BoxScaleLoss=4.924, ClassLoss=9.533 [Epoch 239][Batch 1499], LR: 1.00E-04, Speed: 9.175 samples/sec, ObjLoss=23.946, BoxCenterLoss=14.349, BoxScaleLoss=4.924, ClassLoss=9.532 [Epoch 239][Batch 1599], LR: 1.00E-04, Speed: 7.534 samples/sec, ObjLoss=23.946, BoxCenterLoss=14.349, BoxScaleLoss=4.923, ClassLoss=9.532 [Epoch 239][Batch 1699], LR: 1.00E-04, Speed: 9.970 samples/sec, ObjLoss=23.945, BoxCenterLoss=14.349, BoxScaleLoss=4.923, ClassLoss=9.531 [Epoch 239][Batch 1799], LR: 1.00E-04, Speed: 13.250 samples/sec, ObjLoss=23.944, BoxCenterLoss=14.348, BoxScaleLoss=4.923, ClassLoss=9.531 [Epoch 239] Training cost: 2181.843, ObjLoss=23.944, BoxCenterLoss=14.349, BoxScaleLoss=4.923, ClassLoss=9.531 [Epoch 239] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.330 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.439 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.592 person=44.5 bicycle=24.6 car=33.4 motorcycle=37.9 airplane=54.1 bus=59.7 train=59.0 truck=30.3 boat=19.6 traffic light=20.2 fire hydrant=59.3 stop sign=55.4 parking meter=42.0 bench=19.4 bird=27.8 cat=57.2 dog=52.0 horse=46.5 sheep=43.6 cow=48.0 elephant=56.5 bear=63.2 zebra=56.0 giraffe=57.3 backpack=10.5 umbrella=32.7 handbag=9.3 tie=22.8 suitcase=28.2 frisbee=48.9 skis=17.5 snowboard=27.6 sports ball=34.6 kite=32.4 baseball bat=21.4 baseball glove=27.2 skateboard=41.9 surfboard=28.9 tennis racket=37.2 bottle=27.1 wine glass=27.3 cup=32.8 fork=23.7 knife=7.9 spoon=8.8 bowl=32.0 banana=18.0 apple=11.7 sandwich=28.5 orange=24.3 broccoli=13.6 carrot=15.0 hot dog=26.1 pizza=44.7 donut=40.3 cake=29.7 chair=22.3 couch=37.3 potted plant=20.6 bed=39.4 dining table=24.5 toilet=50.4 tv=48.5 laptop=47.4 mouse=47.8 remote=20.2 keyboard=44.7 cell phone=26.3 microwave=44.8 oven=27.7 toaster=7.1 sink=30.5 refrigerator=46.9 book=7.8 clock=41.1 vase=29.7 scissors=29.6 teddy bear=38.2 hair drier=0.0 toothbrush=10.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.0 [Epoch 240][Batch 99], LR: 1.00E-04, Speed: 8.723 samples/sec, ObjLoss=23.944, BoxCenterLoss=14.349, BoxScaleLoss=4.923, ClassLoss=9.530 [Epoch 240][Batch 199], LR: 1.00E-04, Speed: 12.154 samples/sec, ObjLoss=23.943, BoxCenterLoss=14.349, BoxScaleLoss=4.923, ClassLoss=9.530 [Epoch 240][Batch 299], LR: 1.00E-04, Speed: 12.810 samples/sec, ObjLoss=23.943, BoxCenterLoss=14.348, BoxScaleLoss=4.923, ClassLoss=9.529 [Epoch 240][Batch 399], LR: 1.00E-04, Speed: 9.641 samples/sec, ObjLoss=23.942, BoxCenterLoss=14.348, BoxScaleLoss=4.923, ClassLoss=9.529 [Epoch 240][Batch 499], LR: 1.00E-04, Speed: 10.952 samples/sec, ObjLoss=23.941, BoxCenterLoss=14.348, BoxScaleLoss=4.922, ClassLoss=9.528 [Epoch 240][Batch 599], LR: 1.00E-04, Speed: 8.895 samples/sec, ObjLoss=23.941, BoxCenterLoss=14.348, BoxScaleLoss=4.922, ClassLoss=9.528 [Epoch 240][Batch 699], LR: 1.00E-04, Speed: 7.289 samples/sec, ObjLoss=23.940, BoxCenterLoss=14.348, BoxScaleLoss=4.922, ClassLoss=9.527 [Epoch 240][Batch 799], LR: 1.00E-04, Speed: 8.398 samples/sec, ObjLoss=23.940, BoxCenterLoss=14.348, BoxScaleLoss=4.922, ClassLoss=9.527 [Epoch 240][Batch 899], LR: 1.00E-04, Speed: 7.893 samples/sec, ObjLoss=23.940, BoxCenterLoss=14.348, BoxScaleLoss=4.922, ClassLoss=9.527 [Epoch 240][Batch 999], LR: 1.00E-04, Speed: 92.754 samples/sec, ObjLoss=23.939, BoxCenterLoss=14.348, BoxScaleLoss=4.922, ClassLoss=9.526 [Epoch 240][Batch 1099], LR: 1.00E-04, Speed: 9.187 samples/sec, ObjLoss=23.939, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.526 [Epoch 240][Batch 1199], LR: 1.00E-04, Speed: 112.651 samples/sec, ObjLoss=23.938, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.525 [Epoch 240][Batch 1299], LR: 1.00E-04, Speed: 9.448 samples/sec, ObjLoss=23.938, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.525 [Epoch 240][Batch 1399], LR: 1.00E-04, Speed: 11.979 samples/sec, ObjLoss=23.937, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.524 [Epoch 240][Batch 1499], LR: 1.00E-04, Speed: 96.884 samples/sec, ObjLoss=23.936, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.524 [Epoch 240][Batch 1599], LR: 1.00E-04, Speed: 8.329 samples/sec, ObjLoss=23.936, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.524 [Epoch 240][Batch 1699], LR: 1.00E-04, Speed: 9.423 samples/sec, ObjLoss=23.935, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.523 [Epoch 240][Batch 1799], LR: 1.00E-04, Speed: 132.705 samples/sec, ObjLoss=23.934, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.523 [Epoch 240] Training cost: 2186.193, ObjLoss=23.934, BoxCenterLoss=14.348, BoxScaleLoss=4.921, ClassLoss=9.523 [Epoch 240] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.536 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.203 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596 person=45.1 bicycle=23.9 car=34.2 motorcycle=38.6 airplane=54.7 bus=59.6 train=61.4 truck=30.4 boat=19.3 traffic light=19.5 fire hydrant=59.2 stop sign=53.6 parking meter=38.9 bench=19.9 bird=27.6 cat=59.9 dog=53.4 horse=49.1 sheep=44.2 cow=47.7 elephant=56.1 bear=60.1 zebra=56.3 giraffe=57.6 backpack=10.5 umbrella=32.0 handbag=9.1 tie=23.9 suitcase=25.9 frisbee=48.4 skis=16.7 snowboard=25.2 sports ball=34.8 kite=33.9 baseball bat=22.0 baseball glove=28.7 skateboard=42.3 surfboard=28.5 tennis racket=38.2 bottle=26.8 wine glass=26.5 cup=33.0 fork=24.4 knife=9.2 spoon=9.5 bowl=33.1 banana=19.5 apple=12.0 sandwich=28.6 orange=24.9 broccoli=14.5 carrot=15.8 hot dog=25.8 pizza=45.3 donut=37.9 cake=29.2 chair=23.1 couch=37.3 potted plant=20.1 bed=41.3 dining table=26.1 toilet=52.3 tv=48.4 laptop=49.7 mouse=50.1 remote=19.6 keyboard=41.7 cell phone=27.5 microwave=43.3 oven=26.9 toaster=7.1 sink=30.9 refrigerator=43.9 book=7.1 clock=42.7 vase=28.3 scissors=28.6 teddy bear=37.9 hair drier=0.0 toothbrush=13.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.2 [Epoch 241][Batch 99], LR: 1.00E-04, Speed: 8.912 samples/sec, ObjLoss=23.934, BoxCenterLoss=14.348, BoxScaleLoss=4.920, ClassLoss=9.522 [Epoch 241][Batch 199], LR: 1.00E-04, Speed: 116.029 samples/sec, ObjLoss=23.933, BoxCenterLoss=14.348, BoxScaleLoss=4.920, ClassLoss=9.522 [Epoch 241][Batch 299], LR: 1.00E-04, Speed: 8.229 samples/sec, ObjLoss=23.933, BoxCenterLoss=14.348, BoxScaleLoss=4.920, ClassLoss=9.521 [Epoch 241][Batch 399], LR: 1.00E-04, Speed: 9.698 samples/sec, ObjLoss=23.932, BoxCenterLoss=14.347, BoxScaleLoss=4.920, ClassLoss=9.521 [Epoch 241][Batch 499], LR: 1.00E-04, Speed: 12.239 samples/sec, ObjLoss=23.932, BoxCenterLoss=14.347, BoxScaleLoss=4.920, ClassLoss=9.520 [Epoch 241][Batch 599], LR: 1.00E-04, Speed: 11.289 samples/sec, ObjLoss=23.931, BoxCenterLoss=14.347, BoxScaleLoss=4.920, ClassLoss=9.520 [Epoch 241][Batch 699], LR: 1.00E-04, Speed: 98.189 samples/sec, ObjLoss=23.931, BoxCenterLoss=14.347, BoxScaleLoss=4.920, ClassLoss=9.519 [Epoch 241][Batch 799], LR: 1.00E-04, Speed: 8.640 samples/sec, ObjLoss=23.930, BoxCenterLoss=14.347, BoxScaleLoss=4.920, ClassLoss=9.519 [Epoch 241][Batch 899], LR: 1.00E-04, Speed: 9.181 samples/sec, ObjLoss=23.929, BoxCenterLoss=14.347, BoxScaleLoss=4.919, ClassLoss=9.519 [Epoch 241][Batch 999], LR: 1.00E-04, Speed: 8.177 samples/sec, ObjLoss=23.929, BoxCenterLoss=14.347, BoxScaleLoss=4.919, ClassLoss=9.518 [Epoch 241][Batch 1099], LR: 1.00E-04, Speed: 6.729 samples/sec, ObjLoss=23.928, BoxCenterLoss=14.347, BoxScaleLoss=4.919, ClassLoss=9.518 [Epoch 241][Batch 1199], LR: 1.00E-04, Speed: 9.649 samples/sec, ObjLoss=23.927, BoxCenterLoss=14.347, BoxScaleLoss=4.919, ClassLoss=9.517 [Epoch 241][Batch 1299], LR: 1.00E-04, Speed: 114.085 samples/sec, ObjLoss=23.926, BoxCenterLoss=14.347, BoxScaleLoss=4.919, ClassLoss=9.517 [Epoch 241][Batch 1399], LR: 1.00E-04, Speed: 132.946 samples/sec, ObjLoss=23.926, BoxCenterLoss=14.346, BoxScaleLoss=4.919, ClassLoss=9.516 [Epoch 241][Batch 1499], LR: 1.00E-04, Speed: 10.746 samples/sec, ObjLoss=23.925, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.516 [Epoch 241][Batch 1599], LR: 1.00E-04, Speed: 9.469 samples/sec, ObjLoss=23.925, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.515 [Epoch 241][Batch 1699], LR: 1.00E-04, Speed: 8.345 samples/sec, ObjLoss=23.924, BoxCenterLoss=14.347, BoxScaleLoss=4.918, ClassLoss=9.515 [Epoch 241][Batch 1799], LR: 1.00E-04, Speed: 11.925 samples/sec, ObjLoss=23.924, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.514 [Epoch 241] Training cost: 2090.286, ObjLoss=23.924, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.514 [Epoch 241] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.401 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.411 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.581 person=44.6 bicycle=23.8 car=33.8 motorcycle=37.4 airplane=53.2 bus=58.0 train=57.5 truck=30.2 boat=19.6 traffic light=19.9 fire hydrant=57.1 stop sign=55.3 parking meter=37.3 bench=18.7 bird=28.5 cat=58.4 dog=53.2 horse=48.2 sheep=43.0 cow=45.6 elephant=55.1 bear=58.1 zebra=55.4 giraffe=56.2 backpack=10.7 umbrella=31.8 handbag=9.9 tie=23.5 suitcase=26.5 frisbee=49.3 skis=16.6 snowboard=26.2 sports ball=35.3 kite=34.2 baseball bat=21.1 baseball glove=29.5 skateboard=41.9 surfboard=28.1 tennis racket=39.0 bottle=26.5 wine glass=27.5 cup=33.1 fork=24.9 knife=9.5 spoon=10.3 bowl=32.4 banana=18.6 apple=12.8 sandwich=27.1 orange=24.8 broccoli=15.1 carrot=14.6 hot dog=26.3 pizza=43.8 donut=37.4 cake=30.5 chair=22.7 couch=36.9 potted plant=19.6 bed=38.7 dining table=23.4 toilet=49.8 tv=47.1 laptop=49.2 mouse=50.3 remote=20.0 keyboard=38.9 cell phone=26.9 microwave=43.2 oven=26.4 toaster=7.1 sink=29.9 refrigerator=46.2 book=7.5 clock=43.4 vase=30.1 scissors=29.5 teddy bear=38.0 hair drier=0.0 toothbrush=13.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.8 [Epoch 242][Batch 99], LR: 1.00E-04, Speed: 8.741 samples/sec, ObjLoss=23.923, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.514 [Epoch 242][Batch 199], LR: 1.00E-04, Speed: 9.147 samples/sec, ObjLoss=23.922, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.513 [Epoch 242][Batch 299], LR: 1.00E-04, Speed: 9.319 samples/sec, ObjLoss=23.922, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.513 [Epoch 242][Batch 399], LR: 1.00E-04, Speed: 9.796 samples/sec, ObjLoss=23.921, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.512 [Epoch 242][Batch 499], LR: 1.00E-04, Speed: 9.897 samples/sec, ObjLoss=23.921, BoxCenterLoss=14.346, BoxScaleLoss=4.918, ClassLoss=9.512 [Epoch 242][Batch 599], LR: 1.00E-04, Speed: 10.939 samples/sec, ObjLoss=23.921, BoxCenterLoss=14.346, BoxScaleLoss=4.917, ClassLoss=9.512 [Epoch 242][Batch 699], LR: 1.00E-04, Speed: 92.876 samples/sec, ObjLoss=23.920, BoxCenterLoss=14.346, BoxScaleLoss=4.917, ClassLoss=9.511 [Epoch 242][Batch 799], LR: 1.00E-04, Speed: 9.381 samples/sec, ObjLoss=23.919, BoxCenterLoss=14.346, BoxScaleLoss=4.917, ClassLoss=9.511 [Epoch 242][Batch 899], LR: 1.00E-04, Speed: 8.944 samples/sec, ObjLoss=23.919, BoxCenterLoss=14.346, BoxScaleLoss=4.917, ClassLoss=9.510 [Epoch 242][Batch 999], LR: 1.00E-04, Speed: 8.961 samples/sec, ObjLoss=23.919, BoxCenterLoss=14.346, BoxScaleLoss=4.917, ClassLoss=9.510 [Epoch 242][Batch 1099], LR: 1.00E-04, Speed: 9.461 samples/sec, ObjLoss=23.918, BoxCenterLoss=14.346, BoxScaleLoss=4.917, ClassLoss=9.509 [Epoch 242][Batch 1199], LR: 1.00E-04, Speed: 96.042 samples/sec, ObjLoss=23.917, BoxCenterLoss=14.346, BoxScaleLoss=4.917, ClassLoss=9.509 [Epoch 242][Batch 1299], LR: 1.00E-04, Speed: 10.673 samples/sec, ObjLoss=23.916, BoxCenterLoss=14.346, BoxScaleLoss=4.916, ClassLoss=9.508 [Epoch 242][Batch 1399], LR: 1.00E-04, Speed: 11.533 samples/sec, ObjLoss=23.916, BoxCenterLoss=14.346, BoxScaleLoss=4.916, ClassLoss=9.508 [Epoch 242][Batch 1499], LR: 1.00E-04, Speed: 10.239 samples/sec, ObjLoss=23.915, BoxCenterLoss=14.346, BoxScaleLoss=4.916, ClassLoss=9.507 [Epoch 242][Batch 1599], LR: 1.00E-04, Speed: 9.986 samples/sec, ObjLoss=23.914, BoxCenterLoss=14.345, BoxScaleLoss=4.916, ClassLoss=9.507 [Epoch 242][Batch 1699], LR: 1.00E-04, Speed: 12.800 samples/sec, ObjLoss=23.914, BoxCenterLoss=14.345, BoxScaleLoss=4.916, ClassLoss=9.506 [Epoch 242][Batch 1799], LR: 1.00E-04, Speed: 14.400 samples/sec, ObjLoss=23.913, BoxCenterLoss=14.345, BoxScaleLoss=4.916, ClassLoss=9.506 [Epoch 242] Training cost: 2218.257, ObjLoss=23.913, BoxCenterLoss=14.345, BoxScaleLoss=4.916, ClassLoss=9.506 [Epoch 242] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.330 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.413 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.591 person=44.5 bicycle=24.9 car=33.6 motorcycle=38.2 airplane=56.0 bus=59.7 train=60.7 truck=30.7 boat=18.2 traffic light=18.1 fire hydrant=59.0 stop sign=55.1 parking meter=39.5 bench=19.0 bird=29.0 cat=59.7 dog=53.5 horse=48.2 sheep=42.4 cow=46.2 elephant=55.3 bear=60.0 zebra=57.0 giraffe=57.5 backpack=10.5 umbrella=33.5 handbag=10.0 tie=22.6 suitcase=27.6 frisbee=50.1 skis=15.4 snowboard=27.3 sports ball=31.9 kite=31.3 baseball bat=21.0 baseball glove=28.1 skateboard=39.5 surfboard=27.9 tennis racket=36.9 bottle=27.5 wine glass=26.8 cup=33.0 fork=23.9 knife=9.0 spoon=9.2 bowl=33.7 banana=18.4 apple=11.6 sandwich=25.8 orange=26.0 broccoli=15.4 carrot=15.6 hot dog=24.9 pizza=43.9 donut=37.5 cake=29.8 chair=22.5 couch=37.7 potted plant=20.6 bed=41.2 dining table=25.6 toilet=51.5 tv=48.5 laptop=47.8 mouse=49.4 remote=19.8 keyboard=41.9 cell phone=26.6 microwave=43.5 oven=28.4 toaster=7.1 sink=31.0 refrigerator=45.7 book=7.3 clock=40.8 vase=28.3 scissors=31.2 teddy bear=36.8 hair drier=0.0 toothbrush=14.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.0 [Epoch 243][Batch 99], LR: 1.00E-04, Speed: 11.389 samples/sec, ObjLoss=23.912, BoxCenterLoss=14.345, BoxScaleLoss=4.915, ClassLoss=9.505 [Epoch 243][Batch 199], LR: 1.00E-04, Speed: 9.365 samples/sec, ObjLoss=23.911, BoxCenterLoss=14.345, BoxScaleLoss=4.915, ClassLoss=9.505 [Epoch 243][Batch 299], LR: 1.00E-04, Speed: 8.477 samples/sec, ObjLoss=23.911, BoxCenterLoss=14.345, BoxScaleLoss=4.915, ClassLoss=9.504 [Epoch 243][Batch 399], LR: 1.00E-04, Speed: 9.600 samples/sec, ObjLoss=23.910, BoxCenterLoss=14.344, BoxScaleLoss=4.915, ClassLoss=9.504 [Epoch 243][Batch 499], LR: 1.00E-04, Speed: 123.170 samples/sec, ObjLoss=23.910, BoxCenterLoss=14.344, BoxScaleLoss=4.915, ClassLoss=9.503 [Epoch 243][Batch 599], LR: 1.00E-04, Speed: 83.619 samples/sec, ObjLoss=23.909, BoxCenterLoss=14.344, BoxScaleLoss=4.915, ClassLoss=9.503 [Epoch 243][Batch 699], LR: 1.00E-04, Speed: 92.092 samples/sec, ObjLoss=23.909, BoxCenterLoss=14.344, BoxScaleLoss=4.915, ClassLoss=9.502 [Epoch 243][Batch 799], LR: 1.00E-04, Speed: 8.790 samples/sec, ObjLoss=23.908, BoxCenterLoss=14.344, BoxScaleLoss=4.914, ClassLoss=9.502 [Epoch 243][Batch 899], LR: 1.00E-04, Speed: 8.510 samples/sec, ObjLoss=23.907, BoxCenterLoss=14.344, BoxScaleLoss=4.914, ClassLoss=9.502 [Epoch 243][Batch 999], LR: 1.00E-04, Speed: 128.624 samples/sec, ObjLoss=23.907, BoxCenterLoss=14.344, BoxScaleLoss=4.914, ClassLoss=9.501 [Epoch 243][Batch 1099], LR: 1.00E-04, Speed: 9.243 samples/sec, ObjLoss=23.906, BoxCenterLoss=14.344, BoxScaleLoss=4.914, ClassLoss=9.501 [Epoch 243][Batch 1199], LR: 1.00E-04, Speed: 102.616 samples/sec, ObjLoss=23.905, BoxCenterLoss=14.344, BoxScaleLoss=4.914, ClassLoss=9.500 [Epoch 243][Batch 1299], LR: 1.00E-04, Speed: 11.246 samples/sec, ObjLoss=23.905, BoxCenterLoss=14.344, BoxScaleLoss=4.914, ClassLoss=9.500 [Epoch 243][Batch 1399], LR: 1.00E-04, Speed: 8.645 samples/sec, ObjLoss=23.904, BoxCenterLoss=14.344, BoxScaleLoss=4.913, ClassLoss=9.499 [Epoch 243][Batch 1499], LR: 1.00E-04, Speed: 10.762 samples/sec, ObjLoss=23.904, BoxCenterLoss=14.344, BoxScaleLoss=4.913, ClassLoss=9.499 [Epoch 243][Batch 1599], LR: 1.00E-04, Speed: 8.310 samples/sec, ObjLoss=23.903, BoxCenterLoss=14.344, BoxScaleLoss=4.913, ClassLoss=9.498 [Epoch 243][Batch 1699], LR: 1.00E-04, Speed: 9.626 samples/sec, ObjLoss=23.903, BoxCenterLoss=14.343, BoxScaleLoss=4.913, ClassLoss=9.498 [Epoch 243][Batch 1799], LR: 1.00E-04, Speed: 13.223 samples/sec, ObjLoss=23.902, BoxCenterLoss=14.343, BoxScaleLoss=4.913, ClassLoss=9.497 [Epoch 243] Training cost: 2194.008, ObjLoss=23.902, BoxCenterLoss=14.343, BoxScaleLoss=4.913, ClassLoss=9.497 [Epoch 243] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.490 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.406 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.595 person=44.7 bicycle=24.4 car=34.1 motorcycle=38.7 airplane=54.9 bus=60.6 train=61.0 truck=31.5 boat=19.3 traffic light=18.8 fire hydrant=58.1 stop sign=55.5 parking meter=38.5 bench=20.4 bird=29.4 cat=60.1 dog=54.1 horse=48.4 sheep=44.9 cow=45.8 elephant=57.5 bear=60.8 zebra=57.1 giraffe=57.0 backpack=12.1 umbrella=33.5 handbag=9.6 tie=23.5 suitcase=28.4 frisbee=50.1 skis=17.8 snowboard=25.7 sports ball=33.9 kite=33.6 baseball bat=21.3 baseball glove=28.0 skateboard=42.3 surfboard=28.0 tennis racket=38.0 bottle=26.4 wine glass=27.1 cup=33.2 fork=25.5 knife=9.7 spoon=9.8 bowl=33.2 banana=18.2 apple=10.6 sandwich=27.4 orange=24.5 broccoli=13.3 carrot=14.0 hot dog=23.5 pizza=44.5 donut=40.2 cake=31.5 chair=23.1 couch=37.1 potted plant=20.4 bed=40.5 dining table=25.1 toilet=52.0 tv=49.0 laptop=49.4 mouse=48.3 remote=19.2 keyboard=43.0 cell phone=27.1 microwave=42.3 oven=30.4 toaster=7.1 sink=31.2 refrigerator=46.9 book=7.9 clock=42.7 vase=29.5 scissors=29.5 teddy bear=38.0 hair drier=0.0 toothbrush=12.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.3 [Epoch 244][Batch 99], LR: 1.00E-04, Speed: 14.430 samples/sec, ObjLoss=23.901, BoxCenterLoss=14.343, BoxScaleLoss=4.913, ClassLoss=9.496 [Epoch 244][Batch 199], LR: 1.00E-04, Speed: 9.285 samples/sec, ObjLoss=23.900, BoxCenterLoss=14.343, BoxScaleLoss=4.912, ClassLoss=9.496 [Epoch 244][Batch 299], LR: 1.00E-04, Speed: 97.143 samples/sec, ObjLoss=23.900, BoxCenterLoss=14.343, BoxScaleLoss=4.912, ClassLoss=9.495 [Epoch 244][Batch 399], LR: 1.00E-04, Speed: 118.526 samples/sec, ObjLoss=23.899, BoxCenterLoss=14.343, BoxScaleLoss=4.912, ClassLoss=9.495 [Epoch 244][Batch 499], LR: 1.00E-04, Speed: 10.453 samples/sec, ObjLoss=23.899, BoxCenterLoss=14.343, BoxScaleLoss=4.912, ClassLoss=9.495 [Epoch 244][Batch 599], LR: 1.00E-04, Speed: 9.788 samples/sec, ObjLoss=23.898, BoxCenterLoss=14.343, BoxScaleLoss=4.912, ClassLoss=9.494 [Epoch 244][Batch 699], LR: 1.00E-04, Speed: 104.651 samples/sec, ObjLoss=23.897, BoxCenterLoss=14.343, BoxScaleLoss=4.912, ClassLoss=9.494 [Epoch 244][Batch 799], LR: 1.00E-04, Speed: 11.261 samples/sec, ObjLoss=23.897, BoxCenterLoss=14.342, BoxScaleLoss=4.911, ClassLoss=9.493 [Epoch 244][Batch 899], LR: 1.00E-04, Speed: 8.886 samples/sec, ObjLoss=23.896, BoxCenterLoss=14.342, BoxScaleLoss=4.911, ClassLoss=9.492 [Epoch 244][Batch 999], LR: 1.00E-04, Speed: 8.840 samples/sec, ObjLoss=23.895, BoxCenterLoss=14.342, BoxScaleLoss=4.911, ClassLoss=9.492 [Epoch 244][Batch 1099], LR: 1.00E-04, Speed: 12.822 samples/sec, ObjLoss=23.895, BoxCenterLoss=14.342, BoxScaleLoss=4.911, ClassLoss=9.492 [Epoch 244][Batch 1199], LR: 1.00E-04, Speed: 11.629 samples/sec, ObjLoss=23.894, BoxCenterLoss=14.342, BoxScaleLoss=4.911, ClassLoss=9.491 [Epoch 244][Batch 1299], LR: 1.00E-04, Speed: 108.269 samples/sec, ObjLoss=23.894, BoxCenterLoss=14.342, BoxScaleLoss=4.911, ClassLoss=9.491 [Epoch 244][Batch 1399], LR: 1.00E-04, Speed: 106.881 samples/sec, ObjLoss=23.893, BoxCenterLoss=14.342, BoxScaleLoss=4.911, ClassLoss=9.490 [Epoch 244][Batch 1499], LR: 1.00E-04, Speed: 13.751 samples/sec, ObjLoss=23.892, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.490 [Epoch 244][Batch 1599], LR: 1.00E-04, Speed: 9.918 samples/sec, ObjLoss=23.891, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.489 [Epoch 244][Batch 1699], LR: 1.00E-04, Speed: 9.250 samples/sec, ObjLoss=23.891, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.489 [Epoch 244][Batch 1799], LR: 1.00E-04, Speed: 12.189 samples/sec, ObjLoss=23.890, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.488 [Epoch 244] Training cost: 2094.124, ObjLoss=23.890, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.488 [Epoch 244] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.330 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.144 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.276 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.413 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.202 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.586 person=44.9 bicycle=24.5 car=33.9 motorcycle=37.2 airplane=55.2 bus=59.2 train=60.7 truck=31.0 boat=19.4 traffic light=19.9 fire hydrant=58.1 stop sign=53.0 parking meter=38.7 bench=19.2 bird=29.0 cat=58.0 dog=54.4 horse=46.5 sheep=44.1 cow=46.0 elephant=55.6 bear=59.2 zebra=56.5 giraffe=58.2 backpack=10.8 umbrella=32.3 handbag=9.7 tie=23.5 suitcase=28.1 frisbee=46.7 skis=17.3 snowboard=25.6 sports ball=33.7 kite=34.3 baseball bat=20.5 baseball glove=27.9 skateboard=40.9 surfboard=28.4 tennis racket=37.6 bottle=27.3 wine glass=27.6 cup=33.1 fork=24.5 knife=9.5 spoon=9.9 bowl=32.0 banana=19.1 apple=12.9 sandwich=27.7 orange=25.1 broccoli=14.3 carrot=14.2 hot dog=27.4 pizza=43.7 donut=35.7 cake=28.3 chair=22.6 couch=38.0 potted plant=19.8 bed=42.1 dining table=25.4 toilet=51.3 tv=47.8 laptop=48.2 mouse=48.0 remote=18.9 keyboard=41.2 cell phone=25.7 microwave=44.5 oven=28.8 toaster=7.1 sink=31.7 refrigerator=46.1 book=7.0 clock=43.8 vase=29.9 scissors=31.0 teddy bear=39.4 hair drier=0.0 toothbrush=11.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.0 [Epoch 245][Batch 99], LR: 1.00E-04, Speed: 122.857 samples/sec, ObjLoss=23.890, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.488 [Epoch 245][Batch 199], LR: 1.00E-04, Speed: 11.312 samples/sec, ObjLoss=23.890, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.487 [Epoch 245][Batch 299], LR: 1.00E-04, Speed: 10.128 samples/sec, ObjLoss=23.889, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.487 [Epoch 245][Batch 399], LR: 1.00E-04, Speed: 8.511 samples/sec, ObjLoss=23.889, BoxCenterLoss=14.342, BoxScaleLoss=4.910, ClassLoss=9.486 [Epoch 245][Batch 499], LR: 1.00E-04, Speed: 9.800 samples/sec, ObjLoss=23.888, BoxCenterLoss=14.342, BoxScaleLoss=4.909, ClassLoss=9.486 [Epoch 245][Batch 599], LR: 1.00E-04, Speed: 10.446 samples/sec, ObjLoss=23.887, BoxCenterLoss=14.342, BoxScaleLoss=4.909, ClassLoss=9.486 [Epoch 245][Batch 699], LR: 1.00E-04, Speed: 9.098 samples/sec, ObjLoss=23.887, BoxCenterLoss=14.342, BoxScaleLoss=4.909, ClassLoss=9.485 [Epoch 245][Batch 799], LR: 1.00E-04, Speed: 11.606 samples/sec, ObjLoss=23.886, BoxCenterLoss=14.341, BoxScaleLoss=4.909, ClassLoss=9.485 [Epoch 245][Batch 899], LR: 1.00E-04, Speed: 10.674 samples/sec, ObjLoss=23.885, BoxCenterLoss=14.341, BoxScaleLoss=4.909, ClassLoss=9.484 [Epoch 245][Batch 999], LR: 1.00E-04, Speed: 8.102 samples/sec, ObjLoss=23.885, BoxCenterLoss=14.341, BoxScaleLoss=4.909, ClassLoss=9.484 [Epoch 245][Batch 1099], LR: 1.00E-04, Speed: 109.504 samples/sec, ObjLoss=23.885, BoxCenterLoss=14.341, BoxScaleLoss=4.909, ClassLoss=9.483 [Epoch 245][Batch 1199], LR: 1.00E-04, Speed: 9.483 samples/sec, ObjLoss=23.884, BoxCenterLoss=14.342, BoxScaleLoss=4.908, ClassLoss=9.483 [Epoch 245][Batch 1299], LR: 1.00E-04, Speed: 11.945 samples/sec, ObjLoss=23.884, BoxCenterLoss=14.342, BoxScaleLoss=4.908, ClassLoss=9.482 [Epoch 245][Batch 1399], LR: 1.00E-04, Speed: 119.451 samples/sec, ObjLoss=23.883, BoxCenterLoss=14.342, BoxScaleLoss=4.908, ClassLoss=9.482 [Epoch 245][Batch 1499], LR: 1.00E-04, Speed: 89.062 samples/sec, ObjLoss=23.883, BoxCenterLoss=14.341, BoxScaleLoss=4.908, ClassLoss=9.481 [Epoch 245][Batch 1599], LR: 1.00E-04, Speed: 8.087 samples/sec, ObjLoss=23.882, BoxCenterLoss=14.341, BoxScaleLoss=4.908, ClassLoss=9.481 [Epoch 245][Batch 1699], LR: 1.00E-04, Speed: 10.483 samples/sec, ObjLoss=23.881, BoxCenterLoss=14.341, BoxScaleLoss=4.908, ClassLoss=9.480 [Epoch 245][Batch 1799], LR: 1.00E-04, Speed: 14.180 samples/sec, ObjLoss=23.881, BoxCenterLoss=14.341, BoxScaleLoss=4.908, ClassLoss=9.480 [Epoch 245] Training cost: 2179.837, ObjLoss=23.881, BoxCenterLoss=14.341, BoxScaleLoss=4.908, ClassLoss=9.480 [Epoch 245] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.541 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.205 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596 person=44.5 bicycle=24.2 car=34.1 motorcycle=38.8 airplane=55.1 bus=60.3 train=60.6 truck=30.5 boat=18.9 traffic light=19.4 fire hydrant=59.8 stop sign=55.7 parking meter=39.1 bench=19.6 bird=27.7 cat=58.2 dog=54.3 horse=47.0 sheep=43.8 cow=46.3 elephant=57.6 bear=59.6 zebra=56.7 giraffe=58.2 backpack=10.8 umbrella=33.0 handbag=9.1 tie=22.1 suitcase=27.8 frisbee=49.7 skis=17.1 snowboard=25.7 sports ball=35.7 kite=32.6 baseball bat=19.7 baseball glove=27.6 skateboard=41.3 surfboard=28.9 tennis racket=38.5 bottle=27.2 wine glass=26.6 cup=33.1 fork=24.0 knife=8.9 spoon=10.5 bowl=34.0 banana=18.7 apple=13.3 sandwich=27.8 orange=23.9 broccoli=16.0 carrot=15.7 hot dog=26.1 pizza=43.5 donut=38.0 cake=31.4 chair=23.3 couch=36.8 potted plant=19.6 bed=39.6 dining table=25.6 toilet=52.4 tv=48.9 laptop=49.5 mouse=47.8 remote=18.8 keyboard=42.4 cell phone=25.3 microwave=41.6 oven=31.2 toaster=5.9 sink=30.2 refrigerator=44.5 book=6.7 clock=43.6 vase=28.5 scissors=32.4 teddy bear=39.2 hair drier=0.0 toothbrush=10.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.2 [Epoch 246][Batch 99], LR: 1.00E-04, Speed: 9.314 samples/sec, ObjLoss=23.880, BoxCenterLoss=14.341, BoxScaleLoss=4.907, ClassLoss=9.479 [Epoch 246][Batch 199], LR: 1.00E-04, Speed: 9.403 samples/sec, ObjLoss=23.880, BoxCenterLoss=14.341, BoxScaleLoss=4.907, ClassLoss=9.479 [Epoch 246][Batch 299], LR: 1.00E-04, Speed: 8.837 samples/sec, ObjLoss=23.879, BoxCenterLoss=14.341, BoxScaleLoss=4.907, ClassLoss=9.479 [Epoch 246][Batch 399], LR: 1.00E-04, Speed: 8.838 samples/sec, ObjLoss=23.879, BoxCenterLoss=14.341, BoxScaleLoss=4.907, ClassLoss=9.478 [Epoch 246][Batch 499], LR: 1.00E-04, Speed: 8.669 samples/sec, ObjLoss=23.878, BoxCenterLoss=14.341, BoxScaleLoss=4.907, ClassLoss=9.478 [Epoch 246][Batch 599], LR: 1.00E-04, Speed: 9.028 samples/sec, ObjLoss=23.877, BoxCenterLoss=14.341, BoxScaleLoss=4.907, ClassLoss=9.477 [Epoch 246][Batch 699], LR: 1.00E-04, Speed: 9.075 samples/sec, ObjLoss=23.877, BoxCenterLoss=14.341, BoxScaleLoss=4.907, ClassLoss=9.477 [Epoch 246][Batch 799], LR: 1.00E-04, Speed: 8.989 samples/sec, ObjLoss=23.876, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.476 [Epoch 246][Batch 899], LR: 1.00E-04, Speed: 8.980 samples/sec, ObjLoss=23.875, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.475 [Epoch 246][Batch 999], LR: 1.00E-04, Speed: 9.637 samples/sec, ObjLoss=23.875, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.475 [Epoch 246][Batch 1099], LR: 1.00E-04, Speed: 9.594 samples/sec, ObjLoss=23.874, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.475 [Epoch 246][Batch 1199], LR: 1.00E-04, Speed: 12.664 samples/sec, ObjLoss=23.874, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.474 [Epoch 246][Batch 1299], LR: 1.00E-04, Speed: 116.525 samples/sec, ObjLoss=23.873, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.474 [Epoch 246][Batch 1399], LR: 1.00E-04, Speed: 120.154 samples/sec, ObjLoss=23.873, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.473 [Epoch 246][Batch 1499], LR: 1.00E-04, Speed: 12.068 samples/sec, ObjLoss=23.872, BoxCenterLoss=14.340, BoxScaleLoss=4.906, ClassLoss=9.473 [Epoch 246][Batch 1599], LR: 1.00E-04, Speed: 13.660 samples/sec, ObjLoss=23.871, BoxCenterLoss=14.340, BoxScaleLoss=4.905, ClassLoss=9.472 [Epoch 246][Batch 1699], LR: 1.00E-04, Speed: 9.329 samples/sec, ObjLoss=23.870, BoxCenterLoss=14.340, BoxScaleLoss=4.905, ClassLoss=9.472 [Epoch 246][Batch 1799], LR: 1.00E-04, Speed: 13.865 samples/sec, ObjLoss=23.869, BoxCenterLoss=14.339, BoxScaleLoss=4.905, ClassLoss=9.471 [Epoch 246] Training cost: 2129.877, ObjLoss=23.869, BoxCenterLoss=14.339, BoxScaleLoss=4.905, ClassLoss=9.471 [Epoch 246] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.414 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.204 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.435 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.593 person=44.6 bicycle=25.0 car=33.9 motorcycle=38.3 airplane=54.8 bus=59.3 train=59.7 truck=29.6 boat=20.0 traffic light=17.5 fire hydrant=58.9 stop sign=54.5 parking meter=37.0 bench=19.6 bird=28.2 cat=61.5 dog=54.6 horse=47.8 sheep=43.1 cow=45.7 elephant=56.8 bear=60.2 zebra=56.9 giraffe=57.6 backpack=10.3 umbrella=32.2 handbag=9.0 tie=22.0 suitcase=27.5 frisbee=47.1 skis=17.5 snowboard=24.6 sports ball=33.2 kite=32.1 baseball bat=20.9 baseball glove=26.1 skateboard=42.7 surfboard=28.7 tennis racket=38.6 bottle=26.8 wine glass=26.9 cup=33.1 fork=23.3 knife=9.0 spoon=10.1 bowl=34.1 banana=17.9 apple=12.8 sandwich=28.3 orange=24.0 broccoli=15.7 carrot=14.8 hot dog=25.8 pizza=45.8 donut=37.5 cake=30.6 chair=23.0 couch=37.3 potted plant=21.1 bed=42.4 dining table=24.9 toilet=52.0 tv=48.2 laptop=49.0 mouse=48.8 remote=20.1 keyboard=43.0 cell phone=25.3 microwave=41.5 oven=29.6 toaster=7.1 sink=30.9 refrigerator=48.1 book=6.4 clock=41.7 vase=29.0 scissors=31.9 teddy bear=39.3 hair drier=0.0 toothbrush=12.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.1 [Epoch 247][Batch 99], LR: 1.00E-04, Speed: 7.392 samples/sec, ObjLoss=23.869, BoxCenterLoss=14.339, BoxScaleLoss=4.905, ClassLoss=9.471 [Epoch 247][Batch 199], LR: 1.00E-04, Speed: 9.524 samples/sec, ObjLoss=23.868, BoxCenterLoss=14.339, BoxScaleLoss=4.905, ClassLoss=9.470 [Epoch 247][Batch 299], LR: 1.00E-04, Speed: 104.988 samples/sec, ObjLoss=23.868, BoxCenterLoss=14.339, BoxScaleLoss=4.905, ClassLoss=9.470 [Epoch 247][Batch 399], LR: 1.00E-04, Speed: 108.710 samples/sec, ObjLoss=23.867, BoxCenterLoss=14.339, BoxScaleLoss=4.904, ClassLoss=9.469 [Epoch 247][Batch 499], LR: 1.00E-04, Speed: 11.571 samples/sec, ObjLoss=23.867, BoxCenterLoss=14.339, BoxScaleLoss=4.904, ClassLoss=9.469 [Epoch 247][Batch 599], LR: 1.00E-04, Speed: 9.540 samples/sec, ObjLoss=23.866, BoxCenterLoss=14.339, BoxScaleLoss=4.904, ClassLoss=9.469 [Epoch 247][Batch 699], LR: 1.00E-04, Speed: 11.830 samples/sec, ObjLoss=23.865, BoxCenterLoss=14.339, BoxScaleLoss=4.904, ClassLoss=9.468 [Epoch 247][Batch 799], LR: 1.00E-04, Speed: 10.462 samples/sec, ObjLoss=23.865, BoxCenterLoss=14.339, BoxScaleLoss=4.904, ClassLoss=9.467 [Epoch 247][Batch 899], LR: 1.00E-04, Speed: 9.551 samples/sec, ObjLoss=23.864, BoxCenterLoss=14.339, BoxScaleLoss=4.904, ClassLoss=9.467 [Epoch 247][Batch 999], LR: 1.00E-04, Speed: 13.592 samples/sec, ObjLoss=23.863, BoxCenterLoss=14.339, BoxScaleLoss=4.903, ClassLoss=9.467 [Epoch 247][Batch 1099], LR: 1.00E-04, Speed: 14.075 samples/sec, ObjLoss=23.863, BoxCenterLoss=14.339, BoxScaleLoss=4.903, ClassLoss=9.466 [Epoch 247][Batch 1199], LR: 1.00E-04, Speed: 10.944 samples/sec, ObjLoss=23.863, BoxCenterLoss=14.339, BoxScaleLoss=4.903, ClassLoss=9.466 [Epoch 247][Batch 1299], LR: 1.00E-04, Speed: 12.383 samples/sec, ObjLoss=23.862, BoxCenterLoss=14.339, BoxScaleLoss=4.903, ClassLoss=9.465 [Epoch 247][Batch 1399], LR: 1.00E-04, Speed: 9.591 samples/sec, ObjLoss=23.861, BoxCenterLoss=14.338, BoxScaleLoss=4.903, ClassLoss=9.465 [Epoch 247][Batch 1499], LR: 1.00E-04, Speed: 8.940 samples/sec, ObjLoss=23.861, BoxCenterLoss=14.338, BoxScaleLoss=4.903, ClassLoss=9.464 [Epoch 247][Batch 1599], LR: 1.00E-04, Speed: 84.963 samples/sec, ObjLoss=23.860, BoxCenterLoss=14.338, BoxScaleLoss=4.903, ClassLoss=9.464 [Epoch 247][Batch 1699], LR: 1.00E-04, Speed: 10.203 samples/sec, ObjLoss=23.860, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.464 [Epoch 247][Batch 1799], LR: 1.00E-04, Speed: 129.582 samples/sec, ObjLoss=23.859, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.463 [Epoch 247] Training cost: 2130.884, ObjLoss=23.859, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.463 [Epoch 247] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.541 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.497 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.278 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.406 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.440 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.600 person=44.4 bicycle=25.4 car=34.4 motorcycle=38.4 airplane=53.2 bus=59.5 train=60.3 truck=30.5 boat=19.5 traffic light=18.2 fire hydrant=59.0 stop sign=54.6 parking meter=36.0 bench=18.7 bird=27.7 cat=59.8 dog=53.4 horse=47.4 sheep=44.3 cow=46.7 elephant=57.0 bear=61.1 zebra=57.8 giraffe=57.4 backpack=10.7 umbrella=32.2 handbag=9.3 tie=24.1 suitcase=29.4 frisbee=50.2 skis=16.9 snowboard=24.4 sports ball=32.9 kite=30.8 baseball bat=19.3 baseball glove=27.9 skateboard=40.4 surfboard=29.0 tennis racket=36.9 bottle=27.1 wine glass=26.2 cup=33.8 fork=25.2 knife=9.9 spoon=10.9 bowl=32.8 banana=19.0 apple=13.5 sandwich=27.4 orange=23.9 broccoli=17.1 carrot=14.7 hot dog=27.5 pizza=45.7 donut=38.8 cake=30.6 chair=22.6 couch=38.0 potted plant=20.5 bed=41.8 dining table=24.6 toilet=51.7 tv=49.3 laptop=48.3 mouse=49.7 remote=20.2 keyboard=42.9 cell phone=26.1 microwave=44.1 oven=28.4 toaster=8.5 sink=31.0 refrigerator=48.3 book=7.2 clock=43.2 vase=29.1 scissors=30.8 teddy bear=40.3 hair drier=0.0 toothbrush=16.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =33.3 [Epoch 248][Batch 99], LR: 1.00E-04, Speed: 10.445 samples/sec, ObjLoss=23.859, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.463 [Epoch 248][Batch 199], LR: 1.00E-04, Speed: 87.366 samples/sec, ObjLoss=23.858, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.462 [Epoch 248][Batch 299], LR: 1.00E-04, Speed: 8.688 samples/sec, ObjLoss=23.858, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.462 [Epoch 248][Batch 399], LR: 1.00E-04, Speed: 121.140 samples/sec, ObjLoss=23.857, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.461 [Epoch 248][Batch 499], LR: 1.00E-04, Speed: 20.536 samples/sec, ObjLoss=23.856, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.461 [Epoch 248][Batch 599], LR: 1.00E-04, Speed: 8.934 samples/sec, ObjLoss=23.856, BoxCenterLoss=14.338, BoxScaleLoss=4.902, ClassLoss=9.460 [Epoch 248][Batch 699], LR: 1.00E-04, Speed: 8.375 samples/sec, ObjLoss=23.855, BoxCenterLoss=14.338, BoxScaleLoss=4.901, ClassLoss=9.460 [Epoch 248][Batch 799], LR: 1.00E-04, Speed: 11.632 samples/sec, ObjLoss=23.855, BoxCenterLoss=14.338, BoxScaleLoss=4.901, ClassLoss=9.459 [Epoch 248][Batch 899], LR: 1.00E-04, Speed: 8.898 samples/sec, ObjLoss=23.854, BoxCenterLoss=14.338, BoxScaleLoss=4.901, ClassLoss=9.459 [Epoch 248][Batch 999], LR: 1.00E-04, Speed: 10.410 samples/sec, ObjLoss=23.854, BoxCenterLoss=14.338, BoxScaleLoss=4.901, ClassLoss=9.458 [Epoch 248][Batch 1099], LR: 1.00E-04, Speed: 7.802 samples/sec, ObjLoss=23.854, BoxCenterLoss=14.338, BoxScaleLoss=4.901, ClassLoss=9.458 [Epoch 248][Batch 1199], LR: 1.00E-04, Speed: 11.771 samples/sec, ObjLoss=23.853, BoxCenterLoss=14.338, BoxScaleLoss=4.901, ClassLoss=9.457 [Epoch 248][Batch 1299], LR: 1.00E-04, Speed: 97.082 samples/sec, ObjLoss=23.852, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.457 [Epoch 248][Batch 1399], LR: 1.00E-04, Speed: 11.061 samples/sec, ObjLoss=23.852, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.456 [Epoch 248][Batch 1499], LR: 1.00E-04, Speed: 10.195 samples/sec, ObjLoss=23.852, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.456 [Epoch 248][Batch 1599], LR: 1.00E-04, Speed: 10.161 samples/sec, ObjLoss=23.851, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.456 [Epoch 248][Batch 1699], LR: 1.00E-04, Speed: 9.874 samples/sec, ObjLoss=23.851, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.455 [Epoch 248][Batch 1799], LR: 1.00E-04, Speed: 14.056 samples/sec, ObjLoss=23.851, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.455 [Epoch 248] Training cost: 2179.619, ObjLoss=23.851, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.455 [Epoch 248] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.542 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.401 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.412 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.579 person=44.9 bicycle=24.3 car=33.7 motorcycle=38.2 airplane=53.7 bus=57.6 train=59.9 truck=31.4 boat=19.7 traffic light=19.6 fire hydrant=57.7 stop sign=53.1 parking meter=36.5 bench=19.0 bird=28.4 cat=58.1 dog=54.1 horse=47.8 sheep=43.3 cow=46.5 elephant=55.1 bear=58.2 zebra=55.5 giraffe=56.3 backpack=10.9 umbrella=31.9 handbag=10.1 tie=23.3 suitcase=28.1 frisbee=49.3 skis=16.8 snowboard=27.1 sports ball=30.7 kite=33.0 baseball bat=20.9 baseball glove=28.5 skateboard=40.2 surfboard=28.1 tennis racket=36.1 bottle=27.6 wine glass=26.6 cup=33.5 fork=23.4 knife=7.7 spoon=9.9 bowl=33.4 banana=19.1 apple=15.6 sandwich=27.1 orange=25.1 broccoli=16.0 carrot=15.5 hot dog=26.4 pizza=42.9 donut=38.3 cake=31.6 chair=22.7 couch=38.7 potted plant=19.3 bed=41.8 dining table=26.1 toilet=48.9 tv=49.1 laptop=48.1 mouse=47.8 remote=20.6 keyboard=39.8 cell phone=26.5 microwave=43.1 oven=30.4 toaster=7.1 sink=29.6 refrigerator=43.6 book=6.7 clock=42.8 vase=30.0 scissors=26.9 teddy bear=35.6 hair drier=0.0 toothbrush=13.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.8 [Epoch 249][Batch 99], LR: 1.00E-04, Speed: 9.053 samples/sec, ObjLoss=23.850, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.454 [Epoch 249][Batch 199], LR: 1.00E-04, Speed: 91.344 samples/sec, ObjLoss=23.849, BoxCenterLoss=14.338, BoxScaleLoss=4.900, ClassLoss=9.454 [Epoch 249][Batch 299], LR: 1.00E-04, Speed: 11.121 samples/sec, ObjLoss=23.849, BoxCenterLoss=14.338, BoxScaleLoss=4.899, ClassLoss=9.453 [Epoch 249][Batch 399], LR: 1.00E-04, Speed: 11.978 samples/sec, ObjLoss=23.848, BoxCenterLoss=14.338, BoxScaleLoss=4.899, ClassLoss=9.453 [Epoch 249][Batch 499], LR: 1.00E-04, Speed: 8.989 samples/sec, ObjLoss=23.848, BoxCenterLoss=14.338, BoxScaleLoss=4.899, ClassLoss=9.452 [Epoch 249][Batch 599], LR: 1.00E-04, Speed: 11.408 samples/sec, ObjLoss=23.847, BoxCenterLoss=14.338, BoxScaleLoss=4.899, ClassLoss=9.452 [Epoch 249][Batch 699], LR: 1.00E-04, Speed: 8.574 samples/sec, ObjLoss=23.847, BoxCenterLoss=14.338, BoxScaleLoss=4.899, ClassLoss=9.451 [Epoch 249][Batch 799], LR: 1.00E-04, Speed: 11.094 samples/sec, ObjLoss=23.846, BoxCenterLoss=14.338, BoxScaleLoss=4.899, ClassLoss=9.451 [Epoch 249][Batch 899], LR: 1.00E-04, Speed: 10.317 samples/sec, ObjLoss=23.845, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.450 [Epoch 249][Batch 999], LR: 1.00E-04, Speed: 11.906 samples/sec, ObjLoss=23.845, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.450 [Epoch 249][Batch 1099], LR: 1.00E-04, Speed: 11.947 samples/sec, ObjLoss=23.844, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.450 [Epoch 249][Batch 1199], LR: 1.00E-04, Speed: 10.521 samples/sec, ObjLoss=23.844, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.449 [Epoch 249][Batch 1299], LR: 1.00E-04, Speed: 9.982 samples/sec, ObjLoss=23.843, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.449 [Epoch 249][Batch 1399], LR: 1.00E-04, Speed: 8.775 samples/sec, ObjLoss=23.843, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.448 [Epoch 249][Batch 1499], LR: 1.00E-04, Speed: 11.192 samples/sec, ObjLoss=23.843, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.448 [Epoch 249][Batch 1599], LR: 1.00E-04, Speed: 8.868 samples/sec, ObjLoss=23.842, BoxCenterLoss=14.338, BoxScaleLoss=4.898, ClassLoss=9.447 [Epoch 249][Batch 1699], LR: 1.00E-04, Speed: 9.691 samples/sec, ObjLoss=23.841, BoxCenterLoss=14.337, BoxScaleLoss=4.897, ClassLoss=9.447 [Epoch 249][Batch 1799], LR: 1.00E-04, Speed: 12.646 samples/sec, ObjLoss=23.841, BoxCenterLoss=14.337, BoxScaleLoss=4.897, ClassLoss=9.446 [Epoch 249] Training cost: 2202.403, ObjLoss=23.841, BoxCenterLoss=14.337, BoxScaleLoss=4.897, ClassLoss=9.446 [Epoch 249] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.538 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.351 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.402 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.412 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.439 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.586 person=45.0 bicycle=24.6 car=34.0 motorcycle=38.0 airplane=54.7 bus=59.4 train=60.1 truck=30.4 boat=21.0 traffic light=19.9 fire hydrant=57.9 stop sign=56.3 parking meter=35.4 bench=19.8 bird=29.0 cat=59.7 dog=54.0 horse=49.1 sheep=43.9 cow=46.3 elephant=55.7 bear=60.1 zebra=55.6 giraffe=58.9 backpack=10.3 umbrella=31.9 handbag=9.9 tie=24.2 suitcase=28.2 frisbee=48.1 skis=16.1 snowboard=27.0 sports ball=34.0 kite=33.6 baseball bat=21.4 baseball glove=27.6 skateboard=38.9 surfboard=28.0 tennis racket=38.4 bottle=27.8 wine glass=26.4 cup=32.5 fork=23.6 knife=9.1 spoon=10.1 bowl=33.1 banana=16.0 apple=13.2 sandwich=23.9 orange=24.2 broccoli=14.8 carrot=15.0 hot dog=26.0 pizza=44.2 donut=36.4 cake=28.8 chair=22.7 couch=37.1 potted plant=20.6 bed=39.4 dining table=24.9 toilet=51.0 tv=47.0 laptop=47.8 mouse=47.4 remote=19.0 keyboard=41.7 cell phone=26.0 microwave=43.3 oven=29.4 toaster=5.9 sink=29.2 refrigerator=44.4 book=7.0 clock=42.9 vase=30.6 scissors=28.2 teddy bear=37.2 hair drier=0.0 toothbrush=13.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =32.8 [Epoch 250][Batch 99], LR: 1.00E-05, Speed: 11.168 samples/sec, ObjLoss=23.840, BoxCenterLoss=14.337, BoxScaleLoss=4.897, ClassLoss=9.446 [Epoch 250][Batch 199], LR: 1.00E-05, Speed: 10.049 samples/sec, ObjLoss=23.839, BoxCenterLoss=14.337, BoxScaleLoss=4.897, ClassLoss=9.445 [Epoch 250][Batch 299], LR: 1.00E-05, Speed: 6.151 samples/sec, ObjLoss=23.839, BoxCenterLoss=14.337, BoxScaleLoss=4.897, ClassLoss=9.445 [Epoch 250][Batch 399], LR: 1.00E-05, Speed: 82.227 samples/sec, ObjLoss=23.838, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.444 [Epoch 250][Batch 499], LR: 1.00E-05, Speed: 9.409 samples/sec, ObjLoss=23.838, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.444 [Epoch 250][Batch 599], LR: 1.00E-05, Speed: 10.049 samples/sec, ObjLoss=23.838, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.443 [Epoch 250][Batch 699], LR: 1.00E-05, Speed: 9.795 samples/sec, ObjLoss=23.837, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.443 [Epoch 250][Batch 799], LR: 1.00E-05, Speed: 10.921 samples/sec, ObjLoss=23.837, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.443 [Epoch 250][Batch 899], LR: 1.00E-05, Speed: 10.215 samples/sec, ObjLoss=23.837, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.442 [Epoch 250][Batch 999], LR: 1.00E-05, Speed: 128.608 samples/sec, ObjLoss=23.836, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.442 [Epoch 250][Batch 1099], LR: 1.00E-05, Speed: 11.479 samples/sec, ObjLoss=23.836, BoxCenterLoss=14.337, BoxScaleLoss=4.896, ClassLoss=9.441 [Epoch 250][Batch 1199], LR: 1.00E-05, Speed: 11.571 samples/sec, ObjLoss=23.835, BoxCenterLoss=14.337, BoxScaleLoss=4.895, ClassLoss=9.441 [Epoch 250][Batch 1299], LR: 1.00E-05, Speed: 10.210 samples/sec, ObjLoss=23.834, BoxCenterLoss=14.337, BoxScaleLoss=4.895, ClassLoss=9.440 [Epoch 250][Batch 1399], LR: 1.00E-05, Speed: 7.911 samples/sec, ObjLoss=23.834, BoxCenterLoss=14.337, BoxScaleLoss=4.895, ClassLoss=9.440 [Epoch 250][Batch 1499], LR: 1.00E-05, Speed: 11.418 samples/sec, ObjLoss=23.833, BoxCenterLoss=14.337, BoxScaleLoss=4.895, ClassLoss=9.440 [Epoch 250][Batch 1599], LR: 1.00E-05, Speed: 125.073 samples/sec, ObjLoss=23.833, BoxCenterLoss=14.336, BoxScaleLoss=4.895, ClassLoss=9.439 [Epoch 250][Batch 1699], LR: 1.00E-05, Speed: 94.297 samples/sec, ObjLoss=23.832, BoxCenterLoss=14.336, BoxScaleLoss=4.895, ClassLoss=9.439 [Epoch 250][Batch 1799], LR: 1.00E-05, Speed: 142.122 samples/sec, ObjLoss=23.832, BoxCenterLoss=14.336, BoxScaleLoss=4.895, ClassLoss=9.439 [Epoch 250] Training cost: 2183.211, ObjLoss=23.832, BoxCenterLoss=14.336, BoxScaleLoss=4.895, ClassLoss=9.438 [Epoch 250] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.368 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.367 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.503 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.606 person=45.6 bicycle=25.6 car=34.6 motorcycle=38.8 airplane=55.9 bus=61.3 train=61.5 truck=31.4 boat=21.1 traffic light=20.1 fire hydrant=60.4 stop sign=58.1 parking meter=38.1 bench=19.9 bird=30.1 cat=60.3 dog=54.4 horse=49.7 sheep=44.6 cow=48.1 elephant=58.2 bear=60.2 zebra=57.8 giraffe=60.0 backpack=10.5 umbrella=32.8 handbag=10.1 tie=25.0 suitcase=29.8 frisbee=50.7 skis=17.5 snowboard=28.5 sports ball=36.7 kite=34.2 baseball bat=21.3 baseball glove=29.2 skateboard=42.9 surfboard=29.5 tennis racket=39.4 bottle=28.1 wine glass=27.3 cup=34.3 fork=25.2 knife=10.1 spoon=10.3 bowl=34.7 banana=19.1 apple=13.7 sandwich=27.6 orange=25.7 broccoli=16.7 carrot=16.7 hot dog=28.0 pizza=46.2 donut=39.7 cake=31.6 chair=23.3 couch=38.5 potted plant=21.0 bed=41.3 dining table=25.6 toilet=53.8 tv=50.0 laptop=49.2 mouse=50.4 remote=21.5 keyboard=44.1 cell phone=27.5 microwave=43.7 oven=30.0 toaster=5.9 sink=30.2 refrigerator=47.9 book=7.3 clock=43.8 vase=31.5 scissors=31.0 teddy bear=38.6 hair drier=0.0 toothbrush=14.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.2 [Epoch 251][Batch 99], LR: 1.00E-05, Speed: 9.646 samples/sec, ObjLoss=23.832, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.438 [Epoch 251][Batch 199], LR: 1.00E-05, Speed: 12.426 samples/sec, ObjLoss=23.831, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.438 [Epoch 251][Batch 299], LR: 1.00E-05, Speed: 10.198 samples/sec, ObjLoss=23.830, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.437 [Epoch 251][Batch 399], LR: 1.00E-05, Speed: 106.147 samples/sec, ObjLoss=23.830, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.437 [Epoch 251][Batch 499], LR: 1.00E-05, Speed: 9.307 samples/sec, ObjLoss=23.830, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.436 [Epoch 251][Batch 599], LR: 1.00E-05, Speed: 7.913 samples/sec, ObjLoss=23.829, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.436 [Epoch 251][Batch 699], LR: 1.00E-05, Speed: 31.023 samples/sec, ObjLoss=23.829, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.436 [Epoch 251][Batch 799], LR: 1.00E-05, Speed: 10.636 samples/sec, ObjLoss=23.828, BoxCenterLoss=14.336, BoxScaleLoss=4.894, ClassLoss=9.435 [Epoch 251][Batch 899], LR: 1.00E-05, Speed: 11.263 samples/sec, ObjLoss=23.828, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.435 [Epoch 251][Batch 999], LR: 1.00E-05, Speed: 10.287 samples/sec, ObjLoss=23.828, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.435 [Epoch 251][Batch 1099], LR: 1.00E-05, Speed: 123.405 samples/sec, ObjLoss=23.827, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.434 [Epoch 251][Batch 1199], LR: 1.00E-05, Speed: 14.688 samples/sec, ObjLoss=23.827, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.434 [Epoch 251][Batch 1299], LR: 1.00E-05, Speed: 10.694 samples/sec, ObjLoss=23.826, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.433 [Epoch 251][Batch 1399], LR: 1.00E-05, Speed: 13.617 samples/sec, ObjLoss=23.825, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.433 [Epoch 251][Batch 1499], LR: 1.00E-05, Speed: 10.938 samples/sec, ObjLoss=23.825, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.433 [Epoch 251][Batch 1599], LR: 1.00E-05, Speed: 11.103 samples/sec, ObjLoss=23.825, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.432 [Epoch 251][Batch 1699], LR: 1.00E-05, Speed: 112.401 samples/sec, ObjLoss=23.824, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.432 [Epoch 251][Batch 1799], LR: 1.00E-05, Speed: 12.263 samples/sec, ObjLoss=23.823, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.431 [Epoch 251] Training cost: 2113.026, ObjLoss=23.823, BoxCenterLoss=14.336, BoxScaleLoss=4.893, ClassLoss=9.431 [Epoch 251] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.369 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.502 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605 person=45.5 bicycle=25.8 car=34.2 motorcycle=38.5 airplane=55.7 bus=62.1 train=61.5 truck=31.3 boat=20.9 traffic light=20.1 fire hydrant=59.5 stop sign=56.0 parking meter=38.1 bench=20.0 bird=30.3 cat=60.2 dog=55.0 horse=49.8 sheep=45.3 cow=47.8 elephant=58.4 bear=63.7 zebra=57.9 giraffe=58.4 backpack=10.7 umbrella=33.4 handbag=10.2 tie=24.8 suitcase=29.3 frisbee=51.2 skis=17.7 snowboard=27.6 sports ball=36.8 kite=33.8 baseball bat=21.9 baseball glove=28.9 skateboard=42.6 surfboard=29.6 tennis racket=39.7 bottle=28.0 wine glass=27.8 cup=34.3 fork=25.0 knife=10.2 spoon=10.7 bowl=35.3 banana=19.1 apple=13.8 sandwich=27.9 orange=25.7 broccoli=16.5 carrot=16.7 hot dog=27.4 pizza=45.6 donut=39.3 cake=31.2 chair=23.6 couch=38.5 potted plant=20.9 bed=40.5 dining table=25.1 toilet=52.8 tv=49.9 laptop=50.1 mouse=51.9 remote=21.2 keyboard=44.0 cell phone=27.4 microwave=43.7 oven=30.5 toaster=5.9 sink=30.7 refrigerator=48.6 book=7.6 clock=43.8 vase=31.5 scissors=31.2 teddy bear=38.2 hair drier=0.0 toothbrush=14.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.3 [Epoch 252][Batch 99], LR: 1.00E-05, Speed: 8.210 samples/sec, ObjLoss=23.823, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.431 [Epoch 252][Batch 199], LR: 1.00E-05, Speed: 12.076 samples/sec, ObjLoss=23.822, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.430 [Epoch 252][Batch 299], LR: 1.00E-05, Speed: 74.733 samples/sec, ObjLoss=23.821, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.430 [Epoch 252][Batch 399], LR: 1.00E-05, Speed: 125.954 samples/sec, ObjLoss=23.821, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.430 [Epoch 252][Batch 499], LR: 1.00E-05, Speed: 109.245 samples/sec, ObjLoss=23.821, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.429 [Epoch 252][Batch 599], LR: 1.00E-05, Speed: 9.755 samples/sec, ObjLoss=23.820, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.429 [Epoch 252][Batch 699], LR: 1.00E-05, Speed: 11.640 samples/sec, ObjLoss=23.820, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.429 [Epoch 252][Batch 799], LR: 1.00E-05, Speed: 8.474 samples/sec, ObjLoss=23.820, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.428 [Epoch 252][Batch 899], LR: 1.00E-05, Speed: 8.903 samples/sec, ObjLoss=23.819, BoxCenterLoss=14.336, BoxScaleLoss=4.892, ClassLoss=9.428 [Epoch 252][Batch 999], LR: 1.00E-05, Speed: 13.522 samples/sec, ObjLoss=23.818, BoxCenterLoss=14.336, BoxScaleLoss=4.891, ClassLoss=9.427 [Epoch 252][Batch 1099], LR: 1.00E-05, Speed: 15.216 samples/sec, ObjLoss=23.818, BoxCenterLoss=14.336, BoxScaleLoss=4.891, ClassLoss=9.427 [Epoch 252][Batch 1199], LR: 1.00E-05, Speed: 10.355 samples/sec, ObjLoss=23.818, BoxCenterLoss=14.336, BoxScaleLoss=4.891, ClassLoss=9.427 [Epoch 252][Batch 1299], LR: 1.00E-05, Speed: 10.093 samples/sec, ObjLoss=23.817, BoxCenterLoss=14.336, BoxScaleLoss=4.891, ClassLoss=9.426 [Epoch 252][Batch 1399], LR: 1.00E-05, Speed: 91.233 samples/sec, ObjLoss=23.817, BoxCenterLoss=14.336, BoxScaleLoss=4.891, ClassLoss=9.426 [Epoch 252][Batch 1499], LR: 1.00E-05, Speed: 11.180 samples/sec, ObjLoss=23.816, BoxCenterLoss=14.336, BoxScaleLoss=4.891, ClassLoss=9.426 [Epoch 252][Batch 1599], LR: 1.00E-05, Speed: 9.670 samples/sec, ObjLoss=23.816, BoxCenterLoss=14.336, BoxScaleLoss=4.891, ClassLoss=9.425 [Epoch 252][Batch 1699], LR: 1.00E-05, Speed: 10.642 samples/sec, ObjLoss=23.815, BoxCenterLoss=14.335, BoxScaleLoss=4.891, ClassLoss=9.425 [Epoch 252][Batch 1799], LR: 1.00E-05, Speed: 11.461 samples/sec, ObjLoss=23.814, BoxCenterLoss=14.335, BoxScaleLoss=4.890, ClassLoss=9.424 [Epoch 252] Training cost: 2089.181, ObjLoss=23.814, BoxCenterLoss=14.335, BoxScaleLoss=4.890, ClassLoss=9.424 [Epoch 252] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.368 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.364 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.503 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.452 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605 person=45.5 bicycle=25.3 car=34.4 motorcycle=38.5 airplane=55.8 bus=61.1 train=61.7 truck=31.6 boat=21.3 traffic light=20.1 fire hydrant=59.6 stop sign=57.8 parking meter=37.4 bench=19.6 bird=30.0 cat=60.6 dog=54.9 horse=49.1 sheep=44.7 cow=47.0 elephant=58.1 bear=63.0 zebra=58.3 giraffe=58.7 backpack=11.2 umbrella=33.1 handbag=10.3 tie=24.5 suitcase=29.1 frisbee=51.6 skis=17.8 snowboard=28.4 sports ball=36.8 kite=33.2 baseball bat=21.7 baseball glove=29.7 skateboard=42.9 surfboard=29.5 tennis racket=39.7 bottle=27.9 wine glass=27.6 cup=34.5 fork=24.8 knife=9.9 spoon=10.3 bowl=35.1 banana=18.6 apple=14.3 sandwich=27.3 orange=25.7 broccoli=16.3 carrot=16.8 hot dog=26.9 pizza=45.1 donut=39.2 cake=31.5 chair=23.5 couch=37.9 potted plant=20.7 bed=39.9 dining table=24.0 toilet=53.3 tv=50.1 laptop=50.1 mouse=51.8 remote=20.9 keyboard=43.0 cell phone=27.2 microwave=44.3 oven=29.5 toaster=5.9 sink=30.4 refrigerator=48.5 book=7.2 clock=44.2 vase=31.6 scissors=31.0 teddy bear=38.3 hair drier=0.0 toothbrush=14.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.2 [Epoch 253][Batch 99], LR: 1.00E-05, Speed: 10.444 samples/sec, ObjLoss=23.813, BoxCenterLoss=14.335, BoxScaleLoss=4.890, ClassLoss=9.423 [Epoch 253][Batch 199], LR: 1.00E-05, Speed: 9.892 samples/sec, ObjLoss=23.813, BoxCenterLoss=14.335, BoxScaleLoss=4.890, ClassLoss=9.423 [Epoch 253][Batch 299], LR: 1.00E-05, Speed: 111.229 samples/sec, ObjLoss=23.813, BoxCenterLoss=14.335, BoxScaleLoss=4.890, ClassLoss=9.423 [Epoch 253][Batch 399], LR: 1.00E-05, Speed: 9.775 samples/sec, ObjLoss=23.812, BoxCenterLoss=14.335, BoxScaleLoss=4.890, ClassLoss=9.422 [Epoch 253][Batch 499], LR: 1.00E-05, Speed: 110.715 samples/sec, ObjLoss=23.811, BoxCenterLoss=14.335, BoxScaleLoss=4.890, ClassLoss=9.422 [Epoch 253][Batch 599], LR: 1.00E-05, Speed: 10.674 samples/sec, ObjLoss=23.811, BoxCenterLoss=14.335, BoxScaleLoss=4.889, ClassLoss=9.421 [Epoch 253][Batch 699], LR: 1.00E-05, Speed: 10.658 samples/sec, ObjLoss=23.810, BoxCenterLoss=14.335, BoxScaleLoss=4.889, ClassLoss=9.421 [Epoch 253][Batch 799], LR: 1.00E-05, Speed: 9.821 samples/sec, ObjLoss=23.810, BoxCenterLoss=14.334, BoxScaleLoss=4.889, ClassLoss=9.420 [Epoch 253][Batch 899], LR: 1.00E-05, Speed: 10.214 samples/sec, ObjLoss=23.809, BoxCenterLoss=14.334, BoxScaleLoss=4.889, ClassLoss=9.420 [Epoch 253][Batch 999], LR: 1.00E-05, Speed: 11.116 samples/sec, ObjLoss=23.808, BoxCenterLoss=14.334, BoxScaleLoss=4.889, ClassLoss=9.419 [Epoch 253][Batch 1099], LR: 1.00E-05, Speed: 10.505 samples/sec, ObjLoss=23.808, BoxCenterLoss=14.334, BoxScaleLoss=4.889, ClassLoss=9.419 [Epoch 253][Batch 1199], LR: 1.00E-05, Speed: 7.471 samples/sec, ObjLoss=23.807, BoxCenterLoss=14.334, BoxScaleLoss=4.889, ClassLoss=9.419 [Epoch 253][Batch 1299], LR: 1.00E-05, Speed: 9.686 samples/sec, ObjLoss=23.807, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.418 [Epoch 253][Batch 1399], LR: 1.00E-05, Speed: 121.733 samples/sec, ObjLoss=23.806, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.418 [Epoch 253][Batch 1499], LR: 1.00E-05, Speed: 8.990 samples/sec, ObjLoss=23.806, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.418 [Epoch 253][Batch 1599], LR: 1.00E-05, Speed: 113.108 samples/sec, ObjLoss=23.805, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.417 [Epoch 253][Batch 1699], LR: 1.00E-05, Speed: 9.121 samples/sec, ObjLoss=23.805, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.417 [Epoch 253][Batch 1799], LR: 1.00E-05, Speed: 10.395 samples/sec, ObjLoss=23.804, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.416 [Epoch 253] Training cost: 2117.336, ObjLoss=23.804, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.416 [Epoch 253] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.152 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.368 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.506 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.415 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.425 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.452 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608 person=45.6 bicycle=25.8 car=34.3 motorcycle=38.6 airplane=55.9 bus=61.7 train=62.3 truck=31.7 boat=20.8 traffic light=20.1 fire hydrant=59.2 stop sign=57.1 parking meter=38.7 bench=19.4 bird=30.0 cat=61.0 dog=56.2 horse=49.4 sheep=44.9 cow=47.4 elephant=58.2 bear=62.9 zebra=59.0 giraffe=59.2 backpack=10.8 umbrella=33.1 handbag=10.1 tie=24.9 suitcase=28.9 frisbee=52.2 skis=18.2 snowboard=28.0 sports ball=36.4 kite=33.9 baseball bat=21.8 baseball glove=29.1 skateboard=41.8 surfboard=29.3 tennis racket=39.9 bottle=27.4 wine glass=28.2 cup=34.3 fork=25.5 knife=10.5 spoon=10.9 bowl=34.6 banana=19.0 apple=13.3 sandwich=27.2 orange=25.8 broccoli=16.1 carrot=16.4 hot dog=27.8 pizza=45.3 donut=38.3 cake=31.4 chair=23.7 couch=38.8 potted plant=21.0 bed=42.2 dining table=25.9 toilet=53.8 tv=50.0 laptop=49.8 mouse=51.3 remote=20.7 keyboard=43.1 cell phone=27.2 microwave=45.2 oven=29.8 toaster=5.9 sink=31.2 refrigerator=48.6 book=7.4 clock=44.1 vase=30.8 scissors=31.2 teddy bear=39.0 hair drier=0.0 toothbrush=14.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.3 [Epoch 254][Batch 99], LR: 1.00E-05, Speed: 10.808 samples/sec, ObjLoss=23.803, BoxCenterLoss=14.334, BoxScaleLoss=4.888, ClassLoss=9.416 [Epoch 254][Batch 199], LR: 1.00E-05, Speed: 11.420 samples/sec, ObjLoss=23.803, BoxCenterLoss=14.334, BoxScaleLoss=4.887, ClassLoss=9.415 [Epoch 254][Batch 299], LR: 1.00E-05, Speed: 110.602 samples/sec, ObjLoss=23.802, BoxCenterLoss=14.334, BoxScaleLoss=4.887, ClassLoss=9.415 [Epoch 254][Batch 399], LR: 1.00E-05, Speed: 8.567 samples/sec, ObjLoss=23.802, BoxCenterLoss=14.333, BoxScaleLoss=4.887, ClassLoss=9.414 [Epoch 254][Batch 499], LR: 1.00E-05, Speed: 104.888 samples/sec, ObjLoss=23.801, BoxCenterLoss=14.333, BoxScaleLoss=4.887, ClassLoss=9.414 [Epoch 254][Batch 599], LR: 1.00E-05, Speed: 7.963 samples/sec, ObjLoss=23.801, BoxCenterLoss=14.333, BoxScaleLoss=4.887, ClassLoss=9.413 [Epoch 254][Batch 699], LR: 1.00E-05, Speed: 11.352 samples/sec, ObjLoss=23.801, BoxCenterLoss=14.334, BoxScaleLoss=4.887, ClassLoss=9.413 [Epoch 254][Batch 799], LR: 1.00E-05, Speed: 10.212 samples/sec, ObjLoss=23.800, BoxCenterLoss=14.334, BoxScaleLoss=4.887, ClassLoss=9.413 [Epoch 254][Batch 899], LR: 1.00E-05, Speed: 10.005 samples/sec, ObjLoss=23.800, BoxCenterLoss=14.334, BoxScaleLoss=4.887, ClassLoss=9.412 [Epoch 254][Batch 999], LR: 1.00E-05, Speed: 110.742 samples/sec, ObjLoss=23.799, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.412 [Epoch 254][Batch 1099], LR: 1.00E-05, Speed: 11.751 samples/sec, ObjLoss=23.799, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.411 [Epoch 254][Batch 1199], LR: 1.00E-05, Speed: 10.430 samples/sec, ObjLoss=23.798, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.411 [Epoch 254][Batch 1299], LR: 1.00E-05, Speed: 9.410 samples/sec, ObjLoss=23.798, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.411 [Epoch 254][Batch 1399], LR: 1.00E-05, Speed: 11.111 samples/sec, ObjLoss=23.797, BoxCenterLoss=14.333, BoxScaleLoss=4.886, ClassLoss=9.410 [Epoch 254][Batch 1499], LR: 1.00E-05, Speed: 12.392 samples/sec, ObjLoss=23.797, BoxCenterLoss=14.333, BoxScaleLoss=4.886, ClassLoss=9.410 [Epoch 254][Batch 1599], LR: 1.00E-05, Speed: 8.319 samples/sec, ObjLoss=23.796, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.410 [Epoch 254][Batch 1699], LR: 1.00E-05, Speed: 8.188 samples/sec, ObjLoss=23.796, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.409 [Epoch 254][Batch 1799], LR: 1.00E-05, Speed: 133.119 samples/sec, ObjLoss=23.796, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.409 [Epoch 254] Training cost: 2120.054, ObjLoss=23.796, BoxCenterLoss=14.334, BoxScaleLoss=4.886, ClassLoss=9.409 [Epoch 254] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.368 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605 person=45.3 bicycle=25.3 car=34.4 motorcycle=38.9 airplane=56.7 bus=61.5 train=62.0 truck=32.0 boat=20.8 traffic light=20.2 fire hydrant=60.4 stop sign=58.5 parking meter=38.5 bench=19.9 bird=29.8 cat=60.4 dog=55.4 horse=49.9 sheep=44.9 cow=47.6 elephant=57.6 bear=62.9 zebra=58.3 giraffe=59.2 backpack=10.9 umbrella=33.2 handbag=10.1 tie=24.9 suitcase=28.8 frisbee=51.2 skis=17.5 snowboard=28.3 sports ball=36.6 kite=33.8 baseball bat=22.2 baseball glove=29.0 skateboard=42.6 surfboard=29.2 tennis racket=40.0 bottle=27.8 wine glass=27.8 cup=34.7 fork=25.2 knife=10.0 spoon=10.6 bowl=35.4 banana=19.2 apple=14.1 sandwich=27.5 orange=26.5 broccoli=16.6 carrot=16.3 hot dog=28.8 pizza=45.7 donut=38.5 cake=32.4 chair=23.5 couch=38.0 potted plant=21.1 bed=41.6 dining table=25.0 toilet=52.8 tv=49.9 laptop=50.3 mouse=51.7 remote=21.6 keyboard=43.4 cell phone=28.3 microwave=43.8 oven=30.0 toaster=5.9 sink=31.2 refrigerator=49.1 book=7.7 clock=44.3 vase=31.2 scissors=30.0 teddy bear=39.3 hair drier=0.0 toothbrush=14.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.4 [Epoch 255][Batch 99], LR: 1.00E-05, Speed: 121.914 samples/sec, ObjLoss=23.795, BoxCenterLoss=14.333, BoxScaleLoss=4.885, ClassLoss=9.408 [Epoch 255][Batch 199], LR: 1.00E-05, Speed: 118.492 samples/sec, ObjLoss=23.794, BoxCenterLoss=14.333, BoxScaleLoss=4.885, ClassLoss=9.408 [Epoch 255][Batch 299], LR: 1.00E-05, Speed: 9.896 samples/sec, ObjLoss=23.794, BoxCenterLoss=14.333, BoxScaleLoss=4.885, ClassLoss=9.408 [Epoch 255][Batch 399], LR: 1.00E-05, Speed: 10.907 samples/sec, ObjLoss=23.793, BoxCenterLoss=14.333, BoxScaleLoss=4.885, ClassLoss=9.407 [Epoch 255][Batch 499], LR: 1.00E-05, Speed: 83.473 samples/sec, ObjLoss=23.792, BoxCenterLoss=14.333, BoxScaleLoss=4.885, ClassLoss=9.407 [Epoch 255][Batch 599], LR: 1.00E-05, Speed: 13.276 samples/sec, ObjLoss=23.792, BoxCenterLoss=14.333, BoxScaleLoss=4.885, ClassLoss=9.406 [Epoch 255][Batch 699], LR: 1.00E-05, Speed: 11.525 samples/sec, ObjLoss=23.791, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.406 [Epoch 255][Batch 799], LR: 1.00E-05, Speed: 8.969 samples/sec, ObjLoss=23.791, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.406 [Epoch 255][Batch 899], LR: 1.00E-05, Speed: 11.702 samples/sec, ObjLoss=23.790, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.405 [Epoch 255][Batch 999], LR: 1.00E-05, Speed: 10.980 samples/sec, ObjLoss=23.790, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.405 [Epoch 255][Batch 1099], LR: 1.00E-05, Speed: 11.737 samples/sec, ObjLoss=23.789, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.404 [Epoch 255][Batch 1199], LR: 1.00E-05, Speed: 8.049 samples/sec, ObjLoss=23.789, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.404 [Epoch 255][Batch 1299], LR: 1.00E-05, Speed: 10.157 samples/sec, ObjLoss=23.788, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.403 [Epoch 255][Batch 1399], LR: 1.00E-05, Speed: 12.907 samples/sec, ObjLoss=23.788, BoxCenterLoss=14.333, BoxScaleLoss=4.884, ClassLoss=9.403 [Epoch 255][Batch 1499], LR: 1.00E-05, Speed: 11.022 samples/sec, ObjLoss=23.788, BoxCenterLoss=14.333, BoxScaleLoss=4.883, ClassLoss=9.402 [Epoch 255][Batch 1599], LR: 1.00E-05, Speed: 9.952 samples/sec, ObjLoss=23.787, BoxCenterLoss=14.332, BoxScaleLoss=4.883, ClassLoss=9.402 [Epoch 255][Batch 1699], LR: 1.00E-05, Speed: 7.637 samples/sec, ObjLoss=23.787, BoxCenterLoss=14.332, BoxScaleLoss=4.883, ClassLoss=9.402 [Epoch 255][Batch 1799], LR: 1.00E-05, Speed: 10.818 samples/sec, ObjLoss=23.786, BoxCenterLoss=14.332, BoxScaleLoss=4.883, ClassLoss=9.401 [Epoch 255] Training cost: 2144.178, ObjLoss=23.786, BoxCenterLoss=14.332, BoxScaleLoss=4.883, ClassLoss=9.401 [Epoch 255] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.372 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.369 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611 person=45.7 bicycle=25.8 car=34.4 motorcycle=39.1 airplane=56.6 bus=61.7 train=61.9 truck=32.0 boat=21.0 traffic light=19.9 fire hydrant=60.3 stop sign=57.1 parking meter=39.3 bench=20.1 bird=30.5 cat=60.7 dog=55.8 horse=49.1 sheep=45.5 cow=47.8 elephant=58.2 bear=63.6 zebra=57.8 giraffe=59.2 backpack=11.1 umbrella=33.3 handbag=10.0 tie=24.8 suitcase=29.9 frisbee=50.7 skis=18.2 snowboard=27.6 sports ball=35.8 kite=33.3 baseball bat=21.8 baseball glove=29.3 skateboard=42.8 surfboard=29.5 tennis racket=39.8 bottle=27.9 wine glass=27.5 cup=34.3 fork=25.4 knife=9.9 spoon=10.9 bowl=35.3 banana=18.7 apple=13.2 sandwich=28.1 orange=25.9 broccoli=17.1 carrot=16.4 hot dog=29.2 pizza=46.1 donut=38.1 cake=32.6 chair=23.4 couch=38.9 potted plant=21.4 bed=42.8 dining table=25.7 toilet=54.3 tv=50.5 laptop=50.2 mouse=51.8 remote=20.9 keyboard=43.2 cell phone=27.5 microwave=43.0 oven=31.2 toaster=5.9 sink=31.2 refrigerator=49.2 book=7.4 clock=44.6 vase=30.8 scissors=30.9 teddy bear=39.9 hair drier=0.0 toothbrush=13.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 256][Batch 99], LR: 1.00E-05, Speed: 10.625 samples/sec, ObjLoss=23.785, BoxCenterLoss=14.332, BoxScaleLoss=4.883, ClassLoss=9.401 [Epoch 256][Batch 199], LR: 1.00E-05, Speed: 9.697 samples/sec, ObjLoss=23.785, BoxCenterLoss=14.332, BoxScaleLoss=4.883, ClassLoss=9.400 [Epoch 256][Batch 299], LR: 1.00E-05, Speed: 7.819 samples/sec, ObjLoss=23.784, BoxCenterLoss=14.332, BoxScaleLoss=4.883, ClassLoss=9.400 [Epoch 256][Batch 399], LR: 1.00E-05, Speed: 88.177 samples/sec, ObjLoss=23.784, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.399 [Epoch 256][Batch 499], LR: 1.00E-05, Speed: 11.757 samples/sec, ObjLoss=23.783, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.399 [Epoch 256][Batch 599], LR: 1.00E-05, Speed: 99.227 samples/sec, ObjLoss=23.783, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.399 [Epoch 256][Batch 699], LR: 1.00E-05, Speed: 10.474 samples/sec, ObjLoss=23.782, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.398 [Epoch 256][Batch 799], LR: 1.00E-05, Speed: 9.796 samples/sec, ObjLoss=23.781, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.398 [Epoch 256][Batch 899], LR: 1.00E-05, Speed: 105.740 samples/sec, ObjLoss=23.781, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.397 [Epoch 256][Batch 999], LR: 1.00E-05, Speed: 12.881 samples/sec, ObjLoss=23.781, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.397 [Epoch 256][Batch 1099], LR: 1.00E-05, Speed: 8.740 samples/sec, ObjLoss=23.780, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.397 [Epoch 256][Batch 1199], LR: 1.00E-05, Speed: 6.810 samples/sec, ObjLoss=23.780, BoxCenterLoss=14.332, BoxScaleLoss=4.882, ClassLoss=9.396 [Epoch 256][Batch 1299], LR: 1.00E-05, Speed: 77.696 samples/sec, ObjLoss=23.779, BoxCenterLoss=14.332, BoxScaleLoss=4.881, ClassLoss=9.396 [Epoch 256][Batch 1399], LR: 1.00E-05, Speed: 11.497 samples/sec, ObjLoss=23.779, BoxCenterLoss=14.332, BoxScaleLoss=4.881, ClassLoss=9.395 [Epoch 256][Batch 1499], LR: 1.00E-05, Speed: 10.957 samples/sec, ObjLoss=23.778, BoxCenterLoss=14.332, BoxScaleLoss=4.881, ClassLoss=9.395 [Epoch 256][Batch 1599], LR: 1.00E-05, Speed: 10.263 samples/sec, ObjLoss=23.778, BoxCenterLoss=14.332, BoxScaleLoss=4.881, ClassLoss=9.394 [Epoch 256][Batch 1699], LR: 1.00E-05, Speed: 9.436 samples/sec, ObjLoss=23.777, BoxCenterLoss=14.332, BoxScaleLoss=4.881, ClassLoss=9.394 [Epoch 256][Batch 1799], LR: 1.00E-05, Speed: 41.251 samples/sec, ObjLoss=23.776, BoxCenterLoss=14.331, BoxScaleLoss=4.881, ClassLoss=9.394 [Epoch 256] Training cost: 2143.251, ObjLoss=23.776, BoxCenterLoss=14.331, BoxScaleLoss=4.881, ClassLoss=9.394 [Epoch 256] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.368 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.425 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.452 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608 person=45.5 bicycle=25.8 car=34.5 motorcycle=39.0 airplane=56.8 bus=61.9 train=61.9 truck=31.7 boat=20.3 traffic light=20.3 fire hydrant=60.9 stop sign=56.5 parking meter=39.0 bench=19.9 bird=29.9 cat=60.9 dog=56.1 horse=49.6 sheep=44.8 cow=47.3 elephant=58.4 bear=62.7 zebra=58.4 giraffe=59.5 backpack=10.8 umbrella=33.6 handbag=10.1 tie=24.4 suitcase=29.3 frisbee=50.8 skis=18.6 snowboard=28.5 sports ball=36.5 kite=34.2 baseball bat=21.6 baseball glove=28.9 skateboard=43.5 surfboard=29.3 tennis racket=40.2 bottle=28.1 wine glass=27.8 cup=34.8 fork=25.6 knife=10.3 spoon=11.0 bowl=34.8 banana=19.1 apple=12.9 sandwich=28.1 orange=25.0 broccoli=16.5 carrot=16.5 hot dog=28.2 pizza=46.3 donut=37.8 cake=32.2 chair=23.6 couch=39.0 potted plant=21.0 bed=42.5 dining table=25.7 toilet=53.4 tv=50.0 laptop=49.9 mouse=51.1 remote=21.2 keyboard=43.5 cell phone=27.7 microwave=44.8 oven=29.4 toaster=5.9 sink=31.0 refrigerator=48.3 book=7.4 clock=43.9 vase=31.2 scissors=31.8 teddy bear=39.9 hair drier=0.0 toothbrush=15.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.4 [Epoch 257][Batch 99], LR: 1.00E-05, Speed: 8.855 samples/sec, ObjLoss=23.776, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.393 [Epoch 257][Batch 199], LR: 1.00E-05, Speed: 119.290 samples/sec, ObjLoss=23.775, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.393 [Epoch 257][Batch 299], LR: 1.00E-05, Speed: 9.804 samples/sec, ObjLoss=23.775, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.392 [Epoch 257][Batch 399], LR: 1.00E-05, Speed: 9.524 samples/sec, ObjLoss=23.774, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.392 [Epoch 257][Batch 499], LR: 1.00E-05, Speed: 8.890 samples/sec, ObjLoss=23.774, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.392 [Epoch 257][Batch 599], LR: 1.00E-05, Speed: 10.768 samples/sec, ObjLoss=23.774, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.391 [Epoch 257][Batch 699], LR: 1.00E-05, Speed: 11.426 samples/sec, ObjLoss=23.773, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.391 [Epoch 257][Batch 799], LR: 1.00E-05, Speed: 14.234 samples/sec, ObjLoss=23.773, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.390 [Epoch 257][Batch 899], LR: 1.00E-05, Speed: 9.355 samples/sec, ObjLoss=23.772, BoxCenterLoss=14.331, BoxScaleLoss=4.880, ClassLoss=9.390 [Epoch 257][Batch 999], LR: 1.00E-05, Speed: 9.411 samples/sec, ObjLoss=23.772, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.390 [Epoch 257][Batch 1099], LR: 1.00E-05, Speed: 12.878 samples/sec, ObjLoss=23.772, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.390 [Epoch 257][Batch 1199], LR: 1.00E-05, Speed: 12.283 samples/sec, ObjLoss=23.771, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.389 [Epoch 257][Batch 1299], LR: 1.00E-05, Speed: 123.506 samples/sec, ObjLoss=23.770, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.389 [Epoch 257][Batch 1399], LR: 1.00E-05, Speed: 9.066 samples/sec, ObjLoss=23.770, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.388 [Epoch 257][Batch 1499], LR: 1.00E-05, Speed: 8.754 samples/sec, ObjLoss=23.769, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.388 [Epoch 257][Batch 1599], LR: 1.00E-05, Speed: 8.482 samples/sec, ObjLoss=23.769, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.388 [Epoch 257][Batch 1699], LR: 1.00E-05, Speed: 8.308 samples/sec, ObjLoss=23.769, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.387 [Epoch 257][Batch 1799], LR: 1.00E-05, Speed: 126.416 samples/sec, ObjLoss=23.768, BoxCenterLoss=14.331, BoxScaleLoss=4.879, ClassLoss=9.387 [Epoch 257] Training cost: 2157.393, ObjLoss=23.768, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.387 [Epoch 257] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.456 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610 person=45.6 bicycle=26.0 car=34.8 motorcycle=39.0 airplane=56.3 bus=61.6 train=62.1 truck=31.9 boat=20.3 traffic light=20.1 fire hydrant=60.9 stop sign=56.4 parking meter=38.9 bench=20.3 bird=30.2 cat=60.7 dog=55.9 horse=49.3 sheep=45.0 cow=47.5 elephant=58.3 bear=60.1 zebra=58.3 giraffe=59.0 backpack=10.9 umbrella=33.6 handbag=10.3 tie=24.9 suitcase=29.8 frisbee=51.2 skis=18.4 snowboard=28.3 sports ball=36.4 kite=34.2 baseball bat=21.7 baseball glove=29.7 skateboard=42.4 surfboard=30.0 tennis racket=40.1 bottle=28.1 wine glass=27.7 cup=34.6 fork=26.1 knife=10.2 spoon=10.9 bowl=35.3 banana=19.5 apple=13.3 sandwich=28.1 orange=26.1 broccoli=16.7 carrot=16.4 hot dog=27.5 pizza=46.1 donut=38.9 cake=32.5 chair=23.9 couch=39.1 potted plant=20.9 bed=42.3 dining table=25.8 toilet=53.3 tv=50.4 laptop=50.1 mouse=51.7 remote=21.0 keyboard=44.2 cell phone=28.0 microwave=43.6 oven=29.0 toaster=5.9 sink=31.4 refrigerator=48.7 book=7.5 clock=44.1 vase=30.9 scissors=31.4 teddy bear=40.5 hair drier=0.0 toothbrush=14.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 258][Batch 99], LR: 1.00E-05, Speed: 9.818 samples/sec, ObjLoss=23.767, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.386 [Epoch 258][Batch 199], LR: 1.00E-05, Speed: 9.129 samples/sec, ObjLoss=23.767, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.386 [Epoch 258][Batch 299], LR: 1.00E-05, Speed: 12.176 samples/sec, ObjLoss=23.767, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.385 [Epoch 258][Batch 399], LR: 1.00E-05, Speed: 10.747 samples/sec, ObjLoss=23.766, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.385 [Epoch 258][Batch 499], LR: 1.00E-05, Speed: 8.964 samples/sec, ObjLoss=23.765, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.385 [Epoch 258][Batch 599], LR: 1.00E-05, Speed: 11.465 samples/sec, ObjLoss=23.765, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.384 [Epoch 258][Batch 699], LR: 1.00E-05, Speed: 9.597 samples/sec, ObjLoss=23.765, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.384 [Epoch 258][Batch 799], LR: 1.00E-05, Speed: 10.988 samples/sec, ObjLoss=23.764, BoxCenterLoss=14.331, BoxScaleLoss=4.878, ClassLoss=9.383 [Epoch 258][Batch 899], LR: 1.00E-05, Speed: 13.580 samples/sec, ObjLoss=23.764, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.383 [Epoch 258][Batch 999], LR: 1.00E-05, Speed: 11.335 samples/sec, ObjLoss=23.763, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.383 [Epoch 258][Batch 1099], LR: 1.00E-05, Speed: 142.771 samples/sec, ObjLoss=23.763, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.382 [Epoch 258][Batch 1199], LR: 1.00E-05, Speed: 139.173 samples/sec, ObjLoss=23.763, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.382 [Epoch 258][Batch 1299], LR: 1.00E-05, Speed: 10.042 samples/sec, ObjLoss=23.763, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.382 [Epoch 258][Batch 1399], LR: 1.00E-05, Speed: 14.532 samples/sec, ObjLoss=23.762, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.382 [Epoch 258][Batch 1499], LR: 1.00E-05, Speed: 9.913 samples/sec, ObjLoss=23.761, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.381 [Epoch 258][Batch 1599], LR: 1.00E-05, Speed: 9.566 samples/sec, ObjLoss=23.761, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.381 [Epoch 258][Batch 1699], LR: 1.00E-05, Speed: 9.352 samples/sec, ObjLoss=23.760, BoxCenterLoss=14.331, BoxScaleLoss=4.877, ClassLoss=9.380 [Epoch 258][Batch 1799], LR: 1.00E-05, Speed: 16.131 samples/sec, ObjLoss=23.760, BoxCenterLoss=14.331, BoxScaleLoss=4.876, ClassLoss=9.380 [Epoch 258] Training cost: 2108.752, ObjLoss=23.760, BoxCenterLoss=14.331, BoxScaleLoss=4.876, ClassLoss=9.380 [Epoch 258] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.456 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611 person=45.5 bicycle=25.8 car=34.8 motorcycle=39.2 airplane=56.7 bus=61.6 train=62.8 truck=32.3 boat=20.3 traffic light=20.3 fire hydrant=60.5 stop sign=57.1 parking meter=38.8 bench=20.1 bird=29.8 cat=61.4 dog=55.9 horse=49.1 sheep=45.3 cow=46.4 elephant=58.1 bear=64.3 zebra=58.4 giraffe=58.7 backpack=11.0 umbrella=34.1 handbag=10.0 tie=24.4 suitcase=29.1 frisbee=52.3 skis=17.6 snowboard=28.2 sports ball=35.8 kite=34.2 baseball bat=21.6 baseball glove=29.6 skateboard=43.4 surfboard=29.1 tennis racket=39.7 bottle=28.2 wine glass=28.1 cup=34.4 fork=26.3 knife=9.7 spoon=10.6 bowl=35.5 banana=19.0 apple=13.3 sandwich=28.5 orange=26.0 broccoli=16.2 carrot=16.2 hot dog=27.5 pizza=46.6 donut=38.4 cake=32.2 chair=23.7 couch=38.5 potted plant=21.6 bed=41.1 dining table=26.0 toilet=53.2 tv=50.0 laptop=49.9 mouse=51.4 remote=21.4 keyboard=44.0 cell phone=27.8 microwave=44.0 oven=29.8 toaster=5.9 sink=31.1 refrigerator=49.1 book=7.7 clock=43.7 vase=31.2 scissors=29.8 teddy bear=40.0 hair drier=0.0 toothbrush=14.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.4 [Epoch 259][Batch 99], LR: 1.00E-05, Speed: 10.635 samples/sec, ObjLoss=23.759, BoxCenterLoss=14.331, BoxScaleLoss=4.876, ClassLoss=9.379 [Epoch 259][Batch 199], LR: 1.00E-05, Speed: 9.701 samples/sec, ObjLoss=23.759, BoxCenterLoss=14.330, BoxScaleLoss=4.876, ClassLoss=9.379 [Epoch 259][Batch 299], LR: 1.00E-05, Speed: 83.883 samples/sec, ObjLoss=23.758, BoxCenterLoss=14.330, BoxScaleLoss=4.876, ClassLoss=9.378 [Epoch 259][Batch 399], LR: 1.00E-05, Speed: 7.653 samples/sec, ObjLoss=23.758, BoxCenterLoss=14.330, BoxScaleLoss=4.876, ClassLoss=9.378 [Epoch 259][Batch 499], LR: 1.00E-05, Speed: 95.871 samples/sec, ObjLoss=23.757, BoxCenterLoss=14.330, BoxScaleLoss=4.876, ClassLoss=9.378 [Epoch 259][Batch 599], LR: 1.00E-05, Speed: 8.948 samples/sec, ObjLoss=23.757, BoxCenterLoss=14.330, BoxScaleLoss=4.876, ClassLoss=9.377 [Epoch 259][Batch 699], LR: 1.00E-05, Speed: 7.233 samples/sec, ObjLoss=23.756, BoxCenterLoss=14.330, BoxScaleLoss=4.875, ClassLoss=9.377 [Epoch 259][Batch 799], LR: 1.00E-05, Speed: 10.274 samples/sec, ObjLoss=23.756, BoxCenterLoss=14.330, BoxScaleLoss=4.875, ClassLoss=9.376 [Epoch 259][Batch 899], LR: 1.00E-05, Speed: 11.053 samples/sec, ObjLoss=23.755, BoxCenterLoss=14.330, BoxScaleLoss=4.875, ClassLoss=9.376 [Epoch 259][Batch 999], LR: 1.00E-05, Speed: 8.387 samples/sec, ObjLoss=23.754, BoxCenterLoss=14.330, BoxScaleLoss=4.875, ClassLoss=9.376 [Epoch 259][Batch 1099], LR: 1.00E-05, Speed: 14.971 samples/sec, ObjLoss=23.754, BoxCenterLoss=14.330, BoxScaleLoss=4.875, ClassLoss=9.375 [Epoch 259][Batch 1199], LR: 1.00E-05, Speed: 14.421 samples/sec, ObjLoss=23.753, BoxCenterLoss=14.330, BoxScaleLoss=4.875, ClassLoss=9.375 [Epoch 259][Batch 1299], LR: 1.00E-05, Speed: 8.511 samples/sec, ObjLoss=23.753, BoxCenterLoss=14.330, BoxScaleLoss=4.875, ClassLoss=9.374 [Epoch 259][Batch 1399], LR: 1.00E-05, Speed: 7.867 samples/sec, ObjLoss=23.752, BoxCenterLoss=14.330, BoxScaleLoss=4.874, ClassLoss=9.374 [Epoch 259][Batch 1499], LR: 1.00E-05, Speed: 11.561 samples/sec, ObjLoss=23.752, BoxCenterLoss=14.330, BoxScaleLoss=4.874, ClassLoss=9.374 [Epoch 259][Batch 1599], LR: 1.00E-05, Speed: 8.898 samples/sec, ObjLoss=23.751, BoxCenterLoss=14.330, BoxScaleLoss=4.874, ClassLoss=9.373 [Epoch 259][Batch 1699], LR: 1.00E-05, Speed: 10.649 samples/sec, ObjLoss=23.751, BoxCenterLoss=14.329, BoxScaleLoss=4.874, ClassLoss=9.373 [Epoch 259][Batch 1799], LR: 1.00E-05, Speed: 11.264 samples/sec, ObjLoss=23.750, BoxCenterLoss=14.329, BoxScaleLoss=4.874, ClassLoss=9.373 [Epoch 259] Training cost: 2104.827, ObjLoss=23.750, BoxCenterLoss=14.329, BoxScaleLoss=4.874, ClassLoss=9.372 [Epoch 259] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.367 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.607 person=45.5 bicycle=25.6 car=34.7 motorcycle=38.8 airplane=57.0 bus=62.0 train=61.8 truck=32.6 boat=20.3 traffic light=20.1 fire hydrant=59.0 stop sign=56.2 parking meter=39.6 bench=20.1 bird=30.2 cat=60.8 dog=55.7 horse=49.6 sheep=46.4 cow=47.4 elephant=58.3 bear=63.6 zebra=58.1 giraffe=59.1 backpack=11.0 umbrella=33.3 handbag=10.3 tie=23.9 suitcase=28.9 frisbee=51.9 skis=18.1 snowboard=28.4 sports ball=36.0 kite=33.9 baseball bat=21.2 baseball glove=29.8 skateboard=43.3 surfboard=29.3 tennis racket=39.7 bottle=27.8 wine glass=28.1 cup=34.5 fork=25.4 knife=10.1 spoon=10.6 bowl=35.1 banana=19.2 apple=13.6 sandwich=28.0 orange=25.2 broccoli=16.1 carrot=15.9 hot dog=27.3 pizza=46.3 donut=39.6 cake=31.9 chair=23.6 couch=39.1 potted plant=21.1 bed=42.0 dining table=26.2 toilet=53.1 tv=50.0 laptop=49.9 mouse=51.1 remote=21.2 keyboard=43.2 cell phone=28.0 microwave=44.3 oven=30.1 toaster=5.9 sink=31.1 refrigerator=48.9 book=7.6 clock=43.9 vase=30.9 scissors=30.9 teddy bear=39.4 hair drier=0.0 toothbrush=14.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.4 [Epoch 260][Batch 99], LR: 1.00E-05, Speed: 9.218 samples/sec, ObjLoss=23.777, BoxCenterLoss=14.401, BoxScaleLoss=4.895, ClassLoss=9.401 [Epoch 260][Batch 199], LR: 1.00E-05, Speed: 10.480 samples/sec, ObjLoss=23.799, BoxCenterLoss=14.473, BoxScaleLoss=4.915, ClassLoss=9.428 [Epoch 260][Batch 299], LR: 1.00E-05, Speed: 10.515 samples/sec, ObjLoss=23.822, BoxCenterLoss=14.544, BoxScaleLoss=4.936, ClassLoss=9.455 [Epoch 260][Batch 399], LR: 1.00E-05, Speed: 8.000 samples/sec, ObjLoss=23.844, BoxCenterLoss=14.615, BoxScaleLoss=4.956, ClassLoss=9.480 [Epoch 260][Batch 499], LR: 1.00E-05, Speed: 8.112 samples/sec, ObjLoss=23.866, BoxCenterLoss=14.684, BoxScaleLoss=4.974, ClassLoss=9.505 [Epoch 260][Batch 599], LR: 1.00E-05, Speed: 10.377 samples/sec, ObjLoss=23.889, BoxCenterLoss=14.757, BoxScaleLoss=4.995, ClassLoss=9.534 [Epoch 260][Batch 699], LR: 1.00E-05, Speed: 8.887 samples/sec, ObjLoss=23.910, BoxCenterLoss=14.828, BoxScaleLoss=5.016, ClassLoss=9.563 [Epoch 260][Batch 799], LR: 1.00E-05, Speed: 8.629 samples/sec, ObjLoss=23.932, BoxCenterLoss=14.899, BoxScaleLoss=5.037, ClassLoss=9.589 [Epoch 260][Batch 899], LR: 1.00E-05, Speed: 123.748 samples/sec, ObjLoss=23.954, BoxCenterLoss=14.968, BoxScaleLoss=5.056, ClassLoss=9.615 [Epoch 260][Batch 999], LR: 1.00E-05, Speed: 10.366 samples/sec, ObjLoss=23.977, BoxCenterLoss=15.041, BoxScaleLoss=5.076, ClassLoss=9.642 [Epoch 260][Batch 1099], LR: 1.00E-05, Speed: 9.981 samples/sec, ObjLoss=24.000, BoxCenterLoss=15.114, BoxScaleLoss=5.097, ClassLoss=9.670 [Epoch 260][Batch 1199], LR: 1.00E-05, Speed: 10.792 samples/sec, ObjLoss=24.022, BoxCenterLoss=15.183, BoxScaleLoss=5.117, ClassLoss=9.696 [Epoch 260][Batch 1299], LR: 1.00E-05, Speed: 9.701 samples/sec, ObjLoss=24.043, BoxCenterLoss=15.253, BoxScaleLoss=5.137, ClassLoss=9.724 [Epoch 260][Batch 1399], LR: 1.00E-05, Speed: 10.903 samples/sec, ObjLoss=24.066, BoxCenterLoss=15.325, BoxScaleLoss=5.157, ClassLoss=9.752 [Epoch 260][Batch 1499], LR: 1.00E-05, Speed: 9.545 samples/sec, ObjLoss=24.088, BoxCenterLoss=15.397, BoxScaleLoss=5.178, ClassLoss=9.779 [Epoch 260][Batch 1599], LR: 1.00E-05, Speed: 92.585 samples/sec, ObjLoss=24.112, BoxCenterLoss=15.470, BoxScaleLoss=5.197, ClassLoss=9.805 [Epoch 260][Batch 1699], LR: 1.00E-05, Speed: 9.278 samples/sec, ObjLoss=24.135, BoxCenterLoss=15.542, BoxScaleLoss=5.218, ClassLoss=9.832 [Epoch 260][Batch 1799], LR: 1.00E-05, Speed: 11.994 samples/sec, ObjLoss=24.158, BoxCenterLoss=15.615, BoxScaleLoss=5.239, ClassLoss=9.862 [Epoch 260] Training cost: 2111.606, ObjLoss=24.164, BoxCenterLoss=15.638, BoxScaleLoss=5.246, ClassLoss=9.872 [Epoch 260] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.288 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.277 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.320 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.411 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.465 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.586 person=25.0 bicycle=22.2 car=29.6 motorcycle=34.0 airplane=47.7 bus=55.0 train=56.7 truck=30.3 boat=19.2 traffic light=18.0 fire hydrant=54.2 stop sign=48.5 parking meter=36.0 bench=16.5 bird=27.4 cat=52.5 dog=43.7 horse=42.2 sheep=40.9 cow=43.8 elephant=54.0 bear=53.0 zebra=54.0 giraffe=52.0 backpack=6.9 umbrella=29.6 handbag=8.3 tie=19.0 suitcase=25.0 frisbee=49.9 skis=14.4 snowboard=23.6 sports ball=27.7 kite=31.6 baseball bat=14.6 baseball glove=23.5 skateboard=36.1 surfboard=23.8 tennis racket=32.6 bottle=24.6 wine glass=20.9 cup=26.8 fork=20.4 knife=6.7 spoon=6.0 bowl=26.5 banana=16.3 apple=12.4 sandwich=28.1 orange=20.9 broccoli=17.6 carrot=13.8 hot dog=20.5 pizza=33.6 donut=32.9 cake=25.3 chair=20.0 couch=26.3 potted plant=19.8 bed=29.5 dining table=17.1 toilet=48.2 tv=44.3 laptop=41.0 mouse=38.1 remote=15.8 keyboard=35.0 cell phone=16.3 microwave=40.0 oven=26.2 toaster=11.7 sink=27.7 refrigerator=47.0 book=6.7 clock=33.5 vase=24.8 scissors=16.6 teddy bear=33.3 hair drier=0.0 toothbrush=8.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.8 [Epoch 261][Batch 99], LR: 1.00E-05, Speed: 8.031 samples/sec, ObjLoss=24.186, BoxCenterLoss=15.710, BoxScaleLoss=5.266, ClassLoss=9.897 [Epoch 261][Batch 199], LR: 1.00E-05, Speed: 9.798 samples/sec, ObjLoss=24.209, BoxCenterLoss=15.782, BoxScaleLoss=5.285, ClassLoss=9.921 [Epoch 261][Batch 299], LR: 1.00E-05, Speed: 8.953 samples/sec, ObjLoss=24.231, BoxCenterLoss=15.855, BoxScaleLoss=5.305, ClassLoss=9.947 [Epoch 261][Batch 399], LR: 1.00E-05, Speed: 11.115 samples/sec, ObjLoss=24.252, BoxCenterLoss=15.923, BoxScaleLoss=5.323, ClassLoss=9.971 [Epoch 261][Batch 499], LR: 1.00E-05, Speed: 109.125 samples/sec, ObjLoss=24.273, BoxCenterLoss=15.994, BoxScaleLoss=5.344, ClassLoss=9.999 [Epoch 261][Batch 599], LR: 1.00E-05, Speed: 10.745 samples/sec, ObjLoss=24.295, BoxCenterLoss=16.065, BoxScaleLoss=5.364, ClassLoss=10.025 [Epoch 261][Batch 699], LR: 1.00E-05, Speed: 9.282 samples/sec, ObjLoss=24.317, BoxCenterLoss=16.136, BoxScaleLoss=5.383, ClassLoss=10.052 [Epoch 261][Batch 799], LR: 1.00E-05, Speed: 7.556 samples/sec, ObjLoss=24.338, BoxCenterLoss=16.208, BoxScaleLoss=5.405, ClassLoss=10.079 [Epoch 261][Batch 899], LR: 1.00E-05, Speed: 11.777 samples/sec, ObjLoss=24.361, BoxCenterLoss=16.277, BoxScaleLoss=5.424, ClassLoss=10.104 [Epoch 261][Batch 999], LR: 1.00E-05, Speed: 143.551 samples/sec, ObjLoss=24.382, BoxCenterLoss=16.347, BoxScaleLoss=5.443, ClassLoss=10.130 [Epoch 261][Batch 1099], LR: 1.00E-05, Speed: 11.735 samples/sec, ObjLoss=24.403, BoxCenterLoss=16.419, BoxScaleLoss=5.465, ClassLoss=10.158 [Epoch 261][Batch 1199], LR: 1.00E-05, Speed: 8.544 samples/sec, ObjLoss=24.425, BoxCenterLoss=16.492, BoxScaleLoss=5.486, ClassLoss=10.186 [Epoch 261][Batch 1299], LR: 1.00E-05, Speed: 9.959 samples/sec, ObjLoss=24.447, BoxCenterLoss=16.562, BoxScaleLoss=5.505, ClassLoss=10.212 [Epoch 261][Batch 1399], LR: 1.00E-05, Speed: 11.492 samples/sec, ObjLoss=24.468, BoxCenterLoss=16.630, BoxScaleLoss=5.524, ClassLoss=10.239 [Epoch 261][Batch 1499], LR: 1.00E-05, Speed: 9.463 samples/sec, ObjLoss=24.490, BoxCenterLoss=16.703, BoxScaleLoss=5.544, ClassLoss=10.266 [Epoch 261][Batch 1599], LR: 1.00E-05, Speed: 10.434 samples/sec, ObjLoss=24.511, BoxCenterLoss=16.773, BoxScaleLoss=5.565, ClassLoss=10.292 [Epoch 261][Batch 1699], LR: 1.00E-05, Speed: 9.424 samples/sec, ObjLoss=24.534, BoxCenterLoss=16.845, BoxScaleLoss=5.585, ClassLoss=10.319 [Epoch 261][Batch 1799], LR: 1.00E-05, Speed: 10.792 samples/sec, ObjLoss=24.555, BoxCenterLoss=16.914, BoxScaleLoss=5.604, ClassLoss=10.345 [Epoch 261] Training cost: 2086.539, ObjLoss=24.563, BoxCenterLoss=16.938, BoxScaleLoss=5.610, ClassLoss=10.353 [Epoch 261] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.546 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.273 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.136 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.304 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.408 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.434 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.254 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.580 person=17.5 bicycle=20.5 car=29.7 motorcycle=36.8 airplane=48.7 bus=53.1 train=55.7 truck=28.5 boat=19.0 traffic light=19.1 fire hydrant=53.7 stop sign=48.2 parking meter=34.0 bench=17.3 bird=25.8 cat=54.7 dog=45.6 horse=44.9 sheep=41.0 cow=43.5 elephant=52.1 bear=56.4 zebra=52.1 giraffe=55.4 backpack=6.3 umbrella=28.0 handbag=7.5 tie=20.5 suitcase=26.0 frisbee=45.0 skis=15.3 snowboard=23.2 sports ball=30.0 kite=31.1 baseball bat=16.2 baseball glove=25.3 skateboard=31.7 surfboard=26.2 tennis racket=31.4 bottle=23.6 wine glass=21.8 cup=29.4 fork=18.1 knife=6.5 spoon=5.9 bowl=22.3 banana=14.9 apple=11.4 sandwich=23.8 orange=20.6 broccoli=16.5 carrot=11.8 hot dog=16.9 pizza=32.0 donut=33.5 cake=24.6 chair=19.9 couch=37.1 potted plant=20.2 bed=39.6 dining table=25.4 toilet=46.2 tv=44.5 laptop=43.9 mouse=41.8 remote=13.6 keyboard=36.4 cell phone=19.6 microwave=28.5 oven=26.6 toaster=6.9 sink=28.4 refrigerator=43.3 book=5.6 clock=38.3 vase=21.9 scissors=19.5 teddy bear=34.3 hair drier=0.0 toothbrush=6.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.7 [Epoch 262][Batch 99], LR: 1.00E-05, Speed: 9.329 samples/sec, ObjLoss=24.584, BoxCenterLoss=17.009, BoxScaleLoss=5.630, ClassLoss=10.378 [Epoch 262][Batch 199], LR: 1.00E-05, Speed: 8.210 samples/sec, ObjLoss=24.606, BoxCenterLoss=17.079, BoxScaleLoss=5.649, ClassLoss=10.403 [Epoch 262][Batch 299], LR: 1.00E-05, Speed: 8.948 samples/sec, ObjLoss=24.627, BoxCenterLoss=17.149, BoxScaleLoss=5.668, ClassLoss=10.430 [Epoch 262][Batch 399], LR: 1.00E-05, Speed: 8.884 samples/sec, ObjLoss=24.649, BoxCenterLoss=17.221, BoxScaleLoss=5.689, ClassLoss=10.458 [Epoch 262][Batch 499], LR: 1.00E-05, Speed: 9.269 samples/sec, ObjLoss=24.669, BoxCenterLoss=17.289, BoxScaleLoss=5.708, ClassLoss=10.482 [Epoch 262][Batch 599], LR: 1.00E-05, Speed: 10.338 samples/sec, ObjLoss=24.690, BoxCenterLoss=17.360, BoxScaleLoss=5.729, ClassLoss=10.508 [Epoch 262][Batch 699], LR: 1.00E-05, Speed: 9.840 samples/sec, ObjLoss=24.712, BoxCenterLoss=17.430, BoxScaleLoss=5.748, ClassLoss=10.533 [Epoch 262][Batch 799], LR: 1.00E-05, Speed: 8.710 samples/sec, ObjLoss=24.733, BoxCenterLoss=17.503, BoxScaleLoss=5.769, ClassLoss=10.560 [Epoch 262][Batch 899], LR: 1.00E-05, Speed: 11.985 samples/sec, ObjLoss=24.755, BoxCenterLoss=17.574, BoxScaleLoss=5.789, ClassLoss=10.586 [Epoch 262][Batch 999], LR: 1.00E-05, Speed: 9.800 samples/sec, ObjLoss=24.776, BoxCenterLoss=17.647, BoxScaleLoss=5.811, ClassLoss=10.613 [Epoch 262][Batch 1099], LR: 1.00E-05, Speed: 11.636 samples/sec, ObjLoss=24.798, BoxCenterLoss=17.718, BoxScaleLoss=5.830, ClassLoss=10.638 [Epoch 262][Batch 1199], LR: 1.00E-05, Speed: 11.426 samples/sec, ObjLoss=24.820, BoxCenterLoss=17.787, BoxScaleLoss=5.848, ClassLoss=10.664 [Epoch 262][Batch 1299], LR: 1.00E-05, Speed: 9.219 samples/sec, ObjLoss=24.841, BoxCenterLoss=17.854, BoxScaleLoss=5.867, ClassLoss=10.688 [Epoch 262][Batch 1399], LR: 1.00E-05, Speed: 7.374 samples/sec, ObjLoss=24.862, BoxCenterLoss=17.926, BoxScaleLoss=5.888, ClassLoss=10.716 [Epoch 262][Batch 1499], LR: 1.00E-05, Speed: 8.320 samples/sec, ObjLoss=24.884, BoxCenterLoss=17.997, BoxScaleLoss=5.908, ClassLoss=10.742 [Epoch 262][Batch 1599], LR: 1.00E-05, Speed: 8.978 samples/sec, ObjLoss=24.905, BoxCenterLoss=18.066, BoxScaleLoss=5.928, ClassLoss=10.768 [Epoch 262][Batch 1699], LR: 1.00E-05, Speed: 13.349 samples/sec, ObjLoss=24.927, BoxCenterLoss=18.136, BoxScaleLoss=5.947, ClassLoss=10.794 [Epoch 262][Batch 1799], LR: 1.00E-05, Speed: 9.306 samples/sec, ObjLoss=24.948, BoxCenterLoss=18.205, BoxScaleLoss=5.967, ClassLoss=10.821 [Epoch 262] Training cost: 2123.821, ObjLoss=24.955, BoxCenterLoss=18.226, BoxScaleLoss=5.973, ClassLoss=10.828 [Epoch 262] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.544 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.296 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.403 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.255 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.402 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.426 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.575 person=10.7 bicycle=20.1 car=30.7 motorcycle=34.4 airplane=51.5 bus=54.2 train=53.5 truck=29.0 boat=18.5 traffic light=17.9 fire hydrant=47.3 stop sign=49.9 parking meter=31.0 bench=17.0 bird=25.3 cat=48.3 dog=43.9 horse=45.0 sheep=39.6 cow=39.6 elephant=52.0 bear=50.6 zebra=55.7 giraffe=50.4 backpack=7.7 umbrella=31.5 handbag=8.0 tie=22.4 suitcase=27.8 frisbee=42.1 skis=15.3 snowboard=25.6 sports ball=29.8 kite=32.0 baseball bat=17.5 baseball glove=25.7 skateboard=34.0 surfboard=26.0 tennis racket=31.9 bottle=23.9 wine glass=23.1 cup=26.5 fork=21.4 knife=5.5 spoon=5.0 bowl=23.5 banana=17.5 apple=10.9 sandwich=22.0 orange=19.8 broccoli=16.0 carrot=12.8 hot dog=17.6 pizza=35.3 donut=27.6 cake=24.4 chair=20.0 couch=30.7 potted plant=18.7 bed=38.2 dining table=23.9 toilet=42.6 tv=42.0 laptop=43.7 mouse=41.2 remote=12.8 keyboard=32.6 cell phone=16.7 microwave=36.1 oven=23.1 toaster=9.1 sink=28.8 refrigerator=38.1 book=6.5 clock=35.7 vase=24.4 scissors=19.0 teddy bear=29.4 hair drier=0.0 toothbrush=8.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.1 [Epoch 263][Batch 99], LR: 1.00E-05, Speed: 9.688 samples/sec, ObjLoss=24.977, BoxCenterLoss=18.297, BoxScaleLoss=5.991, ClassLoss=10.852 [Epoch 263][Batch 199], LR: 1.00E-05, Speed: 10.440 samples/sec, ObjLoss=24.998, BoxCenterLoss=18.366, BoxScaleLoss=6.010, ClassLoss=10.876 [Epoch 263][Batch 299], LR: 1.00E-05, Speed: 9.472 samples/sec, ObjLoss=25.017, BoxCenterLoss=18.436, BoxScaleLoss=6.031, ClassLoss=10.902 [Epoch 263][Batch 399], LR: 1.00E-05, Speed: 9.565 samples/sec, ObjLoss=25.038, BoxCenterLoss=18.505, BoxScaleLoss=6.049, ClassLoss=10.926 [Epoch 263][Batch 499], LR: 1.00E-05, Speed: 8.966 samples/sec, ObjLoss=25.059, BoxCenterLoss=18.575, BoxScaleLoss=6.070, ClassLoss=10.952 [Epoch 263][Batch 599], LR: 1.00E-05, Speed: 9.463 samples/sec, ObjLoss=25.081, BoxCenterLoss=18.648, BoxScaleLoss=6.090, ClassLoss=10.976 [Epoch 263][Batch 699], LR: 1.00E-05, Speed: 10.733 samples/sec, ObjLoss=25.101, BoxCenterLoss=18.716, BoxScaleLoss=6.109, ClassLoss=11.000 [Epoch 263][Batch 799], LR: 1.00E-05, Speed: 11.347 samples/sec, ObjLoss=25.122, BoxCenterLoss=18.785, BoxScaleLoss=6.129, ClassLoss=11.025 [Epoch 263][Batch 899], LR: 1.00E-05, Speed: 10.882 samples/sec, ObjLoss=25.143, BoxCenterLoss=18.855, BoxScaleLoss=6.149, ClassLoss=11.052 [Epoch 263][Batch 999], LR: 1.00E-05, Speed: 12.082 samples/sec, ObjLoss=25.164, BoxCenterLoss=18.925, BoxScaleLoss=6.169, ClassLoss=11.077 [Epoch 263][Batch 1099], LR: 1.00E-05, Speed: 11.769 samples/sec, ObjLoss=25.185, BoxCenterLoss=18.994, BoxScaleLoss=6.188, ClassLoss=11.101 [Epoch 263][Batch 1199], LR: 1.00E-05, Speed: 8.922 samples/sec, ObjLoss=25.205, BoxCenterLoss=19.064, BoxScaleLoss=6.208, ClassLoss=11.127 [Epoch 263][Batch 1299], LR: 1.00E-05, Speed: 10.658 samples/sec, ObjLoss=25.226, BoxCenterLoss=19.134, BoxScaleLoss=6.228, ClassLoss=11.153 [Epoch 263][Batch 1399], LR: 1.00E-05, Speed: 135.066 samples/sec, ObjLoss=25.247, BoxCenterLoss=19.205, BoxScaleLoss=6.248, ClassLoss=11.178 [Epoch 263][Batch 1499], LR: 1.00E-05, Speed: 9.444 samples/sec, ObjLoss=25.267, BoxCenterLoss=19.274, BoxScaleLoss=6.267, ClassLoss=11.204 [Epoch 263][Batch 1599], LR: 1.00E-05, Speed: 9.575 samples/sec, ObjLoss=25.289, BoxCenterLoss=19.345, BoxScaleLoss=6.287, ClassLoss=11.229 [Epoch 263][Batch 1699], LR: 1.00E-05, Speed: 8.196 samples/sec, ObjLoss=25.309, BoxCenterLoss=19.415, BoxScaleLoss=6.308, ClassLoss=11.255 [Epoch 263][Batch 1799], LR: 1.00E-05, Speed: 12.596 samples/sec, ObjLoss=25.329, BoxCenterLoss=19.482, BoxScaleLoss=6.326, ClassLoss=11.280 [Epoch 263] Training cost: 2072.772, ObjLoss=25.336, BoxCenterLoss=19.504, BoxScaleLoss=6.333, ClassLoss=11.288 [Epoch 263] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.311 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.439 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.423 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.449 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.470 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.595 person=11.0 bicycle=20.9 car=28.6 motorcycle=36.3 airplane=56.3 bus=58.6 train=58.2 truck=30.7 boat=20.1 traffic light=16.9 fire hydrant=55.4 stop sign=52.8 parking meter=40.4 bench=18.7 bird=25.5 cat=51.4 dog=51.0 horse=45.8 sheep=40.7 cow=40.5 elephant=51.9 bear=60.5 zebra=55.6 giraffe=56.2 backpack=8.0 umbrella=32.2 handbag=8.3 tie=21.7 suitcase=26.1 frisbee=44.9 skis=15.6 snowboard=25.1 sports ball=28.2 kite=31.2 baseball bat=18.4 baseball glove=26.7 skateboard=36.9 surfboard=27.2 tennis racket=35.8 bottle=25.3 wine glass=24.0 cup=28.7 fork=20.0 knife=6.9 spoon=5.9 bowl=25.1 banana=17.8 apple=13.3 sandwich=23.9 orange=20.3 broccoli=15.8 carrot=13.2 hot dog=15.0 pizza=35.2 donut=36.9 cake=29.4 chair=19.4 couch=36.3 potted plant=20.7 bed=38.3 dining table=25.3 toilet=52.8 tv=42.3 laptop=44.6 mouse=38.8 remote=14.4 keyboard=37.4 cell phone=19.0 microwave=25.5 oven=27.8 toaster=11.4 sink=27.5 refrigerator=45.1 book=5.2 clock=38.3 vase=24.5 scissors=20.4 teddy bear=34.5 hair drier=0.0 toothbrush=12.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =29.9 [Epoch 264][Batch 99], LR: 1.00E-05, Speed: 10.212 samples/sec, ObjLoss=25.356, BoxCenterLoss=19.572, BoxScaleLoss=6.353, ClassLoss=11.313 [Epoch 264][Batch 199], LR: 1.00E-05, Speed: 8.766 samples/sec, ObjLoss=25.378, BoxCenterLoss=19.642, BoxScaleLoss=6.371, ClassLoss=11.336 [Epoch 264][Batch 299], LR: 1.00E-05, Speed: 8.878 samples/sec, ObjLoss=25.398, BoxCenterLoss=19.712, BoxScaleLoss=6.390, ClassLoss=11.361 [Epoch 264][Batch 399], LR: 1.00E-05, Speed: 97.735 samples/sec, ObjLoss=25.417, BoxCenterLoss=19.781, BoxScaleLoss=6.410, ClassLoss=11.386 [Epoch 264][Batch 499], LR: 1.00E-05, Speed: 8.710 samples/sec, ObjLoss=25.438, BoxCenterLoss=19.853, BoxScaleLoss=6.430, ClassLoss=11.410 [Epoch 264][Batch 599], LR: 1.00E-05, Speed: 11.691 samples/sec, ObjLoss=25.459, BoxCenterLoss=19.923, BoxScaleLoss=6.449, ClassLoss=11.434 [Epoch 264][Batch 699], LR: 1.00E-05, Speed: 104.181 samples/sec, ObjLoss=25.479, BoxCenterLoss=19.991, BoxScaleLoss=6.467, ClassLoss=11.457 [Epoch 264][Batch 799], LR: 1.00E-05, Speed: 11.061 samples/sec, ObjLoss=25.500, BoxCenterLoss=20.062, BoxScaleLoss=6.488, ClassLoss=11.484 [Epoch 264][Batch 899], LR: 1.00E-05, Speed: 9.050 samples/sec, ObjLoss=25.521, BoxCenterLoss=20.133, BoxScaleLoss=6.507, ClassLoss=11.508 [Epoch 264][Batch 999], LR: 1.00E-05, Speed: 10.262 samples/sec, ObjLoss=25.542, BoxCenterLoss=20.202, BoxScaleLoss=6.527, ClassLoss=11.533 [Epoch 264][Batch 1099], LR: 1.00E-05, Speed: 103.964 samples/sec, ObjLoss=25.563, BoxCenterLoss=20.272, BoxScaleLoss=6.546, ClassLoss=11.558 [Epoch 264][Batch 1199], LR: 1.00E-05, Speed: 10.854 samples/sec, ObjLoss=25.583, BoxCenterLoss=20.340, BoxScaleLoss=6.567, ClassLoss=11.584 [Epoch 264][Batch 1299], LR: 1.00E-05, Speed: 10.236 samples/sec, ObjLoss=25.604, BoxCenterLoss=20.407, BoxScaleLoss=6.586, ClassLoss=11.609 [Epoch 264][Batch 1399], LR: 1.00E-05, Speed: 9.289 samples/sec, ObjLoss=25.623, BoxCenterLoss=20.474, BoxScaleLoss=6.604, ClassLoss=11.632 [Epoch 264][Batch 1499], LR: 1.00E-05, Speed: 8.231 samples/sec, ObjLoss=25.644, BoxCenterLoss=20.541, BoxScaleLoss=6.623, ClassLoss=11.659 [Epoch 264][Batch 1599], LR: 1.00E-05, Speed: 10.832 samples/sec, ObjLoss=25.665, BoxCenterLoss=20.612, BoxScaleLoss=6.643, ClassLoss=11.684 [Epoch 264][Batch 1699], LR: 1.00E-05, Speed: 9.448 samples/sec, ObjLoss=25.687, BoxCenterLoss=20.683, BoxScaleLoss=6.662, ClassLoss=11.709 [Epoch 264][Batch 1799], LR: 1.00E-05, Speed: 12.786 samples/sec, ObjLoss=25.709, BoxCenterLoss=20.754, BoxScaleLoss=6.681, ClassLoss=11.732 [Epoch 264] Training cost: 2083.709, ObjLoss=25.715, BoxCenterLoss=20.776, BoxScaleLoss=6.688, ClassLoss=11.741 [Epoch 264] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.158 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.318 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.421 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.269 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.467 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596 person=7.3 bicycle=20.0 car=31.1 motorcycle=37.4 airplane=52.6 bus=58.5 train=58.3 truck=29.5 boat=20.1 traffic light=18.7 fire hydrant=55.8 stop sign=48.3 parking meter=35.8 bench=17.5 bird=26.9 cat=53.2 dog=48.7 horse=46.9 sheep=41.2 cow=43.6 elephant=53.6 bear=58.9 zebra=54.2 giraffe=55.4 backpack=9.4 umbrella=29.9 handbag=9.0 tie=23.5 suitcase=27.9 frisbee=44.6 skis=16.3 snowboard=26.8 sports ball=26.5 kite=33.6 baseball bat=20.0 baseball glove=25.6 skateboard=37.1 surfboard=25.7 tennis racket=31.8 bottle=25.3 wine glass=23.4 cup=30.1 fork=18.9 knife=7.6 spoon=8.6 bowl=22.4 banana=17.3 apple=11.0 sandwich=25.5 orange=21.2 broccoli=17.9 carrot=15.7 hot dog=15.5 pizza=34.6 donut=39.6 cake=31.7 chair=21.3 couch=30.0 potted plant=19.7 bed=41.4 dining table=26.2 toilet=48.7 tv=43.5 laptop=44.6 mouse=48.3 remote=15.9 keyboard=34.5 cell phone=18.5 microwave=35.4 oven=26.8 toaster=17.3 sink=29.4 refrigerator=41.5 book=7.6 clock=31.9 vase=25.4 scissors=23.2 teddy bear=35.0 hair drier=0.0 toothbrush=6.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =30.0 [Epoch 265][Batch 99], LR: 1.00E-05, Speed: 11.937 samples/sec, ObjLoss=25.734, BoxCenterLoss=20.842, BoxScaleLoss=6.707, ClassLoss=11.765 [Epoch 265][Batch 199], LR: 1.00E-05, Speed: 10.755 samples/sec, ObjLoss=25.754, BoxCenterLoss=20.910, BoxScaleLoss=6.727, ClassLoss=11.790 [Epoch 265][Batch 299], LR: 1.00E-05, Speed: 9.588 samples/sec, ObjLoss=25.775, BoxCenterLoss=20.980, BoxScaleLoss=6.746, ClassLoss=11.814 [Epoch 265][Batch 399], LR: 1.00E-05, Speed: 9.811 samples/sec, ObjLoss=25.796, BoxCenterLoss=21.049, BoxScaleLoss=6.764, ClassLoss=11.837 [Epoch 265][Batch 499], LR: 1.00E-05, Speed: 10.410 samples/sec, ObjLoss=25.815, BoxCenterLoss=21.116, BoxScaleLoss=6.784, ClassLoss=11.862 [Epoch 265][Batch 599], LR: 1.00E-05, Speed: 9.673 samples/sec, ObjLoss=25.835, BoxCenterLoss=21.183, BoxScaleLoss=6.803, ClassLoss=11.886 [Epoch 265][Batch 699], LR: 1.00E-05, Speed: 10.962 samples/sec, ObjLoss=25.856, BoxCenterLoss=21.252, BoxScaleLoss=6.822, ClassLoss=11.909 [Epoch 265][Batch 799], LR: 1.00E-05, Speed: 9.649 samples/sec, ObjLoss=25.877, BoxCenterLoss=21.322, BoxScaleLoss=6.841, ClassLoss=11.932 [Epoch 265][Batch 899], LR: 1.00E-05, Speed: 10.742 samples/sec, ObjLoss=25.896, BoxCenterLoss=21.388, BoxScaleLoss=6.859, ClassLoss=11.955 [Epoch 265][Batch 999], LR: 1.00E-05, Speed: 10.170 samples/sec, ObjLoss=25.916, BoxCenterLoss=21.456, BoxScaleLoss=6.877, ClassLoss=11.977 [Epoch 265][Batch 1099], LR: 1.00E-05, Speed: 97.467 samples/sec, ObjLoss=25.935, BoxCenterLoss=21.524, BoxScaleLoss=6.898, ClassLoss=12.003 [Epoch 265][Batch 1199], LR: 1.00E-05, Speed: 94.135 samples/sec, ObjLoss=25.959, BoxCenterLoss=21.598, BoxScaleLoss=6.918, ClassLoss=12.027 [Epoch 265][Batch 1299], LR: 1.00E-05, Speed: 10.412 samples/sec, ObjLoss=25.981, BoxCenterLoss=21.669, BoxScaleLoss=6.936, ClassLoss=12.050 [Epoch 265][Batch 1399], LR: 1.00E-05, Speed: 46.510 samples/sec, ObjLoss=26.002, BoxCenterLoss=21.738, BoxScaleLoss=6.954, ClassLoss=12.073 [Epoch 265][Batch 1499], LR: 1.00E-05, Speed: 9.560 samples/sec, ObjLoss=26.022, BoxCenterLoss=21.805, BoxScaleLoss=6.972, ClassLoss=12.097 [Epoch 265][Batch 1599], LR: 1.00E-05, Speed: 9.727 samples/sec, ObjLoss=26.042, BoxCenterLoss=21.872, BoxScaleLoss=6.990, ClassLoss=12.119 [Epoch 265][Batch 1699], LR: 1.00E-05, Speed: 8.489 samples/sec, ObjLoss=26.063, BoxCenterLoss=21.941, BoxScaleLoss=7.010, ClassLoss=12.144 [Epoch 265][Batch 1799], LR: 1.00E-05, Speed: 9.662 samples/sec, ObjLoss=26.084, BoxCenterLoss=22.009, BoxScaleLoss=7.028, ClassLoss=12.168 [Epoch 265] Training cost: 2096.920, ObjLoss=26.090, BoxCenterLoss=22.031, BoxScaleLoss=7.034, ClassLoss=12.175 [Epoch 265] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.546 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.423 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.449 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.486 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.586 person=4.4 bicycle=20.6 car=29.6 motorcycle=36.5 airplane=51.3 bus=55.2 train=53.7 truck=31.2 boat=20.0 traffic light=17.0 fire hydrant=51.5 stop sign=55.8 parking meter=36.4 bench=17.7 bird=24.4 cat=50.0 dog=50.4 horse=47.4 sheep=41.3 cow=40.5 elephant=53.1 bear=60.1 zebra=56.9 giraffe=52.7 backpack=7.7 umbrella=31.7 handbag=8.6 tie=20.5 suitcase=27.5 frisbee=47.0 skis=15.2 snowboard=28.3 sports ball=29.4 kite=32.6 baseball bat=18.3 baseball glove=25.0 skateboard=39.0 surfboard=28.0 tennis racket=36.1 bottle=26.4 wine glass=25.1 cup=28.8 fork=20.9 knife=6.5 spoon=8.0 bowl=27.0 banana=17.0 apple=11.7 sandwich=23.6 orange=19.2 broccoli=15.2 carrot=12.8 hot dog=19.8 pizza=33.1 donut=35.6 cake=28.0 chair=21.1 couch=33.8 potted plant=20.6 bed=37.8 dining table=26.0 toilet=47.6 tv=48.4 laptop=44.8 mouse=38.3 remote=15.7 keyboard=29.5 cell phone=19.1 microwave=34.7 oven=27.0 toaster=16.8 sink=28.5 refrigerator=42.8 book=6.6 clock=35.9 vase=26.1 scissors=28.9 teddy bear=28.1 hair drier=0.4 toothbrush=9.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =29.7 [Epoch 266][Batch 99], LR: 1.00E-05, Speed: 9.004 samples/sec, ObjLoss=26.111, BoxCenterLoss=22.103, BoxScaleLoss=7.053, ClassLoss=12.198 [Epoch 266][Batch 199], LR: 1.00E-05, Speed: 8.996 samples/sec, ObjLoss=26.131, BoxCenterLoss=22.172, BoxScaleLoss=7.073, ClassLoss=12.221 [Epoch 266][Batch 299], LR: 1.00E-05, Speed: 7.683 samples/sec, ObjLoss=26.152, BoxCenterLoss=22.241, BoxScaleLoss=7.092, ClassLoss=12.245 [Epoch 266][Batch 399], LR: 1.00E-05, Speed: 9.224 samples/sec, ObjLoss=26.173, BoxCenterLoss=22.313, BoxScaleLoss=7.111, ClassLoss=12.268 [Epoch 266][Batch 499], LR: 1.00E-05, Speed: 75.272 samples/sec, ObjLoss=26.193, BoxCenterLoss=22.381, BoxScaleLoss=7.129, ClassLoss=12.290 [Epoch 266][Batch 599], LR: 1.00E-05, Speed: 9.828 samples/sec, ObjLoss=26.212, BoxCenterLoss=22.447, BoxScaleLoss=7.147, ClassLoss=12.312 [Epoch 266][Batch 699], LR: 1.00E-05, Speed: 9.531 samples/sec, ObjLoss=26.233, BoxCenterLoss=22.517, BoxScaleLoss=7.167, ClassLoss=12.336 [Epoch 266][Batch 799], LR: 1.00E-05, Speed: 11.115 samples/sec, ObjLoss=26.253, BoxCenterLoss=22.583, BoxScaleLoss=7.184, ClassLoss=12.358 [Epoch 266][Batch 899], LR: 1.00E-05, Speed: 8.102 samples/sec, ObjLoss=26.273, BoxCenterLoss=22.650, BoxScaleLoss=7.203, ClassLoss=12.382 [Epoch 266][Batch 999], LR: 1.00E-05, Speed: 11.684 samples/sec, ObjLoss=26.293, BoxCenterLoss=22.720, BoxScaleLoss=7.222, ClassLoss=12.404 [Epoch 266][Batch 1099], LR: 1.00E-05, Speed: 10.963 samples/sec, ObjLoss=26.312, BoxCenterLoss=22.786, BoxScaleLoss=7.242, ClassLoss=12.429 [Epoch 266][Batch 1199], LR: 1.00E-05, Speed: 133.802 samples/sec, ObjLoss=26.333, BoxCenterLoss=22.855, BoxScaleLoss=7.260, ClassLoss=12.452 [Epoch 266][Batch 1299], LR: 1.00E-05, Speed: 8.508 samples/sec, ObjLoss=26.353, BoxCenterLoss=22.924, BoxScaleLoss=7.279, ClassLoss=12.475 [Epoch 266][Batch 1399], LR: 1.00E-05, Speed: 9.133 samples/sec, ObjLoss=26.373, BoxCenterLoss=22.989, BoxScaleLoss=7.296, ClassLoss=12.496 [Epoch 266][Batch 1499], LR: 1.00E-05, Speed: 8.246 samples/sec, ObjLoss=26.392, BoxCenterLoss=23.056, BoxScaleLoss=7.314, ClassLoss=12.518 [Epoch 266][Batch 1599], LR: 1.00E-05, Speed: 9.180 samples/sec, ObjLoss=26.412, BoxCenterLoss=23.123, BoxScaleLoss=7.334, ClassLoss=12.541 [Epoch 266][Batch 1699], LR: 1.00E-05, Speed: 72.731 samples/sec, ObjLoss=26.432, BoxCenterLoss=23.191, BoxScaleLoss=7.352, ClassLoss=12.564 [Epoch 266][Batch 1799], LR: 1.00E-05, Speed: 12.509 samples/sec, ObjLoss=26.453, BoxCenterLoss=23.261, BoxScaleLoss=7.370, ClassLoss=12.587 [Epoch 266] Training cost: 2124.218, ObjLoss=26.460, BoxCenterLoss=23.284, BoxScaleLoss=7.377, ClassLoss=12.595 [Epoch 266] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.546 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.282 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.310 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.465 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596 person=4.5 bicycle=19.0 car=29.5 motorcycle=36.8 airplane=48.7 bus=55.1 train=57.9 truck=28.8 boat=18.1 traffic light=14.8 fire hydrant=55.6 stop sign=50.2 parking meter=36.9 bench=18.9 bird=25.7 cat=51.3 dog=47.0 horse=45.1 sheep=41.6 cow=39.6 elephant=54.4 bear=60.6 zebra=54.4 giraffe=57.2 backpack=7.3 umbrella=30.1 handbag=8.1 tie=20.2 suitcase=26.1 frisbee=39.9 skis=16.5 snowboard=26.5 sports ball=28.5 kite=30.9 baseball bat=19.9 baseball glove=26.3 skateboard=31.8 surfboard=24.0 tennis racket=34.7 bottle=25.5 wine glass=24.4 cup=29.1 fork=20.3 knife=5.9 spoon=6.3 bowl=28.3 banana=16.7 apple=9.0 sandwich=23.7 orange=22.3 broccoli=15.8 carrot=12.9 hot dog=13.9 pizza=33.1 donut=31.2 cake=30.4 chair=19.7 couch=37.4 potted plant=18.5 bed=40.0 dining table=24.8 toilet=49.3 tv=48.3 laptop=45.1 mouse=41.0 remote=13.0 keyboard=32.2 cell phone=19.6 microwave=32.6 oven=26.7 toaster=16.4 sink=26.6 refrigerator=43.1 book=8.3 clock=36.8 vase=27.9 scissors=25.3 teddy bear=35.5 hair drier=2.0 toothbrush=7.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =29.4 [Epoch 267][Batch 99], LR: 1.00E-05, Speed: 8.730 samples/sec, ObjLoss=26.480, BoxCenterLoss=23.352, BoxScaleLoss=7.396, ClassLoss=12.618 [Epoch 267][Batch 199], LR: 1.00E-05, Speed: 8.739 samples/sec, ObjLoss=26.501, BoxCenterLoss=23.423, BoxScaleLoss=7.416, ClassLoss=12.643 [Epoch 267][Batch 299], LR: 1.00E-05, Speed: 9.967 samples/sec, ObjLoss=26.520, BoxCenterLoss=23.488, BoxScaleLoss=7.434, ClassLoss=12.666 [Epoch 267][Batch 399], LR: 1.00E-05, Speed: 9.992 samples/sec, ObjLoss=26.540, BoxCenterLoss=23.557, BoxScaleLoss=7.453, ClassLoss=12.688 [Epoch 267][Batch 499], LR: 1.00E-05, Speed: 80.592 samples/sec, ObjLoss=26.560, BoxCenterLoss=23.626, BoxScaleLoss=7.473, ClassLoss=12.711 [Epoch 267][Batch 599], LR: 1.00E-05, Speed: 9.410 samples/sec, ObjLoss=26.579, BoxCenterLoss=23.692, BoxScaleLoss=7.492, ClassLoss=12.734 [Epoch 267][Batch 699], LR: 1.00E-05, Speed: 9.284 samples/sec, ObjLoss=26.597, BoxCenterLoss=23.758, BoxScaleLoss=7.511, ClassLoss=12.758 [Epoch 267][Batch 799], LR: 1.00E-05, Speed: 9.804 samples/sec, ObjLoss=26.616, BoxCenterLoss=23.826, BoxScaleLoss=7.531, ClassLoss=12.781 [Epoch 267][Batch 899], LR: 1.00E-05, Speed: 121.955 samples/sec, ObjLoss=26.636, BoxCenterLoss=23.893, BoxScaleLoss=7.550, ClassLoss=12.804 [Epoch 267][Batch 999], LR: 1.00E-05, Speed: 8.599 samples/sec, ObjLoss=26.655, BoxCenterLoss=23.962, BoxScaleLoss=7.569, ClassLoss=12.829 [Epoch 267][Batch 1099], LR: 1.00E-05, Speed: 13.550 samples/sec, ObjLoss=26.675, BoxCenterLoss=24.029, BoxScaleLoss=7.587, ClassLoss=12.852 [Epoch 267][Batch 1199], LR: 1.00E-05, Speed: 9.356 samples/sec, ObjLoss=26.695, BoxCenterLoss=24.096, BoxScaleLoss=7.606, ClassLoss=12.875 [Epoch 267][Batch 1299], LR: 1.00E-05, Speed: 11.519 samples/sec, ObjLoss=26.716, BoxCenterLoss=24.164, BoxScaleLoss=7.624, ClassLoss=12.897 [Epoch 267][Batch 1399], LR: 1.00E-05, Speed: 8.309 samples/sec, ObjLoss=26.737, BoxCenterLoss=24.235, BoxScaleLoss=7.644, ClassLoss=12.919 [Epoch 267][Batch 1499], LR: 1.00E-05, Speed: 9.866 samples/sec, ObjLoss=26.757, BoxCenterLoss=24.303, BoxScaleLoss=7.662, ClassLoss=12.943 [Epoch 267][Batch 1599], LR: 1.00E-05, Speed: 10.331 samples/sec, ObjLoss=26.776, BoxCenterLoss=24.372, BoxScaleLoss=7.681, ClassLoss=12.967 [Epoch 267][Batch 1699], LR: 1.00E-05, Speed: 8.765 samples/sec, ObjLoss=26.798, BoxCenterLoss=24.440, BoxScaleLoss=7.699, ClassLoss=12.989 [Epoch 267][Batch 1799], LR: 1.00E-05, Speed: 12.883 samples/sec, ObjLoss=26.817, BoxCenterLoss=24.506, BoxScaleLoss=7.717, ClassLoss=13.011 [Epoch 267] Training cost: 2163.881, ObjLoss=26.824, BoxCenterLoss=24.527, BoxScaleLoss=7.723, ClassLoss=13.018 [Epoch 267] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.300 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.142 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.327 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.422 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.480 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608 person=2.8 bicycle=20.8 car=28.9 motorcycle=36.5 airplane=54.2 bus=55.0 train=55.9 truck=32.0 boat=19.0 traffic light=16.8 fire hydrant=53.7 stop sign=51.1 parking meter=34.6 bench=19.5 bird=25.7 cat=55.8 dog=46.3 horse=48.5 sheep=38.8 cow=44.2 elephant=53.9 bear=55.1 zebra=56.8 giraffe=55.8 backpack=7.4 umbrella=32.5 handbag=8.5 tie=21.1 suitcase=28.0 frisbee=41.3 skis=15.3 snowboard=22.8 sports ball=21.8 kite=29.4 baseball bat=21.5 baseball glove=26.9 skateboard=34.2 surfboard=25.4 tennis racket=33.9 bottle=25.5 wine glass=22.3 cup=28.0 fork=21.7 knife=8.8 spoon=8.2 bowl=28.6 banana=18.4 apple=10.8 sandwich=25.5 orange=20.7 broccoli=17.0 carrot=14.3 hot dog=21.6 pizza=37.1 donut=37.9 cake=27.4 chair=20.3 couch=35.1 potted plant=21.2 bed=36.0 dining table=24.0 toilet=49.0 tv=47.6 laptop=47.2 mouse=42.5 remote=17.4 keyboard=39.4 cell phone=17.4 microwave=42.3 oven=26.1 toaster=17.3 sink=27.0 refrigerator=45.0 book=7.3 clock=39.1 vase=25.8 scissors=24.0 teddy bear=34.4 hair drier=1.0 toothbrush=10.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =30.0 [Epoch 268][Batch 99], LR: 1.00E-05, Speed: 116.296 samples/sec, ObjLoss=26.843, BoxCenterLoss=24.595, BoxScaleLoss=7.742, ClassLoss=13.039 [Epoch 268][Batch 199], LR: 1.00E-05, Speed: 105.747 samples/sec, ObjLoss=26.863, BoxCenterLoss=24.661, BoxScaleLoss=7.760, ClassLoss=13.062 [Epoch 268][Batch 299], LR: 1.00E-05, Speed: 10.292 samples/sec, ObjLoss=26.882, BoxCenterLoss=24.729, BoxScaleLoss=7.779, ClassLoss=13.085 [Epoch 268][Batch 399], LR: 1.00E-05, Speed: 9.947 samples/sec, ObjLoss=26.901, BoxCenterLoss=24.797, BoxScaleLoss=7.799, ClassLoss=13.109 Namespace(batch_size=64, data_shape=416, dataset='coco', epochs=280, gpus='0,1,2,3,4,5,6,7', log_interval=100, lr=0.001, lr_decay=0.1, lr_decay_epoch='220,250', momentum=0.9, network='darknet53', num_samples=117266, num_workers=48, resume='yolo3_darknet53_coco_best.params', save_interval=10, save_prefix='yolo3_darknet53_coco', seed=233, start_epoch=250, syncbn=True, val_interval=1, wd=0.0005) Start training from [Epoch 250] [Epoch 250][Batch 99], LR: 1.00E-05, Speed: 109.560 samples/sec, ObjLoss=21.487, BoxCenterLoss=14.208, BoxScaleLoss=4.263, ClassLoss=7.551 [Epoch 250][Batch 199], LR: 1.00E-05, Speed: 46.163 samples/sec, ObjLoss=21.899, BoxCenterLoss=14.490, BoxScaleLoss=4.495, ClassLoss=7.677 [Epoch 250][Batch 299], LR: 1.00E-05, Speed: 8.594 samples/sec, ObjLoss=21.789, BoxCenterLoss=14.540, BoxScaleLoss=4.524, ClassLoss=7.701 [Epoch 250][Batch 399], LR: 1.00E-05, Speed: 6.592 samples/sec, ObjLoss=21.857, BoxCenterLoss=14.572, BoxScaleLoss=4.583, ClassLoss=7.813 [Epoch 250][Batch 499], LR: 1.00E-05, Speed: 9.106 samples/sec, ObjLoss=21.628, BoxCenterLoss=14.404, BoxScaleLoss=4.455, ClassLoss=7.756 [Epoch 250][Batch 599], LR: 1.00E-05, Speed: 7.203 samples/sec, ObjLoss=21.682, BoxCenterLoss=14.394, BoxScaleLoss=4.421, ClassLoss=7.680 [Epoch 250][Batch 699], LR: 1.00E-05, Speed: 117.144 samples/sec, ObjLoss=21.710, BoxCenterLoss=14.434, BoxScaleLoss=4.453, ClassLoss=7.716 [Epoch 250][Batch 799], LR: 1.00E-05, Speed: 7.817 samples/sec, ObjLoss=21.765, BoxCenterLoss=14.462, BoxScaleLoss=4.465, ClassLoss=7.722 [Epoch 250][Batch 899], LR: 1.00E-05, Speed: 9.757 samples/sec, ObjLoss=21.892, BoxCenterLoss=14.519, BoxScaleLoss=4.461, ClassLoss=7.714 [Epoch 250][Batch 999], LR: 1.00E-05, Speed: 111.820 samples/sec, ObjLoss=21.895, BoxCenterLoss=14.508, BoxScaleLoss=4.433, ClassLoss=7.680 [Epoch 250][Batch 1099], LR: 1.00E-05, Speed: 9.539 samples/sec, ObjLoss=21.805, BoxCenterLoss=14.429, BoxScaleLoss=4.393, ClassLoss=7.632 [Epoch 250][Batch 1199], LR: 1.00E-05, Speed: 8.733 samples/sec, ObjLoss=21.691, BoxCenterLoss=14.337, BoxScaleLoss=4.356, ClassLoss=7.574 [Epoch 250][Batch 1299], LR: 1.00E-05, Speed: 9.016 samples/sec, ObjLoss=21.707, BoxCenterLoss=14.347, BoxScaleLoss=4.355, ClassLoss=7.597 [Epoch 250][Batch 1399], LR: 1.00E-05, Speed: 110.682 samples/sec, ObjLoss=21.699, BoxCenterLoss=14.349, BoxScaleLoss=4.403, ClassLoss=7.656 [Epoch 250][Batch 1499], LR: 1.00E-05, Speed: 10.598 samples/sec, ObjLoss=21.651, BoxCenterLoss=14.324, BoxScaleLoss=4.393, ClassLoss=7.636 [Epoch 250][Batch 1599], LR: 1.00E-05, Speed: 11.382 samples/sec, ObjLoss=21.609, BoxCenterLoss=14.293, BoxScaleLoss=4.367, ClassLoss=7.601 [Epoch 250][Batch 1699], LR: 1.00E-05, Speed: 8.557 samples/sec, ObjLoss=21.550, BoxCenterLoss=14.259, BoxScaleLoss=4.355, ClassLoss=7.577 [Epoch 250][Batch 1799], LR: 1.00E-05, Speed: 115.046 samples/sec, ObjLoss=21.600, BoxCenterLoss=14.282, BoxScaleLoss=4.342, ClassLoss=7.555 [Epoch 250] Training cost: 2236.399, ObjLoss=21.595, BoxCenterLoss=14.278, BoxScaleLoss=4.348, ClassLoss=7.563 [Epoch 250] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.372 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.369 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.419 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.430 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610 person=45.6 bicycle=25.6 car=34.5 motorcycle=39.6 airplane=57.2 bus=62.4 train=61.9 truck=32.1 boat=20.8 traffic light=20.2 fire hydrant=60.1 stop sign=57.0 parking meter=39.1 bench=19.8 bird=30.6 cat=61.2 dog=56.5 horse=49.1 sheep=44.8 cow=47.8 elephant=58.4 bear=63.0 zebra=58.4 giraffe=58.1 backpack=10.9 umbrella=33.5 handbag=10.3 tie=24.9 suitcase=29.5 frisbee=51.3 skis=17.8 snowboard=29.4 sports ball=36.4 kite=34.0 baseball bat=22.3 baseball glove=29.8 skateboard=43.2 surfboard=29.9 tennis racket=39.5 bottle=28.3 wine glass=27.9 cup=34.8 fork=25.6 knife=10.1 spoon=10.6 bowl=35.2 banana=18.6 apple=13.8 sandwich=27.8 orange=26.7 broccoli=16.5 carrot=16.6 hot dog=27.8 pizza=45.7 donut=38.7 cake=32.5 chair=23.9 couch=38.6 potted plant=20.6 bed=42.9 dining table=26.2 toilet=53.9 tv=49.8 laptop=50.3 mouse=52.0 remote=21.2 keyboard=44.0 cell phone=27.6 microwave=44.7 oven=30.3 toaster=5.9 sink=31.5 refrigerator=48.3 book=7.6 clock=44.5 vase=31.2 scissors=30.7 teddy bear=39.3 hair drier=0.0 toothbrush=15.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 251][Batch 99], LR: 1.00E-05, Speed: 9.674 samples/sec, ObjLoss=21.545, BoxCenterLoss=14.234, BoxScaleLoss=4.328, ClassLoss=7.536 [Epoch 251][Batch 199], LR: 1.00E-05, Speed: 90.384 samples/sec, ObjLoss=21.548, BoxCenterLoss=14.224, BoxScaleLoss=4.322, ClassLoss=7.542 [Epoch 251][Batch 299], LR: 1.00E-05, Speed: 9.306 samples/sec, ObjLoss=21.532, BoxCenterLoss=14.211, BoxScaleLoss=4.317, ClassLoss=7.537 [Epoch 251][Batch 399], LR: 1.00E-05, Speed: 9.198 samples/sec, ObjLoss=21.477, BoxCenterLoss=14.172, BoxScaleLoss=4.301, ClassLoss=7.520 [Epoch 251][Batch 499], LR: 1.00E-05, Speed: 6.673 samples/sec, ObjLoss=21.500, BoxCenterLoss=14.183, BoxScaleLoss=4.308, ClassLoss=7.537 [Epoch 251][Batch 599], LR: 1.00E-05, Speed: 10.831 samples/sec, ObjLoss=21.444, BoxCenterLoss=14.145, BoxScaleLoss=4.289, ClassLoss=7.516 [Epoch 251][Batch 699], LR: 1.00E-05, Speed: 7.742 samples/sec, ObjLoss=21.452, BoxCenterLoss=14.153, BoxScaleLoss=4.290, ClassLoss=7.516 [Epoch 251][Batch 799], LR: 1.00E-05, Speed: 6.998 samples/sec, ObjLoss=21.485, BoxCenterLoss=14.167, BoxScaleLoss=4.295, ClassLoss=7.526 [Epoch 251][Batch 899], LR: 1.00E-05, Speed: 106.697 samples/sec, ObjLoss=21.478, BoxCenterLoss=14.165, BoxScaleLoss=4.305, ClassLoss=7.548 [Epoch 251][Batch 999], LR: 1.00E-05, Speed: 7.571 samples/sec, ObjLoss=21.474, BoxCenterLoss=14.172, BoxScaleLoss=4.315, ClassLoss=7.558 [Epoch 251][Batch 1099], LR: 1.00E-05, Speed: 9.981 samples/sec, ObjLoss=21.463, BoxCenterLoss=14.167, BoxScaleLoss=4.315, ClassLoss=7.554 [Epoch 251][Batch 1199], LR: 1.00E-05, Speed: 9.272 samples/sec, ObjLoss=21.476, BoxCenterLoss=14.175, BoxScaleLoss=4.335, ClassLoss=7.576 [Epoch 251][Batch 1299], LR: 1.00E-05, Speed: 7.846 samples/sec, ObjLoss=21.505, BoxCenterLoss=14.188, BoxScaleLoss=4.336, ClassLoss=7.586 [Epoch 251][Batch 1399], LR: 1.00E-05, Speed: 9.479 samples/sec, ObjLoss=21.516, BoxCenterLoss=14.183, BoxScaleLoss=4.327, ClassLoss=7.580 [Epoch 251][Batch 1499], LR: 1.00E-05, Speed: 109.709 samples/sec, ObjLoss=21.509, BoxCenterLoss=14.180, BoxScaleLoss=4.325, ClassLoss=7.579 [Epoch 251][Batch 1599], LR: 1.00E-05, Speed: 109.952 samples/sec, ObjLoss=21.530, BoxCenterLoss=14.186, BoxScaleLoss=4.322, ClassLoss=7.580 [Epoch 251][Batch 1699], LR: 1.00E-05, Speed: 9.916 samples/sec, ObjLoss=21.538, BoxCenterLoss=14.194, BoxScaleLoss=4.321, ClassLoss=7.581 [Epoch 251][Batch 1799], LR: 1.00E-05, Speed: 10.475 samples/sec, ObjLoss=21.526, BoxCenterLoss=14.181, BoxScaleLoss=4.316, ClassLoss=7.566 [Epoch 251] Training cost: 2201.166, ObjLoss=21.509, BoxCenterLoss=14.170, BoxScaleLoss=4.313, ClassLoss=7.561 [Epoch 251] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.369 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.509 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.419 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.430 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.218 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.456 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.609 person=45.6 bicycle=25.9 car=34.8 motorcycle=39.1 airplane=57.2 bus=62.3 train=62.2 truck=31.0 boat=20.5 traffic light=19.8 fire hydrant=60.6 stop sign=58.1 parking meter=40.3 bench=19.8 bird=30.2 cat=61.2 dog=56.0 horse=49.7 sheep=44.9 cow=47.5 elephant=58.6 bear=62.4 zebra=58.5 giraffe=58.5 backpack=11.0 umbrella=33.4 handbag=10.0 tie=25.1 suitcase=29.9 frisbee=51.7 skis=17.9 snowboard=28.3 sports ball=36.6 kite=33.8 baseball bat=22.6 baseball glove=29.2 skateboard=43.5 surfboard=29.4 tennis racket=39.3 bottle=28.2 wine glass=28.3 cup=34.4 fork=25.2 knife=10.2 spoon=10.6 bowl=34.9 banana=19.1 apple=13.5 sandwich=27.2 orange=25.8 broccoli=16.7 carrot=16.8 hot dog=28.2 pizza=46.2 donut=38.2 cake=32.6 chair=23.6 couch=38.6 potted plant=20.4 bed=42.6 dining table=26.2 toilet=53.5 tv=50.3 laptop=49.9 mouse=52.0 remote=21.7 keyboard=44.2 cell phone=27.9 microwave=43.3 oven=29.8 toaster=5.9 sink=30.9 refrigerator=47.9 book=7.3 clock=44.3 vase=31.5 scissors=30.1 teddy bear=39.3 hair drier=0.0 toothbrush=15.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 252][Batch 99], LR: 1.00E-05, Speed: 10.733 samples/sec, ObjLoss=21.490, BoxCenterLoss=14.154, BoxScaleLoss=4.306, ClassLoss=7.553 [Epoch 252][Batch 199], LR: 1.00E-05, Speed: 7.321 samples/sec, ObjLoss=21.485, BoxCenterLoss=14.148, BoxScaleLoss=4.304, ClassLoss=7.553 [Epoch 252][Batch 299], LR: 1.00E-05, Speed: 6.447 samples/sec, ObjLoss=21.469, BoxCenterLoss=14.137, BoxScaleLoss=4.306, ClassLoss=7.562 [Epoch 252][Batch 399], LR: 1.00E-05, Speed: 93.304 samples/sec, ObjLoss=21.439, BoxCenterLoss=14.121, BoxScaleLoss=4.300, ClassLoss=7.550 [Epoch 252][Batch 499], LR: 1.00E-05, Speed: 8.799 samples/sec, ObjLoss=21.404, BoxCenterLoss=14.098, BoxScaleLoss=4.291, ClassLoss=7.530 [Epoch 252][Batch 599], LR: 1.00E-05, Speed: 120.385 samples/sec, ObjLoss=21.375, BoxCenterLoss=14.078, BoxScaleLoss=4.281, ClassLoss=7.515 [Epoch 252][Batch 699], LR: 1.00E-05, Speed: 11.304 samples/sec, ObjLoss=21.388, BoxCenterLoss=14.082, BoxScaleLoss=4.278, ClassLoss=7.515 [Epoch 252][Batch 799], LR: 1.00E-05, Speed: 8.142 samples/sec, ObjLoss=21.418, BoxCenterLoss=14.103, BoxScaleLoss=4.281, ClassLoss=7.520 [Epoch 252][Batch 899], LR: 1.00E-05, Speed: 7.222 samples/sec, ObjLoss=21.388, BoxCenterLoss=14.088, BoxScaleLoss=4.278, ClassLoss=7.511 [Epoch 252][Batch 999], LR: 1.00E-05, Speed: 7.664 samples/sec, ObjLoss=21.372, BoxCenterLoss=14.078, BoxScaleLoss=4.279, ClassLoss=7.515 [Epoch 252][Batch 1099], LR: 1.00E-05, Speed: 10.402 samples/sec, ObjLoss=21.384, BoxCenterLoss=14.082, BoxScaleLoss=4.274, ClassLoss=7.509 [Epoch 252][Batch 1199], LR: 1.00E-05, Speed: 9.341 samples/sec, ObjLoss=21.400, BoxCenterLoss=14.088, BoxScaleLoss=4.275, ClassLoss=7.506 [Epoch 252][Batch 1299], LR: 1.00E-05, Speed: 9.961 samples/sec, ObjLoss=21.376, BoxCenterLoss=14.070, BoxScaleLoss=4.268, ClassLoss=7.496 [Epoch 252][Batch 1399], LR: 1.00E-05, Speed: 10.442 samples/sec, ObjLoss=21.376, BoxCenterLoss=14.075, BoxScaleLoss=4.277, ClassLoss=7.507 [Epoch 252][Batch 1499], LR: 1.00E-05, Speed: 8.580 samples/sec, ObjLoss=21.364, BoxCenterLoss=14.073, BoxScaleLoss=4.272, ClassLoss=7.494 [Epoch 252][Batch 1599], LR: 1.00E-05, Speed: 8.022 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.069, BoxScaleLoss=4.274, ClassLoss=7.496 [Epoch 252][Batch 1699], LR: 1.00E-05, Speed: 96.265 samples/sec, ObjLoss=21.351, BoxCenterLoss=14.080, BoxScaleLoss=4.281, ClassLoss=7.501 [Epoch 252][Batch 1799], LR: 1.00E-05, Speed: 9.292 samples/sec, ObjLoss=21.341, BoxCenterLoss=14.077, BoxScaleLoss=4.282, ClassLoss=7.504 [Epoch 252] Training cost: 2251.668, ObjLoss=21.343, BoxCenterLoss=14.077, BoxScaleLoss=4.279, ClassLoss=7.500 [Epoch 252] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611 person=45.5 bicycle=25.8 car=34.7 motorcycle=39.0 airplane=56.9 bus=62.1 train=62.3 truck=31.3 boat=20.6 traffic light=20.2 fire hydrant=60.5 stop sign=58.3 parking meter=38.6 bench=20.3 bird=29.9 cat=60.9 dog=55.7 horse=49.4 sheep=44.5 cow=47.2 elephant=58.5 bear=61.3 zebra=58.2 giraffe=59.3 backpack=11.0 umbrella=32.9 handbag=10.5 tie=25.1 suitcase=29.1 frisbee=52.7 skis=17.8 snowboard=28.4 sports ball=36.3 kite=34.2 baseball bat=21.3 baseball glove=29.6 skateboard=42.8 surfboard=29.6 tennis racket=39.9 bottle=28.1 wine glass=28.2 cup=34.7 fork=26.5 knife=10.1 spoon=10.8 bowl=35.3 banana=19.5 apple=13.4 sandwich=27.8 orange=25.4 broccoli=16.4 carrot=16.1 hot dog=27.5 pizza=46.1 donut=39.0 cake=31.9 chair=23.9 couch=38.6 potted plant=20.9 bed=42.2 dining table=25.8 toilet=53.9 tv=50.3 laptop=50.2 mouse=51.7 remote=21.6 keyboard=43.4 cell phone=27.7 microwave=43.4 oven=29.9 toaster=5.9 sink=30.9 refrigerator=47.7 book=7.9 clock=44.1 vase=31.1 scissors=31.9 teddy bear=40.1 hair drier=0.0 toothbrush=15.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 253][Batch 99], LR: 1.00E-05, Speed: 9.178 samples/sec, ObjLoss=21.344, BoxCenterLoss=14.084, BoxScaleLoss=4.283, ClassLoss=7.501 [Epoch 253][Batch 199], LR: 1.00E-05, Speed: 7.417 samples/sec, ObjLoss=21.367, BoxCenterLoss=14.101, BoxScaleLoss=4.290, ClassLoss=7.501 [Epoch 253][Batch 299], LR: 1.00E-05, Speed: 6.831 samples/sec, ObjLoss=21.352, BoxCenterLoss=14.092, BoxScaleLoss=4.291, ClassLoss=7.502 [Epoch 253][Batch 399], LR: 1.00E-05, Speed: 7.448 samples/sec, ObjLoss=21.350, BoxCenterLoss=14.085, BoxScaleLoss=4.286, ClassLoss=7.498 [Epoch 253][Batch 499], LR: 1.00E-05, Speed: 9.779 samples/sec, ObjLoss=21.344, BoxCenterLoss=14.085, BoxScaleLoss=4.285, ClassLoss=7.491 [Epoch 253][Batch 599], LR: 1.00E-05, Speed: 7.116 samples/sec, ObjLoss=21.328, BoxCenterLoss=14.077, BoxScaleLoss=4.282, ClassLoss=7.485 [Epoch 253][Batch 699], LR: 1.00E-05, Speed: 87.676 samples/sec, ObjLoss=21.333, BoxCenterLoss=14.081, BoxScaleLoss=4.286, ClassLoss=7.489 [Epoch 253][Batch 799], LR: 1.00E-05, Speed: 10.266 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.090, BoxScaleLoss=4.286, ClassLoss=7.491 [Epoch 253][Batch 899], LR: 1.00E-05, Speed: 7.437 samples/sec, ObjLoss=21.339, BoxCenterLoss=14.085, BoxScaleLoss=4.280, ClassLoss=7.480 [Epoch 253][Batch 999], LR: 1.00E-05, Speed: 10.293 samples/sec, ObjLoss=21.353, BoxCenterLoss=14.090, BoxScaleLoss=4.279, ClassLoss=7.482 [Epoch 253][Batch 1099], LR: 1.00E-05, Speed: 9.454 samples/sec, ObjLoss=21.364, BoxCenterLoss=14.097, BoxScaleLoss=4.281, ClassLoss=7.483 [Epoch 253][Batch 1199], LR: 1.00E-05, Speed: 9.327 samples/sec, ObjLoss=21.393, BoxCenterLoss=14.110, BoxScaleLoss=4.280, ClassLoss=7.484 [Epoch 253][Batch 1299], LR: 1.00E-05, Speed: 7.357 samples/sec, ObjLoss=21.403, BoxCenterLoss=14.117, BoxScaleLoss=4.283, ClassLoss=7.483 [Epoch 253][Batch 1399], LR: 1.00E-05, Speed: 8.066 samples/sec, ObjLoss=21.408, BoxCenterLoss=14.120, BoxScaleLoss=4.281, ClassLoss=7.481 [Epoch 253][Batch 1499], LR: 1.00E-05, Speed: 8.109 samples/sec, ObjLoss=21.404, BoxCenterLoss=14.114, BoxScaleLoss=4.273, ClassLoss=7.473 [Epoch 253][Batch 1599], LR: 1.00E-05, Speed: 9.258 samples/sec, ObjLoss=21.402, BoxCenterLoss=14.113, BoxScaleLoss=4.271, ClassLoss=7.469 [Epoch 253][Batch 1699], LR: 1.00E-05, Speed: 10.512 samples/sec, ObjLoss=21.395, BoxCenterLoss=14.110, BoxScaleLoss=4.272, ClassLoss=7.467 [Epoch 253][Batch 1799], LR: 1.00E-05, Speed: 91.314 samples/sec, ObjLoss=21.395, BoxCenterLoss=14.110, BoxScaleLoss=4.270, ClassLoss=7.466 [Epoch 253] Training cost: 2275.565, ObjLoss=21.393, BoxCenterLoss=14.108, BoxScaleLoss=4.271, ClassLoss=7.467 [Epoch 253] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.369 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.502 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.418 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.457 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604 person=45.3 bicycle=25.8 car=34.4 motorcycle=39.1 airplane=56.3 bus=62.0 train=61.1 truck=31.7 boat=20.7 traffic light=20.0 fire hydrant=60.0 stop sign=58.0 parking meter=38.2 bench=19.9 bird=30.3 cat=60.3 dog=55.9 horse=49.3 sheep=45.2 cow=47.4 elephant=58.1 bear=64.7 zebra=58.6 giraffe=58.4 backpack=11.0 umbrella=32.9 handbag=10.2 tie=24.5 suitcase=29.3 frisbee=51.7 skis=18.0 snowboard=28.7 sports ball=36.7 kite=34.4 baseball bat=21.9 baseball glove=29.8 skateboard=42.9 surfboard=29.6 tennis racket=39.2 bottle=27.8 wine glass=28.0 cup=34.6 fork=25.6 knife=9.5 spoon=10.4 bowl=35.0 banana=18.9 apple=14.2 sandwich=28.0 orange=26.3 broccoli=16.1 carrot=16.1 hot dog=26.5 pizza=44.5 donut=39.8 cake=31.5 chair=23.4 couch=38.5 potted plant=21.1 bed=39.6 dining table=24.1 toilet=53.0 tv=50.4 laptop=49.6 mouse=52.1 remote=21.8 keyboard=44.7 cell phone=27.9 microwave=43.8 oven=30.5 toaster=5.9 sink=31.1 refrigerator=48.9 book=7.6 clock=44.2 vase=31.3 scissors=30.7 teddy bear=38.1 hair drier=0.0 toothbrush=13.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.3 [Epoch 254][Batch 99], LR: 1.00E-05, Speed: 84.581 samples/sec, ObjLoss=21.396, BoxCenterLoss=14.113, BoxScaleLoss=4.267, ClassLoss=7.462 [Epoch 254][Batch 199], LR: 1.00E-05, Speed: 8.950 samples/sec, ObjLoss=21.396, BoxCenterLoss=14.110, BoxScaleLoss=4.264, ClassLoss=7.459 [Epoch 254][Batch 299], LR: 1.00E-05, Speed: 7.727 samples/sec, ObjLoss=21.398, BoxCenterLoss=14.116, BoxScaleLoss=4.268, ClassLoss=7.463 [Epoch 254][Batch 399], LR: 1.00E-05, Speed: 10.114 samples/sec, ObjLoss=21.384, BoxCenterLoss=14.107, BoxScaleLoss=4.268, ClassLoss=7.460 [Epoch 254][Batch 499], LR: 1.00E-05, Speed: 102.060 samples/sec, ObjLoss=21.377, BoxCenterLoss=14.106, BoxScaleLoss=4.269, ClassLoss=7.459 [Epoch 254][Batch 599], LR: 1.00E-05, Speed: 9.962 samples/sec, ObjLoss=21.377, BoxCenterLoss=14.106, BoxScaleLoss=4.270, ClassLoss=7.460 [Epoch 254][Batch 699], LR: 1.00E-05, Speed: 9.181 samples/sec, ObjLoss=21.391, BoxCenterLoss=14.114, BoxScaleLoss=4.271, ClassLoss=7.468 [Epoch 254][Batch 799], LR: 1.00E-05, Speed: 9.436 samples/sec, ObjLoss=21.396, BoxCenterLoss=14.115, BoxScaleLoss=4.271, ClassLoss=7.470 [Epoch 254][Batch 899], LR: 1.00E-05, Speed: 7.892 samples/sec, ObjLoss=21.374, BoxCenterLoss=14.101, BoxScaleLoss=4.265, ClassLoss=7.465 [Epoch 254][Batch 999], LR: 1.00E-05, Speed: 10.784 samples/sec, ObjLoss=21.363, BoxCenterLoss=14.093, BoxScaleLoss=4.259, ClassLoss=7.454 [Epoch 254][Batch 1099], LR: 1.00E-05, Speed: 7.940 samples/sec, ObjLoss=21.379, BoxCenterLoss=14.099, BoxScaleLoss=4.259, ClassLoss=7.453 [Epoch 254][Batch 1199], LR: 1.00E-05, Speed: 10.370 samples/sec, ObjLoss=21.371, BoxCenterLoss=14.091, BoxScaleLoss=4.258, ClassLoss=7.450 [Epoch 254][Batch 1299], LR: 1.00E-05, Speed: 10.199 samples/sec, ObjLoss=21.382, BoxCenterLoss=14.099, BoxScaleLoss=4.257, ClassLoss=7.449 [Epoch 254][Batch 1399], LR: 1.00E-05, Speed: 8.112 samples/sec, ObjLoss=21.380, BoxCenterLoss=14.093, BoxScaleLoss=4.254, ClassLoss=7.449 [Epoch 254][Batch 1499], LR: 1.00E-05, Speed: 9.269 samples/sec, ObjLoss=21.377, BoxCenterLoss=14.093, BoxScaleLoss=4.254, ClassLoss=7.449 [Epoch 254][Batch 1599], LR: 1.00E-05, Speed: 11.092 samples/sec, ObjLoss=21.382, BoxCenterLoss=14.100, BoxScaleLoss=4.258, ClassLoss=7.457 [Epoch 254][Batch 1699], LR: 1.00E-05, Speed: 103.515 samples/sec, ObjLoss=21.383, BoxCenterLoss=14.101, BoxScaleLoss=4.256, ClassLoss=7.454 [Epoch 254][Batch 1799], LR: 1.00E-05, Speed: 11.767 samples/sec, ObjLoss=21.384, BoxCenterLoss=14.104, BoxScaleLoss=4.260, ClassLoss=7.461 [Epoch 254] Training cost: 2177.038, ObjLoss=21.383, BoxCenterLoss=14.104, BoxScaleLoss=4.260, ClassLoss=7.463 [Epoch 254] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.374 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.506 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.419 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.430 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.457 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.607 person=45.6 bicycle=25.9 car=34.6 motorcycle=39.0 airplane=56.7 bus=62.3 train=61.8 truck=32.0 boat=20.5 traffic light=19.6 fire hydrant=60.5 stop sign=58.4 parking meter=38.0 bench=20.0 bird=30.0 cat=61.0 dog=55.7 horse=49.9 sheep=45.2 cow=46.8 elephant=58.4 bear=62.8 zebra=58.6 giraffe=58.6 backpack=10.9 umbrella=33.3 handbag=10.5 tie=24.9 suitcase=29.6 frisbee=52.6 skis=18.2 snowboard=29.1 sports ball=36.1 kite=34.3 baseball bat=22.3 baseball glove=29.4 skateboard=43.3 surfboard=29.4 tennis racket=39.6 bottle=27.8 wine glass=27.8 cup=34.7 fork=25.7 knife=10.3 spoon=11.1 bowl=35.7 banana=18.9 apple=14.0 sandwich=27.8 orange=26.5 broccoli=16.0 carrot=16.2 hot dog=27.7 pizza=45.5 donut=38.9 cake=32.7 chair=23.6 couch=39.6 potted plant=21.8 bed=41.4 dining table=25.3 toilet=52.6 tv=50.0 laptop=50.4 mouse=52.3 remote=21.6 keyboard=44.1 cell phone=28.0 microwave=44.5 oven=30.7 toaster=5.9 sink=31.6 refrigerator=49.1 book=7.3 clock=44.2 vase=31.0 scissors=30.0 teddy bear=39.5 hair drier=0.0 toothbrush=14.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 255][Batch 99], LR: 1.00E-05, Speed: 10.563 samples/sec, ObjLoss=21.379, BoxCenterLoss=14.101, BoxScaleLoss=4.260, ClassLoss=7.461 [Epoch 255][Batch 199], LR: 1.00E-05, Speed: 7.453 samples/sec, ObjLoss=21.376, BoxCenterLoss=14.099, BoxScaleLoss=4.259, ClassLoss=7.457 [Epoch 255][Batch 299], LR: 1.00E-05, Speed: 8.466 samples/sec, ObjLoss=21.376, BoxCenterLoss=14.099, BoxScaleLoss=4.258, ClassLoss=7.455 [Epoch 255][Batch 399], LR: 1.00E-05, Speed: 90.665 samples/sec, ObjLoss=21.381, BoxCenterLoss=14.103, BoxScaleLoss=4.259, ClassLoss=7.453 [Epoch 255][Batch 499], LR: 1.00E-05, Speed: 6.787 samples/sec, ObjLoss=21.381, BoxCenterLoss=14.103, BoxScaleLoss=4.258, ClassLoss=7.451 [Epoch 255][Batch 599], LR: 1.00E-05, Speed: 9.159 samples/sec, ObjLoss=21.389, BoxCenterLoss=14.109, BoxScaleLoss=4.259, ClassLoss=7.452 [Epoch 255][Batch 699], LR: 1.00E-05, Speed: 7.624 samples/sec, ObjLoss=21.393, BoxCenterLoss=14.112, BoxScaleLoss=4.260, ClassLoss=7.450 [Epoch 255][Batch 799], LR: 1.00E-05, Speed: 10.033 samples/sec, ObjLoss=21.382, BoxCenterLoss=14.108, BoxScaleLoss=4.260, ClassLoss=7.447 [Epoch 255][Batch 899], LR: 1.00E-05, Speed: 8.604 samples/sec, ObjLoss=21.373, BoxCenterLoss=14.100, BoxScaleLoss=4.258, ClassLoss=7.444 [Epoch 255][Batch 999], LR: 1.00E-05, Speed: 8.049 samples/sec, ObjLoss=21.374, BoxCenterLoss=14.102, BoxScaleLoss=4.258, ClassLoss=7.444 [Epoch 255][Batch 1099], LR: 1.00E-05, Speed: 103.179 samples/sec, ObjLoss=21.378, BoxCenterLoss=14.103, BoxScaleLoss=4.254, ClassLoss=7.440 [Epoch 255][Batch 1199], LR: 1.00E-05, Speed: 9.410 samples/sec, ObjLoss=21.378, BoxCenterLoss=14.103, BoxScaleLoss=4.251, ClassLoss=7.438 [Epoch 255][Batch 1299], LR: 1.00E-05, Speed: 16.606 samples/sec, ObjLoss=21.382, BoxCenterLoss=14.106, BoxScaleLoss=4.252, ClassLoss=7.435 [Epoch 255][Batch 1399], LR: 1.00E-05, Speed: 101.993 samples/sec, ObjLoss=21.370, BoxCenterLoss=14.099, BoxScaleLoss=4.253, ClassLoss=7.436 [Epoch 255][Batch 1499], LR: 1.00E-05, Speed: 124.083 samples/sec, ObjLoss=21.350, BoxCenterLoss=14.085, BoxScaleLoss=4.249, ClassLoss=7.431 [Epoch 255][Batch 1599], LR: 1.00E-05, Speed: 7.277 samples/sec, ObjLoss=21.356, BoxCenterLoss=14.087, BoxScaleLoss=4.248, ClassLoss=7.433 [Epoch 255][Batch 1699], LR: 1.00E-05, Speed: 117.337 samples/sec, ObjLoss=21.360, BoxCenterLoss=14.088, BoxScaleLoss=4.249, ClassLoss=7.434 [Epoch 255][Batch 1799], LR: 1.00E-05, Speed: 12.238 samples/sec, ObjLoss=21.362, BoxCenterLoss=14.089, BoxScaleLoss=4.250, ClassLoss=7.436 [Epoch 255] Training cost: 2189.619, ObjLoss=21.362, BoxCenterLoss=14.089, BoxScaleLoss=4.249, ClassLoss=7.435 [Epoch 255] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.373 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611 person=45.5 bicycle=25.6 car=34.7 motorcycle=39.3 airplane=56.4 bus=61.9 train=62.8 truck=31.9 boat=20.6 traffic light=20.1 fire hydrant=59.9 stop sign=57.8 parking meter=38.1 bench=20.0 bird=30.5 cat=61.1 dog=56.9 horse=49.3 sheep=44.8 cow=47.5 elephant=59.2 bear=62.4 zebra=58.4 giraffe=58.8 backpack=10.8 umbrella=33.8 handbag=10.3 tie=25.1 suitcase=30.0 frisbee=52.0 skis=18.2 snowboard=28.2 sports ball=36.4 kite=34.0 baseball bat=22.6 baseball glove=29.4 skateboard=42.7 surfboard=29.7 tennis racket=40.2 bottle=27.9 wine glass=28.3 cup=34.5 fork=25.7 knife=10.2 spoon=10.5 bowl=34.5 banana=19.1 apple=13.1 sandwich=28.3 orange=24.9 broccoli=16.6 carrot=15.8 hot dog=28.2 pizza=46.2 donut=38.7 cake=32.2 chair=23.7 couch=38.4 potted plant=21.7 bed=42.0 dining table=26.4 toilet=54.3 tv=50.0 laptop=49.8 mouse=52.2 remote=21.8 keyboard=44.0 cell phone=27.6 microwave=45.3 oven=29.6 toaster=5.9 sink=31.2 refrigerator=48.1 book=7.4 clock=44.1 vase=31.2 scissors=31.2 teddy bear=39.9 hair drier=0.0 toothbrush=14.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 256][Batch 99], LR: 1.00E-05, Speed: 10.971 samples/sec, ObjLoss=21.353, BoxCenterLoss=14.083, BoxScaleLoss=4.248, ClassLoss=7.432 [Epoch 256][Batch 199], LR: 1.00E-05, Speed: 118.105 samples/sec, ObjLoss=21.339, BoxCenterLoss=14.076, BoxScaleLoss=4.245, ClassLoss=7.427 [Epoch 256][Batch 299], LR: 1.00E-05, Speed: 109.843 samples/sec, ObjLoss=21.332, BoxCenterLoss=14.070, BoxScaleLoss=4.244, ClassLoss=7.426 [Epoch 256][Batch 399], LR: 1.00E-05, Speed: 97.285 samples/sec, ObjLoss=21.330, BoxCenterLoss=14.070, BoxScaleLoss=4.243, ClassLoss=7.425 [Epoch 256][Batch 499], LR: 1.00E-05, Speed: 75.071 samples/sec, ObjLoss=21.324, BoxCenterLoss=14.066, BoxScaleLoss=4.241, ClassLoss=7.423 [Epoch 256][Batch 599], LR: 1.00E-05, Speed: 8.119 samples/sec, ObjLoss=21.315, BoxCenterLoss=14.062, BoxScaleLoss=4.241, ClassLoss=7.420 [Epoch 256][Batch 699], LR: 1.00E-05, Speed: 9.927 samples/sec, ObjLoss=21.326, BoxCenterLoss=14.071, BoxScaleLoss=4.245, ClassLoss=7.420 [Epoch 256][Batch 799], LR: 1.00E-05, Speed: 9.306 samples/sec, ObjLoss=21.311, BoxCenterLoss=14.062, BoxScaleLoss=4.241, ClassLoss=7.417 [Epoch 256][Batch 899], LR: 1.00E-05, Speed: 8.351 samples/sec, ObjLoss=21.307, BoxCenterLoss=14.063, BoxScaleLoss=4.241, ClassLoss=7.418 [Epoch 256][Batch 999], LR: 1.00E-05, Speed: 11.518 samples/sec, ObjLoss=21.313, BoxCenterLoss=14.068, BoxScaleLoss=4.244, ClassLoss=7.420 [Epoch 256][Batch 1099], LR: 1.00E-05, Speed: 7.787 samples/sec, ObjLoss=21.319, BoxCenterLoss=14.072, BoxScaleLoss=4.244, ClassLoss=7.419 [Epoch 256][Batch 1199], LR: 1.00E-05, Speed: 116.274 samples/sec, ObjLoss=21.318, BoxCenterLoss=14.071, BoxScaleLoss=4.244, ClassLoss=7.421 [Epoch 256][Batch 1299], LR: 1.00E-05, Speed: 8.573 samples/sec, ObjLoss=21.328, BoxCenterLoss=14.076, BoxScaleLoss=4.244, ClassLoss=7.424 [Epoch 256][Batch 1399], LR: 1.00E-05, Speed: 12.210 samples/sec, ObjLoss=21.328, BoxCenterLoss=14.075, BoxScaleLoss=4.243, ClassLoss=7.422 [Epoch 256][Batch 1499], LR: 1.00E-05, Speed: 8.048 samples/sec, ObjLoss=21.336, BoxCenterLoss=14.080, BoxScaleLoss=4.244, ClassLoss=7.423 [Epoch 256][Batch 1599], LR: 1.00E-05, Speed: 8.023 samples/sec, ObjLoss=21.332, BoxCenterLoss=14.077, BoxScaleLoss=4.243, ClassLoss=7.420 [Epoch 256][Batch 1699], LR: 1.00E-05, Speed: 6.828 samples/sec, ObjLoss=21.349, BoxCenterLoss=14.089, BoxScaleLoss=4.246, ClassLoss=7.426 [Epoch 256][Batch 1799], LR: 1.00E-05, Speed: 13.542 samples/sec, ObjLoss=21.349, BoxCenterLoss=14.087, BoxScaleLoss=4.245, ClassLoss=7.427 [Epoch 256] Training cost: 2201.956, ObjLoss=21.348, BoxCenterLoss=14.086, BoxScaleLoss=4.244, ClassLoss=7.426 [Epoch 256] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.507 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.426 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610 person=45.5 bicycle=25.3 car=34.4 motorcycle=39.0 airplane=56.9 bus=62.2 train=62.4 truck=32.1 boat=20.7 traffic light=20.4 fire hydrant=59.7 stop sign=57.9 parking meter=38.5 bench=20.5 bird=29.8 cat=59.9 dog=56.6 horse=49.8 sheep=45.0 cow=47.1 elephant=58.4 bear=62.0 zebra=57.9 giraffe=58.7 backpack=11.1 umbrella=33.2 handbag=10.3 tie=25.3 suitcase=30.4 frisbee=51.2 skis=18.2 snowboard=29.1 sports ball=36.3 kite=34.2 baseball bat=22.5 baseball glove=28.6 skateboard=42.6 surfboard=29.7 tennis racket=39.4 bottle=27.8 wine glass=28.1 cup=34.4 fork=26.1 knife=10.4 spoon=10.8 bowl=35.1 banana=19.6 apple=12.8 sandwich=26.9 orange=25.3 broccoli=16.2 carrot=15.9 hot dog=27.8 pizza=45.8 donut=39.6 cake=32.5 chair=23.9 couch=38.7 potted plant=21.1 bed=42.3 dining table=26.1 toilet=52.7 tv=49.4 laptop=49.7 mouse=52.1 remote=21.8 keyboard=43.4 cell phone=27.1 microwave=43.6 oven=29.8 toaster=5.9 sink=31.4 refrigerator=49.2 book=7.7 clock=43.4 vase=30.8 scissors=31.6 teddy bear=39.5 hair drier=0.0 toothbrush=15.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.4 [Epoch 257][Batch 99], LR: 1.00E-05, Speed: 9.926 samples/sec, ObjLoss=21.335, BoxCenterLoss=14.076, BoxScaleLoss=4.241, ClassLoss=7.421 [Epoch 257][Batch 199], LR: 1.00E-05, Speed: 8.280 samples/sec, ObjLoss=21.335, BoxCenterLoss=14.076, BoxScaleLoss=4.240, ClassLoss=7.419 [Epoch 257][Batch 299], LR: 1.00E-05, Speed: 84.372 samples/sec, ObjLoss=21.327, BoxCenterLoss=14.070, BoxScaleLoss=4.237, ClassLoss=7.415 [Epoch 257][Batch 399], LR: 1.00E-05, Speed: 89.518 samples/sec, ObjLoss=21.337, BoxCenterLoss=14.076, BoxScaleLoss=4.238, ClassLoss=7.418 [Epoch 257][Batch 499], LR: 1.00E-05, Speed: 7.186 samples/sec, ObjLoss=21.334, BoxCenterLoss=14.075, BoxScaleLoss=4.238, ClassLoss=7.416 [Epoch 257][Batch 599], LR: 1.00E-05, Speed: 7.512 samples/sec, ObjLoss=21.335, BoxCenterLoss=14.076, BoxScaleLoss=4.238, ClassLoss=7.414 [Epoch 257][Batch 699], LR: 1.00E-05, Speed: 8.092 samples/sec, ObjLoss=21.338, BoxCenterLoss=14.079, BoxScaleLoss=4.238, ClassLoss=7.413 [Epoch 257][Batch 799], LR: 1.00E-05, Speed: 8.737 samples/sec, ObjLoss=21.340, BoxCenterLoss=14.080, BoxScaleLoss=4.237, ClassLoss=7.410 [Epoch 257][Batch 899], LR: 1.00E-05, Speed: 9.780 samples/sec, ObjLoss=21.335, BoxCenterLoss=14.075, BoxScaleLoss=4.234, ClassLoss=7.406 [Epoch 257][Batch 999], LR: 1.00E-05, Speed: 128.874 samples/sec, ObjLoss=21.330, BoxCenterLoss=14.073, BoxScaleLoss=4.235, ClassLoss=7.407 [Epoch 257][Batch 1099], LR: 1.00E-05, Speed: 72.109 samples/sec, ObjLoss=21.328, BoxCenterLoss=14.071, BoxScaleLoss=4.234, ClassLoss=7.406 [Epoch 257][Batch 1199], LR: 1.00E-05, Speed: 10.179 samples/sec, ObjLoss=21.330, BoxCenterLoss=14.072, BoxScaleLoss=4.236, ClassLoss=7.407 [Epoch 257][Batch 1299], LR: 1.00E-05, Speed: 10.338 samples/sec, ObjLoss=21.340, BoxCenterLoss=14.078, BoxScaleLoss=4.238, ClassLoss=7.412 [Epoch 257][Batch 1399], LR: 1.00E-05, Speed: 11.372 samples/sec, ObjLoss=21.349, BoxCenterLoss=14.084, BoxScaleLoss=4.241, ClassLoss=7.416 [Epoch 257][Batch 1499], LR: 1.00E-05, Speed: 11.649 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.082, BoxScaleLoss=4.242, ClassLoss=7.419 [Epoch 257][Batch 1599], LR: 1.00E-05, Speed: 7.737 samples/sec, ObjLoss=21.345, BoxCenterLoss=14.080, BoxScaleLoss=4.241, ClassLoss=7.416 [Epoch 257][Batch 1699], LR: 1.00E-05, Speed: 8.590 samples/sec, ObjLoss=21.340, BoxCenterLoss=14.078, BoxScaleLoss=4.240, ClassLoss=7.413 [Epoch 257][Batch 1799], LR: 1.00E-05, Speed: 8.127 samples/sec, ObjLoss=21.340, BoxCenterLoss=14.078, BoxScaleLoss=4.241, ClassLoss=7.413 [Epoch 257] Training cost: 2228.286, ObjLoss=21.339, BoxCenterLoss=14.077, BoxScaleLoss=4.239, ClassLoss=7.411 [Epoch 257] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.426 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.609 person=45.6 bicycle=25.3 car=35.0 motorcycle=39.5 airplane=56.4 bus=61.6 train=62.4 truck=32.0 boat=20.6 traffic light=20.4 fire hydrant=59.4 stop sign=58.0 parking meter=39.7 bench=20.6 bird=30.1 cat=61.0 dog=57.0 horse=49.5 sheep=44.8 cow=48.2 elephant=58.0 bear=62.2 zebra=58.7 giraffe=59.1 backpack=11.2 umbrella=33.4 handbag=10.4 tie=25.3 suitcase=29.7 frisbee=51.5 skis=18.1 snowboard=28.6 sports ball=36.3 kite=34.2 baseball bat=21.8 baseball glove=29.3 skateboard=42.6 surfboard=29.7 tennis racket=39.3 bottle=27.7 wine glass=28.1 cup=34.5 fork=25.7 knife=10.3 spoon=10.7 bowl=34.1 banana=19.5 apple=13.1 sandwich=27.6 orange=25.0 broccoli=16.0 carrot=15.7 hot dog=28.2 pizza=46.9 donut=39.7 cake=32.3 chair=23.7 couch=38.8 potted plant=21.1 bed=42.2 dining table=25.8 toilet=54.2 tv=49.9 laptop=49.7 mouse=52.1 remote=21.4 keyboard=42.7 cell phone=28.1 microwave=43.9 oven=29.8 toaster=5.9 sink=31.3 refrigerator=48.1 book=7.7 clock=43.6 vase=31.2 scissors=31.7 teddy bear=40.0 hair drier=0.0 toothbrush=16.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 258][Batch 99], LR: 1.00E-05, Speed: 8.747 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.083, BoxScaleLoss=4.240, ClassLoss=7.413 [Epoch 258][Batch 199], LR: 1.00E-05, Speed: 115.893 samples/sec, ObjLoss=21.349, BoxCenterLoss=14.087, BoxScaleLoss=4.243, ClassLoss=7.415 [Epoch 258][Batch 299], LR: 1.00E-05, Speed: 117.304 samples/sec, ObjLoss=21.354, BoxCenterLoss=14.090, BoxScaleLoss=4.245, ClassLoss=7.417 [Epoch 258][Batch 399], LR: 1.00E-05, Speed: 109.991 samples/sec, ObjLoss=21.359, BoxCenterLoss=14.095, BoxScaleLoss=4.247, ClassLoss=7.419 [Epoch 258][Batch 499], LR: 1.00E-05, Speed: 106.967 samples/sec, ObjLoss=21.350, BoxCenterLoss=14.088, BoxScaleLoss=4.246, ClassLoss=7.417 [Epoch 258][Batch 599], LR: 1.00E-05, Speed: 7.126 samples/sec, ObjLoss=21.352, BoxCenterLoss=14.091, BoxScaleLoss=4.245, ClassLoss=7.415 [Epoch 258][Batch 699], LR: 1.00E-05, Speed: 7.698 samples/sec, ObjLoss=21.359, BoxCenterLoss=14.095, BoxScaleLoss=4.244, ClassLoss=7.415 [Epoch 258][Batch 799], LR: 1.00E-05, Speed: 8.627 samples/sec, ObjLoss=21.355, BoxCenterLoss=14.092, BoxScaleLoss=4.244, ClassLoss=7.415 [Epoch 258][Batch 899], LR: 1.00E-05, Speed: 9.676 samples/sec, ObjLoss=21.356, BoxCenterLoss=14.094, BoxScaleLoss=4.245, ClassLoss=7.416 [Epoch 258][Batch 999], LR: 1.00E-05, Speed: 12.104 samples/sec, ObjLoss=21.364, BoxCenterLoss=14.098, BoxScaleLoss=4.246, ClassLoss=7.416 [Epoch 258][Batch 1099], LR: 1.00E-05, Speed: 96.420 samples/sec, ObjLoss=21.363, BoxCenterLoss=14.099, BoxScaleLoss=4.247, ClassLoss=7.419 [Epoch 258][Batch 1199], LR: 1.00E-05, Speed: 8.954 samples/sec, ObjLoss=21.358, BoxCenterLoss=14.095, BoxScaleLoss=4.245, ClassLoss=7.415 [Epoch 258][Batch 1299], LR: 1.00E-05, Speed: 12.475 samples/sec, ObjLoss=21.357, BoxCenterLoss=14.095, BoxScaleLoss=4.245, ClassLoss=7.414 [Epoch 258][Batch 1399], LR: 1.00E-05, Speed: 109.163 samples/sec, ObjLoss=21.358, BoxCenterLoss=14.095, BoxScaleLoss=4.245, ClassLoss=7.415 [Epoch 258][Batch 1499], LR: 1.00E-05, Speed: 9.720 samples/sec, ObjLoss=21.354, BoxCenterLoss=14.094, BoxScaleLoss=4.244, ClassLoss=7.413 [Epoch 258][Batch 1599], LR: 1.00E-05, Speed: 9.646 samples/sec, ObjLoss=21.354, BoxCenterLoss=14.094, BoxScaleLoss=4.244, ClassLoss=7.411 [Epoch 258][Batch 1699], LR: 1.00E-05, Speed: 8.813 samples/sec, ObjLoss=21.345, BoxCenterLoss=14.087, BoxScaleLoss=4.242, ClassLoss=7.409 [Epoch 258][Batch 1799], LR: 1.00E-05, Speed: 11.719 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.088, BoxScaleLoss=4.242, ClassLoss=7.409 [Epoch 258] Training cost: 2187.241, ObjLoss=21.349, BoxCenterLoss=14.090, BoxScaleLoss=4.244, ClassLoss=7.411 [Epoch 258] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.372 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.418 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.613 person=45.5 bicycle=25.6 car=34.9 motorcycle=39.6 airplane=57.2 bus=61.2 train=61.9 truck=32.5 boat=20.9 traffic light=20.0 fire hydrant=59.3 stop sign=57.1 parking meter=39.3 bench=20.3 bird=30.7 cat=60.8 dog=55.8 horse=49.6 sheep=44.8 cow=46.8 elephant=57.9 bear=65.8 zebra=57.5 giraffe=58.2 backpack=11.2 umbrella=33.6 handbag=10.4 tie=24.5 suitcase=30.0 frisbee=51.7 skis=17.7 snowboard=29.0 sports ball=36.3 kite=34.4 baseball bat=22.0 baseball glove=29.1 skateboard=43.5 surfboard=29.2 tennis racket=38.7 bottle=27.5 wine glass=27.4 cup=34.1 fork=25.2 knife=9.7 spoon=10.5 bowl=34.7 banana=19.1 apple=14.3 sandwich=28.2 orange=25.7 broccoli=16.3 carrot=16.3 hot dog=27.5 pizza=45.3 donut=38.0 cake=31.9 chair=23.7 couch=39.5 potted plant=21.4 bed=41.3 dining table=25.6 toilet=53.0 tv=50.5 laptop=49.5 mouse=51.5 remote=21.4 keyboard=44.1 cell phone=27.9 microwave=45.2 oven=30.5 toaster=7.1 sink=30.8 refrigerator=50.3 book=7.3 clock=43.6 vase=31.0 scissors=31.5 teddy bear=39.5 hair drier=0.0 toothbrush=15.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 259][Batch 99], LR: 1.00E-05, Speed: 87.407 samples/sec, ObjLoss=21.351, BoxCenterLoss=14.093, BoxScaleLoss=4.246, ClassLoss=7.414 [Epoch 259][Batch 199], LR: 1.00E-05, Speed: 9.126 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.091, BoxScaleLoss=4.245, ClassLoss=7.411 [Epoch 259][Batch 299], LR: 1.00E-05, Speed: 10.925 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.090, BoxScaleLoss=4.244, ClassLoss=7.411 [Epoch 259][Batch 399], LR: 1.00E-05, Speed: 98.889 samples/sec, ObjLoss=21.356, BoxCenterLoss=14.095, BoxScaleLoss=4.246, ClassLoss=7.415 [Epoch 259][Batch 499], LR: 1.00E-05, Speed: 86.253 samples/sec, ObjLoss=21.360, BoxCenterLoss=14.098, BoxScaleLoss=4.247, ClassLoss=7.417 [Epoch 259][Batch 599], LR: 1.00E-05, Speed: 10.237 samples/sec, ObjLoss=21.358, BoxCenterLoss=14.096, BoxScaleLoss=4.246, ClassLoss=7.417 [Epoch 259][Batch 699], LR: 1.00E-05, Speed: 11.489 samples/sec, ObjLoss=21.358, BoxCenterLoss=14.093, BoxScaleLoss=4.242, ClassLoss=7.413 [Epoch 259][Batch 799], LR: 1.00E-05, Speed: 11.322 samples/sec, ObjLoss=21.360, BoxCenterLoss=14.094, BoxScaleLoss=4.242, ClassLoss=7.411 [Epoch 259][Batch 899], LR: 1.00E-05, Speed: 11.508 samples/sec, ObjLoss=21.365, BoxCenterLoss=14.096, BoxScaleLoss=4.241, ClassLoss=7.409 [Epoch 259][Batch 999], LR: 1.00E-05, Speed: 12.023 samples/sec, ObjLoss=21.364, BoxCenterLoss=14.095, BoxScaleLoss=4.240, ClassLoss=7.408 [Epoch 259][Batch 1099], LR: 1.00E-05, Speed: 91.323 samples/sec, ObjLoss=21.359, BoxCenterLoss=14.092, BoxScaleLoss=4.240, ClassLoss=7.407 [Epoch 259][Batch 1199], LR: 1.00E-05, Speed: 8.641 samples/sec, ObjLoss=21.350, BoxCenterLoss=14.085, BoxScaleLoss=4.238, ClassLoss=7.405 [Epoch 259][Batch 1299], LR: 1.00E-05, Speed: 11.297 samples/sec, ObjLoss=21.346, BoxCenterLoss=14.085, BoxScaleLoss=4.239, ClassLoss=7.405 [Epoch 259][Batch 1399], LR: 1.00E-05, Speed: 7.909 samples/sec, ObjLoss=21.339, BoxCenterLoss=14.081, BoxScaleLoss=4.238, ClassLoss=7.402 [Epoch 259][Batch 1499], LR: 1.00E-05, Speed: 7.394 samples/sec, ObjLoss=21.338, BoxCenterLoss=14.082, BoxScaleLoss=4.238, ClassLoss=7.402 [Epoch 259][Batch 1599], LR: 1.00E-05, Speed: 10.869 samples/sec, ObjLoss=21.341, BoxCenterLoss=14.086, BoxScaleLoss=4.239, ClassLoss=7.401 [Epoch 259][Batch 1699], LR: 1.00E-05, Speed: 107.099 samples/sec, ObjLoss=21.340, BoxCenterLoss=14.086, BoxScaleLoss=4.240, ClassLoss=7.402 [Epoch 259][Batch 1799], LR: 1.00E-05, Speed: 7.861 samples/sec, ObjLoss=21.339, BoxCenterLoss=14.085, BoxScaleLoss=4.240, ClassLoss=7.403 [Epoch 259] Training cost: 2178.555, ObjLoss=21.338, BoxCenterLoss=14.084, BoxScaleLoss=4.240, ClassLoss=7.403 [Epoch 259] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.285 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.456 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610 person=45.6 bicycle=25.7 car=34.8 motorcycle=39.6 airplane=56.5 bus=61.4 train=62.2 truck=32.4 boat=21.1 traffic light=20.4 fire hydrant=60.3 stop sign=57.0 parking meter=39.0 bench=20.0 bird=30.3 cat=61.3 dog=56.5 horse=50.4 sheep=44.7 cow=47.2 elephant=57.6 bear=63.9 zebra=58.5 giraffe=58.8 backpack=10.9 umbrella=33.7 handbag=10.1 tie=25.3 suitcase=29.0 frisbee=50.7 skis=17.2 snowboard=29.4 sports ball=35.8 kite=33.9 baseball bat=21.9 baseball glove=29.3 skateboard=43.4 surfboard=29.8 tennis racket=39.9 bottle=28.0 wine glass=27.6 cup=34.5 fork=25.5 knife=10.3 spoon=11.1 bowl=34.8 banana=19.1 apple=13.7 sandwich=28.4 orange=25.5 broccoli=16.1 carrot=16.0 hot dog=27.0 pizza=46.0 donut=38.8 cake=32.3 chair=23.9 couch=38.9 potted plant=21.2 bed=41.6 dining table=25.6 toilet=53.2 tv=50.1 laptop=50.1 mouse=51.8 remote=21.7 keyboard=43.4 cell phone=28.0 microwave=44.0 oven=30.4 toaster=5.9 sink=31.2 refrigerator=48.9 book=7.5 clock=43.4 vase=31.5 scissors=31.7 teddy bear=38.6 hair drier=0.0 toothbrush=16.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =34.5 [Epoch 260][Batch 99], LR: 1.00E-05, Speed: 10.354 samples/sec, ObjLoss=21.303, BoxCenterLoss=14.081, BoxScaleLoss=4.236, ClassLoss=7.385 [Epoch 260][Batch 199], LR: 1.00E-05, Speed: 6.906 samples/sec, ObjLoss=21.268, BoxCenterLoss=14.080, BoxScaleLoss=4.232, ClassLoss=7.368 [Epoch 260][Batch 299], LR: 1.00E-05, Speed: 10.625 samples/sec, ObjLoss=21.235, BoxCenterLoss=14.078, BoxScaleLoss=4.228, ClassLoss=7.351 [Epoch 260][Batch 399], LR: 1.00E-05, Speed: 10.737 samples/sec, ObjLoss=21.203, BoxCenterLoss=14.078, BoxScaleLoss=4.225, ClassLoss=7.335 [Epoch 260][Batch 499], LR: 1.00E-05, Speed: 10.822 samples/sec, ObjLoss=21.170, BoxCenterLoss=14.078, BoxScaleLoss=4.222, ClassLoss=7.319 [Epoch 260][Batch 599], LR: 1.00E-05, Speed: 7.644 samples/sec, ObjLoss=21.138, BoxCenterLoss=14.079, BoxScaleLoss=4.219, ClassLoss=7.301 [Epoch 260][Batch 699], LR: 1.00E-05, Speed: 7.138 samples/sec, ObjLoss=21.105, BoxCenterLoss=14.078, BoxScaleLoss=4.215, ClassLoss=7.284 [Epoch 260][Batch 799], LR: 1.00E-05, Speed: 9.203 samples/sec, ObjLoss=21.074, BoxCenterLoss=14.077, BoxScaleLoss=4.210, ClassLoss=7.266 [Epoch 260][Batch 899], LR: 1.00E-05, Speed: 10.163 samples/sec, ObjLoss=21.042, BoxCenterLoss=14.076, BoxScaleLoss=4.207, ClassLoss=7.250 [Epoch 260][Batch 999], LR: 1.00E-05, Speed: 8.776 samples/sec, ObjLoss=21.012, BoxCenterLoss=14.077, BoxScaleLoss=4.203, ClassLoss=7.233 [Epoch 260][Batch 1099], LR: 1.00E-05, Speed: 9.001 samples/sec, ObjLoss=20.981, BoxCenterLoss=14.075, BoxScaleLoss=4.199, ClassLoss=7.217 [Epoch 260][Batch 1199], LR: 1.00E-05, Speed: 7.222 samples/sec, ObjLoss=20.949, BoxCenterLoss=14.074, BoxScaleLoss=4.196, ClassLoss=7.202 [Epoch 260][Batch 1299], LR: 1.00E-05, Speed: 9.420 samples/sec, ObjLoss=20.922, BoxCenterLoss=14.075, BoxScaleLoss=4.193, ClassLoss=7.186 [Epoch 260][Batch 1399], LR: 1.00E-05, Speed: 9.526 samples/sec, ObjLoss=20.890, BoxCenterLoss=14.073, BoxScaleLoss=4.188, ClassLoss=7.170 [Epoch 260][Batch 1499], LR: 1.00E-05, Speed: 9.204 samples/sec, ObjLoss=20.859, BoxCenterLoss=14.072, BoxScaleLoss=4.184, ClassLoss=7.154 [Epoch 260][Batch 1599], LR: 1.00E-05, Speed: 10.166 samples/sec, ObjLoss=20.827, BoxCenterLoss=14.070, BoxScaleLoss=4.181, ClassLoss=7.138 [Epoch 260][Batch 1699], LR: 1.00E-05, Speed: 7.958 samples/sec, ObjLoss=20.796, BoxCenterLoss=14.068, BoxScaleLoss=4.177, ClassLoss=7.122 [Epoch 260][Batch 1799], LR: 1.00E-05, Speed: 13.241 samples/sec, ObjLoss=20.766, BoxCenterLoss=14.066, BoxScaleLoss=4.173, ClassLoss=7.106 [Epoch 260] Training cost: 2096.204, ObjLoss=20.757, BoxCenterLoss=14.066, BoxScaleLoss=4.171, ClassLoss=7.101 [Epoch 260] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.563 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.377 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.165 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.378 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.426 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.439 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.468 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618 person=46.2 bicycle=26.6 car=35.2 motorcycle=40.5 airplane=57.1 bus=62.7 train=62.2 truck=32.8 boat=21.5 traffic light=20.7 fire hydrant=59.9 stop sign=59.1 parking meter=42.2 bench=21.3 bird=30.9 cat=61.0 dog=57.3 horse=50.5 sheep=45.4 cow=49.2 elephant=59.2 bear=66.5 zebra=59.9 giraffe=60.1 backpack=10.6 umbrella=34.4 handbag=10.6 tie=25.7 suitcase=30.9 frisbee=52.5 skis=18.1 snowboard=28.9 sports ball=35.9 kite=34.9 baseball bat=24.1 baseball glove=29.3 skateboard=43.3 surfboard=30.9 tennis racket=40.3 bottle=29.2 wine glass=28.1 cup=35.7 fork=27.0 knife=11.3 spoon=11.8 bowl=35.4 banana=19.5 apple=13.5 sandwich=30.4 orange=26.9 broccoli=16.3 carrot=16.7 hot dog=29.7 pizza=47.7 donut=41.2 cake=33.6 chair=24.2 couch=39.7 potted plant=21.5 bed=42.5 dining table=25.4 toilet=53.5 tv=50.6 laptop=49.9 mouse=53.3 remote=22.3 keyboard=44.6 cell phone=28.1 microwave=45.3 oven=30.3 toaster=5.9 sink=32.0 refrigerator=48.4 book=8.2 clock=43.8 vase=32.1 scissors=31.9 teddy bear=40.1 hair drier=0.0 toothbrush=17.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.3 [Epoch 261][Batch 99], LR: 1.00E-05, Speed: 10.035 samples/sec, ObjLoss=20.731, BoxCenterLoss=14.066, BoxScaleLoss=4.167, ClassLoss=7.085 [Epoch 261][Batch 199], LR: 1.00E-05, Speed: 125.998 samples/sec, ObjLoss=20.702, BoxCenterLoss=14.065, BoxScaleLoss=4.164, ClassLoss=7.070 [Epoch 261][Batch 299], LR: 1.00E-05, Speed: 7.404 samples/sec, ObjLoss=20.670, BoxCenterLoss=14.061, BoxScaleLoss=4.160, ClassLoss=7.055 [Epoch 261][Batch 399], LR: 1.00E-05, Speed: 7.728 samples/sec, ObjLoss=20.641, BoxCenterLoss=14.060, BoxScaleLoss=4.156, ClassLoss=7.040 [Epoch 261][Batch 499], LR: 1.00E-05, Speed: 8.154 samples/sec, ObjLoss=20.613, BoxCenterLoss=14.059, BoxScaleLoss=4.151, ClassLoss=7.024 [Epoch 261][Batch 599], LR: 1.00E-05, Speed: 9.666 samples/sec, ObjLoss=20.588, BoxCenterLoss=14.059, BoxScaleLoss=4.148, ClassLoss=7.010 [Epoch 261][Batch 699], LR: 1.00E-05, Speed: 73.376 samples/sec, ObjLoss=20.563, BoxCenterLoss=14.060, BoxScaleLoss=4.146, ClassLoss=6.996 [Epoch 261][Batch 799], LR: 1.00E-05, Speed: 7.413 samples/sec, ObjLoss=20.534, BoxCenterLoss=14.058, BoxScaleLoss=4.143, ClassLoss=6.984 [Epoch 261][Batch 899], LR: 1.00E-05, Speed: 100.262 samples/sec, ObjLoss=20.505, BoxCenterLoss=14.056, BoxScaleLoss=4.140, ClassLoss=6.970 [Epoch 261][Batch 999], LR: 1.00E-05, Speed: 129.165 samples/sec, ObjLoss=20.476, BoxCenterLoss=14.053, BoxScaleLoss=4.136, ClassLoss=6.955 [Epoch 261][Batch 1099], LR: 1.00E-05, Speed: 9.058 samples/sec, ObjLoss=20.450, BoxCenterLoss=14.052, BoxScaleLoss=4.133, ClassLoss=6.941 [Epoch 261][Batch 1199], LR: 1.00E-05, Speed: 8.131 samples/sec, ObjLoss=20.424, BoxCenterLoss=14.052, BoxScaleLoss=4.131, ClassLoss=6.928 [Epoch 261][Batch 1299], LR: 1.00E-05, Speed: 12.736 samples/sec, ObjLoss=20.399, BoxCenterLoss=14.052, BoxScaleLoss=4.127, ClassLoss=6.914 [Epoch 261][Batch 1399], LR: 1.00E-05, Speed: 10.219 samples/sec, ObjLoss=20.371, BoxCenterLoss=14.050, BoxScaleLoss=4.124, ClassLoss=6.901 [Epoch 261][Batch 1499], LR: 1.00E-05, Speed: 116.828 samples/sec, ObjLoss=20.346, BoxCenterLoss=14.048, BoxScaleLoss=4.121, ClassLoss=6.887 [Epoch 261][Batch 1599], LR: 1.00E-05, Speed: 10.530 samples/sec, ObjLoss=20.321, BoxCenterLoss=14.048, BoxScaleLoss=4.118, ClassLoss=6.874 [Epoch 261][Batch 1699], LR: 1.00E-05, Speed: 10.251 samples/sec, ObjLoss=20.292, BoxCenterLoss=14.045, BoxScaleLoss=4.114, ClassLoss=6.860 [Epoch 261][Batch 1799], LR: 1.00E-05, Speed: 9.777 samples/sec, ObjLoss=20.267, BoxCenterLoss=14.043, BoxScaleLoss=4.110, ClassLoss=6.846 [Epoch 261] Training cost: 2211.788, ObjLoss=20.260, BoxCenterLoss=14.043, BoxScaleLoss=4.109, ClassLoss=6.841 [Epoch 261] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.380 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.470 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.617 person=46.3 bicycle=26.2 car=35.1 motorcycle=40.3 airplane=57.3 bus=62.8 train=63.0 truck=32.5 boat=21.2 traffic light=21.0 fire hydrant=60.5 stop sign=58.5 parking meter=41.6 bench=21.2 bird=31.1 cat=61.1 dog=56.6 horse=50.5 sheep=45.7 cow=49.0 elephant=59.5 bear=66.9 zebra=60.0 giraffe=59.6 backpack=10.9 umbrella=34.7 handbag=10.9 tie=25.5 suitcase=31.4 frisbee=52.8 skis=18.1 snowboard=28.9 sports ball=36.3 kite=35.6 baseball bat=24.2 baseball glove=30.0 skateboard=43.9 surfboard=30.7 tennis racket=40.2 bottle=29.5 wine glass=28.2 cup=35.7 fork=26.8 knife=11.1 spoon=11.4 bowl=35.3 banana=20.0 apple=13.8 sandwich=30.2 orange=26.9 broccoli=17.0 carrot=16.9 hot dog=28.9 pizza=47.8 donut=41.7 cake=33.8 chair=24.5 couch=40.0 potted plant=21.3 bed=42.7 dining table=26.5 toilet=53.6 tv=50.2 laptop=51.0 mouse=52.9 remote=22.9 keyboard=45.8 cell phone=28.8 microwave=45.6 oven=31.4 toaster=5.9 sink=32.0 refrigerator=48.1 book=8.0 clock=43.8 vase=32.6 scissors=31.8 teddy bear=39.5 hair drier=0.0 toothbrush=15.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.4 [Epoch 262][Batch 99], LR: 1.00E-05, Speed: 9.940 samples/sec, ObjLoss=20.235, BoxCenterLoss=14.042, BoxScaleLoss=4.107, ClassLoss=6.829 [Epoch 262][Batch 199], LR: 1.00E-05, Speed: 8.376 samples/sec, ObjLoss=20.212, BoxCenterLoss=14.042, BoxScaleLoss=4.105, ClassLoss=6.818 [Epoch 262][Batch 299], LR: 1.00E-05, Speed: 9.013 samples/sec, ObjLoss=20.187, BoxCenterLoss=14.041, BoxScaleLoss=4.102, ClassLoss=6.805 [Epoch 262][Batch 399], LR: 1.00E-05, Speed: 9.990 samples/sec, ObjLoss=20.164, BoxCenterLoss=14.040, BoxScaleLoss=4.098, ClassLoss=6.792 [Epoch 262][Batch 499], LR: 1.00E-05, Speed: 10.422 samples/sec, ObjLoss=20.140, BoxCenterLoss=14.039, BoxScaleLoss=4.094, ClassLoss=6.779 [Epoch 262][Batch 599], LR: 1.00E-05, Speed: 9.398 samples/sec, ObjLoss=20.115, BoxCenterLoss=14.038, BoxScaleLoss=4.092, ClassLoss=6.766 [Epoch 262][Batch 699], LR: 1.00E-05, Speed: 121.259 samples/sec, ObjLoss=20.092, BoxCenterLoss=14.038, BoxScaleLoss=4.090, ClassLoss=6.755 [Epoch 262][Batch 799], LR: 1.00E-05, Speed: 115.785 samples/sec, ObjLoss=20.070, BoxCenterLoss=14.039, BoxScaleLoss=4.087, ClassLoss=6.743 [Epoch 262][Batch 899], LR: 1.00E-05, Speed: 10.260 samples/sec, ObjLoss=20.048, BoxCenterLoss=14.039, BoxScaleLoss=4.085, ClassLoss=6.730 [Epoch 262][Batch 999], LR: 1.00E-05, Speed: 7.917 samples/sec, ObjLoss=20.023, BoxCenterLoss=14.036, BoxScaleLoss=4.082, ClassLoss=6.719 [Epoch 262][Batch 1099], LR: 1.00E-05, Speed: 7.831 samples/sec, ObjLoss=19.999, BoxCenterLoss=14.034, BoxScaleLoss=4.079, ClassLoss=6.707 [Epoch 262][Batch 1199], LR: 1.00E-05, Speed: 10.264 samples/sec, ObjLoss=19.978, BoxCenterLoss=14.035, BoxScaleLoss=4.076, ClassLoss=6.695 [Epoch 262][Batch 1299], LR: 1.00E-05, Speed: 11.078 samples/sec, ObjLoss=19.956, BoxCenterLoss=14.034, BoxScaleLoss=4.073, ClassLoss=6.683 [Epoch 262][Batch 1399], LR: 1.00E-05, Speed: 8.002 samples/sec, ObjLoss=19.932, BoxCenterLoss=14.032, BoxScaleLoss=4.071, ClassLoss=6.672 [Epoch 262][Batch 1499], LR: 1.00E-05, Speed: 75.369 samples/sec, ObjLoss=19.908, BoxCenterLoss=14.029, BoxScaleLoss=4.067, ClassLoss=6.660 [Epoch 262][Batch 1599], LR: 1.00E-05, Speed: 10.879 samples/sec, ObjLoss=19.885, BoxCenterLoss=14.027, BoxScaleLoss=4.065, ClassLoss=6.648 [Epoch 262][Batch 1699], LR: 1.00E-05, Speed: 10.059 samples/sec, ObjLoss=19.863, BoxCenterLoss=14.025, BoxScaleLoss=4.061, ClassLoss=6.636 [Epoch 262][Batch 1799], LR: 1.00E-05, Speed: 11.437 samples/sec, ObjLoss=19.841, BoxCenterLoss=14.023, BoxScaleLoss=4.059, ClassLoss=6.625 [Epoch 262] Training cost: 2076.706, ObjLoss=19.834, BoxCenterLoss=14.023, BoxScaleLoss=4.058, ClassLoss=6.622 [Epoch 262] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.378 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.469 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.619 person=46.3 bicycle=26.6 car=35.2 motorcycle=40.3 airplane=57.4 bus=63.1 train=62.3 truck=32.7 boat=20.7 traffic light=20.6 fire hydrant=60.6 stop sign=58.4 parking meter=40.2 bench=21.0 bird=30.9 cat=61.1 dog=56.8 horse=50.5 sheep=46.3 cow=47.9 elephant=59.4 bear=67.6 zebra=60.2 giraffe=59.7 backpack=11.3 umbrella=34.3 handbag=10.9 tie=25.0 suitcase=31.3 frisbee=52.4 skis=17.9 snowboard=29.7 sports ball=35.5 kite=35.5 baseball bat=23.7 baseball glove=29.5 skateboard=43.4 surfboard=30.6 tennis racket=40.1 bottle=29.2 wine glass=28.0 cup=35.3 fork=26.3 knife=11.0 spoon=11.7 bowl=35.9 banana=20.2 apple=14.1 sandwich=30.4 orange=27.3 broccoli=17.0 carrot=16.9 hot dog=29.5 pizza=47.6 donut=41.8 cake=34.8 chair=24.4 couch=39.7 potted plant=21.6 bed=42.0 dining table=26.3 toilet=54.0 tv=51.4 laptop=51.4 mouse=52.5 remote=22.6 keyboard=45.7 cell phone=28.5 microwave=45.8 oven=30.7 toaster=5.9 sink=32.4 refrigerator=48.4 book=8.0 clock=44.5 vase=33.0 scissors=31.4 teddy bear=39.9 hair drier=0.0 toothbrush=15.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.4 [Epoch 263][Batch 99], LR: 1.00E-05, Speed: 88.029 samples/sec, ObjLoss=19.814, BoxCenterLoss=14.024, BoxScaleLoss=4.057, ClassLoss=6.611 [Epoch 263][Batch 199], LR: 1.00E-05, Speed: 87.235 samples/sec, ObjLoss=19.793, BoxCenterLoss=14.023, BoxScaleLoss=4.054, ClassLoss=6.600 [Epoch 263][Batch 299], LR: 1.00E-05, Speed: 9.958 samples/sec, ObjLoss=19.773, BoxCenterLoss=14.023, BoxScaleLoss=4.051, ClassLoss=6.589 [Epoch 263][Batch 399], LR: 1.00E-05, Speed: 9.976 samples/sec, ObjLoss=19.753, BoxCenterLoss=14.022, BoxScaleLoss=4.049, ClassLoss=6.578 [Epoch 263][Batch 499], LR: 1.00E-05, Speed: 10.749 samples/sec, ObjLoss=19.734, BoxCenterLoss=14.022, BoxScaleLoss=4.047, ClassLoss=6.568 [Epoch 263][Batch 599], LR: 1.00E-05, Speed: 10.842 samples/sec, ObjLoss=19.713, BoxCenterLoss=14.021, BoxScaleLoss=4.045, ClassLoss=6.558 [Epoch 263][Batch 699], LR: 1.00E-05, Speed: 11.866 samples/sec, ObjLoss=19.694, BoxCenterLoss=14.020, BoxScaleLoss=4.042, ClassLoss=6.547 [Epoch 263][Batch 799], LR: 1.00E-05, Speed: 11.574 samples/sec, ObjLoss=19.673, BoxCenterLoss=14.020, BoxScaleLoss=4.040, ClassLoss=6.538 [Epoch 263][Batch 899], LR: 1.00E-05, Speed: 10.918 samples/sec, ObjLoss=19.652, BoxCenterLoss=14.018, BoxScaleLoss=4.038, ClassLoss=6.528 [Epoch 263][Batch 999], LR: 1.00E-05, Speed: 104.205 samples/sec, ObjLoss=19.632, BoxCenterLoss=14.017, BoxScaleLoss=4.036, ClassLoss=6.519 [Epoch 263][Batch 1099], LR: 1.00E-05, Speed: 8.895 samples/sec, ObjLoss=19.614, BoxCenterLoss=14.017, BoxScaleLoss=4.034, ClassLoss=6.509 [Epoch 263][Batch 1199], LR: 1.00E-05, Speed: 125.589 samples/sec, ObjLoss=19.595, BoxCenterLoss=14.018, BoxScaleLoss=4.032, ClassLoss=6.499 [Epoch 263][Batch 1299], LR: 1.00E-05, Speed: 7.674 samples/sec, ObjLoss=19.574, BoxCenterLoss=14.016, BoxScaleLoss=4.030, ClassLoss=6.489 [Epoch 263][Batch 1399], LR: 1.00E-05, Speed: 7.644 samples/sec, ObjLoss=19.556, BoxCenterLoss=14.016, BoxScaleLoss=4.027, ClassLoss=6.479 [Epoch 263][Batch 1499], LR: 1.00E-05, Speed: 9.529 samples/sec, ObjLoss=19.538, BoxCenterLoss=14.016, BoxScaleLoss=4.024, ClassLoss=6.468 [Epoch 263][Batch 1599], LR: 1.00E-05, Speed: 94.576 samples/sec, ObjLoss=19.519, BoxCenterLoss=14.015, BoxScaleLoss=4.020, ClassLoss=6.457 [Epoch 263][Batch 1699], LR: 1.00E-05, Speed: 10.037 samples/sec, ObjLoss=19.500, BoxCenterLoss=14.013, BoxScaleLoss=4.017, ClassLoss=6.445 [Epoch 263][Batch 1799], LR: 1.00E-05, Speed: 11.229 samples/sec, ObjLoss=19.482, BoxCenterLoss=14.013, BoxScaleLoss=4.015, ClassLoss=6.435 [Epoch 263] Training cost: 2126.623, ObjLoss=19.477, BoxCenterLoss=14.014, BoxScaleLoss=4.014, ClassLoss=6.433 [Epoch 263] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.355 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.566 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.380 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.441 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.469 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621 person=46.3 bicycle=26.3 car=35.4 motorcycle=40.3 airplane=57.8 bus=63.4 train=63.8 truck=32.7 boat=20.9 traffic light=20.9 fire hydrant=60.5 stop sign=59.0 parking meter=40.3 bench=21.3 bird=31.3 cat=61.5 dog=57.0 horse=50.0 sheep=46.1 cow=48.8 elephant=60.1 bear=66.5 zebra=60.0 giraffe=60.4 backpack=11.0 umbrella=34.5 handbag=10.7 tie=25.3 suitcase=31.5 frisbee=53.0 skis=18.3 snowboard=29.3 sports ball=36.2 kite=35.7 baseball bat=23.5 baseball glove=29.8 skateboard=42.9 surfboard=30.8 tennis racket=40.2 bottle=29.4 wine glass=28.8 cup=35.5 fork=27.4 knife=11.4 spoon=11.9 bowl=35.1 banana=19.8 apple=13.7 sandwich=29.3 orange=26.7 broccoli=17.2 carrot=17.0 hot dog=29.5 pizza=48.0 donut=41.6 cake=34.2 chair=24.7 couch=40.3 potted plant=21.6 bed=42.9 dining table=26.7 toilet=54.9 tv=51.4 laptop=50.8 mouse=52.7 remote=22.5 keyboard=44.8 cell phone=28.5 microwave=46.2 oven=30.3 toaster=5.9 sink=32.5 refrigerator=48.7 book=8.3 clock=44.5 vase=32.5 scissors=32.4 teddy bear=40.9 hair drier=0.0 toothbrush=15.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.5 [Epoch 264][Batch 99], LR: 1.00E-05, Speed: 10.902 samples/sec, ObjLoss=19.460, BoxCenterLoss=14.014, BoxScaleLoss=4.012, ClassLoss=6.423 [Epoch 264][Batch 199], LR: 1.00E-05, Speed: 128.352 samples/sec, ObjLoss=19.439, BoxCenterLoss=14.010, BoxScaleLoss=4.008, ClassLoss=6.413 [Epoch 264][Batch 299], LR: 1.00E-05, Speed: 8.392 samples/sec, ObjLoss=19.422, BoxCenterLoss=14.009, BoxScaleLoss=4.005, ClassLoss=6.403 [Epoch 264][Batch 399], LR: 1.00E-05, Speed: 10.992 samples/sec, ObjLoss=19.405, BoxCenterLoss=14.010, BoxScaleLoss=4.003, ClassLoss=6.394 [Epoch 264][Batch 499], LR: 1.00E-05, Speed: 10.966 samples/sec, ObjLoss=19.385, BoxCenterLoss=14.008, BoxScaleLoss=4.001, ClassLoss=6.384 [Epoch 264][Batch 599], LR: 1.00E-05, Speed: 11.286 samples/sec, ObjLoss=19.368, BoxCenterLoss=14.008, BoxScaleLoss=3.999, ClassLoss=6.375 [Epoch 264][Batch 699], LR: 1.00E-05, Speed: 10.505 samples/sec, ObjLoss=19.351, BoxCenterLoss=14.007, BoxScaleLoss=3.996, ClassLoss=6.365 [Epoch 264][Batch 799], LR: 1.00E-05, Speed: 8.493 samples/sec, ObjLoss=19.333, BoxCenterLoss=14.006, BoxScaleLoss=3.994, ClassLoss=6.356 [Epoch 264][Batch 899], LR: 1.00E-05, Speed: 6.636 samples/sec, ObjLoss=19.317, BoxCenterLoss=14.005, BoxScaleLoss=3.992, ClassLoss=6.347 [Epoch 264][Batch 999], LR: 1.00E-05, Speed: 10.452 samples/sec, ObjLoss=19.301, BoxCenterLoss=14.005, BoxScaleLoss=3.989, ClassLoss=6.338 [Epoch 264][Batch 1099], LR: 1.00E-05, Speed: 8.656 samples/sec, ObjLoss=19.282, BoxCenterLoss=14.003, BoxScaleLoss=3.988, ClassLoss=6.330 [Epoch 264][Batch 1199], LR: 1.00E-05, Speed: 8.274 samples/sec, ObjLoss=19.264, BoxCenterLoss=14.002, BoxScaleLoss=3.986, ClassLoss=6.321 [Epoch 264][Batch 1299], LR: 1.00E-05, Speed: 7.101 samples/sec, ObjLoss=19.245, BoxCenterLoss=14.000, BoxScaleLoss=3.984, ClassLoss=6.313 [Epoch 264][Batch 1399], LR: 1.00E-05, Speed: 9.031 samples/sec, ObjLoss=19.230, BoxCenterLoss=14.001, BoxScaleLoss=3.982, ClassLoss=6.304 [Epoch 264][Batch 1499], LR: 1.00E-05, Speed: 11.263 samples/sec, ObjLoss=19.211, BoxCenterLoss=14.000, BoxScaleLoss=3.981, ClassLoss=6.296 [Epoch 264][Batch 1599], LR: 1.00E-05, Speed: 10.219 samples/sec, ObjLoss=19.194, BoxCenterLoss=13.998, BoxScaleLoss=3.979, ClassLoss=6.287 [Epoch 264][Batch 1699], LR: 1.00E-05, Speed: 36.717 samples/sec, ObjLoss=19.178, BoxCenterLoss=13.998, BoxScaleLoss=3.977, ClassLoss=6.278 [Epoch 264][Batch 1799], LR: 1.00E-05, Speed: 11.578 samples/sec, ObjLoss=19.163, BoxCenterLoss=13.998, BoxScaleLoss=3.975, ClassLoss=6.271 [Epoch 264] Training cost: 2077.176, ObjLoss=19.159, BoxCenterLoss=13.999, BoxScaleLoss=3.975, ClassLoss=6.268 [Epoch 264] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.355 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.566 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.165 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.521 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622 person=46.3 bicycle=26.7 car=35.0 motorcycle=40.5 airplane=57.6 bus=62.9 train=63.5 truck=32.8 boat=20.9 traffic light=20.4 fire hydrant=60.7 stop sign=59.0 parking meter=40.3 bench=21.5 bird=30.9 cat=61.4 dog=57.7 horse=50.3 sheep=46.0 cow=48.5 elephant=59.5 bear=68.6 zebra=60.0 giraffe=60.5 backpack=11.1 umbrella=34.9 handbag=10.9 tie=25.2 suitcase=31.9 frisbee=52.7 skis=18.4 snowboard=28.9 sports ball=35.4 kite=35.2 baseball bat=23.0 baseball glove=29.9 skateboard=43.2 surfboard=30.6 tennis racket=40.3 bottle=29.3 wine glass=28.7 cup=35.4 fork=26.7 knife=11.0 spoon=11.7 bowl=35.6 banana=19.9 apple=14.2 sandwich=30.3 orange=27.2 broccoli=17.3 carrot=16.8 hot dog=29.3 pizza=47.5 donut=42.0 cake=33.8 chair=24.8 couch=40.2 potted plant=21.5 bed=44.0 dining table=27.0 toilet=54.5 tv=51.1 laptop=52.0 mouse=51.9 remote=22.7 keyboard=46.5 cell phone=28.4 microwave=46.7 oven=30.7 toaster=5.9 sink=32.4 refrigerator=48.0 book=8.1 clock=44.6 vase=32.8 scissors=31.5 teddy bear=40.0 hair drier=0.0 toothbrush=16.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.5 [Epoch 265][Batch 99], LR: 1.00E-05, Speed: 9.223 samples/sec, ObjLoss=19.143, BoxCenterLoss=13.999, BoxScaleLoss=3.973, ClassLoss=6.260 [Epoch 265][Batch 199], LR: 1.00E-05, Speed: 28.485 samples/sec, ObjLoss=19.125, BoxCenterLoss=13.997, BoxScaleLoss=3.972, ClassLoss=6.253 [Epoch 265][Batch 299], LR: 1.00E-05, Speed: 8.608 samples/sec, ObjLoss=19.109, BoxCenterLoss=13.996, BoxScaleLoss=3.970, ClassLoss=6.245 [Epoch 265][Batch 399], LR: 1.00E-05, Speed: 9.818 samples/sec, ObjLoss=19.095, BoxCenterLoss=13.997, BoxScaleLoss=3.968, ClassLoss=6.237 [Epoch 265][Batch 499], LR: 1.00E-05, Speed: 113.017 samples/sec, ObjLoss=19.081, BoxCenterLoss=13.998, BoxScaleLoss=3.966, ClassLoss=6.229 [Epoch 265][Batch 599], LR: 1.00E-05, Speed: 9.689 samples/sec, ObjLoss=19.066, BoxCenterLoss=13.996, BoxScaleLoss=3.964, ClassLoss=6.220 [Epoch 265][Batch 699], LR: 1.00E-05, Speed: 10.717 samples/sec, ObjLoss=19.049, BoxCenterLoss=13.995, BoxScaleLoss=3.962, ClassLoss=6.212 [Epoch 265][Batch 799], LR: 1.00E-05, Speed: 10.651 samples/sec, ObjLoss=19.033, BoxCenterLoss=13.994, BoxScaleLoss=3.960, ClassLoss=6.203 [Epoch 265][Batch 899], LR: 1.00E-05, Speed: 9.352 samples/sec, ObjLoss=19.019, BoxCenterLoss=13.994, BoxScaleLoss=3.958, ClassLoss=6.196 [Epoch 265][Batch 999], LR: 1.00E-05, Speed: 8.270 samples/sec, ObjLoss=19.002, BoxCenterLoss=13.992, BoxScaleLoss=3.956, ClassLoss=6.188 [Epoch 265][Batch 1099], LR: 1.00E-05, Speed: 12.489 samples/sec, ObjLoss=18.987, BoxCenterLoss=13.991, BoxScaleLoss=3.953, ClassLoss=6.178 [Epoch 265][Batch 1199], LR: 1.00E-05, Speed: 7.986 samples/sec, ObjLoss=18.972, BoxCenterLoss=13.990, BoxScaleLoss=3.952, ClassLoss=6.171 [Epoch 265][Batch 1299], LR: 1.00E-05, Speed: 10.066 samples/sec, ObjLoss=18.958, BoxCenterLoss=13.991, BoxScaleLoss=3.950, ClassLoss=6.164 [Epoch 265][Batch 1399], LR: 1.00E-05, Speed: 10.159 samples/sec, ObjLoss=18.942, BoxCenterLoss=13.990, BoxScaleLoss=3.948, ClassLoss=6.156 [Epoch 265][Batch 1499], LR: 1.00E-05, Speed: 100.692 samples/sec, ObjLoss=18.928, BoxCenterLoss=13.989, BoxScaleLoss=3.946, ClassLoss=6.148 [Epoch 265][Batch 1599], LR: 1.00E-05, Speed: 9.902 samples/sec, ObjLoss=18.912, BoxCenterLoss=13.988, BoxScaleLoss=3.945, ClassLoss=6.141 [Epoch 265][Batch 1699], LR: 1.00E-05, Speed: 10.207 samples/sec, ObjLoss=18.896, BoxCenterLoss=13.986, BoxScaleLoss=3.943, ClassLoss=6.133 [Epoch 265][Batch 1799], LR: 1.00E-05, Speed: 14.405 samples/sec, ObjLoss=18.883, BoxCenterLoss=13.987, BoxScaleLoss=3.941, ClassLoss=6.125 [Epoch 265] Training cost: 2142.296, ObjLoss=18.878, BoxCenterLoss=13.986, BoxScaleLoss=3.940, ClassLoss=6.123 [Epoch 265] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.567 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.381 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.521 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.430 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.233 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.470 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622 person=46.5 bicycle=26.8 car=35.5 motorcycle=41.0 airplane=57.6 bus=63.0 train=62.9 truck=32.6 boat=21.1 traffic light=21.2 fire hydrant=60.3 stop sign=60.0 parking meter=40.3 bench=21.6 bird=31.0 cat=61.2 dog=57.6 horse=50.5 sheep=46.2 cow=49.2 elephant=60.2 bear=66.3 zebra=60.3 giraffe=60.7 backpack=11.3 umbrella=34.3 handbag=10.7 tie=25.4 suitcase=32.2 frisbee=53.0 skis=18.3 snowboard=29.0 sports ball=36.5 kite=35.4 baseball bat=23.9 baseball glove=29.7 skateboard=43.7 surfboard=30.5 tennis racket=40.3 bottle=29.5 wine glass=28.8 cup=35.9 fork=27.4 knife=11.2 spoon=11.9 bowl=35.5 banana=20.1 apple=13.7 sandwich=30.4 orange=26.9 broccoli=17.5 carrot=16.9 hot dog=29.0 pizza=47.7 donut=41.1 cake=33.6 chair=24.8 couch=39.6 potted plant=21.5 bed=42.7 dining table=26.2 toilet=55.5 tv=51.9 laptop=51.3 mouse=53.1 remote=23.3 keyboard=45.4 cell phone=28.9 microwave=44.9 oven=30.5 toaster=5.9 sink=32.3 refrigerator=48.5 book=8.4 clock=44.5 vase=32.3 scissors=32.8 teddy bear=41.0 hair drier=0.0 toothbrush=16.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.6 [Epoch 266][Batch 99], LR: 1.00E-05, Speed: 109.640 samples/sec, ObjLoss=18.865, BoxCenterLoss=13.986, BoxScaleLoss=3.938, ClassLoss=6.115 [Epoch 266][Batch 199], LR: 1.00E-05, Speed: 10.317 samples/sec, ObjLoss=18.850, BoxCenterLoss=13.986, BoxScaleLoss=3.937, ClassLoss=6.108 [Epoch 266][Batch 299], LR: 1.00E-05, Speed: 7.377 samples/sec, ObjLoss=18.836, BoxCenterLoss=13.985, BoxScaleLoss=3.936, ClassLoss=6.102 [Epoch 266][Batch 399], LR: 1.00E-05, Speed: 8.143 samples/sec, ObjLoss=18.824, BoxCenterLoss=13.985, BoxScaleLoss=3.934, ClassLoss=6.094 [Epoch 266][Batch 499], LR: 1.00E-05, Speed: 87.020 samples/sec, ObjLoss=18.809, BoxCenterLoss=13.984, BoxScaleLoss=3.932, ClassLoss=6.086 [Epoch 266][Batch 599], LR: 1.00E-05, Speed: 11.628 samples/sec, ObjLoss=18.795, BoxCenterLoss=13.984, BoxScaleLoss=3.930, ClassLoss=6.079 [Epoch 266][Batch 699], LR: 1.00E-05, Speed: 7.917 samples/sec, ObjLoss=18.782, BoxCenterLoss=13.984, BoxScaleLoss=3.929, ClassLoss=6.073 [Epoch 266][Batch 799], LR: 1.00E-05, Speed: 11.047 samples/sec, ObjLoss=18.768, BoxCenterLoss=13.983, BoxScaleLoss=3.927, ClassLoss=6.065 [Epoch 266][Batch 899], LR: 1.00E-05, Speed: 10.512 samples/sec, ObjLoss=18.755, BoxCenterLoss=13.982, BoxScaleLoss=3.925, ClassLoss=6.058 [Epoch 266][Batch 999], LR: 1.00E-05, Speed: 111.853 samples/sec, ObjLoss=18.741, BoxCenterLoss=13.982, BoxScaleLoss=3.924, ClassLoss=6.052 [Epoch 266][Batch 1099], LR: 1.00E-05, Speed: 11.052 samples/sec, ObjLoss=18.728, BoxCenterLoss=13.981, BoxScaleLoss=3.922, ClassLoss=6.044 [Epoch 266][Batch 1199], LR: 1.00E-05, Speed: 8.613 samples/sec, ObjLoss=18.713, BoxCenterLoss=13.980, BoxScaleLoss=3.921, ClassLoss=6.038 [Epoch 266][Batch 1299], LR: 1.00E-05, Speed: 7.921 samples/sec, ObjLoss=18.699, BoxCenterLoss=13.979, BoxScaleLoss=3.920, ClassLoss=6.031 [Epoch 266][Batch 1399], LR: 1.00E-05, Speed: 7.608 samples/sec, ObjLoss=18.687, BoxCenterLoss=13.979, BoxScaleLoss=3.918, ClassLoss=6.024 [Epoch 266][Batch 1499], LR: 1.00E-05, Speed: 10.649 samples/sec, ObjLoss=18.672, BoxCenterLoss=13.978, BoxScaleLoss=3.916, ClassLoss=6.016 [Epoch 266][Batch 1599], LR: 1.00E-05, Speed: 8.298 samples/sec, ObjLoss=18.659, BoxCenterLoss=13.976, BoxScaleLoss=3.914, ClassLoss=6.010 [Epoch 266][Batch 1699], LR: 1.00E-05, Speed: 117.641 samples/sec, ObjLoss=18.646, BoxCenterLoss=13.975, BoxScaleLoss=3.912, ClassLoss=6.003 [Epoch 266][Batch 1799], LR: 1.00E-05, Speed: 9.284 samples/sec, ObjLoss=18.633, BoxCenterLoss=13.974, BoxScaleLoss=3.910, ClassLoss=5.995 [Epoch 266] Training cost: 2139.265, ObjLoss=18.630, BoxCenterLoss=13.974, BoxScaleLoss=3.909, ClassLoss=5.993 [Epoch 266] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.471 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620 person=46.4 bicycle=26.7 car=35.5 motorcycle=40.4 airplane=56.9 bus=63.1 train=63.6 truck=32.8 boat=21.6 traffic light=20.6 fire hydrant=60.4 stop sign=59.5 parking meter=40.7 bench=21.3 bird=31.2 cat=62.7 dog=57.0 horse=50.4 sheep=46.2 cow=49.2 elephant=59.9 bear=66.9 zebra=60.1 giraffe=60.3 backpack=11.1 umbrella=34.9 handbag=10.8 tie=25.4 suitcase=32.4 frisbee=53.7 skis=18.0 snowboard=29.2 sports ball=35.8 kite=35.7 baseball bat=24.3 baseball glove=29.9 skateboard=43.9 surfboard=30.7 tennis racket=39.9 bottle=29.4 wine glass=28.9 cup=36.1 fork=27.3 knife=10.8 spoon=11.8 bowl=36.1 banana=19.9 apple=13.8 sandwich=30.1 orange=26.8 broccoli=17.5 carrot=16.9 hot dog=28.8 pizza=47.9 donut=41.4 cake=34.6 chair=24.6 couch=40.3 potted plant=21.7 bed=43.6 dining table=26.9 toilet=54.6 tv=51.3 laptop=51.1 mouse=52.7 remote=23.1 keyboard=46.0 cell phone=28.6 microwave=46.2 oven=31.1 toaster=5.9 sink=33.0 refrigerator=48.4 book=8.3 clock=44.3 vase=32.6 scissors=31.8 teddy bear=40.9 hair drier=0.0 toothbrush=14.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.6 [Epoch 267][Batch 99], LR: 1.00E-05, Speed: 9.792 samples/sec, ObjLoss=18.616, BoxCenterLoss=13.974, BoxScaleLoss=3.908, ClassLoss=5.987 [Epoch 267][Batch 199], LR: 1.00E-05, Speed: 9.056 samples/sec, ObjLoss=18.604, BoxCenterLoss=13.973, BoxScaleLoss=3.907, ClassLoss=5.980 [Epoch 267][Batch 299], LR: 1.00E-05, Speed: 11.219 samples/sec, ObjLoss=18.592, BoxCenterLoss=13.973, BoxScaleLoss=3.905, ClassLoss=5.974 [Epoch 267][Batch 399], LR: 1.00E-05, Speed: 7.104 samples/sec, ObjLoss=18.580, BoxCenterLoss=13.972, BoxScaleLoss=3.904, ClassLoss=5.968 [Epoch 267][Batch 499], LR: 1.00E-05, Speed: 114.401 samples/sec, ObjLoss=18.567, BoxCenterLoss=13.972, BoxScaleLoss=3.902, ClassLoss=5.961 [Epoch 267][Batch 599], LR: 1.00E-05, Speed: 8.783 samples/sec, ObjLoss=18.555, BoxCenterLoss=13.972, BoxScaleLoss=3.901, ClassLoss=5.955 [Epoch 267][Batch 699], LR: 1.00E-05, Speed: 8.110 samples/sec, ObjLoss=18.543, BoxCenterLoss=13.972, BoxScaleLoss=3.900, ClassLoss=5.949 [Epoch 267][Batch 799], LR: 1.00E-05, Speed: 10.134 samples/sec, ObjLoss=18.530, BoxCenterLoss=13.971, BoxScaleLoss=3.899, ClassLoss=5.943 [Epoch 267][Batch 899], LR: 1.00E-05, Speed: 10.652 samples/sec, ObjLoss=18.517, BoxCenterLoss=13.970, BoxScaleLoss=3.897, ClassLoss=5.936 [Epoch 267][Batch 999], LR: 1.00E-05, Speed: 11.891 samples/sec, ObjLoss=18.505, BoxCenterLoss=13.969, BoxScaleLoss=3.895, ClassLoss=5.930 [Epoch 267][Batch 1099], LR: 1.00E-05, Speed: 10.950 samples/sec, ObjLoss=18.494, BoxCenterLoss=13.970, BoxScaleLoss=3.896, ClassLoss=5.926 [Epoch 267][Batch 1199], LR: 1.00E-05, Speed: 10.869 samples/sec, ObjLoss=18.483, BoxCenterLoss=13.969, BoxScaleLoss=3.894, ClassLoss=5.918 [Epoch 267][Batch 1299], LR: 1.00E-05, Speed: 7.914 samples/sec, ObjLoss=18.471, BoxCenterLoss=13.969, BoxScaleLoss=3.892, ClassLoss=5.912 [Epoch 267][Batch 1399], LR: 1.00E-05, Speed: 90.754 samples/sec, ObjLoss=18.459, BoxCenterLoss=13.968, BoxScaleLoss=3.890, ClassLoss=5.905 [Epoch 267][Batch 1499], LR: 1.00E-05, Speed: 96.379 samples/sec, ObjLoss=18.447, BoxCenterLoss=13.967, BoxScaleLoss=3.888, ClassLoss=5.899 [Epoch 267][Batch 1599], LR: 1.00E-05, Speed: 116.577 samples/sec, ObjLoss=18.434, BoxCenterLoss=13.966, BoxScaleLoss=3.887, ClassLoss=5.894 [Epoch 267][Batch 1699], LR: 1.00E-05, Speed: 8.707 samples/sec, ObjLoss=18.422, BoxCenterLoss=13.965, BoxScaleLoss=3.886, ClassLoss=5.888 [Epoch 267][Batch 1799], LR: 1.00E-05, Speed: 12.869 samples/sec, ObjLoss=18.412, BoxCenterLoss=13.965, BoxScaleLoss=3.885, ClassLoss=5.882 [Epoch 267] Training cost: 2046.484, ObjLoss=18.408, BoxCenterLoss=13.965, BoxScaleLoss=3.885, ClassLoss=5.880 [Epoch 267] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.567 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.381 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.619 person=46.4 bicycle=26.8 car=35.4 motorcycle=40.6 airplane=57.9 bus=63.2 train=63.8 truck=33.0 boat=21.3 traffic light=20.7 fire hydrant=61.0 stop sign=59.2 parking meter=40.7 bench=21.3 bird=30.8 cat=61.5 dog=57.0 horse=50.8 sheep=46.2 cow=48.9 elephant=59.7 bear=66.6 zebra=60.1 giraffe=60.1 backpack=11.3 umbrella=34.8 handbag=10.7 tie=25.6 suitcase=32.6 frisbee=53.0 skis=18.3 snowboard=28.7 sports ball=36.2 kite=35.5 baseball bat=23.7 baseball glove=30.0 skateboard=43.6 surfboard=30.5 tennis racket=40.2 bottle=29.4 wine glass=28.5 cup=35.5 fork=26.9 knife=11.3 spoon=12.0 bowl=36.0 banana=20.1 apple=13.9 sandwich=30.3 orange=27.4 broccoli=17.2 carrot=16.9 hot dog=29.5 pizza=47.6 donut=41.8 cake=34.4 chair=24.7 couch=40.2 potted plant=22.1 bed=43.4 dining table=26.8 toilet=55.1 tv=51.7 laptop=51.6 mouse=52.0 remote=23.1 keyboard=46.3 cell phone=28.7 microwave=45.8 oven=30.2 toaster=5.9 sink=32.7 refrigerator=48.5 book=8.3 clock=44.7 vase=32.5 scissors=32.4 teddy bear=39.9 hair drier=0.0 toothbrush=15.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.6 [Epoch 268][Batch 99], LR: 1.00E-05, Speed: 121.045 samples/sec, ObjLoss=18.399, BoxCenterLoss=13.967, BoxScaleLoss=3.884, ClassLoss=5.875 [Epoch 268][Batch 199], LR: 1.00E-05, Speed: 8.617 samples/sec, ObjLoss=18.388, BoxCenterLoss=13.967, BoxScaleLoss=3.883, ClassLoss=5.869 [Epoch 268][Batch 299], LR: 1.00E-05, Speed: 86.958 samples/sec, ObjLoss=18.378, BoxCenterLoss=13.969, BoxScaleLoss=3.882, ClassLoss=5.864 [Epoch 268][Batch 399], LR: 1.00E-05, Speed: 8.644 samples/sec, ObjLoss=18.367, BoxCenterLoss=13.968, BoxScaleLoss=3.881, ClassLoss=5.859 [Epoch 268][Batch 499], LR: 1.00E-05, Speed: 10.394 samples/sec, ObjLoss=18.356, BoxCenterLoss=13.968, BoxScaleLoss=3.879, ClassLoss=5.853 [Epoch 268][Batch 599], LR: 1.00E-05, Speed: 125.519 samples/sec, ObjLoss=18.345, BoxCenterLoss=13.967, BoxScaleLoss=3.878, ClassLoss=5.847 [Epoch 268][Batch 699], LR: 1.00E-05, Speed: 7.396 samples/sec, ObjLoss=18.332, BoxCenterLoss=13.965, BoxScaleLoss=3.876, ClassLoss=5.841 [Epoch 268][Batch 799], LR: 1.00E-05, Speed: 10.938 samples/sec, ObjLoss=18.321, BoxCenterLoss=13.964, BoxScaleLoss=3.874, ClassLoss=5.835 [Epoch 268][Batch 899], LR: 1.00E-05, Speed: 7.622 samples/sec, ObjLoss=18.309, BoxCenterLoss=13.963, BoxScaleLoss=3.873, ClassLoss=5.829 [Epoch 268][Batch 999], LR: 1.00E-05, Speed: 7.910 samples/sec, ObjLoss=18.298, BoxCenterLoss=13.962, BoxScaleLoss=3.872, ClassLoss=5.824 [Epoch 268][Batch 1099], LR: 1.00E-05, Speed: 9.235 samples/sec, ObjLoss=18.287, BoxCenterLoss=13.961, BoxScaleLoss=3.870, ClassLoss=5.818 [Epoch 268][Batch 1199], LR: 1.00E-05, Speed: 12.077 samples/sec, ObjLoss=18.276, BoxCenterLoss=13.960, BoxScaleLoss=3.870, ClassLoss=5.813 [Epoch 268][Batch 1299], LR: 1.00E-05, Speed: 8.810 samples/sec, ObjLoss=18.266, BoxCenterLoss=13.961, BoxScaleLoss=3.868, ClassLoss=5.807 [Epoch 268][Batch 1399], LR: 1.00E-05, Speed: 8.103 samples/sec, ObjLoss=18.254, BoxCenterLoss=13.959, BoxScaleLoss=3.866, ClassLoss=5.801 [Epoch 268][Batch 1499], LR: 1.00E-05, Speed: 10.905 samples/sec, ObjLoss=18.245, BoxCenterLoss=13.959, BoxScaleLoss=3.865, ClassLoss=5.796 [Epoch 268][Batch 1599], LR: 1.00E-05, Speed: 11.372 samples/sec, ObjLoss=18.234, BoxCenterLoss=13.958, BoxScaleLoss=3.864, ClassLoss=5.791 [Epoch 268][Batch 1699], LR: 1.00E-05, Speed: 10.080 samples/sec, ObjLoss=18.223, BoxCenterLoss=13.957, BoxScaleLoss=3.863, ClassLoss=5.785 [Epoch 268][Batch 1799], LR: 1.00E-05, Speed: 8.543 samples/sec, ObjLoss=18.212, BoxCenterLoss=13.957, BoxScaleLoss=3.861, ClassLoss=5.779 [Epoch 268] Training cost: 2133.152, ObjLoss=18.209, BoxCenterLoss=13.956, BoxScaleLoss=3.861, ClassLoss=5.777 [Epoch 268] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.383 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.233 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622 person=46.6 bicycle=26.5 car=35.7 motorcycle=40.4 airplane=57.9 bus=62.8 train=63.7 truck=33.0 boat=21.0 traffic light=20.8 fire hydrant=60.4 stop sign=58.7 parking meter=41.1 bench=21.5 bird=31.1 cat=61.8 dog=56.7 horse=51.1 sheep=46.7 cow=48.5 elephant=60.2 bear=67.2 zebra=60.0 giraffe=60.4 backpack=11.3 umbrella=35.2 handbag=10.7 tie=25.7 suitcase=32.3 frisbee=53.2 skis=18.4 snowboard=29.0 sports ball=36.5 kite=35.4 baseball bat=24.1 baseball glove=30.5 skateboard=43.8 surfboard=30.5 tennis racket=40.3 bottle=29.6 wine glass=28.7 cup=36.1 fork=26.8 knife=11.1 spoon=11.8 bowl=35.7 banana=20.1 apple=13.9 sandwich=30.7 orange=27.5 broccoli=17.8 carrot=17.1 hot dog=28.8 pizza=47.6 donut=41.2 cake=34.2 chair=24.9 couch=39.9 potted plant=21.2 bed=43.6 dining table=26.8 toilet=55.3 tv=51.8 laptop=51.7 mouse=53.3 remote=22.9 keyboard=46.0 cell phone=28.8 microwave=47.0 oven=30.2 toaster=5.9 sink=33.1 refrigerator=48.2 book=8.3 clock=44.4 vase=32.7 scissors=32.1 teddy bear=40.5 hair drier=0.0 toothbrush=15.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.7 [Epoch 269][Batch 99], LR: 1.00E-05, Speed: 9.285 samples/sec, ObjLoss=18.199, BoxCenterLoss=13.957, BoxScaleLoss=3.860, ClassLoss=5.773 [Epoch 269][Batch 199], LR: 1.00E-05, Speed: 7.844 samples/sec, ObjLoss=18.190, BoxCenterLoss=13.958, BoxScaleLoss=3.859, ClassLoss=5.768 [Epoch 269][Batch 299], LR: 1.00E-05, Speed: 8.798 samples/sec, ObjLoss=18.182, BoxCenterLoss=13.958, BoxScaleLoss=3.858, ClassLoss=5.763 [Epoch 269][Batch 399], LR: 1.00E-05, Speed: 10.989 samples/sec, ObjLoss=18.170, BoxCenterLoss=13.956, BoxScaleLoss=3.856, ClassLoss=5.757 [Epoch 269][Batch 499], LR: 1.00E-05, Speed: 83.228 samples/sec, ObjLoss=18.158, BoxCenterLoss=13.955, BoxScaleLoss=3.855, ClassLoss=5.752 [Epoch 269][Batch 599], LR: 1.00E-05, Speed: 8.489 samples/sec, ObjLoss=18.150, BoxCenterLoss=13.956, BoxScaleLoss=3.854, ClassLoss=5.747 [Epoch 269][Batch 699], LR: 1.00E-05, Speed: 10.772 samples/sec, ObjLoss=18.140, BoxCenterLoss=13.955, BoxScaleLoss=3.853, ClassLoss=5.742 [Epoch 269][Batch 799], LR: 1.00E-05, Speed: 8.370 samples/sec, ObjLoss=18.129, BoxCenterLoss=13.954, BoxScaleLoss=3.851, ClassLoss=5.737 [Epoch 269][Batch 899], LR: 1.00E-05, Speed: 7.034 samples/sec, ObjLoss=18.119, BoxCenterLoss=13.955, BoxScaleLoss=3.851, ClassLoss=5.732 [Epoch 269][Batch 999], LR: 1.00E-05, Speed: 120.879 samples/sec, ObjLoss=18.110, BoxCenterLoss=13.954, BoxScaleLoss=3.849, ClassLoss=5.727 [Epoch 269][Batch 1099], LR: 1.00E-05, Speed: 88.557 samples/sec, ObjLoss=18.101, BoxCenterLoss=13.954, BoxScaleLoss=3.848, ClassLoss=5.721 [Epoch 269][Batch 1199], LR: 1.00E-05, Speed: 7.685 samples/sec, ObjLoss=18.091, BoxCenterLoss=13.954, BoxScaleLoss=3.847, ClassLoss=5.716 [Epoch 269][Batch 1299], LR: 1.00E-05, Speed: 10.975 samples/sec, ObjLoss=18.082, BoxCenterLoss=13.954, BoxScaleLoss=3.846, ClassLoss=5.712 [Epoch 269][Batch 1399], LR: 1.00E-05, Speed: 88.016 samples/sec, ObjLoss=18.074, BoxCenterLoss=13.954, BoxScaleLoss=3.845, ClassLoss=5.707 [Epoch 269][Batch 1499], LR: 1.00E-05, Speed: 8.110 samples/sec, ObjLoss=18.064, BoxCenterLoss=13.953, BoxScaleLoss=3.844, ClassLoss=5.703 [Epoch 269][Batch 1599], LR: 1.00E-05, Speed: 9.812 samples/sec, ObjLoss=18.055, BoxCenterLoss=13.953, BoxScaleLoss=3.843, ClassLoss=5.698 [Epoch 269][Batch 1699], LR: 1.00E-05, Speed: 11.362 samples/sec, ObjLoss=18.045, BoxCenterLoss=13.953, BoxScaleLoss=3.842, ClassLoss=5.693 [Epoch 269][Batch 1799], LR: 1.00E-05, Speed: 12.679 samples/sec, ObjLoss=18.036, BoxCenterLoss=13.953, BoxScaleLoss=3.841, ClassLoss=5.688 [Epoch 269] Training cost: 2107.016, ObjLoss=18.033, BoxCenterLoss=13.953, BoxScaleLoss=3.841, ClassLoss=5.687 [Epoch 269] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.233 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621 person=46.4 bicycle=26.4 car=35.5 motorcycle=40.5 airplane=57.7 bus=63.7 train=63.5 truck=32.9 boat=21.6 traffic light=20.7 fire hydrant=60.6 stop sign=59.5 parking meter=41.3 bench=21.2 bird=30.7 cat=61.9 dog=57.1 horse=50.4 sheep=46.5 cow=49.1 elephant=59.8 bear=66.6 zebra=60.4 giraffe=60.4 backpack=11.0 umbrella=35.0 handbag=10.8 tie=25.5 suitcase=31.8 frisbee=54.0 skis=18.5 snowboard=29.3 sports ball=36.1 kite=35.8 baseball bat=23.0 baseball glove=30.6 skateboard=43.8 surfboard=30.8 tennis racket=40.5 bottle=29.5 wine glass=28.9 cup=36.0 fork=27.3 knife=10.9 spoon=11.6 bowl=36.0 banana=19.8 apple=14.4 sandwich=30.2 orange=27.7 broccoli=17.3 carrot=17.4 hot dog=28.6 pizza=47.8 donut=42.0 cake=34.2 chair=24.8 couch=39.9 potted plant=21.5 bed=43.6 dining table=27.0 toilet=55.7 tv=51.5 laptop=51.2 mouse=51.4 remote=22.9 keyboard=46.0 cell phone=28.8 microwave=46.9 oven=30.4 toaster=5.9 sink=33.1 refrigerator=48.1 book=8.4 clock=44.1 vase=32.4 scissors=32.7 teddy bear=40.1 hair drier=0.0 toothbrush=15.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.7 [Epoch 270][Batch 99], LR: 1.00E-05, Speed: 7.285 samples/sec, ObjLoss=18.024, BoxCenterLoss=13.952, BoxScaleLoss=3.840, ClassLoss=5.682 [Epoch 270][Batch 199], LR: 1.00E-05, Speed: 61.344 samples/sec, ObjLoss=18.014, BoxCenterLoss=13.951, BoxScaleLoss=3.838, ClassLoss=5.676 [Epoch 270][Batch 299], LR: 1.00E-05, Speed: 11.767 samples/sec, ObjLoss=18.005, BoxCenterLoss=13.951, BoxScaleLoss=3.836, ClassLoss=5.671 [Epoch 270][Batch 399], LR: 1.00E-05, Speed: 11.974 samples/sec, ObjLoss=17.996, BoxCenterLoss=13.950, BoxScaleLoss=3.835, ClassLoss=5.666 [Epoch 270][Batch 499], LR: 1.00E-05, Speed: 10.175 samples/sec, ObjLoss=17.986, BoxCenterLoss=13.949, BoxScaleLoss=3.834, ClassLoss=5.661 [Epoch 270][Batch 599], LR: 1.00E-05, Speed: 8.043 samples/sec, ObjLoss=17.974, BoxCenterLoss=13.946, BoxScaleLoss=3.832, ClassLoss=5.655 [Epoch 270][Batch 699], LR: 1.00E-05, Speed: 7.380 samples/sec, ObjLoss=17.966, BoxCenterLoss=13.946, BoxScaleLoss=3.830, ClassLoss=5.650 [Epoch 270][Batch 799], LR: 1.00E-05, Speed: 8.311 samples/sec, ObjLoss=17.958, BoxCenterLoss=13.945, BoxScaleLoss=3.829, ClassLoss=5.645 [Epoch 270][Batch 899], LR: 1.00E-05, Speed: 7.902 samples/sec, ObjLoss=17.950, BoxCenterLoss=13.945, BoxScaleLoss=3.827, ClassLoss=5.641 [Epoch 270][Batch 999], LR: 1.00E-05, Speed: 11.155 samples/sec, ObjLoss=17.941, BoxCenterLoss=13.945, BoxScaleLoss=3.826, ClassLoss=5.636 [Epoch 270][Batch 1099], LR: 1.00E-05, Speed: 9.819 samples/sec, ObjLoss=17.934, BoxCenterLoss=13.945, BoxScaleLoss=3.825, ClassLoss=5.631 [Epoch 270][Batch 1199], LR: 1.00E-05, Speed: 138.118 samples/sec, ObjLoss=17.924, BoxCenterLoss=13.944, BoxScaleLoss=3.824, ClassLoss=5.626 [Epoch 270][Batch 1299], LR: 1.00E-05, Speed: 7.752 samples/sec, ObjLoss=17.915, BoxCenterLoss=13.944, BoxScaleLoss=3.823, ClassLoss=5.622 [Epoch 270][Batch 1399], LR: 1.00E-05, Speed: 97.288 samples/sec, ObjLoss=17.905, BoxCenterLoss=13.943, BoxScaleLoss=3.822, ClassLoss=5.617 [Epoch 270][Batch 1499], LR: 1.00E-05, Speed: 87.442 samples/sec, ObjLoss=17.897, BoxCenterLoss=13.942, BoxScaleLoss=3.821, ClassLoss=5.612 [Epoch 270][Batch 1599], LR: 1.00E-05, Speed: 7.540 samples/sec, ObjLoss=17.889, BoxCenterLoss=13.942, BoxScaleLoss=3.819, ClassLoss=5.607 [Epoch 270][Batch 1699], LR: 1.00E-05, Speed: 11.074 samples/sec, ObjLoss=17.880, BoxCenterLoss=13.942, BoxScaleLoss=3.819, ClassLoss=5.604 [Epoch 270][Batch 1799], LR: 1.00E-05, Speed: 13.042 samples/sec, ObjLoss=17.872, BoxCenterLoss=13.942, BoxScaleLoss=3.818, ClassLoss=5.599 [Epoch 270] Training cost: 2136.778, ObjLoss=17.869, BoxCenterLoss=13.942, BoxScaleLoss=3.818, ClassLoss=5.598 [Epoch 270] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.381 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.524 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.624 person=46.5 bicycle=26.6 car=35.4 motorcycle=40.5 airplane=57.8 bus=63.2 train=64.3 truck=32.8 boat=21.4 traffic light=20.7 fire hydrant=59.9 stop sign=59.1 parking meter=40.7 bench=21.5 bird=30.8 cat=62.2 dog=57.6 horse=51.4 sheep=46.8 cow=48.6 elephant=59.5 bear=67.7 zebra=60.1 giraffe=60.5 backpack=11.1 umbrella=34.5 handbag=10.9 tie=25.6 suitcase=32.4 frisbee=54.0 skis=18.5 snowboard=28.5 sports ball=36.5 kite=35.9 baseball bat=23.9 baseball glove=30.1 skateboard=43.5 surfboard=30.0 tennis racket=39.8 bottle=29.4 wine glass=28.3 cup=35.6 fork=27.4 knife=11.1 spoon=12.0 bowl=36.1 banana=20.4 apple=14.2 sandwich=30.2 orange=27.7 broccoli=17.2 carrot=17.2 hot dog=28.7 pizza=48.2 donut=41.8 cake=33.8 chair=24.8 couch=40.6 potted plant=21.7 bed=43.4 dining table=27.1 toilet=56.6 tv=51.8 laptop=51.4 mouse=52.5 remote=23.1 keyboard=46.3 cell phone=28.8 microwave=46.5 oven=30.5 toaster=5.9 sink=32.7 refrigerator=48.4 book=8.4 clock=44.3 vase=32.4 scissors=32.2 teddy bear=40.4 hair drier=0.0 toothbrush=15.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.7 [Epoch 271][Batch 99], LR: 1.00E-05, Speed: 11.204 samples/sec, ObjLoss=17.861, BoxCenterLoss=13.942, BoxScaleLoss=3.817, ClassLoss=5.594 [Epoch 271][Batch 199], LR: 1.00E-05, Speed: 8.055 samples/sec, ObjLoss=17.852, BoxCenterLoss=13.941, BoxScaleLoss=3.816, ClassLoss=5.589 [Epoch 271][Batch 299], LR: 1.00E-05, Speed: 87.537 samples/sec, ObjLoss=17.844, BoxCenterLoss=13.940, BoxScaleLoss=3.814, ClassLoss=5.585 [Epoch 271][Batch 399], LR: 1.00E-05, Speed: 8.099 samples/sec, ObjLoss=17.836, BoxCenterLoss=13.940, BoxScaleLoss=3.813, ClassLoss=5.580 [Epoch 271][Batch 499], LR: 1.00E-05, Speed: 122.323 samples/sec, ObjLoss=17.827, BoxCenterLoss=13.940, BoxScaleLoss=3.813, ClassLoss=5.577 [Epoch 271][Batch 599], LR: 1.00E-05, Speed: 8.861 samples/sec, ObjLoss=17.819, BoxCenterLoss=13.939, BoxScaleLoss=3.812, ClassLoss=5.572 [Epoch 271][Batch 699], LR: 1.00E-05, Speed: 12.133 samples/sec, ObjLoss=17.810, BoxCenterLoss=13.939, BoxScaleLoss=3.811, ClassLoss=5.568 [Epoch 271][Batch 799], LR: 1.00E-05, Speed: 10.559 samples/sec, ObjLoss=17.802, BoxCenterLoss=13.938, BoxScaleLoss=3.810, ClassLoss=5.564 [Epoch 271][Batch 899], LR: 1.00E-05, Speed: 9.269 samples/sec, ObjLoss=17.793, BoxCenterLoss=13.937, BoxScaleLoss=3.809, ClassLoss=5.560 [Epoch 271][Batch 999], LR: 1.00E-05, Speed: 11.213 samples/sec, ObjLoss=17.785, BoxCenterLoss=13.938, BoxScaleLoss=3.809, ClassLoss=5.556 [Epoch 271][Batch 1099], LR: 1.00E-05, Speed: 133.517 samples/sec, ObjLoss=17.778, BoxCenterLoss=13.938, BoxScaleLoss=3.808, ClassLoss=5.552 [Epoch 271][Batch 1199], LR: 1.00E-05, Speed: 7.731 samples/sec, ObjLoss=17.770, BoxCenterLoss=13.938, BoxScaleLoss=3.807, ClassLoss=5.548 [Epoch 271][Batch 1299], LR: 1.00E-05, Speed: 11.081 samples/sec, ObjLoss=17.763, BoxCenterLoss=13.938, BoxScaleLoss=3.806, ClassLoss=5.544 [Epoch 271][Batch 1399], LR: 1.00E-05, Speed: 10.418 samples/sec, ObjLoss=17.756, BoxCenterLoss=13.938, BoxScaleLoss=3.805, ClassLoss=5.540 [Epoch 271][Batch 1499], LR: 1.00E-05, Speed: 9.535 samples/sec, ObjLoss=17.748, BoxCenterLoss=13.938, BoxScaleLoss=3.804, ClassLoss=5.535 [Epoch 271][Batch 1599], LR: 1.00E-05, Speed: 108.555 samples/sec, ObjLoss=17.738, BoxCenterLoss=13.936, BoxScaleLoss=3.803, ClassLoss=5.530 [Epoch 271][Batch 1699], LR: 1.00E-05, Speed: 7.087 samples/sec, ObjLoss=17.730, BoxCenterLoss=13.935, BoxScaleLoss=3.802, ClassLoss=5.527 [Epoch 271][Batch 1799], LR: 1.00E-05, Speed: 11.348 samples/sec, ObjLoss=17.723, BoxCenterLoss=13.936, BoxScaleLoss=3.801, ClassLoss=5.523 [Epoch 271] Training cost: 2060.058, ObjLoss=17.720, BoxCenterLoss=13.936, BoxScaleLoss=3.801, ClassLoss=5.522 [Epoch 271] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.381 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.623 person=46.7 bicycle=26.6 car=35.6 motorcycle=40.4 airplane=57.8 bus=62.8 train=63.8 truck=33.1 boat=21.3 traffic light=21.1 fire hydrant=60.8 stop sign=59.1 parking meter=40.3 bench=21.7 bird=31.1 cat=62.0 dog=57.3 horse=50.9 sheep=46.9 cow=48.4 elephant=60.1 bear=67.7 zebra=60.2 giraffe=60.7 backpack=11.4 umbrella=34.8 handbag=10.9 tie=25.9 suitcase=32.9 frisbee=53.5 skis=17.9 snowboard=28.9 sports ball=36.5 kite=35.2 baseball bat=24.2 baseball glove=29.9 skateboard=43.8 surfboard=30.4 tennis racket=40.1 bottle=29.3 wine glass=28.7 cup=36.0 fork=27.4 knife=11.1 spoon=11.8 bowl=36.1 banana=19.9 apple=14.1 sandwich=30.3 orange=27.1 broccoli=17.6 carrot=16.9 hot dog=29.0 pizza=48.0 donut=41.2 cake=34.4 chair=25.1 couch=40.4 potted plant=22.2 bed=43.7 dining table=26.8 toilet=55.8 tv=51.5 laptop=51.4 mouse=52.5 remote=22.7 keyboard=46.7 cell phone=29.4 microwave=47.0 oven=30.8 toaster=5.9 sink=32.8 refrigerator=48.3 book=8.4 clock=44.6 vase=32.5 scissors=32.0 teddy bear=40.0 hair drier=0.0 toothbrush=15.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.7 [Epoch 272][Batch 99], LR: 1.00E-05, Speed: 8.054 samples/sec, ObjLoss=17.712, BoxCenterLoss=13.935, BoxScaleLoss=3.800, ClassLoss=5.518 [Epoch 272][Batch 199], LR: 1.00E-05, Speed: 8.610 samples/sec, ObjLoss=17.706, BoxCenterLoss=13.935, BoxScaleLoss=3.799, ClassLoss=5.513 [Epoch 272][Batch 299], LR: 1.00E-05, Speed: 119.425 samples/sec, ObjLoss=17.697, BoxCenterLoss=13.934, BoxScaleLoss=3.798, ClassLoss=5.509 [Epoch 272][Batch 399], LR: 1.00E-05, Speed: 10.256 samples/sec, ObjLoss=17.690, BoxCenterLoss=13.934, BoxScaleLoss=3.797, ClassLoss=5.505 [Epoch 272][Batch 499], LR: 1.00E-05, Speed: 8.019 samples/sec, ObjLoss=17.682, BoxCenterLoss=13.933, BoxScaleLoss=3.796, ClassLoss=5.501 [Epoch 272][Batch 599], LR: 1.00E-05, Speed: 8.196 samples/sec, ObjLoss=17.675, BoxCenterLoss=13.933, BoxScaleLoss=3.795, ClassLoss=5.497 [Epoch 272][Batch 699], LR: 1.00E-05, Speed: 7.554 samples/sec, ObjLoss=17.667, BoxCenterLoss=13.932, BoxScaleLoss=3.794, ClassLoss=5.493 [Epoch 272][Batch 799], LR: 1.00E-05, Speed: 9.206 samples/sec, ObjLoss=17.660, BoxCenterLoss=13.932, BoxScaleLoss=3.792, ClassLoss=5.488 [Epoch 272][Batch 899], LR: 1.00E-05, Speed: 8.822 samples/sec, ObjLoss=17.653, BoxCenterLoss=13.931, BoxScaleLoss=3.792, ClassLoss=5.485 [Epoch 272][Batch 999], LR: 1.00E-05, Speed: 91.167 samples/sec, ObjLoss=17.644, BoxCenterLoss=13.930, BoxScaleLoss=3.790, ClassLoss=5.480 [Epoch 272][Batch 1099], LR: 1.00E-05, Speed: 11.185 samples/sec, ObjLoss=17.637, BoxCenterLoss=13.930, BoxScaleLoss=3.789, ClassLoss=5.476 [Epoch 272][Batch 1199], LR: 1.00E-05, Speed: 10.865 samples/sec, ObjLoss=17.630, BoxCenterLoss=13.930, BoxScaleLoss=3.789, ClassLoss=5.473 [Epoch 272][Batch 1299], LR: 1.00E-05, Speed: 10.199 samples/sec, ObjLoss=17.624, BoxCenterLoss=13.930, BoxScaleLoss=3.788, ClassLoss=5.469 [Epoch 272][Batch 1399], LR: 1.00E-05, Speed: 10.695 samples/sec, ObjLoss=17.618, BoxCenterLoss=13.931, BoxScaleLoss=3.787, ClassLoss=5.466 [Epoch 272][Batch 1499], LR: 1.00E-05, Speed: 11.789 samples/sec, ObjLoss=17.610, BoxCenterLoss=13.930, BoxScaleLoss=3.786, ClassLoss=5.462 [Epoch 272][Batch 1599], LR: 1.00E-05, Speed: 9.103 samples/sec, ObjLoss=17.604, BoxCenterLoss=13.931, BoxScaleLoss=3.785, ClassLoss=5.458 [Epoch 272][Batch 1699], LR: 1.00E-05, Speed: 78.223 samples/sec, ObjLoss=17.597, BoxCenterLoss=13.930, BoxScaleLoss=3.784, ClassLoss=5.454 [Epoch 272][Batch 1799], LR: 1.00E-05, Speed: 10.179 samples/sec, ObjLoss=17.590, BoxCenterLoss=13.930, BoxScaleLoss=3.783, ClassLoss=5.450 [Epoch 272] Training cost: 2247.744, ObjLoss=17.587, BoxCenterLoss=13.930, BoxScaleLoss=3.783, ClassLoss=5.449 [Epoch 272] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.567 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.165 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.430 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.470 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.626 person=46.7 bicycle=26.4 car=35.3 motorcycle=40.5 airplane=58.3 bus=63.3 train=64.1 truck=32.8 boat=21.2 traffic light=20.7 fire hydrant=60.5 stop sign=59.6 parking meter=40.7 bench=21.4 bird=30.7 cat=61.7 dog=57.6 horse=51.2 sheep=46.9 cow=48.5 elephant=59.8 bear=66.7 zebra=60.5 giraffe=60.4 backpack=11.3 umbrella=34.9 handbag=10.6 tie=25.3 suitcase=32.5 frisbee=53.9 skis=18.4 snowboard=29.0 sports ball=35.9 kite=35.5 baseball bat=23.5 baseball glove=29.7 skateboard=44.2 surfboard=30.7 tennis racket=40.2 bottle=29.8 wine glass=28.7 cup=35.8 fork=27.5 knife=11.1 spoon=11.9 bowl=35.7 banana=20.2 apple=13.9 sandwich=30.1 orange=27.6 broccoli=17.4 carrot=17.1 hot dog=29.1 pizza=48.7 donut=41.4 cake=34.2 chair=24.9 couch=40.6 potted plant=21.8 bed=44.0 dining table=26.6 toilet=55.9 tv=51.9 laptop=51.1 mouse=51.9 remote=23.0 keyboard=45.6 cell phone=29.1 microwave=46.3 oven=30.0 toaster=5.9 sink=32.7 refrigerator=47.6 book=8.3 clock=44.5 vase=32.5 scissors=32.7 teddy bear=40.6 hair drier=0.0 toothbrush=16.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.7 [Epoch 273][Batch 99], LR: 1.00E-05, Speed: 7.829 samples/sec, ObjLoss=17.581, BoxCenterLoss=13.930, BoxScaleLoss=3.781, ClassLoss=5.445 [Epoch 273][Batch 199], LR: 1.00E-05, Speed: 123.312 samples/sec, ObjLoss=17.574, BoxCenterLoss=13.929, BoxScaleLoss=3.780, ClassLoss=5.441 [Epoch 273][Batch 299], LR: 1.00E-05, Speed: 10.668 samples/sec, ObjLoss=17.566, BoxCenterLoss=13.929, BoxScaleLoss=3.779, ClassLoss=5.437 [Epoch 273][Batch 399], LR: 1.00E-05, Speed: 10.988 samples/sec, ObjLoss=17.559, BoxCenterLoss=13.928, BoxScaleLoss=3.778, ClassLoss=5.432 [Epoch 273][Batch 499], LR: 1.00E-05, Speed: 10.668 samples/sec, ObjLoss=17.552, BoxCenterLoss=13.928, BoxScaleLoss=3.777, ClassLoss=5.429 [Epoch 273][Batch 599], LR: 1.00E-05, Speed: 11.310 samples/sec, ObjLoss=17.545, BoxCenterLoss=13.928, BoxScaleLoss=3.777, ClassLoss=5.425 [Epoch 273][Batch 699], LR: 1.00E-05, Speed: 10.723 samples/sec, ObjLoss=17.538, BoxCenterLoss=13.927, BoxScaleLoss=3.776, ClassLoss=5.422 [Epoch 273][Batch 799], LR: 1.00E-05, Speed: 9.042 samples/sec, ObjLoss=17.530, BoxCenterLoss=13.926, BoxScaleLoss=3.774, ClassLoss=5.418 [Epoch 273][Batch 899], LR: 1.00E-05, Speed: 12.331 samples/sec, ObjLoss=17.523, BoxCenterLoss=13.926, BoxScaleLoss=3.774, ClassLoss=5.414 [Epoch 273][Batch 999], LR: 1.00E-05, Speed: 8.873 samples/sec, ObjLoss=17.516, BoxCenterLoss=13.926, BoxScaleLoss=3.773, ClassLoss=5.412 [Epoch 273][Batch 1099], LR: 1.00E-05, Speed: 7.370 samples/sec, ObjLoss=17.509, BoxCenterLoss=13.924, BoxScaleLoss=3.772, ClassLoss=5.408 [Epoch 273][Batch 1199], LR: 1.00E-05, Speed: 9.700 samples/sec, ObjLoss=17.501, BoxCenterLoss=13.924, BoxScaleLoss=3.771, ClassLoss=5.404 [Epoch 273][Batch 1299], LR: 1.00E-05, Speed: 110.321 samples/sec, ObjLoss=17.495, BoxCenterLoss=13.924, BoxScaleLoss=3.770, ClassLoss=5.401 [Epoch 273][Batch 1399], LR: 1.00E-05, Speed: 9.138 samples/sec, ObjLoss=17.489, BoxCenterLoss=13.923, BoxScaleLoss=3.769, ClassLoss=5.397 [Epoch 273][Batch 1499], LR: 1.00E-05, Speed: 8.339 samples/sec, ObjLoss=17.482, BoxCenterLoss=13.923, BoxScaleLoss=3.769, ClassLoss=5.394 [Epoch 273][Batch 1599], LR: 1.00E-05, Speed: 7.572 samples/sec, ObjLoss=17.476, BoxCenterLoss=13.924, BoxScaleLoss=3.769, ClassLoss=5.391 [Epoch 273][Batch 1699], LR: 1.00E-05, Speed: 85.936 samples/sec, ObjLoss=17.470, BoxCenterLoss=13.924, BoxScaleLoss=3.768, ClassLoss=5.387 [Epoch 273][Batch 1799], LR: 1.00E-05, Speed: 130.116 samples/sec, ObjLoss=17.463, BoxCenterLoss=13.923, BoxScaleLoss=3.767, ClassLoss=5.383 [Epoch 273] Training cost: 2063.436, ObjLoss=17.460, BoxCenterLoss=13.923, BoxScaleLoss=3.766, ClassLoss=5.383 [Epoch 273] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.384 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621 person=46.8 bicycle=26.6 car=35.4 motorcycle=40.6 airplane=57.3 bus=63.1 train=63.9 truck=32.9 boat=21.3 traffic light=20.8 fire hydrant=60.7 stop sign=59.3 parking meter=41.0 bench=21.5 bird=31.0 cat=61.1 dog=57.4 horse=50.6 sheep=46.6 cow=48.5 elephant=59.8 bear=66.8 zebra=59.6 giraffe=59.5 backpack=11.5 umbrella=35.1 handbag=10.9 tie=25.2 suitcase=32.5 frisbee=53.3 skis=18.1 snowboard=29.4 sports ball=36.0 kite=35.3 baseball bat=24.1 baseball glove=30.4 skateboard=43.9 surfboard=30.3 tennis racket=40.6 bottle=29.4 wine glass=28.4 cup=35.5 fork=27.3 knife=11.2 spoon=11.5 bowl=35.9 banana=20.0 apple=14.4 sandwich=30.5 orange=27.1 broccoli=17.0 carrot=17.3 hot dog=28.4 pizza=47.5 donut=41.9 cake=34.2 chair=25.0 couch=39.7 potted plant=22.1 bed=43.3 dining table=26.9 toilet=55.6 tv=51.1 laptop=51.7 mouse=52.0 remote=22.9 keyboard=46.5 cell phone=29.1 microwave=46.7 oven=30.5 toaster=7.1 sink=32.7 refrigerator=48.3 book=8.4 clock=44.3 vase=32.6 scissors=32.8 teddy bear=39.8 hair drier=0.0 toothbrush=16.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.6 [Epoch 274][Batch 99], LR: 1.00E-05, Speed: 11.820 samples/sec, ObjLoss=17.454, BoxCenterLoss=13.922, BoxScaleLoss=3.766, ClassLoss=5.379 [Epoch 274][Batch 199], LR: 1.00E-05, Speed: 9.428 samples/sec, ObjLoss=17.447, BoxCenterLoss=13.923, BoxScaleLoss=3.765, ClassLoss=5.376 [Epoch 274][Batch 299], LR: 1.00E-05, Speed: 10.802 samples/sec, ObjLoss=17.441, BoxCenterLoss=13.923, BoxScaleLoss=3.764, ClassLoss=5.373 [Epoch 274][Batch 399], LR: 1.00E-05, Speed: 9.743 samples/sec, ObjLoss=17.434, BoxCenterLoss=13.922, BoxScaleLoss=3.763, ClassLoss=5.369 [Epoch 274][Batch 499], LR: 1.00E-05, Speed: 8.216 samples/sec, ObjLoss=17.427, BoxCenterLoss=13.921, BoxScaleLoss=3.762, ClassLoss=5.365 [Epoch 274][Batch 599], LR: 1.00E-05, Speed: 10.165 samples/sec, ObjLoss=17.421, BoxCenterLoss=13.921, BoxScaleLoss=3.762, ClassLoss=5.362 [Epoch 274][Batch 699], LR: 1.00E-05, Speed: 7.829 samples/sec, ObjLoss=17.414, BoxCenterLoss=13.920, BoxScaleLoss=3.761, ClassLoss=5.359 [Epoch 274][Batch 799], LR: 1.00E-05, Speed: 90.991 samples/sec, ObjLoss=17.409, BoxCenterLoss=13.921, BoxScaleLoss=3.760, ClassLoss=5.355 [Epoch 274][Batch 899], LR: 1.00E-05, Speed: 12.204 samples/sec, ObjLoss=17.402, BoxCenterLoss=13.920, BoxScaleLoss=3.759, ClassLoss=5.351 [Epoch 274][Batch 999], LR: 1.00E-05, Speed: 77.454 samples/sec, ObjLoss=17.394, BoxCenterLoss=13.918, BoxScaleLoss=3.758, ClassLoss=5.348 [Epoch 274][Batch 1099], LR: 1.00E-05, Speed: 9.907 samples/sec, ObjLoss=17.388, BoxCenterLoss=13.918, BoxScaleLoss=3.757, ClassLoss=5.345 [Epoch 274][Batch 1199], LR: 1.00E-05, Speed: 8.863 samples/sec, ObjLoss=17.382, BoxCenterLoss=13.918, BoxScaleLoss=3.756, ClassLoss=5.342 [Epoch 274][Batch 1299], LR: 1.00E-05, Speed: 10.962 samples/sec, ObjLoss=17.376, BoxCenterLoss=13.918, BoxScaleLoss=3.756, ClassLoss=5.338 [Epoch 274][Batch 1399], LR: 1.00E-05, Speed: 10.607 samples/sec, ObjLoss=17.370, BoxCenterLoss=13.918, BoxScaleLoss=3.755, ClassLoss=5.335 [Epoch 274][Batch 1499], LR: 1.00E-05, Speed: 11.713 samples/sec, ObjLoss=17.365, BoxCenterLoss=13.918, BoxScaleLoss=3.754, ClassLoss=5.332 [Epoch 274][Batch 1599], LR: 1.00E-05, Speed: 10.156 samples/sec, ObjLoss=17.359, BoxCenterLoss=13.918, BoxScaleLoss=3.754, ClassLoss=5.329 [Epoch 274][Batch 1699], LR: 1.00E-05, Speed: 7.530 samples/sec, ObjLoss=17.354, BoxCenterLoss=13.919, BoxScaleLoss=3.753, ClassLoss=5.325 [Epoch 274][Batch 1799], LR: 1.00E-05, Speed: 10.109 samples/sec, ObjLoss=17.348, BoxCenterLoss=13.918, BoxScaleLoss=3.752, ClassLoss=5.322 [Epoch 274] Training cost: 2149.429, ObjLoss=17.346, BoxCenterLoss=13.918, BoxScaleLoss=3.752, ClassLoss=5.321 [Epoch 274] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.384 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.170 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.381 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.240 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618 person=46.4 bicycle=26.0 car=35.3 motorcycle=40.5 airplane=57.7 bus=62.2 train=63.9 truck=33.1 boat=21.7 traffic light=20.9 fire hydrant=60.3 stop sign=58.6 parking meter=41.4 bench=21.5 bird=30.7 cat=61.8 dog=56.7 horse=50.8 sheep=46.5 cow=48.7 elephant=59.1 bear=68.7 zebra=60.2 giraffe=60.8 backpack=11.3 umbrella=34.8 handbag=10.8 tie=25.3 suitcase=32.3 frisbee=53.9 skis=18.2 snowboard=29.5 sports ball=36.2 kite=36.0 baseball bat=23.2 baseball glove=30.2 skateboard=43.4 surfboard=30.2 tennis racket=40.4 bottle=29.2 wine glass=28.6 cup=35.6 fork=26.9 knife=11.1 spoon=11.4 bowl=36.0 banana=19.7 apple=14.3 sandwich=30.7 orange=26.7 broccoli=17.5 carrot=16.9 hot dog=28.6 pizza=47.1 donut=41.4 cake=34.7 chair=24.9 couch=39.5 potted plant=21.8 bed=42.7 dining table=25.6 toilet=55.4 tv=51.5 laptop=51.8 mouse=51.9 remote=22.9 keyboard=45.5 cell phone=28.4 microwave=46.2 oven=30.6 toaster=7.1 sink=32.9 refrigerator=49.3 book=8.5 clock=44.2 vase=32.5 scissors=31.9 teddy bear=40.0 hair drier=0.0 toothbrush=14.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.6 [Epoch 275][Batch 99], LR: 1.00E-05, Speed: 13.319 samples/sec, ObjLoss=17.339, BoxCenterLoss=13.918, BoxScaleLoss=3.751, ClassLoss=5.317 [Epoch 275][Batch 199], LR: 1.00E-05, Speed: 107.104 samples/sec, ObjLoss=17.333, BoxCenterLoss=13.917, BoxScaleLoss=3.750, ClassLoss=5.315 [Epoch 275][Batch 299], LR: 1.00E-05, Speed: 8.801 samples/sec, ObjLoss=17.328, BoxCenterLoss=13.918, BoxScaleLoss=3.750, ClassLoss=5.311 [Epoch 275][Batch 399], LR: 1.00E-05, Speed: 10.467 samples/sec, ObjLoss=17.322, BoxCenterLoss=13.918, BoxScaleLoss=3.749, ClassLoss=5.308 [Epoch 275][Batch 499], LR: 1.00E-05, Speed: 10.822 samples/sec, ObjLoss=17.317, BoxCenterLoss=13.918, BoxScaleLoss=3.749, ClassLoss=5.305 [Epoch 275][Batch 599], LR: 1.00E-05, Speed: 7.502 samples/sec, ObjLoss=17.310, BoxCenterLoss=13.917, BoxScaleLoss=3.748, ClassLoss=5.302 [Epoch 275][Batch 699], LR: 1.00E-05, Speed: 7.817 samples/sec, ObjLoss=17.304, BoxCenterLoss=13.916, BoxScaleLoss=3.747, ClassLoss=5.299 [Epoch 275][Batch 799], LR: 1.00E-05, Speed: 9.180 samples/sec, ObjLoss=17.296, BoxCenterLoss=13.914, BoxScaleLoss=3.746, ClassLoss=5.295 [Epoch 275][Batch 899], LR: 1.00E-05, Speed: 11.785 samples/sec, ObjLoss=17.291, BoxCenterLoss=13.914, BoxScaleLoss=3.745, ClassLoss=5.293 [Epoch 275][Batch 999], LR: 1.00E-05, Speed: 137.307 samples/sec, ObjLoss=17.286, BoxCenterLoss=13.915, BoxScaleLoss=3.745, ClassLoss=5.290 [Epoch 275][Batch 1099], LR: 1.00E-05, Speed: 102.070 samples/sec, ObjLoss=17.279, BoxCenterLoss=13.914, BoxScaleLoss=3.744, ClassLoss=5.287 [Epoch 275][Batch 1199], LR: 1.00E-05, Speed: 8.482 samples/sec, ObjLoss=17.274, BoxCenterLoss=13.914, BoxScaleLoss=3.743, ClassLoss=5.284 [Epoch 275][Batch 1299], LR: 1.00E-05, Speed: 8.107 samples/sec, ObjLoss=17.269, BoxCenterLoss=13.914, BoxScaleLoss=3.743, ClassLoss=5.281 [Epoch 275][Batch 1399], LR: 1.00E-05, Speed: 129.255 samples/sec, ObjLoss=17.262, BoxCenterLoss=13.913, BoxScaleLoss=3.742, ClassLoss=5.278 [Epoch 275][Batch 1499], LR: 1.00E-05, Speed: 8.130 samples/sec, ObjLoss=17.256, BoxCenterLoss=13.913, BoxScaleLoss=3.742, ClassLoss=5.275 [Epoch 275][Batch 1599], LR: 1.00E-05, Speed: 9.655 samples/sec, ObjLoss=17.251, BoxCenterLoss=13.914, BoxScaleLoss=3.741, ClassLoss=5.273 [Epoch 275][Batch 1699], LR: 1.00E-05, Speed: 10.403 samples/sec, ObjLoss=17.245, BoxCenterLoss=13.913, BoxScaleLoss=3.740, ClassLoss=5.270 [Epoch 275][Batch 1799], LR: 1.00E-05, Speed: 13.002 samples/sec, ObjLoss=17.239, BoxCenterLoss=13.913, BoxScaleLoss=3.740, ClassLoss=5.267 [Epoch 275] Training cost: 2159.895, ObjLoss=17.237, BoxCenterLoss=13.913, BoxScaleLoss=3.740, ClassLoss=5.266 [Epoch 275] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622 person=46.6 bicycle=26.7 car=35.7 motorcycle=40.5 airplane=57.1 bus=62.7 train=63.6 truck=32.8 boat=21.7 traffic light=20.7 fire hydrant=60.1 stop sign=59.4 parking meter=40.5 bench=21.7 bird=31.1 cat=62.3 dog=57.6 horse=51.3 sheep=46.8 cow=48.9 elephant=59.9 bear=68.1 zebra=60.1 giraffe=61.0 backpack=11.1 umbrella=34.7 handbag=10.7 tie=25.2 suitcase=32.6 frisbee=53.3 skis=18.4 snowboard=30.1 sports ball=35.6 kite=35.5 baseball bat=24.0 baseball glove=30.2 skateboard=44.2 surfboard=30.9 tennis racket=40.3 bottle=29.4 wine glass=28.8 cup=35.7 fork=27.0 knife=11.3 spoon=11.7 bowl=35.8 banana=20.4 apple=14.0 sandwich=30.2 orange=27.2 broccoli=17.4 carrot=17.3 hot dog=29.3 pizza=47.7 donut=41.2 cake=33.7 chair=24.9 couch=40.3 potted plant=21.5 bed=43.7 dining table=26.9 toilet=55.8 tv=52.1 laptop=51.9 mouse=52.0 remote=23.2 keyboard=46.3 cell phone=28.7 microwave=46.9 oven=29.8 toaster=7.1 sink=32.4 refrigerator=49.0 book=8.6 clock=44.4 vase=32.6 scissors=32.0 teddy bear=40.8 hair drier=0.0 toothbrush=15.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.7 [Epoch 276][Batch 99], LR: 1.00E-05, Speed: 8.077 samples/sec, ObjLoss=17.231, BoxCenterLoss=13.912, BoxScaleLoss=3.739, ClassLoss=5.263 [Epoch 276][Batch 199], LR: 1.00E-05, Speed: 9.122 samples/sec, ObjLoss=17.226, BoxCenterLoss=13.912, BoxScaleLoss=3.738, ClassLoss=5.259 [Epoch 276][Batch 299], LR: 1.00E-05, Speed: 8.941 samples/sec, ObjLoss=17.221, BoxCenterLoss=13.912, BoxScaleLoss=3.737, ClassLoss=5.256 [Epoch 276][Batch 399], LR: 1.00E-05, Speed: 10.202 samples/sec, ObjLoss=17.215, BoxCenterLoss=13.912, BoxScaleLoss=3.736, ClassLoss=5.253 [Epoch 276][Batch 499], LR: 1.00E-05, Speed: 9.064 samples/sec, ObjLoss=17.210, BoxCenterLoss=13.912, BoxScaleLoss=3.735, ClassLoss=5.250 [Epoch 276][Batch 599], LR: 1.00E-05, Speed: 11.167 samples/sec, ObjLoss=17.206, BoxCenterLoss=13.912, BoxScaleLoss=3.735, ClassLoss=5.247 [Epoch 276][Batch 699], LR: 1.00E-05, Speed: 120.977 samples/sec, ObjLoss=17.200, BoxCenterLoss=13.912, BoxScaleLoss=3.734, ClassLoss=5.244 [Epoch 276][Batch 799], LR: 1.00E-05, Speed: 11.234 samples/sec, ObjLoss=17.195, BoxCenterLoss=13.911, BoxScaleLoss=3.733, ClassLoss=5.241 [Epoch 276][Batch 899], LR: 1.00E-05, Speed: 11.633 samples/sec, ObjLoss=17.189, BoxCenterLoss=13.911, BoxScaleLoss=3.733, ClassLoss=5.238 [Epoch 276][Batch 999], LR: 1.00E-05, Speed: 7.208 samples/sec, ObjLoss=17.183, BoxCenterLoss=13.911, BoxScaleLoss=3.733, ClassLoss=5.236 [Epoch 276][Batch 1099], LR: 1.00E-05, Speed: 9.461 samples/sec, ObjLoss=17.178, BoxCenterLoss=13.910, BoxScaleLoss=3.732, ClassLoss=5.233 [Epoch 276][Batch 1199], LR: 1.00E-05, Speed: 93.100 samples/sec, ObjLoss=17.173, BoxCenterLoss=13.911, BoxScaleLoss=3.731, ClassLoss=5.230 [Epoch 276][Batch 1299], LR: 1.00E-05, Speed: 94.721 samples/sec, ObjLoss=17.168, BoxCenterLoss=13.911, BoxScaleLoss=3.731, ClassLoss=5.228 [Epoch 276][Batch 1399], LR: 1.00E-05, Speed: 10.038 samples/sec, ObjLoss=17.163, BoxCenterLoss=13.911, BoxScaleLoss=3.730, ClassLoss=5.225 [Epoch 276][Batch 1499], LR: 1.00E-05, Speed: 10.120 samples/sec, ObjLoss=17.157, BoxCenterLoss=13.910, BoxScaleLoss=3.729, ClassLoss=5.222 [Epoch 276][Batch 1599], LR: 1.00E-05, Speed: 9.207 samples/sec, ObjLoss=17.152, BoxCenterLoss=13.910, BoxScaleLoss=3.729, ClassLoss=5.220 [Epoch 276][Batch 1699], LR: 1.00E-05, Speed: 8.220 samples/sec, ObjLoss=17.146, BoxCenterLoss=13.909, BoxScaleLoss=3.728, ClassLoss=5.217 [Epoch 276][Batch 1799], LR: 1.00E-05, Speed: 11.484 samples/sec, ObjLoss=17.141, BoxCenterLoss=13.909, BoxScaleLoss=3.728, ClassLoss=5.214 [Epoch 276] Training cost: 2244.946, ObjLoss=17.140, BoxCenterLoss=13.909, BoxScaleLoss=3.728, ClassLoss=5.213 [Epoch 276] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.385 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.384 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.524 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622 person=46.8 bicycle=26.7 car=35.5 motorcycle=41.0 airplane=58.1 bus=62.9 train=64.1 truck=33.2 boat=21.7 traffic light=21.0 fire hydrant=61.4 stop sign=59.3 parking meter=41.1 bench=21.7 bird=31.2 cat=62.3 dog=57.8 horse=50.8 sheep=46.9 cow=49.4 elephant=60.4 bear=67.6 zebra=60.6 giraffe=60.9 backpack=11.2 umbrella=35.1 handbag=10.8 tie=25.4 suitcase=32.4 frisbee=54.2 skis=18.4 snowboard=28.7 sports ball=36.5 kite=35.5 baseball bat=24.1 baseball glove=30.3 skateboard=44.1 surfboard=31.1 tennis racket=40.2 bottle=29.7 wine glass=28.8 cup=35.9 fork=27.7 knife=10.9 spoon=11.9 bowl=36.3 banana=19.8 apple=13.5 sandwich=30.3 orange=27.5 broccoli=17.1 carrot=16.8 hot dog=28.6 pizza=47.9 donut=41.3 cake=34.4 chair=24.8 couch=40.3 potted plant=21.8 bed=43.8 dining table=26.8 toilet=56.4 tv=51.4 laptop=51.7 mouse=52.7 remote=23.0 keyboard=45.9 cell phone=29.0 microwave=46.1 oven=30.1 toaster=5.9 sink=32.7 refrigerator=49.3 book=8.5 clock=44.3 vase=32.9 scissors=31.9 teddy bear=40.8 hair drier=0.0 toothbrush=15.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.8 [Epoch 277][Batch 99], LR: 1.00E-05, Speed: 7.354 samples/sec, ObjLoss=17.135, BoxCenterLoss=13.909, BoxScaleLoss=3.727, ClassLoss=5.211 [Epoch 277][Batch 199], LR: 1.00E-05, Speed: 10.510 samples/sec, ObjLoss=17.129, BoxCenterLoss=13.909, BoxScaleLoss=3.727, ClassLoss=5.208 [Epoch 277][Batch 299], LR: 1.00E-05, Speed: 7.120 samples/sec, ObjLoss=17.124, BoxCenterLoss=13.909, BoxScaleLoss=3.726, ClassLoss=5.206 [Epoch 277][Batch 399], LR: 1.00E-05, Speed: 10.732 samples/sec, ObjLoss=17.120, BoxCenterLoss=13.909, BoxScaleLoss=3.726, ClassLoss=5.204 [Epoch 277][Batch 499], LR: 1.00E-05, Speed: 9.326 samples/sec, ObjLoss=17.114, BoxCenterLoss=13.909, BoxScaleLoss=3.726, ClassLoss=5.201 [Epoch 277][Batch 599], LR: 1.00E-05, Speed: 107.053 samples/sec, ObjLoss=17.109, BoxCenterLoss=13.909, BoxScaleLoss=3.725, ClassLoss=5.198 [Epoch 277][Batch 699], LR: 1.00E-05, Speed: 10.749 samples/sec, ObjLoss=17.103, BoxCenterLoss=13.908, BoxScaleLoss=3.724, ClassLoss=5.195 [Epoch 277][Batch 799], LR: 1.00E-05, Speed: 10.278 samples/sec, ObjLoss=17.098, BoxCenterLoss=13.908, BoxScaleLoss=3.723, ClassLoss=5.192 [Epoch 277][Batch 899], LR: 1.00E-05, Speed: 11.414 samples/sec, ObjLoss=17.093, BoxCenterLoss=13.907, BoxScaleLoss=3.723, ClassLoss=5.190 [Epoch 277][Batch 999], LR: 1.00E-05, Speed: 8.127 samples/sec, ObjLoss=17.088, BoxCenterLoss=13.907, BoxScaleLoss=3.722, ClassLoss=5.187 [Epoch 277][Batch 1099], LR: 1.00E-05, Speed: 8.157 samples/sec, ObjLoss=17.083, BoxCenterLoss=13.907, BoxScaleLoss=3.721, ClassLoss=5.184 [Epoch 277][Batch 1199], LR: 1.00E-05, Speed: 7.484 samples/sec, ObjLoss=17.078, BoxCenterLoss=13.907, BoxScaleLoss=3.721, ClassLoss=5.182 [Epoch 277][Batch 1299], LR: 1.00E-05, Speed: 9.678 samples/sec, ObjLoss=17.073, BoxCenterLoss=13.907, BoxScaleLoss=3.720, ClassLoss=5.179 [Epoch 277][Batch 1399], LR: 1.00E-05, Speed: 10.622 samples/sec, ObjLoss=17.068, BoxCenterLoss=13.906, BoxScaleLoss=3.720, ClassLoss=5.177 [Epoch 277][Batch 1499], LR: 1.00E-05, Speed: 106.520 samples/sec, ObjLoss=17.063, BoxCenterLoss=13.906, BoxScaleLoss=3.719, ClassLoss=5.174 [Epoch 277][Batch 1599], LR: 1.00E-05, Speed: 11.126 samples/sec, ObjLoss=17.057, BoxCenterLoss=13.905, BoxScaleLoss=3.718, ClassLoss=5.171 [Epoch 277][Batch 1699], LR: 1.00E-05, Speed: 11.764 samples/sec, ObjLoss=17.052, BoxCenterLoss=13.904, BoxScaleLoss=3.718, ClassLoss=5.169 [Epoch 277][Batch 1799], LR: 1.00E-05, Speed: 11.370 samples/sec, ObjLoss=17.046, BoxCenterLoss=13.904, BoxScaleLoss=3.717, ClassLoss=5.166 [Epoch 277] Training cost: 2151.695, ObjLoss=17.044, BoxCenterLoss=13.903, BoxScaleLoss=3.717, ClassLoss=5.165 [Epoch 277] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.385 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.446 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621 person=46.7 bicycle=26.5 car=35.5 motorcycle=40.6 airplane=58.0 bus=63.6 train=63.6 truck=33.1 boat=21.4 traffic light=21.2 fire hydrant=60.3 stop sign=59.4 parking meter=41.4 bench=21.7 bird=31.0 cat=62.5 dog=57.4 horse=50.3 sheep=46.9 cow=48.5 elephant=59.6 bear=68.2 zebra=60.7 giraffe=60.9 backpack=11.2 umbrella=34.7 handbag=10.7 tie=25.5 suitcase=33.3 frisbee=53.3 skis=17.9 snowboard=29.2 sports ball=36.5 kite=35.8 baseball bat=24.1 baseball glove=30.7 skateboard=43.9 surfboard=30.7 tennis racket=40.7 bottle=29.6 wine glass=28.9 cup=35.9 fork=27.4 knife=11.1 spoon=12.0 bowl=36.0 banana=20.2 apple=13.6 sandwich=30.5 orange=27.5 broccoli=17.1 carrot=17.2 hot dog=29.3 pizza=47.8 donut=42.4 cake=34.1 chair=24.8 couch=40.3 potted plant=22.1 bed=43.3 dining table=26.9 toilet=56.4 tv=51.3 laptop=51.6 mouse=51.5 remote=23.4 keyboard=45.8 cell phone=29.4 microwave=46.9 oven=30.5 toaster=5.9 sink=33.1 refrigerator=49.1 book=8.7 clock=43.8 vase=32.6 scissors=33.1 teddy bear=40.5 hair drier=0.0 toothbrush=16.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.8 [Epoch 278][Batch 99], LR: 1.00E-05, Speed: 9.102 samples/sec, ObjLoss=17.040, BoxCenterLoss=13.903, BoxScaleLoss=3.716, ClassLoss=5.163 [Epoch 278][Batch 199], LR: 1.00E-05, Speed: 6.436 samples/sec, ObjLoss=17.035, BoxCenterLoss=13.903, BoxScaleLoss=3.716, ClassLoss=5.160 [Epoch 278][Batch 299], LR: 1.00E-05, Speed: 7.954 samples/sec, ObjLoss=17.030, BoxCenterLoss=13.902, BoxScaleLoss=3.715, ClassLoss=5.158 [Epoch 278][Batch 399], LR: 1.00E-05, Speed: 9.579 samples/sec, ObjLoss=17.026, BoxCenterLoss=13.903, BoxScaleLoss=3.714, ClassLoss=5.155 [Epoch 278][Batch 499], LR: 1.00E-05, Speed: 10.107 samples/sec, ObjLoss=17.020, BoxCenterLoss=13.902, BoxScaleLoss=3.714, ClassLoss=5.153 [Epoch 278][Batch 599], LR: 1.00E-05, Speed: 8.889 samples/sec, ObjLoss=17.016, BoxCenterLoss=13.902, BoxScaleLoss=3.713, ClassLoss=5.150 [Epoch 278][Batch 699], LR: 1.00E-05, Speed: 9.417 samples/sec, ObjLoss=17.011, BoxCenterLoss=13.902, BoxScaleLoss=3.713, ClassLoss=5.148 [Epoch 278][Batch 799], LR: 1.00E-05, Speed: 99.708 samples/sec, ObjLoss=17.007, BoxCenterLoss=13.901, BoxScaleLoss=3.712, ClassLoss=5.145 [Epoch 278][Batch 899], LR: 1.00E-05, Speed: 7.557 samples/sec, ObjLoss=17.002, BoxCenterLoss=13.901, BoxScaleLoss=3.711, ClassLoss=5.143 [Epoch 278][Batch 999], LR: 1.00E-05, Speed: 73.429 samples/sec, ObjLoss=16.996, BoxCenterLoss=13.901, BoxScaleLoss=3.711, ClassLoss=5.140 [Epoch 278][Batch 1099], LR: 1.00E-05, Speed: 8.892 samples/sec, ObjLoss=16.992, BoxCenterLoss=13.900, BoxScaleLoss=3.710, ClassLoss=5.138 [Epoch 278][Batch 1199], LR: 1.00E-05, Speed: 9.900 samples/sec, ObjLoss=16.987, BoxCenterLoss=13.900, BoxScaleLoss=3.710, ClassLoss=5.135 [Epoch 278][Batch 1299], LR: 1.00E-05, Speed: 10.942 samples/sec, ObjLoss=16.984, BoxCenterLoss=13.901, BoxScaleLoss=3.710, ClassLoss=5.133 [Epoch 278][Batch 1399], LR: 1.00E-05, Speed: 10.043 samples/sec, ObjLoss=16.979, BoxCenterLoss=13.901, BoxScaleLoss=3.709, ClassLoss=5.131 [Epoch 278][Batch 1499], LR: 1.00E-05, Speed: 10.010 samples/sec, ObjLoss=16.974, BoxCenterLoss=13.900, BoxScaleLoss=3.708, ClassLoss=5.128 [Epoch 278][Batch 1599], LR: 1.00E-05, Speed: 10.306 samples/sec, ObjLoss=16.969, BoxCenterLoss=13.900, BoxScaleLoss=3.707, ClassLoss=5.126 [Epoch 278][Batch 1699], LR: 1.00E-05, Speed: 9.491 samples/sec, ObjLoss=16.965, BoxCenterLoss=13.900, BoxScaleLoss=3.707, ClassLoss=5.124 [Epoch 278][Batch 1799], LR: 1.00E-05, Speed: 12.640 samples/sec, ObjLoss=16.961, BoxCenterLoss=13.900, BoxScaleLoss=3.706, ClassLoss=5.121 [Epoch 278] Training cost: 2234.737, ObjLoss=16.960, BoxCenterLoss=13.901, BoxScaleLoss=3.706, ClassLoss=5.120 [Epoch 278] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.385 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.526 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.475 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.624 person=46.8 bicycle=26.7 car=35.7 motorcycle=41.0 airplane=58.0 bus=62.8 train=63.5 truck=32.9 boat=21.6 traffic light=21.4 fire hydrant=59.8 stop sign=58.7 parking meter=41.4 bench=21.7 bird=30.9 cat=61.9 dog=57.3 horse=50.7 sheep=46.7 cow=48.4 elephant=60.1 bear=67.5 zebra=59.8 giraffe=61.1 backpack=11.3 umbrella=35.0 handbag=10.6 tie=25.9 suitcase=33.0 frisbee=53.6 skis=18.4 snowboard=29.0 sports ball=36.6 kite=35.8 baseball bat=23.6 baseball glove=30.3 skateboard=44.1 surfboard=30.6 tennis racket=40.3 bottle=29.6 wine glass=28.8 cup=36.0 fork=27.1 knife=10.9 spoon=11.6 bowl=36.1 banana=19.8 apple=14.0 sandwich=31.2 orange=27.8 broccoli=17.5 carrot=17.3 hot dog=29.5 pizza=47.7 donut=41.9 cake=34.6 chair=25.0 couch=40.4 potted plant=22.4 bed=44.1 dining table=26.9 toilet=56.3 tv=51.3 laptop=51.0 mouse=52.1 remote=23.7 keyboard=46.7 cell phone=28.7 microwave=46.9 oven=30.7 toaster=5.9 sink=32.6 refrigerator=49.1 book=8.5 clock=44.4 vase=32.6 scissors=32.0 teddy bear=40.5 hair drier=0.0 toothbrush=15.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.8 [Epoch 279][Batch 99], LR: 1.00E-05, Speed: 9.493 samples/sec, ObjLoss=16.956, BoxCenterLoss=13.901, BoxScaleLoss=3.706, ClassLoss=5.118 [Epoch 279][Batch 199], LR: 1.00E-05, Speed: 8.496 samples/sec, ObjLoss=16.952, BoxCenterLoss=13.902, BoxScaleLoss=3.705, ClassLoss=5.116 [Epoch 279][Batch 299], LR: 1.00E-05, Speed: 10.219 samples/sec, ObjLoss=16.947, BoxCenterLoss=13.901, BoxScaleLoss=3.704, ClassLoss=5.113 [Epoch 279][Batch 399], LR: 1.00E-05, Speed: 98.864 samples/sec, ObjLoss=16.943, BoxCenterLoss=13.900, BoxScaleLoss=3.703, ClassLoss=5.110 [Epoch 279][Batch 499], LR: 1.00E-05, Speed: 8.053 samples/sec, ObjLoss=16.938, BoxCenterLoss=13.900, BoxScaleLoss=3.703, ClassLoss=5.108 [Epoch 279][Batch 599], LR: 1.00E-05, Speed: 8.352 samples/sec, ObjLoss=16.934, BoxCenterLoss=13.900, BoxScaleLoss=3.702, ClassLoss=5.106 [Epoch 279][Batch 699], LR: 1.00E-05, Speed: 9.231 samples/sec, ObjLoss=16.930, BoxCenterLoss=13.901, BoxScaleLoss=3.702, ClassLoss=5.103 [Epoch 279][Batch 799], LR: 1.00E-05, Speed: 11.916 samples/sec, ObjLoss=16.926, BoxCenterLoss=13.901, BoxScaleLoss=3.701, ClassLoss=5.101 [Epoch 279][Batch 899], LR: 1.00E-05, Speed: 9.828 samples/sec, ObjLoss=16.921, BoxCenterLoss=13.900, BoxScaleLoss=3.701, ClassLoss=5.098 [Epoch 279][Batch 999], LR: 1.00E-05, Speed: 136.369 samples/sec, ObjLoss=16.916, BoxCenterLoss=13.899, BoxScaleLoss=3.700, ClassLoss=5.096 [Epoch 279][Batch 1099], LR: 1.00E-05, Speed: 9.472 samples/sec, ObjLoss=16.912, BoxCenterLoss=13.899, BoxScaleLoss=3.700, ClassLoss=5.094 [Epoch 279][Batch 1199], LR: 1.00E-05, Speed: 9.425 samples/sec, ObjLoss=16.908, BoxCenterLoss=13.899, BoxScaleLoss=3.699, ClassLoss=5.092 [Epoch 279][Batch 1299], LR: 1.00E-05, Speed: 8.222 samples/sec, ObjLoss=16.904, BoxCenterLoss=13.899, BoxScaleLoss=3.698, ClassLoss=5.089 [Epoch 279][Batch 1399], LR: 1.00E-05, Speed: 10.733 samples/sec, ObjLoss=16.899, BoxCenterLoss=13.899, BoxScaleLoss=3.697, ClassLoss=5.086 [Epoch 279][Batch 1499], LR: 1.00E-05, Speed: 9.383 samples/sec, ObjLoss=16.895, BoxCenterLoss=13.899, BoxScaleLoss=3.697, ClassLoss=5.084 [Epoch 279][Batch 1599], LR: 1.00E-05, Speed: 9.827 samples/sec, ObjLoss=16.890, BoxCenterLoss=13.898, BoxScaleLoss=3.696, ClassLoss=5.082 [Epoch 279][Batch 1699], LR: 1.00E-05, Speed: 10.669 samples/sec, ObjLoss=16.885, BoxCenterLoss=13.897, BoxScaleLoss=3.695, ClassLoss=5.080 [Epoch 279][Batch 1799], LR: 1.00E-05, Speed: 12.677 samples/sec, ObjLoss=16.880, BoxCenterLoss=13.897, BoxScaleLoss=3.695, ClassLoss=5.077 [Epoch 279] Training cost: 2165.922, ObjLoss=16.880, BoxCenterLoss=13.897, BoxScaleLoss=3.695, ClassLoss=5.077 [Epoch 279] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.384 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.431 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.471 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.625 person=46.6 bicycle=27.2 car=35.6 motorcycle=40.8 airplane=57.3 bus=62.9 train=63.8 truck=32.7 boat=21.5 traffic light=21.1 fire hydrant=59.9 stop sign=58.7 parking meter=41.6 bench=21.7 bird=31.2 cat=63.1 dog=58.1 horse=50.9 sheep=46.3 cow=48.9 elephant=60.2 bear=65.5 zebra=60.1 giraffe=61.2 backpack=11.4 umbrella=34.8 handbag=10.5 tie=25.5 suitcase=33.0 frisbee=54.0 skis=18.3 snowboard=28.8 sports ball=36.4 kite=35.3 baseball bat=23.5 baseball glove=30.3 skateboard=43.7 surfboard=30.9 tennis racket=40.5 bottle=29.3 wine glass=28.9 cup=36.0 fork=27.6 knife=10.9 spoon=12.2 bowl=36.1 banana=20.1 apple=13.7 sandwich=30.3 orange=27.6 broccoli=17.4 carrot=16.9 hot dog=30.2 pizza=48.0 donut=42.3 cake=34.2 chair=25.0 couch=40.3 potted plant=21.6 bed=44.1 dining table=27.1 toilet=56.2 tv=51.5 laptop=51.5 mouse=52.7 remote=23.2 keyboard=46.0 cell phone=29.0 microwave=46.5 oven=30.0 toaster=5.9 sink=32.6 refrigerator=49.1 book=8.5 clock=44.2 vase=32.2 scissors=32.5 teddy bear=41.2 hair drier=0.0 toothbrush=15.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =35.8