Namespace(batch_size=64, data_shape=416, dataset='coco', epochs=280, gpus='0,1,2,3,4,5,6,7', label_smooth=False, log_interval=100, lr=0.001, lr_decay=0.1, lr_decay_epoch='220,250', lr_decay_period=0, lr_mode='step', mixup=False, momentum=0.9, network='mobilenet1.0', no_mixup_epochs=20, no_random_shape=False, no_wd=False, num_samples=117266, num_workers=32, resume='', save_interval=10, save_prefix='yolo3_mobilenet1.0_coco', seed=233, start_epoch=0, syncbn=True, val_interval=1, warmup_epochs=2, warmup_lr=0.0, wd=0.0005) Start training from [Epoch 0] [Epoch 0][Batch 99], LR: 2.70E-05, Speed: 126.004 samples/sec, ObjLoss=869.390, BoxCenterLoss=17.834, BoxScaleLoss=16.303, ClassLoss=339.027 [Epoch 0][Batch 199], LR: 5.43E-05, Speed: 115.747 samples/sec, ObjLoss=460.669, BoxCenterLoss=17.739, BoxScaleLoss=14.559, ClassLoss=240.273 [Epoch 0][Batch 299], LR: 8.16E-05, Speed: 141.053 samples/sec, ObjLoss=319.517, BoxCenterLoss=17.240, BoxScaleLoss=13.195, ClassLoss=176.314 [Epoch 0][Batch 399], LR: 1.09E-04, Speed: 95.144 samples/sec, ObjLoss=248.286, BoxCenterLoss=16.955, BoxScaleLoss=12.230, ClassLoss=140.716 [Epoch 0][Batch 499], LR: 1.36E-04, Speed: 153.377 samples/sec, ObjLoss=205.438, BoxCenterLoss=16.840, BoxScaleLoss=11.525, ClassLoss=118.501 [Epoch 0][Batch 599], LR: 1.63E-04, Speed: 138.787 samples/sec, ObjLoss=176.795, BoxCenterLoss=16.733, BoxScaleLoss=10.933, ClassLoss=103.484 [Epoch 0][Batch 699], LR: 1.91E-04, Speed: 134.117 samples/sec, ObjLoss=156.022, BoxCenterLoss=16.552, BoxScaleLoss=10.404, ClassLoss=92.462 [Epoch 0][Batch 799], LR: 2.18E-04, Speed: 140.266 samples/sec, ObjLoss=140.590, BoxCenterLoss=16.513, BoxScaleLoss=10.046, ClassLoss=84.286 [Epoch 0][Batch 899], LR: 2.45E-04, Speed: 124.203 samples/sec, ObjLoss=128.596, BoxCenterLoss=16.483, BoxScaleLoss=9.747, ClassLoss=77.756 [Epoch 0][Batch 999], LR: 2.73E-04, Speed: 127.079 samples/sec, ObjLoss=118.839, BoxCenterLoss=16.431, BoxScaleLoss=9.478, ClassLoss=72.465 [Epoch 0][Batch 1099], LR: 3.00E-04, Speed: 134.263 samples/sec, ObjLoss=110.931, BoxCenterLoss=16.391, BoxScaleLoss=9.265, ClassLoss=68.087 [Epoch 0][Batch 1199], LR: 3.27E-04, Speed: 131.434 samples/sec, ObjLoss=104.277, BoxCenterLoss=16.347, BoxScaleLoss=9.090, ClassLoss=64.442 [Epoch 0][Batch 1299], LR: 3.55E-04, Speed: 143.662 samples/sec, ObjLoss=98.565, BoxCenterLoss=16.294, BoxScaleLoss=8.950, ClassLoss=61.300 [Epoch 0][Batch 1399], LR: 3.82E-04, Speed: 122.840 samples/sec, ObjLoss=93.630, BoxCenterLoss=16.250, BoxScaleLoss=8.832, ClassLoss=58.572 [Epoch 0][Batch 1499], LR: 4.09E-04, Speed: 143.433 samples/sec, ObjLoss=89.314, BoxCenterLoss=16.188, BoxScaleLoss=8.740, ClassLoss=56.170 [Epoch 0][Batch 1599], LR: 4.36E-04, Speed: 136.728 samples/sec, ObjLoss=85.648, BoxCenterLoss=16.173, BoxScaleLoss=8.634, ClassLoss=54.076 [Epoch 0][Batch 1699], LR: 4.64E-04, Speed: 152.527 samples/sec, ObjLoss=82.364, BoxCenterLoss=16.126, BoxScaleLoss=8.518, ClassLoss=52.189 [Epoch 0][Batch 1799], LR: 4.91E-04, Speed: 131.505 samples/sec, ObjLoss=79.506, BoxCenterLoss=16.106, BoxScaleLoss=8.422, ClassLoss=50.488 [Epoch 0] Training cost: 1175.634, ObjLoss=78.616, BoxCenterLoss=16.084, BoxScaleLoss=8.398, ClassLoss=49.972 [Epoch 0] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.015 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.002 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 ] = 0.004 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.009 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.011 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.011 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.021 person=6.6 bicycle=0.0 car=2.5 motorcycle=0.2 airplane=3.6 bus=1.0 train=1.6 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=0.8 dog=0.3 horse=1.1 sheep=0.3 cow=0.4 elephant=0.4 bear=0.0 zebra=2.7 giraffe=3.7 backpack=0.0 umbrella=0.0 handbag=0.0 tie=0.0 suitcase=0.0 frisbee=0.0 skis=1.0 snowboard=0.0 sports ball=0.1 kite=0.1 baseball bat=0.0 baseball glove=0.0 skateboard=0.1 surfboard=0.0 tennis racket=0.0 bottle=0.1 wine glass=0.0 cup=0.2 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=4.2 donut=0.0 cake=0.1 chair=0.5 couch=0.0 potted plant=0.0 bed=0.0 dining table=0.8 toilet=0.5 tv=0.6 laptop=1.0 mouse=0.0 remote=0.0 keyboard=0.0 cell phone=0.0 microwave=0.0 oven=0.0 toaster=0.0 sink=0.1 refrigerator=0.0 book=0.5 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] ~~~~ =0.5 [Epoch 1][Batch 99], LR: 5.27E-04, Speed: 165.389 samples/sec, ObjLoss=76.060, BoxCenterLoss=16.043, BoxScaleLoss=8.317, ClassLoss=48.472 [Epoch 1][Batch 199], LR: 5.54E-04, Speed: 126.204 samples/sec, ObjLoss=73.793, BoxCenterLoss=16.039, BoxScaleLoss=8.264, ClassLoss=47.134 [Epoch 1][Batch 299], LR: 5.82E-04, Speed: 150.706 samples/sec, ObjLoss=71.680, BoxCenterLoss=16.008, BoxScaleLoss=8.209, ClassLoss=45.888 [Epoch 1][Batch 399], LR: 6.09E-04, Speed: 147.238 samples/sec, ObjLoss=69.758, BoxCenterLoss=15.980, BoxScaleLoss=8.162, ClassLoss=44.741 [Epoch 1][Batch 499], LR: 6.36E-04, Speed: 115.549 samples/sec, ObjLoss=68.029, BoxCenterLoss=15.962, BoxScaleLoss=8.122, ClassLoss=43.692 [Epoch 1][Batch 599], LR: 6.63E-04, Speed: 152.893 samples/sec, ObjLoss=66.406, BoxCenterLoss=15.937, BoxScaleLoss=8.078, ClassLoss=42.695 [Epoch 1][Batch 699], LR: 6.91E-04, Speed: 126.042 samples/sec, ObjLoss=64.953, BoxCenterLoss=15.918, BoxScaleLoss=8.038, ClassLoss=41.778 [Epoch 1][Batch 799], LR: 7.18E-04, Speed: 157.230 samples/sec, ObjLoss=63.587, BoxCenterLoss=15.902, BoxScaleLoss=8.003, ClassLoss=40.917 [Epoch 1][Batch 899], LR: 7.45E-04, Speed: 132.468 samples/sec, ObjLoss=62.295, BoxCenterLoss=15.884, BoxScaleLoss=7.971, ClassLoss=40.114 [Epoch 1][Batch 999], LR: 7.73E-04, Speed: 136.475 samples/sec, ObjLoss=61.098, BoxCenterLoss=15.876, BoxScaleLoss=7.957, ClassLoss=39.379 [Epoch 1][Batch 1099], LR: 8.00E-04, Speed: 145.460 samples/sec, ObjLoss=59.974, BoxCenterLoss=15.847, BoxScaleLoss=7.931, ClassLoss=38.646 [Epoch 1][Batch 1199], LR: 8.27E-04, Speed: 156.462 samples/sec, ObjLoss=58.894, BoxCenterLoss=15.821, BoxScaleLoss=7.909, ClassLoss=37.969 [Epoch 1][Batch 1299], LR: 8.55E-04, Speed: 139.215 samples/sec, ObjLoss=57.970, BoxCenterLoss=15.819, BoxScaleLoss=7.893, ClassLoss=37.343 [Epoch 1][Batch 1399], LR: 8.82E-04, Speed: 125.000 samples/sec, ObjLoss=57.071, BoxCenterLoss=15.803, BoxScaleLoss=7.867, ClassLoss=36.731 [Epoch 1][Batch 1499], LR: 9.09E-04, Speed: 130.773 samples/sec, ObjLoss=56.219, BoxCenterLoss=15.794, BoxScaleLoss=7.843, ClassLoss=36.157 [Epoch 1][Batch 1599], LR: 9.36E-04, Speed: 135.662 samples/sec, ObjLoss=55.410, BoxCenterLoss=15.777, BoxScaleLoss=7.820, ClassLoss=35.599 [Epoch 1][Batch 1699], LR: 9.64E-04, Speed: 132.677 samples/sec, ObjLoss=54.645, BoxCenterLoss=15.763, BoxScaleLoss=7.806, ClassLoss=35.084 [Epoch 1][Batch 1799], LR: 9.91E-04, Speed: 173.339 samples/sec, ObjLoss=53.926, BoxCenterLoss=15.757, BoxScaleLoss=7.792, ClassLoss=34.597 [Epoch 1] Training cost: 1100.104, ObjLoss=53.705, BoxCenterLoss=15.757, BoxScaleLoss=7.788, ClassLoss=34.444 [Epoch 1] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.011 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.040 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.002 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.007 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.021 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.030 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.030 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.019 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.050 person=4.5 bicycle=0.6 car=1.6 motorcycle=3.7 airplane=3.5 bus=5.9 train=3.9 truck=1.4 boat=0.1 traffic light=0.4 fire hydrant=0.0 stop sign=5.5 parking meter=0.0 bench=0.4 bird=0.0 cat=5.4 dog=1.6 horse=3.4 sheep=1.5 cow=0.6 elephant=3.9 bear=2.8 zebra=1.2 giraffe=3.5 backpack=0.0 umbrella=0.2 handbag=0.0 tie=0.4 suitcase=0.7 frisbee=0.0 skis=0.9 snowboard=0.0 sports ball=0.3 kite=0.2 baseball bat=0.0 baseball glove=0.0 skateboard=0.1 surfboard=0.0 tennis racket=0.0 bottle=0.5 wine glass=0.3 cup=1.3 fork=0.0 knife=0.0 spoon=0.0 bowl=1.5 banana=0.2 apple=0.0 sandwich=0.6 orange=0.2 broccoli=0.0 carrot=0.0 hot dog=0.0 pizza=2.5 donut=0.4 cake=0.2 chair=0.2 couch=1.7 potted plant=0.0 bed=1.4 dining table=3.6 toilet=2.8 tv=1.4 laptop=1.1 mouse=0.0 remote=0.0 keyboard=0.1 cell phone=0.3 microwave=0.0 oven=0.7 toaster=0.0 sink=1.2 refrigerator=0.7 book=0.1 clock=4.8 vase=0.2 scissors=0.0 teddy bear=2.0 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =1.1 [Epoch 2][Batch 99], LR: 1.00E-03, Speed: 141.171 samples/sec, ObjLoss=53.037, BoxCenterLoss=15.757, BoxScaleLoss=7.779, ClassLoss=33.992 [Epoch 2][Batch 199], LR: 1.00E-03, Speed: 123.757 samples/sec, ObjLoss=52.402, BoxCenterLoss=15.751, BoxScaleLoss=7.762, ClassLoss=33.557 [Epoch 2][Batch 299], LR: 1.00E-03, Speed: 132.233 samples/sec, ObjLoss=51.789, BoxCenterLoss=15.747, BoxScaleLoss=7.751, ClassLoss=33.132 [Epoch 2][Batch 399], LR: 1.00E-03, Speed: 128.277 samples/sec, ObjLoss=51.201, BoxCenterLoss=15.733, BoxScaleLoss=7.730, ClassLoss=32.720 [Epoch 2][Batch 499], LR: 1.00E-03, Speed: 133.318 samples/sec, ObjLoss=50.647, BoxCenterLoss=15.718, BoxScaleLoss=7.709, ClassLoss=32.331 [Epoch 2][Batch 599], LR: 1.00E-03, Speed: 117.142 samples/sec, ObjLoss=50.133, BoxCenterLoss=15.716, BoxScaleLoss=7.691, ClassLoss=31.959 [Epoch 2][Batch 699], LR: 1.00E-03, Speed: 167.762 samples/sec, ObjLoss=49.623, BoxCenterLoss=15.708, BoxScaleLoss=7.678, ClassLoss=31.599 [Epoch 2][Batch 799], LR: 1.00E-03, Speed: 125.490 samples/sec, ObjLoss=49.145, BoxCenterLoss=15.702, BoxScaleLoss=7.657, ClassLoss=31.245 [Epoch 2][Batch 899], LR: 1.00E-03, Speed: 139.651 samples/sec, ObjLoss=48.690, BoxCenterLoss=15.697, BoxScaleLoss=7.641, ClassLoss=30.917 [Epoch 2][Batch 999], LR: 1.00E-03, Speed: 114.315 samples/sec, ObjLoss=48.226, BoxCenterLoss=15.685, BoxScaleLoss=7.624, ClassLoss=30.593 [Epoch 2][Batch 1099], LR: 1.00E-03, Speed: 136.735 samples/sec, ObjLoss=47.799, BoxCenterLoss=15.681, BoxScaleLoss=7.606, ClassLoss=30.273 [Epoch 2][Batch 1199], LR: 1.00E-03, Speed: 132.461 samples/sec, ObjLoss=47.393, BoxCenterLoss=15.674, BoxScaleLoss=7.585, ClassLoss=29.965 [Epoch 2][Batch 1299], LR: 1.00E-03, Speed: 134.395 samples/sec, ObjLoss=47.010, BoxCenterLoss=15.678, BoxScaleLoss=7.576, ClassLoss=29.686 [Epoch 2][Batch 1399], LR: 1.00E-03, Speed: 103.461 samples/sec, ObjLoss=46.601, BoxCenterLoss=15.661, BoxScaleLoss=7.556, ClassLoss=29.394 [Epoch 2][Batch 1499], LR: 1.00E-03, Speed: 160.280 samples/sec, ObjLoss=46.230, BoxCenterLoss=15.653, BoxScaleLoss=7.538, ClassLoss=29.120 [Epoch 2][Batch 1599], LR: 1.00E-03, Speed: 122.223 samples/sec, ObjLoss=45.861, BoxCenterLoss=15.640, BoxScaleLoss=7.518, ClassLoss=28.852 [Epoch 2][Batch 1699], LR: 1.00E-03, Speed: 133.170 samples/sec, ObjLoss=45.506, BoxCenterLoss=15.630, BoxScaleLoss=7.500, ClassLoss=28.587 [Epoch 2][Batch 1799], LR: 1.00E-03, Speed: 134.668 samples/sec, ObjLoss=45.181, BoxCenterLoss=15.622, BoxScaleLoss=7.484, ClassLoss=28.341 [Epoch 2] Training cost: 1159.341, ObjLoss=45.083, BoxCenterLoss=15.622, BoxScaleLoss=7.478, ClassLoss=28.263 [Epoch 2] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.034 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.015 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.032 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.055 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.050 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.070 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.070 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.060 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.112 person=13.3 bicycle=0.6 car=3.8 motorcycle=3.1 airplane=6.6 bus=14.5 train=5.8 truck=3.0 boat=0.5 traffic light=0.4 fire hydrant=5.5 stop sign=9.5 parking meter=0.0 bench=1.2 bird=1.6 cat=12.9 dog=5.5 horse=9.5 sheep=5.8 cow=5.9 elephant=11.6 bear=14.9 zebra=15.9 giraffe=11.3 backpack=0.0 umbrella=1.4 handbag=0.0 tie=0.9 suitcase=1.3 frisbee=1.1 skis=1.5 snowboard=0.5 sports ball=5.1 kite=1.6 baseball bat=0.0 baseball glove=0.6 skateboard=1.4 surfboard=0.4 tennis racket=4.1 bottle=1.6 wine glass=1.3 cup=4.1 fork=0.0 knife=0.3 spoon=0.0 bowl=2.9 banana=1.1 apple=0.0 sandwich=1.2 orange=2.1 broccoli=1.4 carrot=0.0 hot dog=0.0 pizza=8.9 donut=1.2 cake=0.7 chair=1.8 couch=4.1 potted plant=0.8 bed=6.8 dining table=2.0 toilet=7.6 tv=7.7 laptop=9.5 mouse=0.0 remote=0.0 keyboard=2.8 cell phone=1.1 microwave=3.3 oven=1.6 toaster=0.0 sink=3.1 refrigerator=1.5 book=0.4 clock=5.7 vase=0.7 scissors=0.0 teddy bear=5.4 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =3.4 [Epoch 3][Batch 99], LR: 1.00E-03, Speed: 126.132 samples/sec, ObjLoss=44.774, BoxCenterLoss=15.619, BoxScaleLoss=7.465, ClassLoss=28.026 [Epoch 3][Batch 199], LR: 1.00E-03, Speed: 132.020 samples/sec, ObjLoss=44.476, BoxCenterLoss=15.618, BoxScaleLoss=7.449, ClassLoss=27.791 [Epoch 3][Batch 299], LR: 1.00E-03, Speed: 148.751 samples/sec, ObjLoss=44.176, BoxCenterLoss=15.612, BoxScaleLoss=7.439, ClassLoss=27.572 [Epoch 3][Batch 399], LR: 1.00E-03, Speed: 151.286 samples/sec, ObjLoss=43.876, BoxCenterLoss=15.602, BoxScaleLoss=7.421, ClassLoss=27.347 [Epoch 3][Batch 499], LR: 1.00E-03, Speed: 127.919 samples/sec, ObjLoss=43.604, BoxCenterLoss=15.598, BoxScaleLoss=7.407, ClassLoss=27.136 [Epoch 3][Batch 599], LR: 1.00E-03, Speed: 125.089 samples/sec, ObjLoss=43.341, BoxCenterLoss=15.589, BoxScaleLoss=7.388, ClassLoss=26.929 [Epoch 3][Batch 699], LR: 1.00E-03, Speed: 103.374 samples/sec, ObjLoss=43.093, BoxCenterLoss=15.580, BoxScaleLoss=7.371, ClassLoss=26.733 [Epoch 3][Batch 799], LR: 1.00E-03, Speed: 149.142 samples/sec, ObjLoss=42.839, BoxCenterLoss=15.568, BoxScaleLoss=7.351, ClassLoss=26.532 [Epoch 3][Batch 899], LR: 1.00E-03, Speed: 125.518 samples/sec, ObjLoss=42.614, BoxCenterLoss=15.567, BoxScaleLoss=7.338, ClassLoss=26.342 [Epoch 3][Batch 999], LR: 1.00E-03, Speed: 136.879 samples/sec, ObjLoss=42.384, BoxCenterLoss=15.565, BoxScaleLoss=7.323, ClassLoss=26.156 [Epoch 3][Batch 1099], LR: 1.00E-03, Speed: 109.984 samples/sec, ObjLoss=42.164, BoxCenterLoss=15.560, BoxScaleLoss=7.305, ClassLoss=25.973 [Epoch 3][Batch 1199], LR: 1.00E-03, Speed: 143.598 samples/sec, ObjLoss=41.944, BoxCenterLoss=15.556, BoxScaleLoss=7.287, ClassLoss=25.793 [Epoch 3][Batch 1299], LR: 1.00E-03, Speed: 128.756 samples/sec, ObjLoss=41.720, BoxCenterLoss=15.549, BoxScaleLoss=7.274, ClassLoss=25.621 [Epoch 3][Batch 1399], LR: 1.00E-03, Speed: 132.101 samples/sec, ObjLoss=41.501, BoxCenterLoss=15.545, BoxScaleLoss=7.260, ClassLoss=25.451 [Epoch 3][Batch 1499], LR: 1.00E-03, Speed: 143.280 samples/sec, ObjLoss=41.315, BoxCenterLoss=15.543, BoxScaleLoss=7.245, ClassLoss=25.287 [Epoch 3][Batch 1599], LR: 1.00E-03, Speed: 117.052 samples/sec, ObjLoss=41.113, BoxCenterLoss=15.540, BoxScaleLoss=7.229, ClassLoss=25.125 [Epoch 3][Batch 1699], LR: 1.00E-03, Speed: 146.539 samples/sec, ObjLoss=40.902, BoxCenterLoss=15.533, BoxScaleLoss=7.218, ClassLoss=24.970 [Epoch 3][Batch 1799], LR: 1.00E-03, Speed: 120.851 samples/sec, ObjLoss=40.698, BoxCenterLoss=15.526, BoxScaleLoss=7.205, ClassLoss=24.811 [Epoch 3] Training cost: 1138.813, ObjLoss=40.626, BoxCenterLoss=15.522, BoxScaleLoss=7.202, ClassLoss=24.762 [Epoch 3] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.047 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.135 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.020 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.045 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.073 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.063 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.087 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.088 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.016 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.073 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.139 person=10.1 bicycle=1.9 car=8.0 motorcycle=8.0 airplane=10.1 bus=15.9 train=15.9 truck=4.6 boat=1.4 traffic light=1.8 fire hydrant=12.9 stop sign=15.3 parking meter=0.0 bench=2.4 bird=3.5 cat=9.1 dog=7.5 horse=8.1 sheep=5.6 cow=7.6 elephant=11.8 bear=10.4 zebra=22.1 giraffe=17.8 backpack=0.2 umbrella=3.3 handbag=0.0 tie=2.0 suitcase=0.8 frisbee=1.2 skis=1.3 snowboard=0.3 sports ball=8.0 kite=6.4 baseball bat=0.2 baseball glove=0.5 skateboard=2.8 surfboard=2.0 tennis racket=4.7 bottle=2.8 wine glass=2.8 cup=5.6 fork=0.3 knife=0.6 spoon=0.1 bowl=3.2 banana=1.6 apple=0.4 sandwich=2.4 orange=4.4 broccoli=3.1 carrot=0.8 hot dog=1.6 pizza=8.2 donut=3.6 cake=0.9 chair=2.3 couch=3.5 potted plant=1.2 bed=10.8 dining table=7.7 toilet=9.1 tv=8.5 laptop=4.6 mouse=1.1 remote=0.1 keyboard=4.7 cell phone=3.0 microwave=6.7 oven=1.2 toaster=0.0 sink=2.8 refrigerator=2.8 book=0.9 clock=8.9 vase=2.8 scissors=0.0 teddy bear=5.5 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =4.7 [Epoch 4][Batch 99], LR: 1.00E-03, Speed: 130.050 samples/sec, ObjLoss=40.442, BoxCenterLoss=15.515, BoxScaleLoss=7.189, ClassLoss=24.609 [Epoch 4][Batch 199], LR: 1.00E-03, Speed: 118.296 samples/sec, ObjLoss=40.261, BoxCenterLoss=15.514, BoxScaleLoss=7.179, ClassLoss=24.466 [Epoch 4][Batch 299], LR: 1.00E-03, Speed: 136.635 samples/sec, ObjLoss=40.084, BoxCenterLoss=15.507, BoxScaleLoss=7.167, ClassLoss=24.324 [Epoch 4][Batch 399], LR: 1.00E-03, Speed: 148.076 samples/sec, ObjLoss=39.913, BoxCenterLoss=15.501, BoxScaleLoss=7.156, ClassLoss=24.190 [Epoch 4][Batch 499], LR: 1.00E-03, Speed: 149.493 samples/sec, ObjLoss=39.768, BoxCenterLoss=15.496, BoxScaleLoss=7.145, ClassLoss=24.057 [Epoch 4][Batch 599], LR: 1.00E-03, Speed: 127.430 samples/sec, ObjLoss=39.598, BoxCenterLoss=15.493, BoxScaleLoss=7.136, ClassLoss=23.925 [Epoch 4][Batch 699], LR: 1.00E-03, Speed: 150.295 samples/sec, ObjLoss=39.430, BoxCenterLoss=15.486, BoxScaleLoss=7.121, ClassLoss=23.789 [Epoch 4][Batch 799], LR: 1.00E-03, Speed: 154.945 samples/sec, ObjLoss=39.271, BoxCenterLoss=15.482, BoxScaleLoss=7.109, ClassLoss=23.659 [Epoch 4][Batch 899], LR: 1.00E-03, Speed: 124.549 samples/sec, ObjLoss=39.103, BoxCenterLoss=15.474, BoxScaleLoss=7.097, ClassLoss=23.531 [Epoch 4][Batch 999], LR: 1.00E-03, Speed: 119.059 samples/sec, ObjLoss=38.951, BoxCenterLoss=15.468, BoxScaleLoss=7.086, ClassLoss=23.405 [Epoch 4][Batch 1099], LR: 1.00E-03, Speed: 126.557 samples/sec, ObjLoss=38.809, BoxCenterLoss=15.469, BoxScaleLoss=7.075, ClassLoss=23.285 [Epoch 4][Batch 1199], LR: 1.00E-03, Speed: 148.100 samples/sec, ObjLoss=38.664, BoxCenterLoss=15.471, BoxScaleLoss=7.068, ClassLoss=23.170 [Epoch 4][Batch 1299], LR: 1.00E-03, Speed: 128.653 samples/sec, ObjLoss=38.522, BoxCenterLoss=15.467, BoxScaleLoss=7.057, ClassLoss=23.050 [Epoch 4][Batch 1399], LR: 1.00E-03, Speed: 150.427 samples/sec, ObjLoss=38.395, BoxCenterLoss=15.463, BoxScaleLoss=7.047, ClassLoss=22.938 [Epoch 4][Batch 1499], LR: 1.00E-03, Speed: 146.054 samples/sec, ObjLoss=38.269, BoxCenterLoss=15.462, BoxScaleLoss=7.042, ClassLoss=22.834 [Epoch 4][Batch 1599], LR: 1.00E-03, Speed: 163.349 samples/sec, ObjLoss=38.122, BoxCenterLoss=15.454, BoxScaleLoss=7.033, ClassLoss=22.730 [Epoch 4][Batch 1699], LR: 1.00E-03, Speed: 116.332 samples/sec, ObjLoss=38.007, BoxCenterLoss=15.458, BoxScaleLoss=7.023, ClassLoss=22.627 [Epoch 4][Batch 1799], LR: 1.00E-03, Speed: 155.155 samples/sec, ObjLoss=37.868, BoxCenterLoss=15.451, BoxScaleLoss=7.014, ClassLoss=22.523 [Epoch 4] Training cost: 1103.824, ObjLoss=37.824, BoxCenterLoss=15.449, BoxScaleLoss=7.010, ClassLoss=22.489 [Epoch 4] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.056 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.162 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.023 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.050 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.093 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.074 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.105 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.107 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 ] = 0.091 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.178 person=15.8 bicycle=2.6 car=7.6 motorcycle=9.9 airplane=12.9 bus=20.6 train=16.4 truck=6.8 boat=1.4 traffic light=1.1 fire hydrant=12.0 stop sign=12.6 parking meter=2.7 bench=2.5 bird=4.2 cat=12.2 dog=10.1 horse=9.2 sheep=8.3 cow=7.6 elephant=11.5 bear=14.2 zebra=18.4 giraffe=9.2 backpack=0.1 umbrella=6.0 handbag=0.0 tie=2.0 suitcase=2.0 frisbee=3.6 skis=1.7 snowboard=1.3 sports ball=6.6 kite=9.0 baseball bat=1.3 baseball glove=1.2 skateboard=3.0 surfboard=1.5 tennis racket=5.4 bottle=3.7 wine glass=3.6 cup=4.7 fork=0.2 knife=0.5 spoon=0.2 bowl=4.7 banana=1.3 apple=1.0 sandwich=4.3 orange=4.5 broccoli=2.1 carrot=0.8 hot dog=1.9 pizza=11.3 donut=4.0 cake=1.9 chair=2.5 couch=7.1 potted plant=2.7 bed=11.1 dining table=4.1 toilet=12.2 tv=11.0 laptop=14.3 mouse=0.0 remote=0.3 keyboard=10.0 cell phone=3.4 microwave=4.9 oven=4.8 toaster=0.0 sink=3.1 refrigerator=6.1 book=0.5 clock=11.3 vase=2.7 scissors=0.0 teddy bear=7.0 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =5.6 [Epoch 5][Batch 99], LR: 1.00E-03, Speed: 126.290 samples/sec, ObjLoss=37.695, BoxCenterLoss=15.444, BoxScaleLoss=7.001, ClassLoss=22.384 [Epoch 5][Batch 199], LR: 1.00E-03, Speed: 129.265 samples/sec, ObjLoss=37.580, BoxCenterLoss=15.443, BoxScaleLoss=6.992, ClassLoss=22.284 [Epoch 5][Batch 299], LR: 1.00E-03, Speed: 145.957 samples/sec, ObjLoss=37.459, BoxCenterLoss=15.442, BoxScaleLoss=6.983, ClassLoss=22.184 [Epoch 5][Batch 399], LR: 1.00E-03, Speed: 114.222 samples/sec, ObjLoss=37.340, BoxCenterLoss=15.440, BoxScaleLoss=6.972, ClassLoss=22.084 [Epoch 5][Batch 499], LR: 1.00E-03, Speed: 142.887 samples/sec, ObjLoss=37.227, BoxCenterLoss=15.439, BoxScaleLoss=6.964, ClassLoss=21.990 [Epoch 5][Batch 599], LR: 1.00E-03, Speed: 126.557 samples/sec, ObjLoss=37.118, BoxCenterLoss=15.440, BoxScaleLoss=6.955, ClassLoss=21.896 [Epoch 5][Batch 699], LR: 1.00E-03, Speed: 138.815 samples/sec, ObjLoss=37.010, BoxCenterLoss=15.436, BoxScaleLoss=6.946, ClassLoss=21.801 [Epoch 5][Batch 799], LR: 1.00E-03, Speed: 109.159 samples/sec, ObjLoss=36.894, BoxCenterLoss=15.430, BoxScaleLoss=6.935, ClassLoss=21.708 [Epoch 5][Batch 899], LR: 1.00E-03, Speed: 138.400 samples/sec, ObjLoss=36.788, BoxCenterLoss=15.425, BoxScaleLoss=6.927, ClassLoss=21.617 [Epoch 5][Batch 999], LR: 1.00E-03, Speed: 161.896 samples/sec, ObjLoss=36.683, BoxCenterLoss=15.423, BoxScaleLoss=6.917, ClassLoss=21.528 [Epoch 5][Batch 1099], LR: 1.00E-03, Speed: 119.515 samples/sec, ObjLoss=36.582, BoxCenterLoss=15.420, BoxScaleLoss=6.908, ClassLoss=21.441 [Epoch 5][Batch 1199], LR: 1.00E-03, Speed: 143.271 samples/sec, ObjLoss=36.471, BoxCenterLoss=15.415, BoxScaleLoss=6.899, ClassLoss=21.353 [Epoch 5][Batch 1299], LR: 1.00E-03, Speed: 138.782 samples/sec, ObjLoss=36.372, BoxCenterLoss=15.412, BoxScaleLoss=6.888, ClassLoss=21.268 [Epoch 5][Batch 1399], LR: 1.00E-03, Speed: 124.404 samples/sec, ObjLoss=36.279, BoxCenterLoss=15.411, BoxScaleLoss=6.884, ClassLoss=21.190 [Epoch 5][Batch 1499], LR: 1.00E-03, Speed: 136.038 samples/sec, ObjLoss=36.171, BoxCenterLoss=15.405, BoxScaleLoss=6.875, ClassLoss=21.114 [Epoch 5][Batch 1599], LR: 1.00E-03, Speed: 134.213 samples/sec, ObjLoss=36.075, BoxCenterLoss=15.401, BoxScaleLoss=6.866, ClassLoss=21.033 [Epoch 5][Batch 1699], LR: 1.00E-03, Speed: 127.989 samples/sec, ObjLoss=35.976, BoxCenterLoss=15.395, BoxScaleLoss=6.858, ClassLoss=20.955 [Epoch 5][Batch 1799], LR: 1.00E-03, Speed: 147.672 samples/sec, ObjLoss=35.879, BoxCenterLoss=15.390, BoxScaleLoss=6.850, ClassLoss=20.878 [Epoch 5] Training cost: 1124.584, ObjLoss=35.850, BoxCenterLoss=15.390, BoxScaleLoss=6.847, ClassLoss=20.853 [Epoch 5] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.072 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.040 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.067 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.106 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.092 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.131 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.194 person=19.0 bicycle=2.5 car=10.3 motorcycle=8.1 airplane=16.1 bus=24.5 train=23.0 truck=6.9 boat=2.2 traffic light=4.7 fire hydrant=15.8 stop sign=10.4 parking meter=2.2 bench=3.8 bird=6.8 cat=22.6 dog=16.1 horse=11.6 sheep=10.4 cow=11.6 elephant=17.8 bear=21.3 zebra=29.9 giraffe=22.9 backpack=0.5 umbrella=3.2 handbag=0.6 tie=4.7 suitcase=1.8 frisbee=3.8 skis=2.9 snowboard=2.7 sports ball=8.7 kite=7.4 baseball bat=1.1 baseball glove=3.6 skateboard=6.5 surfboard=2.3 tennis racket=7.6 bottle=5.1 wine glass=3.5 cup=5.9 fork=1.2 knife=0.4 spoon=0.2 bowl=5.8 banana=2.0 apple=0.3 sandwich=4.0 orange=2.4 broccoli=3.7 carrot=0.9 hot dog=1.7 pizza=15.9 donut=3.5 cake=2.9 chair=3.1 couch=10.3 potted plant=1.8 bed=17.1 dining table=8.8 toilet=14.9 tv=9.7 laptop=10.1 mouse=3.8 remote=0.9 keyboard=7.0 cell phone=3.2 microwave=6.3 oven=2.7 toaster=0.0 sink=4.8 refrigerator=4.4 book=1.2 clock=12.2 vase=3.7 scissors=0.0 teddy bear=10.6 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =7.2 [Epoch 6][Batch 99], LR: 1.00E-03, Speed: 133.095 samples/sec, ObjLoss=35.752, BoxCenterLoss=15.386, BoxScaleLoss=6.840, ClassLoss=20.777 [Epoch 6][Batch 199], LR: 1.00E-03, Speed: 141.986 samples/sec, ObjLoss=35.652, BoxCenterLoss=15.381, BoxScaleLoss=6.833, ClassLoss=20.703 [Epoch 6][Batch 299], LR: 1.00E-03, Speed: 136.258 samples/sec, ObjLoss=35.565, BoxCenterLoss=15.381, BoxScaleLoss=6.827, ClassLoss=20.631 [Epoch 6][Batch 399], LR: 1.00E-03, Speed: 129.745 samples/sec, ObjLoss=35.484, BoxCenterLoss=15.378, BoxScaleLoss=6.818, ClassLoss=20.559 [Epoch 6][Batch 499], LR: 1.00E-03, Speed: 132.986 samples/sec, ObjLoss=35.394, BoxCenterLoss=15.374, BoxScaleLoss=6.811, ClassLoss=20.487 [Epoch 6][Batch 599], LR: 1.00E-03, Speed: 130.717 samples/sec, ObjLoss=35.309, BoxCenterLoss=15.371, BoxScaleLoss=6.803, ClassLoss=20.417 [Epoch 6][Batch 699], LR: 1.00E-03, Speed: 150.102 samples/sec, ObjLoss=35.222, BoxCenterLoss=15.366, BoxScaleLoss=6.795, ClassLoss=20.342 [Epoch 6][Batch 799], LR: 1.00E-03, Speed: 128.070 samples/sec, ObjLoss=35.135, BoxCenterLoss=15.360, BoxScaleLoss=6.786, ClassLoss=20.274 [Epoch 6][Batch 899], LR: 1.00E-03, Speed: 128.402 samples/sec, ObjLoss=35.055, BoxCenterLoss=15.358, BoxScaleLoss=6.779, ClassLoss=20.204 [Epoch 6][Batch 999], LR: 1.00E-03, Speed: 149.170 samples/sec, ObjLoss=34.964, BoxCenterLoss=15.354, BoxScaleLoss=6.773, ClassLoss=20.137 [Epoch 6][Batch 1099], LR: 1.00E-03, Speed: 149.866 samples/sec, ObjLoss=34.886, BoxCenterLoss=15.352, BoxScaleLoss=6.764, ClassLoss=20.070 [Epoch 6][Batch 1199], LR: 1.00E-03, Speed: 126.705 samples/sec, ObjLoss=34.808, BoxCenterLoss=15.348, BoxScaleLoss=6.758, ClassLoss=20.007 [Epoch 6][Batch 1299], LR: 1.00E-03, Speed: 122.180 samples/sec, ObjLoss=34.731, BoxCenterLoss=15.345, BoxScaleLoss=6.752, ClassLoss=19.943 [Epoch 6][Batch 1399], LR: 1.00E-03, Speed: 154.712 samples/sec, ObjLoss=34.668, BoxCenterLoss=15.344, BoxScaleLoss=6.748, ClassLoss=19.884 [Epoch 6][Batch 1499], LR: 1.00E-03, Speed: 112.490 samples/sec, ObjLoss=34.601, BoxCenterLoss=15.344, BoxScaleLoss=6.742, ClassLoss=19.822 [Epoch 6][Batch 1599], LR: 1.00E-03, Speed: 121.986 samples/sec, ObjLoss=34.529, BoxCenterLoss=15.341, BoxScaleLoss=6.734, ClassLoss=19.758 [Epoch 6][Batch 1699], LR: 1.00E-03, Speed: 96.502 samples/sec, ObjLoss=34.457, BoxCenterLoss=15.339, BoxScaleLoss=6.727, ClassLoss=19.694 [Epoch 6][Batch 1799], LR: 1.00E-03, Speed: 158.463 samples/sec, ObjLoss=34.401, BoxCenterLoss=15.341, BoxScaleLoss=6.721, ClassLoss=19.638 [Epoch 6] Training cost: 1133.844, ObjLoss=34.380, BoxCenterLoss=15.341, BoxScaleLoss=6.719, ClassLoss=19.619 [Epoch 6] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.039 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.069 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.116 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.097 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.145 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.152 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 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.207 person=19.9 bicycle=4.2 car=10.0 motorcycle=7.7 airplane=18.2 bus=27.1 train=22.6 truck=8.1 boat=4.4 traffic light=4.2 fire hydrant=15.1 stop sign=17.6 parking meter=2.8 bench=4.9 bird=6.1 cat=23.0 dog=18.8 horse=15.4 sheep=7.3 cow=11.6 elephant=21.2 bear=23.8 zebra=23.8 giraffe=22.0 backpack=1.0 umbrella=4.7 handbag=0.2 tie=4.8 suitcase=3.2 frisbee=6.2 skis=2.8 snowboard=2.0 sports ball=9.1 kite=14.3 baseball bat=1.1 baseball glove=3.1 skateboard=4.8 surfboard=3.5 tennis racket=4.7 bottle=4.0 wine glass=2.1 cup=6.9 fork=0.5 knife=0.1 spoon=0.5 bowl=5.0 banana=3.5 apple=0.9 sandwich=5.4 orange=2.5 broccoli=4.3 carrot=1.7 hot dog=2.0 pizza=12.1 donut=3.2 cake=2.1 chair=3.4 couch=9.1 potted plant=2.1 bed=14.1 dining table=9.9 toilet=17.8 tv=13.9 laptop=16.2 mouse=6.8 remote=1.0 keyboard=3.3 cell phone=2.5 microwave=6.9 oven=5.4 toaster=0.0 sink=5.8 refrigerator=9.5 book=1.6 clock=11.1 vase=4.0 scissors=0.0 teddy bear=8.0 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =7.7 [Epoch 7][Batch 99], LR: 1.00E-03, Speed: 124.810 samples/sec, ObjLoss=34.311, BoxCenterLoss=15.340, BoxScaleLoss=6.713, ClassLoss=19.561 [Epoch 7][Batch 199], LR: 1.00E-03, Speed: 123.788 samples/sec, ObjLoss=34.242, BoxCenterLoss=15.337, BoxScaleLoss=6.706, ClassLoss=19.502 [Epoch 7][Batch 299], LR: 1.00E-03, Speed: 117.126 samples/sec, ObjLoss=34.174, BoxCenterLoss=15.333, BoxScaleLoss=6.698, ClassLoss=19.443 [Epoch 7][Batch 399], LR: 1.00E-03, Speed: 130.168 samples/sec, ObjLoss=34.113, BoxCenterLoss=15.333, BoxScaleLoss=6.691, ClassLoss=19.386 [Epoch 7][Batch 499], LR: 1.00E-03, Speed: 118.441 samples/sec, ObjLoss=34.049, BoxCenterLoss=15.333, BoxScaleLoss=6.686, ClassLoss=19.333 [Epoch 7][Batch 599], LR: 1.00E-03, Speed: 143.260 samples/sec, ObjLoss=33.980, BoxCenterLoss=15.329, BoxScaleLoss=6.680, ClassLoss=19.277 [Epoch 7][Batch 699], LR: 1.00E-03, Speed: 117.767 samples/sec, ObjLoss=33.921, BoxCenterLoss=15.326, BoxScaleLoss=6.672, ClassLoss=19.219 [Epoch 7][Batch 799], LR: 1.00E-03, Speed: 122.274 samples/sec, ObjLoss=33.860, BoxCenterLoss=15.324, BoxScaleLoss=6.664, ClassLoss=19.162 [Epoch 7][Batch 899], LR: 1.00E-03, Speed: 128.470 samples/sec, ObjLoss=33.796, BoxCenterLoss=15.323, BoxScaleLoss=6.658, ClassLoss=19.107 [Epoch 7][Batch 999], LR: 1.00E-03, Speed: 104.321 samples/sec, ObjLoss=33.732, BoxCenterLoss=15.320, BoxScaleLoss=6.652, ClassLoss=19.055 [Epoch 7][Batch 1099], LR: 1.00E-03, Speed: 119.620 samples/sec, ObjLoss=33.677, BoxCenterLoss=15.321, BoxScaleLoss=6.648, ClassLoss=19.003 [Epoch 7][Batch 1199], LR: 1.00E-03, Speed: 144.308 samples/sec, ObjLoss=33.610, BoxCenterLoss=15.317, BoxScaleLoss=6.643, ClassLoss=18.951 [Epoch 7][Batch 1299], LR: 1.00E-03, Speed: 158.712 samples/sec, ObjLoss=33.550, BoxCenterLoss=15.314, BoxScaleLoss=6.637, ClassLoss=18.900 [Epoch 7][Batch 1399], LR: 1.00E-03, Speed: 104.720 samples/sec, ObjLoss=33.495, BoxCenterLoss=15.313, BoxScaleLoss=6.633, ClassLoss=18.854 [Epoch 7][Batch 1499], LR: 1.00E-03, Speed: 152.946 samples/sec, ObjLoss=33.432, BoxCenterLoss=15.309, BoxScaleLoss=6.627, ClassLoss=18.805 [Epoch 7][Batch 1599], LR: 1.00E-03, Speed: 119.992 samples/sec, ObjLoss=33.379, BoxCenterLoss=15.309, BoxScaleLoss=6.622, ClassLoss=18.757 [Epoch 7][Batch 1699], LR: 1.00E-03, Speed: 134.221 samples/sec, ObjLoss=33.317, BoxCenterLoss=15.305, BoxScaleLoss=6.616, ClassLoss=18.707 [Epoch 7][Batch 1799], LR: 1.00E-03, Speed: 131.836 samples/sec, ObjLoss=33.262, BoxCenterLoss=15.303, BoxScaleLoss=6.610, ClassLoss=18.659 [Epoch 7] Training cost: 1160.560, ObjLoss=33.243, BoxCenterLoss=15.302, BoxScaleLoss=6.608, ClassLoss=18.642 [Epoch 7] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.072 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.034 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.079 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.109 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.092 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.137 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 ] = 0.199 person=17.9 bicycle=5.6 car=9.4 motorcycle=9.1 airplane=12.7 bus=19.9 train=14.9 truck=6.4 boat=3.7 traffic light=4.9 fire hydrant=20.1 stop sign=14.8 parking meter=2.2 bench=3.8 bird=7.5 cat=19.9 dog=14.1 horse=7.5 sheep=8.9 cow=10.9 elephant=15.0 bear=15.1 zebra=26.3 giraffe=20.9 backpack=0.8 umbrella=5.0 handbag=0.2 tie=2.9 suitcase=2.7 frisbee=5.9 skis=2.7 snowboard=2.2 sports ball=6.2 kite=10.2 baseball bat=2.9 baseball glove=3.9 skateboard=6.1 surfboard=3.1 tennis racket=9.7 bottle=5.0 wine glass=4.3 cup=7.5 fork=0.9 knife=0.7 spoon=0.7 bowl=7.0 banana=3.6 apple=0.4 sandwich=5.3 orange=4.4 broccoli=4.6 carrot=1.5 hot dog=3.0 pizza=12.6 donut=7.5 cake=2.1 chair=4.3 couch=7.5 potted plant=3.1 bed=8.8 dining table=5.3 toilet=11.2 tv=16.1 laptop=13.3 mouse=6.7 remote=1.4 keyboard=6.6 cell phone=4.3 microwave=12.0 oven=3.8 toaster=0.0 sink=7.2 refrigerator=3.9 book=1.5 clock=19.3 vase=3.7 scissors=0.0 teddy bear=9.4 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =7.2 [Epoch 8][Batch 99], LR: 1.00E-03, Speed: 138.777 samples/sec, ObjLoss=33.187, BoxCenterLoss=15.300, BoxScaleLoss=6.601, ClassLoss=18.592 [Epoch 8][Batch 199], LR: 1.00E-03, Speed: 138.031 samples/sec, ObjLoss=33.129, BoxCenterLoss=15.301, BoxScaleLoss=6.599, ClassLoss=18.547 [Epoch 8][Batch 299], LR: 1.00E-03, Speed: 140.008 samples/sec, ObjLoss=33.072, BoxCenterLoss=15.298, BoxScaleLoss=6.593, ClassLoss=18.499 [Epoch 8][Batch 399], LR: 1.00E-03, Speed: 129.694 samples/sec, ObjLoss=33.019, BoxCenterLoss=15.298, BoxScaleLoss=6.589, ClassLoss=18.455 [Epoch 8][Batch 499], LR: 1.00E-03, Speed: 121.884 samples/sec, ObjLoss=32.964, BoxCenterLoss=15.292, BoxScaleLoss=6.584, ClassLoss=18.409 [Epoch 8][Batch 599], LR: 1.00E-03, Speed: 133.214 samples/sec, ObjLoss=32.913, BoxCenterLoss=15.291, BoxScaleLoss=6.577, ClassLoss=18.362 [Epoch 8][Batch 699], LR: 1.00E-03, Speed: 120.934 samples/sec, ObjLoss=32.862, BoxCenterLoss=15.290, BoxScaleLoss=6.571, ClassLoss=18.317 [Epoch 8][Batch 799], LR: 1.00E-03, Speed: 134.054 samples/sec, ObjLoss=32.806, BoxCenterLoss=15.287, BoxScaleLoss=6.567, ClassLoss=18.275 [Epoch 8][Batch 899], LR: 1.00E-03, Speed: 135.962 samples/sec, ObjLoss=32.753, BoxCenterLoss=15.285, BoxScaleLoss=6.563, ClassLoss=18.232 [Epoch 8][Batch 999], LR: 1.00E-03, Speed: 144.993 samples/sec, ObjLoss=32.706, BoxCenterLoss=15.284, BoxScaleLoss=6.557, ClassLoss=18.187 [Epoch 8][Batch 1099], LR: 1.00E-03, Speed: 112.554 samples/sec, ObjLoss=32.656, BoxCenterLoss=15.281, BoxScaleLoss=6.550, ClassLoss=18.143 [Epoch 8][Batch 1199], LR: 1.00E-03, Speed: 134.691 samples/sec, ObjLoss=32.609, BoxCenterLoss=15.279, BoxScaleLoss=6.544, ClassLoss=18.100 [Epoch 8][Batch 1299], LR: 1.00E-03, Speed: 133.945 samples/sec, ObjLoss=32.562, BoxCenterLoss=15.276, BoxScaleLoss=6.538, ClassLoss=18.059 [Epoch 8][Batch 1399], LR: 1.00E-03, Speed: 103.828 samples/sec, ObjLoss=32.515, BoxCenterLoss=15.275, BoxScaleLoss=6.534, ClassLoss=18.018 [Epoch 8][Batch 1499], LR: 1.00E-03, Speed: 134.807 samples/sec, ObjLoss=32.470, BoxCenterLoss=15.274, BoxScaleLoss=6.528, ClassLoss=17.976 [Epoch 8][Batch 1599], LR: 1.00E-03, Speed: 129.197 samples/sec, ObjLoss=32.418, BoxCenterLoss=15.271, BoxScaleLoss=6.523, ClassLoss=17.935 [Epoch 8][Batch 1699], LR: 1.00E-03, Speed: 103.353 samples/sec, ObjLoss=32.371, BoxCenterLoss=15.269, BoxScaleLoss=6.518, ClassLoss=17.894 [Epoch 8][Batch 1799], LR: 1.00E-03, Speed: 137.302 samples/sec, ObjLoss=32.330, BoxCenterLoss=15.269, BoxScaleLoss=6.514, ClassLoss=17.855 [Epoch 8] Training cost: 1137.643, ObjLoss=32.315, BoxCenterLoss=15.269, BoxScaleLoss=6.513, ClassLoss=17.843 [Epoch 8] 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 ] = 0.228 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.052 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.022 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 ] = 0.144 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.104 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.030 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.136 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.244 person=19.0 bicycle=3.8 car=11.4 motorcycle=10.4 airplane=19.2 bus=26.4 train=19.2 truck=7.9 boat=3.3 traffic light=5.2 fire hydrant=20.8 stop sign=21.7 parking meter=6.7 bench=3.7 bird=8.5 cat=23.3 dog=19.0 horse=13.7 sheep=15.1 cow=13.2 elephant=20.3 bear=21.6 zebra=28.8 giraffe=26.7 backpack=1.1 umbrella=7.4 handbag=0.3 tie=5.2 suitcase=3.5 frisbee=9.5 skis=2.3 snowboard=2.1 sports ball=10.8 kite=11.3 baseball bat=1.9 baseball glove=5.3 skateboard=5.4 surfboard=5.3 tennis racket=5.3 bottle=5.8 wine glass=6.2 cup=8.6 fork=0.4 knife=0.5 spoon=0.8 bowl=9.1 banana=3.8 apple=1.0 sandwich=8.4 orange=6.6 broccoli=5.9 carrot=1.5 hot dog=2.9 pizza=19.1 donut=8.4 cake=5.6 chair=4.3 couch=9.3 potted plant=3.9 bed=17.1 dining table=8.6 toilet=12.9 tv=16.4 laptop=15.6 mouse=8.2 remote=0.8 keyboard=10.8 cell phone=4.8 microwave=8.6 oven=7.2 toaster=0.0 sink=7.7 refrigerator=8.8 book=2.0 clock=14.3 vase=5.1 scissors=2.0 teddy bear=16.1 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =9.1 [Epoch 9][Batch 99], LR: 1.00E-03, Speed: 99.622 samples/sec, ObjLoss=32.269, BoxCenterLoss=15.266, BoxScaleLoss=6.510, ClassLoss=17.807 [Epoch 9][Batch 199], LR: 1.00E-03, Speed: 126.001 samples/sec, ObjLoss=32.219, BoxCenterLoss=15.262, BoxScaleLoss=6.503, ClassLoss=17.765 [Epoch 9][Batch 299], LR: 1.00E-03, Speed: 158.455 samples/sec, ObjLoss=32.175, BoxCenterLoss=15.258, BoxScaleLoss=6.498, ClassLoss=17.725 [Epoch 9][Batch 399], LR: 1.00E-03, Speed: 127.126 samples/sec, ObjLoss=32.129, BoxCenterLoss=15.256, BoxScaleLoss=6.493, ClassLoss=17.687 [Epoch 9][Batch 499], LR: 1.00E-03, Speed: 122.810 samples/sec, ObjLoss=32.085, BoxCenterLoss=15.254, BoxScaleLoss=6.489, ClassLoss=17.650 [Epoch 9][Batch 599], LR: 1.00E-03, Speed: 114.383 samples/sec, ObjLoss=32.048, BoxCenterLoss=15.254, BoxScaleLoss=6.484, ClassLoss=17.614 [Epoch 9][Batch 699], LR: 1.00E-03, Speed: 132.883 samples/sec, ObjLoss=32.009, BoxCenterLoss=15.254, BoxScaleLoss=6.480, ClassLoss=17.576 [Epoch 9][Batch 799], LR: 1.00E-03, Speed: 124.330 samples/sec, ObjLoss=31.970, BoxCenterLoss=15.253, BoxScaleLoss=6.474, ClassLoss=17.538 [Epoch 9][Batch 899], LR: 1.00E-03, Speed: 127.981 samples/sec, ObjLoss=31.931, BoxCenterLoss=15.253, BoxScaleLoss=6.469, ClassLoss=17.500 [Epoch 9][Batch 999], LR: 1.00E-03, Speed: 156.558 samples/sec, ObjLoss=31.892, BoxCenterLoss=15.252, BoxScaleLoss=6.466, ClassLoss=17.466 [Epoch 9][Batch 1099], LR: 1.00E-03, Speed: 130.253 samples/sec, ObjLoss=31.848, BoxCenterLoss=15.250, BoxScaleLoss=6.462, ClassLoss=17.431 [Epoch 9][Batch 1199], LR: 1.00E-03, Speed: 130.177 samples/sec, ObjLoss=31.805, BoxCenterLoss=15.249, BoxScaleLoss=6.459, ClassLoss=17.397 [Epoch 9][Batch 1299], LR: 1.00E-03, Speed: 134.021 samples/sec, ObjLoss=31.764, BoxCenterLoss=15.248, BoxScaleLoss=6.455, ClassLoss=17.360 [Epoch 9][Batch 1399], LR: 1.00E-03, Speed: 138.504 samples/sec, ObjLoss=31.722, BoxCenterLoss=15.247, BoxScaleLoss=6.451, ClassLoss=17.326 [Epoch 9][Batch 1499], LR: 1.00E-03, Speed: 141.882 samples/sec, ObjLoss=31.681, BoxCenterLoss=15.245, BoxScaleLoss=6.446, ClassLoss=17.291 [Epoch 9][Batch 1599], LR: 1.00E-03, Speed: 150.361 samples/sec, ObjLoss=31.641, BoxCenterLoss=15.243, BoxScaleLoss=6.441, ClassLoss=17.257 [Epoch 9][Batch 1699], LR: 1.00E-03, Speed: 137.369 samples/sec, ObjLoss=31.601, BoxCenterLoss=15.241, BoxScaleLoss=6.439, ClassLoss=17.225 [Epoch 9][Batch 1799], LR: 1.00E-03, Speed: 141.667 samples/sec, ObjLoss=31.561, BoxCenterLoss=15.239, BoxScaleLoss=6.434, ClassLoss=17.192 [Epoch 9] Training cost: 1163.053, ObjLoss=31.549, BoxCenterLoss=15.239, BoxScaleLoss=6.433, ClassLoss=17.182 [Epoch 9] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.221 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.052 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 ] = 0.081 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.142 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.103 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.148 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.026 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 ] = 0.246 person=21.5 bicycle=4.0 car=9.8 motorcycle=12.5 airplane=14.2 bus=28.4 train=25.9 truck=7.8 boat=3.4 traffic light=5.0 fire hydrant=21.8 stop sign=20.3 parking meter=4.5 bench=3.8 bird=5.2 cat=21.9 dog=20.5 horse=15.1 sheep=13.2 cow=12.7 elephant=24.0 bear=27.9 zebra=28.0 giraffe=25.5 backpack=1.0 umbrella=5.8 handbag=0.5 tie=5.1 suitcase=4.2 frisbee=8.8 skis=3.2 snowboard=3.6 sports ball=9.6 kite=6.4 baseball bat=1.1 baseball glove=5.3 skateboard=5.2 surfboard=4.4 tennis racket=6.3 bottle=5.2 wine glass=5.3 cup=8.6 fork=0.7 knife=0.3 spoon=0.5 bowl=8.1 banana=2.7 apple=0.5 sandwich=6.4 orange=4.8 broccoli=3.3 carrot=1.4 hot dog=4.5 pizza=17.9 donut=5.9 cake=4.1 chair=4.3 couch=14.2 potted plant=2.8 bed=15.4 dining table=8.5 toilet=16.1 tv=11.7 laptop=13.0 mouse=9.3 remote=1.4 keyboard=8.3 cell phone=3.7 microwave=7.1 oven=7.3 toaster=0.0 sink=9.5 refrigerator=7.9 book=2.1 clock=16.9 vase=2.2 scissors=0.6 teddy bear=13.1 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =8.8 [Epoch 10][Batch 99], LR: 1.00E-03, Speed: 150.617 samples/sec, ObjLoss=31.511, BoxCenterLoss=15.237, BoxScaleLoss=6.430, ClassLoss=17.150 [Epoch 10][Batch 199], LR: 1.00E-03, Speed: 135.641 samples/sec, ObjLoss=31.473, BoxCenterLoss=15.236, BoxScaleLoss=6.426, ClassLoss=17.116 [Epoch 10][Batch 299], LR: 1.00E-03, Speed: 148.075 samples/sec, ObjLoss=31.436, BoxCenterLoss=15.234, BoxScaleLoss=6.422, ClassLoss=17.085 [Epoch 10][Batch 399], LR: 1.00E-03, Speed: 157.742 samples/sec, ObjLoss=31.397, BoxCenterLoss=15.233, BoxScaleLoss=6.418, ClassLoss=17.052 [Epoch 10][Batch 499], LR: 1.00E-03, Speed: 135.395 samples/sec, ObjLoss=31.360, BoxCenterLoss=15.232, BoxScaleLoss=6.415, ClassLoss=17.023 [Epoch 10][Batch 599], LR: 1.00E-03, Speed: 139.038 samples/sec, ObjLoss=31.322, BoxCenterLoss=15.231, BoxScaleLoss=6.412, ClassLoss=16.991 [Epoch 10][Batch 699], LR: 1.00E-03, Speed: 122.083 samples/sec, ObjLoss=31.286, BoxCenterLoss=15.230, BoxScaleLoss=6.408, ClassLoss=16.960 [Epoch 10][Batch 799], LR: 1.00E-03, Speed: 135.572 samples/sec, ObjLoss=31.251, BoxCenterLoss=15.228, BoxScaleLoss=6.403, ClassLoss=16.927 [Epoch 10][Batch 899], LR: 1.00E-03, Speed: 118.431 samples/sec, ObjLoss=31.218, BoxCenterLoss=15.227, BoxScaleLoss=6.399, ClassLoss=16.896 [Epoch 10][Batch 999], LR: 1.00E-03, Speed: 134.205 samples/sec, ObjLoss=31.183, BoxCenterLoss=15.227, BoxScaleLoss=6.396, ClassLoss=16.866 [Epoch 10][Batch 1099], LR: 1.00E-03, Speed: 125.963 samples/sec, ObjLoss=31.148, BoxCenterLoss=15.225, BoxScaleLoss=6.391, ClassLoss=16.836 [Epoch 10][Batch 1199], LR: 1.00E-03, Speed: 132.417 samples/sec, ObjLoss=31.108, BoxCenterLoss=15.222, BoxScaleLoss=6.387, ClassLoss=16.806 [Epoch 10][Batch 1299], LR: 1.00E-03, Speed: 130.322 samples/sec, ObjLoss=31.078, BoxCenterLoss=15.222, BoxScaleLoss=6.384, ClassLoss=16.777 [Epoch 10][Batch 1399], LR: 1.00E-03, Speed: 150.013 samples/sec, ObjLoss=31.044, BoxCenterLoss=15.220, BoxScaleLoss=6.380, ClassLoss=16.748 [Epoch 10][Batch 1499], LR: 1.00E-03, Speed: 123.923 samples/sec, ObjLoss=31.009, BoxCenterLoss=15.218, BoxScaleLoss=6.375, ClassLoss=16.716 [Epoch 10][Batch 1599], LR: 1.00E-03, Speed: 151.315 samples/sec, ObjLoss=30.974, BoxCenterLoss=15.217, BoxScaleLoss=6.372, ClassLoss=16.689 [Epoch 10][Batch 1699], LR: 1.00E-03, Speed: 120.139 samples/sec, ObjLoss=30.938, BoxCenterLoss=15.216, BoxScaleLoss=6.369, ClassLoss=16.661 [Epoch 10][Batch 1799], LR: 1.00E-03, Speed: 169.213 samples/sec, ObjLoss=30.907, BoxCenterLoss=15.214, BoxScaleLoss=6.365, ClassLoss=16.633 [Epoch 10] Training cost: 1154.693, ObjLoss=30.901, BoxCenterLoss=15.215, BoxScaleLoss=6.364, ClassLoss=16.624 [Epoch 10] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.206 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.039 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.080 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.120 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.094 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.142 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 ] = 0.145 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.207 person=18.3 bicycle=4.3 car=7.9 motorcycle=8.5 airplane=7.7 bus=20.1 train=16.6 truck=5.8 boat=4.0 traffic light=2.3 fire hydrant=19.6 stop sign=23.1 parking meter=6.2 bench=3.5 bird=3.5 cat=18.9 dog=13.8 horse=14.2 sheep=9.8 cow=11.1 elephant=16.8 bear=22.6 zebra=24.4 giraffe=28.7 backpack=1.0 umbrella=6.9 handbag=0.2 tie=6.5 suitcase=3.2 frisbee=8.5 skis=1.8 snowboard=1.2 sports ball=2.2 kite=5.3 baseball bat=1.6 baseball glove=5.2 skateboard=6.8 surfboard=5.1 tennis racket=6.2 bottle=4.8 wine glass=5.8 cup=6.8 fork=1.2 knife=0.5 spoon=1.2 bowl=7.9 banana=2.7 apple=2.0 sandwich=4.7 orange=6.3 broccoli=4.7 carrot=1.6 hot dog=1.6 pizza=15.2 donut=9.0 cake=3.2 chair=3.5 couch=10.3 potted plant=2.3 bed=7.7 dining table=4.3 toilet=14.2 tv=18.7 laptop=18.7 mouse=5.8 remote=1.1 keyboard=13.4 cell phone=5.5 microwave=11.5 oven=4.2 toaster=0.0 sink=5.8 refrigerator=8.4 book=2.6 clock=13.1 vase=5.5 scissors=0.6 teddy bear=8.5 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =7.7 [Epoch 11][Batch 99], LR: 1.00E-03, Speed: 105.858 samples/sec, ObjLoss=30.873, BoxCenterLoss=15.216, BoxScaleLoss=6.360, ClassLoss=16.596 [Epoch 11][Batch 199], LR: 1.00E-03, Speed: 141.113 samples/sec, ObjLoss=30.842, BoxCenterLoss=15.215, BoxScaleLoss=6.357, ClassLoss=16.568 [Epoch 11][Batch 299], LR: 1.00E-03, Speed: 132.778 samples/sec, ObjLoss=30.810, BoxCenterLoss=15.214, BoxScaleLoss=6.353, ClassLoss=16.540 [Epoch 11][Batch 399], LR: 1.00E-03, Speed: 159.963 samples/sec, ObjLoss=30.779, BoxCenterLoss=15.213, BoxScaleLoss=6.349, ClassLoss=16.511 [Epoch 11][Batch 499], LR: 1.00E-03, Speed: 149.625 samples/sec, ObjLoss=30.750, BoxCenterLoss=15.212, BoxScaleLoss=6.345, ClassLoss=16.482 [Epoch 11][Batch 599], LR: 1.00E-03, Speed: 136.096 samples/sec, ObjLoss=30.722, BoxCenterLoss=15.212, BoxScaleLoss=6.340, ClassLoss=16.454 [Epoch 11][Batch 699], LR: 1.00E-03, Speed: 128.142 samples/sec, ObjLoss=30.691, BoxCenterLoss=15.211, BoxScaleLoss=6.337, ClassLoss=16.426 [Epoch 11][Batch 799], LR: 1.00E-03, Speed: 123.968 samples/sec, ObjLoss=30.658, BoxCenterLoss=15.211, BoxScaleLoss=6.334, ClassLoss=16.401 [Epoch 11][Batch 899], LR: 1.00E-03, Speed: 133.585 samples/sec, ObjLoss=30.625, BoxCenterLoss=15.208, BoxScaleLoss=6.330, ClassLoss=16.373 [Epoch 11][Batch 999], LR: 1.00E-03, Speed: 161.418 samples/sec, ObjLoss=30.590, BoxCenterLoss=15.205, BoxScaleLoss=6.327, ClassLoss=16.346 [Epoch 11][Batch 1099], LR: 1.00E-03, Speed: 142.592 samples/sec, ObjLoss=30.560, BoxCenterLoss=15.204, BoxScaleLoss=6.322, ClassLoss=16.319 [Epoch 11][Batch 1199], LR: 1.00E-03, Speed: 140.917 samples/sec, ObjLoss=30.529, BoxCenterLoss=15.202, BoxScaleLoss=6.319, ClassLoss=16.293 [Epoch 11][Batch 1299], LR: 1.00E-03, Speed: 148.292 samples/sec, ObjLoss=30.500, BoxCenterLoss=15.201, BoxScaleLoss=6.316, ClassLoss=16.267 [Epoch 11][Batch 1399], LR: 1.00E-03, Speed: 141.115 samples/sec, ObjLoss=30.470, BoxCenterLoss=15.200, BoxScaleLoss=6.313, ClassLoss=16.241 [Epoch 11][Batch 1499], LR: 1.00E-03, Speed: 156.111 samples/sec, ObjLoss=30.442, BoxCenterLoss=15.200, BoxScaleLoss=6.310, ClassLoss=16.216 [Epoch 11][Batch 1599], LR: 1.00E-03, Speed: 156.975 samples/sec, ObjLoss=30.416, BoxCenterLoss=15.201, BoxScaleLoss=6.306, ClassLoss=16.191 [Epoch 11][Batch 1699], LR: 1.00E-03, Speed: 139.645 samples/sec, ObjLoss=30.383, BoxCenterLoss=15.198, BoxScaleLoss=6.303, ClassLoss=16.165 [Epoch 11][Batch 1799], LR: 1.00E-03, Speed: 138.627 samples/sec, ObjLoss=30.356, BoxCenterLoss=15.197, BoxScaleLoss=6.299, ClassLoss=16.140 [Epoch 11] Training cost: 1148.093, ObjLoss=30.348, BoxCenterLoss=15.197, BoxScaleLoss=6.297, ClassLoss=16.131 [Epoch 11] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.090 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.240 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.041 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.027 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.143 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.105 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.239 person=22.2 bicycle=6.3 car=12.0 motorcycle=11.6 airplane=18.0 bus=23.9 train=22.4 truck=8.7 boat=3.6 traffic light=4.4 fire hydrant=23.9 stop sign=18.7 parking meter=8.1 bench=4.6 bird=8.3 cat=19.6 dog=14.2 horse=12.0 sheep=11.5 cow=11.2 elephant=22.9 bear=15.7 zebra=29.7 giraffe=24.5 backpack=1.0 umbrella=7.8 handbag=0.6 tie=3.1 suitcase=3.3 frisbee=12.9 skis=2.1 snowboard=2.2 sports ball=12.0 kite=9.6 baseball bat=2.7 baseball glove=8.1 skateboard=6.7 surfboard=5.5 tennis racket=9.2 bottle=5.3 wine glass=4.5 cup=7.8 fork=1.0 knife=0.7 spoon=0.6 bowl=8.0 banana=3.7 apple=1.1 sandwich=6.0 orange=7.3 broccoli=2.8 carrot=2.0 hot dog=4.1 pizza=16.1 donut=9.2 cake=4.6 chair=4.5 couch=8.0 potted plant=1.8 bed=11.1 dining table=8.1 toilet=11.9 tv=19.8 laptop=21.6 mouse=10.8 remote=1.3 keyboard=10.3 cell phone=5.2 microwave=8.7 oven=8.3 toaster=0.0 sink=7.2 refrigerator=8.9 book=2.0 clock=13.9 vase=5.9 scissors=2.1 teddy bear=16.2 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =9.0 [Epoch 12][Batch 99], LR: 1.00E-03, Speed: 149.638 samples/sec, ObjLoss=30.318, BoxCenterLoss=15.195, BoxScaleLoss=6.293, ClassLoss=16.106 [Epoch 12][Batch 199], LR: 1.00E-03, Speed: 133.757 samples/sec, ObjLoss=30.288, BoxCenterLoss=15.193, BoxScaleLoss=6.290, ClassLoss=16.081 [Epoch 12][Batch 299], LR: 1.00E-03, Speed: 125.156 samples/sec, ObjLoss=30.260, BoxCenterLoss=15.193, BoxScaleLoss=6.288, ClassLoss=16.057 [Epoch 12][Batch 399], LR: 1.00E-03, Speed: 131.856 samples/sec, ObjLoss=30.231, BoxCenterLoss=15.190, BoxScaleLoss=6.284, ClassLoss=16.032 [Epoch 12][Batch 499], LR: 1.00E-03, Speed: 130.896 samples/sec, ObjLoss=30.205, BoxCenterLoss=15.189, BoxScaleLoss=6.280, ClassLoss=16.007 [Epoch 12][Batch 599], LR: 1.00E-03, Speed: 163.212 samples/sec, ObjLoss=30.178, BoxCenterLoss=15.188, BoxScaleLoss=6.278, ClassLoss=15.984 [Epoch 12][Batch 699], LR: 1.00E-03, Speed: 126.384 samples/sec, ObjLoss=30.151, BoxCenterLoss=15.188, BoxScaleLoss=6.275, ClassLoss=15.962 [Epoch 12][Batch 799], LR: 1.00E-03, Speed: 122.573 samples/sec, ObjLoss=30.121, BoxCenterLoss=15.185, BoxScaleLoss=6.270, ClassLoss=15.935 [Epoch 12][Batch 899], LR: 1.00E-03, Speed: 160.733 samples/sec, ObjLoss=30.096, BoxCenterLoss=15.185, BoxScaleLoss=6.268, ClassLoss=15.912 [Epoch 12][Batch 999], LR: 1.00E-03, Speed: 123.851 samples/sec, ObjLoss=30.069, BoxCenterLoss=15.183, BoxScaleLoss=6.264, ClassLoss=15.889 [Epoch 12][Batch 1099], LR: 1.00E-03, Speed: 137.291 samples/sec, ObjLoss=30.039, BoxCenterLoss=15.182, BoxScaleLoss=6.262, ClassLoss=15.866 [Epoch 12][Batch 1199], LR: 1.00E-03, Speed: 145.557 samples/sec, ObjLoss=30.016, BoxCenterLoss=15.182, BoxScaleLoss=6.259, ClassLoss=15.845 [Epoch 12][Batch 1299], LR: 1.00E-03, Speed: 171.991 samples/sec, ObjLoss=29.991, BoxCenterLoss=15.182, BoxScaleLoss=6.257, ClassLoss=15.821 [Epoch 12][Batch 1399], LR: 1.00E-03, Speed: 163.997 samples/sec, ObjLoss=29.961, BoxCenterLoss=15.179, BoxScaleLoss=6.254, ClassLoss=15.798 [Epoch 12][Batch 1499], LR: 1.00E-03, Speed: 143.505 samples/sec, ObjLoss=29.933, BoxCenterLoss=15.177, BoxScaleLoss=6.250, ClassLoss=15.775 [Epoch 12][Batch 1599], LR: 1.00E-03, Speed: 156.416 samples/sec, ObjLoss=29.906, BoxCenterLoss=15.175, BoxScaleLoss=6.247, ClassLoss=15.752 [Epoch 12][Batch 1699], LR: 1.00E-03, Speed: 131.429 samples/sec, ObjLoss=29.881, BoxCenterLoss=15.174, BoxScaleLoss=6.243, ClassLoss=15.729 [Epoch 12][Batch 1799], LR: 1.00E-03, Speed: 118.773 samples/sec, ObjLoss=29.857, BoxCenterLoss=15.172, BoxScaleLoss=6.240, ClassLoss=15.706 [Epoch 12] Training cost: 1141.911, ObjLoss=29.848, BoxCenterLoss=15.172, BoxScaleLoss=6.239, ClassLoss=15.700 [Epoch 12] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.051 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.032 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 ] = 0.149 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.118 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 ] = 0.184 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.062 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.267 person=22.0 bicycle=5.0 car=10.8 motorcycle=10.6 airplane=19.1 bus=25.6 train=29.4 truck=9.2 boat=2.7 traffic light=6.4 fire hydrant=18.0 stop sign=19.9 parking meter=8.0 bench=3.4 bird=6.5 cat=24.7 dog=21.0 horse=15.6 sheep=13.5 cow=17.6 elephant=25.0 bear=21.2 zebra=29.7 giraffe=25.2 backpack=1.0 umbrella=5.3 handbag=1.3 tie=6.8 suitcase=3.7 frisbee=8.0 skis=1.6 snowboard=1.4 sports ball=14.3 kite=9.4 baseball bat=4.4 baseball glove=10.3 skateboard=7.8 surfboard=3.2 tennis racket=6.7 bottle=8.1 wine glass=7.0 cup=10.4 fork=2.0 knife=1.0 spoon=0.3 bowl=9.1 banana=3.6 apple=1.6 sandwich=6.4 orange=6.9 broccoli=5.6 carrot=3.1 hot dog=4.5 pizza=18.7 donut=9.0 cake=5.0 chair=5.9 couch=14.4 potted plant=5.2 bed=19.8 dining table=9.6 toilet=15.4 tv=22.1 laptop=14.0 mouse=14.1 remote=2.1 keyboard=12.6 cell phone=6.9 microwave=15.5 oven=4.7 toaster=0.0 sink=8.4 refrigerator=9.1 book=2.8 clock=17.5 vase=6.5 scissors=2.7 teddy bear=14.8 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =10.0 [Epoch 13][Batch 99], LR: 1.00E-03, Speed: 127.125 samples/sec, ObjLoss=29.823, BoxCenterLoss=15.172, BoxScaleLoss=6.237, ClassLoss=15.679 [Epoch 13][Batch 199], LR: 1.00E-03, Speed: 114.923 samples/sec, ObjLoss=29.799, BoxCenterLoss=15.171, BoxScaleLoss=6.234, ClassLoss=15.657 [Epoch 13][Batch 299], LR: 1.00E-03, Speed: 137.026 samples/sec, ObjLoss=29.773, BoxCenterLoss=15.169, BoxScaleLoss=6.230, ClassLoss=15.634 [Epoch 13][Batch 399], LR: 1.00E-03, Speed: 137.338 samples/sec, ObjLoss=29.746, BoxCenterLoss=15.168, BoxScaleLoss=6.228, ClassLoss=15.614 [Epoch 13][Batch 499], LR: 1.00E-03, Speed: 146.919 samples/sec, ObjLoss=29.722, BoxCenterLoss=15.168, BoxScaleLoss=6.226, ClassLoss=15.593 [Epoch 13][Batch 599], LR: 1.00E-03, Speed: 137.467 samples/sec, ObjLoss=29.696, BoxCenterLoss=15.166, BoxScaleLoss=6.222, ClassLoss=15.571 [Epoch 13][Batch 699], LR: 1.00E-03, Speed: 150.907 samples/sec, ObjLoss=29.673, BoxCenterLoss=15.164, BoxScaleLoss=6.219, ClassLoss=15.549 [Epoch 13][Batch 799], LR: 1.00E-03, Speed: 148.038 samples/sec, ObjLoss=29.648, BoxCenterLoss=15.163, BoxScaleLoss=6.217, ClassLoss=15.529 [Epoch 13][Batch 899], LR: 1.00E-03, Speed: 148.847 samples/sec, ObjLoss=29.627, BoxCenterLoss=15.163, BoxScaleLoss=6.214, ClassLoss=15.508 [Epoch 13][Batch 999], LR: 1.00E-03, Speed: 130.187 samples/sec, ObjLoss=29.603, BoxCenterLoss=15.162, BoxScaleLoss=6.211, ClassLoss=15.488 [Epoch 13][Batch 1099], LR: 1.00E-03, Speed: 130.479 samples/sec, ObjLoss=29.579, BoxCenterLoss=15.161, BoxScaleLoss=6.209, ClassLoss=15.468 [Epoch 13][Batch 1199], LR: 1.00E-03, Speed: 117.969 samples/sec, ObjLoss=29.556, BoxCenterLoss=15.161, BoxScaleLoss=6.206, ClassLoss=15.447 [Epoch 13][Batch 1299], LR: 1.00E-03, Speed: 123.995 samples/sec, ObjLoss=29.530, BoxCenterLoss=15.159, BoxScaleLoss=6.204, ClassLoss=15.428 [Epoch 13][Batch 1399], LR: 1.00E-03, Speed: 119.319 samples/sec, ObjLoss=29.509, BoxCenterLoss=15.159, BoxScaleLoss=6.201, ClassLoss=15.408 [Epoch 13][Batch 1499], LR: 1.00E-03, Speed: 130.799 samples/sec, ObjLoss=29.487, BoxCenterLoss=15.157, BoxScaleLoss=6.197, ClassLoss=15.386 [Epoch 13][Batch 1599], LR: 1.00E-03, Speed: 127.062 samples/sec, ObjLoss=29.462, BoxCenterLoss=15.155, BoxScaleLoss=6.193, ClassLoss=15.365 [Epoch 13][Batch 1699], LR: 1.00E-03, Speed: 131.556 samples/sec, ObjLoss=29.440, BoxCenterLoss=15.154, BoxScaleLoss=6.190, ClassLoss=15.345 [Epoch 13][Batch 1799], LR: 1.00E-03, Speed: 127.510 samples/sec, ObjLoss=29.421, BoxCenterLoss=15.153, BoxScaleLoss=6.187, ClassLoss=15.326 [Epoch 13] Training cost: 1137.536, ObjLoss=29.415, BoxCenterLoss=15.153, BoxScaleLoss=6.186, ClassLoss=15.319 [Epoch 13] 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 ] = 0.278 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.066 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.029 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.111 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.183 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 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.184 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 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295 person=23.4 bicycle=8.8 car=13.3 motorcycle=15.1 airplane=24.8 bus=31.9 train=28.2 truck=8.9 boat=5.3 traffic light=4.7 fire hydrant=19.3 stop sign=28.0 parking meter=9.6 bench=3.3 bird=10.4 cat=27.7 dog=19.5 horse=15.9 sheep=19.2 cow=17.0 elephant=21.9 bear=29.2 zebra=31.6 giraffe=27.7 backpack=1.9 umbrella=11.5 handbag=1.0 tie=5.5 suitcase=4.9 frisbee=14.9 skis=3.2 snowboard=5.1 sports ball=9.2 kite=14.1 baseball bat=3.5 baseball glove=5.2 skateboard=7.5 surfboard=6.6 tennis racket=10.7 bottle=7.9 wine glass=6.7 cup=10.5 fork=2.5 knife=1.1 spoon=0.7 bowl=11.5 banana=6.0 apple=1.7 sandwich=6.5 orange=8.0 broccoli=6.3 carrot=3.1 hot dog=4.7 pizza=17.8 donut=12.4 cake=5.2 chair=6.0 couch=14.0 potted plant=4.5 bed=15.4 dining table=7.8 toilet=22.7 tv=21.4 laptop=20.2 mouse=12.7 remote=1.6 keyboard=11.5 cell phone=5.9 microwave=17.0 oven=8.5 toaster=0.0 sink=10.1 refrigerator=10.3 book=3.5 clock=18.5 vase=6.7 scissors=5.0 teddy bear=16.4 hair drier=0.0 toothbrush=0.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =11.3 [Epoch 14][Batch 99], LR: 1.00E-03, Speed: 146.660 samples/sec, ObjLoss=29.394, BoxCenterLoss=15.153, BoxScaleLoss=6.183, ClassLoss=15.300 [Epoch 14][Batch 199], LR: 1.00E-03, Speed: 136.902 samples/sec, ObjLoss=29.370, BoxCenterLoss=15.152, BoxScaleLoss=6.180, ClassLoss=15.281 [Epoch 14][Batch 299], LR: 1.00E-03, Speed: 150.455 samples/sec, ObjLoss=29.351, BoxCenterLoss=15.152, BoxScaleLoss=6.177, ClassLoss=15.261 [Epoch 14][Batch 399], LR: 1.00E-03, Speed: 115.775 samples/sec, ObjLoss=29.329, BoxCenterLoss=15.151, BoxScaleLoss=6.175, ClassLoss=15.242 [Epoch 14][Batch 499], LR: 1.00E-03, Speed: 116.287 samples/sec, ObjLoss=29.305, BoxCenterLoss=15.149, BoxScaleLoss=6.171, ClassLoss=15.223 [Epoch 14][Batch 599], LR: 1.00E-03, Speed: 146.631 samples/sec, ObjLoss=29.283, BoxCenterLoss=15.147, BoxScaleLoss=6.169, ClassLoss=15.204 [Epoch 14][Batch 699], LR: 1.00E-03, Speed: 137.424 samples/sec, ObjLoss=29.265, BoxCenterLoss=15.147, BoxScaleLoss=6.166, ClassLoss=15.184 [Epoch 14][Batch 799], LR: 1.00E-03, Speed: 140.210 samples/sec, ObjLoss=29.241, BoxCenterLoss=15.145, BoxScaleLoss=6.163, ClassLoss=15.164 [Epoch 14][Batch 899], LR: 1.00E-03, Speed: 136.327 samples/sec, ObjLoss=29.220, BoxCenterLoss=15.144, BoxScaleLoss=6.160, ClassLoss=15.145 [Epoch 14][Batch 999], LR: 1.00E-03, Speed: 128.140 samples/sec, ObjLoss=29.198, BoxCenterLoss=15.143, BoxScaleLoss=6.157, ClassLoss=15.126 [Epoch 14][Batch 1099], LR: 1.00E-03, Speed: 145.835 samples/sec, ObjLoss=29.175, BoxCenterLoss=15.142, BoxScaleLoss=6.155, ClassLoss=15.109 [Epoch 14][Batch 1199], LR: 1.00E-03, Speed: 145.642 samples/sec, ObjLoss=29.151, BoxCenterLoss=15.139, BoxScaleLoss=6.152, ClassLoss=15.091 [Epoch 14][Batch 1299], LR: 1.00E-03, Speed: 114.001 samples/sec, ObjLoss=29.128, BoxCenterLoss=15.138, BoxScaleLoss=6.150, ClassLoss=15.074 [Epoch 14][Batch 1399], LR: 1.00E-03, Speed: 135.320 samples/sec, ObjLoss=29.106, BoxCenterLoss=15.136, BoxScaleLoss=6.147, ClassLoss=15.055 [Epoch 14][Batch 1499], LR: 1.00E-03, Speed: 122.546 samples/sec, ObjLoss=29.083, BoxCenterLoss=15.135, BoxScaleLoss=6.145, ClassLoss=15.038 [Epoch 14][Batch 1599], LR: 1.00E-03, Speed: 110.014 samples/sec, ObjLoss=29.064, BoxCenterLoss=15.135, BoxScaleLoss=6.143, ClassLoss=15.020 [Epoch 14][Batch 1699], LR: 1.00E-03, Speed: 101.232 samples/sec, ObjLoss=29.043, BoxCenterLoss=15.133, BoxScaleLoss=6.140, ClassLoss=15.002 [Epoch 14][Batch 1799], LR: 1.00E-03, Speed: 148.201 samples/sec, ObjLoss=29.025, BoxCenterLoss=15.133, BoxScaleLoss=6.138, ClassLoss=14.985 [Epoch 14] Training cost: 1105.273, ObjLoss=29.019, BoxCenterLoss=15.133, BoxScaleLoss=6.138, ClassLoss=14.980 [Epoch 14] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.081 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.033 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.187 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.052 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.302 person=22.4 bicycle=8.3 car=13.9 motorcycle=14.5 airplane=26.8 bus=33.6 train=31.0 truck=10.6 boat=5.5 traffic light=4.4 fire hydrant=16.8 stop sign=24.7 parking meter=8.8 bench=5.2 bird=8.4 cat=27.8 dog=25.2 horse=21.0 sheep=16.0 cow=18.9 elephant=26.8 bear=32.7 zebra=34.8 giraffe=31.3 backpack=2.1 umbrella=9.9 handbag=0.9 tie=4.8 suitcase=5.8 frisbee=16.6 skis=3.4 snowboard=4.5 sports ball=9.5 kite=14.6 baseball bat=3.8 baseball glove=10.1 skateboard=10.2 surfboard=7.2 tennis racket=10.0 bottle=5.9 wine glass=3.1 cup=9.1 fork=2.3 knife=1.6 spoon=0.6 bowl=11.8 banana=5.4 apple=2.2 sandwich=8.2 orange=6.1 broccoli=4.2 carrot=3.1 hot dog=6.6 pizza=21.5 donut=9.3 cake=5.6 chair=6.0 couch=16.2 potted plant=4.7 bed=18.9 dining table=9.2 toilet=23.5 tv=22.0 laptop=23.1 mouse=19.4 remote=1.5 keyboard=15.4 cell phone=5.2 microwave=18.1 oven=8.3 toaster=0.0 sink=9.8 refrigerator=15.0 book=2.8 clock=21.2 vase=8.1 scissors=7.3 teddy bear=15.3 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.0 [Epoch 15][Batch 99], LR: 1.00E-03, Speed: 148.580 samples/sec, ObjLoss=28.999, BoxCenterLoss=15.133, BoxScaleLoss=6.136, ClassLoss=14.963 [Epoch 15][Batch 199], LR: 1.00E-03, Speed: 143.654 samples/sec, ObjLoss=28.979, BoxCenterLoss=15.132, BoxScaleLoss=6.134, ClassLoss=14.946 [Epoch 15][Batch 299], LR: 1.00E-03, Speed: 102.774 samples/sec, ObjLoss=28.956, BoxCenterLoss=15.131, BoxScaleLoss=6.133, ClassLoss=14.930 [Epoch 15][Batch 399], LR: 1.00E-03, Speed: 139.192 samples/sec, ObjLoss=28.938, BoxCenterLoss=15.131, BoxScaleLoss=6.130, ClassLoss=14.912 [Epoch 15][Batch 499], LR: 1.00E-03, Speed: 140.370 samples/sec, ObjLoss=28.919, BoxCenterLoss=15.130, BoxScaleLoss=6.127, ClassLoss=14.894 [Epoch 15][Batch 599], LR: 1.00E-03, Speed: 125.331 samples/sec, ObjLoss=28.898, BoxCenterLoss=15.128, BoxScaleLoss=6.124, ClassLoss=14.876 [Epoch 15][Batch 699], LR: 1.00E-03, Speed: 139.435 samples/sec, ObjLoss=28.879, BoxCenterLoss=15.128, BoxScaleLoss=6.122, ClassLoss=14.859 [Epoch 15][Batch 799], LR: 1.00E-03, Speed: 132.031 samples/sec, ObjLoss=28.859, BoxCenterLoss=15.126, BoxScaleLoss=6.119, ClassLoss=14.841 [Epoch 15][Batch 899], LR: 1.00E-03, Speed: 132.004 samples/sec, ObjLoss=28.840, BoxCenterLoss=15.125, BoxScaleLoss=6.117, ClassLoss=14.824 [Epoch 15][Batch 999], LR: 1.00E-03, Speed: 124.045 samples/sec, ObjLoss=28.821, BoxCenterLoss=15.125, BoxScaleLoss=6.114, ClassLoss=14.807 [Epoch 15][Batch 1099], LR: 1.00E-03, Speed: 168.924 samples/sec, ObjLoss=28.801, BoxCenterLoss=15.123, BoxScaleLoss=6.111, ClassLoss=14.789 [Epoch 15][Batch 1199], LR: 1.00E-03, Speed: 132.223 samples/sec, ObjLoss=28.782, BoxCenterLoss=15.122, BoxScaleLoss=6.109, ClassLoss=14.773 [Epoch 15][Batch 1299], LR: 1.00E-03, Speed: 124.046 samples/sec, ObjLoss=28.765, BoxCenterLoss=15.122, BoxScaleLoss=6.107, ClassLoss=14.757 [Epoch 15][Batch 1399], LR: 1.00E-03, Speed: 149.234 samples/sec, ObjLoss=28.744, BoxCenterLoss=15.120, BoxScaleLoss=6.105, ClassLoss=14.740 [Epoch 15][Batch 1499], LR: 1.00E-03, Speed: 131.200 samples/sec, ObjLoss=28.727, BoxCenterLoss=15.120, BoxScaleLoss=6.103, ClassLoss=14.723 [Epoch 15][Batch 1599], LR: 1.00E-03, Speed: 155.158 samples/sec, ObjLoss=28.707, BoxCenterLoss=15.119, BoxScaleLoss=6.101, ClassLoss=14.707 [Epoch 15][Batch 1699], LR: 1.00E-03, Speed: 119.170 samples/sec, ObjLoss=28.687, BoxCenterLoss=15.117, BoxScaleLoss=6.099, ClassLoss=14.691 [Epoch 15][Batch 1799], LR: 1.00E-03, Speed: 155.431 samples/sec, ObjLoss=28.669, BoxCenterLoss=15.117, BoxScaleLoss=6.097, ClassLoss=14.676 [Epoch 15] Training cost: 1122.034, ObjLoss=28.663, BoxCenterLoss=15.117, BoxScaleLoss=6.096, ClassLoss=14.670 [Epoch 15] 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 ] = 0.283 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.059 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 ] = 0.114 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.173 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.178 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 ] = 0.058 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.279 person=24.1 bicycle=7.2 car=12.1 motorcycle=13.9 airplane=21.4 bus=33.3 train=26.0 truck=12.3 boat=6.8 traffic light=6.5 fire hydrant=24.7 stop sign=21.5 parking meter=12.3 bench=5.7 bird=9.0 cat=26.5 dog=20.8 horse=18.9 sheep=12.4 cow=15.7 elephant=22.9 bear=23.0 zebra=29.2 giraffe=29.7 backpack=1.7 umbrella=6.6 handbag=1.2 tie=7.6 suitcase=6.6 frisbee=14.7 skis=4.1 snowboard=4.4 sports ball=13.2 kite=13.7 baseball bat=4.4 baseball glove=8.7 skateboard=7.8 surfboard=6.8 tennis racket=8.1 bottle=8.7 wine glass=7.3 cup=11.4 fork=1.5 knife=1.2 spoon=1.0 bowl=11.1 banana=4.8 apple=1.1 sandwich=8.5 orange=10.0 broccoli=5.8 carrot=3.0 hot dog=4.1 pizza=14.9 donut=10.5 cake=6.9 chair=6.1 couch=12.5 potted plant=5.6 bed=10.2 dining table=5.9 toilet=23.2 tv=22.7 laptop=22.2 mouse=13.6 remote=2.2 keyboard=11.0 cell phone=8.1 microwave=12.6 oven=9.1 toaster=0.0 sink=8.7 refrigerator=9.8 book=2.9 clock=20.1 vase=7.7 scissors=3.3 teddy bear=13.1 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =11.1 [Epoch 16][Batch 99], LR: 1.00E-03, Speed: 145.765 samples/sec, ObjLoss=28.647, BoxCenterLoss=15.117, BoxScaleLoss=6.093, ClassLoss=14.653 [Epoch 16][Batch 199], LR: 1.00E-03, Speed: 134.248 samples/sec, ObjLoss=28.628, BoxCenterLoss=15.117, BoxScaleLoss=6.091, ClassLoss=14.638 [Epoch 16][Batch 299], LR: 1.00E-03, Speed: 105.542 samples/sec, ObjLoss=28.609, BoxCenterLoss=15.115, BoxScaleLoss=6.089, ClassLoss=14.623 [Epoch 16][Batch 399], LR: 1.00E-03, Speed: 134.538 samples/sec, ObjLoss=28.594, BoxCenterLoss=15.115, BoxScaleLoss=6.087, ClassLoss=14.607 [Epoch 16][Batch 499], LR: 1.00E-03, Speed: 109.431 samples/sec, ObjLoss=28.575, BoxCenterLoss=15.114, BoxScaleLoss=6.085, ClassLoss=14.591 [Epoch 16][Batch 599], LR: 1.00E-03, Speed: 157.257 samples/sec, ObjLoss=28.556, BoxCenterLoss=15.113, BoxScaleLoss=6.084, ClassLoss=14.576 [Epoch 16][Batch 699], LR: 1.00E-03, Speed: 128.597 samples/sec, ObjLoss=28.539, BoxCenterLoss=15.112, BoxScaleLoss=6.082, ClassLoss=14.561 [Epoch 16][Batch 799], LR: 1.00E-03, Speed: 126.107 samples/sec, ObjLoss=28.522, BoxCenterLoss=15.112, BoxScaleLoss=6.079, ClassLoss=14.544 [Epoch 16][Batch 899], LR: 1.00E-03, Speed: 135.857 samples/sec, ObjLoss=28.504, BoxCenterLoss=15.111, BoxScaleLoss=6.077, ClassLoss=14.529 [Epoch 16][Batch 999], LR: 1.00E-03, Speed: 118.664 samples/sec, ObjLoss=28.485, BoxCenterLoss=15.110, BoxScaleLoss=6.075, ClassLoss=14.514 [Epoch 16][Batch 1099], LR: 1.00E-03, Speed: 136.495 samples/sec, ObjLoss=28.467, BoxCenterLoss=15.108, BoxScaleLoss=6.072, ClassLoss=14.497 [Epoch 16][Batch 1199], LR: 1.00E-03, Speed: 124.634 samples/sec, ObjLoss=28.450, BoxCenterLoss=15.108, BoxScaleLoss=6.070, ClassLoss=14.482 [Epoch 16][Batch 1299], LR: 1.00E-03, Speed: 134.755 samples/sec, ObjLoss=28.430, BoxCenterLoss=15.106, BoxScaleLoss=6.068, ClassLoss=14.467 [Epoch 16][Batch 1399], LR: 1.00E-03, Speed: 131.661 samples/sec, ObjLoss=28.413, BoxCenterLoss=15.105, BoxScaleLoss=6.066, ClassLoss=14.452 [Epoch 16][Batch 1499], LR: 1.00E-03, Speed: 143.103 samples/sec, ObjLoss=28.394, BoxCenterLoss=15.103, BoxScaleLoss=6.063, ClassLoss=14.436 [Epoch 16][Batch 1599], LR: 1.00E-03, Speed: 129.667 samples/sec, ObjLoss=28.376, BoxCenterLoss=15.101, BoxScaleLoss=6.060, ClassLoss=14.420 [Epoch 16][Batch 1699], LR: 1.00E-03, Speed: 139.248 samples/sec, ObjLoss=28.360, BoxCenterLoss=15.100, BoxScaleLoss=6.058, ClassLoss=14.405 [Epoch 16][Batch 1799], LR: 1.00E-03, Speed: 132.059 samples/sec, ObjLoss=28.344, BoxCenterLoss=15.098, BoxScaleLoss=6.055, ClassLoss=14.390 [Epoch 16] Training cost: 1142.918, ObjLoss=28.339, BoxCenterLoss=15.098, BoxScaleLoss=6.054, ClassLoss=14.385 [Epoch 16] 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 ] = 0.074 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 ] = 0.112 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.177 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.125 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.173 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.175 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 ] = 0.168 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.283 person=23.3 bicycle=7.3 car=11.3 motorcycle=13.3 airplane=25.0 bus=32.0 train=27.7 truck=12.1 boat=4.8 traffic light=5.9 fire hydrant=18.9 stop sign=17.5 parking meter=10.4 bench=4.8 bird=8.7 cat=24.1 dog=20.8 horse=16.1 sheep=14.6 cow=16.5 elephant=29.4 bear=30.2 zebra=28.2 giraffe=29.4 backpack=1.5 umbrella=10.9 handbag=0.8 tie=6.1 suitcase=4.7 frisbee=16.6 skis=3.1 snowboard=4.9 sports ball=11.3 kite=11.2 baseball bat=3.8 baseball glove=9.2 skateboard=6.3 surfboard=7.3 tennis racket=11.7 bottle=7.0 wine glass=5.2 cup=9.2 fork=1.8 knife=1.1 spoon=0.4 bowl=12.0 banana=5.5 apple=2.1 sandwich=8.8 orange=10.5 broccoli=5.5 carrot=2.8 hot dog=6.9 pizza=21.2 donut=8.6 cake=6.4 chair=5.7 couch=16.4 potted plant=4.9 bed=17.5 dining table=9.3 toilet=20.1 tv=20.0 laptop=21.8 mouse=17.0 remote=1.3 keyboard=16.1 cell phone=6.6 microwave=15.9 oven=10.3 toaster=0.0 sink=10.2 refrigerator=9.4 book=2.0 clock=18.4 vase=6.3 scissors=2.9 teddy bear=15.1 hair drier=0.0 toothbrush=0.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =11.3 [Epoch 17][Batch 99], LR: 1.00E-03, Speed: 94.660 samples/sec, ObjLoss=28.323, BoxCenterLoss=15.098, BoxScaleLoss=6.052, ClassLoss=14.370 [Epoch 17][Batch 199], LR: 1.00E-03, Speed: 122.753 samples/sec, ObjLoss=28.306, BoxCenterLoss=15.097, BoxScaleLoss=6.050, ClassLoss=14.355 [Epoch 17][Batch 299], LR: 1.00E-03, Speed: 142.211 samples/sec, ObjLoss=28.290, BoxCenterLoss=15.096, BoxScaleLoss=6.048, ClassLoss=14.342 [Epoch 17][Batch 399], LR: 1.00E-03, Speed: 131.327 samples/sec, ObjLoss=28.274, BoxCenterLoss=15.095, BoxScaleLoss=6.045, ClassLoss=14.327 [Epoch 17][Batch 499], LR: 1.00E-03, Speed: 129.209 samples/sec, ObjLoss=28.257, BoxCenterLoss=15.094, BoxScaleLoss=6.043, ClassLoss=14.312 [Epoch 17][Batch 599], LR: 1.00E-03, Speed: 118.947 samples/sec, ObjLoss=28.241, BoxCenterLoss=15.093, BoxScaleLoss=6.041, ClassLoss=14.297 [Epoch 17][Batch 699], LR: 1.00E-03, Speed: 143.098 samples/sec, ObjLoss=28.224, BoxCenterLoss=15.092, BoxScaleLoss=6.040, ClassLoss=14.284 [Epoch 17][Batch 799], LR: 1.00E-03, Speed: 133.551 samples/sec, ObjLoss=28.206, BoxCenterLoss=15.091, BoxScaleLoss=6.037, ClassLoss=14.269 [Epoch 17][Batch 899], LR: 1.00E-03, Speed: 141.181 samples/sec, ObjLoss=28.189, BoxCenterLoss=15.089, BoxScaleLoss=6.035, ClassLoss=14.255 [Epoch 17][Batch 999], LR: 1.00E-03, Speed: 139.632 samples/sec, ObjLoss=28.173, BoxCenterLoss=15.088, BoxScaleLoss=6.033, ClassLoss=14.240 [Epoch 17][Batch 1099], LR: 1.00E-03, Speed: 119.400 samples/sec, ObjLoss=28.155, BoxCenterLoss=15.087, BoxScaleLoss=6.031, ClassLoss=14.227 [Epoch 17][Batch 1199], LR: 1.00E-03, Speed: 136.963 samples/sec, ObjLoss=28.142, BoxCenterLoss=15.087, BoxScaleLoss=6.029, ClassLoss=14.213 [Epoch 17][Batch 1299], LR: 1.00E-03, Speed: 127.803 samples/sec, ObjLoss=28.127, BoxCenterLoss=15.087, BoxScaleLoss=6.027, ClassLoss=14.199 [Epoch 17][Batch 1399], LR: 1.00E-03, Speed: 109.173 samples/sec, ObjLoss=28.113, BoxCenterLoss=15.086, BoxScaleLoss=6.025, ClassLoss=14.185 [Epoch 17][Batch 1499], LR: 1.00E-03, Speed: 133.852 samples/sec, ObjLoss=28.097, BoxCenterLoss=15.085, BoxScaleLoss=6.024, ClassLoss=14.171 [Epoch 17][Batch 1599], LR: 1.00E-03, Speed: 140.771 samples/sec, ObjLoss=28.080, BoxCenterLoss=15.084, BoxScaleLoss=6.021, ClassLoss=14.158 [Epoch 17][Batch 1699], LR: 1.00E-03, Speed: 135.407 samples/sec, ObjLoss=28.063, BoxCenterLoss=15.082, BoxScaleLoss=6.019, ClassLoss=14.144 [Epoch 17][Batch 1799], LR: 1.00E-03, Speed: 143.826 samples/sec, ObjLoss=28.048, BoxCenterLoss=15.081, BoxScaleLoss=6.017, ClassLoss=14.132 [Epoch 17] Training cost: 1096.988, ObjLoss=28.042, BoxCenterLoss=15.081, BoxScaleLoss=6.017, ClassLoss=14.128 [Epoch 17] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.291 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.078 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 ] = 0.104 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.188 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.131 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.188 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.174 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.293 person=22.2 bicycle=8.1 car=13.7 motorcycle=17.0 airplane=29.2 bus=31.7 train=29.3 truck=11.4 boat=6.3 traffic light=6.8 fire hydrant=25.2 stop sign=16.6 parking meter=9.9 bench=5.8 bird=11.2 cat=28.7 dog=19.5 horse=16.0 sheep=19.1 cow=17.0 elephant=25.3 bear=26.6 zebra=33.8 giraffe=31.4 backpack=1.6 umbrella=10.4 handbag=1.1 tie=7.0 suitcase=4.9 frisbee=19.1 skis=3.2 snowboard=4.6 sports ball=11.2 kite=16.0 baseball bat=4.2 baseball glove=10.3 skateboard=9.1 surfboard=8.3 tennis racket=10.4 bottle=7.4 wine glass=6.7 cup=9.2 fork=2.6 knife=1.3 spoon=0.8 bowl=13.3 banana=5.9 apple=2.9 sandwich=10.8 orange=9.1 broccoli=4.3 carrot=3.6 hot dog=7.4 pizza=21.8 donut=12.4 cake=6.7 chair=5.5 couch=16.0 potted plant=4.8 bed=18.1 dining table=9.4 toilet=23.6 tv=17.6 laptop=18.7 mouse=16.4 remote=1.7 keyboard=11.3 cell phone=5.5 microwave=17.5 oven=9.8 toaster=0.0 sink=11.3 refrigerator=14.7 book=2.3 clock=18.7 vase=8.7 scissors=5.5 teddy bear=15.3 hair drier=0.0 toothbrush=0.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.0 [Epoch 18][Batch 99], LR: 1.00E-03, Speed: 153.173 samples/sec, ObjLoss=28.027, BoxCenterLoss=15.080, BoxScaleLoss=6.015, ClassLoss=14.115 [Epoch 18][Batch 199], LR: 1.00E-03, Speed: 131.850 samples/sec, ObjLoss=28.014, BoxCenterLoss=15.081, BoxScaleLoss=6.014, ClassLoss=14.102 [Epoch 18][Batch 299], LR: 1.00E-03, Speed: 123.626 samples/sec, ObjLoss=28.000, BoxCenterLoss=15.081, BoxScaleLoss=6.012, ClassLoss=14.089 [Epoch 18][Batch 399], LR: 1.00E-03, Speed: 126.526 samples/sec, ObjLoss=27.985, BoxCenterLoss=15.080, BoxScaleLoss=6.010, ClassLoss=14.075 [Epoch 18][Batch 499], LR: 1.00E-03, Speed: 150.428 samples/sec, ObjLoss=27.970, BoxCenterLoss=15.079, BoxScaleLoss=6.008, ClassLoss=14.061 [Epoch 18][Batch 599], LR: 1.00E-03, Speed: 128.363 samples/sec, ObjLoss=27.954, BoxCenterLoss=15.078, BoxScaleLoss=6.007, ClassLoss=14.049 [Epoch 18][Batch 699], LR: 1.00E-03, Speed: 100.984 samples/sec, ObjLoss=27.940, BoxCenterLoss=15.077, BoxScaleLoss=6.004, ClassLoss=14.036 [Epoch 18][Batch 799], LR: 1.00E-03, Speed: 155.445 samples/sec, ObjLoss=27.924, BoxCenterLoss=15.076, BoxScaleLoss=6.003, ClassLoss=14.023 [Epoch 18][Batch 899], LR: 1.00E-03, Speed: 127.641 samples/sec, ObjLoss=27.908, BoxCenterLoss=15.075, BoxScaleLoss=6.001, ClassLoss=14.010 [Epoch 18][Batch 999], LR: 1.00E-03, Speed: 132.578 samples/sec, ObjLoss=27.892, BoxCenterLoss=15.073, BoxScaleLoss=5.999, ClassLoss=13.998 [Epoch 18][Batch 1099], LR: 1.00E-03, Speed: 116.679 samples/sec, ObjLoss=27.875, BoxCenterLoss=15.071, BoxScaleLoss=5.997, ClassLoss=13.984 [Epoch 18][Batch 1199], LR: 1.00E-03, Speed: 129.178 samples/sec, ObjLoss=27.861, BoxCenterLoss=15.070, BoxScaleLoss=5.995, ClassLoss=13.971 [Epoch 18][Batch 1299], LR: 1.00E-03, Speed: 119.819 samples/sec, ObjLoss=27.846, BoxCenterLoss=15.068, BoxScaleLoss=5.992, ClassLoss=13.958 [Epoch 18][Batch 1399], LR: 1.00E-03, Speed: 148.707 samples/sec, ObjLoss=27.832, BoxCenterLoss=15.067, BoxScaleLoss=5.990, ClassLoss=13.945 [Epoch 18][Batch 1499], LR: 1.00E-03, Speed: 114.584 samples/sec, ObjLoss=27.820, BoxCenterLoss=15.067, BoxScaleLoss=5.989, ClassLoss=13.934 [Epoch 18][Batch 1599], LR: 1.00E-03, Speed: 133.560 samples/sec, ObjLoss=27.806, BoxCenterLoss=15.066, BoxScaleLoss=5.987, ClassLoss=13.922 [Epoch 18][Batch 1699], LR: 1.00E-03, Speed: 139.013 samples/sec, ObjLoss=27.790, BoxCenterLoss=15.065, BoxScaleLoss=5.984, ClassLoss=13.909 [Epoch 18][Batch 1799], LR: 1.00E-03, Speed: 148.334 samples/sec, ObjLoss=27.777, BoxCenterLoss=15.064, BoxScaleLoss=5.982, ClassLoss=13.896 [Epoch 18] Training cost: 1099.749, ObjLoss=27.771, BoxCenterLoss=15.063, BoxScaleLoss=5.982, ClassLoss=13.893 [Epoch 18] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.301 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 ] = 0.123 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.198 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.201 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.207 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 ] = 0.199 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.313 person=25.1 bicycle=10.1 car=12.0 motorcycle=17.9 airplane=26.6 bus=28.1 train=32.9 truck=12.4 boat=6.3 traffic light=6.1 fire hydrant=23.3 stop sign=21.5 parking meter=12.0 bench=6.7 bird=11.1 cat=19.6 dog=24.9 horse=16.7 sheep=16.6 cow=17.3 elephant=28.5 bear=28.2 zebra=30.3 giraffe=31.8 backpack=1.7 umbrella=8.4 handbag=1.0 tie=9.3 suitcase=7.8 frisbee=21.4 skis=2.7 snowboard=3.6 sports ball=18.7 kite=17.4 baseball bat=4.4 baseball glove=9.8 skateboard=11.4 surfboard=7.1 tennis racket=13.2 bottle=9.4 wine glass=8.5 cup=12.8 fork=3.8 knife=0.8 spoon=0.7 bowl=11.2 banana=6.6 apple=1.6 sandwich=9.3 orange=8.6 broccoli=6.0 carrot=3.3 hot dog=7.6 pizza=18.6 donut=9.2 cake=8.5 chair=7.0 couch=19.3 potted plant=4.0 bed=19.4 dining table=11.4 toilet=21.2 tv=23.8 laptop=22.6 mouse=20.5 remote=1.9 keyboard=15.1 cell phone=8.3 microwave=15.9 oven=12.2 toaster=0.0 sink=13.3 refrigerator=17.5 book=2.4 clock=21.4 vase=8.6 scissors=7.5 teddy bear=15.4 hair drier=0.0 toothbrush=0.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.7 [Epoch 19][Batch 99], LR: 1.00E-03, Speed: 133.909 samples/sec, ObjLoss=27.757, BoxCenterLoss=15.063, BoxScaleLoss=5.980, ClassLoss=13.881 [Epoch 19][Batch 199], LR: 1.00E-03, Speed: 133.801 samples/sec, ObjLoss=27.746, BoxCenterLoss=15.063, BoxScaleLoss=5.979, ClassLoss=13.868 [Epoch 19][Batch 299], LR: 1.00E-03, Speed: 124.451 samples/sec, ObjLoss=27.733, BoxCenterLoss=15.062, BoxScaleLoss=5.976, ClassLoss=13.856 [Epoch 19][Batch 399], LR: 1.00E-03, Speed: 147.553 samples/sec, ObjLoss=27.718, BoxCenterLoss=15.061, BoxScaleLoss=5.975, ClassLoss=13.844 [Epoch 19][Batch 499], LR: 1.00E-03, Speed: 115.502 samples/sec, ObjLoss=27.705, BoxCenterLoss=15.060, BoxScaleLoss=5.973, ClassLoss=13.831 [Epoch 19][Batch 599], LR: 1.00E-03, Speed: 133.021 samples/sec, ObjLoss=27.692, BoxCenterLoss=15.060, BoxScaleLoss=5.971, ClassLoss=13.819 [Epoch 19][Batch 699], LR: 1.00E-03, Speed: 122.618 samples/sec, ObjLoss=27.679, BoxCenterLoss=15.059, BoxScaleLoss=5.969, ClassLoss=13.807 [Epoch 19][Batch 799], LR: 1.00E-03, Speed: 116.527 samples/sec, ObjLoss=27.665, BoxCenterLoss=15.058, BoxScaleLoss=5.967, ClassLoss=13.795 [Epoch 19][Batch 899], LR: 1.00E-03, Speed: 125.553 samples/sec, ObjLoss=27.651, BoxCenterLoss=15.057, BoxScaleLoss=5.965, ClassLoss=13.783 [Epoch 19][Batch 999], LR: 1.00E-03, Speed: 143.535 samples/sec, ObjLoss=27.637, BoxCenterLoss=15.056, BoxScaleLoss=5.963, ClassLoss=13.770 [Epoch 19][Batch 1099], LR: 1.00E-03, Speed: 121.401 samples/sec, ObjLoss=27.626, BoxCenterLoss=15.056, BoxScaleLoss=5.961, ClassLoss=13.759 [Epoch 19][Batch 1199], LR: 1.00E-03, Speed: 114.865 samples/sec, ObjLoss=27.612, BoxCenterLoss=15.055, BoxScaleLoss=5.959, ClassLoss=13.746 [Epoch 19][Batch 1299], LR: 1.00E-03, Speed: 142.270 samples/sec, ObjLoss=27.600, BoxCenterLoss=15.054, BoxScaleLoss=5.957, ClassLoss=13.735 [Epoch 19][Batch 1399], LR: 1.00E-03, Speed: 151.924 samples/sec, ObjLoss=27.588, BoxCenterLoss=15.054, BoxScaleLoss=5.956, ClassLoss=13.724 [Epoch 19][Batch 1499], LR: 1.00E-03, Speed: 134.105 samples/sec, ObjLoss=27.573, BoxCenterLoss=15.053, BoxScaleLoss=5.954, ClassLoss=13.713 [Epoch 19][Batch 1599], LR: 1.00E-03, Speed: 135.475 samples/sec, ObjLoss=27.561, BoxCenterLoss=15.052, BoxScaleLoss=5.952, ClassLoss=13.701 [Epoch 19][Batch 1699], LR: 1.00E-03, Speed: 128.851 samples/sec, ObjLoss=27.550, BoxCenterLoss=15.051, BoxScaleLoss=5.950, ClassLoss=13.689 [Epoch 19][Batch 1799], LR: 1.00E-03, Speed: 158.894 samples/sec, ObjLoss=27.535, BoxCenterLoss=15.050, BoxScaleLoss=5.948, ClassLoss=13.678 [Epoch 19] Training cost: 1100.082, ObjLoss=27.531, BoxCenterLoss=15.050, BoxScaleLoss=5.948, ClassLoss=13.675 [Epoch 19] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.123 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.079 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.039 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.119 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.133 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 ] = 0.201 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 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.292 person=23.4 bicycle=5.9 car=11.8 motorcycle=14.8 airplane=25.6 bus=31.3 train=27.0 truck=10.9 boat=5.3 traffic light=7.2 fire hydrant=20.9 stop sign=27.6 parking meter=14.4 bench=5.7 bird=12.3 cat=31.1 dog=23.8 horse=18.4 sheep=17.5 cow=18.1 elephant=28.9 bear=33.3 zebra=32.3 giraffe=30.5 backpack=1.9 umbrella=10.9 handbag=0.9 tie=5.9 suitcase=7.2 frisbee=17.7 skis=4.7 snowboard=5.0 sports ball=11.2 kite=15.5 baseball bat=4.0 baseball glove=11.2 skateboard=11.4 surfboard=4.9 tennis racket=12.5 bottle=6.7 wine glass=5.4 cup=12.6 fork=1.8 knife=0.6 spoon=0.4 bowl=11.5 banana=5.8 apple=2.1 sandwich=9.6 orange=9.5 broccoli=6.4 carrot=2.8 hot dog=4.6 pizza=20.4 donut=15.0 cake=8.7 chair=5.9 couch=18.3 potted plant=3.8 bed=14.0 dining table=6.7 toilet=22.5 tv=20.4 laptop=23.1 mouse=15.6 remote=2.3 keyboard=18.3 cell phone=7.4 microwave=21.0 oven=9.0 toaster=0.0 sink=11.4 refrigerator=10.6 book=2.5 clock=19.6 vase=8.8 scissors=1.0 teddy bear=15.1 hair drier=0.0 toothbrush=0.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.3 [Epoch 20][Batch 99], LR: 1.00E-03, Speed: 131.033 samples/sec, ObjLoss=27.521, BoxCenterLoss=15.050, BoxScaleLoss=5.946, ClassLoss=13.663 [Epoch 20][Batch 199], LR: 1.00E-03, Speed: 113.019 samples/sec, ObjLoss=27.508, BoxCenterLoss=15.049, BoxScaleLoss=5.944, ClassLoss=13.650 [Epoch 20][Batch 299], LR: 1.00E-03, Speed: 144.951 samples/sec, ObjLoss=27.498, BoxCenterLoss=15.050, BoxScaleLoss=5.942, ClassLoss=13.640 [Epoch 20][Batch 399], LR: 1.00E-03, Speed: 133.954 samples/sec, ObjLoss=27.484, BoxCenterLoss=15.048, BoxScaleLoss=5.940, ClassLoss=13.627 [Epoch 20][Batch 499], LR: 1.00E-03, Speed: 112.865 samples/sec, ObjLoss=27.471, BoxCenterLoss=15.048, BoxScaleLoss=5.938, ClassLoss=13.616 [Epoch 20][Batch 599], LR: 1.00E-03, Speed: 131.095 samples/sec, ObjLoss=27.458, BoxCenterLoss=15.046, BoxScaleLoss=5.936, ClassLoss=13.605 [Epoch 20][Batch 699], LR: 1.00E-03, Speed: 107.312 samples/sec, ObjLoss=27.448, BoxCenterLoss=15.046, BoxScaleLoss=5.934, ClassLoss=13.594 [Epoch 20][Batch 799], LR: 1.00E-03, Speed: 116.532 samples/sec, ObjLoss=27.438, BoxCenterLoss=15.046, BoxScaleLoss=5.932, ClassLoss=13.582 [Epoch 20][Batch 899], LR: 1.00E-03, Speed: 116.004 samples/sec, ObjLoss=27.426, BoxCenterLoss=15.046, BoxScaleLoss=5.930, ClassLoss=13.571 [Epoch 20][Batch 999], LR: 1.00E-03, Speed: 162.403 samples/sec, ObjLoss=27.414, BoxCenterLoss=15.044, BoxScaleLoss=5.928, ClassLoss=13.560 [Epoch 20][Batch 1099], LR: 1.00E-03, Speed: 138.162 samples/sec, ObjLoss=27.404, BoxCenterLoss=15.045, BoxScaleLoss=5.926, ClassLoss=13.548 [Epoch 20][Batch 1199], LR: 1.00E-03, Speed: 137.985 samples/sec, ObjLoss=27.391, BoxCenterLoss=15.044, BoxScaleLoss=5.924, ClassLoss=13.536 [Epoch 20][Batch 1299], LR: 1.00E-03, Speed: 121.692 samples/sec, ObjLoss=27.378, BoxCenterLoss=15.043, BoxScaleLoss=5.922, ClassLoss=13.527 [Epoch 20][Batch 1399], LR: 1.00E-03, Speed: 109.906 samples/sec, ObjLoss=27.364, BoxCenterLoss=15.042, BoxScaleLoss=5.921, ClassLoss=13.516 [Epoch 20][Batch 1499], LR: 1.00E-03, Speed: 127.712 samples/sec, ObjLoss=27.354, BoxCenterLoss=15.041, BoxScaleLoss=5.919, ClassLoss=13.505 [Epoch 20][Batch 1599], LR: 1.00E-03, Speed: 131.413 samples/sec, ObjLoss=27.342, BoxCenterLoss=15.040, BoxScaleLoss=5.917, ClassLoss=13.493 [Epoch 20][Batch 1699], LR: 1.00E-03, Speed: 129.500 samples/sec, ObjLoss=27.330, BoxCenterLoss=15.040, BoxScaleLoss=5.915, ClassLoss=13.483 [Epoch 20][Batch 1799], LR: 1.00E-03, Speed: 144.956 samples/sec, ObjLoss=27.318, BoxCenterLoss=15.040, BoxScaleLoss=5.914, ClassLoss=13.473 [Epoch 20] Training cost: 1128.638, ObjLoss=27.315, BoxCenterLoss=15.040, BoxScaleLoss=5.913, ClassLoss=13.470 [Epoch 20] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.301 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.083 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 ] = 0.128 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.135 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 ] = 0.198 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 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.305 person=21.5 bicycle=6.6 car=13.8 motorcycle=14.3 airplane=27.3 bus=30.6 train=30.7 truck=11.4 boat=7.6 traffic light=6.8 fire hydrant=24.4 stop sign=30.5 parking meter=13.2 bench=5.4 bird=11.5 cat=30.3 dog=23.5 horse=17.0 sheep=15.1 cow=19.7 elephant=29.1 bear=28.3 zebra=31.0 giraffe=31.2 backpack=1.3 umbrella=11.3 handbag=0.7 tie=5.9 suitcase=5.2 frisbee=14.7 skis=4.8 snowboard=6.3 sports ball=15.5 kite=14.7 baseball bat=5.5 baseball glove=11.2 skateboard=12.8 surfboard=7.2 tennis racket=9.7 bottle=7.2 wine glass=8.1 cup=11.7 fork=2.2 knife=1.3 spoon=0.3 bowl=13.5 banana=6.0 apple=3.1 sandwich=11.7 orange=8.8 broccoli=7.4 carrot=4.1 hot dog=7.6 pizza=26.6 donut=14.5 cake=8.9 chair=5.6 couch=13.6 potted plant=4.4 bed=15.2 dining table=8.2 toilet=23.2 tv=21.5 laptop=22.9 mouse=21.7 remote=3.0 keyboard=16.2 cell phone=9.1 microwave=14.2 oven=8.9 toaster=0.0 sink=13.6 refrigerator=13.2 book=2.5 clock=21.0 vase=9.6 scissors=2.6 teddy bear=14.8 hair drier=0.0 toothbrush=1.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.7 [Epoch 21][Batch 99], LR: 1.00E-03, Speed: 161.371 samples/sec, ObjLoss=27.304, BoxCenterLoss=15.039, BoxScaleLoss=5.912, ClassLoss=13.459 [Epoch 21][Batch 199], LR: 1.00E-03, Speed: 121.245 samples/sec, ObjLoss=27.291, BoxCenterLoss=15.038, BoxScaleLoss=5.910, ClassLoss=13.448 [Epoch 21][Batch 299], LR: 1.00E-03, Speed: 121.365 samples/sec, ObjLoss=27.280, BoxCenterLoss=15.038, BoxScaleLoss=5.908, ClassLoss=13.437 [Epoch 21][Batch 399], LR: 1.00E-03, Speed: 139.765 samples/sec, ObjLoss=27.267, BoxCenterLoss=15.037, BoxScaleLoss=5.907, ClassLoss=13.427 [Epoch 21][Batch 499], LR: 1.00E-03, Speed: 147.322 samples/sec, ObjLoss=27.254, BoxCenterLoss=15.036, BoxScaleLoss=5.906, ClassLoss=13.418 [Epoch 21][Batch 599], LR: 1.00E-03, Speed: 144.292 samples/sec, ObjLoss=27.243, BoxCenterLoss=15.036, BoxScaleLoss=5.904, ClassLoss=13.407 [Epoch 21][Batch 699], LR: 1.00E-03, Speed: 126.534 samples/sec, ObjLoss=27.231, BoxCenterLoss=15.036, BoxScaleLoss=5.903, ClassLoss=13.398 [Epoch 21][Batch 799], LR: 1.00E-03, Speed: 137.144 samples/sec, ObjLoss=27.219, BoxCenterLoss=15.035, BoxScaleLoss=5.902, ClassLoss=13.389 [Epoch 21][Batch 899], LR: 1.00E-03, Speed: 141.033 samples/sec, ObjLoss=27.208, BoxCenterLoss=15.034, BoxScaleLoss=5.899, ClassLoss=13.378 [Epoch 21][Batch 999], LR: 1.00E-03, Speed: 130.923 samples/sec, ObjLoss=27.196, BoxCenterLoss=15.033, BoxScaleLoss=5.898, ClassLoss=13.368 [Epoch 21][Batch 1099], LR: 1.00E-03, Speed: 137.302 samples/sec, ObjLoss=27.183, BoxCenterLoss=15.032, BoxScaleLoss=5.896, ClassLoss=13.357 [Epoch 21][Batch 1199], LR: 1.00E-03, Speed: 122.124 samples/sec, ObjLoss=27.172, BoxCenterLoss=15.032, BoxScaleLoss=5.895, ClassLoss=13.347 [Epoch 21][Batch 1299], LR: 1.00E-03, Speed: 147.788 samples/sec, ObjLoss=27.161, BoxCenterLoss=15.031, BoxScaleLoss=5.893, ClassLoss=13.338 [Epoch 21][Batch 1399], LR: 1.00E-03, Speed: 141.371 samples/sec, ObjLoss=27.151, BoxCenterLoss=15.031, BoxScaleLoss=5.892, ClassLoss=13.328 [Epoch 21][Batch 1499], LR: 1.00E-03, Speed: 138.454 samples/sec, ObjLoss=27.138, BoxCenterLoss=15.030, BoxScaleLoss=5.890, ClassLoss=13.318 [Epoch 21][Batch 1599], LR: 1.00E-03, Speed: 133.554 samples/sec, ObjLoss=27.125, BoxCenterLoss=15.027, BoxScaleLoss=5.888, ClassLoss=13.307 [Epoch 21][Batch 1699], LR: 1.00E-03, Speed: 115.611 samples/sec, ObjLoss=27.111, BoxCenterLoss=15.026, BoxScaleLoss=5.886, ClassLoss=13.298 [Epoch 21][Batch 1799], LR: 1.00E-03, Speed: 178.759 samples/sec, ObjLoss=27.100, BoxCenterLoss=15.025, BoxScaleLoss=5.885, ClassLoss=13.287 [Epoch 21] Training cost: 1129.431, ObjLoss=27.096, BoxCenterLoss=15.025, BoxScaleLoss=5.884, ClassLoss=13.284 [Epoch 21] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.119 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.066 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.042 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.198 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.132 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.191 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.196 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.067 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.175 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.314 person=23.7 bicycle=8.5 car=13.0 motorcycle=17.6 airplane=27.3 bus=31.4 train=24.6 truck=11.3 boat=4.6 traffic light=7.7 fire hydrant=27.1 stop sign=20.0 parking meter=13.1 bench=6.5 bird=10.0 cat=29.6 dog=19.5 horse=18.3 sheep=13.5 cow=19.6 elephant=25.2 bear=25.4 zebra=29.2 giraffe=29.0 backpack=1.4 umbrella=10.8 handbag=1.1 tie=5.9 suitcase=5.7 frisbee=19.9 skis=3.1 snowboard=6.5 sports ball=12.1 kite=15.1 baseball bat=4.4 baseball glove=10.8 skateboard=9.2 surfboard=7.5 tennis racket=8.1 bottle=9.6 wine glass=5.4 cup=10.1 fork=3.6 knife=1.3 spoon=0.7 bowl=9.9 banana=5.2 apple=2.3 sandwich=11.4 orange=6.4 broccoli=6.5 carrot=2.6 hot dog=6.1 pizza=17.9 donut=4.6 cake=7.7 chair=6.1 couch=12.3 potted plant=4.8 bed=18.7 dining table=11.8 toilet=21.8 tv=23.7 laptop=19.9 mouse=14.2 remote=2.3 keyboard=11.3 cell phone=7.7 microwave=18.6 oven=11.8 toaster=0.0 sink=11.4 refrigerator=16.3 book=2.5 clock=21.2 vase=7.6 scissors=6.2 teddy bear=13.9 hair drier=0.0 toothbrush=0.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =11.9 [Epoch 22][Batch 99], LR: 1.00E-03, Speed: 132.395 samples/sec, ObjLoss=27.084, BoxCenterLoss=15.024, BoxScaleLoss=5.883, ClassLoss=13.274 [Epoch 22][Batch 199], LR: 1.00E-03, Speed: 130.529 samples/sec, ObjLoss=27.075, BoxCenterLoss=15.024, BoxScaleLoss=5.881, ClassLoss=13.264 [Epoch 22][Batch 299], LR: 1.00E-03, Speed: 152.952 samples/sec, ObjLoss=27.063, BoxCenterLoss=15.023, BoxScaleLoss=5.879, ClassLoss=13.254 [Epoch 22][Batch 399], LR: 1.00E-03, Speed: 109.267 samples/sec, ObjLoss=27.052, BoxCenterLoss=15.023, BoxScaleLoss=5.878, ClassLoss=13.244 [Epoch 22][Batch 499], LR: 1.00E-03, Speed: 129.799 samples/sec, ObjLoss=27.040, BoxCenterLoss=15.022, BoxScaleLoss=5.877, ClassLoss=13.235 [Epoch 22][Batch 599], LR: 1.00E-03, Speed: 137.357 samples/sec, ObjLoss=27.029, BoxCenterLoss=15.021, BoxScaleLoss=5.875, ClassLoss=13.225 [Epoch 22][Batch 699], LR: 1.00E-03, Speed: 138.816 samples/sec, ObjLoss=27.019, BoxCenterLoss=15.021, BoxScaleLoss=5.873, ClassLoss=13.216 [Epoch 22][Batch 799], LR: 1.00E-03, Speed: 146.795 samples/sec, ObjLoss=27.008, BoxCenterLoss=15.020, BoxScaleLoss=5.871, ClassLoss=13.206 [Epoch 22][Batch 899], LR: 1.00E-03, Speed: 148.295 samples/sec, ObjLoss=26.997, BoxCenterLoss=15.019, BoxScaleLoss=5.870, ClassLoss=13.197 [Epoch 22][Batch 999], LR: 1.00E-03, Speed: 124.304 samples/sec, ObjLoss=26.985, BoxCenterLoss=15.017, BoxScaleLoss=5.868, ClassLoss=13.186 [Epoch 22][Batch 1099], LR: 1.00E-03, Speed: 126.278 samples/sec, ObjLoss=26.974, BoxCenterLoss=15.016, BoxScaleLoss=5.867, ClassLoss=13.177 [Epoch 22][Batch 1199], LR: 1.00E-03, Speed: 133.359 samples/sec, ObjLoss=26.963, BoxCenterLoss=15.015, BoxScaleLoss=5.865, ClassLoss=13.168 [Epoch 22][Batch 1299], LR: 1.00E-03, Speed: 122.771 samples/sec, ObjLoss=26.955, BoxCenterLoss=15.016, BoxScaleLoss=5.864, ClassLoss=13.158 [Epoch 22][Batch 1399], LR: 1.00E-03, Speed: 120.825 samples/sec, ObjLoss=26.945, BoxCenterLoss=15.015, BoxScaleLoss=5.862, ClassLoss=13.149 [Epoch 22][Batch 1499], LR: 1.00E-03, Speed: 142.014 samples/sec, ObjLoss=26.934, BoxCenterLoss=15.014, BoxScaleLoss=5.861, ClassLoss=13.139 [Epoch 22][Batch 1599], LR: 1.00E-03, Speed: 144.891 samples/sec, ObjLoss=26.923, BoxCenterLoss=15.014, BoxScaleLoss=5.860, ClassLoss=13.131 [Epoch 22][Batch 1699], LR: 1.00E-03, Speed: 131.315 samples/sec, ObjLoss=26.915, BoxCenterLoss=15.014, BoxScaleLoss=5.858, ClassLoss=13.122 [Epoch 22][Batch 1799], LR: 1.00E-03, Speed: 167.011 samples/sec, ObjLoss=26.903, BoxCenterLoss=15.012, BoxScaleLoss=5.856, ClassLoss=13.112 [Epoch 22] Training cost: 1167.307, ObjLoss=26.900, BoxCenterLoss=15.012, BoxScaleLoss=5.856, ClassLoss=13.109 [Epoch 22] 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 ] = 0.319 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.075 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 ] = 0.138 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.187 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.137 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.204 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.297 person=21.3 bicycle=8.7 car=13.7 motorcycle=15.4 airplane=27.4 bus=30.2 train=27.8 truck=11.8 boat=6.2 traffic light=6.2 fire hydrant=21.8 stop sign=22.2 parking meter=14.1 bench=6.2 bird=11.2 cat=30.2 dog=19.4 horse=24.5 sheep=18.9 cow=21.4 elephant=30.9 bear=18.3 zebra=26.6 giraffe=28.4 backpack=1.8 umbrella=12.4 handbag=1.2 tie=6.8 suitcase=6.5 frisbee=20.6 skis=6.9 snowboard=4.0 sports ball=12.0 kite=17.7 baseball bat=5.9 baseball glove=10.9 skateboard=11.0 surfboard=6.5 tennis racket=12.5 bottle=9.7 wine glass=9.5 cup=13.2 fork=4.0 knife=1.6 spoon=0.6 bowl=13.4 banana=6.5 apple=3.0 sandwich=12.7 orange=10.7 broccoli=6.3 carrot=4.1 hot dog=6.2 pizza=22.4 donut=10.2 cake=9.4 chair=6.8 couch=15.6 potted plant=5.7 bed=15.6 dining table=8.0 toilet=26.9 tv=25.4 laptop=25.5 mouse=18.4 remote=1.6 keyboard=20.8 cell phone=8.8 microwave=18.1 oven=7.3 toaster=0.0 sink=12.3 refrigerator=13.1 book=3.1 clock=21.1 vase=10.7 scissors=6.6 teddy bear=13.2 hair drier=0.0 toothbrush=1.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.9 [Epoch 23][Batch 99], LR: 1.00E-03, Speed: 131.832 samples/sec, ObjLoss=26.890, BoxCenterLoss=15.012, BoxScaleLoss=5.854, ClassLoss=13.099 [Epoch 23][Batch 199], LR: 1.00E-03, Speed: 133.716 samples/sec, ObjLoss=26.880, BoxCenterLoss=15.012, BoxScaleLoss=5.852, ClassLoss=13.089 [Epoch 23][Batch 299], LR: 1.00E-03, Speed: 124.382 samples/sec, ObjLoss=26.868, BoxCenterLoss=15.011, BoxScaleLoss=5.851, ClassLoss=13.080 [Epoch 23][Batch 399], LR: 1.00E-03, Speed: 133.347 samples/sec, ObjLoss=26.859, BoxCenterLoss=15.010, BoxScaleLoss=5.848, ClassLoss=13.069 [Epoch 23][Batch 499], LR: 1.00E-03, Speed: 149.368 samples/sec, ObjLoss=26.848, BoxCenterLoss=15.010, BoxScaleLoss=5.848, ClassLoss=13.061 [Epoch 23][Batch 599], LR: 1.00E-03, Speed: 121.580 samples/sec, ObjLoss=26.837, BoxCenterLoss=15.008, BoxScaleLoss=5.846, ClassLoss=13.052 [Epoch 23][Batch 699], LR: 1.00E-03, Speed: 112.005 samples/sec, ObjLoss=26.827, BoxCenterLoss=15.008, BoxScaleLoss=5.845, ClassLoss=13.043 [Epoch 23][Batch 799], LR: 1.00E-03, Speed: 123.285 samples/sec, ObjLoss=26.816, BoxCenterLoss=15.007, BoxScaleLoss=5.843, ClassLoss=13.034 [Epoch 23][Batch 899], LR: 1.00E-03, Speed: 127.494 samples/sec, ObjLoss=26.805, BoxCenterLoss=15.006, BoxScaleLoss=5.842, ClassLoss=13.025 [Epoch 23][Batch 999], LR: 1.00E-03, Speed: 126.050 samples/sec, ObjLoss=26.796, BoxCenterLoss=15.006, BoxScaleLoss=5.841, ClassLoss=13.017 [Epoch 23][Batch 1099], LR: 1.00E-03, Speed: 112.059 samples/sec, ObjLoss=26.785, BoxCenterLoss=15.005, BoxScaleLoss=5.840, ClassLoss=13.008 [Epoch 23][Batch 1199], LR: 1.00E-03, Speed: 155.930 samples/sec, ObjLoss=26.775, BoxCenterLoss=15.004, BoxScaleLoss=5.838, ClassLoss=12.999 [Epoch 23][Batch 1299], LR: 1.00E-03, Speed: 128.122 samples/sec, ObjLoss=26.765, BoxCenterLoss=15.004, BoxScaleLoss=5.837, ClassLoss=12.991 [Epoch 23][Batch 1399], LR: 1.00E-03, Speed: 171.129 samples/sec, ObjLoss=26.756, BoxCenterLoss=15.004, BoxScaleLoss=5.836, ClassLoss=12.982 [Epoch 23][Batch 1499], LR: 1.00E-03, Speed: 133.129 samples/sec, ObjLoss=26.748, BoxCenterLoss=15.004, BoxScaleLoss=5.834, ClassLoss=12.973 [Epoch 23][Batch 1599], LR: 1.00E-03, Speed: 121.615 samples/sec, ObjLoss=26.739, BoxCenterLoss=15.003, BoxScaleLoss=5.833, ClassLoss=12.966 [Epoch 23][Batch 1699], LR: 1.00E-03, Speed: 122.007 samples/sec, ObjLoss=26.730, BoxCenterLoss=15.003, BoxScaleLoss=5.832, ClassLoss=12.957 [Epoch 23][Batch 1799], LR: 1.00E-03, Speed: 166.198 samples/sec, ObjLoss=26.719, BoxCenterLoss=15.002, BoxScaleLoss=5.830, ClassLoss=12.948 [Epoch 23] Training cost: 1120.407, ObjLoss=26.716, BoxCenterLoss=15.001, BoxScaleLoss=5.830, ClassLoss=12.945 [Epoch 23] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.131 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.316 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.082 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 ] = 0.138 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.195 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.204 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210 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 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.314 person=24.8 bicycle=8.6 car=14.6 motorcycle=16.3 airplane=30.3 bus=27.6 train=31.7 truck=13.2 boat=6.8 traffic light=5.9 fire hydrant=27.4 stop sign=26.8 parking meter=9.1 bench=6.9 bird=10.5 cat=28.7 dog=22.5 horse=17.0 sheep=16.2 cow=16.6 elephant=27.5 bear=24.3 zebra=31.0 giraffe=30.6 backpack=2.2 umbrella=11.1 handbag=1.5 tie=9.2 suitcase=7.8 frisbee=19.9 skis=3.9 snowboard=6.0 sports ball=16.9 kite=16.5 baseball bat=6.1 baseball glove=10.3 skateboard=11.8 surfboard=8.6 tennis racket=11.6 bottle=9.7 wine glass=8.5 cup=11.5 fork=3.3 knife=1.6 spoon=1.2 bowl=13.8 banana=6.1 apple=2.7 sandwich=6.3 orange=8.4 broccoli=7.2 carrot=3.7 hot dog=8.4 pizza=22.5 donut=12.1 cake=8.2 chair=7.4 couch=15.3 potted plant=6.6 bed=23.0 dining table=11.6 toilet=25.0 tv=23.5 laptop=20.9 mouse=21.6 remote=2.8 keyboard=14.7 cell phone=8.7 microwave=17.6 oven=11.9 toaster=0.0 sink=10.8 refrigerator=20.8 book=2.8 clock=22.8 vase=9.2 scissors=7.4 teddy bear=13.1 hair drier=0.0 toothbrush=0.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.1 [Epoch 24][Batch 99], LR: 1.00E-03, Speed: 156.672 samples/sec, ObjLoss=26.706, BoxCenterLoss=15.001, BoxScaleLoss=5.829, ClassLoss=12.936 [Epoch 24][Batch 199], LR: 1.00E-03, Speed: 133.653 samples/sec, ObjLoss=26.697, BoxCenterLoss=15.001, BoxScaleLoss=5.827, ClassLoss=12.928 [Epoch 24][Batch 299], LR: 1.00E-03, Speed: 115.767 samples/sec, ObjLoss=26.689, BoxCenterLoss=15.000, BoxScaleLoss=5.826, ClassLoss=12.918 [Epoch 24][Batch 399], LR: 1.00E-03, Speed: 119.944 samples/sec, ObjLoss=26.679, BoxCenterLoss=15.000, BoxScaleLoss=5.824, ClassLoss=12.910 [Epoch 24][Batch 499], LR: 1.00E-03, Speed: 160.819 samples/sec, ObjLoss=26.668, BoxCenterLoss=14.998, BoxScaleLoss=5.823, ClassLoss=12.901 [Epoch 24][Batch 599], LR: 1.00E-03, Speed: 147.923 samples/sec, ObjLoss=26.658, BoxCenterLoss=14.998, BoxScaleLoss=5.821, ClassLoss=12.893 [Epoch 24][Batch 699], LR: 1.00E-03, Speed: 140.228 samples/sec, ObjLoss=26.650, BoxCenterLoss=14.998, BoxScaleLoss=5.820, ClassLoss=12.883 [Epoch 24][Batch 799], LR: 1.00E-03, Speed: 128.483 samples/sec, ObjLoss=26.640, BoxCenterLoss=14.997, BoxScaleLoss=5.818, ClassLoss=12.875 [Epoch 24][Batch 899], LR: 1.00E-03, Speed: 110.667 samples/sec, ObjLoss=26.629, BoxCenterLoss=14.996, BoxScaleLoss=5.817, ClassLoss=12.866 [Epoch 24][Batch 999], LR: 1.00E-03, Speed: 132.433 samples/sec, ObjLoss=26.618, BoxCenterLoss=14.994, BoxScaleLoss=5.815, ClassLoss=12.858 [Epoch 24][Batch 1099], LR: 1.00E-03, Speed: 139.791 samples/sec, ObjLoss=26.609, BoxCenterLoss=14.993, BoxScaleLoss=5.814, ClassLoss=12.849 [Epoch 24][Batch 1199], LR: 1.00E-03, Speed: 107.373 samples/sec, ObjLoss=26.600, BoxCenterLoss=14.993, BoxScaleLoss=5.813, ClassLoss=12.841 [Epoch 24][Batch 1299], LR: 1.00E-03, Speed: 132.361 samples/sec, ObjLoss=26.590, BoxCenterLoss=14.992, BoxScaleLoss=5.811, ClassLoss=12.832 [Epoch 24][Batch 1399], LR: 1.00E-03, Speed: 126.992 samples/sec, ObjLoss=26.583, BoxCenterLoss=14.992, BoxScaleLoss=5.809, ClassLoss=12.824 [Epoch 24][Batch 1499], LR: 1.00E-03, Speed: 118.412 samples/sec, ObjLoss=26.573, BoxCenterLoss=14.991, BoxScaleLoss=5.808, ClassLoss=12.816 [Epoch 24][Batch 1599], LR: 1.00E-03, Speed: 142.361 samples/sec, ObjLoss=26.565, BoxCenterLoss=14.991, BoxScaleLoss=5.807, ClassLoss=12.807 [Epoch 24][Batch 1699], LR: 1.00E-03, Speed: 120.529 samples/sec, ObjLoss=26.557, BoxCenterLoss=14.991, BoxScaleLoss=5.805, ClassLoss=12.799 [Epoch 24][Batch 1799], LR: 1.00E-03, Speed: 134.150 samples/sec, ObjLoss=26.547, BoxCenterLoss=14.990, BoxScaleLoss=5.804, ClassLoss=12.791 [Epoch 24] Training cost: 1171.031, ObjLoss=26.544, BoxCenterLoss=14.990, BoxScaleLoss=5.804, ClassLoss=12.789 [Epoch 24] 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 ] = 0.321 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.099 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 ] = 0.143 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.204 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221 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 ] = 0.224 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.313 person=25.7 bicycle=8.3 car=15.5 motorcycle=15.1 airplane=24.9 bus=29.7 train=34.4 truck=12.8 boat=7.0 traffic light=7.0 fire hydrant=26.7 stop sign=30.4 parking meter=13.3 bench=6.7 bird=12.0 cat=32.9 dog=24.2 horse=19.0 sheep=17.6 cow=19.5 elephant=31.2 bear=27.5 zebra=28.8 giraffe=34.1 backpack=1.4 umbrella=13.3 handbag=1.2 tie=8.2 suitcase=8.2 frisbee=24.9 skis=4.7 snowboard=6.8 sports ball=16.8 kite=15.4 baseball bat=6.3 baseball glove=12.3 skateboard=12.3 surfboard=9.9 tennis racket=12.4 bottle=9.6 wine glass=6.8 cup=12.8 fork=2.9 knife=1.3 spoon=0.6 bowl=13.3 banana=7.5 apple=3.1 sandwich=11.2 orange=12.3 broccoli=7.6 carrot=3.7 hot dog=9.8 pizza=19.1 donut=12.0 cake=10.7 chair=8.3 couch=19.3 potted plant=6.1 bed=26.9 dining table=14.4 toilet=23.7 tv=21.4 laptop=24.1 mouse=21.8 remote=2.9 keyboard=11.8 cell phone=9.6 microwave=17.7 oven=12.5 toaster=0.0 sink=13.7 refrigerator=16.5 book=3.3 clock=22.6 vase=10.6 scissors=5.2 teddy bear=17.3 hair drier=0.0 toothbrush=0.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.9 [Epoch 25][Batch 99], LR: 1.00E-03, Speed: 134.563 samples/sec, ObjLoss=26.533, BoxCenterLoss=14.988, BoxScaleLoss=5.802, ClassLoss=12.780 [Epoch 25][Batch 199], LR: 1.00E-03, Speed: 135.988 samples/sec, ObjLoss=26.523, BoxCenterLoss=14.987, BoxScaleLoss=5.801, ClassLoss=12.771 [Epoch 25][Batch 299], LR: 1.00E-03, Speed: 117.148 samples/sec, ObjLoss=26.515, BoxCenterLoss=14.987, BoxScaleLoss=5.799, ClassLoss=12.763 [Epoch 25][Batch 399], LR: 1.00E-03, Speed: 154.769 samples/sec, ObjLoss=26.505, BoxCenterLoss=14.986, BoxScaleLoss=5.798, ClassLoss=12.754 [Epoch 25][Batch 499], LR: 1.00E-03, Speed: 104.324 samples/sec, ObjLoss=26.495, BoxCenterLoss=14.985, BoxScaleLoss=5.796, ClassLoss=12.746 [Epoch 25][Batch 599], LR: 1.00E-03, Speed: 128.993 samples/sec, ObjLoss=26.486, BoxCenterLoss=14.984, BoxScaleLoss=5.795, ClassLoss=12.738 [Epoch 25][Batch 699], LR: 1.00E-03, Speed: 136.886 samples/sec, ObjLoss=26.478, BoxCenterLoss=14.984, BoxScaleLoss=5.793, ClassLoss=12.729 [Epoch 25][Batch 799], LR: 1.00E-03, Speed: 126.406 samples/sec, ObjLoss=26.467, BoxCenterLoss=14.983, BoxScaleLoss=5.793, ClassLoss=12.722 [Epoch 25][Batch 899], LR: 1.00E-03, Speed: 137.906 samples/sec, ObjLoss=26.458, BoxCenterLoss=14.982, BoxScaleLoss=5.791, ClassLoss=12.714 [Epoch 25][Batch 999], LR: 1.00E-03, Speed: 145.797 samples/sec, ObjLoss=26.449, BoxCenterLoss=14.981, BoxScaleLoss=5.790, ClassLoss=12.706 [Epoch 25][Batch 1099], LR: 1.00E-03, Speed: 136.307 samples/sec, ObjLoss=26.440, BoxCenterLoss=14.981, BoxScaleLoss=5.789, ClassLoss=12.699 [Epoch 25][Batch 1199], LR: 1.00E-03, Speed: 149.422 samples/sec, ObjLoss=26.432, BoxCenterLoss=14.981, BoxScaleLoss=5.788, ClassLoss=12.691 [Epoch 25][Batch 1299], LR: 1.00E-03, Speed: 122.051 samples/sec, ObjLoss=26.424, BoxCenterLoss=14.981, BoxScaleLoss=5.786, ClassLoss=12.683 [Epoch 25][Batch 1399], LR: 1.00E-03, Speed: 113.779 samples/sec, ObjLoss=26.414, BoxCenterLoss=14.980, BoxScaleLoss=5.785, ClassLoss=12.676 [Epoch 25][Batch 1499], LR: 1.00E-03, Speed: 129.813 samples/sec, ObjLoss=26.406, BoxCenterLoss=14.979, BoxScaleLoss=5.784, ClassLoss=12.668 [Epoch 25][Batch 1599], LR: 1.00E-03, Speed: 124.545 samples/sec, ObjLoss=26.398, BoxCenterLoss=14.979, BoxScaleLoss=5.784, ClassLoss=12.660 [Epoch 25][Batch 1699], LR: 1.00E-03, Speed: 151.156 samples/sec, ObjLoss=26.389, BoxCenterLoss=14.978, BoxScaleLoss=5.782, ClassLoss=12.652 [Epoch 25][Batch 1799], LR: 1.00E-03, Speed: 157.237 samples/sec, ObjLoss=26.380, BoxCenterLoss=14.978, BoxScaleLoss=5.781, ClassLoss=12.645 [Epoch 25] Training cost: 1109.845, ObjLoss=26.378, BoxCenterLoss=14.978, BoxScaleLoss=5.780, ClassLoss=12.642 [Epoch 25] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.334 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.048 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145 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 ] = 0.150 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 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.335 person=26.4 bicycle=10.0 car=16.0 motorcycle=17.5 airplane=29.8 bus=37.0 train=29.9 truck=13.1 boat=6.2 traffic light=7.6 fire hydrant=26.4 stop sign=32.4 parking meter=10.2 bench=5.9 bird=11.9 cat=31.0 dog=25.7 horse=22.5 sheep=15.6 cow=21.5 elephant=30.9 bear=34.2 zebra=33.0 giraffe=35.5 backpack=2.4 umbrella=13.9 handbag=1.9 tie=8.6 suitcase=9.1 frisbee=23.7 skis=4.7 snowboard=8.6 sports ball=15.9 kite=13.2 baseball bat=5.9 baseball glove=15.3 skateboard=9.9 surfboard=8.8 tennis racket=14.3 bottle=10.3 wine glass=8.1 cup=14.2 fork=3.5 knife=1.5 spoon=0.8 bowl=14.3 banana=8.1 apple=3.6 sandwich=11.8 orange=13.4 broccoli=8.5 carrot=4.3 hot dog=9.2 pizza=21.7 donut=17.2 cake=11.9 chair=7.9 couch=19.0 potted plant=5.2 bed=27.3 dining table=10.4 toilet=25.8 tv=24.7 laptop=25.9 mouse=22.9 remote=5.1 keyboard=14.7 cell phone=10.7 microwave=18.0 oven=10.7 toaster=0.0 sink=10.2 refrigerator=17.8 book=3.0 clock=27.3 vase=8.4 scissors=4.1 teddy bear=21.7 hair drier=0.0 toothbrush=1.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.6 [Epoch 26][Batch 99], LR: 1.00E-03, Speed: 150.152 samples/sec, ObjLoss=26.368, BoxCenterLoss=14.976, BoxScaleLoss=5.778, ClassLoss=12.634 [Epoch 26][Batch 199], LR: 1.00E-03, Speed: 157.237 samples/sec, ObjLoss=26.360, BoxCenterLoss=14.976, BoxScaleLoss=5.777, ClassLoss=12.627 [Epoch 26][Batch 299], LR: 1.00E-03, Speed: 161.502 samples/sec, ObjLoss=26.352, BoxCenterLoss=14.975, BoxScaleLoss=5.776, ClassLoss=12.619 [Epoch 26][Batch 399], LR: 1.00E-03, Speed: 136.622 samples/sec, ObjLoss=26.343, BoxCenterLoss=14.975, BoxScaleLoss=5.774, ClassLoss=12.611 [Epoch 26][Batch 499], LR: 1.00E-03, Speed: 146.237 samples/sec, ObjLoss=26.336, BoxCenterLoss=14.975, BoxScaleLoss=5.773, ClassLoss=12.603 [Epoch 26][Batch 599], LR: 1.00E-03, Speed: 144.868 samples/sec, ObjLoss=26.329, BoxCenterLoss=14.975, BoxScaleLoss=5.772, ClassLoss=12.596 [Epoch 26][Batch 699], LR: 1.00E-03, Speed: 126.429 samples/sec, ObjLoss=26.320, BoxCenterLoss=14.974, BoxScaleLoss=5.770, ClassLoss=12.588 [Epoch 26][Batch 799], LR: 1.00E-03, Speed: 147.765 samples/sec, ObjLoss=26.312, BoxCenterLoss=14.974, BoxScaleLoss=5.769, ClassLoss=12.580 [Epoch 26][Batch 899], LR: 1.00E-03, Speed: 130.977 samples/sec, ObjLoss=26.303, BoxCenterLoss=14.973, BoxScaleLoss=5.768, ClassLoss=12.573 [Epoch 26][Batch 999], LR: 1.00E-03, Speed: 139.245 samples/sec, ObjLoss=26.295, BoxCenterLoss=14.973, BoxScaleLoss=5.767, ClassLoss=12.566 [Epoch 26][Batch 1099], LR: 1.00E-03, Speed: 133.628 samples/sec, ObjLoss=26.287, BoxCenterLoss=14.973, BoxScaleLoss=5.766, ClassLoss=12.559 [Epoch 26][Batch 1199], LR: 1.00E-03, Speed: 131.833 samples/sec, ObjLoss=26.280, BoxCenterLoss=14.972, BoxScaleLoss=5.765, ClassLoss=12.551 [Epoch 26][Batch 1299], LR: 1.00E-03, Speed: 123.395 samples/sec, ObjLoss=26.272, BoxCenterLoss=14.972, BoxScaleLoss=5.764, ClassLoss=12.544 [Epoch 26][Batch 1399], LR: 1.00E-03, Speed: 128.817 samples/sec, ObjLoss=26.263, BoxCenterLoss=14.971, BoxScaleLoss=5.762, ClassLoss=12.536 [Epoch 26][Batch 1499], LR: 1.00E-03, Speed: 146.713 samples/sec, ObjLoss=26.255, BoxCenterLoss=14.971, BoxScaleLoss=5.761, ClassLoss=12.529 [Epoch 26][Batch 1599], LR: 1.00E-03, Speed: 132.088 samples/sec, ObjLoss=26.247, BoxCenterLoss=14.970, BoxScaleLoss=5.760, ClassLoss=12.522 [Epoch 26][Batch 1699], LR: 1.00E-03, Speed: 163.170 samples/sec, ObjLoss=26.238, BoxCenterLoss=14.969, BoxScaleLoss=5.759, ClassLoss=12.515 [Epoch 26][Batch 1799], LR: 1.00E-03, Speed: 158.088 samples/sec, ObjLoss=26.230, BoxCenterLoss=14.969, BoxScaleLoss=5.758, ClassLoss=12.508 [Epoch 26] Training cost: 1130.609, ObjLoss=26.227, BoxCenterLoss=14.969, BoxScaleLoss=5.757, ClassLoss=12.506 [Epoch 26] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.136 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.318 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.037 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.139 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.208 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.145 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 ] = 0.221 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.075 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321 person=24.0 bicycle=8.7 car=12.9 motorcycle=17.3 airplane=24.5 bus=34.8 train=31.6 truck=12.2 boat=5.2 traffic light=5.1 fire hydrant=24.2 stop sign=27.9 parking meter=19.3 bench=5.7 bird=8.6 cat=31.4 dog=24.5 horse=22.4 sheep=17.3 cow=18.9 elephant=26.8 bear=32.8 zebra=34.3 giraffe=31.0 backpack=2.2 umbrella=12.4 handbag=1.4 tie=9.7 suitcase=7.6 frisbee=19.0 skis=1.8 snowboard=4.8 sports ball=12.6 kite=7.8 baseball bat=5.8 baseball glove=11.7 skateboard=11.7 surfboard=8.5 tennis racket=11.0 bottle=8.4 wine glass=7.9 cup=14.6 fork=3.4 knife=1.3 spoon=0.6 bowl=13.8 banana=7.0 apple=3.4 sandwich=12.4 orange=9.2 broccoli=6.6 carrot=4.6 hot dog=8.9 pizza=20.6 donut=10.8 cake=8.7 chair=8.4 couch=20.6 potted plant=6.5 bed=23.4 dining table=13.4 toilet=25.1 tv=25.3 laptop=27.7 mouse=22.1 remote=2.3 keyboard=17.7 cell phone=10.4 microwave=15.7 oven=9.1 toaster=0.0 sink=10.6 refrigerator=19.6 book=2.6 clock=20.6 vase=11.8 scissors=4.8 teddy bear=16.6 hair drier=0.0 toothbrush=0.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.6 [Epoch 27][Batch 99], LR: 1.00E-03, Speed: 135.366 samples/sec, ObjLoss=26.218, BoxCenterLoss=14.968, BoxScaleLoss=5.756, ClassLoss=12.499 [Epoch 27][Batch 199], LR: 1.00E-03, Speed: 142.680 samples/sec, ObjLoss=26.211, BoxCenterLoss=14.969, BoxScaleLoss=5.756, ClassLoss=12.493 [Epoch 27][Batch 299], LR: 1.00E-03, Speed: 156.836 samples/sec, ObjLoss=26.203, BoxCenterLoss=14.969, BoxScaleLoss=5.755, ClassLoss=12.486 [Epoch 27][Batch 399], LR: 1.00E-03, Speed: 149.650 samples/sec, ObjLoss=26.196, BoxCenterLoss=14.968, BoxScaleLoss=5.754, ClassLoss=12.480 [Epoch 27][Batch 499], LR: 1.00E-03, Speed: 132.337 samples/sec, ObjLoss=26.188, BoxCenterLoss=14.968, BoxScaleLoss=5.753, ClassLoss=12.472 [Epoch 27][Batch 599], LR: 1.00E-03, Speed: 137.115 samples/sec, ObjLoss=26.180, BoxCenterLoss=14.967, BoxScaleLoss=5.751, ClassLoss=12.465 [Epoch 27][Batch 699], LR: 1.00E-03, Speed: 143.590 samples/sec, ObjLoss=26.172, BoxCenterLoss=14.966, BoxScaleLoss=5.750, ClassLoss=12.457 [Epoch 27][Batch 799], LR: 1.00E-03, Speed: 139.468 samples/sec, ObjLoss=26.164, BoxCenterLoss=14.966, BoxScaleLoss=5.749, ClassLoss=12.449 [Epoch 27][Batch 899], LR: 1.00E-03, Speed: 146.784 samples/sec, ObjLoss=26.156, BoxCenterLoss=14.965, BoxScaleLoss=5.747, ClassLoss=12.442 [Epoch 27][Batch 999], LR: 1.00E-03, Speed: 126.051 samples/sec, ObjLoss=26.149, BoxCenterLoss=14.965, BoxScaleLoss=5.746, ClassLoss=12.435 [Epoch 27][Batch 1099], LR: 1.00E-03, Speed: 141.162 samples/sec, ObjLoss=26.140, BoxCenterLoss=14.964, BoxScaleLoss=5.745, ClassLoss=12.428 [Epoch 27][Batch 1199], LR: 1.00E-03, Speed: 118.548 samples/sec, ObjLoss=26.132, BoxCenterLoss=14.964, BoxScaleLoss=5.744, ClassLoss=12.421 [Epoch 27][Batch 1299], LR: 1.00E-03, Speed: 134.256 samples/sec, ObjLoss=26.125, BoxCenterLoss=14.963, BoxScaleLoss=5.743, ClassLoss=12.414 [Epoch 27][Batch 1399], LR: 1.00E-03, Speed: 123.062 samples/sec, ObjLoss=26.115, BoxCenterLoss=14.962, BoxScaleLoss=5.741, ClassLoss=12.407 [Epoch 27][Batch 1499], LR: 1.00E-03, Speed: 140.704 samples/sec, ObjLoss=26.107, BoxCenterLoss=14.961, BoxScaleLoss=5.740, ClassLoss=12.401 [Epoch 27][Batch 1599], LR: 1.00E-03, Speed: 127.234 samples/sec, ObjLoss=26.099, BoxCenterLoss=14.960, BoxScaleLoss=5.739, ClassLoss=12.394 [Epoch 27][Batch 1699], LR: 1.00E-03, Speed: 126.180 samples/sec, ObjLoss=26.090, BoxCenterLoss=14.959, BoxScaleLoss=5.738, ClassLoss=12.387 [Epoch 27][Batch 1799], LR: 1.00E-03, Speed: 168.532 samples/sec, ObjLoss=26.081, BoxCenterLoss=14.958, BoxScaleLoss=5.737, ClassLoss=12.380 [Epoch 27] Training cost: 1085.909, ObjLoss=26.078, BoxCenterLoss=14.957, BoxScaleLoss=5.736, ClassLoss=12.377 [Epoch 27] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.327 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.090 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 ] = 0.139 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.214 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 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220 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 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.326 person=27.3 bicycle=10.4 car=14.4 motorcycle=19.3 airplane=29.2 bus=35.0 train=34.5 truck=12.3 boat=5.7 traffic light=7.6 fire hydrant=25.0 stop sign=28.5 parking meter=10.6 bench=7.4 bird=10.4 cat=33.5 dog=25.9 horse=22.9 sheep=16.9 cow=18.3 elephant=24.4 bear=24.4 zebra=36.0 giraffe=35.6 backpack=2.2 umbrella=15.0 handbag=1.4 tie=9.0 suitcase=9.7 frisbee=20.1 skis=1.7 snowboard=5.6 sports ball=8.2 kite=12.2 baseball bat=5.1 baseball glove=9.5 skateboard=12.1 surfboard=9.2 tennis racket=12.2 bottle=9.1 wine glass=7.8 cup=13.4 fork=3.0 knife=1.7 spoon=0.8 bowl=13.5 banana=6.4 apple=4.5 sandwich=11.8 orange=12.3 broccoli=7.3 carrot=2.2 hot dog=8.0 pizza=18.8 donut=12.7 cake=8.7 chair=7.3 couch=18.2 potted plant=6.8 bed=25.7 dining table=14.1 toilet=19.4 tv=22.6 laptop=25.0 mouse=25.9 remote=3.6 keyboard=17.4 cell phone=9.5 microwave=12.0 oven=11.8 toaster=0.0 sink=12.2 refrigerator=15.6 book=3.2 clock=21.0 vase=10.9 scissors=6.7 teddy bear=19.8 hair drier=0.0 toothbrush=1.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.8 [Epoch 28][Batch 99], LR: 1.00E-03, Speed: 106.366 samples/sec, ObjLoss=26.069, BoxCenterLoss=14.956, BoxScaleLoss=5.735, ClassLoss=12.370 [Epoch 28][Batch 199], LR: 1.00E-03, Speed: 120.459 samples/sec, ObjLoss=26.063, BoxCenterLoss=14.956, BoxScaleLoss=5.734, ClassLoss=12.364 [Epoch 28][Batch 299], LR: 1.00E-03, Speed: 117.511 samples/sec, ObjLoss=26.056, BoxCenterLoss=14.956, BoxScaleLoss=5.733, ClassLoss=12.357 [Epoch 28][Batch 399], LR: 1.00E-03, Speed: 147.808 samples/sec, ObjLoss=26.049, BoxCenterLoss=14.956, BoxScaleLoss=5.732, ClassLoss=12.350 [Epoch 28][Batch 499], LR: 1.00E-03, Speed: 142.391 samples/sec, ObjLoss=26.040, BoxCenterLoss=14.955, BoxScaleLoss=5.731, ClassLoss=12.344 [Epoch 28][Batch 599], LR: 1.00E-03, Speed: 132.772 samples/sec, ObjLoss=26.033, BoxCenterLoss=14.955, BoxScaleLoss=5.730, ClassLoss=12.337 [Epoch 28][Batch 699], LR: 1.00E-03, Speed: 145.768 samples/sec, ObjLoss=26.025, BoxCenterLoss=14.954, BoxScaleLoss=5.728, ClassLoss=12.329 [Epoch 28][Batch 799], LR: 1.00E-03, Speed: 143.194 samples/sec, ObjLoss=26.018, BoxCenterLoss=14.953, BoxScaleLoss=5.727, ClassLoss=12.323 [Epoch 28][Batch 899], LR: 1.00E-03, Speed: 133.119 samples/sec, ObjLoss=26.010, BoxCenterLoss=14.953, BoxScaleLoss=5.726, ClassLoss=12.316 [Epoch 28][Batch 999], LR: 1.00E-03, Speed: 119.744 samples/sec, ObjLoss=26.003, BoxCenterLoss=14.953, BoxScaleLoss=5.725, ClassLoss=12.310 [Epoch 28][Batch 1099], LR: 1.00E-03, Speed: 142.380 samples/sec, ObjLoss=25.996, BoxCenterLoss=14.952, BoxScaleLoss=5.724, ClassLoss=12.303 [Epoch 28][Batch 1199], LR: 1.00E-03, Speed: 150.244 samples/sec, ObjLoss=25.989, BoxCenterLoss=14.952, BoxScaleLoss=5.723, ClassLoss=12.297 [Epoch 28][Batch 1299], LR: 1.00E-03, Speed: 126.668 samples/sec, ObjLoss=25.982, BoxCenterLoss=14.952, BoxScaleLoss=5.722, ClassLoss=12.291 [Epoch 28][Batch 1399], LR: 1.00E-03, Speed: 132.087 samples/sec, ObjLoss=25.975, BoxCenterLoss=14.952, BoxScaleLoss=5.721, ClassLoss=12.284 [Epoch 28][Batch 1499], LR: 1.00E-03, Speed: 167.733 samples/sec, ObjLoss=25.966, BoxCenterLoss=14.950, BoxScaleLoss=5.719, ClassLoss=12.277 [Epoch 28][Batch 1599], LR: 1.00E-03, Speed: 116.219 samples/sec, ObjLoss=25.959, BoxCenterLoss=14.950, BoxScaleLoss=5.718, ClassLoss=12.271 [Epoch 28][Batch 1699], LR: 1.00E-03, Speed: 98.162 samples/sec, ObjLoss=25.951, BoxCenterLoss=14.949, BoxScaleLoss=5.718, ClassLoss=12.265 [Epoch 28][Batch 1799], LR: 1.00E-03, Speed: 156.209 samples/sec, ObjLoss=25.944, BoxCenterLoss=14.949, BoxScaleLoss=5.716, ClassLoss=12.257 [Epoch 28] Training cost: 1097.981, ObjLoss=25.942, BoxCenterLoss=14.949, BoxScaleLoss=5.715, ClassLoss=12.255 [Epoch 28] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.148 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.333 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.103 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 ] = 0.149 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.153 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 ] = 0.233 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.087 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.230 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.342 person=27.0 bicycle=10.9 car=16.9 motorcycle=18.6 airplane=31.5 bus=33.7 train=37.4 truck=14.3 boat=7.6 traffic light=6.8 fire hydrant=29.3 stop sign=29.1 parking meter=16.3 bench=7.2 bird=11.3 cat=26.2 dog=24.2 horse=24.6 sheep=20.4 cow=17.3 elephant=26.9 bear=25.8 zebra=32.6 giraffe=37.5 backpack=1.6 umbrella=13.2 handbag=1.1 tie=8.2 suitcase=6.9 frisbee=22.9 skis=4.4 snowboard=8.2 sports ball=12.2 kite=17.5 baseball bat=5.6 baseball glove=12.9 skateboard=14.3 surfboard=9.5 tennis racket=15.2 bottle=10.2 wine glass=6.2 cup=13.8 fork=3.0 knife=2.6 spoon=0.8 bowl=14.6 banana=7.8 apple=5.3 sandwich=15.6 orange=11.7 broccoli=6.6 carrot=3.9 hot dog=9.1 pizza=20.8 donut=13.5 cake=9.1 chair=8.0 couch=22.9 potted plant=6.2 bed=27.5 dining table=15.6 toilet=23.2 tv=24.9 laptop=23.0 mouse=24.4 remote=3.2 keyboard=21.5 cell phone=9.5 microwave=15.1 oven=13.0 toaster=0.0 sink=13.2 refrigerator=19.7 book=3.9 clock=23.1 vase=10.9 scissors=11.2 teddy bear=19.9 hair drier=0.0 toothbrush=1.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.8 [Epoch 29][Batch 99], LR: 1.00E-03, Speed: 105.161 samples/sec, ObjLoss=25.935, BoxCenterLoss=14.949, BoxScaleLoss=5.714, ClassLoss=12.249 [Epoch 29][Batch 199], LR: 1.00E-03, Speed: 148.512 samples/sec, ObjLoss=25.929, BoxCenterLoss=14.949, BoxScaleLoss=5.713, ClassLoss=12.242 [Epoch 29][Batch 299], LR: 1.00E-03, Speed: 139.754 samples/sec, ObjLoss=25.921, BoxCenterLoss=14.948, BoxScaleLoss=5.712, ClassLoss=12.235 [Epoch 29][Batch 399], LR: 1.00E-03, Speed: 122.882 samples/sec, ObjLoss=25.914, BoxCenterLoss=14.947, BoxScaleLoss=5.711, ClassLoss=12.228 [Epoch 29][Batch 499], LR: 1.00E-03, Speed: 138.373 samples/sec, ObjLoss=25.907, BoxCenterLoss=14.947, BoxScaleLoss=5.710, ClassLoss=12.222 [Epoch 29][Batch 599], LR: 1.00E-03, Speed: 134.836 samples/sec, ObjLoss=25.901, BoxCenterLoss=14.947, BoxScaleLoss=5.709, ClassLoss=12.215 [Epoch 29][Batch 699], LR: 1.00E-03, Speed: 170.413 samples/sec, ObjLoss=25.894, BoxCenterLoss=14.947, BoxScaleLoss=5.708, ClassLoss=12.209 [Epoch 29][Batch 799], LR: 1.00E-03, Speed: 176.858 samples/sec, ObjLoss=25.888, BoxCenterLoss=14.947, BoxScaleLoss=5.706, ClassLoss=12.202 [Epoch 29][Batch 899], LR: 1.00E-03, Speed: 134.220 samples/sec, ObjLoss=25.880, BoxCenterLoss=14.946, BoxScaleLoss=5.705, ClassLoss=12.195 [Epoch 29][Batch 999], LR: 1.00E-03, Speed: 148.172 samples/sec, ObjLoss=25.872, BoxCenterLoss=14.945, BoxScaleLoss=5.704, ClassLoss=12.189 [Epoch 29][Batch 1099], LR: 1.00E-03, Speed: 120.800 samples/sec, ObjLoss=25.865, BoxCenterLoss=14.945, BoxScaleLoss=5.703, ClassLoss=12.183 [Epoch 29][Batch 1199], LR: 1.00E-03, Speed: 136.506 samples/sec, ObjLoss=25.860, BoxCenterLoss=14.945, BoxScaleLoss=5.702, ClassLoss=12.177 [Epoch 29][Batch 1299], LR: 1.00E-03, Speed: 136.036 samples/sec, ObjLoss=25.854, BoxCenterLoss=14.945, BoxScaleLoss=5.701, ClassLoss=12.171 [Epoch 29][Batch 1399], LR: 1.00E-03, Speed: 150.333 samples/sec, ObjLoss=25.846, BoxCenterLoss=14.944, BoxScaleLoss=5.700, ClassLoss=12.164 [Epoch 29][Batch 1499], LR: 1.00E-03, Speed: 129.111 samples/sec, ObjLoss=25.840, BoxCenterLoss=14.944, BoxScaleLoss=5.699, ClassLoss=12.158 [Epoch 29][Batch 1599], LR: 1.00E-03, Speed: 138.425 samples/sec, ObjLoss=25.832, BoxCenterLoss=14.943, BoxScaleLoss=5.698, ClassLoss=12.152 [Epoch 29][Batch 1699], LR: 1.00E-03, Speed: 142.536 samples/sec, ObjLoss=25.824, BoxCenterLoss=14.942, BoxScaleLoss=5.697, ClassLoss=12.146 [Epoch 29][Batch 1799], LR: 1.00E-03, Speed: 162.945 samples/sec, ObjLoss=25.817, BoxCenterLoss=14.942, BoxScaleLoss=5.696, ClassLoss=12.140 [Epoch 29] Training cost: 1120.123, ObjLoss=25.816, BoxCenterLoss=14.942, BoxScaleLoss=5.696, ClassLoss=12.138 [Epoch 29] 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 ] = 0.334 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.048 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.142 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 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221 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 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.334 person=27.5 bicycle=9.9 car=15.6 motorcycle=16.5 airplane=29.9 bus=35.6 train=31.1 truck=12.3 boat=6.3 traffic light=7.4 fire hydrant=27.0 stop sign=27.5 parking meter=15.0 bench=6.7 bird=11.0 cat=34.1 dog=25.0 horse=23.8 sheep=22.6 cow=22.4 elephant=30.6 bear=31.1 zebra=34.2 giraffe=32.6 backpack=2.4 umbrella=14.1 handbag=1.3 tie=7.3 suitcase=9.9 frisbee=23.2 skis=5.2 snowboard=7.7 sports ball=16.3 kite=16.2 baseball bat=6.2 baseball glove=11.5 skateboard=11.8 surfboard=10.5 tennis racket=13.2 bottle=10.7 wine glass=8.1 cup=12.7 fork=3.8 knife=1.8 spoon=0.5 bowl=12.6 banana=6.4 apple=3.1 sandwich=7.4 orange=10.5 broccoli=7.7 carrot=3.6 hot dog=9.1 pizza=18.4 donut=11.6 cake=9.7 chair=8.2 couch=21.5 potted plant=5.4 bed=20.2 dining table=10.8 toilet=29.0 tv=25.9 laptop=24.2 mouse=18.0 remote=3.6 keyboard=18.5 cell phone=8.6 microwave=18.9 oven=12.5 toaster=0.0 sink=10.5 refrigerator=18.0 book=2.9 clock=24.1 vase=10.4 scissors=7.2 teddy bear=20.4 hair drier=0.0 toothbrush=1.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.4 [Epoch 30][Batch 99], LR: 1.00E-03, Speed: 126.429 samples/sec, ObjLoss=25.808, BoxCenterLoss=14.941, BoxScaleLoss=5.694, ClassLoss=12.131 [Epoch 30][Batch 199], LR: 1.00E-03, Speed: 148.851 samples/sec, ObjLoss=25.803, BoxCenterLoss=14.941, BoxScaleLoss=5.693, ClassLoss=12.125 [Epoch 30][Batch 299], LR: 1.00E-03, Speed: 177.795 samples/sec, ObjLoss=25.796, BoxCenterLoss=14.941, BoxScaleLoss=5.693, ClassLoss=12.119 [Epoch 30][Batch 399], LR: 1.00E-03, Speed: 123.478 samples/sec, ObjLoss=25.788, BoxCenterLoss=14.940, BoxScaleLoss=5.691, ClassLoss=12.112 [Epoch 30][Batch 499], LR: 1.00E-03, Speed: 154.539 samples/sec, ObjLoss=25.782, BoxCenterLoss=14.940, BoxScaleLoss=5.691, ClassLoss=12.106 [Epoch 30][Batch 599], LR: 1.00E-03, Speed: 155.843 samples/sec, ObjLoss=25.774, BoxCenterLoss=14.939, BoxScaleLoss=5.690, ClassLoss=12.101 [Epoch 30][Batch 699], LR: 1.00E-03, Speed: 160.853 samples/sec, ObjLoss=25.767, BoxCenterLoss=14.939, BoxScaleLoss=5.689, ClassLoss=12.095 [Epoch 30][Batch 799], LR: 1.00E-03, Speed: 141.897 samples/sec, ObjLoss=25.761, BoxCenterLoss=14.939, BoxScaleLoss=5.688, ClassLoss=12.089 [Epoch 30][Batch 899], LR: 1.00E-03, Speed: 119.771 samples/sec, ObjLoss=25.753, BoxCenterLoss=14.938, BoxScaleLoss=5.687, ClassLoss=12.083 [Epoch 30][Batch 999], LR: 1.00E-03, Speed: 127.329 samples/sec, ObjLoss=25.746, BoxCenterLoss=14.938, BoxScaleLoss=5.687, ClassLoss=12.077 [Epoch 30][Batch 1099], LR: 1.00E-03, Speed: 112.751 samples/sec, ObjLoss=25.740, BoxCenterLoss=14.938, BoxScaleLoss=5.686, ClassLoss=12.072 [Epoch 30][Batch 1199], LR: 1.00E-03, Speed: 129.654 samples/sec, ObjLoss=25.732, BoxCenterLoss=14.937, BoxScaleLoss=5.685, ClassLoss=12.066 [Epoch 30][Batch 1299], LR: 1.00E-03, Speed: 139.239 samples/sec, ObjLoss=25.727, BoxCenterLoss=14.937, BoxScaleLoss=5.684, ClassLoss=12.060 [Epoch 30][Batch 1399], LR: 1.00E-03, Speed: 131.266 samples/sec, ObjLoss=25.720, BoxCenterLoss=14.936, BoxScaleLoss=5.683, ClassLoss=12.054 [Epoch 30][Batch 1499], LR: 1.00E-03, Speed: 124.300 samples/sec, ObjLoss=25.712, BoxCenterLoss=14.935, BoxScaleLoss=5.682, ClassLoss=12.048 [Epoch 30][Batch 1599], LR: 1.00E-03, Speed: 141.506 samples/sec, ObjLoss=25.707, BoxCenterLoss=14.936, BoxScaleLoss=5.681, ClassLoss=12.041 [Epoch 30][Batch 1699], LR: 1.00E-03, Speed: 162.272 samples/sec, ObjLoss=25.700, BoxCenterLoss=14.935, BoxScaleLoss=5.680, ClassLoss=12.036 [Epoch 30][Batch 1799], LR: 1.00E-03, Speed: 147.880 samples/sec, ObjLoss=25.693, BoxCenterLoss=14.934, BoxScaleLoss=5.679, ClassLoss=12.030 [Epoch 30] Training cost: 1112.670, ObjLoss=25.691, BoxCenterLoss=14.934, BoxScaleLoss=5.678, ClassLoss=12.028 [Epoch 30] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.149 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.336 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.108 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 ] = 0.142 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.155 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 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.082 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357 person=25.9 bicycle=11.6 car=13.3 motorcycle=19.9 airplane=26.4 bus=33.9 train=34.9 truck=11.2 boat=5.6 traffic light=7.2 fire hydrant=24.7 stop sign=20.6 parking meter=18.3 bench=8.0 bird=13.3 cat=34.7 dog=28.1 horse=20.8 sheep=17.9 cow=21.2 elephant=29.7 bear=38.3 zebra=33.4 giraffe=31.8 backpack=2.9 umbrella=12.5 handbag=1.9 tie=10.7 suitcase=10.2 frisbee=24.4 skis=5.5 snowboard=4.2 sports ball=14.5 kite=15.6 baseball bat=8.6 baseball glove=13.5 skateboard=10.6 surfboard=10.8 tennis racket=14.2 bottle=11.5 wine glass=9.2 cup=13.7 fork=4.2 knife=2.1 spoon=0.8 bowl=15.0 banana=6.8 apple=3.9 sandwich=12.0 orange=13.4 broccoli=5.6 carrot=6.1 hot dog=11.1 pizza=22.0 donut=13.8 cake=7.8 chair=7.6 couch=20.2 potted plant=6.2 bed=23.3 dining table=13.0 toilet=28.2 tv=26.2 laptop=25.9 mouse=21.6 remote=3.9 keyboard=17.9 cell phone=12.5 microwave=18.3 oven=15.4 toaster=0.0 sink=16.9 refrigerator=17.1 book=3.7 clock=21.1 vase=12.1 scissors=11.2 teddy bear=21.6 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.9 [Epoch 31][Batch 99], LR: 1.00E-03, Speed: 125.181 samples/sec, ObjLoss=25.684, BoxCenterLoss=14.934, BoxScaleLoss=5.677, ClassLoss=12.022 [Epoch 31][Batch 199], LR: 1.00E-03, Speed: 119.474 samples/sec, ObjLoss=25.678, BoxCenterLoss=14.933, BoxScaleLoss=5.677, ClassLoss=12.016 [Epoch 31][Batch 299], LR: 1.00E-03, Speed: 136.245 samples/sec, ObjLoss=25.671, BoxCenterLoss=14.933, BoxScaleLoss=5.676, ClassLoss=12.011 [Epoch 31][Batch 399], LR: 1.00E-03, Speed: 129.242 samples/sec, ObjLoss=25.664, BoxCenterLoss=14.932, BoxScaleLoss=5.674, ClassLoss=12.004 [Epoch 31][Batch 499], LR: 1.00E-03, Speed: 116.226 samples/sec, ObjLoss=25.658, BoxCenterLoss=14.932, BoxScaleLoss=5.673, ClassLoss=11.998 [Epoch 31][Batch 599], LR: 1.00E-03, Speed: 112.149 samples/sec, ObjLoss=25.652, BoxCenterLoss=14.931, BoxScaleLoss=5.672, ClassLoss=11.992 [Epoch 31][Batch 699], LR: 1.00E-03, Speed: 141.138 samples/sec, ObjLoss=25.645, BoxCenterLoss=14.931, BoxScaleLoss=5.671, ClassLoss=11.986 [Epoch 31][Batch 799], LR: 1.00E-03, Speed: 142.204 samples/sec, ObjLoss=25.638, BoxCenterLoss=14.930, BoxScaleLoss=5.670, ClassLoss=11.980 [Epoch 31][Batch 899], LR: 1.00E-03, Speed: 130.365 samples/sec, ObjLoss=25.632, BoxCenterLoss=14.930, BoxScaleLoss=5.669, ClassLoss=11.975 [Epoch 31][Batch 999], LR: 1.00E-03, Speed: 145.412 samples/sec, ObjLoss=25.626, BoxCenterLoss=14.930, BoxScaleLoss=5.669, ClassLoss=11.969 [Epoch 31][Batch 1099], LR: 1.00E-03, Speed: 136.150 samples/sec, ObjLoss=25.620, BoxCenterLoss=14.929, BoxScaleLoss=5.668, ClassLoss=11.963 [Epoch 31][Batch 1199], LR: 1.00E-03, Speed: 142.216 samples/sec, ObjLoss=25.614, BoxCenterLoss=14.929, BoxScaleLoss=5.666, ClassLoss=11.957 [Epoch 31][Batch 1299], LR: 1.00E-03, Speed: 154.278 samples/sec, ObjLoss=25.608, BoxCenterLoss=14.928, BoxScaleLoss=5.665, ClassLoss=11.952 [Epoch 31][Batch 1399], LR: 1.00E-03, Speed: 120.007 samples/sec, ObjLoss=25.600, BoxCenterLoss=14.927, BoxScaleLoss=5.664, ClassLoss=11.946 [Epoch 31][Batch 1499], LR: 1.00E-03, Speed: 123.487 samples/sec, ObjLoss=25.594, BoxCenterLoss=14.927, BoxScaleLoss=5.664, ClassLoss=11.941 [Epoch 31][Batch 1599], LR: 1.00E-03, Speed: 142.641 samples/sec, ObjLoss=25.588, BoxCenterLoss=14.927, BoxScaleLoss=5.662, ClassLoss=11.935 [Epoch 31][Batch 1699], LR: 1.00E-03, Speed: 125.617 samples/sec, ObjLoss=25.580, BoxCenterLoss=14.926, BoxScaleLoss=5.662, ClassLoss=11.930 [Epoch 31][Batch 1799], LR: 1.00E-03, Speed: 197.019 samples/sec, ObjLoss=25.574, BoxCenterLoss=14.926, BoxScaleLoss=5.661, ClassLoss=11.925 [Epoch 31] Training cost: 1113.270, ObjLoss=25.572, BoxCenterLoss=14.925, BoxScaleLoss=5.661, ClassLoss=11.923 [Epoch 31] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.339 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.098 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 ] = 0.149 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 ] = 0.152 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 ] = 0.232 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 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 ] = 0.332 person=26.8 bicycle=8.6 car=16.6 motorcycle=16.4 airplane=33.1 bus=29.7 train=34.8 truck=12.7 boat=6.8 traffic light=7.2 fire hydrant=30.3 stop sign=25.3 parking meter=16.1 bench=7.2 bird=13.4 cat=33.7 dog=25.6 horse=22.9 sheep=22.1 cow=22.0 elephant=32.5 bear=31.6 zebra=34.4 giraffe=37.7 backpack=2.7 umbrella=12.7 handbag=1.5 tie=7.4 suitcase=9.0 frisbee=24.5 skis=4.3 snowboard=10.7 sports ball=12.3 kite=14.4 baseball bat=7.4 baseball glove=12.5 skateboard=11.2 surfboard=8.8 tennis racket=10.5 bottle=11.2 wine glass=8.1 cup=13.0 fork=4.2 knife=1.7 spoon=0.5 bowl=12.0 banana=6.2 apple=4.4 sandwich=12.3 orange=11.2 broccoli=6.2 carrot=4.1 hot dog=7.1 pizza=20.7 donut=16.3 cake=11.5 chair=8.1 couch=18.6 potted plant=5.2 bed=23.6 dining table=12.6 toilet=25.1 tv=21.7 laptop=23.4 mouse=24.8 remote=3.7 keyboard=19.4 cell phone=9.2 microwave=17.8 oven=13.1 toaster=0.0 sink=12.1 refrigerator=19.3 book=2.6 clock=26.5 vase=10.4 scissors=4.3 teddy bear=18.4 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.6 [Epoch 32][Batch 99], LR: 1.00E-03, Speed: 137.237 samples/sec, ObjLoss=25.567, BoxCenterLoss=14.925, BoxScaleLoss=5.660, ClassLoss=11.917 [Epoch 32][Batch 199], LR: 1.00E-03, Speed: 125.862 samples/sec, ObjLoss=25.560, BoxCenterLoss=14.925, BoxScaleLoss=5.659, ClassLoss=11.911 [Epoch 32][Batch 299], LR: 1.00E-03, Speed: 163.795 samples/sec, ObjLoss=25.554, BoxCenterLoss=14.924, BoxScaleLoss=5.658, ClassLoss=11.905 [Epoch 32][Batch 399], LR: 1.00E-03, Speed: 133.800 samples/sec, ObjLoss=25.548, BoxCenterLoss=14.924, BoxScaleLoss=5.657, ClassLoss=11.899 [Epoch 32][Batch 499], LR: 1.00E-03, Speed: 149.498 samples/sec, ObjLoss=25.541, BoxCenterLoss=14.923, BoxScaleLoss=5.656, ClassLoss=11.893 [Epoch 32][Batch 599], LR: 1.00E-03, Speed: 163.952 samples/sec, ObjLoss=25.535, BoxCenterLoss=14.923, BoxScaleLoss=5.655, ClassLoss=11.888 [Epoch 32][Batch 699], LR: 1.00E-03, Speed: 111.607 samples/sec, ObjLoss=25.530, BoxCenterLoss=14.923, BoxScaleLoss=5.654, ClassLoss=11.882 [Epoch 32][Batch 799], LR: 1.00E-03, Speed: 122.721 samples/sec, ObjLoss=25.525, BoxCenterLoss=14.923, BoxScaleLoss=5.653, ClassLoss=11.877 [Epoch 32][Batch 899], LR: 1.00E-03, Speed: 121.231 samples/sec, ObjLoss=25.517, BoxCenterLoss=14.922, BoxScaleLoss=5.652, ClassLoss=11.871 [Epoch 32][Batch 999], LR: 1.00E-03, Speed: 124.015 samples/sec, ObjLoss=25.512, BoxCenterLoss=14.922, BoxScaleLoss=5.651, ClassLoss=11.866 [Epoch 32][Batch 1099], LR: 1.00E-03, Speed: 115.672 samples/sec, ObjLoss=25.507, BoxCenterLoss=14.922, BoxScaleLoss=5.651, ClassLoss=11.861 [Epoch 32][Batch 1199], LR: 1.00E-03, Speed: 110.173 samples/sec, ObjLoss=25.501, BoxCenterLoss=14.922, BoxScaleLoss=5.650, ClassLoss=11.855 [Epoch 32][Batch 1299], LR: 1.00E-03, Speed: 132.609 samples/sec, ObjLoss=25.494, BoxCenterLoss=14.921, BoxScaleLoss=5.649, ClassLoss=11.850 [Epoch 32][Batch 1399], LR: 1.00E-03, Speed: 133.643 samples/sec, ObjLoss=25.487, BoxCenterLoss=14.920, BoxScaleLoss=5.647, ClassLoss=11.844 [Epoch 32][Batch 1499], LR: 1.00E-03, Speed: 138.268 samples/sec, ObjLoss=25.481, BoxCenterLoss=14.920, BoxScaleLoss=5.646, ClassLoss=11.838 [Epoch 32][Batch 1599], LR: 1.00E-03, Speed: 139.018 samples/sec, ObjLoss=25.475, BoxCenterLoss=14.919, BoxScaleLoss=5.645, ClassLoss=11.832 [Epoch 32][Batch 1699], LR: 1.00E-03, Speed: 129.520 samples/sec, ObjLoss=25.467, BoxCenterLoss=14.918, BoxScaleLoss=5.645, ClassLoss=11.827 [Epoch 32][Batch 1799], LR: 1.00E-03, Speed: 175.429 samples/sec, ObjLoss=25.463, BoxCenterLoss=14.918, BoxScaleLoss=5.643, ClassLoss=11.821 [Epoch 32] Training cost: 1104.592, ObjLoss=25.461, BoxCenterLoss=14.918, BoxScaleLoss=5.643, ClassLoss=11.820 [Epoch 32] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.149 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.329 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.108 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.046 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.234 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 ] = 0.220 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224 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 ] = 0.216 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.354 person=26.3 bicycle=8.2 car=16.0 motorcycle=18.3 airplane=24.9 bus=33.1 train=33.7 truck=13.3 boat=6.0 traffic light=7.4 fire hydrant=31.6 stop sign=31.3 parking meter=20.0 bench=6.0 bird=11.2 cat=32.7 dog=25.6 horse=18.9 sheep=17.7 cow=19.6 elephant=28.2 bear=32.2 zebra=33.7 giraffe=33.1 backpack=2.1 umbrella=13.1 handbag=1.5 tie=7.3 suitcase=9.8 frisbee=22.2 skis=4.2 snowboard=8.6 sports ball=18.1 kite=16.6 baseball bat=6.2 baseball glove=15.1 skateboard=12.6 surfboard=10.7 tennis racket=13.9 bottle=11.0 wine glass=7.7 cup=13.5 fork=4.1 knife=2.2 spoon=0.7 bowl=15.3 banana=6.0 apple=4.0 sandwich=13.0 orange=12.5 broccoli=6.6 carrot=4.0 hot dog=11.7 pizza=23.1 donut=13.1 cake=10.6 chair=7.6 couch=17.7 potted plant=6.7 bed=21.1 dining table=10.7 toilet=26.1 tv=23.6 laptop=27.2 mouse=25.7 remote=4.4 keyboard=22.6 cell phone=10.4 microwave=19.5 oven=14.7 toaster=0.0 sink=13.5 refrigerator=22.3 book=2.6 clock=21.4 vase=10.1 scissors=11.2 teddy bear=18.8 hair drier=0.0 toothbrush=0.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.9 [Epoch 33][Batch 99], LR: 1.00E-03, Speed: 162.953 samples/sec, ObjLoss=25.454, BoxCenterLoss=14.917, BoxScaleLoss=5.642, ClassLoss=11.815 [Epoch 33][Batch 199], LR: 1.00E-03, Speed: 118.974 samples/sec, ObjLoss=25.448, BoxCenterLoss=14.916, BoxScaleLoss=5.641, ClassLoss=11.809 [Epoch 33][Batch 299], LR: 1.00E-03, Speed: 129.530 samples/sec, ObjLoss=25.441, BoxCenterLoss=14.916, BoxScaleLoss=5.640, ClassLoss=11.804 [Epoch 33][Batch 399], LR: 1.00E-03, Speed: 130.829 samples/sec, ObjLoss=25.436, BoxCenterLoss=14.915, BoxScaleLoss=5.639, ClassLoss=11.798 [Epoch 33][Batch 499], LR: 1.00E-03, Speed: 154.432 samples/sec, ObjLoss=25.430, BoxCenterLoss=14.915, BoxScaleLoss=5.638, ClassLoss=11.793 [Epoch 33][Batch 599], LR: 1.00E-03, Speed: 116.607 samples/sec, ObjLoss=25.425, BoxCenterLoss=14.915, BoxScaleLoss=5.637, ClassLoss=11.787 [Epoch 33][Batch 699], LR: 1.00E-03, Speed: 135.182 samples/sec, ObjLoss=25.418, BoxCenterLoss=14.914, BoxScaleLoss=5.636, ClassLoss=11.782 [Epoch 33][Batch 799], LR: 1.00E-03, Speed: 139.652 samples/sec, ObjLoss=25.413, BoxCenterLoss=14.914, BoxScaleLoss=5.635, ClassLoss=11.777 [Epoch 33][Batch 899], LR: 1.00E-03, Speed: 143.393 samples/sec, ObjLoss=25.407, BoxCenterLoss=14.914, BoxScaleLoss=5.634, ClassLoss=11.771 [Epoch 33][Batch 999], LR: 1.00E-03, Speed: 131.927 samples/sec, ObjLoss=25.401, BoxCenterLoss=14.913, BoxScaleLoss=5.633, ClassLoss=11.766 [Epoch 33][Batch 1099], LR: 1.00E-03, Speed: 151.107 samples/sec, ObjLoss=25.396, BoxCenterLoss=14.913, BoxScaleLoss=5.632, ClassLoss=11.760 [Epoch 33][Batch 1199], LR: 1.00E-03, Speed: 139.339 samples/sec, ObjLoss=25.390, BoxCenterLoss=14.913, BoxScaleLoss=5.632, ClassLoss=11.756 [Epoch 33][Batch 1299], LR: 1.00E-03, Speed: 125.863 samples/sec, ObjLoss=25.384, BoxCenterLoss=14.912, BoxScaleLoss=5.631, ClassLoss=11.751 [Epoch 33][Batch 1399], LR: 1.00E-03, Speed: 128.621 samples/sec, ObjLoss=25.378, BoxCenterLoss=14.912, BoxScaleLoss=5.630, ClassLoss=11.745 [Epoch 33][Batch 1499], LR: 1.00E-03, Speed: 139.340 samples/sec, ObjLoss=25.373, BoxCenterLoss=14.911, BoxScaleLoss=5.629, ClassLoss=11.740 [Epoch 33][Batch 1599], LR: 1.00E-03, Speed: 116.050 samples/sec, ObjLoss=25.368, BoxCenterLoss=14.911, BoxScaleLoss=5.628, ClassLoss=11.735 [Epoch 33][Batch 1699], LR: 1.00E-03, Speed: 138.344 samples/sec, ObjLoss=25.363, BoxCenterLoss=14.911, BoxScaleLoss=5.627, ClassLoss=11.730 [Epoch 33][Batch 1799], LR: 1.00E-03, Speed: 140.505 samples/sec, ObjLoss=25.356, BoxCenterLoss=14.910, BoxScaleLoss=5.627, ClassLoss=11.725 [Epoch 33] Training cost: 1120.869, ObjLoss=25.354, BoxCenterLoss=14.909, BoxScaleLoss=5.626, ClassLoss=11.724 [Epoch 33] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.340 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.106 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.049 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 ] = 0.227 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 ] = 0.220 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 ] = 0.074 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.346 person=26.1 bicycle=9.9 car=17.3 motorcycle=19.1 airplane=32.3 bus=32.8 train=36.9 truck=11.7 boat=7.2 traffic light=6.8 fire hydrant=24.6 stop sign=27.1 parking meter=16.7 bench=9.1 bird=10.5 cat=34.0 dog=25.0 horse=17.3 sheep=18.9 cow=19.2 elephant=30.0 bear=38.1 zebra=36.4 giraffe=32.7 backpack=2.5 umbrella=15.1 handbag=1.9 tie=10.4 suitcase=9.8 frisbee=24.1 skis=6.1 snowboard=7.2 sports ball=12.8 kite=15.7 baseball bat=8.1 baseball glove=11.5 skateboard=13.8 surfboard=8.5 tennis racket=15.9 bottle=9.1 wine glass=9.8 cup=13.8 fork=4.0 knife=1.9 spoon=0.8 bowl=16.0 banana=6.6 apple=5.0 sandwich=14.6 orange=13.8 broccoli=7.7 carrot=2.9 hot dog=11.8 pizza=22.7 donut=17.3 cake=9.9 chair=8.1 couch=20.5 potted plant=5.7 bed=21.2 dining table=10.1 toilet=21.8 tv=28.8 laptop=27.4 mouse=22.7 remote=4.8 keyboard=22.1 cell phone=10.0 microwave=16.8 oven=12.1 toaster=0.0 sink=12.9 refrigerator=15.2 book=3.5 clock=25.3 vase=11.3 scissors=7.7 teddy bear=19.4 hair drier=0.0 toothbrush=0.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.0 [Epoch 34][Batch 99], LR: 1.00E-03, Speed: 141.446 samples/sec, ObjLoss=25.348, BoxCenterLoss=14.909, BoxScaleLoss=5.625, ClassLoss=11.718 [Epoch 34][Batch 199], LR: 1.00E-03, Speed: 139.248 samples/sec, ObjLoss=25.343, BoxCenterLoss=14.909, BoxScaleLoss=5.624, ClassLoss=11.713 [Epoch 34][Batch 299], LR: 1.00E-03, Speed: 116.156 samples/sec, ObjLoss=25.337, BoxCenterLoss=14.908, BoxScaleLoss=5.623, ClassLoss=11.707 [Epoch 34][Batch 399], LR: 1.00E-03, Speed: 133.662 samples/sec, ObjLoss=25.331, BoxCenterLoss=14.908, BoxScaleLoss=5.623, ClassLoss=11.703 [Epoch 34][Batch 499], LR: 1.00E-03, Speed: 109.667 samples/sec, ObjLoss=25.325, BoxCenterLoss=14.907, BoxScaleLoss=5.622, ClassLoss=11.698 [Epoch 34][Batch 599], LR: 1.00E-03, Speed: 134.807 samples/sec, ObjLoss=25.319, BoxCenterLoss=14.907, BoxScaleLoss=5.622, ClassLoss=11.693 [Epoch 34][Batch 699], LR: 1.00E-03, Speed: 110.006 samples/sec, ObjLoss=25.313, BoxCenterLoss=14.907, BoxScaleLoss=5.621, ClassLoss=11.689 [Epoch 34][Batch 799], LR: 1.00E-03, Speed: 176.044 samples/sec, ObjLoss=25.307, BoxCenterLoss=14.906, BoxScaleLoss=5.620, ClassLoss=11.683 [Epoch 34][Batch 899], LR: 1.00E-03, Speed: 131.222 samples/sec, ObjLoss=25.301, BoxCenterLoss=14.906, BoxScaleLoss=5.619, ClassLoss=11.678 [Epoch 34][Batch 999], LR: 1.00E-03, Speed: 142.888 samples/sec, ObjLoss=25.295, BoxCenterLoss=14.906, BoxScaleLoss=5.619, ClassLoss=11.674 [Epoch 34][Batch 1099], LR: 1.00E-03, Speed: 142.859 samples/sec, ObjLoss=25.289, BoxCenterLoss=14.906, BoxScaleLoss=5.619, ClassLoss=11.670 [Epoch 34][Batch 1199], LR: 1.00E-03, Speed: 134.518 samples/sec, ObjLoss=25.283, BoxCenterLoss=14.905, BoxScaleLoss=5.618, ClassLoss=11.665 [Epoch 34][Batch 1299], LR: 1.00E-03, Speed: 142.992 samples/sec, ObjLoss=25.278, BoxCenterLoss=14.905, BoxScaleLoss=5.617, ClassLoss=11.660 [Epoch 34][Batch 1399], LR: 1.00E-03, Speed: 150.174 samples/sec, ObjLoss=25.273, BoxCenterLoss=14.905, BoxScaleLoss=5.616, ClassLoss=11.655 [Epoch 34][Batch 1499], LR: 1.00E-03, Speed: 130.210 samples/sec, ObjLoss=25.267, BoxCenterLoss=14.904, BoxScaleLoss=5.615, ClassLoss=11.650 [Epoch 34][Batch 1599], LR: 1.00E-03, Speed: 113.955 samples/sec, ObjLoss=25.261, BoxCenterLoss=14.904, BoxScaleLoss=5.614, ClassLoss=11.645 [Epoch 34][Batch 1699], LR: 1.00E-03, Speed: 150.669 samples/sec, ObjLoss=25.255, BoxCenterLoss=14.903, BoxScaleLoss=5.613, ClassLoss=11.640 [Epoch 34][Batch 1799], LR: 1.00E-03, Speed: 109.850 samples/sec, ObjLoss=25.250, BoxCenterLoss=14.903, BoxScaleLoss=5.612, ClassLoss=11.636 [Epoch 34] Training cost: 1085.150, ObjLoss=25.248, BoxCenterLoss=14.902, BoxScaleLoss=5.612, ClassLoss=11.634 [Epoch 34] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.341 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.098 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.058 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.222 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 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.229 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 ] = 0.231 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.339 person=27.2 bicycle=9.5 car=16.4 motorcycle=19.5 airplane=25.6 bus=28.6 train=31.5 truck=12.4 boat=8.8 traffic light=8.3 fire hydrant=31.1 stop sign=27.0 parking meter=16.5 bench=7.2 bird=12.3 cat=32.2 dog=26.5 horse=21.7 sheep=20.4 cow=24.7 elephant=28.2 bear=23.7 zebra=34.7 giraffe=35.4 backpack=2.5 umbrella=15.6 handbag=1.9 tie=9.2 suitcase=10.5 frisbee=24.6 skis=4.0 snowboard=5.4 sports ball=8.7 kite=16.8 baseball bat=6.1 baseball glove=12.2 skateboard=13.5 surfboard=10.1 tennis racket=15.0 bottle=9.4 wine glass=10.1 cup=12.9 fork=4.3 knife=1.7 spoon=1.1 bowl=15.5 banana=5.5 apple=4.3 sandwich=13.2 orange=12.7 broccoli=6.7 carrot=4.6 hot dog=9.6 pizza=21.1 donut=17.2 cake=10.4 chair=8.2 couch=17.3 potted plant=7.2 bed=23.6 dining table=12.6 toilet=26.9 tv=23.1 laptop=26.4 mouse=24.4 remote=3.7 keyboard=22.2 cell phone=10.4 microwave=14.0 oven=13.4 toaster=0.0 sink=12.3 refrigerator=15.8 book=3.2 clock=21.0 vase=11.1 scissors=9.3 teddy bear=16.9 hair drier=0.0 toothbrush=1.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.6 [Epoch 35][Batch 99], LR: 1.00E-03, Speed: 123.419 samples/sec, ObjLoss=25.242, BoxCenterLoss=14.902, BoxScaleLoss=5.611, ClassLoss=11.629 [Epoch 35][Batch 199], LR: 1.00E-03, Speed: 121.122 samples/sec, ObjLoss=25.237, BoxCenterLoss=14.902, BoxScaleLoss=5.610, ClassLoss=11.624 [Epoch 35][Batch 299], LR: 1.00E-03, Speed: 135.274 samples/sec, ObjLoss=25.231, BoxCenterLoss=14.901, BoxScaleLoss=5.609, ClassLoss=11.618 [Epoch 35][Batch 399], LR: 1.00E-03, Speed: 122.153 samples/sec, ObjLoss=25.227, BoxCenterLoss=14.901, BoxScaleLoss=5.608, ClassLoss=11.613 [Epoch 35][Batch 499], LR: 1.00E-03, Speed: 138.669 samples/sec, ObjLoss=25.222, BoxCenterLoss=14.901, BoxScaleLoss=5.608, ClassLoss=11.608 [Epoch 35][Batch 599], LR: 1.00E-03, Speed: 125.165 samples/sec, ObjLoss=25.216, BoxCenterLoss=14.900, BoxScaleLoss=5.606, ClassLoss=11.603 [Epoch 35][Batch 699], LR: 1.00E-03, Speed: 149.266 samples/sec, ObjLoss=25.211, BoxCenterLoss=14.900, BoxScaleLoss=5.606, ClassLoss=11.598 [Epoch 35][Batch 799], LR: 1.00E-03, Speed: 132.333 samples/sec, ObjLoss=25.205, BoxCenterLoss=14.899, BoxScaleLoss=5.605, ClassLoss=11.594 [Epoch 35][Batch 899], LR: 1.00E-03, Speed: 124.224 samples/sec, ObjLoss=25.199, BoxCenterLoss=14.899, BoxScaleLoss=5.604, ClassLoss=11.590 [Epoch 35][Batch 999], LR: 1.00E-03, Speed: 131.507 samples/sec, ObjLoss=25.194, BoxCenterLoss=14.898, BoxScaleLoss=5.603, ClassLoss=11.585 [Epoch 35][Batch 1099], LR: 1.00E-03, Speed: 131.948 samples/sec, ObjLoss=25.189, BoxCenterLoss=14.898, BoxScaleLoss=5.602, ClassLoss=11.579 [Epoch 35][Batch 1199], LR: 1.00E-03, Speed: 159.384 samples/sec, ObjLoss=25.184, BoxCenterLoss=14.898, BoxScaleLoss=5.602, ClassLoss=11.575 [Epoch 35][Batch 1299], LR: 1.00E-03, Speed: 126.636 samples/sec, ObjLoss=25.179, BoxCenterLoss=14.898, BoxScaleLoss=5.601, ClassLoss=11.570 [Epoch 35][Batch 1399], LR: 1.00E-03, Speed: 133.946 samples/sec, ObjLoss=25.174, BoxCenterLoss=14.898, BoxScaleLoss=5.601, ClassLoss=11.566 [Epoch 35][Batch 1499], LR: 1.00E-03, Speed: 148.111 samples/sec, ObjLoss=25.169, BoxCenterLoss=14.897, BoxScaleLoss=5.600, ClassLoss=11.562 [Epoch 35][Batch 1599], LR: 1.00E-03, Speed: 135.902 samples/sec, ObjLoss=25.163, BoxCenterLoss=14.897, BoxScaleLoss=5.599, ClassLoss=11.557 [Epoch 35][Batch 1699], LR: 1.00E-03, Speed: 159.480 samples/sec, ObjLoss=25.158, BoxCenterLoss=14.896, BoxScaleLoss=5.598, ClassLoss=11.552 [Epoch 35][Batch 1799], LR: 1.00E-03, Speed: 143.209 samples/sec, ObjLoss=25.152, BoxCenterLoss=14.896, BoxScaleLoss=5.597, ClassLoss=11.548 [Epoch 35] Training cost: 1140.870, ObjLoss=25.150, BoxCenterLoss=14.895, BoxScaleLoss=5.597, ClassLoss=11.546 [Epoch 35] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.128 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.321 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.039 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.198 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 ] = 0.199 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.204 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.067 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.209 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.304 person=26.7 bicycle=9.5 car=12.7 motorcycle=17.5 airplane=23.5 bus=31.6 train=28.1 truck=10.1 boat=4.8 traffic light=6.0 fire hydrant=26.6 stop sign=27.3 parking meter=19.7 bench=5.2 bird=10.4 cat=31.6 dog=22.7 horse=23.0 sheep=18.5 cow=20.4 elephant=28.7 bear=28.3 zebra=34.5 giraffe=33.0 backpack=2.3 umbrella=8.3 handbag=1.4 tie=8.0 suitcase=9.0 frisbee=11.7 skis=4.3 snowboard=6.1 sports ball=15.0 kite=12.4 baseball bat=6.0 baseball glove=9.5 skateboard=10.9 surfboard=6.4 tennis racket=13.0 bottle=9.6 wine glass=9.5 cup=10.8 fork=3.4 knife=1.5 spoon=0.9 bowl=11.1 banana=6.3 apple=3.1 sandwich=7.0 orange=9.0 broccoli=5.8 carrot=4.2 hot dog=5.0 pizza=19.3 donut=7.2 cake=7.4 chair=7.7 couch=20.4 potted plant=6.8 bed=18.8 dining table=9.0 toilet=18.7 tv=23.7 laptop=23.4 mouse=13.3 remote=4.2 keyboard=6.8 cell phone=9.9 microwave=15.1 oven=12.3 toaster=0.0 sink=7.8 refrigerator=15.4 book=2.3 clock=21.7 vase=8.8 scissors=8.7 teddy bear=22.0 hair drier=0.0 toothbrush=0.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =12.8 [Epoch 36][Batch 99], LR: 1.00E-03, Speed: 142.628 samples/sec, ObjLoss=25.144, BoxCenterLoss=14.895, BoxScaleLoss=5.596, ClassLoss=11.540 [Epoch 36][Batch 199], LR: 1.00E-03, Speed: 144.243 samples/sec, ObjLoss=25.138, BoxCenterLoss=14.894, BoxScaleLoss=5.595, ClassLoss=11.535 [Epoch 36][Batch 299], LR: 1.00E-03, Speed: 142.529 samples/sec, ObjLoss=25.132, BoxCenterLoss=14.893, BoxScaleLoss=5.594, ClassLoss=11.531 [Epoch 36][Batch 399], LR: 1.00E-03, Speed: 134.911 samples/sec, ObjLoss=25.126, BoxCenterLoss=14.892, BoxScaleLoss=5.593, ClassLoss=11.526 [Epoch 36][Batch 499], LR: 1.00E-03, Speed: 133.854 samples/sec, ObjLoss=25.121, BoxCenterLoss=14.892, BoxScaleLoss=5.592, ClassLoss=11.521 [Epoch 36][Batch 599], LR: 1.00E-03, Speed: 123.050 samples/sec, ObjLoss=25.115, BoxCenterLoss=14.891, BoxScaleLoss=5.592, ClassLoss=11.517 [Epoch 36][Batch 699], LR: 1.00E-03, Speed: 131.626 samples/sec, ObjLoss=25.110, BoxCenterLoss=14.891, BoxScaleLoss=5.591, ClassLoss=11.512 [Epoch 36][Batch 799], LR: 1.00E-03, Speed: 135.985 samples/sec, ObjLoss=25.105, BoxCenterLoss=14.891, BoxScaleLoss=5.590, ClassLoss=11.507 [Epoch 36][Batch 899], LR: 1.00E-03, Speed: 132.192 samples/sec, ObjLoss=25.099, BoxCenterLoss=14.890, BoxScaleLoss=5.589, ClassLoss=11.502 [Epoch 36][Batch 999], LR: 1.00E-03, Speed: 121.570 samples/sec, ObjLoss=25.096, BoxCenterLoss=14.890, BoxScaleLoss=5.588, ClassLoss=11.497 [Epoch 36][Batch 1099], LR: 1.00E-03, Speed: 121.940 samples/sec, ObjLoss=25.091, BoxCenterLoss=14.890, BoxScaleLoss=5.587, ClassLoss=11.492 [Epoch 36][Batch 1199], LR: 1.00E-03, Speed: 125.634 samples/sec, ObjLoss=25.086, BoxCenterLoss=14.890, BoxScaleLoss=5.586, ClassLoss=11.488 [Epoch 36][Batch 1299], LR: 1.00E-03, Speed: 123.828 samples/sec, ObjLoss=25.081, BoxCenterLoss=14.889, BoxScaleLoss=5.585, ClassLoss=11.483 [Epoch 36][Batch 1399], LR: 1.00E-03, Speed: 134.651 samples/sec, ObjLoss=25.075, BoxCenterLoss=14.888, BoxScaleLoss=5.584, ClassLoss=11.479 [Epoch 36][Batch 1499], LR: 1.00E-03, Speed: 136.901 samples/sec, ObjLoss=25.071, BoxCenterLoss=14.888, BoxScaleLoss=5.584, ClassLoss=11.474 [Epoch 36][Batch 1599], LR: 1.00E-03, Speed: 111.403 samples/sec, ObjLoss=25.066, BoxCenterLoss=14.888, BoxScaleLoss=5.583, ClassLoss=11.469 [Epoch 36][Batch 1699], LR: 1.00E-03, Speed: 145.370 samples/sec, ObjLoss=25.062, BoxCenterLoss=14.888, BoxScaleLoss=5.582, ClassLoss=11.465 [Epoch 36][Batch 1799], LR: 1.00E-03, Speed: 130.418 samples/sec, ObjLoss=25.057, BoxCenterLoss=14.888, BoxScaleLoss=5.582, ClassLoss=11.462 [Epoch 36] Training cost: 1106.410, ObjLoss=25.055, BoxCenterLoss=14.888, BoxScaleLoss=5.581, ClassLoss=11.460 [Epoch 36] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.122 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 ] = 0.160 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.238 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 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.349 person=27.4 bicycle=11.9 car=17.4 motorcycle=18.3 airplane=30.7 bus=33.7 train=38.7 truck=14.9 boat=7.9 traffic light=9.3 fire hydrant=29.5 stop sign=31.9 parking meter=14.6 bench=8.1 bird=12.0 cat=36.4 dog=28.8 horse=25.2 sheep=22.1 cow=23.8 elephant=32.4 bear=36.6 zebra=37.0 giraffe=35.6 backpack=2.9 umbrella=13.4 handbag=2.0 tie=9.5 suitcase=11.5 frisbee=19.2 skis=4.9 snowboard=7.1 sports ball=11.1 kite=15.7 baseball bat=4.5 baseball glove=10.0 skateboard=14.4 surfboard=8.7 tennis racket=14.9 bottle=11.2 wine glass=9.1 cup=14.6 fork=3.1 knife=1.2 spoon=0.5 bowl=14.9 banana=8.4 apple=4.1 sandwich=12.2 orange=12.5 broccoli=6.5 carrot=4.8 hot dog=11.1 pizza=24.0 donut=12.1 cake=11.2 chair=9.1 couch=22.1 potted plant=7.3 bed=25.7 dining table=12.4 toilet=28.1 tv=30.4 laptop=28.8 mouse=29.3 remote=4.0 keyboard=23.2 cell phone=12.4 microwave=22.8 oven=15.1 toaster=0.0 sink=16.1 refrigerator=21.5 book=2.8 clock=26.0 vase=10.6 scissors=7.4 teddy bear=19.3 hair drier=0.0 toothbrush=1.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.9 [Epoch 37][Batch 99], LR: 1.00E-03, Speed: 117.388 samples/sec, ObjLoss=25.050, BoxCenterLoss=14.888, BoxScaleLoss=5.581, ClassLoss=11.455 [Epoch 37][Batch 199], LR: 1.00E-03, Speed: 169.107 samples/sec, ObjLoss=25.045, BoxCenterLoss=14.888, BoxScaleLoss=5.580, ClassLoss=11.451 [Epoch 37][Batch 299], LR: 1.00E-03, Speed: 154.570 samples/sec, ObjLoss=25.041, BoxCenterLoss=14.888, BoxScaleLoss=5.579, ClassLoss=11.447 [Epoch 37][Batch 399], LR: 1.00E-03, Speed: 136.769 samples/sec, ObjLoss=25.035, BoxCenterLoss=14.887, BoxScaleLoss=5.579, ClassLoss=11.442 [Epoch 37][Batch 499], LR: 1.00E-03, Speed: 138.272 samples/sec, ObjLoss=25.030, BoxCenterLoss=14.886, BoxScaleLoss=5.578, ClassLoss=11.438 [Epoch 37][Batch 599], LR: 1.00E-03, Speed: 156.227 samples/sec, ObjLoss=25.024, BoxCenterLoss=14.886, BoxScaleLoss=5.577, ClassLoss=11.433 [Epoch 37][Batch 699], LR: 1.00E-03, Speed: 154.543 samples/sec, ObjLoss=25.020, BoxCenterLoss=14.885, BoxScaleLoss=5.576, ClassLoss=11.428 [Epoch 37][Batch 799], LR: 1.00E-03, Speed: 146.173 samples/sec, ObjLoss=25.014, BoxCenterLoss=14.885, BoxScaleLoss=5.575, ClassLoss=11.424 [Epoch 37][Batch 899], LR: 1.00E-03, Speed: 148.290 samples/sec, ObjLoss=25.009, BoxCenterLoss=14.885, BoxScaleLoss=5.575, ClassLoss=11.419 [Epoch 37][Batch 999], LR: 1.00E-03, Speed: 153.969 samples/sec, ObjLoss=25.004, BoxCenterLoss=14.884, BoxScaleLoss=5.574, ClassLoss=11.415 [Epoch 37][Batch 1099], LR: 1.00E-03, Speed: 150.928 samples/sec, ObjLoss=24.999, BoxCenterLoss=14.884, BoxScaleLoss=5.574, ClassLoss=11.411 [Epoch 37][Batch 1199], LR: 1.00E-03, Speed: 122.297 samples/sec, ObjLoss=24.995, BoxCenterLoss=14.884, BoxScaleLoss=5.573, ClassLoss=11.407 [Epoch 37][Batch 1299], LR: 1.00E-03, Speed: 166.447 samples/sec, ObjLoss=24.991, BoxCenterLoss=14.884, BoxScaleLoss=5.572, ClassLoss=11.402 [Epoch 37][Batch 1399], LR: 1.00E-03, Speed: 117.706 samples/sec, ObjLoss=24.986, BoxCenterLoss=14.883, BoxScaleLoss=5.571, ClassLoss=11.397 [Epoch 37][Batch 1499], LR: 1.00E-03, Speed: 137.583 samples/sec, ObjLoss=24.981, BoxCenterLoss=14.883, BoxScaleLoss=5.571, ClassLoss=11.393 [Epoch 37][Batch 1599], LR: 1.00E-03, Speed: 117.959 samples/sec, ObjLoss=24.975, BoxCenterLoss=14.883, BoxScaleLoss=5.570, ClassLoss=11.390 [Epoch 37][Batch 1699], LR: 1.00E-03, Speed: 154.802 samples/sec, ObjLoss=24.970, BoxCenterLoss=14.882, BoxScaleLoss=5.569, ClassLoss=11.385 [Epoch 37][Batch 1799], LR: 1.00E-03, Speed: 141.691 samples/sec, ObjLoss=24.965, BoxCenterLoss=14.882, BoxScaleLoss=5.568, ClassLoss=11.381 [Epoch 37] Training cost: 1102.253, ObjLoss=24.963, BoxCenterLoss=14.882, BoxScaleLoss=5.568, ClassLoss=11.379 [Epoch 37] 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 ] = 0.336 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.046 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 ] = 0.226 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.150 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.223 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 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.334 person=25.1 bicycle=9.2 car=14.0 motorcycle=16.0 airplane=27.3 bus=33.4 train=33.0 truck=13.1 boat=7.2 traffic light=8.2 fire hydrant=27.3 stop sign=31.0 parking meter=17.6 bench=6.7 bird=13.7 cat=31.4 dog=28.4 horse=20.5 sheep=21.3 cow=20.5 elephant=30.2 bear=31.4 zebra=35.1 giraffe=36.1 backpack=2.7 umbrella=11.9 handbag=1.4 tie=9.0 suitcase=8.3 frisbee=18.8 skis=3.9 snowboard=10.9 sports ball=18.1 kite=14.6 baseball bat=6.5 baseball glove=11.0 skateboard=11.6 surfboard=10.9 tennis racket=17.7 bottle=9.4 wine glass=9.9 cup=15.2 fork=4.8 knife=1.8 spoon=0.6 bowl=12.9 banana=6.9 apple=4.7 sandwich=13.3 orange=9.0 broccoli=7.7 carrot=5.1 hot dog=8.8 pizza=21.8 donut=18.3 cake=11.4 chair=8.4 couch=17.3 potted plant=5.9 bed=24.1 dining table=12.9 toilet=21.5 tv=25.0 laptop=28.5 mouse=18.7 remote=3.5 keyboard=19.5 cell phone=10.5 microwave=18.0 oven=12.5 toaster=0.0 sink=13.3 refrigerator=15.0 book=2.5 clock=22.0 vase=11.6 scissors=5.1 teddy bear=20.8 hair drier=0.0 toothbrush=1.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =14.7 [Epoch 38][Batch 99], LR: 1.00E-03, Speed: 110.488 samples/sec, ObjLoss=24.959, BoxCenterLoss=14.881, BoxScaleLoss=5.567, ClassLoss=11.374 [Epoch 38][Batch 199], LR: 1.00E-03, Speed: 135.073 samples/sec, ObjLoss=24.955, BoxCenterLoss=14.882, BoxScaleLoss=5.566, ClassLoss=11.370 [Epoch 38][Batch 299], LR: 1.00E-03, Speed: 131.050 samples/sec, ObjLoss=24.950, BoxCenterLoss=14.881, BoxScaleLoss=5.565, ClassLoss=11.365 [Epoch 38][Batch 399], LR: 1.00E-03, Speed: 127.791 samples/sec, ObjLoss=24.945, BoxCenterLoss=14.881, BoxScaleLoss=5.564, ClassLoss=11.360 [Epoch 38][Batch 499], LR: 1.00E-03, Speed: 124.508 samples/sec, ObjLoss=24.941, BoxCenterLoss=14.880, BoxScaleLoss=5.563, ClassLoss=11.355 [Epoch 38][Batch 599], LR: 1.00E-03, Speed: 134.973 samples/sec, ObjLoss=24.937, BoxCenterLoss=14.880, BoxScaleLoss=5.562, ClassLoss=11.351 [Epoch 38][Batch 699], LR: 1.00E-03, Speed: 131.832 samples/sec, ObjLoss=24.933, BoxCenterLoss=14.881, BoxScaleLoss=5.562, ClassLoss=11.348 [Epoch 38][Batch 799], LR: 1.00E-03, Speed: 117.802 samples/sec, ObjLoss=24.928, BoxCenterLoss=14.880, BoxScaleLoss=5.561, ClassLoss=11.343 [Epoch 38][Batch 899], LR: 1.00E-03, Speed: 137.077 samples/sec, ObjLoss=24.922, BoxCenterLoss=14.879, BoxScaleLoss=5.560, ClassLoss=11.338 [Epoch 38][Batch 999], LR: 1.00E-03, Speed: 110.304 samples/sec, ObjLoss=24.917, BoxCenterLoss=14.879, BoxScaleLoss=5.559, ClassLoss=11.333 [Epoch 38][Batch 1099], LR: 1.00E-03, Speed: 159.893 samples/sec, ObjLoss=24.913, BoxCenterLoss=14.879, BoxScaleLoss=5.559, ClassLoss=11.329 [Epoch 38][Batch 1199], LR: 1.00E-03, Speed: 124.455 samples/sec, ObjLoss=24.908, BoxCenterLoss=14.879, BoxScaleLoss=5.558, ClassLoss=11.325 [Epoch 38][Batch 1299], LR: 1.00E-03, Speed: 127.184 samples/sec, ObjLoss=24.903, BoxCenterLoss=14.878, BoxScaleLoss=5.557, ClassLoss=11.320 [Epoch 38][Batch 1399], LR: 1.00E-03, Speed: 129.193 samples/sec, ObjLoss=24.899, BoxCenterLoss=14.877, BoxScaleLoss=5.556, ClassLoss=11.316 [Epoch 38][Batch 1499], LR: 1.00E-03, Speed: 103.966 samples/sec, ObjLoss=24.894, BoxCenterLoss=14.877, BoxScaleLoss=5.555, ClassLoss=11.312 [Epoch 38][Batch 1599], LR: 1.00E-03, Speed: 146.559 samples/sec, ObjLoss=24.889, BoxCenterLoss=14.877, BoxScaleLoss=5.555, ClassLoss=11.308 [Epoch 38][Batch 1699], LR: 1.00E-03, Speed: 129.245 samples/sec, ObjLoss=24.883, BoxCenterLoss=14.876, BoxScaleLoss=5.554, ClassLoss=11.304 [Epoch 38][Batch 1799], LR: 1.00E-03, Speed: 128.856 samples/sec, ObjLoss=24.879, BoxCenterLoss=14.876, BoxScaleLoss=5.554, ClassLoss=11.300 [Epoch 38] Training cost: 1094.958, ObjLoss=24.877, BoxCenterLoss=14.876, BoxScaleLoss=5.553, ClassLoss=11.299 [Epoch 38] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.137 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.339 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.080 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 ] = 0.160 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.195 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 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224 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 ] = 0.316 person=23.2 bicycle=9.9 car=15.8 motorcycle=16.3 airplane=29.4 bus=28.6 train=28.9 truck=12.1 boat=7.0 traffic light=8.7 fire hydrant=25.3 stop sign=23.3 parking meter=15.3 bench=7.8 bird=11.5 cat=27.8 dog=21.0 horse=25.8 sheep=16.2 cow=20.8 elephant=29.6 bear=17.7 zebra=31.0 giraffe=31.2 backpack=2.6 umbrella=12.2 handbag=1.6 tie=10.0 suitcase=8.9 frisbee=26.3 skis=5.4 snowboard=7.4 sports ball=9.7 kite=18.4 baseball bat=7.5 baseball glove=13.8 skateboard=12.4 surfboard=9.4 tennis racket=16.5 bottle=11.1 wine glass=10.1 cup=15.7 fork=2.8 knife=1.6 spoon=1.0 bowl=14.9 banana=8.2 apple=5.0 sandwich=9.7 orange=10.0 broccoli=6.3 carrot=5.0 hot dog=8.5 pizza=19.1 donut=15.1 cake=9.5 chair=8.6 couch=17.5 potted plant=6.9 bed=15.9 dining table=8.4 toilet=26.7 tv=23.4 laptop=23.0 mouse=21.7 remote=3.6 keyboard=10.6 cell phone=11.5 microwave=17.6 oven=13.7 toaster=0.0 sink=13.5 refrigerator=18.5 book=2.3 clock=21.9 vase=12.3 scissors=10.5 teddy bear=10.5 hair drier=0.0 toothbrush=1.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =13.7 [Epoch 39][Batch 99], LR: 1.00E-03, Speed: 149.805 samples/sec, ObjLoss=24.873, BoxCenterLoss=14.875, BoxScaleLoss=5.552, ClassLoss=11.294 [Epoch 39][Batch 199], LR: 1.00E-03, Speed: 110.597 samples/sec, ObjLoss=24.867, BoxCenterLoss=14.875, BoxScaleLoss=5.551, ClassLoss=11.290 [Epoch 39][Batch 299], LR: 1.00E-03, Speed: 133.238 samples/sec, ObjLoss=24.863, BoxCenterLoss=14.874, BoxScaleLoss=5.550, ClassLoss=11.285 [Epoch 39][Batch 399], LR: 1.00E-03, Speed: 137.930 samples/sec, ObjLoss=24.858, BoxCenterLoss=14.874, BoxScaleLoss=5.550, ClassLoss=11.281 [Epoch 39][Batch 499], LR: 1.00E-03, Speed: 138.506 samples/sec, ObjLoss=24.853, BoxCenterLoss=14.873, BoxScaleLoss=5.549, ClassLoss=11.277 [Epoch 39][Batch 599], LR: 1.00E-03, Speed: 104.840 samples/sec, ObjLoss=24.848, BoxCenterLoss=14.873, BoxScaleLoss=5.548, ClassLoss=11.273 [Epoch 39][Batch 699], LR: 1.00E-03, Speed: 152.366 samples/sec, ObjLoss=24.841, BoxCenterLoss=14.872, BoxScaleLoss=5.548, ClassLoss=11.269 [Epoch 39][Batch 799], LR: 1.00E-03, Speed: 142.968 samples/sec, ObjLoss=24.838, BoxCenterLoss=14.872, BoxScaleLoss=5.548, ClassLoss=11.266 [Epoch 39][Batch 899], LR: 1.00E-03, Speed: 157.631 samples/sec, ObjLoss=24.833, BoxCenterLoss=14.872, BoxScaleLoss=5.547, ClassLoss=11.261 [Epoch 39][Batch 999], LR: 1.00E-03, Speed: 141.824 samples/sec, ObjLoss=24.829, BoxCenterLoss=14.871, BoxScaleLoss=5.546, ClassLoss=11.257 [Epoch 39][Batch 1099], LR: 1.00E-03, Speed: 133.744 samples/sec, ObjLoss=24.823, BoxCenterLoss=14.871, BoxScaleLoss=5.545, ClassLoss=11.253 [Epoch 39][Batch 1199], LR: 1.00E-03, Speed: 130.839 samples/sec, ObjLoss=24.819, BoxCenterLoss=14.871, BoxScaleLoss=5.544, ClassLoss=11.249 [Epoch 39][Batch 1299], LR: 1.00E-03, Speed: 121.109 samples/sec, ObjLoss=24.814, BoxCenterLoss=14.870, BoxScaleLoss=5.543, ClassLoss=11.245 [Epoch 39][Batch 1399], LR: 1.00E-03, Speed: 147.438 samples/sec, ObjLoss=24.811, BoxCenterLoss=14.870, BoxScaleLoss=5.542, ClassLoss=11.240 [Epoch 39][Batch 1499], LR: 1.00E-03, Speed: 135.330 samples/sec, ObjLoss=24.807, BoxCenterLoss=14.870, BoxScaleLoss=5.541, ClassLoss=11.236 [Epoch 39][Batch 1599], LR: 1.00E-03, Speed: 115.401 samples/sec, ObjLoss=24.803, BoxCenterLoss=14.870, BoxScaleLoss=5.540, ClassLoss=11.232 [Epoch 39][Batch 1699], LR: 1.00E-03, Speed: 123.382 samples/sec, ObjLoss=24.799, BoxCenterLoss=14.870, BoxScaleLoss=5.540, ClassLoss=11.228 [Epoch 39][Batch 1799], LR: 1.00E-03, Speed: 142.795 samples/sec, ObjLoss=24.795, BoxCenterLoss=14.870, BoxScaleLoss=5.539, ClassLoss=11.223 [Epoch 39] Training cost: 1095.337, ObjLoss=24.794, BoxCenterLoss=14.870, BoxScaleLoss=5.538, ClassLoss=11.222 [Epoch 39] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.160 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.124 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 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.248 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 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.356 person=27.3 bicycle=11.1 car=17.2 motorcycle=19.9 airplane=29.7 bus=36.6 train=37.0 truck=13.0 boat=8.7 traffic light=8.8 fire hydrant=33.2 stop sign=34.8 parking meter=16.9 bench=8.2 bird=13.4 cat=32.4 dog=27.1 horse=25.8 sheep=20.9 cow=25.5 elephant=32.3 bear=35.2 zebra=31.0 giraffe=37.1 backpack=3.1 umbrella=12.3 handbag=1.7 tie=9.9 suitcase=7.9 frisbee=23.0 skis=4.3 snowboard=7.2 sports ball=18.1 kite=13.8 baseball bat=4.1 baseball glove=13.3 skateboard=16.4 surfboard=10.5 tennis racket=15.1 bottle=12.2 wine glass=9.3 cup=16.4 fork=4.6 knife=2.1 spoon=1.2 bowl=14.4 banana=9.1 apple=4.9 sandwich=17.9 orange=11.8 broccoli=6.9 carrot=3.9 hot dog=11.4 pizza=23.7 donut=16.8 cake=12.1 chair=8.8 couch=22.9 potted plant=6.3 bed=22.4 dining table=8.6 toilet=30.0 tv=29.1 laptop=26.7 mouse=24.0 remote=5.2 keyboard=16.7 cell phone=12.2 microwave=21.4 oven=12.0 toaster=0.0 sink=13.2 refrigerator=20.0 book=4.2 clock=23.3 vase=14.1 scissors=9.2 teddy bear=23.1 hair drier=0.0 toothbrush=2.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.0 [Epoch 40][Batch 99], LR: 1.00E-03, Speed: 144.605 samples/sec, ObjLoss=24.790, BoxCenterLoss=14.870, BoxScaleLoss=5.538, ClassLoss=11.217 [Epoch 40][Batch 199], LR: 1.00E-03, Speed: 120.252 samples/sec, ObjLoss=24.786, BoxCenterLoss=14.870, BoxScaleLoss=5.537, ClassLoss=11.213 [Epoch 40][Batch 299], LR: 1.00E-03, Speed: 146.042 samples/sec, ObjLoss=24.782, BoxCenterLoss=14.870, BoxScaleLoss=5.536, ClassLoss=11.209 [Epoch 40][Batch 399], LR: 1.00E-03, Speed: 111.030 samples/sec, ObjLoss=24.778, BoxCenterLoss=14.870, BoxScaleLoss=5.535, ClassLoss=11.205 [Epoch 40][Batch 499], LR: 1.00E-03, Speed: 139.230 samples/sec, ObjLoss=24.774, BoxCenterLoss=14.870, BoxScaleLoss=5.534, ClassLoss=11.201 [Epoch 40][Batch 599], LR: 1.00E-03, Speed: 137.019 samples/sec, ObjLoss=24.769, BoxCenterLoss=14.869, BoxScaleLoss=5.533, ClassLoss=11.197 [Epoch 40][Batch 699], LR: 1.00E-03, Speed: 124.494 samples/sec, ObjLoss=24.765, BoxCenterLoss=14.869, BoxScaleLoss=5.533, ClassLoss=11.193 [Epoch 40][Batch 799], LR: 1.00E-03, Speed: 145.932 samples/sec, ObjLoss=24.761, BoxCenterLoss=14.869, BoxScaleLoss=5.532, ClassLoss=11.189 [Epoch 40][Batch 899], LR: 1.00E-03, Speed: 135.346 samples/sec, ObjLoss=24.756, BoxCenterLoss=14.868, BoxScaleLoss=5.531, ClassLoss=11.185 [Epoch 40][Batch 999], LR: 1.00E-03, Speed: 142.040 samples/sec, ObjLoss=24.751, BoxCenterLoss=14.868, BoxScaleLoss=5.530, ClassLoss=11.180 [Epoch 40][Batch 1099], LR: 1.00E-03, Speed: 131.083 samples/sec, ObjLoss=24.748, BoxCenterLoss=14.868, BoxScaleLoss=5.529, ClassLoss=11.176 [Epoch 40][Batch 1199], LR: 1.00E-03, Speed: 129.956 samples/sec, ObjLoss=24.743, BoxCenterLoss=14.867, BoxScaleLoss=5.529, ClassLoss=11.172 [Epoch 40][Batch 1299], LR: 1.00E-03, Speed: 135.016 samples/sec, ObjLoss=24.738, BoxCenterLoss=14.867, BoxScaleLoss=5.528, ClassLoss=11.169 [Epoch 40][Batch 1399], LR: 1.00E-03, Speed: 149.125 samples/sec, ObjLoss=24.734, BoxCenterLoss=14.866, BoxScaleLoss=5.527, ClassLoss=11.165 [Epoch 40][Batch 1499], LR: 1.00E-03, Speed: 122.973 samples/sec, ObjLoss=24.728, BoxCenterLoss=14.866, BoxScaleLoss=5.526, ClassLoss=11.161 [Epoch 40][Batch 1599], LR: 1.00E-03, Speed: 125.738 samples/sec, ObjLoss=24.724, BoxCenterLoss=14.865, BoxScaleLoss=5.526, ClassLoss=11.156 [Epoch 40][Batch 1699], LR: 1.00E-03, Speed: 139.496 samples/sec, ObjLoss=24.720, BoxCenterLoss=14.865, BoxScaleLoss=5.525, ClassLoss=11.152 [Epoch 40][Batch 1799], LR: 1.00E-03, Speed: 151.765 samples/sec, ObjLoss=24.716, BoxCenterLoss=14.865, BoxScaleLoss=5.524, ClassLoss=11.148 [Epoch 40] Training cost: 1118.072, ObjLoss=24.714, BoxCenterLoss=14.864, BoxScaleLoss=5.524, ClassLoss=11.146 [Epoch 40] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.161 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.362 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.115 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.061 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.160 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.162 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.245 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 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.356 person=26.0 bicycle=10.5 car=16.7 motorcycle=17.5 airplane=28.3 bus=34.9 train=36.3 truck=13.3 boat=7.7 traffic light=9.3 fire hydrant=24.8 stop sign=26.5 parking meter=14.4 bench=7.4 bird=13.2 cat=36.9 dog=28.1 horse=25.1 sheep=21.5 cow=23.7 elephant=32.1 bear=32.1 zebra=34.1 giraffe=32.5 backpack=2.8 umbrella=13.4 handbag=1.9 tie=9.5 suitcase=11.3 frisbee=20.9 skis=6.6 snowboard=7.6 sports ball=18.1 kite=19.0 baseball bat=6.1 baseball glove=13.9 skateboard=15.6 surfboard=9.7 tennis racket=14.2 bottle=12.0 wine glass=10.3 cup=15.9 fork=4.7 knife=2.4 spoon=0.9 bowl=16.7 banana=7.3 apple=6.1 sandwich=15.5 orange=13.3 broccoli=8.5 carrot=4.3 hot dog=11.0 pizza=28.4 donut=15.1 cake=13.7 chair=9.0 couch=21.3 potted plant=7.1 bed=23.1 dining table=10.5 toilet=26.6 tv=27.6 laptop=29.9 mouse=31.4 remote=5.0 keyboard=21.0 cell phone=10.6 microwave=22.5 oven=13.8 toaster=0.0 sink=18.2 refrigerator=18.9 book=3.4 clock=27.2 vase=14.9 scissors=11.5 teddy bear=21.6 hair drier=0.0 toothbrush=0.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.1 [Epoch 41][Batch 99], LR: 1.00E-03, Speed: 122.328 samples/sec, ObjLoss=24.709, BoxCenterLoss=14.864, BoxScaleLoss=5.523, ClassLoss=11.142 [Epoch 41][Batch 199], LR: 1.00E-03, Speed: 147.714 samples/sec, ObjLoss=24.705, BoxCenterLoss=14.863, BoxScaleLoss=5.522, ClassLoss=11.138 [Epoch 41][Batch 299], LR: 1.00E-03, Speed: 141.310 samples/sec, ObjLoss=24.701, BoxCenterLoss=14.863, BoxScaleLoss=5.521, ClassLoss=11.134 [Epoch 41][Batch 399], LR: 1.00E-03, Speed: 124.979 samples/sec, ObjLoss=24.697, BoxCenterLoss=14.863, BoxScaleLoss=5.520, ClassLoss=11.129 [Epoch 41][Batch 499], LR: 1.00E-03, Speed: 149.385 samples/sec, ObjLoss=24.693, BoxCenterLoss=14.862, BoxScaleLoss=5.519, ClassLoss=11.125 [Epoch 41][Batch 599], LR: 1.00E-03, Speed: 147.352 samples/sec, ObjLoss=24.689, BoxCenterLoss=14.862, BoxScaleLoss=5.519, ClassLoss=11.121 [Epoch 41][Batch 699], LR: 1.00E-03, Speed: 161.758 samples/sec, ObjLoss=24.684, BoxCenterLoss=14.862, BoxScaleLoss=5.518, ClassLoss=11.117 [Epoch 41][Batch 799], LR: 1.00E-03, Speed: 114.663 samples/sec, ObjLoss=24.679, BoxCenterLoss=14.862, BoxScaleLoss=5.517, ClassLoss=11.114 [Epoch 41][Batch 899], LR: 1.00E-03, Speed: 118.444 samples/sec, ObjLoss=24.675, BoxCenterLoss=14.861, BoxScaleLoss=5.517, ClassLoss=11.110 [Epoch 41][Batch 999], LR: 1.00E-03, Speed: 151.960 samples/sec, ObjLoss=24.671, BoxCenterLoss=14.861, BoxScaleLoss=5.516, ClassLoss=11.106 [Epoch 41][Batch 1099], LR: 1.00E-03, Speed: 110.046 samples/sec, ObjLoss=24.667, BoxCenterLoss=14.861, BoxScaleLoss=5.516, ClassLoss=11.102 [Epoch 41][Batch 1199], LR: 1.00E-03, Speed: 118.748 samples/sec, ObjLoss=24.664, BoxCenterLoss=14.861, BoxScaleLoss=5.515, ClassLoss=11.099 [Epoch 41][Batch 1299], LR: 1.00E-03, Speed: 167.405 samples/sec, ObjLoss=24.660, BoxCenterLoss=14.861, BoxScaleLoss=5.514, ClassLoss=11.095 [Epoch 41][Batch 1399], LR: 1.00E-03, Speed: 146.147 samples/sec, ObjLoss=24.655, BoxCenterLoss=14.861, BoxScaleLoss=5.514, ClassLoss=11.091 [Epoch 41][Batch 1499], LR: 1.00E-03, Speed: 142.110 samples/sec, ObjLoss=24.651, BoxCenterLoss=14.860, BoxScaleLoss=5.513, ClassLoss=11.088 [Epoch 41][Batch 1599], LR: 1.00E-03, Speed: 123.735 samples/sec, ObjLoss=24.646, BoxCenterLoss=14.860, BoxScaleLoss=5.512, ClassLoss=11.084 [Epoch 41][Batch 1699], LR: 1.00E-03, Speed: 120.351 samples/sec, ObjLoss=24.642, BoxCenterLoss=14.860, BoxScaleLoss=5.512, ClassLoss=11.080 [Epoch 41][Batch 1799], LR: 1.00E-03, Speed: 134.866 samples/sec, ObjLoss=24.638, BoxCenterLoss=14.859, BoxScaleLoss=5.511, ClassLoss=11.076 [Epoch 41] Training cost: 1082.647, ObjLoss=24.637, BoxCenterLoss=14.859, BoxScaleLoss=5.511, ClassLoss=11.075 [Epoch 41] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.348 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.054 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.165 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 ] = 0.155 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 ] = 0.232 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 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.349 person=26.6 bicycle=11.3 car=17.6 motorcycle=19.3 airplane=31.6 bus=33.3 train=30.9 truck=13.5 boat=7.7 traffic light=8.9 fire hydrant=30.0 stop sign=26.9 parking meter=16.4 bench=7.7 bird=15.7 cat=29.2 dog=25.2 horse=18.8 sheep=18.5 cow=19.8 elephant=27.8 bear=33.1 zebra=31.0 giraffe=30.3 backpack=3.0 umbrella=12.3 handbag=1.6 tie=11.9 suitcase=7.8 frisbee=27.0 skis=4.8 snowboard=9.8 sports ball=18.1 kite=16.6 baseball bat=5.9 baseball glove=12.5 skateboard=18.5 surfboard=10.7 tennis racket=18.0 bottle=10.8 wine glass=10.9 cup=15.2 fork=5.3 knife=1.7 spoon=1.4 bowl=17.2 banana=7.5 apple=5.6 sandwich=12.8 orange=11.5 broccoli=8.3 carrot=4.5 hot dog=10.3 pizza=25.1 donut=14.5 cake=12.2 chair=8.3 couch=12.3 potted plant=7.8 bed=20.8 dining table=9.0 toilet=22.4 tv=29.3 laptop=21.2 mouse=28.1 remote=5.1 keyboard=18.3 cell phone=11.9 microwave=23.4 oven=9.8 toaster=0.0 sink=13.2 refrigerator=13.5 book=3.2 clock=27.1 vase=11.0 scissors=9.6 teddy bear=17.1 hair drier=0.0 toothbrush=1.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.1 [Epoch 42][Batch 99], LR: 1.00E-03, Speed: 122.394 samples/sec, ObjLoss=24.632, BoxCenterLoss=14.859, BoxScaleLoss=5.510, ClassLoss=11.071 [Epoch 42][Batch 199], LR: 1.00E-03, Speed: 155.910 samples/sec, ObjLoss=24.627, BoxCenterLoss=14.858, BoxScaleLoss=5.509, ClassLoss=11.067 [Epoch 42][Batch 299], LR: 1.00E-03, Speed: 144.951 samples/sec, ObjLoss=24.623, BoxCenterLoss=14.858, BoxScaleLoss=5.509, ClassLoss=11.064 [Epoch 42][Batch 399], LR: 1.00E-03, Speed: 137.840 samples/sec, ObjLoss=24.619, BoxCenterLoss=14.858, BoxScaleLoss=5.508, ClassLoss=11.060 [Epoch 42][Batch 499], LR: 1.00E-03, Speed: 148.831 samples/sec, ObjLoss=24.614, BoxCenterLoss=14.858, BoxScaleLoss=5.508, ClassLoss=11.056 [Epoch 42][Batch 599], LR: 1.00E-03, Speed: 157.304 samples/sec, ObjLoss=24.611, BoxCenterLoss=14.858, BoxScaleLoss=5.507, ClassLoss=11.053 [Epoch 42][Batch 699], LR: 1.00E-03, Speed: 112.385 samples/sec, ObjLoss=24.606, BoxCenterLoss=14.857, BoxScaleLoss=5.507, ClassLoss=11.049 [Epoch 42][Batch 799], LR: 1.00E-03, Speed: 123.681 samples/sec, ObjLoss=24.602, BoxCenterLoss=14.857, BoxScaleLoss=5.506, ClassLoss=11.046 [Epoch 42][Batch 899], LR: 1.00E-03, Speed: 114.494 samples/sec, ObjLoss=24.598, BoxCenterLoss=14.856, BoxScaleLoss=5.505, ClassLoss=11.042 [Epoch 42][Batch 999], LR: 1.00E-03, Speed: 126.775 samples/sec, ObjLoss=24.594, BoxCenterLoss=14.856, BoxScaleLoss=5.504, ClassLoss=11.038 [Epoch 42][Batch 1099], LR: 1.00E-03, Speed: 152.258 samples/sec, ObjLoss=24.589, BoxCenterLoss=14.855, BoxScaleLoss=5.503, ClassLoss=11.034 [Epoch 42][Batch 1199], LR: 1.00E-03, Speed: 128.520 samples/sec, ObjLoss=24.585, BoxCenterLoss=14.855, BoxScaleLoss=5.503, ClassLoss=11.030 [Epoch 42][Batch 1299], LR: 1.00E-03, Speed: 132.910 samples/sec, ObjLoss=24.581, BoxCenterLoss=14.855, BoxScaleLoss=5.502, ClassLoss=11.026 [Epoch 42][Batch 1399], LR: 1.00E-03, Speed: 120.223 samples/sec, ObjLoss=24.577, BoxCenterLoss=14.855, BoxScaleLoss=5.502, ClassLoss=11.023 [Epoch 42][Batch 1499], LR: 1.00E-03, Speed: 120.436 samples/sec, ObjLoss=24.574, BoxCenterLoss=14.855, BoxScaleLoss=5.501, ClassLoss=11.019 [Epoch 42][Batch 1599], LR: 1.00E-03, Speed: 139.187 samples/sec, ObjLoss=24.569, BoxCenterLoss=14.855, BoxScaleLoss=5.500, ClassLoss=11.015 [Epoch 42][Batch 1699], LR: 1.00E-03, Speed: 145.808 samples/sec, ObjLoss=24.565, BoxCenterLoss=14.854, BoxScaleLoss=5.500, ClassLoss=11.012 [Epoch 42][Batch 1799], LR: 1.00E-03, Speed: 140.735 samples/sec, ObjLoss=24.561, BoxCenterLoss=14.854, BoxScaleLoss=5.499, ClassLoss=11.009 [Epoch 42] Training cost: 1078.961, ObjLoss=24.559, BoxCenterLoss=14.854, BoxScaleLoss=5.499, ClassLoss=11.008 [Epoch 42] 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 ] = 0.354 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.111 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.056 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.162 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 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.345 person=29.1 bicycle=11.5 car=14.0 motorcycle=17.8 airplane=24.2 bus=30.2 train=34.2 truck=12.1 boat=6.8 traffic light=7.5 fire hydrant=32.2 stop sign=19.9 parking meter=18.5 bench=7.9 bird=13.1 cat=35.3 dog=30.1 horse=26.0 sheep=20.5 cow=20.0 elephant=31.2 bear=33.4 zebra=34.7 giraffe=39.5 backpack=2.7 umbrella=11.2 handbag=2.0 tie=10.6 suitcase=11.6 frisbee=27.1 skis=5.7 snowboard=8.9 sports ball=17.2 kite=16.8 baseball bat=7.8 baseball glove=13.9 skateboard=14.6 surfboard=9.0 tennis racket=18.6 bottle=10.7 wine glass=10.8 cup=14.4 fork=4.5 knife=2.1 spoon=1.4 bowl=12.2 banana=6.5 apple=4.2 sandwich=12.5 orange=8.7 broccoli=7.1 carrot=5.3 hot dog=9.1 pizza=22.5 donut=12.1 cake=10.8 chair=8.0 couch=21.3 potted plant=6.5 bed=25.9 dining table=13.1 toilet=23.8 tv=29.2 laptop=28.5 mouse=26.1 remote=4.6 keyboard=22.1 cell phone=11.9 microwave=18.7 oven=13.3 toaster=0.0 sink=12.7 refrigerator=17.4 book=3.2 clock=25.9 vase=13.8 scissors=11.6 teddy bear=19.5 hair drier=0.0 toothbrush=1.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.5 [Epoch 43][Batch 99], LR: 1.00E-03, Speed: 117.639 samples/sec, ObjLoss=24.555, BoxCenterLoss=14.854, BoxScaleLoss=5.499, ClassLoss=11.005 [Epoch 43][Batch 199], LR: 1.00E-03, Speed: 141.459 samples/sec, ObjLoss=24.551, BoxCenterLoss=14.854, BoxScaleLoss=5.498, ClassLoss=11.001 [Epoch 43][Batch 299], LR: 1.00E-03, Speed: 122.923 samples/sec, ObjLoss=24.547, BoxCenterLoss=14.854, BoxScaleLoss=5.497, ClassLoss=10.997 [Epoch 43][Batch 399], LR: 1.00E-03, Speed: 144.799 samples/sec, ObjLoss=24.544, BoxCenterLoss=14.854, BoxScaleLoss=5.497, ClassLoss=10.993 [Epoch 43][Batch 499], LR: 1.00E-03, Speed: 116.087 samples/sec, ObjLoss=24.541, BoxCenterLoss=14.854, BoxScaleLoss=5.496, ClassLoss=10.989 [Epoch 43][Batch 599], LR: 1.00E-03, Speed: 148.644 samples/sec, ObjLoss=24.537, BoxCenterLoss=14.853, BoxScaleLoss=5.495, ClassLoss=10.986 [Epoch 43][Batch 699], LR: 1.00E-03, Speed: 124.818 samples/sec, ObjLoss=24.534, BoxCenterLoss=14.853, BoxScaleLoss=5.494, ClassLoss=10.982 [Epoch 43][Batch 799], LR: 1.00E-03, Speed: 139.235 samples/sec, ObjLoss=24.530, BoxCenterLoss=14.853, BoxScaleLoss=5.494, ClassLoss=10.978 [Epoch 43][Batch 899], LR: 1.00E-03, Speed: 141.476 samples/sec, ObjLoss=24.526, BoxCenterLoss=14.852, BoxScaleLoss=5.493, ClassLoss=10.974 [Epoch 43][Batch 999], LR: 1.00E-03, Speed: 151.972 samples/sec, ObjLoss=24.521, BoxCenterLoss=14.852, BoxScaleLoss=5.492, ClassLoss=10.971 [Epoch 43][Batch 1099], LR: 1.00E-03, Speed: 146.532 samples/sec, ObjLoss=24.517, BoxCenterLoss=14.851, BoxScaleLoss=5.491, ClassLoss=10.967 [Epoch 43][Batch 1199], LR: 1.00E-03, Speed: 134.373 samples/sec, ObjLoss=24.513, BoxCenterLoss=14.851, BoxScaleLoss=5.491, ClassLoss=10.964 [Epoch 43][Batch 1299], LR: 1.00E-03, Speed: 133.190 samples/sec, ObjLoss=24.508, BoxCenterLoss=14.850, BoxScaleLoss=5.490, ClassLoss=10.960 [Epoch 43][Batch 1399], LR: 1.00E-03, Speed: 117.426 samples/sec, ObjLoss=24.504, BoxCenterLoss=14.849, BoxScaleLoss=5.489, ClassLoss=10.956 [Epoch 43][Batch 1499], LR: 1.00E-03, Speed: 104.563 samples/sec, ObjLoss=24.499, BoxCenterLoss=14.849, BoxScaleLoss=5.488, ClassLoss=10.952 [Epoch 43][Batch 1599], LR: 1.00E-03, Speed: 170.829 samples/sec, ObjLoss=24.495, BoxCenterLoss=14.848, BoxScaleLoss=5.488, ClassLoss=10.949 [Epoch 43][Batch 1699], LR: 1.00E-03, Speed: 131.702 samples/sec, ObjLoss=24.491, BoxCenterLoss=14.848, BoxScaleLoss=5.487, ClassLoss=10.945 [Epoch 43][Batch 1799], LR: 1.00E-03, Speed: 128.194 samples/sec, ObjLoss=24.488, BoxCenterLoss=14.848, BoxScaleLoss=5.487, ClassLoss=10.942 [Epoch 43] Training cost: 1082.332, ObjLoss=24.486, BoxCenterLoss=14.847, BoxScaleLoss=5.486, ClassLoss=10.941 [Epoch 43] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.102 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.055 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.150 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.223 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.157 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.233 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 ] = 0.098 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.233 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.351 person=28.7 bicycle=11.3 car=15.2 motorcycle=19.1 airplane=25.2 bus=36.6 train=35.5 truck=13.2 boat=6.9 traffic light=7.2 fire hydrant=27.4 stop sign=23.2 parking meter=16.6 bench=7.2 bird=12.0 cat=31.7 dog=27.2 horse=22.9 sheep=21.4 cow=23.4 elephant=33.7 bear=37.0 zebra=35.8 giraffe=35.2 backpack=2.3 umbrella=12.5 handbag=1.8 tie=10.2 suitcase=12.0 frisbee=21.0 skis=4.6 snowboard=3.9 sports ball=15.4 kite=16.8 baseball bat=7.2 baseball glove=12.7 skateboard=11.7 surfboard=6.1 tennis racket=12.6 bottle=12.3 wine glass=11.1 cup=14.5 fork=4.0 knife=1.9 spoon=0.8 bowl=11.5 banana=7.0 apple=6.8 sandwich=13.3 orange=11.2 broccoli=7.4 carrot=4.0 hot dog=7.9 pizza=20.4 donut=14.6 cake=9.6 chair=7.9 couch=21.8 potted plant=6.3 bed=26.8 dining table=15.0 toilet=28.9 tv=20.8 laptop=21.7 mouse=23.6 remote=4.5 keyboard=17.2 cell phone=12.1 microwave=13.0 oven=15.0 toaster=0.0 sink=12.6 refrigerator=22.6 book=3.3 clock=22.6 vase=12.6 scissors=7.0 teddy bear=20.0 hair drier=0.0 toothbrush=0.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.0 [Epoch 44][Batch 99], LR: 1.00E-03, Speed: 152.743 samples/sec, ObjLoss=24.482, BoxCenterLoss=14.847, BoxScaleLoss=5.486, ClassLoss=10.937 [Epoch 44][Batch 199], LR: 1.00E-03, Speed: 137.160 samples/sec, ObjLoss=24.477, BoxCenterLoss=14.847, BoxScaleLoss=5.485, ClassLoss=10.934 [Epoch 44][Batch 299], LR: 1.00E-03, Speed: 135.694 samples/sec, ObjLoss=24.473, BoxCenterLoss=14.846, BoxScaleLoss=5.485, ClassLoss=10.931 [Epoch 44][Batch 399], LR: 1.00E-03, Speed: 115.483 samples/sec, ObjLoss=24.469, BoxCenterLoss=14.846, BoxScaleLoss=5.484, ClassLoss=10.928 [Epoch 44][Batch 499], LR: 1.00E-03, Speed: 139.414 samples/sec, ObjLoss=24.465, BoxCenterLoss=14.845, BoxScaleLoss=5.483, ClassLoss=10.924 [Epoch 44][Batch 599], LR: 1.00E-03, Speed: 147.943 samples/sec, ObjLoss=24.461, BoxCenterLoss=14.845, BoxScaleLoss=5.483, ClassLoss=10.920 [Epoch 44][Batch 699], LR: 1.00E-03, Speed: 152.468 samples/sec, ObjLoss=24.458, BoxCenterLoss=14.845, BoxScaleLoss=5.482, ClassLoss=10.917 [Epoch 44][Batch 799], LR: 1.00E-03, Speed: 176.754 samples/sec, ObjLoss=24.454, BoxCenterLoss=14.844, BoxScaleLoss=5.481, ClassLoss=10.914 [Epoch 44][Batch 899], LR: 1.00E-03, Speed: 129.693 samples/sec, ObjLoss=24.450, BoxCenterLoss=14.844, BoxScaleLoss=5.481, ClassLoss=10.910 [Epoch 44][Batch 999], LR: 1.00E-03, Speed: 134.600 samples/sec, ObjLoss=24.446, BoxCenterLoss=14.843, BoxScaleLoss=5.480, ClassLoss=10.906 [Epoch 44][Batch 1099], LR: 1.00E-03, Speed: 149.869 samples/sec, ObjLoss=24.441, BoxCenterLoss=14.843, BoxScaleLoss=5.479, ClassLoss=10.903 [Epoch 44][Batch 1199], LR: 1.00E-03, Speed: 131.211 samples/sec, ObjLoss=24.436, BoxCenterLoss=14.842, BoxScaleLoss=5.479, ClassLoss=10.900 [Epoch 44][Batch 1299], LR: 1.00E-03, Speed: 124.291 samples/sec, ObjLoss=24.432, BoxCenterLoss=14.842, BoxScaleLoss=5.478, ClassLoss=10.896 [Epoch 44][Batch 1399], LR: 1.00E-03, Speed: 134.508 samples/sec, ObjLoss=24.429, BoxCenterLoss=14.841, BoxScaleLoss=5.477, ClassLoss=10.892 [Epoch 44][Batch 1499], LR: 1.00E-03, Speed: 131.141 samples/sec, ObjLoss=24.425, BoxCenterLoss=14.841, BoxScaleLoss=5.476, ClassLoss=10.889 [Epoch 44][Batch 1599], LR: 1.00E-03, Speed: 139.539 samples/sec, ObjLoss=24.421, BoxCenterLoss=14.840, BoxScaleLoss=5.475, ClassLoss=10.885 [Epoch 44][Batch 1699], LR: 1.00E-03, Speed: 150.024 samples/sec, ObjLoss=24.417, BoxCenterLoss=14.840, BoxScaleLoss=5.475, ClassLoss=10.881 [Epoch 44][Batch 1799], LR: 1.00E-03, Speed: 138.668 samples/sec, ObjLoss=24.413, BoxCenterLoss=14.840, BoxScaleLoss=5.474, ClassLoss=10.878 [Epoch 44] Training cost: 1105.851, ObjLoss=24.413, BoxCenterLoss=14.840, BoxScaleLoss=5.474, ClassLoss=10.877 [Epoch 44] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.165 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.124 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.056 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.161 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 ] = 0.164 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 ] = 0.245 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372 person=28.0 bicycle=12.5 car=17.4 motorcycle=19.5 airplane=33.0 bus=35.3 train=35.8 truck=13.9 boat=10.2 traffic light=7.6 fire hydrant=29.9 stop sign=32.6 parking meter=19.9 bench=7.9 bird=12.4 cat=33.9 dog=28.3 horse=26.7 sheep=21.4 cow=25.4 elephant=31.1 bear=35.2 zebra=37.0 giraffe=33.2 backpack=2.8 umbrella=15.9 handbag=1.9 tie=11.4 suitcase=10.7 frisbee=20.2 skis=6.6 snowboard=9.3 sports ball=11.3 kite=19.1 baseball bat=5.2 baseball glove=9.2 skateboard=17.1 surfboard=12.3 tennis racket=18.9 bottle=12.0 wine glass=11.7 cup=16.4 fork=5.7 knife=2.8 spoon=1.4 bowl=19.0 banana=8.4 apple=5.3 sandwich=16.1 orange=12.6 broccoli=7.9 carrot=4.7 hot dog=10.4 pizza=27.4 donut=14.3 cake=12.6 chair=9.2 couch=26.0 potted plant=7.0 bed=24.6 dining table=12.5 toilet=31.0 tv=29.6 laptop=31.5 mouse=27.4 remote=5.2 keyboard=17.0 cell phone=10.0 microwave=20.0 oven=10.1 toaster=0.0 sink=15.1 refrigerator=20.6 book=3.7 clock=25.7 vase=14.5 scissors=9.8 teddy bear=17.1 hair drier=0.0 toothbrush=1.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.5 [Epoch 45][Batch 99], LR: 1.00E-03, Speed: 151.420 samples/sec, ObjLoss=24.408, BoxCenterLoss=14.839, BoxScaleLoss=5.474, ClassLoss=10.874 [Epoch 45][Batch 199], LR: 1.00E-03, Speed: 159.987 samples/sec, ObjLoss=24.404, BoxCenterLoss=14.839, BoxScaleLoss=5.473, ClassLoss=10.871 [Epoch 45][Batch 299], LR: 1.00E-03, Speed: 130.800 samples/sec, ObjLoss=24.401, BoxCenterLoss=14.839, BoxScaleLoss=5.472, ClassLoss=10.867 [Epoch 45][Batch 399], LR: 1.00E-03, Speed: 133.399 samples/sec, ObjLoss=24.396, BoxCenterLoss=14.838, BoxScaleLoss=5.471, ClassLoss=10.864 [Epoch 45][Batch 499], LR: 1.00E-03, Speed: 123.890 samples/sec, ObjLoss=24.392, BoxCenterLoss=14.838, BoxScaleLoss=5.471, ClassLoss=10.861 [Epoch 45][Batch 599], LR: 1.00E-03, Speed: 118.330 samples/sec, ObjLoss=24.388, BoxCenterLoss=14.838, BoxScaleLoss=5.470, ClassLoss=10.857 [Epoch 45][Batch 699], LR: 1.00E-03, Speed: 133.215 samples/sec, ObjLoss=24.384, BoxCenterLoss=14.837, BoxScaleLoss=5.470, ClassLoss=10.854 [Epoch 45][Batch 799], LR: 1.00E-03, Speed: 109.005 samples/sec, ObjLoss=24.380, BoxCenterLoss=14.837, BoxScaleLoss=5.469, ClassLoss=10.851 [Epoch 45][Batch 899], LR: 1.00E-03, Speed: 119.281 samples/sec, ObjLoss=24.377, BoxCenterLoss=14.837, BoxScaleLoss=5.469, ClassLoss=10.847 [Epoch 45][Batch 999], LR: 1.00E-03, Speed: 123.413 samples/sec, ObjLoss=24.373, BoxCenterLoss=14.837, BoxScaleLoss=5.468, ClassLoss=10.844 [Epoch 45][Batch 1099], LR: 1.00E-03, Speed: 125.811 samples/sec, ObjLoss=24.369, BoxCenterLoss=14.836, BoxScaleLoss=5.468, ClassLoss=10.841 [Epoch 45][Batch 1199], LR: 1.00E-03, Speed: 163.870 samples/sec, ObjLoss=24.365, BoxCenterLoss=14.836, BoxScaleLoss=5.467, ClassLoss=10.838 [Epoch 45][Batch 1299], LR: 1.00E-03, Speed: 134.383 samples/sec, ObjLoss=24.361, BoxCenterLoss=14.835, BoxScaleLoss=5.467, ClassLoss=10.835 [Epoch 45][Batch 1399], LR: 1.00E-03, Speed: 132.128 samples/sec, ObjLoss=24.356, BoxCenterLoss=14.835, BoxScaleLoss=5.466, ClassLoss=10.831 [Epoch 45][Batch 1499], LR: 1.00E-03, Speed: 134.338 samples/sec, ObjLoss=24.352, BoxCenterLoss=14.834, BoxScaleLoss=5.465, ClassLoss=10.828 [Epoch 45][Batch 1599], LR: 1.00E-03, Speed: 124.713 samples/sec, ObjLoss=24.349, BoxCenterLoss=14.834, BoxScaleLoss=5.465, ClassLoss=10.825 [Epoch 45][Batch 1699], LR: 1.00E-03, Speed: 162.622 samples/sec, ObjLoss=24.345, BoxCenterLoss=14.834, BoxScaleLoss=5.464, ClassLoss=10.821 [Epoch 45][Batch 1799], LR: 1.00E-03, Speed: 160.604 samples/sec, ObjLoss=24.341, BoxCenterLoss=14.833, BoxScaleLoss=5.464, ClassLoss=10.819 [Epoch 45] Training cost: 1074.018, ObjLoss=24.340, BoxCenterLoss=14.833, BoxScaleLoss=5.464, ClassLoss=10.817 [Epoch 45] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.160 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.354 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.120 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 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.256 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 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 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 ] = 0.363 person=26.7 bicycle=8.5 car=17.7 motorcycle=18.2 airplane=31.9 bus=36.6 train=37.0 truck=13.5 boat=8.7 traffic light=8.2 fire hydrant=29.1 stop sign=28.7 parking meter=20.8 bench=6.8 bird=13.6 cat=33.3 dog=28.6 horse=21.4 sheep=20.8 cow=23.8 elephant=30.8 bear=36.3 zebra=33.9 giraffe=30.4 backpack=2.8 umbrella=13.2 handbag=1.6 tie=12.4 suitcase=11.3 frisbee=24.1 skis=6.1 snowboard=9.9 sports ball=19.0 kite=20.2 baseball bat=5.9 baseball glove=14.3 skateboard=13.8 surfboard=12.3 tennis racket=14.1 bottle=10.1 wine glass=9.7 cup=14.2 fork=5.5 knife=2.2 spoon=0.8 bowl=15.4 banana=8.2 apple=4.7 sandwich=16.0 orange=15.4 broccoli=8.4 carrot=5.5 hot dog=11.0 pizza=26.4 donut=22.0 cake=11.7 chair=8.7 couch=20.7 potted plant=6.2 bed=18.9 dining table=7.2 toilet=30.2 tv=27.8 laptop=27.2 mouse=21.8 remote=6.0 keyboard=22.8 cell phone=10.6 microwave=19.1 oven=12.3 toaster=0.0 sink=16.5 refrigerator=17.4 book=3.6 clock=26.5 vase=12.0 scissors=9.4 teddy bear=20.7 hair drier=0.0 toothbrush=1.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.0 [Epoch 46][Batch 99], LR: 1.00E-03, Speed: 152.981 samples/sec, ObjLoss=24.337, BoxCenterLoss=14.833, BoxScaleLoss=5.463, ClassLoss=10.814 [Epoch 46][Batch 199], LR: 1.00E-03, Speed: 175.487 samples/sec, ObjLoss=24.334, BoxCenterLoss=14.834, BoxScaleLoss=5.463, ClassLoss=10.811 [Epoch 46][Batch 299], LR: 1.00E-03, Speed: 137.701 samples/sec, ObjLoss=24.330, BoxCenterLoss=14.833, BoxScaleLoss=5.462, ClassLoss=10.808 [Epoch 46][Batch 399], LR: 1.00E-03, Speed: 134.229 samples/sec, ObjLoss=24.326, BoxCenterLoss=14.833, BoxScaleLoss=5.462, ClassLoss=10.805 [Epoch 46][Batch 499], LR: 1.00E-03, Speed: 111.101 samples/sec, ObjLoss=24.322, BoxCenterLoss=14.833, BoxScaleLoss=5.461, ClassLoss=10.801 [Epoch 46][Batch 599], LR: 1.00E-03, Speed: 135.076 samples/sec, ObjLoss=24.318, BoxCenterLoss=14.832, BoxScaleLoss=5.461, ClassLoss=10.798 [Epoch 46][Batch 699], LR: 1.00E-03, Speed: 136.042 samples/sec, ObjLoss=24.315, BoxCenterLoss=14.832, BoxScaleLoss=5.460, ClassLoss=10.795 [Epoch 46][Batch 799], LR: 1.00E-03, Speed: 140.188 samples/sec, ObjLoss=24.311, BoxCenterLoss=14.832, BoxScaleLoss=5.460, ClassLoss=10.792 [Epoch 46][Batch 899], LR: 1.00E-03, Speed: 147.112 samples/sec, ObjLoss=24.306, BoxCenterLoss=14.831, BoxScaleLoss=5.459, ClassLoss=10.788 [Epoch 46][Batch 999], LR: 1.00E-03, Speed: 137.145 samples/sec, ObjLoss=24.303, BoxCenterLoss=14.831, BoxScaleLoss=5.459, ClassLoss=10.785 [Epoch 46][Batch 1099], LR: 1.00E-03, Speed: 133.919 samples/sec, ObjLoss=24.300, BoxCenterLoss=14.831, BoxScaleLoss=5.458, ClassLoss=10.782 [Epoch 46][Batch 1199], LR: 1.00E-03, Speed: 155.541 samples/sec, ObjLoss=24.296, BoxCenterLoss=14.830, BoxScaleLoss=5.457, ClassLoss=10.779 [Epoch 46][Batch 1299], LR: 1.00E-03, Speed: 119.793 samples/sec, ObjLoss=24.292, BoxCenterLoss=14.830, BoxScaleLoss=5.457, ClassLoss=10.775 [Epoch 46][Batch 1399], LR: 1.00E-03, Speed: 133.885 samples/sec, ObjLoss=24.288, BoxCenterLoss=14.829, BoxScaleLoss=5.456, ClassLoss=10.772 [Epoch 46][Batch 1499], LR: 1.00E-03, Speed: 152.541 samples/sec, ObjLoss=24.285, BoxCenterLoss=14.829, BoxScaleLoss=5.455, ClassLoss=10.769 [Epoch 46][Batch 1599], LR: 1.00E-03, Speed: 128.889 samples/sec, ObjLoss=24.281, BoxCenterLoss=14.829, BoxScaleLoss=5.455, ClassLoss=10.766 [Epoch 46][Batch 1699], LR: 1.00E-03, Speed: 113.997 samples/sec, ObjLoss=24.278, BoxCenterLoss=14.829, BoxScaleLoss=5.454, ClassLoss=10.763 [Epoch 46][Batch 1799], LR: 1.00E-03, Speed: 136.730 samples/sec, ObjLoss=24.275, BoxCenterLoss=14.829, BoxScaleLoss=5.453, ClassLoss=10.759 [Epoch 46] Training cost: 1116.702, ObjLoss=24.274, BoxCenterLoss=14.829, BoxScaleLoss=5.453, ClassLoss=10.758 [Epoch 46] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.113 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.056 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.161 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.244 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 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.240 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.364 person=28.8 bicycle=11.1 car=15.7 motorcycle=17.4 airplane=28.8 bus=32.7 train=32.3 truck=12.5 boat=7.5 traffic light=8.7 fire hydrant=28.2 stop sign=27.7 parking meter=16.6 bench=6.9 bird=14.2 cat=37.0 dog=24.8 horse=27.0 sheep=21.4 cow=23.7 elephant=30.8 bear=32.7 zebra=36.9 giraffe=36.1 backpack=3.5 umbrella=11.4 handbag=2.0 tie=12.3 suitcase=9.3 frisbee=22.4 skis=5.2 snowboard=8.2 sports ball=17.4 kite=17.6 baseball bat=6.5 baseball glove=14.9 skateboard=16.1 surfboard=8.7 tennis racket=14.6 bottle=10.3 wine glass=11.8 cup=14.8 fork=3.9 knife=2.4 spoon=1.2 bowl=15.2 banana=9.0 apple=4.7 sandwich=13.9 orange=12.1 broccoli=8.6 carrot=5.5 hot dog=9.9 pizza=23.2 donut=18.7 cake=13.2 chair=9.2 couch=20.5 potted plant=8.1 bed=19.3 dining table=9.3 toilet=30.1 tv=26.1 laptop=27.4 mouse=23.7 remote=5.6 keyboard=22.3 cell phone=12.0 microwave=22.7 oven=15.4 toaster=0.0 sink=14.5 refrigerator=23.4 book=3.0 clock=23.3 vase=13.6 scissors=10.5 teddy bear=20.2 hair drier=0.0 toothbrush=2.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.9 [Epoch 47][Batch 99], LR: 1.00E-03, Speed: 126.933 samples/sec, ObjLoss=24.270, BoxCenterLoss=14.828, BoxScaleLoss=5.453, ClassLoss=10.755 [Epoch 47][Batch 199], LR: 1.00E-03, Speed: 117.214 samples/sec, ObjLoss=24.267, BoxCenterLoss=14.828, BoxScaleLoss=5.452, ClassLoss=10.752 [Epoch 47][Batch 299], LR: 1.00E-03, Speed: 129.849 samples/sec, ObjLoss=24.264, BoxCenterLoss=14.828, BoxScaleLoss=5.452, ClassLoss=10.748 [Epoch 47][Batch 399], LR: 1.00E-03, Speed: 146.901 samples/sec, ObjLoss=24.260, BoxCenterLoss=14.828, BoxScaleLoss=5.451, ClassLoss=10.745 [Epoch 47][Batch 499], LR: 1.00E-03, Speed: 138.970 samples/sec, ObjLoss=24.256, BoxCenterLoss=14.828, BoxScaleLoss=5.450, ClassLoss=10.742 [Epoch 47][Batch 599], LR: 1.00E-03, Speed: 140.752 samples/sec, ObjLoss=24.253, BoxCenterLoss=14.827, BoxScaleLoss=5.450, ClassLoss=10.738 [Epoch 47][Batch 699], LR: 1.00E-03, Speed: 131.140 samples/sec, ObjLoss=24.249, BoxCenterLoss=14.827, BoxScaleLoss=5.449, ClassLoss=10.735 [Epoch 47][Batch 799], LR: 1.00E-03, Speed: 135.574 samples/sec, ObjLoss=24.246, BoxCenterLoss=14.826, BoxScaleLoss=5.449, ClassLoss=10.732 [Epoch 47][Batch 899], LR: 1.00E-03, Speed: 136.638 samples/sec, ObjLoss=24.243, BoxCenterLoss=14.827, BoxScaleLoss=5.448, ClassLoss=10.729 [Epoch 47][Batch 999], LR: 1.00E-03, Speed: 135.500 samples/sec, ObjLoss=24.239, BoxCenterLoss=14.826, BoxScaleLoss=5.447, ClassLoss=10.725 [Epoch 47][Batch 1099], LR: 1.00E-03, Speed: 136.686 samples/sec, ObjLoss=24.235, BoxCenterLoss=14.826, BoxScaleLoss=5.447, ClassLoss=10.722 [Epoch 47][Batch 1199], LR: 1.00E-03, Speed: 146.665 samples/sec, ObjLoss=24.232, BoxCenterLoss=14.826, BoxScaleLoss=5.446, ClassLoss=10.719 [Epoch 47][Batch 1299], LR: 1.00E-03, Speed: 133.346 samples/sec, ObjLoss=24.227, BoxCenterLoss=14.825, BoxScaleLoss=5.446, ClassLoss=10.716 [Epoch 47][Batch 1399], LR: 1.00E-03, Speed: 125.199 samples/sec, ObjLoss=24.223, BoxCenterLoss=14.824, BoxScaleLoss=5.445, ClassLoss=10.713 [Epoch 47][Batch 1499], LR: 1.00E-03, Speed: 129.618 samples/sec, ObjLoss=24.220, BoxCenterLoss=14.824, BoxScaleLoss=5.445, ClassLoss=10.710 [Epoch 47][Batch 1599], LR: 1.00E-03, Speed: 135.869 samples/sec, ObjLoss=24.216, BoxCenterLoss=14.824, BoxScaleLoss=5.444, ClassLoss=10.707 [Epoch 47][Batch 1699], LR: 1.00E-03, Speed: 145.703 samples/sec, ObjLoss=24.213, BoxCenterLoss=14.824, BoxScaleLoss=5.443, ClassLoss=10.704 [Epoch 47][Batch 1799], LR: 1.00E-03, Speed: 133.956 samples/sec, ObjLoss=24.209, BoxCenterLoss=14.823, BoxScaleLoss=5.443, ClassLoss=10.701 [Epoch 47] Training cost: 1084.839, ObjLoss=24.208, BoxCenterLoss=14.823, BoxScaleLoss=5.443, ClassLoss=10.700 [Epoch 47] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.117 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 ] = 0.147 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.257 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 ] = 0.233 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240 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 ] = 0.224 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361 person=26.6 bicycle=11.6 car=15.6 motorcycle=19.1 airplane=31.6 bus=32.9 train=38.5 truck=13.6 boat=8.0 traffic light=6.9 fire hydrant=25.6 stop sign=37.1 parking meter=18.4 bench=9.4 bird=11.8 cat=37.3 dog=29.1 horse=25.8 sheep=18.6 cow=22.5 elephant=33.0 bear=31.4 zebra=39.1 giraffe=34.0 backpack=3.0 umbrella=14.7 handbag=1.7 tie=10.8 suitcase=12.3 frisbee=25.9 skis=3.8 snowboard=8.3 sports ball=15.5 kite=13.7 baseball bat=6.8 baseball glove=12.4 skateboard=17.3 surfboard=11.6 tennis racket=13.0 bottle=9.9 wine glass=6.7 cup=14.6 fork=6.0 knife=1.7 spoon=0.9 bowl=15.8 banana=8.8 apple=4.2 sandwich=14.3 orange=13.6 broccoli=8.2 carrot=4.6 hot dog=9.0 pizza=25.7 donut=14.9 cake=13.8 chair=9.5 couch=21.6 potted plant=5.3 bed=20.2 dining table=9.2 toilet=28.7 tv=32.1 laptop=29.3 mouse=20.7 remote=4.6 keyboard=21.1 cell phone=10.6 microwave=23.6 oven=14.9 toaster=0.0 sink=14.7 refrigerator=23.4 book=2.9 clock=21.4 vase=12.6 scissors=10.7 teddy bear=13.9 hair drier=0.0 toothbrush=0.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.9 [Epoch 48][Batch 99], LR: 1.00E-03, Speed: 148.633 samples/sec, ObjLoss=24.204, BoxCenterLoss=14.823, BoxScaleLoss=5.442, ClassLoss=10.698 [Epoch 48][Batch 199], LR: 1.00E-03, Speed: 128.447 samples/sec, ObjLoss=24.200, BoxCenterLoss=14.822, BoxScaleLoss=5.442, ClassLoss=10.694 [Epoch 48][Batch 299], LR: 1.00E-03, Speed: 151.465 samples/sec, ObjLoss=24.197, BoxCenterLoss=14.822, BoxScaleLoss=5.442, ClassLoss=10.691 [Epoch 48][Batch 399], LR: 1.00E-03, Speed: 124.768 samples/sec, ObjLoss=24.193, BoxCenterLoss=14.822, BoxScaleLoss=5.441, ClassLoss=10.688 [Epoch 48][Batch 499], LR: 1.00E-03, Speed: 195.684 samples/sec, ObjLoss=24.189, BoxCenterLoss=14.822, BoxScaleLoss=5.440, ClassLoss=10.685 [Epoch 48][Batch 599], LR: 1.00E-03, Speed: 142.406 samples/sec, ObjLoss=24.186, BoxCenterLoss=14.822, BoxScaleLoss=5.440, ClassLoss=10.682 [Epoch 48][Batch 699], LR: 1.00E-03, Speed: 148.155 samples/sec, ObjLoss=24.182, BoxCenterLoss=14.821, BoxScaleLoss=5.439, ClassLoss=10.679 [Epoch 48][Batch 799], LR: 1.00E-03, Speed: 137.932 samples/sec, ObjLoss=24.179, BoxCenterLoss=14.821, BoxScaleLoss=5.438, ClassLoss=10.676 [Epoch 48][Batch 899], LR: 1.00E-03, Speed: 169.545 samples/sec, ObjLoss=24.176, BoxCenterLoss=14.821, BoxScaleLoss=5.438, ClassLoss=10.673 [Epoch 48][Batch 999], LR: 1.00E-03, Speed: 124.077 samples/sec, ObjLoss=24.172, BoxCenterLoss=14.821, BoxScaleLoss=5.438, ClassLoss=10.670 [Epoch 48][Batch 1099], LR: 1.00E-03, Speed: 132.869 samples/sec, ObjLoss=24.169, BoxCenterLoss=14.821, BoxScaleLoss=5.437, ClassLoss=10.667 [Epoch 48][Batch 1199], LR: 1.00E-03, Speed: 125.994 samples/sec, ObjLoss=24.165, BoxCenterLoss=14.820, BoxScaleLoss=5.437, ClassLoss=10.664 [Epoch 48][Batch 1299], LR: 1.00E-03, Speed: 143.086 samples/sec, ObjLoss=24.162, BoxCenterLoss=14.820, BoxScaleLoss=5.436, ClassLoss=10.661 [Epoch 48][Batch 1399], LR: 1.00E-03, Speed: 139.110 samples/sec, ObjLoss=24.159, BoxCenterLoss=14.820, BoxScaleLoss=5.435, ClassLoss=10.658 [Epoch 48][Batch 1499], LR: 1.00E-03, Speed: 127.552 samples/sec, ObjLoss=24.155, BoxCenterLoss=14.819, BoxScaleLoss=5.434, ClassLoss=10.654 [Epoch 48][Batch 1599], LR: 1.00E-03, Speed: 126.678 samples/sec, ObjLoss=24.152, BoxCenterLoss=14.819, BoxScaleLoss=5.434, ClassLoss=10.651 [Epoch 48][Batch 1699], LR: 1.00E-03, Speed: 111.379 samples/sec, ObjLoss=24.149, BoxCenterLoss=14.819, BoxScaleLoss=5.433, ClassLoss=10.648 [Epoch 48][Batch 1799], LR: 1.00E-03, Speed: 163.589 samples/sec, ObjLoss=24.146, BoxCenterLoss=14.819, BoxScaleLoss=5.432, ClassLoss=10.645 [Epoch 48] Training cost: 1127.032, ObjLoss=24.145, BoxCenterLoss=14.818, BoxScaleLoss=5.432, ClassLoss=10.644 [Epoch 48] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.160 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.117 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 ] = 0.167 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.244 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 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240 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 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.356 person=27.5 bicycle=9.8 car=17.3 motorcycle=19.9 airplane=30.2 bus=33.1 train=32.6 truck=13.5 boat=7.4 traffic light=6.2 fire hydrant=36.1 stop sign=36.1 parking meter=17.6 bench=7.3 bird=11.4 cat=34.1 dog=26.9 horse=23.2 sheep=21.1 cow=24.8 elephant=30.0 bear=30.8 zebra=34.5 giraffe=34.2 backpack=2.9 umbrella=13.2 handbag=2.3 tie=10.9 suitcase=9.5 frisbee=27.9 skis=4.4 snowboard=7.6 sports ball=16.0 kite=14.5 baseball bat=6.9 baseball glove=13.4 skateboard=13.4 surfboard=12.8 tennis racket=18.3 bottle=12.6 wine glass=12.0 cup=16.4 fork=5.2 knife=2.6 spoon=1.2 bowl=13.5 banana=10.8 apple=5.6 sandwich=14.7 orange=12.1 broccoli=8.9 carrot=5.0 hot dog=13.2 pizza=20.4 donut=18.5 cake=11.6 chair=8.9 couch=19.2 potted plant=7.1 bed=18.9 dining table=9.9 toilet=33.4 tv=21.6 laptop=27.3 mouse=22.7 remote=6.1 keyboard=22.1 cell phone=14.2 microwave=18.6 oven=12.6 toaster=0.0 sink=14.5 refrigerator=22.7 book=3.3 clock=25.7 vase=14.7 scissors=12.0 teddy bear=19.2 hair drier=0.0 toothbrush=1.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.0 [Epoch 49][Batch 99], LR: 1.00E-03, Speed: 129.605 samples/sec, ObjLoss=24.142, BoxCenterLoss=14.818, BoxScaleLoss=5.432, ClassLoss=10.641 [Epoch 49][Batch 199], LR: 1.00E-03, Speed: 149.485 samples/sec, ObjLoss=24.138, BoxCenterLoss=14.818, BoxScaleLoss=5.431, ClassLoss=10.637 [Epoch 49][Batch 299], LR: 1.00E-03, Speed: 106.798 samples/sec, ObjLoss=24.134, BoxCenterLoss=14.818, BoxScaleLoss=5.431, ClassLoss=10.635 [Epoch 49][Batch 399], LR: 1.00E-03, Speed: 151.764 samples/sec, ObjLoss=24.131, BoxCenterLoss=14.817, BoxScaleLoss=5.430, ClassLoss=10.632 [Epoch 49][Batch 499], LR: 1.00E-03, Speed: 150.370 samples/sec, ObjLoss=24.128, BoxCenterLoss=14.818, BoxScaleLoss=5.430, ClassLoss=10.629 [Epoch 49][Batch 599], LR: 1.00E-03, Speed: 135.498 samples/sec, ObjLoss=24.125, BoxCenterLoss=14.817, BoxScaleLoss=5.429, ClassLoss=10.626 [Epoch 49][Batch 699], LR: 1.00E-03, Speed: 139.364 samples/sec, ObjLoss=24.121, BoxCenterLoss=14.817, BoxScaleLoss=5.429, ClassLoss=10.623 [Epoch 49][Batch 799], LR: 1.00E-03, Speed: 120.768 samples/sec, ObjLoss=24.117, BoxCenterLoss=14.817, BoxScaleLoss=5.428, ClassLoss=10.621 [Epoch 49][Batch 899], LR: 1.00E-03, Speed: 142.919 samples/sec, ObjLoss=24.114, BoxCenterLoss=14.816, BoxScaleLoss=5.427, ClassLoss=10.617 [Epoch 49][Batch 999], LR: 1.00E-03, Speed: 120.446 samples/sec, ObjLoss=24.110, BoxCenterLoss=14.816, BoxScaleLoss=5.427, ClassLoss=10.614 [Epoch 49][Batch 1099], LR: 1.00E-03, Speed: 160.956 samples/sec, ObjLoss=24.107, BoxCenterLoss=14.815, BoxScaleLoss=5.426, ClassLoss=10.611 [Epoch 49][Batch 1199], LR: 1.00E-03, Speed: 118.023 samples/sec, ObjLoss=24.104, BoxCenterLoss=14.815, BoxScaleLoss=5.426, ClassLoss=10.608 [Epoch 49][Batch 1299], LR: 1.00E-03, Speed: 114.778 samples/sec, ObjLoss=24.101, BoxCenterLoss=14.815, BoxScaleLoss=5.425, ClassLoss=10.605 [Epoch 49][Batch 1399], LR: 1.00E-03, Speed: 156.131 samples/sec, ObjLoss=24.098, BoxCenterLoss=14.814, BoxScaleLoss=5.424, ClassLoss=10.602 [Epoch 49][Batch 1499], LR: 1.00E-03, Speed: 127.989 samples/sec, ObjLoss=24.095, BoxCenterLoss=14.814, BoxScaleLoss=5.424, ClassLoss=10.599 [Epoch 49][Batch 1599], LR: 1.00E-03, Speed: 145.125 samples/sec, ObjLoss=24.091, BoxCenterLoss=14.814, BoxScaleLoss=5.423, ClassLoss=10.596 [Epoch 49][Batch 1699], LR: 1.00E-03, Speed: 129.639 samples/sec, ObjLoss=24.088, BoxCenterLoss=14.814, BoxScaleLoss=5.423, ClassLoss=10.593 [Epoch 49][Batch 1799], LR: 1.00E-03, Speed: 131.838 samples/sec, ObjLoss=24.086, BoxCenterLoss=14.814, BoxScaleLoss=5.422, ClassLoss=10.590 [Epoch 49] Training cost: 1082.490, ObjLoss=24.084, BoxCenterLoss=14.813, BoxScaleLoss=5.422, ClassLoss=10.589 [Epoch 49] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.376 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.135 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.062 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.180 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 ] = 0.169 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 ] = 0.256 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 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.375 person=29.9 bicycle=12.3 car=18.0 motorcycle=19.6 airplane=35.6 bus=33.4 train=36.4 truck=14.9 boat=8.2 traffic light=8.7 fire hydrant=34.8 stop sign=35.7 parking meter=21.6 bench=7.8 bird=15.4 cat=34.8 dog=29.7 horse=28.0 sheep=26.4 cow=26.5 elephant=36.4 bear=38.9 zebra=35.1 giraffe=42.6 backpack=2.6 umbrella=15.2 handbag=2.4 tie=13.0 suitcase=12.0 frisbee=29.7 skis=6.2 snowboard=10.7 sports ball=14.5 kite=19.2 baseball bat=6.1 baseball glove=13.5 skateboard=14.7 surfboard=14.1 tennis racket=18.0 bottle=11.0 wine glass=12.6 cup=18.5 fork=5.5 knife=2.5 spoon=1.4 bowl=15.1 banana=9.7 apple=6.5 sandwich=16.2 orange=14.5 broccoli=9.4 carrot=5.6 hot dog=14.2 pizza=21.3 donut=18.9 cake=12.6 chair=10.5 couch=23.1 potted plant=7.6 bed=22.4 dining table=11.0 toilet=32.4 tv=27.4 laptop=24.2 mouse=25.7 remote=6.8 keyboard=20.0 cell phone=11.0 microwave=22.0 oven=11.5 toaster=0.0 sink=15.9 refrigerator=18.3 book=3.3 clock=23.3 vase=14.9 scissors=11.0 teddy bear=24.9 hair drier=0.0 toothbrush=1.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.3 [Epoch 50][Batch 99], LR: 1.00E-03, Speed: 126.218 samples/sec, ObjLoss=24.081, BoxCenterLoss=14.813, BoxScaleLoss=5.421, ClassLoss=10.586 [Epoch 50][Batch 199], LR: 1.00E-03, Speed: 155.128 samples/sec, ObjLoss=24.078, BoxCenterLoss=14.813, BoxScaleLoss=5.421, ClassLoss=10.583 [Epoch 50][Batch 299], LR: 1.00E-03, Speed: 139.076 samples/sec, ObjLoss=24.074, BoxCenterLoss=14.812, BoxScaleLoss=5.420, ClassLoss=10.579 [Epoch 50][Batch 399], LR: 1.00E-03, Speed: 151.953 samples/sec, ObjLoss=24.072, BoxCenterLoss=14.813, BoxScaleLoss=5.420, ClassLoss=10.577 [Epoch 50][Batch 499], LR: 1.00E-03, Speed: 140.386 samples/sec, ObjLoss=24.069, BoxCenterLoss=14.813, BoxScaleLoss=5.419, ClassLoss=10.574 [Epoch 50][Batch 599], LR: 1.00E-03, Speed: 146.873 samples/sec, ObjLoss=24.066, BoxCenterLoss=14.812, BoxScaleLoss=5.418, ClassLoss=10.571 [Epoch 50][Batch 699], LR: 1.00E-03, Speed: 143.424 samples/sec, ObjLoss=24.062, BoxCenterLoss=14.812, BoxScaleLoss=5.418, ClassLoss=10.568 [Epoch 50][Batch 799], LR: 1.00E-03, Speed: 123.658 samples/sec, ObjLoss=24.059, BoxCenterLoss=14.811, BoxScaleLoss=5.417, ClassLoss=10.565 [Epoch 50][Batch 899], LR: 1.00E-03, Speed: 139.720 samples/sec, ObjLoss=24.056, BoxCenterLoss=14.811, BoxScaleLoss=5.417, ClassLoss=10.562 [Epoch 50][Batch 999], LR: 1.00E-03, Speed: 126.621 samples/sec, ObjLoss=24.052, BoxCenterLoss=14.810, BoxScaleLoss=5.416, ClassLoss=10.559 [Epoch 50][Batch 1099], LR: 1.00E-03, Speed: 113.704 samples/sec, ObjLoss=24.049, BoxCenterLoss=14.810, BoxScaleLoss=5.416, ClassLoss=10.556 [Epoch 50][Batch 1199], LR: 1.00E-03, Speed: 110.656 samples/sec, ObjLoss=24.046, BoxCenterLoss=14.810, BoxScaleLoss=5.415, ClassLoss=10.553 [Epoch 50][Batch 1299], LR: 1.00E-03, Speed: 123.558 samples/sec, ObjLoss=24.044, BoxCenterLoss=14.810, BoxScaleLoss=5.414, ClassLoss=10.550 [Epoch 50][Batch 1399], LR: 1.00E-03, Speed: 145.601 samples/sec, ObjLoss=24.041, BoxCenterLoss=14.810, BoxScaleLoss=5.414, ClassLoss=10.547 [Epoch 50][Batch 1499], LR: 1.00E-03, Speed: 114.352 samples/sec, ObjLoss=24.037, BoxCenterLoss=14.809, BoxScaleLoss=5.413, ClassLoss=10.544 [Epoch 50][Batch 1599], LR: 1.00E-03, Speed: 129.704 samples/sec, ObjLoss=24.034, BoxCenterLoss=14.809, BoxScaleLoss=5.412, ClassLoss=10.541 [Epoch 50][Batch 1699], LR: 1.00E-03, Speed: 126.615 samples/sec, ObjLoss=24.033, BoxCenterLoss=14.810, BoxScaleLoss=5.412, ClassLoss=10.538 [Epoch 50][Batch 1799], LR: 1.00E-03, Speed: 141.603 samples/sec, ObjLoss=24.030, BoxCenterLoss=14.810, BoxScaleLoss=5.411, ClassLoss=10.535 [Epoch 50] Training cost: 1123.875, ObjLoss=24.029, BoxCenterLoss=14.810, BoxScaleLoss=5.411, ClassLoss=10.534 [Epoch 50] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.371 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.087 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.171 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.211 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.106 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.318 person=25.9 bicycle=12.0 car=17.2 motorcycle=14.2 airplane=29.3 bus=29.4 train=26.2 truck=13.9 boat=8.8 traffic light=10.3 fire hydrant=25.8 stop sign=26.1 parking meter=18.5 bench=6.2 bird=16.0 cat=28.5 dog=24.9 horse=21.6 sheep=18.7 cow=24.0 elephant=28.1 bear=23.9 zebra=31.1 giraffe=32.5 backpack=3.3 umbrella=14.3 handbag=2.1 tie=12.3 suitcase=8.6 frisbee=21.6 skis=5.3 snowboard=8.7 sports ball=16.1 kite=20.2 baseball bat=7.6 baseball glove=14.9 skateboard=14.0 surfboard=11.4 tennis racket=18.8 bottle=12.0 wine glass=11.7 cup=16.8 fork=4.1 knife=2.2 spoon=1.3 bowl=13.7 banana=8.4 apple=4.0 sandwich=8.6 orange=10.6 broccoli=9.0 carrot=4.2 hot dog=10.4 pizza=21.1 donut=14.8 cake=11.0 chair=8.7 couch=14.9 potted plant=8.4 bed=10.3 dining table=5.4 toilet=31.6 tv=30.6 laptop=29.2 mouse=26.7 remote=5.9 keyboard=23.7 cell phone=12.0 microwave=24.3 oven=12.2 toaster=0.0 sink=15.9 refrigerator=26.0 book=4.3 clock=27.6 vase=12.6 scissors=9.0 teddy bear=14.4 hair drier=0.0 toothbrush=1.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =15.1 [Epoch 51][Batch 99], LR: 1.00E-03, Speed: 136.389 samples/sec, ObjLoss=24.026, BoxCenterLoss=14.809, BoxScaleLoss=5.411, ClassLoss=10.531 [Epoch 51][Batch 199], LR: 1.00E-03, Speed: 145.193 samples/sec, ObjLoss=24.022, BoxCenterLoss=14.809, BoxScaleLoss=5.410, ClassLoss=10.529 [Epoch 51][Batch 299], LR: 1.00E-03, Speed: 113.809 samples/sec, ObjLoss=24.019, BoxCenterLoss=14.809, BoxScaleLoss=5.410, ClassLoss=10.526 [Epoch 51][Batch 399], LR: 1.00E-03, Speed: 152.063 samples/sec, ObjLoss=24.015, BoxCenterLoss=14.808, BoxScaleLoss=5.409, ClassLoss=10.523 [Epoch 51][Batch 499], LR: 1.00E-03, Speed: 137.718 samples/sec, ObjLoss=24.012, BoxCenterLoss=14.808, BoxScaleLoss=5.409, ClassLoss=10.520 [Epoch 51][Batch 599], LR: 1.00E-03, Speed: 130.376 samples/sec, ObjLoss=24.009, BoxCenterLoss=14.808, BoxScaleLoss=5.408, ClassLoss=10.517 [Epoch 51][Batch 699], LR: 1.00E-03, Speed: 123.061 samples/sec, ObjLoss=24.006, BoxCenterLoss=14.808, BoxScaleLoss=5.407, ClassLoss=10.514 [Epoch 51][Batch 799], LR: 1.00E-03, Speed: 142.183 samples/sec, ObjLoss=24.003, BoxCenterLoss=14.807, BoxScaleLoss=5.407, ClassLoss=10.511 [Epoch 51][Batch 899], LR: 1.00E-03, Speed: 150.083 samples/sec, ObjLoss=23.999, BoxCenterLoss=14.807, BoxScaleLoss=5.406, ClassLoss=10.509 [Epoch 51][Batch 999], LR: 1.00E-03, Speed: 117.808 samples/sec, ObjLoss=23.996, BoxCenterLoss=14.807, BoxScaleLoss=5.406, ClassLoss=10.506 [Epoch 51][Batch 1099], LR: 1.00E-03, Speed: 152.515 samples/sec, ObjLoss=23.993, BoxCenterLoss=14.807, BoxScaleLoss=5.405, ClassLoss=10.503 [Epoch 51][Batch 1199], LR: 1.00E-03, Speed: 148.350 samples/sec, ObjLoss=23.990, BoxCenterLoss=14.807, BoxScaleLoss=5.405, ClassLoss=10.501 [Epoch 51][Batch 1299], LR: 1.00E-03, Speed: 147.301 samples/sec, ObjLoss=23.987, BoxCenterLoss=14.806, BoxScaleLoss=5.404, ClassLoss=10.498 [Epoch 51][Batch 1399], LR: 1.00E-03, Speed: 169.354 samples/sec, ObjLoss=23.984, BoxCenterLoss=14.806, BoxScaleLoss=5.404, ClassLoss=10.495 [Epoch 51][Batch 1499], LR: 1.00E-03, Speed: 150.883 samples/sec, ObjLoss=23.981, BoxCenterLoss=14.806, BoxScaleLoss=5.403, ClassLoss=10.492 [Epoch 51][Batch 1599], LR: 1.00E-03, Speed: 144.159 samples/sec, ObjLoss=23.977, BoxCenterLoss=14.805, BoxScaleLoss=5.402, ClassLoss=10.489 [Epoch 51][Batch 1699], LR: 1.00E-03, Speed: 130.969 samples/sec, ObjLoss=23.974, BoxCenterLoss=14.805, BoxScaleLoss=5.402, ClassLoss=10.486 [Epoch 51][Batch 1799], LR: 1.00E-03, Speed: 152.636 samples/sec, ObjLoss=23.972, BoxCenterLoss=14.805, BoxScaleLoss=5.401, ClassLoss=10.484 [Epoch 51] Training cost: 1098.281, ObjLoss=23.971, BoxCenterLoss=14.805, BoxScaleLoss=5.401, ClassLoss=10.483 [Epoch 51] 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 ] = 0.374 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.143 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 ] = 0.189 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.268 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.258 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 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.375 person=28.4 bicycle=12.8 car=18.1 motorcycle=19.9 airplane=34.0 bus=37.5 train=35.7 truck=15.3 boat=8.9 traffic light=8.7 fire hydrant=33.0 stop sign=32.4 parking meter=22.3 bench=9.5 bird=15.1 cat=33.8 dog=30.0 horse=28.8 sheep=22.0 cow=22.7 elephant=38.9 bear=22.1 zebra=41.8 giraffe=40.9 backpack=3.1 umbrella=17.4 handbag=1.8 tie=12.6 suitcase=9.9 frisbee=26.5 skis=6.8 snowboard=10.0 sports ball=15.7 kite=19.3 baseball bat=9.0 baseball glove=12.8 skateboard=15.5 surfboard=12.7 tennis racket=17.8 bottle=12.1 wine glass=11.4 cup=17.2 fork=7.0 knife=2.7 spoon=1.0 bowl=18.2 banana=9.2 apple=6.4 sandwich=17.7 orange=14.7 broccoli=11.0 carrot=5.4 hot dog=13.4 pizza=26.3 donut=21.1 cake=15.0 chair=9.4 couch=22.6 potted plant=8.7 bed=23.1 dining table=9.2 toilet=28.2 tv=32.4 laptop=33.2 mouse=27.6 remote=6.6 keyboard=25.9 cell phone=12.5 microwave=22.6 oven=14.4 toaster=0.0 sink=14.6 refrigerator=23.8 book=4.1 clock=27.5 vase=15.9 scissors=11.9 teddy bear=19.8 hair drier=0.0 toothbrush=1.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.6 [Epoch 52][Batch 99], LR: 1.00E-03, Speed: 141.460 samples/sec, ObjLoss=23.968, BoxCenterLoss=14.805, BoxScaleLoss=5.401, ClassLoss=10.480 [Epoch 52][Batch 199], LR: 1.00E-03, Speed: 135.823 samples/sec, ObjLoss=23.965, BoxCenterLoss=14.804, BoxScaleLoss=5.400, ClassLoss=10.477 [Epoch 52][Batch 299], LR: 1.00E-03, Speed: 159.481 samples/sec, ObjLoss=23.961, BoxCenterLoss=14.804, BoxScaleLoss=5.399, ClassLoss=10.474 [Epoch 52][Batch 399], LR: 1.00E-03, Speed: 123.949 samples/sec, ObjLoss=23.959, BoxCenterLoss=14.804, BoxScaleLoss=5.399, ClassLoss=10.471 [Epoch 52][Batch 499], LR: 1.00E-03, Speed: 129.427 samples/sec, ObjLoss=23.955, BoxCenterLoss=14.803, BoxScaleLoss=5.398, ClassLoss=10.468 [Epoch 52][Batch 599], LR: 1.00E-03, Speed: 148.569 samples/sec, ObjLoss=23.952, BoxCenterLoss=14.803, BoxScaleLoss=5.397, ClassLoss=10.465 [Epoch 52][Batch 699], LR: 1.00E-03, Speed: 125.274 samples/sec, ObjLoss=23.949, BoxCenterLoss=14.803, BoxScaleLoss=5.397, ClassLoss=10.462 [Epoch 52][Batch 799], LR: 1.00E-03, Speed: 145.839 samples/sec, ObjLoss=23.946, BoxCenterLoss=14.802, BoxScaleLoss=5.396, ClassLoss=10.459 [Epoch 52][Batch 899], LR: 1.00E-03, Speed: 132.519 samples/sec, ObjLoss=23.943, BoxCenterLoss=14.802, BoxScaleLoss=5.396, ClassLoss=10.456 [Epoch 52][Batch 999], LR: 1.00E-03, Speed: 135.438 samples/sec, ObjLoss=23.940, BoxCenterLoss=14.802, BoxScaleLoss=5.395, ClassLoss=10.454 [Epoch 52][Batch 1099], LR: 1.00E-03, Speed: 110.448 samples/sec, ObjLoss=23.936, BoxCenterLoss=14.801, BoxScaleLoss=5.395, ClassLoss=10.451 [Epoch 52][Batch 1199], LR: 1.00E-03, Speed: 124.117 samples/sec, ObjLoss=23.933, BoxCenterLoss=14.801, BoxScaleLoss=5.394, ClassLoss=10.449 [Epoch 52][Batch 1299], LR: 1.00E-03, Speed: 151.713 samples/sec, ObjLoss=23.929, BoxCenterLoss=14.800, BoxScaleLoss=5.394, ClassLoss=10.446 [Epoch 52][Batch 1399], LR: 1.00E-03, Speed: 111.476 samples/sec, ObjLoss=23.926, BoxCenterLoss=14.800, BoxScaleLoss=5.393, ClassLoss=10.443 [Epoch 52][Batch 1499], LR: 1.00E-03, Speed: 135.335 samples/sec, ObjLoss=23.923, BoxCenterLoss=14.800, BoxScaleLoss=5.393, ClassLoss=10.441 [Epoch 52][Batch 1599], LR: 1.00E-03, Speed: 145.308 samples/sec, ObjLoss=23.920, BoxCenterLoss=14.800, BoxScaleLoss=5.392, ClassLoss=10.438 [Epoch 52][Batch 1699], LR: 1.00E-03, Speed: 150.760 samples/sec, ObjLoss=23.917, BoxCenterLoss=14.799, BoxScaleLoss=5.392, ClassLoss=10.435 [Epoch 52][Batch 1799], LR: 1.00E-03, Speed: 141.485 samples/sec, ObjLoss=23.914, BoxCenterLoss=14.799, BoxScaleLoss=5.391, ClassLoss=10.433 [Epoch 52] Training cost: 1088.387, ObjLoss=23.914, BoxCenterLoss=14.799, BoxScaleLoss=5.391, ClassLoss=10.432 [Epoch 52] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.171 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.363 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.140 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.061 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.176 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 ] = 0.167 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.095 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 ] = 0.366 person=29.1 bicycle=13.1 car=17.4 motorcycle=18.4 airplane=31.4 bus=36.4 train=40.5 truck=15.5 boat=9.6 traffic light=6.5 fire hydrant=32.6 stop sign=35.6 parking meter=21.9 bench=9.0 bird=14.4 cat=30.9 dog=33.2 horse=27.2 sheep=25.7 cow=26.2 elephant=34.0 bear=32.2 zebra=38.3 giraffe=42.4 backpack=3.6 umbrella=14.9 handbag=2.1 tie=10.2 suitcase=10.5 frisbee=26.4 skis=5.1 snowboard=9.9 sports ball=19.1 kite=20.4 baseball bat=6.0 baseball glove=15.4 skateboard=15.8 surfboard=13.1 tennis racket=16.1 bottle=12.1 wine glass=12.7 cup=16.5 fork=6.0 knife=2.2 spoon=1.1 bowl=16.2 banana=8.8 apple=5.1 sandwich=12.4 orange=12.2 broccoli=7.8 carrot=5.2 hot dog=8.0 pizza=25.2 donut=19.9 cake=13.2 chair=10.9 couch=23.2 potted plant=7.9 bed=21.5 dining table=9.3 toilet=27.3 tv=29.2 laptop=29.8 mouse=25.5 remote=5.2 keyboard=22.5 cell phone=11.7 microwave=23.8 oven=15.1 toaster=0.0 sink=12.6 refrigerator=25.0 book=4.0 clock=28.9 vase=12.2 scissors=6.4 teddy bear=21.6 hair drier=0.0 toothbrush=1.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.1 [Epoch 53][Batch 99], LR: 1.00E-03, Speed: 160.439 samples/sec, ObjLoss=23.911, BoxCenterLoss=14.799, BoxScaleLoss=5.390, ClassLoss=10.429 [Epoch 53][Batch 199], LR: 1.00E-03, Speed: 149.411 samples/sec, ObjLoss=23.907, BoxCenterLoss=14.799, BoxScaleLoss=5.390, ClassLoss=10.427 [Epoch 53][Batch 299], LR: 1.00E-03, Speed: 148.829 samples/sec, ObjLoss=23.905, BoxCenterLoss=14.799, BoxScaleLoss=5.389, ClassLoss=10.424 [Epoch 53][Batch 399], LR: 1.00E-03, Speed: 139.738 samples/sec, ObjLoss=23.901, BoxCenterLoss=14.798, BoxScaleLoss=5.389, ClassLoss=10.421 [Epoch 53][Batch 499], LR: 1.00E-03, Speed: 133.576 samples/sec, ObjLoss=23.898, BoxCenterLoss=14.798, BoxScaleLoss=5.388, ClassLoss=10.418 [Epoch 53][Batch 599], LR: 1.00E-03, Speed: 129.881 samples/sec, ObjLoss=23.895, BoxCenterLoss=14.798, BoxScaleLoss=5.388, ClassLoss=10.416 [Epoch 53][Batch 699], LR: 1.00E-03, Speed: 153.390 samples/sec, ObjLoss=23.893, BoxCenterLoss=14.798, BoxScaleLoss=5.387, ClassLoss=10.413 [Epoch 53][Batch 799], LR: 1.00E-03, Speed: 149.686 samples/sec, ObjLoss=23.890, BoxCenterLoss=14.798, BoxScaleLoss=5.387, ClassLoss=10.410 [Epoch 53][Batch 899], LR: 1.00E-03, Speed: 129.693 samples/sec, ObjLoss=23.887, BoxCenterLoss=14.797, BoxScaleLoss=5.387, ClassLoss=10.408 [Epoch 53][Batch 999], LR: 1.00E-03, Speed: 133.825 samples/sec, ObjLoss=23.884, BoxCenterLoss=14.797, BoxScaleLoss=5.386, ClassLoss=10.405 [Epoch 53][Batch 1099], LR: 1.00E-03, Speed: 130.221 samples/sec, ObjLoss=23.881, BoxCenterLoss=14.797, BoxScaleLoss=5.385, ClassLoss=10.402 [Epoch 53][Batch 1199], LR: 1.00E-03, Speed: 141.009 samples/sec, ObjLoss=23.878, BoxCenterLoss=14.796, BoxScaleLoss=5.385, ClassLoss=10.400 [Epoch 53][Batch 1299], LR: 1.00E-03, Speed: 150.260 samples/sec, ObjLoss=23.875, BoxCenterLoss=14.796, BoxScaleLoss=5.384, ClassLoss=10.397 [Epoch 53][Batch 1399], LR: 1.00E-03, Speed: 132.951 samples/sec, ObjLoss=23.872, BoxCenterLoss=14.796, BoxScaleLoss=5.384, ClassLoss=10.395 [Epoch 53][Batch 1499], LR: 1.00E-03, Speed: 128.916 samples/sec, ObjLoss=23.868, BoxCenterLoss=14.796, BoxScaleLoss=5.384, ClassLoss=10.393 [Epoch 53][Batch 1599], LR: 1.00E-03, Speed: 137.568 samples/sec, ObjLoss=23.865, BoxCenterLoss=14.796, BoxScaleLoss=5.383, ClassLoss=10.391 [Epoch 53][Batch 1699], LR: 1.00E-03, Speed: 137.062 samples/sec, ObjLoss=23.863, BoxCenterLoss=14.796, BoxScaleLoss=5.383, ClassLoss=10.388 [Epoch 53][Batch 1799], LR: 1.00E-03, Speed: 116.828 samples/sec, ObjLoss=23.860, BoxCenterLoss=14.795, BoxScaleLoss=5.382, ClassLoss=10.385 [Epoch 53] Training cost: 1084.164, ObjLoss=23.858, BoxCenterLoss=14.795, BoxScaleLoss=5.382, ClassLoss=10.384 [Epoch 53] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.175 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.373 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.062 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.177 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.268 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 ] = 0.251 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 ] = 0.095 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 ] = 0.380 person=29.2 bicycle=11.1 car=19.1 motorcycle=22.0 airplane=34.9 bus=40.2 train=34.3 truck=15.3 boat=10.3 traffic light=9.4 fire hydrant=32.3 stop sign=28.9 parking meter=17.8 bench=8.3 bird=12.6 cat=34.5 dog=31.8 horse=25.6 sheep=22.7 cow=25.1 elephant=32.6 bear=41.1 zebra=38.8 giraffe=41.7 backpack=2.6 umbrella=15.2 handbag=2.2 tie=11.2 suitcase=10.0 frisbee=24.7 skis=5.4 snowboard=10.2 sports ball=17.1 kite=18.1 baseball bat=6.9 baseball glove=11.1 skateboard=17.1 surfboard=11.4 tennis racket=15.7 bottle=11.6 wine glass=8.9 cup=14.8 fork=6.0 knife=2.9 spoon=1.1 bowl=15.1 banana=9.2 apple=5.1 sandwich=12.6 orange=13.8 broccoli=10.1 carrot=6.8 hot dog=11.3 pizza=28.0 donut=21.5 cake=14.5 chair=9.8 couch=25.5 potted plant=8.4 bed=24.6 dining table=15.4 toilet=32.9 tv=34.1 laptop=29.5 mouse=31.5 remote=5.5 keyboard=23.8 cell phone=13.1 microwave=25.8 oven=17.1 toaster=0.0 sink=15.4 refrigerator=24.8 book=3.4 clock=25.0 vase=14.7 scissors=10.0 teddy bear=18.8 hair drier=0.0 toothbrush=1.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.5 [Epoch 54][Batch 99], LR: 1.00E-03, Speed: 144.274 samples/sec, ObjLoss=23.856, BoxCenterLoss=14.795, BoxScaleLoss=5.382, ClassLoss=10.382 [Epoch 54][Batch 199], LR: 1.00E-03, Speed: 161.342 samples/sec, ObjLoss=23.852, BoxCenterLoss=14.794, BoxScaleLoss=5.381, ClassLoss=10.379 [Epoch 54][Batch 299], LR: 1.00E-03, Speed: 139.265 samples/sec, ObjLoss=23.848, BoxCenterLoss=14.794, BoxScaleLoss=5.381, ClassLoss=10.376 [Epoch 54][Batch 399], LR: 1.00E-03, Speed: 140.983 samples/sec, ObjLoss=23.845, BoxCenterLoss=14.793, BoxScaleLoss=5.380, ClassLoss=10.374 [Epoch 54][Batch 499], LR: 1.00E-03, Speed: 119.611 samples/sec, ObjLoss=23.842, BoxCenterLoss=14.793, BoxScaleLoss=5.380, ClassLoss=10.371 [Epoch 54][Batch 599], LR: 1.00E-03, Speed: 141.442 samples/sec, ObjLoss=23.839, BoxCenterLoss=14.793, BoxScaleLoss=5.379, ClassLoss=10.368 [Epoch 54][Batch 699], LR: 1.00E-03, Speed: 149.972 samples/sec, ObjLoss=23.836, BoxCenterLoss=14.792, BoxScaleLoss=5.379, ClassLoss=10.366 [Epoch 54][Batch 799], LR: 1.00E-03, Speed: 135.516 samples/sec, ObjLoss=23.833, BoxCenterLoss=14.792, BoxScaleLoss=5.378, ClassLoss=10.363 [Epoch 54][Batch 899], LR: 1.00E-03, Speed: 139.461 samples/sec, ObjLoss=23.830, BoxCenterLoss=14.792, BoxScaleLoss=5.377, ClassLoss=10.360 [Epoch 54][Batch 999], LR: 1.00E-03, Speed: 132.979 samples/sec, ObjLoss=23.827, BoxCenterLoss=14.792, BoxScaleLoss=5.377, ClassLoss=10.358 [Epoch 54][Batch 1099], LR: 1.00E-03, Speed: 150.824 samples/sec, ObjLoss=23.824, BoxCenterLoss=14.792, BoxScaleLoss=5.377, ClassLoss=10.355 [Epoch 54][Batch 1199], LR: 1.00E-03, Speed: 134.001 samples/sec, ObjLoss=23.822, BoxCenterLoss=14.791, BoxScaleLoss=5.376, ClassLoss=10.353 [Epoch 54][Batch 1299], LR: 1.00E-03, Speed: 144.471 samples/sec, ObjLoss=23.819, BoxCenterLoss=14.792, BoxScaleLoss=5.376, ClassLoss=10.350 [Epoch 54][Batch 1399], LR: 1.00E-03, Speed: 152.430 samples/sec, ObjLoss=23.816, BoxCenterLoss=14.791, BoxScaleLoss=5.375, ClassLoss=10.348 [Epoch 54][Batch 1499], LR: 1.00E-03, Speed: 120.558 samples/sec, ObjLoss=23.813, BoxCenterLoss=14.791, BoxScaleLoss=5.375, ClassLoss=10.346 [Epoch 54][Batch 1599], LR: 1.00E-03, Speed: 127.699 samples/sec, ObjLoss=23.810, BoxCenterLoss=14.791, BoxScaleLoss=5.374, ClassLoss=10.343 [Epoch 54][Batch 1699], LR: 1.00E-03, Speed: 126.613 samples/sec, ObjLoss=23.808, BoxCenterLoss=14.791, BoxScaleLoss=5.374, ClassLoss=10.340 [Epoch 54][Batch 1799], LR: 1.00E-03, Speed: 171.344 samples/sec, ObjLoss=23.805, BoxCenterLoss=14.790, BoxScaleLoss=5.373, ClassLoss=10.338 [Epoch 54] Training cost: 1078.164, ObjLoss=23.804, BoxCenterLoss=14.790, BoxScaleLoss=5.373, ClassLoss=10.337 [Epoch 54] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.171 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.370 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.054 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 ] = 0.264 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 ] = 0.252 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 ] = 0.097 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.380 person=30.1 bicycle=12.9 car=16.8 motorcycle=21.9 airplane=33.1 bus=32.9 train=38.2 truck=15.6 boat=9.6 traffic light=9.7 fire hydrant=30.7 stop sign=28.5 parking meter=20.8 bench=9.4 bird=15.3 cat=34.7 dog=31.9 horse=28.0 sheep=23.2 cow=28.1 elephant=36.9 bear=41.6 zebra=40.2 giraffe=35.1 backpack=3.2 umbrella=15.2 handbag=2.8 tie=11.4 suitcase=11.5 frisbee=19.4 skis=3.9 snowboard=8.7 sports ball=18.5 kite=17.4 baseball bat=6.7 baseball glove=12.5 skateboard=15.2 surfboard=11.2 tennis racket=17.2 bottle=13.4 wine glass=10.9 cup=16.6 fork=5.8 knife=2.2 spoon=0.8 bowl=14.2 banana=11.1 apple=4.3 sandwich=16.0 orange=13.7 broccoli=9.6 carrot=6.2 hot dog=14.0 pizza=22.7 donut=19.9 cake=15.0 chair=9.5 couch=21.4 potted plant=8.6 bed=21.8 dining table=15.2 toilet=23.1 tv=31.1 laptop=30.8 mouse=25.2 remote=4.8 keyboard=23.4 cell phone=10.2 microwave=24.0 oven=15.0 toaster=0.0 sink=13.2 refrigerator=20.6 book=2.8 clock=24.9 vase=12.1 scissors=10.9 teddy bear=20.1 hair drier=0.0 toothbrush=1.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.1 [Epoch 55][Batch 99], LR: 1.00E-03, Speed: 123.177 samples/sec, ObjLoss=23.801, BoxCenterLoss=14.790, BoxScaleLoss=5.373, ClassLoss=10.335 [Epoch 55][Batch 199], LR: 1.00E-03, Speed: 155.501 samples/sec, ObjLoss=23.798, BoxCenterLoss=14.789, BoxScaleLoss=5.372, ClassLoss=10.332 [Epoch 55][Batch 299], LR: 1.00E-03, Speed: 110.894 samples/sec, ObjLoss=23.794, BoxCenterLoss=14.789, BoxScaleLoss=5.371, ClassLoss=10.329 [Epoch 55][Batch 399], LR: 1.00E-03, Speed: 138.061 samples/sec, ObjLoss=23.792, BoxCenterLoss=14.789, BoxScaleLoss=5.371, ClassLoss=10.326 [Epoch 55][Batch 499], LR: 1.00E-03, Speed: 151.337 samples/sec, ObjLoss=23.790, BoxCenterLoss=14.788, BoxScaleLoss=5.371, ClassLoss=10.323 [Epoch 55][Batch 599], LR: 1.00E-03, Speed: 126.674 samples/sec, ObjLoss=23.786, BoxCenterLoss=14.788, BoxScaleLoss=5.370, ClassLoss=10.321 [Epoch 55][Batch 699], LR: 1.00E-03, Speed: 121.145 samples/sec, ObjLoss=23.784, BoxCenterLoss=14.788, BoxScaleLoss=5.370, ClassLoss=10.319 [Epoch 55][Batch 799], LR: 1.00E-03, Speed: 131.779 samples/sec, ObjLoss=23.781, BoxCenterLoss=14.788, BoxScaleLoss=5.369, ClassLoss=10.316 [Epoch 55][Batch 899], LR: 1.00E-03, Speed: 166.743 samples/sec, ObjLoss=23.779, BoxCenterLoss=14.788, BoxScaleLoss=5.369, ClassLoss=10.314 [Epoch 55][Batch 999], LR: 1.00E-03, Speed: 144.865 samples/sec, ObjLoss=23.776, BoxCenterLoss=14.788, BoxScaleLoss=5.368, ClassLoss=10.311 [Epoch 55][Batch 1099], LR: 1.00E-03, Speed: 155.967 samples/sec, ObjLoss=23.773, BoxCenterLoss=14.788, BoxScaleLoss=5.368, ClassLoss=10.308 [Epoch 55][Batch 1199], LR: 1.00E-03, Speed: 137.297 samples/sec, ObjLoss=23.771, BoxCenterLoss=14.787, BoxScaleLoss=5.367, ClassLoss=10.306 [Epoch 55][Batch 1299], LR: 1.00E-03, Speed: 147.588 samples/sec, ObjLoss=23.768, BoxCenterLoss=14.787, BoxScaleLoss=5.367, ClassLoss=10.303 [Epoch 55][Batch 1399], LR: 1.00E-03, Speed: 122.037 samples/sec, ObjLoss=23.765, BoxCenterLoss=14.787, BoxScaleLoss=5.366, ClassLoss=10.300 [Epoch 55][Batch 1499], LR: 1.00E-03, Speed: 115.098 samples/sec, ObjLoss=23.762, BoxCenterLoss=14.787, BoxScaleLoss=5.366, ClassLoss=10.298 [Epoch 55][Batch 1599], LR: 1.00E-03, Speed: 127.669 samples/sec, ObjLoss=23.759, BoxCenterLoss=14.786, BoxScaleLoss=5.365, ClassLoss=10.296 [Epoch 55][Batch 1699], LR: 1.00E-03, Speed: 148.378 samples/sec, ObjLoss=23.756, BoxCenterLoss=14.786, BoxScaleLoss=5.365, ClassLoss=10.294 [Epoch 55][Batch 1799], LR: 1.00E-03, Speed: 144.682 samples/sec, ObjLoss=23.753, BoxCenterLoss=14.786, BoxScaleLoss=5.365, ClassLoss=10.292 [Epoch 55] Training cost: 1103.144, ObjLoss=23.752, BoxCenterLoss=14.786, BoxScaleLoss=5.364, ClassLoss=10.291 [Epoch 55] 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 ] = 0.382 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.142 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 ] = 0.180 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 ] = 0.174 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 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.116 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 ] = 0.385 person=28.7 bicycle=12.7 car=19.2 motorcycle=20.9 airplane=34.6 bus=40.0 train=40.8 truck=16.8 boat=8.8 traffic light=10.1 fire hydrant=36.2 stop sign=35.1 parking meter=16.6 bench=10.5 bird=13.9 cat=32.1 dog=27.9 horse=26.4 sheep=24.3 cow=27.3 elephant=33.6 bear=40.5 zebra=37.1 giraffe=40.9 backpack=3.2 umbrella=16.5 handbag=2.7 tie=12.8 suitcase=12.6 frisbee=30.1 skis=6.2 snowboard=10.6 sports ball=16.0 kite=19.4 baseball bat=8.3 baseball glove=13.3 skateboard=18.1 surfboard=14.9 tennis racket=20.8 bottle=12.8 wine glass=11.3 cup=16.6 fork=4.6 knife=2.5 spoon=1.2 bowl=18.7 banana=10.8 apple=6.7 sandwich=15.6 orange=13.4 broccoli=9.3 carrot=6.3 hot dog=9.7 pizza=27.2 donut=22.0 cake=13.9 chair=8.8 couch=23.2 potted plant=7.6 bed=24.7 dining table=13.9 toilet=34.9 tv=31.0 laptop=29.8 mouse=24.0 remote=6.3 keyboard=23.1 cell phone=11.6 microwave=19.3 oven=13.0 toaster=0.0 sink=16.6 refrigerator=22.4 book=4.4 clock=29.7 vase=16.1 scissors=9.1 teddy bear=21.4 hair drier=0.0 toothbrush=0.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.9 [Epoch 56][Batch 99], LR: 1.00E-03, Speed: 129.993 samples/sec, ObjLoss=23.749, BoxCenterLoss=14.785, BoxScaleLoss=5.364, ClassLoss=10.288 [Epoch 56][Batch 199], LR: 1.00E-03, Speed: 148.068 samples/sec, ObjLoss=23.746, BoxCenterLoss=14.785, BoxScaleLoss=5.363, ClassLoss=10.285 [Epoch 56][Batch 299], LR: 1.00E-03, Speed: 154.781 samples/sec, ObjLoss=23.744, BoxCenterLoss=14.785, BoxScaleLoss=5.363, ClassLoss=10.282 [Epoch 56][Batch 399], LR: 1.00E-03, Speed: 129.340 samples/sec, ObjLoss=23.741, BoxCenterLoss=14.784, BoxScaleLoss=5.362, ClassLoss=10.280 [Epoch 56][Batch 499], LR: 1.00E-03, Speed: 132.288 samples/sec, ObjLoss=23.738, BoxCenterLoss=14.784, BoxScaleLoss=5.362, ClassLoss=10.277 [Epoch 56][Batch 599], LR: 1.00E-03, Speed: 148.635 samples/sec, ObjLoss=23.734, BoxCenterLoss=14.783, BoxScaleLoss=5.361, ClassLoss=10.275 [Epoch 56][Batch 699], LR: 1.00E-03, Speed: 137.917 samples/sec, ObjLoss=23.732, BoxCenterLoss=14.783, BoxScaleLoss=5.361, ClassLoss=10.273 [Epoch 56][Batch 799], LR: 1.00E-03, Speed: 140.826 samples/sec, ObjLoss=23.729, BoxCenterLoss=14.783, BoxScaleLoss=5.360, ClassLoss=10.270 [Epoch 56][Batch 899], LR: 1.00E-03, Speed: 136.235 samples/sec, ObjLoss=23.726, BoxCenterLoss=14.783, BoxScaleLoss=5.360, ClassLoss=10.268 [Epoch 56][Batch 999], LR: 1.00E-03, Speed: 138.719 samples/sec, ObjLoss=23.724, BoxCenterLoss=14.783, BoxScaleLoss=5.359, ClassLoss=10.265 [Epoch 56][Batch 1099], LR: 1.00E-03, Speed: 122.908 samples/sec, ObjLoss=23.721, BoxCenterLoss=14.783, BoxScaleLoss=5.359, ClassLoss=10.263 [Epoch 56][Batch 1199], LR: 1.00E-03, Speed: 191.771 samples/sec, ObjLoss=23.718, BoxCenterLoss=14.783, BoxScaleLoss=5.359, ClassLoss=10.261 [Epoch 56][Batch 1299], LR: 1.00E-03, Speed: 107.457 samples/sec, ObjLoss=23.716, BoxCenterLoss=14.783, BoxScaleLoss=5.358, ClassLoss=10.258 [Epoch 56][Batch 1399], LR: 1.00E-03, Speed: 122.516 samples/sec, ObjLoss=23.714, BoxCenterLoss=14.783, BoxScaleLoss=5.358, ClassLoss=10.256 [Epoch 56][Batch 1499], LR: 1.00E-03, Speed: 136.462 samples/sec, ObjLoss=23.712, BoxCenterLoss=14.783, BoxScaleLoss=5.357, ClassLoss=10.254 [Epoch 56][Batch 1599], LR: 1.00E-03, Speed: 141.075 samples/sec, ObjLoss=23.709, BoxCenterLoss=14.783, BoxScaleLoss=5.357, ClassLoss=10.252 [Epoch 56][Batch 1699], LR: 1.00E-03, Speed: 138.787 samples/sec, ObjLoss=23.707, BoxCenterLoss=14.783, BoxScaleLoss=5.357, ClassLoss=10.250 [Epoch 56][Batch 1799], LR: 1.00E-03, Speed: 130.481 samples/sec, ObjLoss=23.704, BoxCenterLoss=14.782, BoxScaleLoss=5.357, ClassLoss=10.247 [Epoch 56] Training cost: 1134.493, ObjLoss=23.703, BoxCenterLoss=14.782, BoxScaleLoss=5.356, ClassLoss=10.246 [Epoch 56] 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 ] = 0.382 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.140 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.178 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 ] = 0.172 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.252 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 ] = 0.102 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389 person=29.9 bicycle=9.8 car=17.4 motorcycle=19.8 airplane=35.6 bus=31.5 train=38.0 truck=16.3 boat=8.7 traffic light=8.9 fire hydrant=34.3 stop sign=33.2 parking meter=20.7 bench=9.6 bird=13.8 cat=38.9 dog=32.6 horse=29.0 sheep=21.7 cow=25.1 elephant=37.3 bear=41.6 zebra=39.5 giraffe=39.8 backpack=3.3 umbrella=15.1 handbag=2.3 tie=12.7 suitcase=11.8 frisbee=31.5 skis=6.9 snowboard=10.3 sports ball=21.2 kite=17.3 baseball bat=6.1 baseball glove=16.7 skateboard=17.4 surfboard=13.4 tennis racket=15.0 bottle=13.4 wine glass=11.8 cup=17.1 fork=6.6 knife=2.5 spoon=1.7 bowl=18.1 banana=9.2 apple=5.3 sandwich=17.8 orange=12.5 broccoli=9.8 carrot=5.8 hot dog=11.7 pizza=23.4 donut=20.3 cake=13.5 chair=10.4 couch=20.9 potted plant=8.6 bed=23.6 dining table=9.9 toilet=30.5 tv=28.4 laptop=31.4 mouse=23.0 remote=6.3 keyboard=23.2 cell phone=13.3 microwave=22.2 oven=13.5 toaster=0.0 sink=13.6 refrigerator=23.6 book=3.1 clock=28.4 vase=15.6 scissors=8.7 teddy bear=22.6 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.7 [Epoch 57][Batch 99], LR: 1.00E-03, Speed: 140.099 samples/sec, ObjLoss=23.700, BoxCenterLoss=14.782, BoxScaleLoss=5.356, ClassLoss=10.244 [Epoch 57][Batch 199], LR: 1.00E-03, Speed: 141.199 samples/sec, ObjLoss=23.698, BoxCenterLoss=14.782, BoxScaleLoss=5.355, ClassLoss=10.241 [Epoch 57][Batch 299], LR: 1.00E-03, Speed: 144.812 samples/sec, ObjLoss=23.695, BoxCenterLoss=14.782, BoxScaleLoss=5.355, ClassLoss=10.239 [Epoch 57][Batch 399], LR: 1.00E-03, Speed: 122.554 samples/sec, ObjLoss=23.692, BoxCenterLoss=14.782, BoxScaleLoss=5.354, ClassLoss=10.236 [Epoch 57][Batch 499], LR: 1.00E-03, Speed: 127.684 samples/sec, ObjLoss=23.690, BoxCenterLoss=14.782, BoxScaleLoss=5.354, ClassLoss=10.234 [Epoch 57][Batch 599], LR: 1.00E-03, Speed: 136.103 samples/sec, ObjLoss=23.687, BoxCenterLoss=14.781, BoxScaleLoss=5.354, ClassLoss=10.231 [Epoch 57][Batch 699], LR: 1.00E-03, Speed: 149.192 samples/sec, ObjLoss=23.685, BoxCenterLoss=14.782, BoxScaleLoss=5.353, ClassLoss=10.229 [Epoch 57][Batch 799], LR: 1.00E-03, Speed: 140.856 samples/sec, ObjLoss=23.682, BoxCenterLoss=14.781, BoxScaleLoss=5.353, ClassLoss=10.227 [Epoch 57][Batch 899], LR: 1.00E-03, Speed: 128.852 samples/sec, ObjLoss=23.679, BoxCenterLoss=14.781, BoxScaleLoss=5.353, ClassLoss=10.225 [Epoch 57][Batch 999], LR: 1.00E-03, Speed: 140.246 samples/sec, ObjLoss=23.676, BoxCenterLoss=14.781, BoxScaleLoss=5.352, ClassLoss=10.222 [Epoch 57][Batch 1099], LR: 1.00E-03, Speed: 130.406 samples/sec, ObjLoss=23.675, BoxCenterLoss=14.781, BoxScaleLoss=5.351, ClassLoss=10.219 [Epoch 57][Batch 1199], LR: 1.00E-03, Speed: 162.946 samples/sec, ObjLoss=23.672, BoxCenterLoss=14.781, BoxScaleLoss=5.351, ClassLoss=10.217 [Epoch 57][Batch 1299], LR: 1.00E-03, Speed: 149.107 samples/sec, ObjLoss=23.669, BoxCenterLoss=14.780, BoxScaleLoss=5.350, ClassLoss=10.215 [Epoch 57][Batch 1399], LR: 1.00E-03, Speed: 139.385 samples/sec, ObjLoss=23.667, BoxCenterLoss=14.780, BoxScaleLoss=5.350, ClassLoss=10.212 [Epoch 57][Batch 1499], LR: 1.00E-03, Speed: 111.111 samples/sec, ObjLoss=23.664, BoxCenterLoss=14.780, BoxScaleLoss=5.349, ClassLoss=10.210 [Epoch 57][Batch 1599], LR: 1.00E-03, Speed: 160.841 samples/sec, ObjLoss=23.661, BoxCenterLoss=14.780, BoxScaleLoss=5.349, ClassLoss=10.208 [Epoch 57][Batch 1699], LR: 1.00E-03, Speed: 152.982 samples/sec, ObjLoss=23.658, BoxCenterLoss=14.779, BoxScaleLoss=5.349, ClassLoss=10.205 [Epoch 57][Batch 1799], LR: 1.00E-03, Speed: 158.884 samples/sec, ObjLoss=23.655, BoxCenterLoss=14.779, BoxScaleLoss=5.348, ClassLoss=10.203 [Epoch 57] Training cost: 1097.496, ObjLoss=23.654, BoxCenterLoss=14.778, BoxScaleLoss=5.348, ClassLoss=10.202 [Epoch 57] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.376 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.136 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.063 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 ] = 0.275 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 ] = 0.246 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.253 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 person=30.0 bicycle=14.1 car=18.9 motorcycle=19.9 airplane=33.8 bus=40.9 train=39.0 truck=15.8 boat=9.2 traffic light=9.8 fire hydrant=35.3 stop sign=35.1 parking meter=22.4 bench=9.7 bird=12.3 cat=39.6 dog=29.7 horse=29.7 sheep=22.5 cow=25.6 elephant=32.0 bear=36.0 zebra=34.9 giraffe=35.7 backpack=3.8 umbrella=14.8 handbag=2.1 tie=13.3 suitcase=10.3 frisbee=29.1 skis=5.0 snowboard=8.3 sports ball=13.0 kite=18.2 baseball bat=5.8 baseball glove=10.2 skateboard=18.9 surfboard=13.6 tennis racket=18.9 bottle=12.8 wine glass=9.6 cup=16.4 fork=5.3 knife=2.4 spoon=1.1 bowl=18.1 banana=8.3 apple=4.9 sandwich=15.9 orange=13.3 broccoli=6.2 carrot=5.9 hot dog=11.9 pizza=26.6 donut=15.1 cake=15.1 chair=10.3 couch=22.7 potted plant=7.4 bed=23.7 dining table=12.8 toilet=33.2 tv=28.0 laptop=31.1 mouse=23.0 remote=4.6 keyboard=15.9 cell phone=10.8 microwave=25.1 oven=14.5 toaster=0.0 sink=12.7 refrigerator=18.2 book=4.1 clock=29.6 vase=14.2 scissors=12.7 teddy bear=23.0 hair drier=0.0 toothbrush=1.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.3 [Epoch 58][Batch 99], LR: 1.00E-03, Speed: 124.746 samples/sec, ObjLoss=23.652, BoxCenterLoss=14.778, BoxScaleLoss=5.347, ClassLoss=10.200 [Epoch 58][Batch 199], LR: 1.00E-03, Speed: 129.919 samples/sec, ObjLoss=23.649, BoxCenterLoss=14.778, BoxScaleLoss=5.347, ClassLoss=10.197 [Epoch 58][Batch 299], LR: 1.00E-03, Speed: 126.823 samples/sec, ObjLoss=23.646, BoxCenterLoss=14.778, BoxScaleLoss=5.346, ClassLoss=10.195 [Epoch 58][Batch 399], LR: 1.00E-03, Speed: 153.706 samples/sec, ObjLoss=23.644, BoxCenterLoss=14.778, BoxScaleLoss=5.346, ClassLoss=10.193 [Epoch 58][Batch 499], LR: 1.00E-03, Speed: 126.258 samples/sec, ObjLoss=23.641, BoxCenterLoss=14.778, BoxScaleLoss=5.346, ClassLoss=10.190 [Epoch 58][Batch 599], LR: 1.00E-03, Speed: 135.489 samples/sec, ObjLoss=23.638, BoxCenterLoss=14.777, BoxScaleLoss=5.345, ClassLoss=10.188 [Epoch 58][Batch 699], LR: 1.00E-03, Speed: 122.243 samples/sec, ObjLoss=23.635, BoxCenterLoss=14.777, BoxScaleLoss=5.345, ClassLoss=10.185 [Epoch 58][Batch 799], LR: 1.00E-03, Speed: 133.757 samples/sec, ObjLoss=23.632, BoxCenterLoss=14.777, BoxScaleLoss=5.344, ClassLoss=10.183 [Epoch 58][Batch 899], LR: 1.00E-03, Speed: 153.984 samples/sec, ObjLoss=23.630, BoxCenterLoss=14.777, BoxScaleLoss=5.344, ClassLoss=10.181 [Epoch 58][Batch 999], LR: 1.00E-03, Speed: 155.845 samples/sec, ObjLoss=23.628, BoxCenterLoss=14.777, BoxScaleLoss=5.343, ClassLoss=10.178 [Epoch 58][Batch 1099], LR: 1.00E-03, Speed: 120.712 samples/sec, ObjLoss=23.626, BoxCenterLoss=14.777, BoxScaleLoss=5.343, ClassLoss=10.176 [Epoch 58][Batch 1199], LR: 1.00E-03, Speed: 118.947 samples/sec, ObjLoss=23.622, BoxCenterLoss=14.776, BoxScaleLoss=5.342, ClassLoss=10.173 [Epoch 58][Batch 1299], LR: 1.00E-03, Speed: 148.285 samples/sec, ObjLoss=23.620, BoxCenterLoss=14.776, BoxScaleLoss=5.342, ClassLoss=10.171 [Epoch 58][Batch 1399], LR: 1.00E-03, Speed: 207.184 samples/sec, ObjLoss=23.618, BoxCenterLoss=14.776, BoxScaleLoss=5.341, ClassLoss=10.169 [Epoch 58][Batch 1499], LR: 1.00E-03, Speed: 147.618 samples/sec, ObjLoss=23.615, BoxCenterLoss=14.776, BoxScaleLoss=5.341, ClassLoss=10.167 [Epoch 58][Batch 1599], LR: 1.00E-03, Speed: 162.360 samples/sec, ObjLoss=23.612, BoxCenterLoss=14.776, BoxScaleLoss=5.341, ClassLoss=10.165 [Epoch 58][Batch 1699], LR: 1.00E-03, Speed: 146.480 samples/sec, ObjLoss=23.609, BoxCenterLoss=14.775, BoxScaleLoss=5.340, ClassLoss=10.162 [Epoch 58][Batch 1799], LR: 1.00E-03, Speed: 183.230 samples/sec, ObjLoss=23.607, BoxCenterLoss=14.775, BoxScaleLoss=5.340, ClassLoss=10.160 [Epoch 58] Training cost: 1141.121, ObjLoss=23.606, BoxCenterLoss=14.775, BoxScaleLoss=5.340, ClassLoss=10.159 [Epoch 58] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.165 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.112 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 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 ] = 0.251 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 ] = 0.245 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.253 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 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368 person=27.9 bicycle=11.7 car=19.1 motorcycle=19.8 airplane=33.3 bus=38.3 train=32.6 truck=15.9 boat=10.3 traffic light=9.1 fire hydrant=32.3 stop sign=24.9 parking meter=14.6 bench=8.3 bird=12.1 cat=35.0 dog=29.2 horse=23.2 sheep=19.8 cow=24.2 elephant=31.9 bear=31.4 zebra=38.1 giraffe=36.8 backpack=3.6 umbrella=13.5 handbag=2.0 tie=8.3 suitcase=11.0 frisbee=22.9 skis=6.2 snowboard=9.7 sports ball=16.5 kite=20.4 baseball bat=9.8 baseball glove=12.6 skateboard=18.8 surfboard=13.0 tennis racket=18.7 bottle=10.4 wine glass=11.9 cup=16.2 fork=7.0 knife=2.2 spoon=1.7 bowl=15.8 banana=9.1 apple=3.9 sandwich=14.4 orange=7.8 broccoli=8.3 carrot=4.3 hot dog=10.6 pizza=23.8 donut=13.6 cake=11.7 chair=10.8 couch=20.0 potted plant=9.2 bed=22.8 dining table=15.3 toilet=26.8 tv=30.3 laptop=27.6 mouse=29.2 remote=5.5 keyboard=19.4 cell phone=10.0 microwave=27.8 oven=14.6 toaster=0.0 sink=12.9 refrigerator=23.6 book=3.5 clock=23.3 vase=14.5 scissors=8.9 teddy bear=19.7 hair drier=0.0 toothbrush=2.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.5 [Epoch 59][Batch 99], LR: 1.00E-03, Speed: 154.105 samples/sec, ObjLoss=23.603, BoxCenterLoss=14.775, BoxScaleLoss=5.339, ClassLoss=10.157 [Epoch 59][Batch 199], LR: 1.00E-03, Speed: 127.709 samples/sec, ObjLoss=23.600, BoxCenterLoss=14.774, BoxScaleLoss=5.338, ClassLoss=10.154 [Epoch 59][Batch 299], LR: 1.00E-03, Speed: 168.374 samples/sec, ObjLoss=23.598, BoxCenterLoss=14.774, BoxScaleLoss=5.338, ClassLoss=10.152 [Epoch 59][Batch 399], LR: 1.00E-03, Speed: 158.769 samples/sec, ObjLoss=23.595, BoxCenterLoss=14.774, BoxScaleLoss=5.338, ClassLoss=10.150 [Epoch 59][Batch 499], LR: 1.00E-03, Speed: 129.644 samples/sec, ObjLoss=23.593, BoxCenterLoss=14.774, BoxScaleLoss=5.337, ClassLoss=10.147 [Epoch 59][Batch 599], LR: 1.00E-03, Speed: 114.486 samples/sec, ObjLoss=23.590, BoxCenterLoss=14.773, BoxScaleLoss=5.337, ClassLoss=10.145 [Epoch 59][Batch 699], LR: 1.00E-03, Speed: 153.713 samples/sec, ObjLoss=23.588, BoxCenterLoss=14.773, BoxScaleLoss=5.336, ClassLoss=10.143 [Epoch 59][Batch 799], LR: 1.00E-03, Speed: 154.663 samples/sec, ObjLoss=23.585, BoxCenterLoss=14.773, BoxScaleLoss=5.336, ClassLoss=10.140 [Epoch 59][Batch 899], LR: 1.00E-03, Speed: 131.704 samples/sec, ObjLoss=23.583, BoxCenterLoss=14.773, BoxScaleLoss=5.335, ClassLoss=10.138 [Epoch 59][Batch 999], LR: 1.00E-03, Speed: 140.985 samples/sec, ObjLoss=23.580, BoxCenterLoss=14.773, BoxScaleLoss=5.335, ClassLoss=10.135 [Epoch 59][Batch 1099], LR: 1.00E-03, Speed: 103.483 samples/sec, ObjLoss=23.577, BoxCenterLoss=14.772, BoxScaleLoss=5.334, ClassLoss=10.133 [Epoch 59][Batch 1199], LR: 1.00E-03, Speed: 144.563 samples/sec, ObjLoss=23.574, BoxCenterLoss=14.772, BoxScaleLoss=5.334, ClassLoss=10.131 [Epoch 59][Batch 1299], LR: 1.00E-03, Speed: 117.990 samples/sec, ObjLoss=23.572, BoxCenterLoss=14.771, BoxScaleLoss=5.333, ClassLoss=10.129 [Epoch 59][Batch 1399], LR: 1.00E-03, Speed: 141.767 samples/sec, ObjLoss=23.569, BoxCenterLoss=14.771, BoxScaleLoss=5.333, ClassLoss=10.126 [Epoch 59][Batch 1499], LR: 1.00E-03, Speed: 131.108 samples/sec, ObjLoss=23.567, BoxCenterLoss=14.771, BoxScaleLoss=5.332, ClassLoss=10.124 [Epoch 59][Batch 1599], LR: 1.00E-03, Speed: 150.063 samples/sec, ObjLoss=23.565, BoxCenterLoss=14.771, BoxScaleLoss=5.332, ClassLoss=10.122 [Epoch 59][Batch 1699], LR: 1.00E-03, Speed: 121.738 samples/sec, ObjLoss=23.563, BoxCenterLoss=14.771, BoxScaleLoss=5.332, ClassLoss=10.120 [Epoch 59][Batch 1799], LR: 1.00E-03, Speed: 137.982 samples/sec, ObjLoss=23.561, BoxCenterLoss=14.771, BoxScaleLoss=5.331, ClassLoss=10.117 [Epoch 59] Training cost: 1164.277, ObjLoss=23.560, BoxCenterLoss=14.771, BoxScaleLoss=5.331, ClassLoss=10.116 [Epoch 59] 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 ] = 0.378 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.137 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 ] = 0.179 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 ] = 0.171 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 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 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 ] = 0.371 person=28.9 bicycle=13.3 car=17.7 motorcycle=20.3 airplane=35.8 bus=37.1 train=36.2 truck=16.5 boat=9.2 traffic light=8.4 fire hydrant=27.5 stop sign=36.1 parking meter=15.2 bench=8.9 bird=14.8 cat=40.5 dog=35.3 horse=29.2 sheep=21.6 cow=25.5 elephant=32.6 bear=42.2 zebra=39.2 giraffe=40.3 backpack=2.9 umbrella=19.0 handbag=2.2 tie=10.3 suitcase=11.2 frisbee=27.5 skis=7.0 snowboard=10.4 sports ball=19.0 kite=20.0 baseball bat=8.1 baseball glove=15.6 skateboard=19.6 surfboard=12.5 tennis racket=17.1 bottle=9.6 wine glass=9.2 cup=12.9 fork=6.7 knife=2.0 spoon=1.1 bowl=15.5 banana=9.9 apple=6.7 sandwich=17.4 orange=13.2 broccoli=8.4 carrot=5.5 hot dog=14.3 pizza=29.0 donut=16.4 cake=14.6 chair=10.3 couch=20.3 potted plant=8.2 bed=18.5 dining table=10.5 toilet=29.7 tv=31.9 laptop=28.4 mouse=26.0 remote=6.3 keyboard=24.4 cell phone=11.4 microwave=27.0 oven=14.4 toaster=0.0 sink=15.6 refrigerator=16.7 book=3.6 clock=27.3 vase=15.6 scissors=8.8 teddy bear=20.5 hair drier=0.0 toothbrush=1.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.6 [Epoch 60][Batch 99], LR: 1.00E-03, Speed: 149.490 samples/sec, ObjLoss=23.558, BoxCenterLoss=14.772, BoxScaleLoss=5.330, ClassLoss=10.114 [Epoch 60][Batch 199], LR: 1.00E-03, Speed: 125.168 samples/sec, ObjLoss=23.556, BoxCenterLoss=14.772, BoxScaleLoss=5.330, ClassLoss=10.112 [Epoch 60][Batch 299], LR: 1.00E-03, Speed: 119.278 samples/sec, ObjLoss=23.554, BoxCenterLoss=14.771, BoxScaleLoss=5.329, ClassLoss=10.109 [Epoch 60][Batch 399], LR: 1.00E-03, Speed: 142.251 samples/sec, ObjLoss=23.552, BoxCenterLoss=14.771, BoxScaleLoss=5.329, ClassLoss=10.107 [Epoch 60][Batch 499], LR: 1.00E-03, Speed: 125.045 samples/sec, ObjLoss=23.549, BoxCenterLoss=14.771, BoxScaleLoss=5.328, ClassLoss=10.104 [Epoch 60][Batch 599], LR: 1.00E-03, Speed: 123.451 samples/sec, ObjLoss=23.546, BoxCenterLoss=14.771, BoxScaleLoss=5.328, ClassLoss=10.102 [Epoch 60][Batch 699], LR: 1.00E-03, Speed: 147.444 samples/sec, ObjLoss=23.543, BoxCenterLoss=14.770, BoxScaleLoss=5.327, ClassLoss=10.100 [Epoch 60][Batch 799], LR: 1.00E-03, Speed: 144.718 samples/sec, ObjLoss=23.541, BoxCenterLoss=14.770, BoxScaleLoss=5.327, ClassLoss=10.097 [Epoch 60][Batch 899], LR: 1.00E-03, Speed: 130.256 samples/sec, ObjLoss=23.538, BoxCenterLoss=14.770, BoxScaleLoss=5.327, ClassLoss=10.095 [Epoch 60][Batch 999], LR: 1.00E-03, Speed: 140.340 samples/sec, ObjLoss=23.536, BoxCenterLoss=14.769, BoxScaleLoss=5.326, ClassLoss=10.093 [Epoch 60][Batch 1099], LR: 1.00E-03, Speed: 141.105 samples/sec, ObjLoss=23.533, BoxCenterLoss=14.769, BoxScaleLoss=5.325, ClassLoss=10.090 [Epoch 60][Batch 1199], LR: 1.00E-03, Speed: 90.808 samples/sec, ObjLoss=23.530, BoxCenterLoss=14.769, BoxScaleLoss=5.325, ClassLoss=10.088 [Epoch 60][Batch 1299], LR: 1.00E-03, Speed: 112.124 samples/sec, ObjLoss=23.528, BoxCenterLoss=14.769, BoxScaleLoss=5.324, ClassLoss=10.086 [Epoch 60][Batch 1399], LR: 1.00E-03, Speed: 95.142 samples/sec, ObjLoss=23.526, BoxCenterLoss=14.769, BoxScaleLoss=5.324, ClassLoss=10.084 [Epoch 60][Batch 1499], LR: 1.00E-03, Speed: 111.316 samples/sec, ObjLoss=23.524, BoxCenterLoss=14.769, BoxScaleLoss=5.324, ClassLoss=10.081 [Epoch 60][Batch 1599], LR: 1.00E-03, Speed: 117.574 samples/sec, ObjLoss=23.521, BoxCenterLoss=14.768, BoxScaleLoss=5.323, ClassLoss=10.080 [Epoch 60][Batch 1699], LR: 1.00E-03, Speed: 147.531 samples/sec, ObjLoss=23.518, BoxCenterLoss=14.768, BoxScaleLoss=5.323, ClassLoss=10.077 [Epoch 60][Batch 1799], LR: 1.00E-03, Speed: 165.751 samples/sec, ObjLoss=23.516, BoxCenterLoss=14.768, BoxScaleLoss=5.323, ClassLoss=10.076 [Epoch 60] Training cost: 1167.292, ObjLoss=23.515, BoxCenterLoss=14.768, BoxScaleLoss=5.323, ClassLoss=10.075 [Epoch 60] 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 ] = 0.380 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.069 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.175 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.268 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 ] = 0.255 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 ] = 0.114 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 person=29.5 bicycle=10.8 car=18.2 motorcycle=21.4 airplane=34.4 bus=40.6 train=43.1 truck=16.5 boat=8.7 traffic light=10.4 fire hydrant=30.4 stop sign=38.2 parking meter=16.6 bench=7.9 bird=15.6 cat=39.6 dog=32.2 horse=28.2 sheep=22.4 cow=26.9 elephant=34.5 bear=43.0 zebra=38.1 giraffe=37.4 backpack=3.8 umbrella=16.1 handbag=2.6 tie=11.0 suitcase=9.8 frisbee=25.0 skis=4.6 snowboard=8.3 sports ball=17.4 kite=17.6 baseball bat=6.7 baseball glove=15.4 skateboard=14.9 surfboard=10.1 tennis racket=11.9 bottle=11.4 wine glass=12.5 cup=16.2 fork=7.4 knife=2.5 spoon=1.8 bowl=17.3 banana=7.6 apple=5.6 sandwich=17.1 orange=9.2 broccoli=8.3 carrot=4.9 hot dog=11.2 pizza=29.8 donut=17.9 cake=11.5 chair=9.6 couch=23.3 potted plant=7.9 bed=25.0 dining table=13.0 toilet=32.0 tv=31.6 laptop=30.6 mouse=28.7 remote=7.4 keyboard=19.3 cell phone=13.1 microwave=23.6 oven=17.2 toaster=0.0 sink=13.4 refrigerator=23.4 book=4.2 clock=28.0 vase=16.0 scissors=12.7 teddy bear=19.3 hair drier=0.0 toothbrush=1.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.7 [Epoch 61][Batch 99], LR: 1.00E-03, Speed: 135.607 samples/sec, ObjLoss=23.513, BoxCenterLoss=14.768, BoxScaleLoss=5.322, ClassLoss=10.072 [Epoch 61][Batch 199], LR: 1.00E-03, Speed: 128.395 samples/sec, ObjLoss=23.511, BoxCenterLoss=14.768, BoxScaleLoss=5.321, ClassLoss=10.070 [Epoch 61][Batch 299], LR: 1.00E-03, Speed: 127.082 samples/sec, ObjLoss=23.508, BoxCenterLoss=14.768, BoxScaleLoss=5.321, ClassLoss=10.067 [Epoch 61][Batch 399], LR: 1.00E-03, Speed: 137.716 samples/sec, ObjLoss=23.506, BoxCenterLoss=14.768, BoxScaleLoss=5.320, ClassLoss=10.065 [Epoch 61][Batch 499], LR: 1.00E-03, Speed: 132.203 samples/sec, ObjLoss=23.504, BoxCenterLoss=14.767, BoxScaleLoss=5.320, ClassLoss=10.063 [Epoch 61][Batch 599], LR: 1.00E-03, Speed: 125.537 samples/sec, ObjLoss=23.501, BoxCenterLoss=14.767, BoxScaleLoss=5.319, ClassLoss=10.061 [Epoch 61][Batch 699], LR: 1.00E-03, Speed: 150.031 samples/sec, ObjLoss=23.499, BoxCenterLoss=14.767, BoxScaleLoss=5.319, ClassLoss=10.058 [Epoch 61][Batch 799], LR: 1.00E-03, Speed: 99.118 samples/sec, ObjLoss=23.496, BoxCenterLoss=14.767, BoxScaleLoss=5.319, ClassLoss=10.056 [Epoch 61][Batch 899], LR: 1.00E-03, Speed: 105.908 samples/sec, ObjLoss=23.492, BoxCenterLoss=14.766, BoxScaleLoss=5.318, ClassLoss=10.054 [Epoch 61][Batch 999], LR: 1.00E-03, Speed: 162.256 samples/sec, ObjLoss=23.490, BoxCenterLoss=14.766, BoxScaleLoss=5.318, ClassLoss=10.052 [Epoch 61][Batch 1099], LR: 1.00E-03, Speed: 100.199 samples/sec, ObjLoss=23.488, BoxCenterLoss=14.766, BoxScaleLoss=5.317, ClassLoss=10.050 [Epoch 61][Batch 1199], LR: 1.00E-03, Speed: 100.729 samples/sec, ObjLoss=23.485, BoxCenterLoss=14.766, BoxScaleLoss=5.317, ClassLoss=10.047 [Epoch 61][Batch 1299], LR: 1.00E-03, Speed: 128.300 samples/sec, ObjLoss=23.483, BoxCenterLoss=14.765, BoxScaleLoss=5.317, ClassLoss=10.045 [Epoch 61][Batch 1399], LR: 1.00E-03, Speed: 109.895 samples/sec, ObjLoss=23.481, BoxCenterLoss=14.765, BoxScaleLoss=5.316, ClassLoss=10.043 [Epoch 61][Batch 1499], LR: 1.00E-03, Speed: 143.903 samples/sec, ObjLoss=23.478, BoxCenterLoss=14.765, BoxScaleLoss=5.316, ClassLoss=10.041 [Epoch 61][Batch 1599], LR: 1.00E-03, Speed: 65.584 samples/sec, ObjLoss=23.476, BoxCenterLoss=14.765, BoxScaleLoss=5.315, ClassLoss=10.039 [Epoch 61][Batch 1699], LR: 1.00E-03, Speed: 143.913 samples/sec, ObjLoss=23.474, BoxCenterLoss=14.765, BoxScaleLoss=5.315, ClassLoss=10.037 [Epoch 61][Batch 1799], LR: 1.00E-03, Speed: 109.081 samples/sec, ObjLoss=23.471, BoxCenterLoss=14.764, BoxScaleLoss=5.315, ClassLoss=10.035 [Epoch 61] Training cost: 1181.686, ObjLoss=23.470, BoxCenterLoss=14.764, BoxScaleLoss=5.314, ClassLoss=10.034 [Epoch 61] 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 ] = 0.387 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.152 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 ] = 0.188 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.285 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 ] = 0.261 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 ] = 0.110 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.400 person=30.2 bicycle=13.4 car=19.8 motorcycle=23.3 airplane=33.5 bus=38.9 train=33.7 truck=17.9 boat=8.0 traffic light=8.5 fire hydrant=32.7 stop sign=36.4 parking meter=21.5 bench=8.7 bird=14.4 cat=39.9 dog=31.1 horse=30.4 sheep=22.6 cow=28.8 elephant=35.4 bear=39.8 zebra=40.5 giraffe=41.1 backpack=4.7 umbrella=16.7 handbag=2.3 tie=8.6 suitcase=13.7 frisbee=30.6 skis=4.3 snowboard=10.1 sports ball=14.2 kite=19.4 baseball bat=8.7 baseball glove=13.3 skateboard=16.8 surfboard=14.9 tennis racket=18.4 bottle=11.9 wine glass=12.2 cup=18.2 fork=6.4 knife=2.7 spoon=2.1 bowl=19.2 banana=10.0 apple=6.4 sandwich=15.3 orange=14.6 broccoli=9.0 carrot=6.0 hot dog=10.3 pizza=28.2 donut=22.5 cake=12.9 chair=10.3 couch=22.6 potted plant=9.7 bed=17.4 dining table=11.3 toilet=31.3 tv=33.1 laptop=30.5 mouse=29.5 remote=6.5 keyboard=25.0 cell phone=13.0 microwave=30.5 oven=16.2 toaster=0.0 sink=17.3 refrigerator=23.6 book=3.2 clock=28.8 vase=15.1 scissors=12.3 teddy bear=22.0 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.3 [Epoch 62][Batch 99], LR: 1.00E-03, Speed: 139.126 samples/sec, ObjLoss=23.467, BoxCenterLoss=14.764, BoxScaleLoss=5.314, ClassLoss=10.032 [Epoch 62][Batch 199], LR: 1.00E-03, Speed: 107.978 samples/sec, ObjLoss=23.465, BoxCenterLoss=14.764, BoxScaleLoss=5.314, ClassLoss=10.030 [Epoch 62][Batch 299], LR: 1.00E-03, Speed: 146.305 samples/sec, ObjLoss=23.462, BoxCenterLoss=14.764, BoxScaleLoss=5.313, ClassLoss=10.028 [Epoch 62][Batch 399], LR: 1.00E-03, Speed: 141.450 samples/sec, ObjLoss=23.460, BoxCenterLoss=14.763, BoxScaleLoss=5.313, ClassLoss=10.025 [Epoch 62][Batch 499], LR: 1.00E-03, Speed: 160.078 samples/sec, ObjLoss=23.458, BoxCenterLoss=14.763, BoxScaleLoss=5.312, ClassLoss=10.023 [Epoch 62][Batch 599], LR: 1.00E-03, Speed: 79.333 samples/sec, ObjLoss=23.455, BoxCenterLoss=14.763, BoxScaleLoss=5.312, ClassLoss=10.020 [Epoch 62][Batch 699], LR: 1.00E-03, Speed: 121.504 samples/sec, ObjLoss=23.453, BoxCenterLoss=14.762, BoxScaleLoss=5.311, ClassLoss=10.018 [Epoch 62][Batch 799], LR: 1.00E-03, Speed: 125.416 samples/sec, ObjLoss=23.450, BoxCenterLoss=14.762, BoxScaleLoss=5.311, ClassLoss=10.016 [Epoch 62][Batch 899], LR: 1.00E-03, Speed: 136.803 samples/sec, ObjLoss=23.447, BoxCenterLoss=14.762, BoxScaleLoss=5.310, ClassLoss=10.014 [Epoch 62][Batch 999], LR: 1.00E-03, Speed: 102.213 samples/sec, ObjLoss=23.445, BoxCenterLoss=14.762, BoxScaleLoss=5.310, ClassLoss=10.012 [Epoch 62][Batch 1099], LR: 1.00E-03, Speed: 120.273 samples/sec, ObjLoss=23.443, BoxCenterLoss=14.762, BoxScaleLoss=5.310, ClassLoss=10.010 [Epoch 62][Batch 1199], LR: 1.00E-03, Speed: 120.490 samples/sec, ObjLoss=23.440, BoxCenterLoss=14.762, BoxScaleLoss=5.309, ClassLoss=10.008 [Epoch 62][Batch 1299], LR: 1.00E-03, Speed: 156.866 samples/sec, ObjLoss=23.438, BoxCenterLoss=14.762, BoxScaleLoss=5.309, ClassLoss=10.005 [Epoch 62][Batch 1399], LR: 1.00E-03, Speed: 124.793 samples/sec, ObjLoss=23.436, BoxCenterLoss=14.762, BoxScaleLoss=5.308, ClassLoss=10.003 [Epoch 62][Batch 1499], LR: 1.00E-03, Speed: 130.459 samples/sec, ObjLoss=23.434, BoxCenterLoss=14.761, BoxScaleLoss=5.308, ClassLoss=10.001 [Epoch 62][Batch 1599], LR: 1.00E-03, Speed: 133.511 samples/sec, ObjLoss=23.431, BoxCenterLoss=14.761, BoxScaleLoss=5.307, ClassLoss=9.999 [Epoch 62][Batch 1699], LR: 1.00E-03, Speed: 109.033 samples/sec, ObjLoss=23.429, BoxCenterLoss=14.761, BoxScaleLoss=5.307, ClassLoss=9.997 [Epoch 62][Batch 1799], LR: 1.00E-03, Speed: 144.655 samples/sec, ObjLoss=23.427, BoxCenterLoss=14.761, BoxScaleLoss=5.307, ClassLoss=9.995 [Epoch 62] Training cost: 1162.630, ObjLoss=23.426, BoxCenterLoss=14.760, BoxScaleLoss=5.307, ClassLoss=9.994 [Epoch 62] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.171 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.382 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.126 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 ] = 0.174 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 ] = 0.169 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 ] = 0.258 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 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.381 person=29.0 bicycle=11.0 car=17.7 motorcycle=15.0 airplane=32.6 bus=35.9 train=39.4 truck=15.5 boat=7.2 traffic light=10.2 fire hydrant=35.7 stop sign=27.2 parking meter=21.1 bench=10.5 bird=14.5 cat=30.5 dog=28.0 horse=29.9 sheep=22.7 cow=26.2 elephant=35.1 bear=33.7 zebra=41.9 giraffe=38.0 backpack=2.7 umbrella=13.9 handbag=2.0 tie=11.7 suitcase=9.9 frisbee=22.2 skis=6.3 snowboard=9.5 sports ball=22.2 kite=16.8 baseball bat=8.4 baseball glove=15.6 skateboard=18.2 surfboard=12.0 tennis racket=20.4 bottle=12.3 wine glass=13.0 cup=18.5 fork=8.2 knife=3.3 spoon=1.3 bowl=15.9 banana=9.2 apple=5.7 sandwich=8.8 orange=14.7 broccoli=9.3 carrot=7.4 hot dog=11.7 pizza=23.6 donut=14.7 cake=11.2 chair=9.7 couch=26.4 potted plant=8.1 bed=22.9 dining table=15.0 toilet=30.6 tv=30.5 laptop=27.3 mouse=21.7 remote=5.8 keyboard=21.8 cell phone=11.8 microwave=21.9 oven=15.9 toaster=0.0 sink=14.3 refrigerator=20.4 book=3.4 clock=23.1 vase=14.0 scissors=7.7 teddy bear=19.9 hair drier=0.0 toothbrush=2.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.1 [Epoch 63][Batch 99], LR: 1.00E-03, Speed: 156.675 samples/sec, ObjLoss=23.424, BoxCenterLoss=14.761, BoxScaleLoss=5.306, ClassLoss=9.992 [Epoch 63][Batch 199], LR: 1.00E-03, Speed: 107.824 samples/sec, ObjLoss=23.422, BoxCenterLoss=14.760, BoxScaleLoss=5.306, ClassLoss=9.990 [Epoch 63][Batch 299], LR: 1.00E-03, Speed: 116.255 samples/sec, ObjLoss=23.419, BoxCenterLoss=14.760, BoxScaleLoss=5.305, ClassLoss=9.988 [Epoch 63][Batch 399], LR: 1.00E-03, Speed: 137.813 samples/sec, ObjLoss=23.417, BoxCenterLoss=14.760, BoxScaleLoss=5.305, ClassLoss=9.986 [Epoch 63][Batch 499], LR: 1.00E-03, Speed: 113.836 samples/sec, ObjLoss=23.415, BoxCenterLoss=14.760, BoxScaleLoss=5.305, ClassLoss=9.984 [Epoch 63][Batch 599], LR: 1.00E-03, Speed: 130.239 samples/sec, ObjLoss=23.412, BoxCenterLoss=14.759, BoxScaleLoss=5.304, ClassLoss=9.981 [Epoch 63][Batch 699], LR: 1.00E-03, Speed: 82.763 samples/sec, ObjLoss=23.409, BoxCenterLoss=14.759, BoxScaleLoss=5.304, ClassLoss=9.979 [Epoch 63][Batch 799], LR: 1.00E-03, Speed: 97.687 samples/sec, ObjLoss=23.407, BoxCenterLoss=14.759, BoxScaleLoss=5.303, ClassLoss=9.977 [Epoch 63][Batch 899], LR: 1.00E-03, Speed: 130.259 samples/sec, ObjLoss=23.405, BoxCenterLoss=14.759, BoxScaleLoss=5.303, ClassLoss=9.975 [Epoch 63][Batch 999], LR: 1.00E-03, Speed: 116.351 samples/sec, ObjLoss=23.402, BoxCenterLoss=14.758, BoxScaleLoss=5.303, ClassLoss=9.973 [Epoch 63][Batch 1099], LR: 1.00E-03, Speed: 87.138 samples/sec, ObjLoss=23.400, BoxCenterLoss=14.758, BoxScaleLoss=5.302, ClassLoss=9.971 [Epoch 63][Batch 1199], LR: 1.00E-03, Speed: 65.548 samples/sec, ObjLoss=23.398, BoxCenterLoss=14.758, BoxScaleLoss=5.302, ClassLoss=9.968 [Epoch 63][Batch 1299], LR: 1.00E-03, Speed: 88.930 samples/sec, ObjLoss=23.395, BoxCenterLoss=14.758, BoxScaleLoss=5.301, ClassLoss=9.966 [Epoch 63][Batch 1399], LR: 1.00E-03, Speed: 89.303 samples/sec, ObjLoss=23.393, BoxCenterLoss=14.757, BoxScaleLoss=5.300, ClassLoss=9.964 [Epoch 63][Batch 1499], LR: 1.00E-03, Speed: 162.807 samples/sec, ObjLoss=23.391, BoxCenterLoss=14.757, BoxScaleLoss=5.300, ClassLoss=9.962 [Epoch 63][Batch 1599], LR: 1.00E-03, Speed: 149.576 samples/sec, ObjLoss=23.388, BoxCenterLoss=14.757, BoxScaleLoss=5.299, ClassLoss=9.959 [Epoch 63][Batch 1699], LR: 1.00E-03, Speed: 156.377 samples/sec, ObjLoss=23.386, BoxCenterLoss=14.757, BoxScaleLoss=5.299, ClassLoss=9.958 [Epoch 63][Batch 1799], LR: 1.00E-03, Speed: 152.207 samples/sec, ObjLoss=23.384, BoxCenterLoss=14.756, BoxScaleLoss=5.299, ClassLoss=9.956 [Epoch 63] Training cost: 1184.470, ObjLoss=23.383, BoxCenterLoss=14.756, BoxScaleLoss=5.299, ClassLoss=9.955 [Epoch 63] 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 ] = 0.388 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.149 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 ] = 0.174 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288 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 ] = 0.259 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 ] = 0.110 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.402 person=29.8 bicycle=13.9 car=19.3 motorcycle=21.3 airplane=36.8 bus=38.3 train=42.2 truck=15.8 boat=9.3 traffic light=10.0 fire hydrant=35.4 stop sign=38.7 parking meter=20.5 bench=8.7 bird=17.2 cat=40.0 dog=32.2 horse=29.2 sheep=23.8 cow=27.5 elephant=33.0 bear=37.5 zebra=36.9 giraffe=40.7 backpack=3.6 umbrella=17.8 handbag=2.3 tie=11.6 suitcase=12.1 frisbee=26.4 skis=6.1 snowboard=10.2 sports ball=19.5 kite=18.1 baseball bat=8.2 baseball glove=9.8 skateboard=16.8 surfboard=12.9 tennis racket=18.1 bottle=13.6 wine glass=12.5 cup=15.4 fork=7.7 knife=2.6 spoon=1.7 bowl=16.0 banana=8.8 apple=6.1 sandwich=17.0 orange=14.3 broccoli=10.4 carrot=6.9 hot dog=10.3 pizza=27.2 donut=20.3 cake=14.7 chair=10.4 couch=25.4 potted plant=8.6 bed=19.4 dining table=14.0 toilet=35.4 tv=28.6 laptop=29.7 mouse=25.4 remote=6.4 keyboard=18.9 cell phone=13.1 microwave=24.9 oven=17.4 toaster=0.0 sink=16.9 refrigerator=24.7 book=2.8 clock=27.2 vase=17.6 scissors=14.1 teddy bear=21.7 hair drier=0.0 toothbrush=1.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.3 [Epoch 64][Batch 99], LR: 1.00E-03, Speed: 165.281 samples/sec, ObjLoss=23.381, BoxCenterLoss=14.756, BoxScaleLoss=5.298, ClassLoss=9.953 [Epoch 64][Batch 199], LR: 1.00E-03, Speed: 159.016 samples/sec, ObjLoss=23.378, BoxCenterLoss=14.756, BoxScaleLoss=5.298, ClassLoss=9.951 [Epoch 64][Batch 299], LR: 1.00E-03, Speed: 118.548 samples/sec, ObjLoss=23.375, BoxCenterLoss=14.755, BoxScaleLoss=5.298, ClassLoss=9.948 [Epoch 64][Batch 399], LR: 1.00E-03, Speed: 123.132 samples/sec, ObjLoss=23.374, BoxCenterLoss=14.755, BoxScaleLoss=5.297, ClassLoss=9.946 [Epoch 64][Batch 499], LR: 1.00E-03, Speed: 111.021 samples/sec, ObjLoss=23.371, BoxCenterLoss=14.755, BoxScaleLoss=5.297, ClassLoss=9.944 [Epoch 64][Batch 599], LR: 1.00E-03, Speed: 120.267 samples/sec, ObjLoss=23.369, BoxCenterLoss=14.755, BoxScaleLoss=5.296, ClassLoss=9.942 [Epoch 64][Batch 699], LR: 1.00E-03, Speed: 135.191 samples/sec, ObjLoss=23.366, BoxCenterLoss=14.755, BoxScaleLoss=5.296, ClassLoss=9.940 [Epoch 64][Batch 799], LR: 1.00E-03, Speed: 76.599 samples/sec, ObjLoss=23.364, BoxCenterLoss=14.755, BoxScaleLoss=5.296, ClassLoss=9.938 [Epoch 64][Batch 899], LR: 1.00E-03, Speed: 129.900 samples/sec, ObjLoss=23.362, BoxCenterLoss=14.755, BoxScaleLoss=5.295, ClassLoss=9.936 [Epoch 64][Batch 999], LR: 1.00E-03, Speed: 132.659 samples/sec, ObjLoss=23.360, BoxCenterLoss=14.755, BoxScaleLoss=5.295, ClassLoss=9.934 [Epoch 64][Batch 1099], LR: 1.00E-03, Speed: 118.734 samples/sec, ObjLoss=23.358, BoxCenterLoss=14.755, BoxScaleLoss=5.295, ClassLoss=9.932 [Epoch 64][Batch 1199], LR: 1.00E-03, Speed: 91.687 samples/sec, ObjLoss=23.356, BoxCenterLoss=14.754, BoxScaleLoss=5.294, ClassLoss=9.930 [Epoch 64][Batch 1299], LR: 1.00E-03, Speed: 101.767 samples/sec, ObjLoss=23.354, BoxCenterLoss=14.754, BoxScaleLoss=5.294, ClassLoss=9.928 [Epoch 64][Batch 1399], LR: 1.00E-03, Speed: 126.300 samples/sec, ObjLoss=23.351, BoxCenterLoss=14.754, BoxScaleLoss=5.293, ClassLoss=9.926 [Epoch 64][Batch 1499], LR: 1.00E-03, Speed: 164.565 samples/sec, ObjLoss=23.349, BoxCenterLoss=14.753, BoxScaleLoss=5.293, ClassLoss=9.924 [Epoch 64][Batch 1599], LR: 1.00E-03, Speed: 129.070 samples/sec, ObjLoss=23.346, BoxCenterLoss=14.753, BoxScaleLoss=5.292, ClassLoss=9.922 [Epoch 64][Batch 1699], LR: 1.00E-03, Speed: 135.899 samples/sec, ObjLoss=23.344, BoxCenterLoss=14.753, BoxScaleLoss=5.292, ClassLoss=9.920 [Epoch 64][Batch 1799], LR: 1.00E-03, Speed: 146.779 samples/sec, ObjLoss=23.342, BoxCenterLoss=14.753, BoxScaleLoss=5.291, ClassLoss=9.918 [Epoch 64] Training cost: 1205.797, ObjLoss=23.341, BoxCenterLoss=14.753, BoxScaleLoss=5.291, ClassLoss=9.917 [Epoch 64] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.171 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.388 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.123 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 ] = 0.169 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 ] = 0.262 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 ] = 0.261 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371 person=27.9 bicycle=12.8 car=18.9 motorcycle=21.2 airplane=31.9 bus=38.2 train=39.6 truck=14.5 boat=10.0 traffic light=9.4 fire hydrant=32.8 stop sign=29.8 parking meter=14.9 bench=8.2 bird=11.6 cat=35.6 dog=30.2 horse=25.8 sheep=22.7 cow=21.2 elephant=29.1 bear=34.6 zebra=27.7 giraffe=31.5 backpack=3.3 umbrella=16.4 handbag=2.2 tie=12.1 suitcase=11.2 frisbee=25.7 skis=6.3 snowboard=9.6 sports ball=20.6 kite=16.4 baseball bat=9.2 baseball glove=15.2 skateboard=18.0 surfboard=11.6 tennis racket=15.6 bottle=12.6 wine glass=8.9 cup=16.8 fork=6.6 knife=2.7 spoon=1.9 bowl=16.6 banana=10.3 apple=6.7 sandwich=12.8 orange=13.2 broccoli=8.2 carrot=5.4 hot dog=9.4 pizza=22.1 donut=18.5 cake=16.3 chair=10.5 couch=22.3 potted plant=7.9 bed=25.9 dining table=15.2 toilet=27.3 tv=33.0 laptop=28.2 mouse=31.8 remote=6.9 keyboard=20.5 cell phone=12.2 microwave=25.0 oven=17.4 toaster=0.0 sink=17.2 refrigerator=26.5 book=4.3 clock=28.7 vase=13.4 scissors=11.8 teddy bear=18.6 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.1 [Epoch 65][Batch 99], LR: 1.00E-03, Speed: 145.079 samples/sec, ObjLoss=23.339, BoxCenterLoss=14.753, BoxScaleLoss=5.291, ClassLoss=9.915 [Epoch 65][Batch 199], LR: 1.00E-03, Speed: 130.192 samples/sec, ObjLoss=23.337, BoxCenterLoss=14.753, BoxScaleLoss=5.291, ClassLoss=9.913 [Epoch 65][Batch 299], LR: 1.00E-03, Speed: 130.667 samples/sec, ObjLoss=23.334, BoxCenterLoss=14.752, BoxScaleLoss=5.290, ClassLoss=9.911 [Epoch 65][Batch 399], LR: 1.00E-03, Speed: 126.437 samples/sec, ObjLoss=23.332, BoxCenterLoss=14.752, BoxScaleLoss=5.290, ClassLoss=9.909 [Epoch 65][Batch 499], LR: 1.00E-03, Speed: 115.256 samples/sec, ObjLoss=23.330, BoxCenterLoss=14.752, BoxScaleLoss=5.289, ClassLoss=9.907 [Epoch 65][Batch 599], LR: 1.00E-03, Speed: 153.903 samples/sec, ObjLoss=23.328, BoxCenterLoss=14.752, BoxScaleLoss=5.289, ClassLoss=9.905 [Epoch 65][Batch 699], LR: 1.00E-03, Speed: 80.645 samples/sec, ObjLoss=23.325, BoxCenterLoss=14.752, BoxScaleLoss=5.288, ClassLoss=9.902 [Epoch 65][Batch 799], LR: 1.00E-03, Speed: 115.740 samples/sec, ObjLoss=23.323, BoxCenterLoss=14.751, BoxScaleLoss=5.288, ClassLoss=9.900 [Epoch 65][Batch 899], LR: 1.00E-03, Speed: 102.781 samples/sec, ObjLoss=23.321, BoxCenterLoss=14.751, BoxScaleLoss=5.288, ClassLoss=9.898 [Epoch 65][Batch 999], LR: 1.00E-03, Speed: 91.165 samples/sec, ObjLoss=23.318, BoxCenterLoss=14.751, BoxScaleLoss=5.287, ClassLoss=9.896 [Epoch 65][Batch 1099], LR: 1.00E-03, Speed: 111.020 samples/sec, ObjLoss=23.316, BoxCenterLoss=14.750, BoxScaleLoss=5.287, ClassLoss=9.894 [Epoch 65][Batch 1199], LR: 1.00E-03, Speed: 139.642 samples/sec, ObjLoss=23.313, BoxCenterLoss=14.750, BoxScaleLoss=5.286, ClassLoss=9.892 [Epoch 65][Batch 1299], LR: 1.00E-03, Speed: 150.444 samples/sec, ObjLoss=23.311, BoxCenterLoss=14.750, BoxScaleLoss=5.286, ClassLoss=9.890 [Epoch 65][Batch 1399], LR: 1.00E-03, Speed: 63.108 samples/sec, ObjLoss=23.309, BoxCenterLoss=14.750, BoxScaleLoss=5.285, ClassLoss=9.888 [Epoch 65][Batch 1499], LR: 1.00E-03, Speed: 77.588 samples/sec, ObjLoss=23.307, BoxCenterLoss=14.749, BoxScaleLoss=5.285, ClassLoss=9.886 [Epoch 65][Batch 1599], LR: 1.00E-03, Speed: 117.967 samples/sec, ObjLoss=23.305, BoxCenterLoss=14.750, BoxScaleLoss=5.285, ClassLoss=9.884 [Epoch 65][Batch 1699], LR: 1.00E-03, Speed: 133.068 samples/sec, ObjLoss=23.303, BoxCenterLoss=14.749, BoxScaleLoss=5.284, ClassLoss=9.882 [Epoch 65][Batch 1799], LR: 1.00E-03, Speed: 173.542 samples/sec, ObjLoss=23.301, BoxCenterLoss=14.749, BoxScaleLoss=5.284, ClassLoss=9.881 [Epoch 65] Training cost: 1205.979, ObjLoss=23.300, BoxCenterLoss=14.749, BoxScaleLoss=5.284, ClassLoss=9.880 [Epoch 65] 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 ] = 0.384 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.143 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 ] = 0.178 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 ] = 0.177 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.260 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.267 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 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.391 person=30.0 bicycle=13.9 car=17.6 motorcycle=23.1 airplane=33.4 bus=38.1 train=35.1 truck=16.5 boat=8.3 traffic light=8.3 fire hydrant=34.1 stop sign=35.8 parking meter=16.1 bench=9.8 bird=15.6 cat=35.8 dog=31.0 horse=27.9 sheep=24.3 cow=26.3 elephant=36.0 bear=36.3 zebra=38.9 giraffe=41.2 backpack=4.2 umbrella=15.6 handbag=2.2 tie=13.2 suitcase=9.5 frisbee=31.1 skis=6.2 snowboard=13.2 sports ball=21.7 kite=17.7 baseball bat=8.5 baseball glove=17.2 skateboard=19.4 surfboard=13.3 tennis racket=18.7 bottle=14.1 wine glass=11.7 cup=16.8 fork=6.8 knife=3.2 spoon=1.3 bowl=18.4 banana=11.7 apple=5.8 sandwich=15.8 orange=11.1 broccoli=8.5 carrot=5.8 hot dog=12.0 pizza=24.0 donut=20.1 cake=12.0 chair=10.1 couch=22.6 potted plant=7.9 bed=23.3 dining table=12.0 toilet=29.0 tv=30.9 laptop=30.4 mouse=27.9 remote=5.3 keyboard=20.0 cell phone=11.5 microwave=25.9 oven=15.2 toaster=0.0 sink=17.1 refrigerator=22.4 book=3.9 clock=25.7 vase=15.2 scissors=10.1 teddy bear=22.2 hair drier=0.0 toothbrush=1.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.9 [Epoch 66][Batch 99], LR: 1.00E-03, Speed: 128.144 samples/sec, ObjLoss=23.297, BoxCenterLoss=14.749, BoxScaleLoss=5.284, ClassLoss=9.878 [Epoch 66][Batch 199], LR: 1.00E-03, Speed: 157.868 samples/sec, ObjLoss=23.295, BoxCenterLoss=14.749, BoxScaleLoss=5.283, ClassLoss=9.876 [Epoch 66][Batch 299], LR: 1.00E-03, Speed: 158.726 samples/sec, ObjLoss=23.293, BoxCenterLoss=14.748, BoxScaleLoss=5.283, ClassLoss=9.874 [Epoch 66][Batch 399], LR: 1.00E-03, Speed: 134.670 samples/sec, ObjLoss=23.291, BoxCenterLoss=14.748, BoxScaleLoss=5.282, ClassLoss=9.872 [Epoch 66][Batch 499], LR: 1.00E-03, Speed: 104.438 samples/sec, ObjLoss=23.288, BoxCenterLoss=14.748, BoxScaleLoss=5.282, ClassLoss=9.870 [Epoch 66][Batch 599], LR: 1.00E-03, Speed: 136.740 samples/sec, ObjLoss=23.286, BoxCenterLoss=14.748, BoxScaleLoss=5.281, ClassLoss=9.868 [Epoch 66][Batch 699], LR: 1.00E-03, Speed: 146.468 samples/sec, ObjLoss=23.284, BoxCenterLoss=14.748, BoxScaleLoss=5.281, ClassLoss=9.866 [Epoch 66][Batch 799], LR: 1.00E-03, Speed: 104.116 samples/sec, ObjLoss=23.281, BoxCenterLoss=14.747, BoxScaleLoss=5.281, ClassLoss=9.864 [Epoch 66][Batch 899], LR: 1.00E-03, Speed: 105.201 samples/sec, ObjLoss=23.279, BoxCenterLoss=14.747, BoxScaleLoss=5.280, ClassLoss=9.862 [Epoch 66][Batch 999], LR: 1.00E-03, Speed: 141.865 samples/sec, ObjLoss=23.277, BoxCenterLoss=14.747, BoxScaleLoss=5.280, ClassLoss=9.860 [Epoch 66][Batch 1099], LR: 1.00E-03, Speed: 147.003 samples/sec, ObjLoss=23.275, BoxCenterLoss=14.747, BoxScaleLoss=5.279, ClassLoss=9.858 [Epoch 66][Batch 1199], LR: 1.00E-03, Speed: 157.040 samples/sec, ObjLoss=23.273, BoxCenterLoss=14.747, BoxScaleLoss=5.279, ClassLoss=9.856 [Epoch 66][Batch 1299], LR: 1.00E-03, Speed: 90.321 samples/sec, ObjLoss=23.271, BoxCenterLoss=14.747, BoxScaleLoss=5.279, ClassLoss=9.854 [Epoch 66][Batch 1399], LR: 1.00E-03, Speed: 125.581 samples/sec, ObjLoss=23.269, BoxCenterLoss=14.746, BoxScaleLoss=5.278, ClassLoss=9.852 [Epoch 66][Batch 1499], LR: 1.00E-03, Speed: 169.386 samples/sec, ObjLoss=23.266, BoxCenterLoss=14.746, BoxScaleLoss=5.278, ClassLoss=9.850 [Epoch 66][Batch 1599], LR: 1.00E-03, Speed: 122.487 samples/sec, ObjLoss=23.265, BoxCenterLoss=14.746, BoxScaleLoss=5.277, ClassLoss=9.849 [Epoch 66][Batch 1699], LR: 1.00E-03, Speed: 148.380 samples/sec, ObjLoss=23.263, BoxCenterLoss=14.746, BoxScaleLoss=5.277, ClassLoss=9.847 [Epoch 66][Batch 1799], LR: 1.00E-03, Speed: 134.701 samples/sec, ObjLoss=23.261, BoxCenterLoss=14.746, BoxScaleLoss=5.277, ClassLoss=9.845 [Epoch 66] Training cost: 1203.179, ObjLoss=23.260, BoxCenterLoss=14.746, BoxScaleLoss=5.276, ClassLoss=9.844 [Epoch 66] 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 ] = 0.389 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.136 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 ] = 0.191 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 ] = 0.171 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 ] = 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.107 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371 person=29.3 bicycle=13.4 car=20.0 motorcycle=23.2 airplane=32.2 bus=30.3 train=34.4 truck=16.2 boat=10.2 traffic light=10.3 fire hydrant=27.4 stop sign=26.4 parking meter=18.0 bench=7.9 bird=16.1 cat=37.4 dog=30.9 horse=27.1 sheep=24.4 cow=23.7 elephant=34.3 bear=38.1 zebra=38.8 giraffe=32.2 backpack=3.8 umbrella=17.4 handbag=2.6 tie=11.0 suitcase=13.2 frisbee=28.8 skis=6.1 snowboard=11.2 sports ball=21.1 kite=19.6 baseball bat=10.4 baseball glove=15.1 skateboard=18.9 surfboard=13.4 tennis racket=19.6 bottle=13.0 wine glass=14.8 cup=17.8 fork=6.1 knife=2.0 spoon=1.5 bowl=17.8 banana=9.5 apple=5.7 sandwich=12.1 orange=13.3 broccoli=9.6 carrot=6.6 hot dog=10.1 pizza=26.4 donut=17.2 cake=16.2 chair=9.5 couch=23.5 potted plant=8.7 bed=21.7 dining table=11.1 toilet=31.7 tv=30.9 laptop=29.8 mouse=32.1 remote=7.4 keyboard=25.5 cell phone=13.7 microwave=27.2 oven=13.0 toaster=0.0 sink=17.2 refrigerator=18.5 book=4.3 clock=28.3 vase=16.2 scissors=11.4 teddy bear=23.2 hair drier=0.0 toothbrush=2.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.8 [Epoch 67][Batch 99], LR: 1.00E-03, Speed: 142.667 samples/sec, ObjLoss=23.258, BoxCenterLoss=14.746, BoxScaleLoss=5.276, ClassLoss=9.841 [Epoch 67][Batch 199], LR: 1.00E-03, Speed: 165.888 samples/sec, ObjLoss=23.256, BoxCenterLoss=14.746, BoxScaleLoss=5.275, ClassLoss=9.840 [Epoch 67][Batch 299], LR: 1.00E-03, Speed: 146.898 samples/sec, ObjLoss=23.253, BoxCenterLoss=14.745, BoxScaleLoss=5.275, ClassLoss=9.838 [Epoch 67][Batch 399], LR: 1.00E-03, Speed: 137.140 samples/sec, ObjLoss=23.251, BoxCenterLoss=14.745, BoxScaleLoss=5.275, ClassLoss=9.836 [Epoch 67][Batch 499], LR: 1.00E-03, Speed: 121.275 samples/sec, ObjLoss=23.249, BoxCenterLoss=14.745, BoxScaleLoss=5.275, ClassLoss=9.834 [Epoch 67][Batch 599], LR: 1.00E-03, Speed: 154.706 samples/sec, ObjLoss=23.246, BoxCenterLoss=14.745, BoxScaleLoss=5.274, ClassLoss=9.832 [Epoch 67][Batch 699], LR: 1.00E-03, Speed: 106.839 samples/sec, ObjLoss=23.244, BoxCenterLoss=14.744, BoxScaleLoss=5.274, ClassLoss=9.831 [Epoch 67][Batch 799], LR: 1.00E-03, Speed: 167.951 samples/sec, ObjLoss=23.242, BoxCenterLoss=14.744, BoxScaleLoss=5.274, ClassLoss=9.829 [Epoch 67][Batch 899], LR: 1.00E-03, Speed: 118.885 samples/sec, ObjLoss=23.240, BoxCenterLoss=14.744, BoxScaleLoss=5.273, ClassLoss=9.827 [Epoch 67][Batch 999], LR: 1.00E-03, Speed: 98.753 samples/sec, ObjLoss=23.238, BoxCenterLoss=14.744, BoxScaleLoss=5.273, ClassLoss=9.825 [Epoch 67][Batch 1099], LR: 1.00E-03, Speed: 151.385 samples/sec, ObjLoss=23.236, BoxCenterLoss=14.744, BoxScaleLoss=5.272, ClassLoss=9.823 [Epoch 67][Batch 1199], LR: 1.00E-03, Speed: 129.261 samples/sec, ObjLoss=23.234, BoxCenterLoss=14.744, BoxScaleLoss=5.272, ClassLoss=9.821 [Epoch 67][Batch 1299], LR: 1.00E-03, Speed: 95.081 samples/sec, ObjLoss=23.232, BoxCenterLoss=14.744, BoxScaleLoss=5.272, ClassLoss=9.819 [Epoch 67][Batch 1399], LR: 1.00E-03, Speed: 107.211 samples/sec, ObjLoss=23.230, BoxCenterLoss=14.744, BoxScaleLoss=5.271, ClassLoss=9.817 [Epoch 67][Batch 1499], LR: 1.00E-03, Speed: 116.486 samples/sec, ObjLoss=23.228, BoxCenterLoss=14.744, BoxScaleLoss=5.271, ClassLoss=9.816 [Epoch 67][Batch 1599], LR: 1.00E-03, Speed: 97.323 samples/sec, ObjLoss=23.226, BoxCenterLoss=14.744, BoxScaleLoss=5.271, ClassLoss=9.814 [Epoch 67][Batch 1699], LR: 1.00E-03, Speed: 141.368 samples/sec, ObjLoss=23.224, BoxCenterLoss=14.743, BoxScaleLoss=5.270, ClassLoss=9.812 [Epoch 67][Batch 1799], LR: 1.00E-03, Speed: 122.463 samples/sec, ObjLoss=23.222, BoxCenterLoss=14.743, BoxScaleLoss=5.270, ClassLoss=9.810 [Epoch 67] Training cost: 1181.650, ObjLoss=23.221, BoxCenterLoss=14.743, BoxScaleLoss=5.270, ClassLoss=9.809 [Epoch 67] 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 ] = 0.383 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156 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 ] = 0.176 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288 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 ] = 0.108 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.394 person=30.7 bicycle=12.4 car=18.7 motorcycle=21.0 airplane=33.4 bus=34.7 train=38.7 truck=16.4 boat=10.0 traffic light=9.5 fire hydrant=38.8 stop sign=29.2 parking meter=19.4 bench=10.6 bird=15.1 cat=39.2 dog=34.8 horse=29.0 sheep=26.0 cow=28.0 elephant=39.4 bear=36.6 zebra=41.2 giraffe=33.6 backpack=3.6 umbrella=14.2 handbag=2.7 tie=11.4 suitcase=13.2 frisbee=29.8 skis=6.8 snowboard=10.3 sports ball=17.7 kite=20.5 baseball bat=8.1 baseball glove=14.5 skateboard=18.8 surfboard=14.8 tennis racket=17.8 bottle=13.1 wine glass=12.7 cup=16.4 fork=6.6 knife=2.6 spoon=2.0 bowl=18.2 banana=9.5 apple=6.2 sandwich=15.9 orange=12.2 broccoli=9.2 carrot=6.1 hot dog=15.1 pizza=25.4 donut=17.6 cake=15.8 chair=10.0 couch=25.1 potted plant=9.7 bed=25.1 dining table=12.5 toilet=33.4 tv=28.2 laptop=30.7 mouse=29.4 remote=5.6 keyboard=28.8 cell phone=12.9 microwave=27.0 oven=15.2 toaster=0.0 sink=18.8 refrigerator=26.4 book=4.4 clock=26.8 vase=16.5 scissors=14.6 teddy bear=21.4 hair drier=0.0 toothbrush=1.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.5 [Epoch 68][Batch 99], LR: 1.00E-03, Speed: 140.205 samples/sec, ObjLoss=23.219, BoxCenterLoss=14.743, BoxScaleLoss=5.269, ClassLoss=9.807 [Epoch 68][Batch 199], LR: 1.00E-03, Speed: 128.900 samples/sec, ObjLoss=23.216, BoxCenterLoss=14.742, BoxScaleLoss=5.269, ClassLoss=9.805 [Epoch 68][Batch 299], LR: 1.00E-03, Speed: 116.731 samples/sec, ObjLoss=23.215, BoxCenterLoss=14.743, BoxScaleLoss=5.268, ClassLoss=9.803 [Epoch 68][Batch 399], LR: 1.00E-03, Speed: 113.398 samples/sec, ObjLoss=23.213, BoxCenterLoss=14.742, BoxScaleLoss=5.268, ClassLoss=9.801 [Epoch 68][Batch 499], LR: 1.00E-03, Speed: 141.960 samples/sec, ObjLoss=23.210, BoxCenterLoss=14.742, BoxScaleLoss=5.268, ClassLoss=9.800 [Epoch 68][Batch 599], LR: 1.00E-03, Speed: 158.642 samples/sec, ObjLoss=23.208, BoxCenterLoss=14.742, BoxScaleLoss=5.267, ClassLoss=9.798 [Epoch 68][Batch 699], LR: 1.00E-03, Speed: 75.699 samples/sec, ObjLoss=23.206, BoxCenterLoss=14.742, BoxScaleLoss=5.267, ClassLoss=9.796 [Epoch 68][Batch 799], LR: 1.00E-03, Speed: 104.788 samples/sec, ObjLoss=23.204, BoxCenterLoss=14.741, BoxScaleLoss=5.267, ClassLoss=9.794 [Epoch 68][Batch 899], LR: 1.00E-03, Speed: 146.886 samples/sec, ObjLoss=23.202, BoxCenterLoss=14.741, BoxScaleLoss=5.266, ClassLoss=9.793 [Epoch 68][Batch 999], LR: 1.00E-03, Speed: 76.909 samples/sec, ObjLoss=23.199, BoxCenterLoss=14.741, BoxScaleLoss=5.266, ClassLoss=9.791 [Epoch 68][Batch 1099], LR: 1.00E-03, Speed: 117.883 samples/sec, ObjLoss=23.198, BoxCenterLoss=14.741, BoxScaleLoss=5.266, ClassLoss=9.789 [Epoch 68][Batch 1199], LR: 1.00E-03, Speed: 93.478 samples/sec, ObjLoss=23.196, BoxCenterLoss=14.741, BoxScaleLoss=5.265, ClassLoss=9.787 [Epoch 68][Batch 1299], LR: 1.00E-03, Speed: 129.595 samples/sec, ObjLoss=23.194, BoxCenterLoss=14.741, BoxScaleLoss=5.265, ClassLoss=9.785 [Epoch 68][Batch 1399], LR: 1.00E-03, Speed: 145.684 samples/sec, ObjLoss=23.192, BoxCenterLoss=14.741, BoxScaleLoss=5.264, ClassLoss=9.783 [Epoch 68][Batch 1499], LR: 1.00E-03, Speed: 124.419 samples/sec, ObjLoss=23.189, BoxCenterLoss=14.740, BoxScaleLoss=5.264, ClassLoss=9.781 [Epoch 68][Batch 1599], LR: 1.00E-03, Speed: 150.086 samples/sec, ObjLoss=23.187, BoxCenterLoss=14.740, BoxScaleLoss=5.263, ClassLoss=9.779 [Epoch 68][Batch 1699], LR: 1.00E-03, Speed: 85.525 samples/sec, ObjLoss=23.185, BoxCenterLoss=14.740, BoxScaleLoss=5.263, ClassLoss=9.777 [Epoch 68][Batch 1799], LR: 1.00E-03, Speed: 110.090 samples/sec, ObjLoss=23.183, BoxCenterLoss=14.740, BoxScaleLoss=5.263, ClassLoss=9.776 [Epoch 68] Training cost: 1176.933, ObjLoss=23.182, BoxCenterLoss=14.740, BoxScaleLoss=5.263, ClassLoss=9.775 [Epoch 68] 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 ] = 0.389 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.117 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.062 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.179 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.268 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 ] = 0.246 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254 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 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382 person=28.5 bicycle=10.6 car=17.9 motorcycle=17.6 airplane=34.0 bus=39.8 train=32.6 truck=16.7 boat=10.3 traffic light=9.5 fire hydrant=30.6 stop sign=32.7 parking meter=20.3 bench=9.5 bird=15.2 cat=29.9 dog=25.1 horse=26.2 sheep=24.0 cow=23.7 elephant=32.5 bear=31.4 zebra=39.4 giraffe=38.1 backpack=3.5 umbrella=15.3 handbag=2.4 tie=12.0 suitcase=11.0 frisbee=24.7 skis=7.5 snowboard=9.6 sports ball=14.9 kite=17.2 baseball bat=9.5 baseball glove=9.4 skateboard=17.9 surfboard=15.3 tennis racket=17.4 bottle=11.6 wine glass=11.3 cup=17.0 fork=6.3 knife=3.4 spoon=1.8 bowl=20.2 banana=8.6 apple=4.8 sandwich=17.4 orange=13.8 broccoli=6.8 carrot=5.7 hot dog=14.6 pizza=25.7 donut=18.7 cake=16.2 chair=10.5 couch=22.8 potted plant=8.0 bed=17.4 dining table=9.3 toilet=31.4 tv=30.4 laptop=26.7 mouse=19.2 remote=6.9 keyboard=25.1 cell phone=14.8 microwave=21.2 oven=15.3 toaster=0.0 sink=16.4 refrigerator=19.5 book=5.3 clock=25.4 vase=16.0 scissors=9.4 teddy bear=18.3 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.0 [Epoch 69][Batch 99], LR: 1.00E-03, Speed: 157.771 samples/sec, ObjLoss=23.180, BoxCenterLoss=14.740, BoxScaleLoss=5.262, ClassLoss=9.773 [Epoch 69][Batch 199], LR: 1.00E-03, Speed: 136.983 samples/sec, ObjLoss=23.178, BoxCenterLoss=14.739, BoxScaleLoss=5.262, ClassLoss=9.771 [Epoch 69][Batch 299], LR: 1.00E-03, Speed: 174.319 samples/sec, ObjLoss=23.176, BoxCenterLoss=14.739, BoxScaleLoss=5.262, ClassLoss=9.769 [Epoch 69][Batch 399], LR: 1.00E-03, Speed: 133.672 samples/sec, ObjLoss=23.173, BoxCenterLoss=14.739, BoxScaleLoss=5.262, ClassLoss=9.767 [Epoch 69][Batch 499], LR: 1.00E-03, Speed: 118.173 samples/sec, ObjLoss=23.171, BoxCenterLoss=14.738, BoxScaleLoss=5.261, ClassLoss=9.766 [Epoch 69][Batch 599], LR: 1.00E-03, Speed: 148.300 samples/sec, ObjLoss=23.169, BoxCenterLoss=14.738, BoxScaleLoss=5.261, ClassLoss=9.764 [Epoch 69][Batch 699], LR: 1.00E-03, Speed: 103.217 samples/sec, ObjLoss=23.166, BoxCenterLoss=14.738, BoxScaleLoss=5.261, ClassLoss=9.762 [Epoch 69][Batch 799], LR: 1.00E-03, Speed: 140.945 samples/sec, ObjLoss=23.165, BoxCenterLoss=14.738, BoxScaleLoss=5.260, ClassLoss=9.760 [Epoch 69][Batch 899], LR: 1.00E-03, Speed: 126.425 samples/sec, ObjLoss=23.163, BoxCenterLoss=14.738, BoxScaleLoss=5.260, ClassLoss=9.758 [Epoch 69][Batch 999], LR: 1.00E-03, Speed: 141.452 samples/sec, ObjLoss=23.161, BoxCenterLoss=14.738, BoxScaleLoss=5.260, ClassLoss=9.757 [Epoch 69][Batch 1099], LR: 1.00E-03, Speed: 196.200 samples/sec, ObjLoss=23.159, BoxCenterLoss=14.737, BoxScaleLoss=5.259, ClassLoss=9.755 [Epoch 69][Batch 1199], LR: 1.00E-03, Speed: 116.433 samples/sec, ObjLoss=23.156, BoxCenterLoss=14.737, BoxScaleLoss=5.259, ClassLoss=9.753 [Epoch 69][Batch 1299], LR: 1.00E-03, Speed: 136.804 samples/sec, ObjLoss=23.155, BoxCenterLoss=14.737, BoxScaleLoss=5.259, ClassLoss=9.751 [Epoch 69][Batch 1399], LR: 1.00E-03, Speed: 143.352 samples/sec, ObjLoss=23.152, BoxCenterLoss=14.737, BoxScaleLoss=5.258, ClassLoss=9.749 [Epoch 69][Batch 1499], LR: 1.00E-03, Speed: 80.638 samples/sec, ObjLoss=23.150, BoxCenterLoss=14.736, BoxScaleLoss=5.258, ClassLoss=9.748 [Epoch 69][Batch 1599], LR: 1.00E-03, Speed: 137.228 samples/sec, ObjLoss=23.148, BoxCenterLoss=14.736, BoxScaleLoss=5.257, ClassLoss=9.746 [Epoch 69][Batch 1699], LR: 1.00E-03, Speed: 122.433 samples/sec, ObjLoss=23.146, BoxCenterLoss=14.736, BoxScaleLoss=5.257, ClassLoss=9.744 [Epoch 69][Batch 1799], LR: 1.00E-03, Speed: 143.859 samples/sec, ObjLoss=23.144, BoxCenterLoss=14.736, BoxScaleLoss=5.257, ClassLoss=9.742 [Epoch 69] Training cost: 1179.198, ObjLoss=23.143, BoxCenterLoss=14.736, BoxScaleLoss=5.257, ClassLoss=9.742 [Epoch 69] 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 ] = 0.387 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.158 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 ] = 0.188 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 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.262 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 ] = 0.118 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389 person=30.5 bicycle=13.0 car=18.4 motorcycle=22.3 airplane=33.3 bus=41.0 train=41.3 truck=15.0 boat=9.4 traffic light=11.0 fire hydrant=36.4 stop sign=31.3 parking meter=13.5 bench=10.1 bird=13.9 cat=33.3 dog=33.4 horse=29.0 sheep=25.4 cow=28.9 elephant=36.4 bear=38.7 zebra=37.4 giraffe=36.3 backpack=3.3 umbrella=16.4 handbag=2.0 tie=11.4 suitcase=13.0 frisbee=33.3 skis=6.3 snowboard=11.6 sports ball=16.4 kite=22.2 baseball bat=9.7 baseball glove=15.1 skateboard=16.8 surfboard=15.1 tennis racket=20.5 bottle=12.8 wine glass=12.9 cup=16.4 fork=5.8 knife=2.5 spoon=1.3 bowl=19.2 banana=10.4 apple=8.2 sandwich=14.7 orange=15.1 broccoli=10.1 carrot=6.2 hot dog=12.9 pizza=26.7 donut=19.4 cake=14.4 chair=9.7 couch=23.6 potted plant=9.7 bed=23.6 dining table=15.4 toilet=33.0 tv=30.4 laptop=28.2 mouse=32.5 remote=7.1 keyboard=23.5 cell phone=13.0 microwave=26.7 oven=16.6 toaster=0.0 sink=16.1 refrigerator=24.3 book=3.6 clock=30.0 vase=18.8 scissors=12.7 teddy bear=23.4 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.6 [Epoch 70][Batch 99], LR: 1.00E-03, Speed: 143.208 samples/sec, ObjLoss=23.141, BoxCenterLoss=14.736, BoxScaleLoss=5.256, ClassLoss=9.740 [Epoch 70][Batch 199], LR: 1.00E-03, Speed: 126.914 samples/sec, ObjLoss=23.139, BoxCenterLoss=14.736, BoxScaleLoss=5.256, ClassLoss=9.739 [Epoch 70][Batch 299], LR: 1.00E-03, Speed: 127.878 samples/sec, ObjLoss=23.137, BoxCenterLoss=14.735, BoxScaleLoss=5.256, ClassLoss=9.737 [Epoch 70][Batch 399], LR: 1.00E-03, Speed: 139.174 samples/sec, ObjLoss=23.135, BoxCenterLoss=14.735, BoxScaleLoss=5.256, ClassLoss=9.735 [Epoch 70][Batch 499], LR: 1.00E-03, Speed: 138.699 samples/sec, ObjLoss=23.132, BoxCenterLoss=14.735, BoxScaleLoss=5.255, ClassLoss=9.733 [Epoch 70][Batch 599], LR: 1.00E-03, Speed: 92.016 samples/sec, ObjLoss=23.130, BoxCenterLoss=14.735, BoxScaleLoss=5.255, ClassLoss=9.732 [Epoch 70][Batch 699], LR: 1.00E-03, Speed: 110.868 samples/sec, ObjLoss=23.128, BoxCenterLoss=14.734, BoxScaleLoss=5.255, ClassLoss=9.730 [Epoch 70][Batch 799], LR: 1.00E-03, Speed: 85.005 samples/sec, ObjLoss=23.126, BoxCenterLoss=14.734, BoxScaleLoss=5.254, ClassLoss=9.728 [Epoch 70][Batch 899], LR: 1.00E-03, Speed: 124.764 samples/sec, ObjLoss=23.124, BoxCenterLoss=14.734, BoxScaleLoss=5.254, ClassLoss=9.726 [Epoch 70][Batch 999], LR: 1.00E-03, Speed: 106.277 samples/sec, ObjLoss=23.122, BoxCenterLoss=14.734, BoxScaleLoss=5.253, ClassLoss=9.724 [Epoch 70][Batch 1099], LR: 1.00E-03, Speed: 128.970 samples/sec, ObjLoss=23.120, BoxCenterLoss=14.734, BoxScaleLoss=5.253, ClassLoss=9.722 [Epoch 70][Batch 1199], LR: 1.00E-03, Speed: 125.971 samples/sec, ObjLoss=23.118, BoxCenterLoss=14.733, BoxScaleLoss=5.253, ClassLoss=9.721 [Epoch 70][Batch 1299], LR: 1.00E-03, Speed: 127.495 samples/sec, ObjLoss=23.116, BoxCenterLoss=14.733, BoxScaleLoss=5.252, ClassLoss=9.719 [Epoch 70][Batch 1399], LR: 1.00E-03, Speed: 125.883 samples/sec, ObjLoss=23.114, BoxCenterLoss=14.733, BoxScaleLoss=5.252, ClassLoss=9.717 [Epoch 70][Batch 1499], LR: 1.00E-03, Speed: 125.043 samples/sec, ObjLoss=23.112, BoxCenterLoss=14.733, BoxScaleLoss=5.252, ClassLoss=9.715 [Epoch 70][Batch 1599], LR: 1.00E-03, Speed: 135.903 samples/sec, ObjLoss=23.110, BoxCenterLoss=14.733, BoxScaleLoss=5.251, ClassLoss=9.713 [Epoch 70][Batch 1699], LR: 1.00E-03, Speed: 142.555 samples/sec, ObjLoss=23.108, BoxCenterLoss=14.733, BoxScaleLoss=5.251, ClassLoss=9.712 [Epoch 70][Batch 1799], LR: 1.00E-03, Speed: 118.949 samples/sec, ObjLoss=23.106, BoxCenterLoss=14.732, BoxScaleLoss=5.250, ClassLoss=9.710 [Epoch 70] Training cost: 1181.509, ObjLoss=23.105, BoxCenterLoss=14.732, BoxScaleLoss=5.250, ClassLoss=9.709 [Epoch 70] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.389 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.067 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 ] = 0.178 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 ] = 0.266 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 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.392 person=29.9 bicycle=13.4 car=19.1 motorcycle=20.4 airplane=36.8 bus=38.4 train=39.2 truck=17.3 boat=7.6 traffic light=9.8 fire hydrant=28.1 stop sign=32.0 parking meter=20.3 bench=8.0 bird=13.4 cat=39.5 dog=34.6 horse=26.3 sheep=23.6 cow=27.1 elephant=32.6 bear=36.7 zebra=37.5 giraffe=39.7 backpack=4.0 umbrella=16.4 handbag=2.9 tie=11.3 suitcase=13.9 frisbee=31.6 skis=6.6 snowboard=12.5 sports ball=19.3 kite=15.2 baseball bat=8.1 baseball glove=17.6 skateboard=17.6 surfboard=12.7 tennis racket=20.7 bottle=13.7 wine glass=13.4 cup=17.3 fork=6.0 knife=3.2 spoon=1.5 bowl=16.9 banana=10.6 apple=5.2 sandwich=15.0 orange=11.9 broccoli=9.4 carrot=6.2 hot dog=11.2 pizza=27.6 donut=14.7 cake=15.5 chair=11.5 couch=21.8 potted plant=9.1 bed=23.3 dining table=10.7 toilet=34.3 tv=34.6 laptop=30.4 mouse=22.7 remote=7.2 keyboard=24.6 cell phone=14.2 microwave=26.5 oven=15.8 toaster=0.0 sink=16.0 refrigerator=25.5 book=3.7 clock=29.2 vase=17.1 scissors=13.6 teddy bear=21.9 hair drier=0.0 toothbrush=1.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.2 [Epoch 71][Batch 99], LR: 1.00E-03, Speed: 138.133 samples/sec, ObjLoss=23.103, BoxCenterLoss=14.732, BoxScaleLoss=5.250, ClassLoss=9.708 [Epoch 71][Batch 199], LR: 1.00E-03, Speed: 144.262 samples/sec, ObjLoss=23.101, BoxCenterLoss=14.732, BoxScaleLoss=5.250, ClassLoss=9.706 [Epoch 71][Batch 299], LR: 1.00E-03, Speed: 131.118 samples/sec, ObjLoss=23.099, BoxCenterLoss=14.732, BoxScaleLoss=5.249, ClassLoss=9.704 [Epoch 71][Batch 399], LR: 1.00E-03, Speed: 128.841 samples/sec, ObjLoss=23.097, BoxCenterLoss=14.732, BoxScaleLoss=5.249, ClassLoss=9.702 [Epoch 71][Batch 499], LR: 1.00E-03, Speed: 125.877 samples/sec, ObjLoss=23.095, BoxCenterLoss=14.731, BoxScaleLoss=5.249, ClassLoss=9.700 [Epoch 71][Batch 599], LR: 1.00E-03, Speed: 91.389 samples/sec, ObjLoss=23.093, BoxCenterLoss=14.731, BoxScaleLoss=5.248, ClassLoss=9.699 [Epoch 71][Batch 699], LR: 1.00E-03, Speed: 169.182 samples/sec, ObjLoss=23.091, BoxCenterLoss=14.731, BoxScaleLoss=5.248, ClassLoss=9.697 [Epoch 71][Batch 799], LR: 1.00E-03, Speed: 148.638 samples/sec, ObjLoss=23.088, BoxCenterLoss=14.731, BoxScaleLoss=5.248, ClassLoss=9.696 [Epoch 71][Batch 899], LR: 1.00E-03, Speed: 127.191 samples/sec, ObjLoss=23.087, BoxCenterLoss=14.731, BoxScaleLoss=5.248, ClassLoss=9.694 [Epoch 71][Batch 999], LR: 1.00E-03, Speed: 122.685 samples/sec, ObjLoss=23.084, BoxCenterLoss=14.731, BoxScaleLoss=5.248, ClassLoss=9.693 [Epoch 71][Batch 1099], LR: 1.00E-03, Speed: 107.116 samples/sec, ObjLoss=23.082, BoxCenterLoss=14.730, BoxScaleLoss=5.247, ClassLoss=9.691 [Epoch 71][Batch 1199], LR: 1.00E-03, Speed: 87.091 samples/sec, ObjLoss=23.080, BoxCenterLoss=14.730, BoxScaleLoss=5.247, ClassLoss=9.689 [Epoch 71][Batch 1299], LR: 1.00E-03, Speed: 139.224 samples/sec, ObjLoss=23.078, BoxCenterLoss=14.730, BoxScaleLoss=5.247, ClassLoss=9.687 [Epoch 71][Batch 1399], LR: 1.00E-03, Speed: 67.964 samples/sec, ObjLoss=23.075, BoxCenterLoss=14.729, BoxScaleLoss=5.246, ClassLoss=9.685 [Epoch 71][Batch 1499], LR: 1.00E-03, Speed: 137.149 samples/sec, ObjLoss=23.074, BoxCenterLoss=14.729, BoxScaleLoss=5.246, ClassLoss=9.684 [Epoch 71][Batch 1599], LR: 1.00E-03, Speed: 136.365 samples/sec, ObjLoss=23.071, BoxCenterLoss=14.729, BoxScaleLoss=5.245, ClassLoss=9.682 [Epoch 71][Batch 1699], LR: 1.00E-03, Speed: 133.437 samples/sec, ObjLoss=23.069, BoxCenterLoss=14.729, BoxScaleLoss=5.245, ClassLoss=9.681 [Epoch 71][Batch 1799], LR: 1.00E-03, Speed: 152.809 samples/sec, ObjLoss=23.067, BoxCenterLoss=14.728, BoxScaleLoss=5.245, ClassLoss=9.679 [Epoch 71] Training cost: 1210.165, ObjLoss=23.067, BoxCenterLoss=14.728, BoxScaleLoss=5.245, ClassLoss=9.678 [Epoch 71] 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 ] = 0.389 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.137 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 ] = 0.178 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 ] = 0.175 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 ] = 0.265 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 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.362 person=30.6 bicycle=11.0 car=18.1 motorcycle=21.3 airplane=37.1 bus=39.5 train=37.2 truck=15.5 boat=8.9 traffic light=11.6 fire hydrant=34.4 stop sign=33.5 parking meter=21.5 bench=8.3 bird=15.1 cat=37.5 dog=33.1 horse=26.0 sheep=22.4 cow=25.4 elephant=38.5 bear=36.5 zebra=37.4 giraffe=40.9 backpack=5.0 umbrella=17.5 handbag=3.1 tie=9.7 suitcase=14.1 frisbee=28.0 skis=5.4 snowboard=10.5 sports ball=19.4 kite=20.5 baseball bat=7.5 baseball glove=17.3 skateboard=16.5 surfboard=12.3 tennis racket=17.0 bottle=13.4 wine glass=12.3 cup=16.9 fork=5.9 knife=2.8 spoon=1.5 bowl=15.4 banana=8.7 apple=5.5 sandwich=16.0 orange=9.8 broccoli=8.3 carrot=7.0 hot dog=11.1 pizza=22.9 donut=17.9 cake=17.4 chair=9.7 couch=22.8 potted plant=8.4 bed=25.2 dining table=15.5 toilet=30.0 tv=26.3 laptop=29.4 mouse=25.7 remote=7.1 keyboard=17.3 cell phone=14.6 microwave=22.7 oven=15.8 toaster=0.0 sink=14.3 refrigerator=23.4 book=3.8 clock=25.7 vase=14.6 scissors=11.9 teddy bear=21.8 hair drier=0.0 toothbrush=4.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.9 [Epoch 72][Batch 99], LR: 1.00E-03, Speed: 130.221 samples/sec, ObjLoss=23.065, BoxCenterLoss=14.728, BoxScaleLoss=5.244, ClassLoss=9.676 [Epoch 72][Batch 199], LR: 1.00E-03, Speed: 148.309 samples/sec, ObjLoss=23.062, BoxCenterLoss=14.728, BoxScaleLoss=5.244, ClassLoss=9.675 [Epoch 72][Batch 299], LR: 1.00E-03, Speed: 83.944 samples/sec, ObjLoss=23.060, BoxCenterLoss=14.728, BoxScaleLoss=5.244, ClassLoss=9.673 [Epoch 72][Batch 399], LR: 1.00E-03, Speed: 145.556 samples/sec, ObjLoss=23.058, BoxCenterLoss=14.727, BoxScaleLoss=5.243, ClassLoss=9.671 [Epoch 72][Batch 499], LR: 1.00E-03, Speed: 94.167 samples/sec, ObjLoss=23.056, BoxCenterLoss=14.727, BoxScaleLoss=5.243, ClassLoss=9.669 [Epoch 72][Batch 599], LR: 1.00E-03, Speed: 163.492 samples/sec, ObjLoss=23.054, BoxCenterLoss=14.727, BoxScaleLoss=5.243, ClassLoss=9.668 [Epoch 72][Batch 699], LR: 1.00E-03, Speed: 97.186 samples/sec, ObjLoss=23.052, BoxCenterLoss=14.726, BoxScaleLoss=5.242, ClassLoss=9.666 [Epoch 72][Batch 799], LR: 1.00E-03, Speed: 118.372 samples/sec, ObjLoss=23.050, BoxCenterLoss=14.726, BoxScaleLoss=5.242, ClassLoss=9.664 [Epoch 72][Batch 899], LR: 1.00E-03, Speed: 125.742 samples/sec, ObjLoss=23.048, BoxCenterLoss=14.726, BoxScaleLoss=5.242, ClassLoss=9.663 [Epoch 72][Batch 999], LR: 1.00E-03, Speed: 148.281 samples/sec, ObjLoss=23.046, BoxCenterLoss=14.726, BoxScaleLoss=5.242, ClassLoss=9.661 [Epoch 72][Batch 1099], LR: 1.00E-03, Speed: 156.012 samples/sec, ObjLoss=23.044, BoxCenterLoss=14.726, BoxScaleLoss=5.241, ClassLoss=9.659 [Epoch 72][Batch 1199], LR: 1.00E-03, Speed: 110.945 samples/sec, ObjLoss=23.042, BoxCenterLoss=14.726, BoxScaleLoss=5.241, ClassLoss=9.657 [Epoch 72][Batch 1299], LR: 1.00E-03, Speed: 126.268 samples/sec, ObjLoss=23.040, BoxCenterLoss=14.726, BoxScaleLoss=5.241, ClassLoss=9.656 [Epoch 72][Batch 1399], LR: 1.00E-03, Speed: 122.301 samples/sec, ObjLoss=23.038, BoxCenterLoss=14.725, BoxScaleLoss=5.240, ClassLoss=9.654 [Epoch 72][Batch 1499], LR: 1.00E-03, Speed: 116.037 samples/sec, ObjLoss=23.037, BoxCenterLoss=14.725, BoxScaleLoss=5.240, ClassLoss=9.652 [Epoch 72][Batch 1599], LR: 1.00E-03, Speed: 147.391 samples/sec, ObjLoss=23.035, BoxCenterLoss=14.725, BoxScaleLoss=5.240, ClassLoss=9.651 [Epoch 72][Batch 1699], LR: 1.00E-03, Speed: 123.088 samples/sec, ObjLoss=23.033, BoxCenterLoss=14.725, BoxScaleLoss=5.239, ClassLoss=9.649 [Epoch 72][Batch 1799], LR: 1.00E-03, Speed: 148.930 samples/sec, ObjLoss=23.031, BoxCenterLoss=14.725, BoxScaleLoss=5.239, ClassLoss=9.647 [Epoch 72] Training cost: 1196.402, ObjLoss=23.030, BoxCenterLoss=14.725, BoxScaleLoss=5.239, ClassLoss=9.647 [Epoch 72] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.175 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.388 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.131 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.268 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 ] = 0.253 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.262 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.107 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.379 person=30.7 bicycle=13.0 car=18.9 motorcycle=21.6 airplane=31.6 bus=36.0 train=39.2 truck=14.1 boat=8.5 traffic light=8.0 fire hydrant=30.4 stop sign=34.0 parking meter=17.7 bench=9.7 bird=14.9 cat=38.3 dog=28.2 horse=28.6 sheep=22.6 cow=24.4 elephant=34.0 bear=27.4 zebra=39.2 giraffe=37.1 backpack=4.5 umbrella=17.2 handbag=3.2 tie=10.3 suitcase=12.4 frisbee=23.0 skis=4.5 snowboard=11.9 sports ball=15.5 kite=11.5 baseball bat=8.7 baseball glove=14.4 skateboard=18.4 surfboard=12.5 tennis racket=17.2 bottle=13.6 wine glass=14.0 cup=17.4 fork=7.1 knife=2.6 spoon=1.5 bowl=18.3 banana=10.3 apple=6.9 sandwich=12.7 orange=12.0 broccoli=10.4 carrot=7.0 hot dog=12.8 pizza=25.2 donut=14.2 cake=15.2 chair=10.6 couch=24.2 potted plant=8.8 bed=24.1 dining table=12.4 toilet=29.8 tv=29.6 laptop=28.3 mouse=26.7 remote=6.5 keyboard=27.8 cell phone=13.2 microwave=26.0 oven=11.1 toaster=0.0 sink=15.1 refrigerator=20.5 book=5.0 clock=28.3 vase=16.4 scissors=12.9 teddy bear=24.0 hair drier=0.0 toothbrush=2.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.5 [Epoch 73][Batch 99], LR: 1.00E-03, Speed: 133.590 samples/sec, ObjLoss=23.028, BoxCenterLoss=14.725, BoxScaleLoss=5.238, ClassLoss=9.645 [Epoch 73][Batch 199], LR: 1.00E-03, Speed: 126.498 samples/sec, ObjLoss=23.026, BoxCenterLoss=14.725, BoxScaleLoss=5.238, ClassLoss=9.643 [Epoch 73][Batch 299], LR: 1.00E-03, Speed: 85.057 samples/sec, ObjLoss=23.024, BoxCenterLoss=14.724, BoxScaleLoss=5.238, ClassLoss=9.642 [Epoch 73][Batch 399], LR: 1.00E-03, Speed: 138.121 samples/sec, ObjLoss=23.022, BoxCenterLoss=14.724, BoxScaleLoss=5.238, ClassLoss=9.640 [Epoch 73][Batch 499], LR: 1.00E-03, Speed: 65.276 samples/sec, ObjLoss=23.020, BoxCenterLoss=14.724, BoxScaleLoss=5.237, ClassLoss=9.638 [Epoch 73][Batch 599], LR: 1.00E-03, Speed: 87.007 samples/sec, ObjLoss=23.018, BoxCenterLoss=14.724, BoxScaleLoss=5.237, ClassLoss=9.636 [Epoch 73][Batch 699], LR: 1.00E-03, Speed: 91.058 samples/sec, ObjLoss=23.016, BoxCenterLoss=14.723, BoxScaleLoss=5.236, ClassLoss=9.635 [Epoch 73][Batch 799], LR: 1.00E-03, Speed: 149.381 samples/sec, ObjLoss=23.014, BoxCenterLoss=14.723, BoxScaleLoss=5.236, ClassLoss=9.633 [Epoch 73][Batch 899], LR: 1.00E-03, Speed: 129.914 samples/sec, ObjLoss=23.012, BoxCenterLoss=14.723, BoxScaleLoss=5.236, ClassLoss=9.632 [Epoch 73][Batch 999], LR: 1.00E-03, Speed: 115.555 samples/sec, ObjLoss=23.010, BoxCenterLoss=14.723, BoxScaleLoss=5.236, ClassLoss=9.630 [Epoch 73][Batch 1099], LR: 1.00E-03, Speed: 80.895 samples/sec, ObjLoss=23.008, BoxCenterLoss=14.723, BoxScaleLoss=5.235, ClassLoss=9.628 [Epoch 73][Batch 1199], LR: 1.00E-03, Speed: 133.395 samples/sec, ObjLoss=23.006, BoxCenterLoss=14.722, BoxScaleLoss=5.235, ClassLoss=9.627 [Epoch 73][Batch 1299], LR: 1.00E-03, Speed: 122.125 samples/sec, ObjLoss=23.004, BoxCenterLoss=14.722, BoxScaleLoss=5.234, ClassLoss=9.625 [Epoch 73][Batch 1399], LR: 1.00E-03, Speed: 139.470 samples/sec, ObjLoss=23.002, BoxCenterLoss=14.722, BoxScaleLoss=5.234, ClassLoss=9.623 [Epoch 73][Batch 1499], LR: 1.00E-03, Speed: 144.900 samples/sec, ObjLoss=23.000, BoxCenterLoss=14.722, BoxScaleLoss=5.234, ClassLoss=9.622 [Epoch 73][Batch 1599], LR: 1.00E-03, Speed: 123.824 samples/sec, ObjLoss=22.998, BoxCenterLoss=14.722, BoxScaleLoss=5.233, ClassLoss=9.620 [Epoch 73][Batch 1699], LR: 1.00E-03, Speed: 72.295 samples/sec, ObjLoss=22.997, BoxCenterLoss=14.722, BoxScaleLoss=5.233, ClassLoss=9.618 [Epoch 73][Batch 1799], LR: 1.00E-03, Speed: 140.522 samples/sec, ObjLoss=22.995, BoxCenterLoss=14.722, BoxScaleLoss=5.233, ClassLoss=9.617 [Epoch 73] Training cost: 1250.807, ObjLoss=22.995, BoxCenterLoss=14.722, BoxScaleLoss=5.233, ClassLoss=9.616 [Epoch 73] 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 ] = 0.393 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.153 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.071 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.195 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 ] = 0.180 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 ] = 0.268 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 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.391 person=31.3 bicycle=13.1 car=20.4 motorcycle=22.8 airplane=38.5 bus=38.3 train=42.8 truck=16.3 boat=11.2 traffic light=11.5 fire hydrant=37.0 stop sign=37.1 parking meter=21.0 bench=9.7 bird=14.6 cat=40.5 dog=32.6 horse=29.6 sheep=25.6 cow=23.2 elephant=31.0 bear=32.4 zebra=36.9 giraffe=41.6 backpack=4.2 umbrella=16.8 handbag=2.3 tie=13.5 suitcase=13.1 frisbee=31.2 skis=6.2 snowboard=11.2 sports ball=19.6 kite=20.3 baseball bat=9.7 baseball glove=15.4 skateboard=16.5 surfboard=14.4 tennis racket=20.1 bottle=14.7 wine glass=12.4 cup=19.2 fork=7.0 knife=3.0 spoon=2.7 bowl=17.9 banana=10.7 apple=6.0 sandwich=13.4 orange=14.1 broccoli=9.2 carrot=7.1 hot dog=11.2 pizza=30.8 donut=14.4 cake=14.0 chair=11.1 couch=24.2 potted plant=10.5 bed=23.7 dining table=11.7 toilet=31.7 tv=35.0 laptop=28.1 mouse=24.9 remote=6.1 keyboard=25.4 cell phone=12.4 microwave=22.8 oven=16.6 toaster=0.0 sink=16.9 refrigerator=23.5 book=4.4 clock=28.6 vase=17.4 scissors=15.3 teddy bear=21.4 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.7 [Epoch 74][Batch 99], LR: 1.00E-03, Speed: 117.610 samples/sec, ObjLoss=22.993, BoxCenterLoss=14.722, BoxScaleLoss=5.233, ClassLoss=9.614 [Epoch 74][Batch 199], LR: 1.00E-03, Speed: 151.673 samples/sec, ObjLoss=22.991, BoxCenterLoss=14.722, BoxScaleLoss=5.232, ClassLoss=9.613 [Epoch 74][Batch 299], LR: 1.00E-03, Speed: 150.405 samples/sec, ObjLoss=22.990, BoxCenterLoss=14.722, BoxScaleLoss=5.232, ClassLoss=9.611 [Epoch 74][Batch 399], LR: 1.00E-03, Speed: 129.847 samples/sec, ObjLoss=22.988, BoxCenterLoss=14.721, BoxScaleLoss=5.231, ClassLoss=9.609 [Epoch 74][Batch 499], LR: 1.00E-03, Speed: 124.614 samples/sec, ObjLoss=22.986, BoxCenterLoss=14.722, BoxScaleLoss=5.231, ClassLoss=9.608 [Epoch 74][Batch 599], LR: 1.00E-03, Speed: 99.911 samples/sec, ObjLoss=22.984, BoxCenterLoss=14.722, BoxScaleLoss=5.231, ClassLoss=9.606 [Epoch 74][Batch 699], LR: 1.00E-03, Speed: 126.746 samples/sec, ObjLoss=22.982, BoxCenterLoss=14.721, BoxScaleLoss=5.231, ClassLoss=9.605 [Epoch 74][Batch 799], LR: 1.00E-03, Speed: 114.734 samples/sec, ObjLoss=22.980, BoxCenterLoss=14.721, BoxScaleLoss=5.230, ClassLoss=9.603 [Epoch 74][Batch 899], LR: 1.00E-03, Speed: 149.749 samples/sec, ObjLoss=22.978, BoxCenterLoss=14.721, BoxScaleLoss=5.230, ClassLoss=9.602 [Epoch 74][Batch 999], LR: 1.00E-03, Speed: 178.463 samples/sec, ObjLoss=22.976, BoxCenterLoss=14.720, BoxScaleLoss=5.230, ClassLoss=9.600 [Epoch 74][Batch 1099], LR: 1.00E-03, Speed: 108.217 samples/sec, ObjLoss=22.974, BoxCenterLoss=14.720, BoxScaleLoss=5.230, ClassLoss=9.599 [Epoch 74][Batch 1199], LR: 1.00E-03, Speed: 122.775 samples/sec, ObjLoss=22.972, BoxCenterLoss=14.720, BoxScaleLoss=5.229, ClassLoss=9.597 [Epoch 74][Batch 1299], LR: 1.00E-03, Speed: 132.484 samples/sec, ObjLoss=22.971, BoxCenterLoss=14.720, BoxScaleLoss=5.229, ClassLoss=9.595 [Epoch 74][Batch 1399], LR: 1.00E-03, Speed: 122.121 samples/sec, ObjLoss=22.969, BoxCenterLoss=14.720, BoxScaleLoss=5.228, ClassLoss=9.593 [Epoch 74][Batch 1499], LR: 1.00E-03, Speed: 124.055 samples/sec, ObjLoss=22.967, BoxCenterLoss=14.720, BoxScaleLoss=5.228, ClassLoss=9.591 [Epoch 74][Batch 1599], LR: 1.00E-03, Speed: 155.901 samples/sec, ObjLoss=22.965, BoxCenterLoss=14.720, BoxScaleLoss=5.228, ClassLoss=9.590 [Epoch 74][Batch 1699], LR: 1.00E-03, Speed: 139.916 samples/sec, ObjLoss=22.963, BoxCenterLoss=14.719, BoxScaleLoss=5.227, ClassLoss=9.588 [Epoch 74][Batch 1799], LR: 1.00E-03, Speed: 81.195 samples/sec, ObjLoss=22.961, BoxCenterLoss=14.719, BoxScaleLoss=5.227, ClassLoss=9.586 [Epoch 74] Training cost: 1190.125, ObjLoss=22.960, BoxCenterLoss=14.719, BoxScaleLoss=5.227, ClassLoss=9.586 [Epoch 74] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.193 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.398 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.165 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 ] = 0.196 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.293 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.273 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 ] = 0.117 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 ] = 0.405 person=31.5 bicycle=14.0 car=19.2 motorcycle=20.9 airplane=32.4 bus=42.1 train=43.1 truck=17.4 boat=8.9 traffic light=9.9 fire hydrant=35.2 stop sign=37.5 parking meter=27.4 bench=10.5 bird=15.5 cat=42.3 dog=30.9 horse=30.8 sheep=24.4 cow=27.0 elephant=40.3 bear=47.2 zebra=40.9 giraffe=38.4 backpack=4.6 umbrella=18.7 handbag=2.8 tie=12.0 suitcase=13.6 frisbee=30.3 skis=4.6 snowboard=10.0 sports ball=11.5 kite=21.4 baseball bat=9.4 baseball glove=15.0 skateboard=17.0 surfboard=14.5 tennis racket=19.9 bottle=14.9 wine glass=11.8 cup=17.8 fork=7.2 knife=3.3 spoon=2.0 bowl=18.2 banana=9.7 apple=6.4 sandwich=13.9 orange=14.3 broccoli=10.5 carrot=8.3 hot dog=13.9 pizza=28.9 donut=19.5 cake=17.4 chair=11.4 couch=22.4 potted plant=10.3 bed=25.4 dining table=13.7 toilet=30.9 tv=34.4 laptop=31.5 mouse=31.5 remote=6.2 keyboard=29.1 cell phone=15.8 microwave=22.3 oven=17.0 toaster=0.0 sink=15.8 refrigerator=26.3 book=3.4 clock=29.4 vase=16.7 scissors=14.6 teddy bear=21.8 hair drier=0.0 toothbrush=3.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.3 [Epoch 75][Batch 99], LR: 1.00E-03, Speed: 147.228 samples/sec, ObjLoss=22.958, BoxCenterLoss=14.719, BoxScaleLoss=5.227, ClassLoss=9.584 [Epoch 75][Batch 199], LR: 1.00E-03, Speed: 148.936 samples/sec, ObjLoss=22.956, BoxCenterLoss=14.719, BoxScaleLoss=5.227, ClassLoss=9.583 [Epoch 75][Batch 299], LR: 1.00E-03, Speed: 125.762 samples/sec, ObjLoss=22.954, BoxCenterLoss=14.718, BoxScaleLoss=5.226, ClassLoss=9.581 [Epoch 75][Batch 399], LR: 1.00E-03, Speed: 87.143 samples/sec, ObjLoss=22.952, BoxCenterLoss=14.718, BoxScaleLoss=5.226, ClassLoss=9.579 [Epoch 75][Batch 499], LR: 1.00E-03, Speed: 130.527 samples/sec, ObjLoss=22.950, BoxCenterLoss=14.718, BoxScaleLoss=5.225, ClassLoss=9.578 [Epoch 75][Batch 599], LR: 1.00E-03, Speed: 111.522 samples/sec, ObjLoss=22.949, BoxCenterLoss=14.718, BoxScaleLoss=5.225, ClassLoss=9.576 [Epoch 75][Batch 699], LR: 1.00E-03, Speed: 104.395 samples/sec, ObjLoss=22.947, BoxCenterLoss=14.718, BoxScaleLoss=5.225, ClassLoss=9.574 [Epoch 75][Batch 799], LR: 1.00E-03, Speed: 135.905 samples/sec, ObjLoss=22.945, BoxCenterLoss=14.718, BoxScaleLoss=5.224, ClassLoss=9.573 [Epoch 75][Batch 899], LR: 1.00E-03, Speed: 116.193 samples/sec, ObjLoss=22.944, BoxCenterLoss=14.718, BoxScaleLoss=5.224, ClassLoss=9.571 [Epoch 75][Batch 999], LR: 1.00E-03, Speed: 124.122 samples/sec, ObjLoss=22.942, BoxCenterLoss=14.717, BoxScaleLoss=5.224, ClassLoss=9.570 [Epoch 75][Batch 1099], LR: 1.00E-03, Speed: 96.146 samples/sec, ObjLoss=22.940, BoxCenterLoss=14.717, BoxScaleLoss=5.223, ClassLoss=9.568 [Epoch 75][Batch 1199], LR: 1.00E-03, Speed: 62.190 samples/sec, ObjLoss=22.938, BoxCenterLoss=14.717, BoxScaleLoss=5.223, ClassLoss=9.567 [Epoch 75][Batch 1299], LR: 1.00E-03, Speed: 65.680 samples/sec, ObjLoss=22.936, BoxCenterLoss=14.717, BoxScaleLoss=5.223, ClassLoss=9.565 [Epoch 75][Batch 1399], LR: 1.00E-03, Speed: 93.945 samples/sec, ObjLoss=22.935, BoxCenterLoss=14.717, BoxScaleLoss=5.222, ClassLoss=9.563 [Epoch 75][Batch 1499], LR: 1.00E-03, Speed: 141.377 samples/sec, ObjLoss=22.932, BoxCenterLoss=14.716, BoxScaleLoss=5.222, ClassLoss=9.562 [Epoch 75][Batch 1599], LR: 1.00E-03, Speed: 162.874 samples/sec, ObjLoss=22.930, BoxCenterLoss=14.716, BoxScaleLoss=5.222, ClassLoss=9.560 [Epoch 75][Batch 1699], LR: 1.00E-03, Speed: 99.648 samples/sec, ObjLoss=22.928, BoxCenterLoss=14.716, BoxScaleLoss=5.221, ClassLoss=9.558 [Epoch 75][Batch 1799], LR: 1.00E-03, Speed: 155.514 samples/sec, ObjLoss=22.927, BoxCenterLoss=14.716, BoxScaleLoss=5.221, ClassLoss=9.557 [Epoch 75] Training cost: 1210.248, ObjLoss=22.926, BoxCenterLoss=14.716, BoxScaleLoss=5.221, ClassLoss=9.556 [Epoch 75] 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 ] = 0.387 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.287 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 ] = 0.256 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 ] = 0.102 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 ] = 0.405 person=30.2 bicycle=11.6 car=20.9 motorcycle=20.2 airplane=30.1 bus=38.1 train=38.9 truck=16.6 boat=9.4 traffic light=10.8 fire hydrant=34.0 stop sign=37.3 parking meter=22.8 bench=8.9 bird=14.3 cat=37.5 dog=28.9 horse=22.4 sheep=22.2 cow=24.2 elephant=36.4 bear=40.0 zebra=40.2 giraffe=41.0 backpack=3.1 umbrella=18.4 handbag=2.3 tie=10.6 suitcase=11.5 frisbee=27.7 skis=6.3 snowboard=9.2 sports ball=18.9 kite=19.8 baseball bat=10.1 baseball glove=14.0 skateboard=20.1 surfboard=15.4 tennis racket=19.0 bottle=13.5 wine glass=14.2 cup=18.5 fork=9.5 knife=3.3 spoon=2.3 bowl=19.8 banana=9.4 apple=7.9 sandwich=15.2 orange=13.6 broccoli=9.6 carrot=6.5 hot dog=14.2 pizza=26.4 donut=24.0 cake=15.1 chair=11.2 couch=23.1 potted plant=8.3 bed=25.2 dining table=14.0 toilet=28.2 tv=33.3 laptop=32.5 mouse=25.8 remote=6.3 keyboard=17.6 cell phone=13.8 microwave=22.0 oven=16.9 toaster=0.0 sink=15.9 refrigerator=24.2 book=3.6 clock=24.4 vase=17.3 scissors=10.9 teddy bear=19.4 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.3 [Epoch 76][Batch 99], LR: 1.00E-03, Speed: 138.478 samples/sec, ObjLoss=22.924, BoxCenterLoss=14.716, BoxScaleLoss=5.220, ClassLoss=9.554 [Epoch 76][Batch 199], LR: 1.00E-03, Speed: 98.427 samples/sec, ObjLoss=22.922, BoxCenterLoss=14.715, BoxScaleLoss=5.220, ClassLoss=9.553 [Epoch 76][Batch 299], LR: 1.00E-03, Speed: 157.522 samples/sec, ObjLoss=22.921, BoxCenterLoss=14.715, BoxScaleLoss=5.220, ClassLoss=9.551 [Epoch 76][Batch 399], LR: 1.00E-03, Speed: 140.043 samples/sec, ObjLoss=22.919, BoxCenterLoss=14.715, BoxScaleLoss=5.219, ClassLoss=9.549 [Epoch 76][Batch 499], LR: 1.00E-03, Speed: 132.013 samples/sec, ObjLoss=22.917, BoxCenterLoss=14.715, BoxScaleLoss=5.219, ClassLoss=9.548 [Epoch 76][Batch 599], LR: 1.00E-03, Speed: 68.079 samples/sec, ObjLoss=22.915, BoxCenterLoss=14.714, BoxScaleLoss=5.219, ClassLoss=9.546 [Epoch 76][Batch 699], LR: 1.00E-03, Speed: 73.504 samples/sec, ObjLoss=22.914, BoxCenterLoss=14.714, BoxScaleLoss=5.218, ClassLoss=9.544 [Epoch 76][Batch 799], LR: 1.00E-03, Speed: 97.516 samples/sec, ObjLoss=22.912, BoxCenterLoss=14.714, BoxScaleLoss=5.218, ClassLoss=9.543 [Epoch 76][Batch 899], LR: 1.00E-03, Speed: 82.425 samples/sec, ObjLoss=22.910, BoxCenterLoss=14.714, BoxScaleLoss=5.218, ClassLoss=9.541 [Epoch 76][Batch 999], LR: 1.00E-03, Speed: 130.027 samples/sec, ObjLoss=22.909, BoxCenterLoss=14.714, BoxScaleLoss=5.217, ClassLoss=9.539 [Epoch 76][Batch 1099], LR: 1.00E-03, Speed: 90.223 samples/sec, ObjLoss=22.907, BoxCenterLoss=14.714, BoxScaleLoss=5.217, ClassLoss=9.538 [Epoch 76][Batch 1199], LR: 1.00E-03, Speed: 109.932 samples/sec, ObjLoss=22.905, BoxCenterLoss=14.714, BoxScaleLoss=5.217, ClassLoss=9.536 [Epoch 76][Batch 1299], LR: 1.00E-03, Speed: 134.017 samples/sec, ObjLoss=22.903, BoxCenterLoss=14.714, BoxScaleLoss=5.216, ClassLoss=9.535 [Epoch 76][Batch 1399], LR: 1.00E-03, Speed: 88.790 samples/sec, ObjLoss=22.902, BoxCenterLoss=14.714, BoxScaleLoss=5.216, ClassLoss=9.533 [Epoch 76][Batch 1499], LR: 1.00E-03, Speed: 122.926 samples/sec, ObjLoss=22.900, BoxCenterLoss=14.714, BoxScaleLoss=5.216, ClassLoss=9.531 [Epoch 76][Batch 1599], LR: 1.00E-03, Speed: 79.267 samples/sec, ObjLoss=22.898, BoxCenterLoss=14.713, BoxScaleLoss=5.215, ClassLoss=9.530 [Epoch 76][Batch 1699], LR: 1.00E-03, Speed: 98.795 samples/sec, ObjLoss=22.896, BoxCenterLoss=14.713, BoxScaleLoss=5.215, ClassLoss=9.528 [Epoch 76][Batch 1799], LR: 1.00E-03, Speed: 153.012 samples/sec, ObjLoss=22.894, BoxCenterLoss=14.713, BoxScaleLoss=5.214, ClassLoss=9.526 [Epoch 76] Training cost: 1214.888, ObjLoss=22.894, BoxCenterLoss=14.713, BoxScaleLoss=5.214, ClassLoss=9.526 [Epoch 76] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.404 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.073 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 ] = 0.297 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 ] = 0.273 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 ] = 0.115 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 ] = 0.410 person=31.7 bicycle=13.5 car=19.9 motorcycle=22.5 airplane=38.0 bus=40.6 train=35.7 truck=18.4 boat=9.2 traffic light=9.8 fire hydrant=36.7 stop sign=32.3 parking meter=24.7 bench=11.0 bird=15.7 cat=43.0 dog=34.5 horse=31.3 sheep=27.1 cow=27.9 elephant=36.6 bear=36.9 zebra=41.6 giraffe=41.2 backpack=4.3 umbrella=18.5 handbag=2.7 tie=12.8 suitcase=16.6 frisbee=32.1 skis=7.4 snowboard=9.6 sports ball=20.6 kite=21.6 baseball bat=8.7 baseball glove=16.8 skateboard=19.5 surfboard=13.2 tennis racket=22.6 bottle=10.7 wine glass=12.6 cup=17.7 fork=7.7 knife=3.1 spoon=2.8 bowl=18.9 banana=9.8 apple=6.4 sandwich=18.4 orange=15.9 broccoli=12.0 carrot=6.8 hot dog=11.7 pizza=32.2 donut=22.8 cake=17.0 chair=11.4 couch=25.4 potted plant=10.9 bed=21.0 dining table=11.7 toilet=35.7 tv=35.2 laptop=31.4 mouse=32.6 remote=6.4 keyboard=23.8 cell phone=15.2 microwave=29.8 oven=16.1 toaster=0.0 sink=19.0 refrigerator=23.1 book=3.9 clock=25.7 vase=16.4 scissors=12.2 teddy bear=21.2 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.5 [Epoch 77][Batch 99], LR: 1.00E-03, Speed: 139.097 samples/sec, ObjLoss=22.892, BoxCenterLoss=14.713, BoxScaleLoss=5.214, ClassLoss=9.524 [Epoch 77][Batch 199], LR: 1.00E-03, Speed: 89.914 samples/sec, ObjLoss=22.890, BoxCenterLoss=14.713, BoxScaleLoss=5.214, ClassLoss=9.523 [Epoch 77][Batch 299], LR: 1.00E-03, Speed: 112.254 samples/sec, ObjLoss=22.888, BoxCenterLoss=14.712, BoxScaleLoss=5.213, ClassLoss=9.521 [Epoch 77][Batch 399], LR: 1.00E-03, Speed: 125.831 samples/sec, ObjLoss=22.886, BoxCenterLoss=14.712, BoxScaleLoss=5.213, ClassLoss=9.519 [Epoch 77][Batch 499], LR: 1.00E-03, Speed: 138.999 samples/sec, ObjLoss=22.884, BoxCenterLoss=14.712, BoxScaleLoss=5.213, ClassLoss=9.518 [Epoch 77][Batch 599], LR: 1.00E-03, Speed: 132.958 samples/sec, ObjLoss=22.882, BoxCenterLoss=14.711, BoxScaleLoss=5.212, ClassLoss=9.516 [Epoch 77][Batch 699], LR: 1.00E-03, Speed: 119.737 samples/sec, ObjLoss=22.880, BoxCenterLoss=14.711, BoxScaleLoss=5.212, ClassLoss=9.515 [Epoch 77][Batch 799], LR: 1.00E-03, Speed: 106.415 samples/sec, ObjLoss=22.879, BoxCenterLoss=14.711, BoxScaleLoss=5.212, ClassLoss=9.513 [Epoch 77][Batch 899], LR: 1.00E-03, Speed: 87.509 samples/sec, ObjLoss=22.876, BoxCenterLoss=14.711, BoxScaleLoss=5.211, ClassLoss=9.511 [Epoch 77][Batch 999], LR: 1.00E-03, Speed: 102.791 samples/sec, ObjLoss=22.875, BoxCenterLoss=14.710, BoxScaleLoss=5.211, ClassLoss=9.509 [Epoch 77][Batch 1099], LR: 1.00E-03, Speed: 120.572 samples/sec, ObjLoss=22.873, BoxCenterLoss=14.710, BoxScaleLoss=5.210, ClassLoss=9.508 [Epoch 77][Batch 1199], LR: 1.00E-03, Speed: 135.896 samples/sec, ObjLoss=22.871, BoxCenterLoss=14.710, BoxScaleLoss=5.210, ClassLoss=9.506 [Epoch 77][Batch 1299], LR: 1.00E-03, Speed: 150.424 samples/sec, ObjLoss=22.870, BoxCenterLoss=14.710, BoxScaleLoss=5.209, ClassLoss=9.504 [Epoch 77][Batch 1399], LR: 1.00E-03, Speed: 116.297 samples/sec, ObjLoss=22.868, BoxCenterLoss=14.710, BoxScaleLoss=5.209, ClassLoss=9.503 [Epoch 77][Batch 1499], LR: 1.00E-03, Speed: 145.782 samples/sec, ObjLoss=22.867, BoxCenterLoss=14.710, BoxScaleLoss=5.208, ClassLoss=9.501 [Epoch 77][Batch 1599], LR: 1.00E-03, Speed: 119.498 samples/sec, ObjLoss=22.865, BoxCenterLoss=14.710, BoxScaleLoss=5.208, ClassLoss=9.499 [Epoch 77][Batch 1699], LR: 1.00E-03, Speed: 109.333 samples/sec, ObjLoss=22.863, BoxCenterLoss=14.709, BoxScaleLoss=5.208, ClassLoss=9.498 [Epoch 77][Batch 1799], LR: 1.00E-03, Speed: 178.660 samples/sec, ObjLoss=22.862, BoxCenterLoss=14.709, BoxScaleLoss=5.207, ClassLoss=9.496 [Epoch 77] Training cost: 1254.555, ObjLoss=22.861, BoxCenterLoss=14.710, BoxScaleLoss=5.207, ClassLoss=9.495 [Epoch 77] 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 ] = 0.397 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.073 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 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.184 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.268 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.277 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 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404 person=30.6 bicycle=13.5 car=20.5 motorcycle=21.8 airplane=36.2 bus=41.6 train=37.5 truck=17.2 boat=10.3 traffic light=10.9 fire hydrant=34.6 stop sign=35.6 parking meter=17.0 bench=10.9 bird=15.7 cat=40.3 dog=35.0 horse=30.2 sheep=23.8 cow=27.3 elephant=35.1 bear=40.9 zebra=42.7 giraffe=41.9 backpack=4.2 umbrella=17.9 handbag=2.9 tie=11.1 suitcase=13.9 frisbee=29.7 skis=7.0 snowboard=12.3 sports ball=22.7 kite=20.1 baseball bat=8.5 baseball glove=19.1 skateboard=20.3 surfboard=13.6 tennis racket=20.7 bottle=13.7 wine glass=13.0 cup=16.6 fork=7.5 knife=3.0 spoon=1.9 bowl=20.0 banana=11.1 apple=7.5 sandwich=16.5 orange=15.3 broccoli=11.2 carrot=8.2 hot dog=9.2 pizza=28.6 donut=23.9 cake=16.3 chair=11.7 couch=24.2 potted plant=9.8 bed=21.8 dining table=12.4 toilet=34.1 tv=34.6 laptop=31.0 mouse=35.8 remote=6.6 keyboard=28.3 cell phone=13.8 microwave=20.9 oven=17.6 toaster=0.0 sink=18.6 refrigerator=23.2 book=3.6 clock=28.6 vase=16.3 scissors=11.4 teddy bear=22.0 hair drier=0.0 toothbrush=3.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.4 [Epoch 78][Batch 99], LR: 1.00E-03, Speed: 139.703 samples/sec, ObjLoss=22.860, BoxCenterLoss=14.709, BoxScaleLoss=5.207, ClassLoss=9.494 [Epoch 78][Batch 199], LR: 1.00E-03, Speed: 131.552 samples/sec, ObjLoss=22.858, BoxCenterLoss=14.709, BoxScaleLoss=5.206, ClassLoss=9.492 [Epoch 78][Batch 299], LR: 1.00E-03, Speed: 127.879 samples/sec, ObjLoss=22.856, BoxCenterLoss=14.709, BoxScaleLoss=5.206, ClassLoss=9.491 [Epoch 78][Batch 399], LR: 1.00E-03, Speed: 81.030 samples/sec, ObjLoss=22.854, BoxCenterLoss=14.709, BoxScaleLoss=5.206, ClassLoss=9.489 [Epoch 78][Batch 499], LR: 1.00E-03, Speed: 135.754 samples/sec, ObjLoss=22.852, BoxCenterLoss=14.708, BoxScaleLoss=5.205, ClassLoss=9.487 [Epoch 78][Batch 599], LR: 1.00E-03, Speed: 147.555 samples/sec, ObjLoss=22.850, BoxCenterLoss=14.708, BoxScaleLoss=5.205, ClassLoss=9.486 [Epoch 78][Batch 699], LR: 1.00E-03, Speed: 89.556 samples/sec, ObjLoss=22.848, BoxCenterLoss=14.708, BoxScaleLoss=5.205, ClassLoss=9.484 [Epoch 78][Batch 799], LR: 1.00E-03, Speed: 88.321 samples/sec, ObjLoss=22.847, BoxCenterLoss=14.708, BoxScaleLoss=5.205, ClassLoss=9.483 [Epoch 78][Batch 899], LR: 1.00E-03, Speed: 109.594 samples/sec, ObjLoss=22.845, BoxCenterLoss=14.708, BoxScaleLoss=5.204, ClassLoss=9.481 [Epoch 78][Batch 999], LR: 1.00E-03, Speed: 134.496 samples/sec, ObjLoss=22.844, BoxCenterLoss=14.708, BoxScaleLoss=5.204, ClassLoss=9.480 [Epoch 78][Batch 1099], LR: 1.00E-03, Speed: 137.062 samples/sec, ObjLoss=22.842, BoxCenterLoss=14.708, BoxScaleLoss=5.203, ClassLoss=9.478 [Epoch 78][Batch 1199], LR: 1.00E-03, Speed: 148.573 samples/sec, ObjLoss=22.840, BoxCenterLoss=14.708, BoxScaleLoss=5.203, ClassLoss=9.476 [Epoch 78][Batch 1299], LR: 1.00E-03, Speed: 134.239 samples/sec, ObjLoss=22.838, BoxCenterLoss=14.707, BoxScaleLoss=5.203, ClassLoss=9.475 [Epoch 78][Batch 1399], LR: 1.00E-03, Speed: 71.530 samples/sec, ObjLoss=22.837, BoxCenterLoss=14.707, BoxScaleLoss=5.202, ClassLoss=9.473 [Epoch 78][Batch 1499], LR: 1.00E-03, Speed: 102.200 samples/sec, ObjLoss=22.835, BoxCenterLoss=14.707, BoxScaleLoss=5.202, ClassLoss=9.471 [Epoch 78][Batch 1599], LR: 1.00E-03, Speed: 149.204 samples/sec, ObjLoss=22.834, BoxCenterLoss=14.707, BoxScaleLoss=5.202, ClassLoss=9.470 [Epoch 78][Batch 1699], LR: 1.00E-03, Speed: 159.391 samples/sec, ObjLoss=22.831, BoxCenterLoss=14.707, BoxScaleLoss=5.202, ClassLoss=9.468 [Epoch 78][Batch 1799], LR: 1.00E-03, Speed: 151.284 samples/sec, ObjLoss=22.830, BoxCenterLoss=14.706, BoxScaleLoss=5.201, ClassLoss=9.467 [Epoch 78] Training cost: 1229.836, ObjLoss=22.829, BoxCenterLoss=14.707, BoxScaleLoss=5.201, ClassLoss=9.467 [Epoch 78] 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 ] = 0.385 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.135 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.171 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 ] = 0.176 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 ] = 0.263 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 ] = 0.256 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389 person=30.6 bicycle=12.7 car=18.4 motorcycle=20.5 airplane=32.7 bus=37.5 train=41.8 truck=15.7 boat=8.1 traffic light=11.0 fire hydrant=37.2 stop sign=34.0 parking meter=21.5 bench=10.0 bird=15.4 cat=39.7 dog=32.5 horse=27.0 sheep=22.5 cow=22.6 elephant=36.5 bear=40.2 zebra=34.4 giraffe=34.1 backpack=4.6 umbrella=12.9 handbag=2.1 tie=12.5 suitcase=11.2 frisbee=28.4 skis=4.9 snowboard=10.9 sports ball=20.6 kite=16.9 baseball bat=6.3 baseball glove=15.6 skateboard=18.3 surfboard=14.1 tennis racket=16.9 bottle=12.6 wine glass=10.7 cup=15.0 fork=7.8 knife=3.3 spoon=2.1 bowl=14.8 banana=8.0 apple=5.8 sandwich=17.1 orange=10.6 broccoli=8.5 carrot=6.1 hot dog=14.4 pizza=24.5 donut=12.0 cake=15.0 chair=9.7 couch=20.7 potted plant=8.3 bed=24.9 dining table=14.7 toilet=29.2 tv=30.1 laptop=28.8 mouse=24.7 remote=6.4 keyboard=18.8 cell phone=13.9 microwave=21.3 oven=15.7 toaster=0.0 sink=15.7 refrigerator=25.8 book=4.3 clock=23.8 vase=15.8 scissors=15.9 teddy bear=22.2 hair drier=0.0 toothbrush=1.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.7 [Epoch 79][Batch 99], LR: 1.00E-03, Speed: 148.887 samples/sec, ObjLoss=22.828, BoxCenterLoss=14.707, BoxScaleLoss=5.201, ClassLoss=9.465 [Epoch 79][Batch 199], LR: 1.00E-03, Speed: 129.708 samples/sec, ObjLoss=22.826, BoxCenterLoss=14.706, BoxScaleLoss=5.201, ClassLoss=9.464 [Epoch 79][Batch 299], LR: 1.00E-03, Speed: 83.001 samples/sec, ObjLoss=22.824, BoxCenterLoss=14.706, BoxScaleLoss=5.201, ClassLoss=9.462 [Epoch 79][Batch 399], LR: 1.00E-03, Speed: 127.521 samples/sec, ObjLoss=22.822, BoxCenterLoss=14.706, BoxScaleLoss=5.200, ClassLoss=9.461 [Epoch 79][Batch 499], LR: 1.00E-03, Speed: 131.541 samples/sec, ObjLoss=22.820, BoxCenterLoss=14.705, BoxScaleLoss=5.200, ClassLoss=9.459 [Epoch 79][Batch 599], LR: 1.00E-03, Speed: 116.039 samples/sec, ObjLoss=22.818, BoxCenterLoss=14.705, BoxScaleLoss=5.200, ClassLoss=9.457 [Epoch 79][Batch 699], LR: 1.00E-03, Speed: 112.034 samples/sec, ObjLoss=22.817, BoxCenterLoss=14.705, BoxScaleLoss=5.199, ClassLoss=9.456 [Epoch 79][Batch 799], LR: 1.00E-03, Speed: 132.515 samples/sec, ObjLoss=22.816, BoxCenterLoss=14.705, BoxScaleLoss=5.199, ClassLoss=9.454 [Epoch 79][Batch 899], LR: 1.00E-03, Speed: 137.584 samples/sec, ObjLoss=22.814, BoxCenterLoss=14.705, BoxScaleLoss=5.198, ClassLoss=9.453 [Epoch 79][Batch 999], LR: 1.00E-03, Speed: 122.160 samples/sec, ObjLoss=22.812, BoxCenterLoss=14.705, BoxScaleLoss=5.198, ClassLoss=9.451 [Epoch 79][Batch 1099], LR: 1.00E-03, Speed: 79.442 samples/sec, ObjLoss=22.810, BoxCenterLoss=14.704, BoxScaleLoss=5.198, ClassLoss=9.450 [Epoch 79][Batch 1199], LR: 1.00E-03, Speed: 138.751 samples/sec, ObjLoss=22.809, BoxCenterLoss=14.705, BoxScaleLoss=5.198, ClassLoss=9.448 [Epoch 79][Batch 1299], LR: 1.00E-03, Speed: 104.489 samples/sec, ObjLoss=22.808, BoxCenterLoss=14.705, BoxScaleLoss=5.197, ClassLoss=9.447 [Epoch 79][Batch 1399], LR: 1.00E-03, Speed: 91.944 samples/sec, ObjLoss=22.806, BoxCenterLoss=14.705, BoxScaleLoss=5.197, ClassLoss=9.446 [Epoch 79][Batch 1499], LR: 1.00E-03, Speed: 116.697 samples/sec, ObjLoss=22.804, BoxCenterLoss=14.704, BoxScaleLoss=5.197, ClassLoss=9.444 [Epoch 79][Batch 1599], LR: 1.00E-03, Speed: 126.836 samples/sec, ObjLoss=22.803, BoxCenterLoss=14.704, BoxScaleLoss=5.196, ClassLoss=9.442 [Epoch 79][Batch 1699], LR: 1.00E-03, Speed: 146.382 samples/sec, ObjLoss=22.802, BoxCenterLoss=14.704, BoxScaleLoss=5.196, ClassLoss=9.441 [Epoch 79][Batch 1799], LR: 1.00E-03, Speed: 106.614 samples/sec, ObjLoss=22.800, BoxCenterLoss=14.704, BoxScaleLoss=5.196, ClassLoss=9.439 [Epoch 79] Training cost: 1222.392, ObjLoss=22.800, BoxCenterLoss=14.704, BoxScaleLoss=5.196, ClassLoss=9.439 [Epoch 79] 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 ] = 0.383 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.138 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 ] = 0.177 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.265 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.173 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.252 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 ] = 0.269 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.376 person=27.6 bicycle=13.6 car=16.9 motorcycle=21.7 airplane=32.5 bus=39.5 train=40.0 truck=17.6 boat=8.4 traffic light=9.7 fire hydrant=36.5 stop sign=30.4 parking meter=19.8 bench=9.2 bird=13.4 cat=39.3 dog=30.6 horse=26.5 sheep=20.3 cow=24.4 elephant=29.3 bear=34.7 zebra=33.6 giraffe=39.3 backpack=3.3 umbrella=17.6 handbag=2.5 tie=10.7 suitcase=9.3 frisbee=28.5 skis=5.6 snowboard=9.9 sports ball=22.3 kite=19.4 baseball bat=8.0 baseball glove=13.7 skateboard=16.8 surfboard=15.2 tennis racket=21.1 bottle=11.2 wine glass=12.9 cup=17.6 fork=8.0 knife=3.0 spoon=1.6 bowl=16.4 banana=8.4 apple=4.6 sandwich=13.9 orange=13.4 broccoli=7.5 carrot=7.0 hot dog=14.8 pizza=27.4 donut=18.3 cake=14.3 chair=9.7 couch=17.5 potted plant=7.6 bed=25.4 dining table=13.2 toilet=30.4 tv=27.1 laptop=32.9 mouse=26.7 remote=5.9 keyboard=22.4 cell phone=11.8 microwave=21.4 oven=16.0 toaster=0.0 sink=16.6 refrigerator=20.5 book=4.2 clock=31.0 vase=14.7 scissors=13.8 teddy bear=20.9 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.6 [Epoch 80][Batch 99], LR: 1.00E-03, Speed: 159.613 samples/sec, ObjLoss=22.798, BoxCenterLoss=14.704, BoxScaleLoss=5.195, ClassLoss=9.437 [Epoch 80][Batch 199], LR: 1.00E-03, Speed: 114.305 samples/sec, ObjLoss=22.796, BoxCenterLoss=14.704, BoxScaleLoss=5.195, ClassLoss=9.436 [Epoch 80][Batch 299], LR: 1.00E-03, Speed: 145.871 samples/sec, ObjLoss=22.794, BoxCenterLoss=14.704, BoxScaleLoss=5.194, ClassLoss=9.434 [Epoch 80][Batch 399], LR: 1.00E-03, Speed: 134.891 samples/sec, ObjLoss=22.793, BoxCenterLoss=14.704, BoxScaleLoss=5.194, ClassLoss=9.432 [Epoch 80][Batch 499], LR: 1.00E-03, Speed: 96.079 samples/sec, ObjLoss=22.791, BoxCenterLoss=14.704, BoxScaleLoss=5.194, ClassLoss=9.431 [Epoch 80][Batch 599], LR: 1.00E-03, Speed: 172.008 samples/sec, ObjLoss=22.789, BoxCenterLoss=14.703, BoxScaleLoss=5.193, ClassLoss=9.429 [Epoch 80][Batch 699], LR: 1.00E-03, Speed: 145.567 samples/sec, ObjLoss=22.788, BoxCenterLoss=14.703, BoxScaleLoss=5.193, ClassLoss=9.428 [Epoch 80][Batch 799], LR: 1.00E-03, Speed: 105.585 samples/sec, ObjLoss=22.786, BoxCenterLoss=14.703, BoxScaleLoss=5.193, ClassLoss=9.426 [Epoch 80][Batch 899], LR: 1.00E-03, Speed: 104.087 samples/sec, ObjLoss=22.784, BoxCenterLoss=14.703, BoxScaleLoss=5.192, ClassLoss=9.425 [Epoch 80][Batch 999], LR: 1.00E-03, Speed: 150.855 samples/sec, ObjLoss=22.783, BoxCenterLoss=14.703, BoxScaleLoss=5.192, ClassLoss=9.423 [Epoch 80][Batch 1099], LR: 1.00E-03, Speed: 136.635 samples/sec, ObjLoss=22.781, BoxCenterLoss=14.703, BoxScaleLoss=5.192, ClassLoss=9.422 [Epoch 80][Batch 1199], LR: 1.00E-03, Speed: 141.560 samples/sec, ObjLoss=22.780, BoxCenterLoss=14.702, BoxScaleLoss=5.192, ClassLoss=9.420 [Epoch 80][Batch 1299], LR: 1.00E-03, Speed: 127.386 samples/sec, ObjLoss=22.778, BoxCenterLoss=14.702, BoxScaleLoss=5.191, ClassLoss=9.419 [Epoch 80][Batch 1399], LR: 1.00E-03, Speed: 113.271 samples/sec, ObjLoss=22.776, BoxCenterLoss=14.702, BoxScaleLoss=5.191, ClassLoss=9.418 [Epoch 80][Batch 1499], LR: 1.00E-03, Speed: 136.168 samples/sec, ObjLoss=22.775, BoxCenterLoss=14.702, BoxScaleLoss=5.191, ClassLoss=9.416 [Epoch 80][Batch 1599], LR: 1.00E-03, Speed: 125.683 samples/sec, ObjLoss=22.773, BoxCenterLoss=14.702, BoxScaleLoss=5.191, ClassLoss=9.415 [Epoch 80][Batch 1699], LR: 1.00E-03, Speed: 138.403 samples/sec, ObjLoss=22.771, BoxCenterLoss=14.702, BoxScaleLoss=5.190, ClassLoss=9.414 [Epoch 80][Batch 1799], LR: 1.00E-03, Speed: 139.775 samples/sec, ObjLoss=22.770, BoxCenterLoss=14.702, BoxScaleLoss=5.190, ClassLoss=9.412 [Epoch 80] Training cost: 1216.975, ObjLoss=22.769, BoxCenterLoss=14.702, BoxScaleLoss=5.190, ClassLoss=9.412 [Epoch 80] 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 ] = 0.400 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 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 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.274 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 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.406 person=31.2 bicycle=13.7 car=19.1 motorcycle=22.3 airplane=37.4 bus=37.0 train=37.7 truck=17.2 boat=7.9 traffic light=9.7 fire hydrant=37.0 stop sign=40.9 parking meter=18.6 bench=11.0 bird=16.0 cat=38.5 dog=31.4 horse=31.1 sheep=24.7 cow=26.8 elephant=37.5 bear=39.1 zebra=41.4 giraffe=42.0 backpack=3.7 umbrella=18.5 handbag=3.1 tie=12.0 suitcase=12.4 frisbee=29.0 skis=6.4 snowboard=13.9 sports ball=20.3 kite=20.4 baseball bat=9.0 baseball glove=17.3 skateboard=19.0 surfboard=12.2 tennis racket=19.0 bottle=14.1 wine glass=11.2 cup=17.9 fork=7.8 knife=2.8 spoon=2.0 bowl=17.6 banana=9.7 apple=6.1 sandwich=15.4 orange=12.7 broccoli=9.3 carrot=8.2 hot dog=14.4 pizza=28.3 donut=19.4 cake=15.1 chair=11.3 couch=24.7 potted plant=9.9 bed=24.2 dining table=13.4 toilet=31.2 tv=37.5 laptop=33.6 mouse=34.7 remote=6.2 keyboard=24.5 cell phone=13.9 microwave=23.6 oven=16.7 toaster=0.0 sink=18.9 refrigerator=22.5 book=4.5 clock=28.4 vase=16.3 scissors=14.6 teddy bear=21.7 hair drier=0.0 toothbrush=3.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.2 [Epoch 81][Batch 99], LR: 1.00E-03, Speed: 134.560 samples/sec, ObjLoss=22.768, BoxCenterLoss=14.702, BoxScaleLoss=5.190, ClassLoss=9.410 [Epoch 81][Batch 199], LR: 1.00E-03, Speed: 112.430 samples/sec, ObjLoss=22.766, BoxCenterLoss=14.701, BoxScaleLoss=5.189, ClassLoss=9.408 [Epoch 81][Batch 299], LR: 1.00E-03, Speed: 154.859 samples/sec, ObjLoss=22.764, BoxCenterLoss=14.701, BoxScaleLoss=5.189, ClassLoss=9.407 [Epoch 81][Batch 399], LR: 1.00E-03, Speed: 119.985 samples/sec, ObjLoss=22.762, BoxCenterLoss=14.701, BoxScaleLoss=5.189, ClassLoss=9.405 [Epoch 81][Batch 499], LR: 1.00E-03, Speed: 133.250 samples/sec, ObjLoss=22.761, BoxCenterLoss=14.701, BoxScaleLoss=5.188, ClassLoss=9.404 [Epoch 81][Batch 599], LR: 1.00E-03, Speed: 130.268 samples/sec, ObjLoss=22.759, BoxCenterLoss=14.701, BoxScaleLoss=5.188, ClassLoss=9.402 [Epoch 81][Batch 699], LR: 1.00E-03, Speed: 95.119 samples/sec, ObjLoss=22.758, BoxCenterLoss=14.701, BoxScaleLoss=5.188, ClassLoss=9.401 [Epoch 81][Batch 799], LR: 1.00E-03, Speed: 132.465 samples/sec, ObjLoss=22.755, BoxCenterLoss=14.701, BoxScaleLoss=5.188, ClassLoss=9.399 [Epoch 81][Batch 899], LR: 1.00E-03, Speed: 80.736 samples/sec, ObjLoss=22.754, BoxCenterLoss=14.701, BoxScaleLoss=5.187, ClassLoss=9.398 [Epoch 81][Batch 999], LR: 1.00E-03, Speed: 147.679 samples/sec, ObjLoss=22.752, BoxCenterLoss=14.701, BoxScaleLoss=5.187, ClassLoss=9.397 [Epoch 81][Batch 1099], LR: 1.00E-03, Speed: 146.311 samples/sec, ObjLoss=22.751, BoxCenterLoss=14.701, BoxScaleLoss=5.187, ClassLoss=9.395 [Epoch 81][Batch 1199], LR: 1.00E-03, Speed: 104.716 samples/sec, ObjLoss=22.750, BoxCenterLoss=14.701, BoxScaleLoss=5.186, ClassLoss=9.393 [Epoch 81][Batch 1299], LR: 1.00E-03, Speed: 131.475 samples/sec, ObjLoss=22.748, BoxCenterLoss=14.701, BoxScaleLoss=5.186, ClassLoss=9.392 [Epoch 81][Batch 1399], LR: 1.00E-03, Speed: 102.514 samples/sec, ObjLoss=22.747, BoxCenterLoss=14.701, BoxScaleLoss=5.186, ClassLoss=9.391 [Epoch 81][Batch 1499], LR: 1.00E-03, Speed: 120.456 samples/sec, ObjLoss=22.745, BoxCenterLoss=14.700, BoxScaleLoss=5.186, ClassLoss=9.390 [Epoch 81][Batch 1599], LR: 1.00E-03, Speed: 72.234 samples/sec, ObjLoss=22.744, BoxCenterLoss=14.700, BoxScaleLoss=5.185, ClassLoss=9.388 [Epoch 81][Batch 1699], LR: 1.00E-03, Speed: 109.222 samples/sec, ObjLoss=22.742, BoxCenterLoss=14.700, BoxScaleLoss=5.185, ClassLoss=9.387 [Epoch 81][Batch 1799], LR: 1.00E-03, Speed: 117.269 samples/sec, ObjLoss=22.740, BoxCenterLoss=14.700, BoxScaleLoss=5.185, ClassLoss=9.385 [Epoch 81] Training cost: 1290.539, ObjLoss=22.740, BoxCenterLoss=14.700, BoxScaleLoss=5.185, ClassLoss=9.384 [Epoch 81] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.166 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.389 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.105 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 ] = 0.190 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 ] = 0.164 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.242 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 ] = 0.104 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.350 person=25.5 bicycle=13.8 car=18.5 motorcycle=18.7 airplane=31.2 bus=31.1 train=32.3 truck=14.5 boat=9.8 traffic light=11.4 fire hydrant=36.4 stop sign=32.9 parking meter=16.7 bench=9.0 bird=13.0 cat=27.9 dog=25.4 horse=23.3 sheep=18.6 cow=23.0 elephant=23.5 bear=16.8 zebra=30.8 giraffe=34.0 backpack=3.9 umbrella=17.6 handbag=2.7 tie=12.6 suitcase=9.8 frisbee=31.2 skis=6.4 snowboard=11.2 sports ball=19.5 kite=21.1 baseball bat=9.2 baseball glove=13.0 skateboard=20.9 surfboard=11.9 tennis racket=20.0 bottle=13.3 wine glass=14.6 cup=17.1 fork=5.5 knife=2.5 spoon=1.4 bowl=18.3 banana=10.7 apple=5.0 sandwich=16.6 orange=15.3 broccoli=9.5 carrot=7.9 hot dog=13.8 pizza=27.8 donut=20.7 cake=16.0 chair=10.8 couch=21.4 potted plant=9.2 bed=21.0 dining table=9.7 toilet=30.1 tv=29.5 laptop=24.0 mouse=23.6 remote=5.6 keyboard=20.4 cell phone=12.2 microwave=23.4 oven=13.4 toaster=0.0 sink=16.1 refrigerator=14.8 book=3.9 clock=28.0 vase=17.0 scissors=12.9 teddy bear=17.1 hair drier=0.0 toothbrush=1.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =16.6 [Epoch 82][Batch 99], LR: 1.00E-03, Speed: 143.204 samples/sec, ObjLoss=22.738, BoxCenterLoss=14.700, BoxScaleLoss=5.184, ClassLoss=9.383 [Epoch 82][Batch 199], LR: 1.00E-03, Speed: 81.690 samples/sec, ObjLoss=22.736, BoxCenterLoss=14.699, BoxScaleLoss=5.184, ClassLoss=9.381 [Epoch 82][Batch 299], LR: 1.00E-03, Speed: 117.808 samples/sec, ObjLoss=22.734, BoxCenterLoss=14.699, BoxScaleLoss=5.184, ClassLoss=9.380 [Epoch 82][Batch 399], LR: 1.00E-03, Speed: 132.856 samples/sec, ObjLoss=22.733, BoxCenterLoss=14.699, BoxScaleLoss=5.183, ClassLoss=9.378 [Epoch 82][Batch 499], LR: 1.00E-03, Speed: 146.006 samples/sec, ObjLoss=22.731, BoxCenterLoss=14.699, BoxScaleLoss=5.183, ClassLoss=9.377 [Epoch 82][Batch 599], LR: 1.00E-03, Speed: 142.678 samples/sec, ObjLoss=22.729, BoxCenterLoss=14.699, BoxScaleLoss=5.183, ClassLoss=9.375 [Epoch 82][Batch 699], LR: 1.00E-03, Speed: 127.140 samples/sec, ObjLoss=22.728, BoxCenterLoss=14.699, BoxScaleLoss=5.182, ClassLoss=9.374 [Epoch 82][Batch 799], LR: 1.00E-03, Speed: 115.389 samples/sec, ObjLoss=22.726, BoxCenterLoss=14.698, BoxScaleLoss=5.182, ClassLoss=9.372 [Epoch 82][Batch 899], LR: 1.00E-03, Speed: 87.818 samples/sec, ObjLoss=22.724, BoxCenterLoss=14.698, BoxScaleLoss=5.181, ClassLoss=9.371 [Epoch 82][Batch 999], LR: 1.00E-03, Speed: 154.281 samples/sec, ObjLoss=22.723, BoxCenterLoss=14.698, BoxScaleLoss=5.181, ClassLoss=9.369 [Epoch 82][Batch 1099], LR: 1.00E-03, Speed: 57.658 samples/sec, ObjLoss=22.721, BoxCenterLoss=14.698, BoxScaleLoss=5.181, ClassLoss=9.368 [Epoch 82][Batch 1199], LR: 1.00E-03, Speed: 123.537 samples/sec, ObjLoss=22.720, BoxCenterLoss=14.698, BoxScaleLoss=5.181, ClassLoss=9.366 [Epoch 82][Batch 1299], LR: 1.00E-03, Speed: 77.288 samples/sec, ObjLoss=22.718, BoxCenterLoss=14.698, BoxScaleLoss=5.180, ClassLoss=9.365 [Epoch 82][Batch 1399], LR: 1.00E-03, Speed: 141.845 samples/sec, ObjLoss=22.716, BoxCenterLoss=14.698, BoxScaleLoss=5.180, ClassLoss=9.363 [Epoch 82][Batch 1499], LR: 1.00E-03, Speed: 90.833 samples/sec, ObjLoss=22.715, BoxCenterLoss=14.698, BoxScaleLoss=5.180, ClassLoss=9.362 [Epoch 82][Batch 1599], LR: 1.00E-03, Speed: 133.527 samples/sec, ObjLoss=22.713, BoxCenterLoss=14.697, BoxScaleLoss=5.179, ClassLoss=9.360 [Epoch 82][Batch 1699], LR: 1.00E-03, Speed: 93.954 samples/sec, ObjLoss=22.712, BoxCenterLoss=14.698, BoxScaleLoss=5.179, ClassLoss=9.359 [Epoch 82][Batch 1799], LR: 1.00E-03, Speed: 156.720 samples/sec, ObjLoss=22.710, BoxCenterLoss=14.697, BoxScaleLoss=5.179, ClassLoss=9.357 [Epoch 82] Training cost: 1270.000, ObjLoss=22.710, BoxCenterLoss=14.697, BoxScaleLoss=5.179, ClassLoss=9.357 [Epoch 82] 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 ] = 0.169 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 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288 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 ] = 0.264 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 ] = 0.107 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.398 person=30.7 bicycle=14.9 car=20.2 motorcycle=22.9 airplane=35.8 bus=37.9 train=41.7 truck=17.7 boat=11.3 traffic light=7.1 fire hydrant=34.4 stop sign=38.8 parking meter=18.6 bench=10.9 bird=16.9 cat=40.1 dog=32.2 horse=30.0 sheep=25.1 cow=27.3 elephant=36.8 bear=44.4 zebra=39.3 giraffe=42.4 backpack=4.8 umbrella=18.6 handbag=2.6 tie=10.9 suitcase=12.5 frisbee=32.2 skis=5.8 snowboard=10.9 sports ball=21.4 kite=23.0 baseball bat=8.5 baseball glove=15.8 skateboard=21.0 surfboard=16.7 tennis racket=19.6 bottle=12.4 wine glass=14.8 cup=17.9 fork=7.3 knife=3.1 spoon=1.9 bowl=18.2 banana=10.9 apple=5.5 sandwich=17.8 orange=15.7 broccoli=10.7 carrot=6.7 hot dog=14.9 pizza=28.0 donut=16.3 cake=13.5 chair=11.9 couch=26.0 potted plant=10.5 bed=22.9 dining table=12.3 toilet=31.8 tv=31.6 laptop=34.9 mouse=32.8 remote=7.8 keyboard=21.3 cell phone=13.7 microwave=23.9 oven=15.2 toaster=0.0 sink=14.9 refrigerator=24.5 book=4.5 clock=28.6 vase=17.0 scissors=11.8 teddy bear=19.5 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.2 [Epoch 83][Batch 99], LR: 1.00E-03, Speed: 138.528 samples/sec, ObjLoss=22.708, BoxCenterLoss=14.697, BoxScaleLoss=5.178, ClassLoss=9.356 [Epoch 83][Batch 199], LR: 1.00E-03, Speed: 166.549 samples/sec, ObjLoss=22.706, BoxCenterLoss=14.697, BoxScaleLoss=5.178, ClassLoss=9.354 [Epoch 83][Batch 299], LR: 1.00E-03, Speed: 135.980 samples/sec, ObjLoss=22.705, BoxCenterLoss=14.697, BoxScaleLoss=5.178, ClassLoss=9.353 [Epoch 83][Batch 399], LR: 1.00E-03, Speed: 133.974 samples/sec, ObjLoss=22.703, BoxCenterLoss=14.697, BoxScaleLoss=5.178, ClassLoss=9.351 [Epoch 83][Batch 499], LR: 1.00E-03, Speed: 130.118 samples/sec, ObjLoss=22.702, BoxCenterLoss=14.697, BoxScaleLoss=5.177, ClassLoss=9.350 [Epoch 83][Batch 599], LR: 1.00E-03, Speed: 105.574 samples/sec, ObjLoss=22.700, BoxCenterLoss=14.697, BoxScaleLoss=5.177, ClassLoss=9.348 [Epoch 83][Batch 699], LR: 1.00E-03, Speed: 110.254 samples/sec, ObjLoss=22.699, BoxCenterLoss=14.697, BoxScaleLoss=5.177, ClassLoss=9.347 [Epoch 83][Batch 799], LR: 1.00E-03, Speed: 114.509 samples/sec, ObjLoss=22.697, BoxCenterLoss=14.696, BoxScaleLoss=5.176, ClassLoss=9.345 [Epoch 83][Batch 899], LR: 1.00E-03, Speed: 57.787 samples/sec, ObjLoss=22.696, BoxCenterLoss=14.696, BoxScaleLoss=5.176, ClassLoss=9.344 [Epoch 83][Batch 999], LR: 1.00E-03, Speed: 136.884 samples/sec, ObjLoss=22.694, BoxCenterLoss=14.696, BoxScaleLoss=5.175, ClassLoss=9.342 [Epoch 83][Batch 1099], LR: 1.00E-03, Speed: 133.423 samples/sec, ObjLoss=22.692, BoxCenterLoss=14.696, BoxScaleLoss=5.175, ClassLoss=9.341 [Epoch 83][Batch 1199], LR: 1.00E-03, Speed: 135.456 samples/sec, ObjLoss=22.691, BoxCenterLoss=14.696, BoxScaleLoss=5.175, ClassLoss=9.339 [Epoch 83][Batch 1299], LR: 1.00E-03, Speed: 97.515 samples/sec, ObjLoss=22.689, BoxCenterLoss=14.696, BoxScaleLoss=5.175, ClassLoss=9.338 [Epoch 83][Batch 1399], LR: 1.00E-03, Speed: 191.911 samples/sec, ObjLoss=22.688, BoxCenterLoss=14.696, BoxScaleLoss=5.174, ClassLoss=9.336 [Epoch 83][Batch 1499], LR: 1.00E-03, Speed: 137.098 samples/sec, ObjLoss=22.686, BoxCenterLoss=14.695, BoxScaleLoss=5.174, ClassLoss=9.335 [Epoch 83][Batch 1599], LR: 1.00E-03, Speed: 123.072 samples/sec, ObjLoss=22.685, BoxCenterLoss=14.695, BoxScaleLoss=5.174, ClassLoss=9.334 [Epoch 83][Batch 1699], LR: 1.00E-03, Speed: 129.151 samples/sec, ObjLoss=22.683, BoxCenterLoss=14.695, BoxScaleLoss=5.173, ClassLoss=9.332 [Epoch 83][Batch 1799], LR: 1.00E-03, Speed: 112.028 samples/sec, ObjLoss=22.682, BoxCenterLoss=14.695, BoxScaleLoss=5.173, ClassLoss=9.331 [Epoch 83] Training cost: 1297.231, ObjLoss=22.682, BoxCenterLoss=14.695, BoxScaleLoss=5.173, ClassLoss=9.330 [Epoch 83] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.137 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 ] = 0.193 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.176 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.273 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 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 person=28.0 bicycle=12.5 car=18.4 motorcycle=22.2 airplane=36.6 bus=38.0 train=40.5 truck=16.9 boat=7.8 traffic light=10.8 fire hydrant=39.0 stop sign=33.4 parking meter=18.8 bench=9.8 bird=14.4 cat=35.8 dog=30.3 horse=24.7 sheep=25.9 cow=27.0 elephant=38.6 bear=41.3 zebra=39.6 giraffe=36.9 backpack=4.2 umbrella=17.6 handbag=2.9 tie=11.1 suitcase=9.4 frisbee=28.2 skis=6.2 snowboard=12.6 sports ball=14.4 kite=18.9 baseball bat=7.7 baseball glove=16.0 skateboard=19.7 surfboard=15.2 tennis racket=18.6 bottle=13.8 wine glass=10.9 cup=15.5 fork=7.4 knife=2.7 spoon=1.7 bowl=17.6 banana=10.2 apple=6.8 sandwich=14.5 orange=12.4 broccoli=10.7 carrot=7.6 hot dog=11.8 pizza=26.0 donut=19.8 cake=15.9 chair=11.9 couch=25.9 potted plant=8.5 bed=23.8 dining table=13.2 toilet=29.7 tv=30.5 laptop=26.7 mouse=27.4 remote=5.4 keyboard=27.8 cell phone=11.4 microwave=26.0 oven=16.1 toaster=0.0 sink=19.0 refrigerator=17.9 book=4.5 clock=23.4 vase=13.9 scissors=16.4 teddy bear=20.8 hair drier=0.0 toothbrush=1.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.2 [Epoch 84][Batch 99], LR: 1.00E-03, Speed: 126.078 samples/sec, ObjLoss=22.680, BoxCenterLoss=14.695, BoxScaleLoss=5.173, ClassLoss=9.329 [Epoch 84][Batch 199], LR: 1.00E-03, Speed: 92.440 samples/sec, ObjLoss=22.678, BoxCenterLoss=14.695, BoxScaleLoss=5.172, ClassLoss=9.328 [Epoch 84][Batch 299], LR: 1.00E-03, Speed: 135.566 samples/sec, ObjLoss=22.677, BoxCenterLoss=14.695, BoxScaleLoss=5.172, ClassLoss=9.326 [Epoch 84][Batch 399], LR: 1.00E-03, Speed: 143.796 samples/sec, ObjLoss=22.675, BoxCenterLoss=14.695, BoxScaleLoss=5.172, ClassLoss=9.325 [Epoch 84][Batch 499], LR: 1.00E-03, Speed: 101.021 samples/sec, ObjLoss=22.674, BoxCenterLoss=14.694, BoxScaleLoss=5.172, ClassLoss=9.323 [Epoch 84][Batch 599], LR: 1.00E-03, Speed: 115.357 samples/sec, ObjLoss=22.672, BoxCenterLoss=14.694, BoxScaleLoss=5.171, ClassLoss=9.322 [Epoch 84][Batch 699], LR: 1.00E-03, Speed: 145.989 samples/sec, ObjLoss=22.670, BoxCenterLoss=14.694, BoxScaleLoss=5.171, ClassLoss=9.321 [Epoch 84][Batch 799], LR: 1.00E-03, Speed: 149.904 samples/sec, ObjLoss=22.668, BoxCenterLoss=14.694, BoxScaleLoss=5.171, ClassLoss=9.319 [Epoch 84][Batch 899], LR: 1.00E-03, Speed: 90.754 samples/sec, ObjLoss=22.667, BoxCenterLoss=14.694, BoxScaleLoss=5.171, ClassLoss=9.318 [Epoch 84][Batch 999], LR: 1.00E-03, Speed: 140.202 samples/sec, ObjLoss=22.665, BoxCenterLoss=14.693, BoxScaleLoss=5.170, ClassLoss=9.316 [Epoch 84][Batch 1099], LR: 1.00E-03, Speed: 164.498 samples/sec, ObjLoss=22.662, BoxCenterLoss=14.693, BoxScaleLoss=5.170, ClassLoss=9.315 [Epoch 84][Batch 1199], LR: 1.00E-03, Speed: 101.322 samples/sec, ObjLoss=22.661, BoxCenterLoss=14.692, BoxScaleLoss=5.170, ClassLoss=9.314 [Epoch 84][Batch 1299], LR: 1.00E-03, Speed: 141.748 samples/sec, ObjLoss=22.660, BoxCenterLoss=14.692, BoxScaleLoss=5.169, ClassLoss=9.312 [Epoch 84][Batch 1399], LR: 1.00E-03, Speed: 78.205 samples/sec, ObjLoss=22.658, BoxCenterLoss=14.692, BoxScaleLoss=5.169, ClassLoss=9.311 [Epoch 84][Batch 1499], LR: 1.00E-03, Speed: 81.934 samples/sec, ObjLoss=22.657, BoxCenterLoss=14.692, BoxScaleLoss=5.169, ClassLoss=9.309 [Epoch 84][Batch 1599], LR: 1.00E-03, Speed: 119.300 samples/sec, ObjLoss=22.655, BoxCenterLoss=14.692, BoxScaleLoss=5.168, ClassLoss=9.308 [Epoch 84][Batch 1699], LR: 1.00E-03, Speed: 126.272 samples/sec, ObjLoss=22.654, BoxCenterLoss=14.692, BoxScaleLoss=5.168, ClassLoss=9.307 [Epoch 84][Batch 1799], LR: 1.00E-03, Speed: 148.757 samples/sec, ObjLoss=22.653, BoxCenterLoss=14.692, BoxScaleLoss=5.168, ClassLoss=9.305 [Epoch 84] Training cost: 1250.810, ObjLoss=22.653, BoxCenterLoss=14.692, BoxScaleLoss=5.167, ClassLoss=9.304 [Epoch 84] 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 ] = 0.403 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.144 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 ] = 0.188 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.285 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 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.116 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.400 person=30.4 bicycle=15.0 car=18.1 motorcycle=23.7 airplane=37.6 bus=38.9 train=36.7 truck=17.2 boat=9.4 traffic light=12.0 fire hydrant=31.5 stop sign=39.3 parking meter=23.9 bench=10.6 bird=15.2 cat=36.9 dog=32.2 horse=28.4 sheep=24.0 cow=24.8 elephant=38.9 bear=30.3 zebra=34.5 giraffe=42.0 backpack=4.8 umbrella=17.8 handbag=3.2 tie=13.8 suitcase=15.0 frisbee=28.7 skis=6.5 snowboard=10.3 sports ball=20.2 kite=21.8 baseball bat=8.5 baseball glove=14.5 skateboard=20.8 surfboard=14.3 tennis racket=18.1 bottle=12.8 wine glass=9.3 cup=13.5 fork=7.2 knife=2.8 spoon=1.3 bowl=18.0 banana=11.5 apple=5.7 sandwich=15.3 orange=11.4 broccoli=8.0 carrot=8.6 hot dog=16.7 pizza=27.7 donut=19.7 cake=15.1 chair=10.6 couch=22.9 potted plant=9.5 bed=25.1 dining table=12.9 toilet=37.9 tv=31.0 laptop=30.2 mouse=29.4 remote=5.8 keyboard=26.8 cell phone=13.2 microwave=26.5 oven=15.4 toaster=0.0 sink=15.3 refrigerator=26.6 book=4.3 clock=30.2 vase=18.1 scissors=9.5 teddy bear=19.1 hair drier=0.0 toothbrush=2.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.7 [Epoch 85][Batch 99], LR: 1.00E-03, Speed: 169.344 samples/sec, ObjLoss=22.652, BoxCenterLoss=14.692, BoxScaleLoss=5.167, ClassLoss=9.303 [Epoch 85][Batch 199], LR: 1.00E-03, Speed: 47.486 samples/sec, ObjLoss=22.650, BoxCenterLoss=14.692, BoxScaleLoss=5.167, ClassLoss=9.302 [Epoch 85][Batch 299], LR: 1.00E-03, Speed: 115.462 samples/sec, ObjLoss=22.649, BoxCenterLoss=14.692, BoxScaleLoss=5.167, ClassLoss=9.300 [Epoch 85][Batch 399], LR: 1.00E-03, Speed: 76.195 samples/sec, ObjLoss=22.647, BoxCenterLoss=14.692, BoxScaleLoss=5.166, ClassLoss=9.299 [Epoch 85][Batch 499], LR: 1.00E-03, Speed: 114.065 samples/sec, ObjLoss=22.645, BoxCenterLoss=14.691, BoxScaleLoss=5.166, ClassLoss=9.297 [Epoch 85][Batch 599], LR: 1.00E-03, Speed: 154.235 samples/sec, ObjLoss=22.644, BoxCenterLoss=14.691, BoxScaleLoss=5.166, ClassLoss=9.296 [Epoch 85][Batch 699], LR: 1.00E-03, Speed: 90.769 samples/sec, ObjLoss=22.642, BoxCenterLoss=14.691, BoxScaleLoss=5.165, ClassLoss=9.294 [Epoch 85][Batch 799], LR: 1.00E-03, Speed: 119.900 samples/sec, ObjLoss=22.641, BoxCenterLoss=14.691, BoxScaleLoss=5.165, ClassLoss=9.293 [Epoch 85][Batch 899], LR: 1.00E-03, Speed: 129.508 samples/sec, ObjLoss=22.639, BoxCenterLoss=14.691, BoxScaleLoss=5.165, ClassLoss=9.292 [Epoch 85][Batch 999], LR: 1.00E-03, Speed: 121.507 samples/sec, ObjLoss=22.638, BoxCenterLoss=14.691, BoxScaleLoss=5.164, ClassLoss=9.290 [Epoch 85][Batch 1099], LR: 1.00E-03, Speed: 116.444 samples/sec, ObjLoss=22.636, BoxCenterLoss=14.691, BoxScaleLoss=5.164, ClassLoss=9.289 [Epoch 85][Batch 1199], LR: 1.00E-03, Speed: 81.028 samples/sec, ObjLoss=22.634, BoxCenterLoss=14.691, BoxScaleLoss=5.164, ClassLoss=9.288 [Epoch 85][Batch 1299], LR: 1.00E-03, Speed: 155.468 samples/sec, ObjLoss=22.633, BoxCenterLoss=14.690, BoxScaleLoss=5.164, ClassLoss=9.287 [Epoch 85][Batch 1399], LR: 1.00E-03, Speed: 129.552 samples/sec, ObjLoss=22.631, BoxCenterLoss=14.690, BoxScaleLoss=5.164, ClassLoss=9.285 [Epoch 85][Batch 1499], LR: 1.00E-03, Speed: 129.755 samples/sec, ObjLoss=22.630, BoxCenterLoss=14.690, BoxScaleLoss=5.163, ClassLoss=9.284 [Epoch 85][Batch 1599], LR: 1.00E-03, Speed: 141.311 samples/sec, ObjLoss=22.628, BoxCenterLoss=14.690, BoxScaleLoss=5.163, ClassLoss=9.283 [Epoch 85][Batch 1699], LR: 1.00E-03, Speed: 126.518 samples/sec, ObjLoss=22.626, BoxCenterLoss=14.690, BoxScaleLoss=5.163, ClassLoss=9.281 [Epoch 85][Batch 1799], LR: 1.00E-03, Speed: 148.247 samples/sec, ObjLoss=22.625, BoxCenterLoss=14.689, BoxScaleLoss=5.162, ClassLoss=9.280 [Epoch 85] Training cost: 1288.821, ObjLoss=22.624, BoxCenterLoss=14.689, BoxScaleLoss=5.162, ClassLoss=9.279 [Epoch 85] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.193 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.079 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.204 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.184 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279 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 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.409 person=31.4 bicycle=13.5 car=21.4 motorcycle=21.4 airplane=33.8 bus=37.2 train=40.5 truck=17.0 boat=10.2 traffic light=10.1 fire hydrant=38.6 stop sign=34.1 parking meter=25.2 bench=10.0 bird=16.3 cat=38.2 dog=34.8 horse=31.4 sheep=21.8 cow=27.4 elephant=34.9 bear=36.3 zebra=41.3 giraffe=37.0 backpack=4.4 umbrella=17.4 handbag=2.7 tie=13.4 suitcase=14.4 frisbee=29.6 skis=7.7 snowboard=11.4 sports ball=19.1 kite=23.2 baseball bat=8.9 baseball glove=15.6 skateboard=21.4 surfboard=15.3 tennis racket=20.2 bottle=15.2 wine glass=15.0 cup=18.0 fork=7.1 knife=2.2 spoon=1.7 bowl=18.6 banana=9.8 apple=6.9 sandwich=16.0 orange=15.1 broccoli=9.3 carrot=7.0 hot dog=13.2 pizza=28.4 donut=20.7 cake=16.1 chair=11.7 couch=23.6 potted plant=9.3 bed=21.6 dining table=11.3 toilet=36.0 tv=29.6 laptop=32.2 mouse=31.2 remote=6.9 keyboard=28.2 cell phone=15.8 microwave=28.9 oven=16.9 toaster=0.0 sink=17.3 refrigerator=23.1 book=3.6 clock=31.0 vase=16.3 scissors=13.5 teddy bear=23.6 hair drier=0.0 toothbrush=1.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.3 [Epoch 86][Batch 99], LR: 1.00E-03, Speed: 99.888 samples/sec, ObjLoss=22.623, BoxCenterLoss=14.689, BoxScaleLoss=5.162, ClassLoss=9.278 [Epoch 86][Batch 199], LR: 1.00E-03, Speed: 122.667 samples/sec, ObjLoss=22.621, BoxCenterLoss=14.689, BoxScaleLoss=5.162, ClassLoss=9.276 [Epoch 86][Batch 299], LR: 1.00E-03, Speed: 123.399 samples/sec, ObjLoss=22.620, BoxCenterLoss=14.689, BoxScaleLoss=5.161, ClassLoss=9.275 [Epoch 86][Batch 399], LR: 1.00E-03, Speed: 122.765 samples/sec, ObjLoss=22.618, BoxCenterLoss=14.689, BoxScaleLoss=5.161, ClassLoss=9.274 [Epoch 86][Batch 499], LR: 1.00E-03, Speed: 73.565 samples/sec, ObjLoss=22.616, BoxCenterLoss=14.688, BoxScaleLoss=5.161, ClassLoss=9.272 [Epoch 86][Batch 599], LR: 1.00E-03, Speed: 109.993 samples/sec, ObjLoss=22.615, BoxCenterLoss=14.689, BoxScaleLoss=5.161, ClassLoss=9.271 [Epoch 86][Batch 699], LR: 1.00E-03, Speed: 119.248 samples/sec, ObjLoss=22.614, BoxCenterLoss=14.688, BoxScaleLoss=5.160, ClassLoss=9.269 [Epoch 86][Batch 799], LR: 1.00E-03, Speed: 140.715 samples/sec, ObjLoss=22.612, BoxCenterLoss=14.688, BoxScaleLoss=5.160, ClassLoss=9.268 [Epoch 86][Batch 899], LR: 1.00E-03, Speed: 128.073 samples/sec, ObjLoss=22.611, BoxCenterLoss=14.688, BoxScaleLoss=5.160, ClassLoss=9.267 [Epoch 86][Batch 999], LR: 1.00E-03, Speed: 79.863 samples/sec, ObjLoss=22.610, BoxCenterLoss=14.688, BoxScaleLoss=5.159, ClassLoss=9.266 [Epoch 86][Batch 1099], LR: 1.00E-03, Speed: 129.045 samples/sec, ObjLoss=22.608, BoxCenterLoss=14.688, BoxScaleLoss=5.159, ClassLoss=9.264 [Epoch 86][Batch 1199], LR: 1.00E-03, Speed: 98.079 samples/sec, ObjLoss=22.607, BoxCenterLoss=14.688, BoxScaleLoss=5.159, ClassLoss=9.263 [Epoch 86][Batch 1299], LR: 1.00E-03, Speed: 116.464 samples/sec, ObjLoss=22.605, BoxCenterLoss=14.688, BoxScaleLoss=5.158, ClassLoss=9.261 [Epoch 86][Batch 1399], LR: 1.00E-03, Speed: 130.194 samples/sec, ObjLoss=22.604, BoxCenterLoss=14.688, BoxScaleLoss=5.158, ClassLoss=9.260 [Epoch 86][Batch 1499], LR: 1.00E-03, Speed: 88.878 samples/sec, ObjLoss=22.603, BoxCenterLoss=14.688, BoxScaleLoss=5.158, ClassLoss=9.259 [Epoch 86][Batch 1599], LR: 1.00E-03, Speed: 132.915 samples/sec, ObjLoss=22.601, BoxCenterLoss=14.688, BoxScaleLoss=5.158, ClassLoss=9.257 [Epoch 86][Batch 1699], LR: 1.00E-03, Speed: 129.094 samples/sec, ObjLoss=22.599, BoxCenterLoss=14.687, BoxScaleLoss=5.157, ClassLoss=9.256 [Epoch 86][Batch 1799], LR: 1.00E-03, Speed: 114.354 samples/sec, ObjLoss=22.598, BoxCenterLoss=14.687, BoxScaleLoss=5.157, ClassLoss=9.254 [Epoch 86] Training cost: 1349.302, ObjLoss=22.598, BoxCenterLoss=14.687, BoxScaleLoss=5.157, ClassLoss=9.254 [Epoch 86] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.196 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.403 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.171 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 ] = 0.195 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.189 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.278 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 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 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410 person=31.4 bicycle=13.5 car=20.7 motorcycle=23.2 airplane=33.9 bus=41.3 train=39.0 truck=18.6 boat=7.0 traffic light=7.7 fire hydrant=37.5 stop sign=33.5 parking meter=24.6 bench=10.6 bird=15.8 cat=40.7 dog=33.9 horse=29.4 sheep=22.2 cow=29.9 elephant=40.1 bear=38.7 zebra=42.7 giraffe=43.6 backpack=4.4 umbrella=18.2 handbag=2.5 tie=11.1 suitcase=14.6 frisbee=31.3 skis=6.9 snowboard=10.1 sports ball=20.0 kite=16.9 baseball bat=9.8 baseball glove=18.3 skateboard=20.2 surfboard=14.7 tennis racket=20.8 bottle=13.3 wine glass=9.2 cup=18.7 fork=8.1 knife=4.4 spoon=2.4 bowl=17.1 banana=10.7 apple=7.7 sandwich=16.4 orange=15.2 broccoli=10.4 carrot=8.1 hot dog=15.1 pizza=29.8 donut=22.6 cake=17.2 chair=10.9 couch=26.2 potted plant=8.4 bed=30.7 dining table=16.9 toilet=31.6 tv=32.0 laptop=34.8 mouse=26.4 remote=6.7 keyboard=27.2 cell phone=12.8 microwave=24.0 oven=17.0 toaster=0.0 sink=18.8 refrigerator=26.0 book=4.1 clock=29.5 vase=17.1 scissors=15.5 teddy bear=22.2 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.6 [Epoch 87][Batch 99], LR: 1.00E-03, Speed: 8.510 samples/sec, ObjLoss=22.596, BoxCenterLoss=14.687, BoxScaleLoss=5.157, ClassLoss=9.253 [Epoch 87][Batch 199], LR: 1.00E-03, Speed: 126.210 samples/sec, ObjLoss=22.595, BoxCenterLoss=14.687, BoxScaleLoss=5.156, ClassLoss=9.251 [Epoch 87][Batch 299], LR: 1.00E-03, Speed: 155.159 samples/sec, ObjLoss=22.593, BoxCenterLoss=14.687, BoxScaleLoss=5.156, ClassLoss=9.250 [Epoch 87][Batch 399], LR: 1.00E-03, Speed: 115.165 samples/sec, ObjLoss=22.592, BoxCenterLoss=14.687, BoxScaleLoss=5.156, ClassLoss=9.249 [Epoch 87][Batch 499], LR: 1.00E-03, Speed: 159.431 samples/sec, ObjLoss=22.591, BoxCenterLoss=14.687, BoxScaleLoss=5.156, ClassLoss=9.247 [Epoch 87][Batch 599], LR: 1.00E-03, Speed: 152.386 samples/sec, ObjLoss=22.589, BoxCenterLoss=14.686, BoxScaleLoss=5.155, ClassLoss=9.246 [Epoch 87][Batch 699], LR: 1.00E-03, Speed: 103.167 samples/sec, ObjLoss=22.587, BoxCenterLoss=14.686, BoxScaleLoss=5.155, ClassLoss=9.245 [Epoch 87][Batch 799], LR: 1.00E-03, Speed: 158.441 samples/sec, ObjLoss=22.586, BoxCenterLoss=14.686, BoxScaleLoss=5.154, ClassLoss=9.243 [Epoch 87][Batch 899], LR: 1.00E-03, Speed: 131.507 samples/sec, ObjLoss=22.584, BoxCenterLoss=14.686, BoxScaleLoss=5.154, ClassLoss=9.242 [Epoch 87][Batch 999], LR: 1.00E-03, Speed: 127.416 samples/sec, ObjLoss=22.583, BoxCenterLoss=14.686, BoxScaleLoss=5.154, ClassLoss=9.241 [Epoch 87][Batch 1099], LR: 1.00E-03, Speed: 125.858 samples/sec, ObjLoss=22.581, BoxCenterLoss=14.686, BoxScaleLoss=5.154, ClassLoss=9.239 [Epoch 87][Batch 1199], LR: 1.00E-03, Speed: 147.774 samples/sec, ObjLoss=22.580, BoxCenterLoss=14.685, BoxScaleLoss=5.153, ClassLoss=9.238 [Epoch 87][Batch 1299], LR: 1.00E-03, Speed: 125.689 samples/sec, ObjLoss=22.578, BoxCenterLoss=14.685, BoxScaleLoss=5.153, ClassLoss=9.237 [Epoch 87][Batch 1399], LR: 1.00E-03, Speed: 127.165 samples/sec, ObjLoss=22.577, BoxCenterLoss=14.685, BoxScaleLoss=5.153, ClassLoss=9.235 [Epoch 87][Batch 1499], LR: 1.00E-03, Speed: 124.710 samples/sec, ObjLoss=22.575, BoxCenterLoss=14.685, BoxScaleLoss=5.152, ClassLoss=9.234 [Epoch 87][Batch 1599], LR: 1.00E-03, Speed: 130.176 samples/sec, ObjLoss=22.574, BoxCenterLoss=14.685, BoxScaleLoss=5.152, ClassLoss=9.232 [Epoch 87][Batch 1699], LR: 1.00E-03, Speed: 132.424 samples/sec, ObjLoss=22.573, BoxCenterLoss=14.685, BoxScaleLoss=5.152, ClassLoss=9.231 [Epoch 87][Batch 1799], LR: 1.00E-03, Speed: 146.362 samples/sec, ObjLoss=22.571, BoxCenterLoss=14.684, BoxScaleLoss=5.151, ClassLoss=9.229 [Epoch 87] Training cost: 1097.876, ObjLoss=22.571, BoxCenterLoss=14.684, BoxScaleLoss=5.151, ClassLoss=9.229 [Epoch 87] 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 ] = 0.398 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.125 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 ] = 0.193 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.173 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 ] = 0.261 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 ] = 0.275 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.348 person=30.1 bicycle=13.5 car=18.6 motorcycle=19.4 airplane=30.2 bus=39.8 train=33.3 truck=15.5 boat=9.6 traffic light=11.8 fire hydrant=19.6 stop sign=30.3 parking meter=17.8 bench=9.1 bird=12.2 cat=38.7 dog=32.8 horse=27.8 sheep=23.3 cow=29.7 elephant=36.9 bear=25.6 zebra=32.9 giraffe=34.7 backpack=4.3 umbrella=14.7 handbag=2.7 tie=13.6 suitcase=12.1 frisbee=28.1 skis=7.4 snowboard=12.1 sports ball=23.1 kite=20.3 baseball bat=9.4 baseball glove=19.0 skateboard=20.2 surfboard=14.0 tennis racket=19.2 bottle=14.2 wine glass=12.5 cup=16.7 fork=7.2 knife=1.8 spoon=2.1 bowl=16.1 banana=9.4 apple=5.3 sandwich=13.5 orange=12.1 broccoli=9.3 carrot=7.5 hot dog=13.7 pizza=24.7 donut=12.3 cake=10.1 chair=10.5 couch=22.4 potted plant=8.4 bed=26.7 dining table=12.5 toilet=24.4 tv=27.4 laptop=27.2 mouse=29.7 remote=7.5 keyboard=23.0 cell phone=13.3 microwave=25.2 oven=16.7 toaster=0.0 sink=16.0 refrigerator=24.9 book=4.9 clock=28.8 vase=12.6 scissors=13.1 teddy bear=25.3 hair drier=0.0 toothbrush=3.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.6 [Epoch 88][Batch 99], LR: 1.00E-03, Speed: 129.141 samples/sec, ObjLoss=22.569, BoxCenterLoss=14.684, BoxScaleLoss=5.151, ClassLoss=9.228 [Epoch 88][Batch 199], LR: 1.00E-03, Speed: 110.551 samples/sec, ObjLoss=22.568, BoxCenterLoss=14.684, BoxScaleLoss=5.151, ClassLoss=9.226 [Epoch 88][Batch 299], LR: 1.00E-03, Speed: 131.208 samples/sec, ObjLoss=22.567, BoxCenterLoss=14.684, BoxScaleLoss=5.150, ClassLoss=9.225 [Epoch 88][Batch 399], LR: 1.00E-03, Speed: 142.313 samples/sec, ObjLoss=22.565, BoxCenterLoss=14.684, BoxScaleLoss=5.150, ClassLoss=9.223 [Epoch 88][Batch 499], LR: 1.00E-03, Speed: 149.326 samples/sec, ObjLoss=22.564, BoxCenterLoss=14.684, BoxScaleLoss=5.150, ClassLoss=9.222 [Epoch 88][Batch 599], LR: 1.00E-03, Speed: 124.030 samples/sec, ObjLoss=22.562, BoxCenterLoss=14.684, BoxScaleLoss=5.149, ClassLoss=9.220 [Epoch 88][Batch 699], LR: 1.00E-03, Speed: 143.653 samples/sec, ObjLoss=22.561, BoxCenterLoss=14.683, BoxScaleLoss=5.149, ClassLoss=9.219 [Epoch 88][Batch 799], LR: 1.00E-03, Speed: 116.207 samples/sec, ObjLoss=22.559, BoxCenterLoss=14.683, BoxScaleLoss=5.149, ClassLoss=9.217 [Epoch 88][Batch 899], LR: 1.00E-03, Speed: 125.736 samples/sec, ObjLoss=22.558, BoxCenterLoss=14.683, BoxScaleLoss=5.148, ClassLoss=9.216 [Epoch 88][Batch 999], LR: 1.00E-03, Speed: 142.096 samples/sec, ObjLoss=22.556, BoxCenterLoss=14.683, BoxScaleLoss=5.148, ClassLoss=9.215 [Epoch 88][Batch 1099], LR: 1.00E-03, Speed: 124.053 samples/sec, ObjLoss=22.555, BoxCenterLoss=14.683, BoxScaleLoss=5.148, ClassLoss=9.213 [Epoch 88][Batch 1199], LR: 1.00E-03, Speed: 149.697 samples/sec, ObjLoss=22.553, BoxCenterLoss=14.683, BoxScaleLoss=5.147, ClassLoss=9.212 [Epoch 88][Batch 1299], LR: 1.00E-03, Speed: 155.283 samples/sec, ObjLoss=22.552, BoxCenterLoss=14.682, BoxScaleLoss=5.147, ClassLoss=9.211 [Epoch 88][Batch 1399], LR: 1.00E-03, Speed: 154.756 samples/sec, ObjLoss=22.550, BoxCenterLoss=14.682, BoxScaleLoss=5.147, ClassLoss=9.209 [Epoch 88][Batch 1499], LR: 1.00E-03, Speed: 131.506 samples/sec, ObjLoss=22.549, BoxCenterLoss=14.682, BoxScaleLoss=5.147, ClassLoss=9.208 [Epoch 88][Batch 1599], LR: 1.00E-03, Speed: 123.527 samples/sec, ObjLoss=22.547, BoxCenterLoss=14.682, BoxScaleLoss=5.147, ClassLoss=9.207 [Epoch 88][Batch 1699], LR: 1.00E-03, Speed: 126.537 samples/sec, ObjLoss=22.546, BoxCenterLoss=14.682, BoxScaleLoss=5.146, ClassLoss=9.206 [Epoch 88][Batch 1799], LR: 1.00E-03, Speed: 163.353 samples/sec, ObjLoss=22.544, BoxCenterLoss=14.682, BoxScaleLoss=5.146, ClassLoss=9.204 [Epoch 88] Training cost: 1152.630, ObjLoss=22.544, BoxCenterLoss=14.682, BoxScaleLoss=5.146, ClassLoss=9.204 [Epoch 88] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.409 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.161 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 ] = 0.196 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.272 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 ] = 0.123 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 ] = 0.410 person=32.7 bicycle=15.0 car=20.8 motorcycle=23.5 airplane=39.1 bus=42.6 train=44.0 truck=17.9 boat=11.3 traffic light=11.1 fire hydrant=38.2 stop sign=34.6 parking meter=19.3 bench=11.0 bird=15.8 cat=37.3 dog=36.9 horse=29.8 sheep=27.2 cow=28.9 elephant=36.9 bear=40.8 zebra=40.6 giraffe=41.9 backpack=4.2 umbrella=17.1 handbag=3.0 tie=15.1 suitcase=12.0 frisbee=31.2 skis=6.3 snowboard=10.7 sports ball=18.8 kite=22.3 baseball bat=7.8 baseball glove=16.3 skateboard=18.0 surfboard=14.5 tennis racket=21.6 bottle=13.7 wine glass=14.4 cup=20.1 fork=8.3 knife=3.8 spoon=2.1 bowl=18.9 banana=11.1 apple=5.6 sandwich=17.3 orange=14.4 broccoli=8.7 carrot=7.7 hot dog=11.2 pizza=28.1 donut=18.4 cake=13.1 chair=11.3 couch=23.1 potted plant=10.6 bed=23.3 dining table=11.3 toilet=34.8 tv=30.7 laptop=31.6 mouse=29.4 remote=8.2 keyboard=24.2 cell phone=14.0 microwave=30.1 oven=16.0 toaster=0.0 sink=16.9 refrigerator=23.6 book=4.8 clock=27.0 vase=17.8 scissors=14.0 teddy bear=23.3 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.5 [Epoch 89][Batch 99], LR: 1.00E-03, Speed: 117.153 samples/sec, ObjLoss=22.542, BoxCenterLoss=14.681, BoxScaleLoss=5.146, ClassLoss=9.202 [Epoch 89][Batch 199], LR: 1.00E-03, Speed: 145.830 samples/sec, ObjLoss=22.541, BoxCenterLoss=14.681, BoxScaleLoss=5.145, ClassLoss=9.201 [Epoch 89][Batch 299], LR: 1.00E-03, Speed: 139.767 samples/sec, ObjLoss=22.539, BoxCenterLoss=14.681, BoxScaleLoss=5.145, ClassLoss=9.199 [Epoch 89][Batch 399], LR: 1.00E-03, Speed: 160.042 samples/sec, ObjLoss=22.537, BoxCenterLoss=14.681, BoxScaleLoss=5.145, ClassLoss=9.198 [Epoch 89][Batch 499], LR: 1.00E-03, Speed: 152.943 samples/sec, ObjLoss=22.536, BoxCenterLoss=14.681, BoxScaleLoss=5.144, ClassLoss=9.197 [Epoch 89][Batch 599], LR: 1.00E-03, Speed: 123.576 samples/sec, ObjLoss=22.534, BoxCenterLoss=14.680, BoxScaleLoss=5.144, ClassLoss=9.196 [Epoch 89][Batch 699], LR: 1.00E-03, Speed: 149.356 samples/sec, ObjLoss=22.533, BoxCenterLoss=14.680, BoxScaleLoss=5.144, ClassLoss=9.194 [Epoch 89][Batch 799], LR: 1.00E-03, Speed: 114.573 samples/sec, ObjLoss=22.531, BoxCenterLoss=14.680, BoxScaleLoss=5.144, ClassLoss=9.193 [Epoch 89][Batch 899], LR: 1.00E-03, Speed: 85.488 samples/sec, ObjLoss=22.530, BoxCenterLoss=14.680, BoxScaleLoss=5.143, ClassLoss=9.192 [Epoch 89][Batch 999], LR: 1.00E-03, Speed: 85.991 samples/sec, ObjLoss=22.528, BoxCenterLoss=14.680, BoxScaleLoss=5.143, ClassLoss=9.190 [Epoch 89][Batch 1099], LR: 1.00E-03, Speed: 133.496 samples/sec, ObjLoss=22.527, BoxCenterLoss=14.679, BoxScaleLoss=5.143, ClassLoss=9.189 [Epoch 89][Batch 1199], LR: 1.00E-03, Speed: 160.166 samples/sec, ObjLoss=22.525, BoxCenterLoss=14.679, BoxScaleLoss=5.142, ClassLoss=9.188 [Epoch 89][Batch 1299], LR: 1.00E-03, Speed: 143.460 samples/sec, ObjLoss=22.524, BoxCenterLoss=14.679, BoxScaleLoss=5.142, ClassLoss=9.187 [Epoch 89][Batch 1399], LR: 1.00E-03, Speed: 141.471 samples/sec, ObjLoss=22.522, BoxCenterLoss=14.679, BoxScaleLoss=5.142, ClassLoss=9.186 [Epoch 89][Batch 1499], LR: 1.00E-03, Speed: 141.747 samples/sec, ObjLoss=22.520, BoxCenterLoss=14.679, BoxScaleLoss=5.142, ClassLoss=9.185 [Epoch 89][Batch 1599], LR: 1.00E-03, Speed: 142.936 samples/sec, ObjLoss=22.519, BoxCenterLoss=14.679, BoxScaleLoss=5.142, ClassLoss=9.183 [Epoch 89][Batch 1699], LR: 1.00E-03, Speed: 95.018 samples/sec, ObjLoss=22.518, BoxCenterLoss=14.679, BoxScaleLoss=5.141, ClassLoss=9.182 [Epoch 89][Batch 1799], LR: 1.00E-03, Speed: 130.957 samples/sec, ObjLoss=22.516, BoxCenterLoss=14.678, BoxScaleLoss=5.141, ClassLoss=9.180 [Epoch 89] Training cost: 1188.544, ObjLoss=22.516, BoxCenterLoss=14.678, BoxScaleLoss=5.141, ClassLoss=9.180 [Epoch 89] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.402 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.075 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 ] = 0.176 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 ] = 0.267 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 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386 person=29.5 bicycle=14.5 car=20.1 motorcycle=22.0 airplane=32.7 bus=34.4 train=35.1 truck=15.2 boat=9.1 traffic light=12.9 fire hydrant=28.7 stop sign=35.1 parking meter=22.8 bench=10.1 bird=16.6 cat=37.0 dog=30.0 horse=27.5 sheep=17.9 cow=23.3 elephant=35.2 bear=34.5 zebra=41.0 giraffe=41.6 backpack=4.2 umbrella=17.1 handbag=3.5 tie=11.7 suitcase=13.6 frisbee=29.7 skis=7.4 snowboard=8.4 sports ball=14.2 kite=19.4 baseball bat=9.0 baseball glove=17.5 skateboard=19.9 surfboard=13.7 tennis racket=20.2 bottle=13.6 wine glass=12.7 cup=17.3 fork=7.8 knife=3.7 spoon=2.1 bowl=17.9 banana=12.0 apple=6.6 sandwich=12.4 orange=14.1 broccoli=8.2 carrot=7.8 hot dog=13.9 pizza=28.8 donut=18.4 cake=15.1 chair=11.0 couch=22.8 potted plant=8.4 bed=25.0 dining table=13.7 toilet=27.5 tv=31.2 laptop=29.5 mouse=27.3 remote=7.6 keyboard=17.4 cell phone=12.2 microwave=15.0 oven=15.8 toaster=0.0 sink=17.2 refrigerator=25.4 book=5.3 clock=27.1 vase=18.5 scissors=11.2 teddy bear=22.1 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.0 [Epoch 90][Batch 99], LR: 1.00E-03, Speed: 153.142 samples/sec, ObjLoss=22.514, BoxCenterLoss=14.678, BoxScaleLoss=5.141, ClassLoss=9.179 [Epoch 90][Batch 199], LR: 1.00E-03, Speed: 149.392 samples/sec, ObjLoss=22.513, BoxCenterLoss=14.678, BoxScaleLoss=5.140, ClassLoss=9.177 [Epoch 90][Batch 299], LR: 1.00E-03, Speed: 148.514 samples/sec, ObjLoss=22.511, BoxCenterLoss=14.678, BoxScaleLoss=5.140, ClassLoss=9.176 [Epoch 90][Batch 399], LR: 1.00E-03, Speed: 134.605 samples/sec, ObjLoss=22.509, BoxCenterLoss=14.677, BoxScaleLoss=5.140, ClassLoss=9.175 [Epoch 90][Batch 499], LR: 1.00E-03, Speed: 145.861 samples/sec, ObjLoss=22.508, BoxCenterLoss=14.677, BoxScaleLoss=5.139, ClassLoss=9.173 [Epoch 90][Batch 599], LR: 1.00E-03, Speed: 154.460 samples/sec, ObjLoss=22.506, BoxCenterLoss=14.677, BoxScaleLoss=5.139, ClassLoss=9.172 [Epoch 90][Batch 699], LR: 1.00E-03, Speed: 117.453 samples/sec, ObjLoss=22.504, BoxCenterLoss=14.677, BoxScaleLoss=5.139, ClassLoss=9.171 [Epoch 90][Batch 799], LR: 1.00E-03, Speed: 129.053 samples/sec, ObjLoss=22.503, BoxCenterLoss=14.677, BoxScaleLoss=5.139, ClassLoss=9.170 [Epoch 90][Batch 899], LR: 1.00E-03, Speed: 131.635 samples/sec, ObjLoss=22.502, BoxCenterLoss=14.676, BoxScaleLoss=5.138, ClassLoss=9.168 [Epoch 90][Batch 999], LR: 1.00E-03, Speed: 79.625 samples/sec, ObjLoss=22.500, BoxCenterLoss=14.676, BoxScaleLoss=5.138, ClassLoss=9.167 [Epoch 90][Batch 1099], LR: 1.00E-03, Speed: 70.484 samples/sec, ObjLoss=22.499, BoxCenterLoss=14.676, BoxScaleLoss=5.138, ClassLoss=9.166 [Epoch 90][Batch 1199], LR: 1.00E-03, Speed: 103.394 samples/sec, ObjLoss=22.497, BoxCenterLoss=14.676, BoxScaleLoss=5.137, ClassLoss=9.164 [Epoch 90][Batch 1299], LR: 1.00E-03, Speed: 103.172 samples/sec, ObjLoss=22.496, BoxCenterLoss=14.676, BoxScaleLoss=5.137, ClassLoss=9.163 [Epoch 90][Batch 1399], LR: 1.00E-03, Speed: 133.627 samples/sec, ObjLoss=22.494, BoxCenterLoss=14.676, BoxScaleLoss=5.137, ClassLoss=9.162 [Epoch 90][Batch 1499], LR: 1.00E-03, Speed: 145.230 samples/sec, ObjLoss=22.493, BoxCenterLoss=14.675, BoxScaleLoss=5.137, ClassLoss=9.161 [Epoch 90][Batch 1599], LR: 1.00E-03, Speed: 99.142 samples/sec, ObjLoss=22.492, BoxCenterLoss=14.675, BoxScaleLoss=5.136, ClassLoss=9.159 [Epoch 90][Batch 1699], LR: 1.00E-03, Speed: 73.878 samples/sec, ObjLoss=22.491, BoxCenterLoss=14.675, BoxScaleLoss=5.136, ClassLoss=9.158 [Epoch 90][Batch 1799], LR: 1.00E-03, Speed: 139.098 samples/sec, ObjLoss=22.489, BoxCenterLoss=14.675, BoxScaleLoss=5.136, ClassLoss=9.157 [Epoch 90] Training cost: 1179.699, ObjLoss=22.489, BoxCenterLoss=14.675, BoxScaleLoss=5.136, ClassLoss=9.157 [Epoch 90] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.190 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.399 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.072 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.284 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 ] = 0.269 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278 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 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.398 person=30.8 bicycle=14.0 car=18.2 motorcycle=22.4 airplane=37.9 bus=41.7 train=36.4 truck=16.0 boat=11.1 traffic light=12.0 fire hydrant=38.2 stop sign=36.3 parking meter=27.0 bench=10.2 bird=16.0 cat=39.3 dog=33.2 horse=29.3 sheep=23.6 cow=27.6 elephant=34.1 bear=46.1 zebra=33.0 giraffe=38.3 backpack=3.5 umbrella=18.8 handbag=3.3 tie=12.1 suitcase=13.3 frisbee=28.3 skis=4.6 snowboard=10.3 sports ball=19.8 kite=21.6 baseball bat=9.2 baseball glove=12.9 skateboard=18.1 surfboard=16.5 tennis racket=20.7 bottle=11.1 wine glass=12.6 cup=17.9 fork=8.2 knife=3.2 spoon=1.5 bowl=17.8 banana=10.9 apple=6.1 sandwich=17.9 orange=13.8 broccoli=9.1 carrot=7.0 hot dog=15.0 pizza=27.5 donut=22.4 cake=17.4 chair=10.9 couch=23.1 potted plant=9.1 bed=24.2 dining table=16.4 toilet=33.2 tv=30.2 laptop=30.4 mouse=26.5 remote=5.6 keyboard=24.0 cell phone=13.5 microwave=25.6 oven=19.0 toaster=0.0 sink=17.7 refrigerator=23.1 book=4.9 clock=27.9 vase=16.0 scissors=10.6 teddy bear=22.6 hair drier=0.0 toothbrush=2.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.0 [Epoch 91][Batch 99], LR: 1.00E-03, Speed: 139.079 samples/sec, ObjLoss=22.487, BoxCenterLoss=14.675, BoxScaleLoss=5.136, ClassLoss=9.155 [Epoch 91][Batch 199], LR: 1.00E-03, Speed: 154.292 samples/sec, ObjLoss=22.486, BoxCenterLoss=14.675, BoxScaleLoss=5.135, ClassLoss=9.154 [Epoch 91][Batch 299], LR: 1.00E-03, Speed: 110.186 samples/sec, ObjLoss=22.484, BoxCenterLoss=14.675, BoxScaleLoss=5.135, ClassLoss=9.153 [Epoch 91][Batch 399], LR: 1.00E-03, Speed: 159.444 samples/sec, ObjLoss=22.484, BoxCenterLoss=14.675, BoxScaleLoss=5.135, ClassLoss=9.151 [Epoch 91][Batch 499], LR: 1.00E-03, Speed: 149.371 samples/sec, ObjLoss=22.482, BoxCenterLoss=14.675, BoxScaleLoss=5.134, ClassLoss=9.150 [Epoch 91][Batch 599], LR: 1.00E-03, Speed: 127.633 samples/sec, ObjLoss=22.481, BoxCenterLoss=14.675, BoxScaleLoss=5.134, ClassLoss=9.149 [Epoch 91][Batch 699], LR: 1.00E-03, Speed: 140.623 samples/sec, ObjLoss=22.479, BoxCenterLoss=14.675, BoxScaleLoss=5.134, ClassLoss=9.148 [Epoch 91][Batch 799], LR: 1.00E-03, Speed: 150.365 samples/sec, ObjLoss=22.478, BoxCenterLoss=14.674, BoxScaleLoss=5.134, ClassLoss=9.146 [Epoch 91][Batch 899], LR: 1.00E-03, Speed: 131.831 samples/sec, ObjLoss=22.476, BoxCenterLoss=14.674, BoxScaleLoss=5.133, ClassLoss=9.145 [Epoch 91][Batch 999], LR: 1.00E-03, Speed: 122.841 samples/sec, ObjLoss=22.475, BoxCenterLoss=14.674, BoxScaleLoss=5.133, ClassLoss=9.144 [Epoch 91][Batch 1099], LR: 1.00E-03, Speed: 156.621 samples/sec, ObjLoss=22.474, BoxCenterLoss=14.674, BoxScaleLoss=5.133, ClassLoss=9.142 [Epoch 91][Batch 1199], LR: 1.00E-03, Speed: 139.480 samples/sec, ObjLoss=22.472, BoxCenterLoss=14.674, BoxScaleLoss=5.132, ClassLoss=9.141 [Epoch 91][Batch 1299], LR: 1.00E-03, Speed: 136.688 samples/sec, ObjLoss=22.471, BoxCenterLoss=14.674, BoxScaleLoss=5.132, ClassLoss=9.140 [Epoch 91][Batch 1399], LR: 1.00E-03, Speed: 155.902 samples/sec, ObjLoss=22.470, BoxCenterLoss=14.674, BoxScaleLoss=5.132, ClassLoss=9.139 [Epoch 91][Batch 1499], LR: 1.00E-03, Speed: 144.890 samples/sec, ObjLoss=22.468, BoxCenterLoss=14.674, BoxScaleLoss=5.132, ClassLoss=9.138 [Epoch 91][Batch 1599], LR: 1.00E-03, Speed: 140.991 samples/sec, ObjLoss=22.467, BoxCenterLoss=14.674, BoxScaleLoss=5.132, ClassLoss=9.136 [Epoch 91][Batch 1699], LR: 1.00E-03, Speed: 110.927 samples/sec, ObjLoss=22.466, BoxCenterLoss=14.674, BoxScaleLoss=5.131, ClassLoss=9.135 [Epoch 91][Batch 1799], LR: 1.00E-03, Speed: 203.229 samples/sec, ObjLoss=22.465, BoxCenterLoss=14.673, BoxScaleLoss=5.131, ClassLoss=9.134 [Epoch 91] Training cost: 1160.121, ObjLoss=22.465, BoxCenterLoss=14.673, BoxScaleLoss=5.131, ClassLoss=9.133 [Epoch 91] 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 ] = 0.398 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.165 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 ] = 0.181 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 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.267 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 ] = 0.118 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404 person=30.7 bicycle=14.1 car=19.9 motorcycle=23.2 airplane=35.7 bus=39.8 train=41.1 truck=17.0 boat=9.6 traffic light=9.5 fire hydrant=31.3 stop sign=36.3 parking meter=20.2 bench=10.4 bird=15.2 cat=39.5 dog=33.3 horse=26.7 sheep=23.1 cow=27.3 elephant=39.0 bear=42.1 zebra=42.4 giraffe=41.4 backpack=3.7 umbrella=18.9 handbag=2.6 tie=10.8 suitcase=14.0 frisbee=23.8 skis=8.0 snowboard=9.0 sports ball=19.1 kite=21.0 baseball bat=9.1 baseball glove=17.3 skateboard=18.0 surfboard=13.0 tennis racket=20.1 bottle=12.0 wine glass=11.1 cup=16.3 fork=9.4 knife=2.9 spoon=1.5 bowl=20.0 banana=10.1 apple=7.3 sandwich=20.7 orange=13.4 broccoli=9.2 carrot=9.0 hot dog=14.7 pizza=32.1 donut=20.1 cake=16.3 chair=10.8 couch=26.3 potted plant=10.8 bed=25.0 dining table=14.0 toilet=34.5 tv=33.2 laptop=33.0 mouse=25.9 remote=7.5 keyboard=27.0 cell phone=14.7 microwave=26.7 oven=17.3 toaster=0.0 sink=19.2 refrigerator=25.4 book=3.9 clock=29.0 vase=16.3 scissors=11.7 teddy bear=22.5 hair drier=0.0 toothbrush=1.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.2 [Epoch 92][Batch 99], LR: 1.00E-03, Speed: 185.423 samples/sec, ObjLoss=22.464, BoxCenterLoss=14.673, BoxScaleLoss=5.130, ClassLoss=9.132 [Epoch 92][Batch 199], LR: 1.00E-03, Speed: 120.168 samples/sec, ObjLoss=22.462, BoxCenterLoss=14.673, BoxScaleLoss=5.130, ClassLoss=9.131 [Epoch 92][Batch 299], LR: 1.00E-03, Speed: 92.137 samples/sec, ObjLoss=22.461, BoxCenterLoss=14.673, BoxScaleLoss=5.130, ClassLoss=9.129 [Epoch 92][Batch 399], LR: 1.00E-03, Speed: 146.229 samples/sec, ObjLoss=22.459, BoxCenterLoss=14.673, BoxScaleLoss=5.130, ClassLoss=9.128 [Epoch 92][Batch 499], LR: 1.00E-03, Speed: 180.333 samples/sec, ObjLoss=22.458, BoxCenterLoss=14.673, BoxScaleLoss=5.129, ClassLoss=9.127 [Epoch 92][Batch 599], LR: 1.00E-03, Speed: 120.908 samples/sec, ObjLoss=22.456, BoxCenterLoss=14.673, BoxScaleLoss=5.129, ClassLoss=9.126 [Epoch 92][Batch 699], LR: 1.00E-03, Speed: 157.918 samples/sec, ObjLoss=22.455, BoxCenterLoss=14.672, BoxScaleLoss=5.129, ClassLoss=9.125 [Epoch 92][Batch 799], LR: 1.00E-03, Speed: 53.207 samples/sec, ObjLoss=22.453, BoxCenterLoss=14.672, BoxScaleLoss=5.129, ClassLoss=9.124 [Epoch 92][Batch 899], LR: 1.00E-03, Speed: 92.341 samples/sec, ObjLoss=22.451, BoxCenterLoss=14.672, BoxScaleLoss=5.129, ClassLoss=9.122 [Epoch 92][Batch 999], LR: 1.00E-03, Speed: 57.826 samples/sec, ObjLoss=22.450, BoxCenterLoss=14.672, BoxScaleLoss=5.128, ClassLoss=9.121 [Epoch 92][Batch 1099], LR: 1.00E-03, Speed: 128.979 samples/sec, ObjLoss=22.449, BoxCenterLoss=14.672, BoxScaleLoss=5.128, ClassLoss=9.120 [Epoch 92][Batch 1199], LR: 1.00E-03, Speed: 130.762 samples/sec, ObjLoss=22.448, BoxCenterLoss=14.672, BoxScaleLoss=5.128, ClassLoss=9.118 [Epoch 92][Batch 1299], LR: 1.00E-03, Speed: 108.643 samples/sec, ObjLoss=22.446, BoxCenterLoss=14.672, BoxScaleLoss=5.127, ClassLoss=9.117 [Epoch 92][Batch 1399], LR: 1.00E-03, Speed: 140.582 samples/sec, ObjLoss=22.445, BoxCenterLoss=14.672, BoxScaleLoss=5.127, ClassLoss=9.116 [Epoch 92][Batch 1499], LR: 1.00E-03, Speed: 157.749 samples/sec, ObjLoss=22.444, BoxCenterLoss=14.672, BoxScaleLoss=5.127, ClassLoss=9.115 [Epoch 92][Batch 1599], LR: 1.00E-03, Speed: 175.638 samples/sec, ObjLoss=22.443, BoxCenterLoss=14.671, BoxScaleLoss=5.127, ClassLoss=9.114 [Epoch 92][Batch 1699], LR: 1.00E-03, Speed: 152.841 samples/sec, ObjLoss=22.441, BoxCenterLoss=14.671, BoxScaleLoss=5.126, ClassLoss=9.112 [Epoch 92][Batch 1799], LR: 1.00E-03, Speed: 115.496 samples/sec, ObjLoss=22.440, BoxCenterLoss=14.671, BoxScaleLoss=5.126, ClassLoss=9.111 [Epoch 92] Training cost: 1227.072, ObjLoss=22.439, BoxCenterLoss=14.671, BoxScaleLoss=5.126, ClassLoss=9.111 [Epoch 92] 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 ] = 0.405 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156 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 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295 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 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.406 person=31.6 bicycle=14.4 car=19.5 motorcycle=25.4 airplane=39.2 bus=36.5 train=35.2 truck=18.5 boat=10.8 traffic light=11.1 fire hydrant=33.2 stop sign=34.4 parking meter=25.9 bench=9.5 bird=14.8 cat=38.0 dog=33.6 horse=27.6 sheep=25.0 cow=28.1 elephant=38.4 bear=29.5 zebra=40.6 giraffe=39.0 backpack=4.3 umbrella=19.9 handbag=3.9 tie=11.1 suitcase=14.3 frisbee=28.8 skis=6.2 snowboard=12.5 sports ball=18.4 kite=18.4 baseball bat=9.6 baseball glove=14.7 skateboard=19.8 surfboard=15.3 tennis racket=20.4 bottle=11.9 wine glass=13.4 cup=17.4 fork=7.1 knife=3.2 spoon=1.5 bowl=19.1 banana=9.8 apple=5.9 sandwich=18.0 orange=13.7 broccoli=10.0 carrot=8.0 hot dog=13.7 pizza=27.1 donut=19.2 cake=16.7 chair=11.1 couch=25.1 potted plant=9.7 bed=25.3 dining table=16.0 toilet=35.4 tv=32.5 laptop=32.5 mouse=28.1 remote=7.1 keyboard=27.2 cell phone=14.8 microwave=26.0 oven=19.5 toaster=0.0 sink=19.2 refrigerator=25.1 book=4.7 clock=26.7 vase=17.6 scissors=12.1 teddy bear=22.1 hair drier=0.0 toothbrush=1.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.2 [Epoch 93][Batch 99], LR: 1.00E-03, Speed: 134.022 samples/sec, ObjLoss=22.438, BoxCenterLoss=14.671, BoxScaleLoss=5.126, ClassLoss=9.110 [Epoch 93][Batch 199], LR: 1.00E-03, Speed: 126.454 samples/sec, ObjLoss=22.437, BoxCenterLoss=14.671, BoxScaleLoss=5.125, ClassLoss=9.108 [Epoch 93][Batch 299], LR: 1.00E-03, Speed: 121.985 samples/sec, ObjLoss=22.435, BoxCenterLoss=14.670, BoxScaleLoss=5.125, ClassLoss=9.107 [Epoch 93][Batch 399], LR: 1.00E-03, Speed: 72.194 samples/sec, ObjLoss=22.434, BoxCenterLoss=14.670, BoxScaleLoss=5.125, ClassLoss=9.105 [Epoch 93][Batch 499], LR: 1.00E-03, Speed: 84.544 samples/sec, ObjLoss=22.432, BoxCenterLoss=14.670, BoxScaleLoss=5.125, ClassLoss=9.105 [Epoch 93][Batch 599], LR: 1.00E-03, Speed: 72.010 samples/sec, ObjLoss=22.431, BoxCenterLoss=14.670, BoxScaleLoss=5.124, ClassLoss=9.103 [Epoch 93][Batch 699], LR: 1.00E-03, Speed: 123.185 samples/sec, ObjLoss=22.430, BoxCenterLoss=14.670, BoxScaleLoss=5.124, ClassLoss=9.102 [Epoch 93][Batch 799], LR: 1.00E-03, Speed: 132.933 samples/sec, ObjLoss=22.428, BoxCenterLoss=14.670, BoxScaleLoss=5.124, ClassLoss=9.101 [Epoch 93][Batch 899], LR: 1.00E-03, Speed: 133.475 samples/sec, ObjLoss=22.426, BoxCenterLoss=14.669, BoxScaleLoss=5.123, ClassLoss=9.100 [Epoch 93][Batch 999], LR: 1.00E-03, Speed: 142.966 samples/sec, ObjLoss=22.425, BoxCenterLoss=14.670, BoxScaleLoss=5.123, ClassLoss=9.098 [Epoch 93][Batch 1099], LR: 1.00E-03, Speed: 145.195 samples/sec, ObjLoss=22.424, BoxCenterLoss=14.670, BoxScaleLoss=5.123, ClassLoss=9.097 [Epoch 93][Batch 1199], LR: 1.00E-03, Speed: 136.882 samples/sec, ObjLoss=22.423, BoxCenterLoss=14.669, BoxScaleLoss=5.123, ClassLoss=9.096 [Epoch 93][Batch 1299], LR: 1.00E-03, Speed: 66.517 samples/sec, ObjLoss=22.421, BoxCenterLoss=14.669, BoxScaleLoss=5.123, ClassLoss=9.095 [Epoch 93][Batch 1399], LR: 1.00E-03, Speed: 84.911 samples/sec, ObjLoss=22.420, BoxCenterLoss=14.669, BoxScaleLoss=5.122, ClassLoss=9.094 [Epoch 93][Batch 1499], LR: 1.00E-03, Speed: 105.348 samples/sec, ObjLoss=22.418, BoxCenterLoss=14.669, BoxScaleLoss=5.122, ClassLoss=9.093 [Epoch 93][Batch 1599], LR: 1.00E-03, Speed: 87.950 samples/sec, ObjLoss=22.417, BoxCenterLoss=14.668, BoxScaleLoss=5.122, ClassLoss=9.091 [Epoch 93][Batch 1699], LR: 1.00E-03, Speed: 121.412 samples/sec, ObjLoss=22.416, BoxCenterLoss=14.669, BoxScaleLoss=5.122, ClassLoss=9.090 [Epoch 93][Batch 1799], LR: 1.00E-03, Speed: 151.075 samples/sec, ObjLoss=22.415, BoxCenterLoss=14.669, BoxScaleLoss=5.122, ClassLoss=9.089 [Epoch 93] Training cost: 1218.155, ObjLoss=22.414, BoxCenterLoss=14.669, BoxScaleLoss=5.121, ClassLoss=9.089 [Epoch 93] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.406 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.166 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 ] = 0.197 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.292 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 ] = 0.288 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 ] = 0.293 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404 person=32.3 bicycle=15.9 car=20.3 motorcycle=23.2 airplane=38.0 bus=40.5 train=39.9 truck=17.4 boat=10.0 traffic light=10.7 fire hydrant=33.9 stop sign=35.9 parking meter=26.2 bench=8.7 bird=15.2 cat=41.1 dog=33.0 horse=30.4 sheep=24.4 cow=26.0 elephant=35.0 bear=37.3 zebra=43.1 giraffe=39.8 backpack=5.2 umbrella=16.2 handbag=3.8 tie=13.5 suitcase=13.4 frisbee=32.1 skis=5.9 snowboard=9.9 sports ball=24.2 kite=17.8 baseball bat=7.8 baseball glove=19.5 skateboard=20.4 surfboard=14.0 tennis racket=21.0 bottle=14.0 wine glass=15.6 cup=20.1 fork=8.3 knife=2.4 spoon=1.5 bowl=17.7 banana=11.9 apple=6.7 sandwich=18.8 orange=13.2 broccoli=10.9 carrot=8.3 hot dog=13.5 pizza=24.2 donut=19.3 cake=16.2 chair=11.1 couch=28.7 potted plant=11.2 bed=27.3 dining table=17.1 toilet=35.4 tv=32.3 laptop=34.1 mouse=31.5 remote=6.9 keyboard=30.5 cell phone=12.8 microwave=25.8 oven=17.1 toaster=0.0 sink=18.3 refrigerator=25.4 book=5.0 clock=29.1 vase=18.0 scissors=12.3 teddy bear=21.3 hair drier=0.0 toothbrush=2.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.7 [Epoch 94][Batch 99], LR: 1.00E-03, Speed: 149.190 samples/sec, ObjLoss=22.413, BoxCenterLoss=14.668, BoxScaleLoss=5.121, ClassLoss=9.087 [Epoch 94][Batch 199], LR: 1.00E-03, Speed: 128.166 samples/sec, ObjLoss=22.411, BoxCenterLoss=14.668, BoxScaleLoss=5.121, ClassLoss=9.086 [Epoch 94][Batch 299], LR: 1.00E-03, Speed: 128.261 samples/sec, ObjLoss=22.410, BoxCenterLoss=14.668, BoxScaleLoss=5.121, ClassLoss=9.085 [Epoch 94][Batch 399], LR: 1.00E-03, Speed: 135.828 samples/sec, ObjLoss=22.409, BoxCenterLoss=14.668, BoxScaleLoss=5.121, ClassLoss=9.084 [Epoch 94][Batch 499], LR: 1.00E-03, Speed: 166.041 samples/sec, ObjLoss=22.407, BoxCenterLoss=14.668, BoxScaleLoss=5.120, ClassLoss=9.083 [Epoch 94][Batch 599], LR: 1.00E-03, Speed: 138.033 samples/sec, ObjLoss=22.406, BoxCenterLoss=14.668, BoxScaleLoss=5.120, ClassLoss=9.082 [Epoch 94][Batch 699], LR: 1.00E-03, Speed: 97.730 samples/sec, ObjLoss=22.404, BoxCenterLoss=14.668, BoxScaleLoss=5.120, ClassLoss=9.080 [Epoch 94][Batch 799], LR: 1.00E-03, Speed: 119.233 samples/sec, ObjLoss=22.403, BoxCenterLoss=14.668, BoxScaleLoss=5.119, ClassLoss=9.079 [Epoch 94][Batch 899], LR: 1.00E-03, Speed: 126.810 samples/sec, ObjLoss=22.402, BoxCenterLoss=14.668, BoxScaleLoss=5.119, ClassLoss=9.078 [Epoch 94][Batch 999], LR: 1.00E-03, Speed: 158.429 samples/sec, ObjLoss=22.400, BoxCenterLoss=14.667, BoxScaleLoss=5.119, ClassLoss=9.077 [Epoch 94][Batch 1099], LR: 1.00E-03, Speed: 136.133 samples/sec, ObjLoss=22.399, BoxCenterLoss=14.667, BoxScaleLoss=5.119, ClassLoss=9.076 [Epoch 94][Batch 1199], LR: 1.00E-03, Speed: 151.711 samples/sec, ObjLoss=22.397, BoxCenterLoss=14.667, BoxScaleLoss=5.119, ClassLoss=9.074 [Epoch 94][Batch 1299], LR: 1.00E-03, Speed: 121.614 samples/sec, ObjLoss=22.396, BoxCenterLoss=14.667, BoxScaleLoss=5.118, ClassLoss=9.073 [Epoch 94][Batch 1399], LR: 1.00E-03, Speed: 134.390 samples/sec, ObjLoss=22.395, BoxCenterLoss=14.667, BoxScaleLoss=5.118, ClassLoss=9.072 [Epoch 94][Batch 1499], LR: 1.00E-03, Speed: 74.509 samples/sec, ObjLoss=22.393, BoxCenterLoss=14.666, BoxScaleLoss=5.117, ClassLoss=9.071 [Epoch 94][Batch 1599], LR: 1.00E-03, Speed: 154.174 samples/sec, ObjLoss=22.392, BoxCenterLoss=14.666, BoxScaleLoss=5.117, ClassLoss=9.069 [Epoch 94][Batch 1699], LR: 1.00E-03, Speed: 76.734 samples/sec, ObjLoss=22.391, BoxCenterLoss=14.666, BoxScaleLoss=5.117, ClassLoss=9.068 [Epoch 94][Batch 1799], LR: 1.00E-03, Speed: 140.831 samples/sec, ObjLoss=22.390, BoxCenterLoss=14.666, BoxScaleLoss=5.117, ClassLoss=9.067 [Epoch 94] Training cost: 1182.321, ObjLoss=22.389, BoxCenterLoss=14.666, BoxScaleLoss=5.117, ClassLoss=9.067 [Epoch 94] 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 ] = 0.403 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.140 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.074 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 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.182 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 ] = 0.277 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 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.392 person=30.7 bicycle=14.5 car=20.1 motorcycle=25.2 airplane=35.4 bus=35.7 train=41.7 truck=16.6 boat=12.6 traffic light=11.3 fire hydrant=29.3 stop sign=31.0 parking meter=21.2 bench=11.0 bird=15.0 cat=41.9 dog=33.0 horse=26.0 sheep=23.3 cow=29.9 elephant=27.9 bear=30.2 zebra=30.4 giraffe=33.2 backpack=4.3 umbrella=16.2 handbag=3.9 tie=13.2 suitcase=14.0 frisbee=30.9 skis=6.5 snowboard=14.2 sports ball=20.1 kite=21.3 baseball bat=8.5 baseball glove=15.6 skateboard=19.9 surfboard=12.8 tennis racket=22.1 bottle=12.3 wine glass=13.6 cup=18.4 fork=5.6 knife=2.2 spoon=2.8 bowl=18.9 banana=9.6 apple=6.0 sandwich=14.2 orange=14.2 broccoli=11.1 carrot=7.0 hot dog=14.8 pizza=24.7 donut=18.9 cake=14.6 chair=11.6 couch=21.9 potted plant=9.4 bed=28.6 dining table=14.9 toilet=31.5 tv=34.1 laptop=31.8 mouse=28.6 remote=6.7 keyboard=27.4 cell phone=14.4 microwave=24.0 oven=18.4 toaster=0.0 sink=18.5 refrigerator=19.0 book=4.2 clock=30.3 vase=15.5 scissors=12.6 teddy bear=22.1 hair drier=0.0 toothbrush=1.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.6 [Epoch 95][Batch 99], LR: 1.00E-03, Speed: 146.349 samples/sec, ObjLoss=22.388, BoxCenterLoss=14.666, BoxScaleLoss=5.116, ClassLoss=9.065 [Epoch 95][Batch 199], LR: 1.00E-03, Speed: 131.109 samples/sec, ObjLoss=22.386, BoxCenterLoss=14.666, BoxScaleLoss=5.116, ClassLoss=9.064 [Epoch 95][Batch 299], LR: 1.00E-03, Speed: 105.863 samples/sec, ObjLoss=22.385, BoxCenterLoss=14.666, BoxScaleLoss=5.116, ClassLoss=9.063 [Epoch 95][Batch 399], LR: 1.00E-03, Speed: 139.611 samples/sec, ObjLoss=22.384, BoxCenterLoss=14.666, BoxScaleLoss=5.116, ClassLoss=9.062 [Epoch 95][Batch 499], LR: 1.00E-03, Speed: 148.432 samples/sec, ObjLoss=22.383, BoxCenterLoss=14.666, BoxScaleLoss=5.115, ClassLoss=9.060 [Epoch 95][Batch 599], LR: 1.00E-03, Speed: 115.205 samples/sec, ObjLoss=22.382, BoxCenterLoss=14.666, BoxScaleLoss=5.115, ClassLoss=9.059 [Epoch 95][Batch 699], LR: 1.00E-03, Speed: 98.499 samples/sec, ObjLoss=22.380, BoxCenterLoss=14.666, BoxScaleLoss=5.115, ClassLoss=9.058 [Epoch 95][Batch 799], LR: 1.00E-03, Speed: 80.742 samples/sec, ObjLoss=22.379, BoxCenterLoss=14.666, BoxScaleLoss=5.115, ClassLoss=9.057 [Epoch 95][Batch 899], LR: 1.00E-03, Speed: 77.753 samples/sec, ObjLoss=22.377, BoxCenterLoss=14.665, BoxScaleLoss=5.114, ClassLoss=9.056 [Epoch 95][Batch 999], LR: 1.00E-03, Speed: 86.564 samples/sec, ObjLoss=22.376, BoxCenterLoss=14.665, BoxScaleLoss=5.114, ClassLoss=9.055 [Epoch 95][Batch 1099], LR: 1.00E-03, Speed: 134.438 samples/sec, ObjLoss=22.374, BoxCenterLoss=14.665, BoxScaleLoss=5.114, ClassLoss=9.054 [Epoch 95][Batch 1199], LR: 1.00E-03, Speed: 127.369 samples/sec, ObjLoss=22.373, BoxCenterLoss=14.665, BoxScaleLoss=5.114, ClassLoss=9.052 [Epoch 95][Batch 1299], LR: 1.00E-03, Speed: 88.295 samples/sec, ObjLoss=22.372, BoxCenterLoss=14.665, BoxScaleLoss=5.113, ClassLoss=9.051 [Epoch 95][Batch 1399], LR: 1.00E-03, Speed: 154.080 samples/sec, ObjLoss=22.371, BoxCenterLoss=14.665, BoxScaleLoss=5.113, ClassLoss=9.050 [Epoch 95][Batch 1499], LR: 1.00E-03, Speed: 137.273 samples/sec, ObjLoss=22.369, BoxCenterLoss=14.664, BoxScaleLoss=5.113, ClassLoss=9.049 [Epoch 95][Batch 1599], LR: 1.00E-03, Speed: 155.822 samples/sec, ObjLoss=22.368, BoxCenterLoss=14.664, BoxScaleLoss=5.112, ClassLoss=9.047 [Epoch 95][Batch 1699], LR: 1.00E-03, Speed: 145.623 samples/sec, ObjLoss=22.367, BoxCenterLoss=14.664, BoxScaleLoss=5.112, ClassLoss=9.046 [Epoch 95][Batch 1799], LR: 1.00E-03, Speed: 84.591 samples/sec, ObjLoss=22.366, BoxCenterLoss=14.664, BoxScaleLoss=5.112, ClassLoss=9.045 [Epoch 95] Training cost: 1236.546, ObjLoss=22.365, BoxCenterLoss=14.664, BoxScaleLoss=5.112, ClassLoss=9.045 [Epoch 95] 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 ] = 0.139 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 ] = 0.202 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 ] = 0.176 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.260 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.268 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 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 person=29.7 bicycle=14.1 car=19.2 motorcycle=21.8 airplane=34.5 bus=31.4 train=36.5 truck=15.5 boat=10.4 traffic light=12.4 fire hydrant=36.9 stop sign=33.8 parking meter=20.0 bench=10.5 bird=14.7 cat=33.2 dog=28.8 horse=24.8 sheep=23.5 cow=26.1 elephant=38.0 bear=31.4 zebra=38.5 giraffe=38.9 backpack=4.7 umbrella=18.1 handbag=2.9 tie=13.7 suitcase=14.5 frisbee=26.4 skis=7.7 snowboard=9.6 sports ball=23.4 kite=21.8 baseball bat=10.9 baseball glove=18.4 skateboard=21.0 surfboard=15.8 tennis racket=20.2 bottle=14.9 wine glass=13.2 cup=19.3 fork=6.8 knife=3.2 spoon=2.6 bowl=19.6 banana=10.7 apple=8.1 sandwich=14.5 orange=13.1 broccoli=9.1 carrot=8.0 hot dog=12.4 pizza=26.7 donut=22.0 cake=17.0 chair=10.7 couch=18.6 potted plant=11.2 bed=12.9 dining table=8.9 toilet=30.4 tv=33.7 laptop=29.2 mouse=31.2 remote=7.7 keyboard=24.9 cell phone=14.6 microwave=22.1 oven=17.1 toaster=0.0 sink=17.8 refrigerator=26.3 book=4.6 clock=28.1 vase=15.9 scissors=15.3 teddy bear=22.8 hair drier=0.0 toothbrush=1.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.5 [Epoch 96][Batch 99], LR: 1.00E-03, Speed: 156.122 samples/sec, ObjLoss=22.364, BoxCenterLoss=14.664, BoxScaleLoss=5.112, ClassLoss=9.044 [Epoch 96][Batch 199], LR: 1.00E-03, Speed: 138.161 samples/sec, ObjLoss=22.362, BoxCenterLoss=14.664, BoxScaleLoss=5.111, ClassLoss=9.042 [Epoch 96][Batch 299], LR: 1.00E-03, Speed: 177.003 samples/sec, ObjLoss=22.361, BoxCenterLoss=14.663, BoxScaleLoss=5.111, ClassLoss=9.041 [Epoch 96][Batch 399], LR: 1.00E-03, Speed: 157.088 samples/sec, ObjLoss=22.360, BoxCenterLoss=14.663, BoxScaleLoss=5.111, ClassLoss=9.040 [Epoch 96][Batch 499], LR: 1.00E-03, Speed: 143.620 samples/sec, ObjLoss=22.358, BoxCenterLoss=14.663, BoxScaleLoss=5.111, ClassLoss=9.039 [Epoch 96][Batch 599], LR: 1.00E-03, Speed: 131.321 samples/sec, ObjLoss=22.357, BoxCenterLoss=14.663, BoxScaleLoss=5.110, ClassLoss=9.037 [Epoch 96][Batch 699], LR: 1.00E-03, Speed: 113.618 samples/sec, ObjLoss=22.355, BoxCenterLoss=14.663, BoxScaleLoss=5.110, ClassLoss=9.036 [Epoch 96][Batch 799], LR: 1.00E-03, Speed: 129.372 samples/sec, ObjLoss=22.354, BoxCenterLoss=14.662, BoxScaleLoss=5.110, ClassLoss=9.035 [Epoch 96][Batch 899], LR: 1.00E-03, Speed: 157.276 samples/sec, ObjLoss=22.353, BoxCenterLoss=14.662, BoxScaleLoss=5.110, ClassLoss=9.034 [Epoch 96][Batch 999], LR: 1.00E-03, Speed: 152.856 samples/sec, ObjLoss=22.351, BoxCenterLoss=14.662, BoxScaleLoss=5.110, ClassLoss=9.033 [Epoch 96][Batch 1099], LR: 1.00E-03, Speed: 62.945 samples/sec, ObjLoss=22.350, BoxCenterLoss=14.662, BoxScaleLoss=5.109, ClassLoss=9.032 [Epoch 96][Batch 1199], LR: 1.00E-03, Speed: 91.885 samples/sec, ObjLoss=22.348, BoxCenterLoss=14.662, BoxScaleLoss=5.109, ClassLoss=9.031 [Epoch 96][Batch 1299], LR: 1.00E-03, Speed: 162.104 samples/sec, ObjLoss=22.347, BoxCenterLoss=14.662, BoxScaleLoss=5.109, ClassLoss=9.030 [Epoch 96][Batch 1399], LR: 1.00E-03, Speed: 145.999 samples/sec, ObjLoss=22.346, BoxCenterLoss=14.662, BoxScaleLoss=5.109, ClassLoss=9.029 [Epoch 96][Batch 1499], LR: 1.00E-03, Speed: 107.495 samples/sec, ObjLoss=22.344, BoxCenterLoss=14.661, BoxScaleLoss=5.108, ClassLoss=9.027 [Epoch 96][Batch 1599], LR: 1.00E-03, Speed: 92.462 samples/sec, ObjLoss=22.343, BoxCenterLoss=14.661, BoxScaleLoss=5.108, ClassLoss=9.027 [Epoch 96][Batch 1699], LR: 1.00E-03, Speed: 142.532 samples/sec, ObjLoss=22.342, BoxCenterLoss=14.661, BoxScaleLoss=5.108, ClassLoss=9.025 [Epoch 96][Batch 1799], LR: 1.00E-03, Speed: 154.255 samples/sec, ObjLoss=22.340, BoxCenterLoss=14.661, BoxScaleLoss=5.108, ClassLoss=9.024 [Epoch 96] Training cost: 1213.622, ObjLoss=22.340, BoxCenterLoss=14.661, BoxScaleLoss=5.108, ClassLoss=9.024 [Epoch 96] 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 ] = 0.402 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.158 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 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.272 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 ] = 0.127 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.403 person=31.0 bicycle=14.4 car=20.9 motorcycle=24.7 airplane=36.8 bus=36.6 train=34.5 truck=16.7 boat=11.2 traffic light=12.1 fire hydrant=34.4 stop sign=32.3 parking meter=22.9 bench=10.6 bird=16.0 cat=39.3 dog=29.2 horse=29.5 sheep=24.1 cow=26.6 elephant=35.3 bear=41.1 zebra=39.2 giraffe=43.4 backpack=4.9 umbrella=17.7 handbag=3.0 tie=13.5 suitcase=15.3 frisbee=32.5 skis=5.9 snowboard=9.8 sports ball=21.0 kite=19.7 baseball bat=11.4 baseball glove=16.1 skateboard=20.7 surfboard=14.2 tennis racket=18.8 bottle=15.2 wine glass=11.2 cup=16.9 fork=7.3 knife=2.8 spoon=1.6 bowl=17.7 banana=9.6 apple=8.4 sandwich=16.8 orange=17.0 broccoli=8.4 carrot=8.2 hot dog=11.8 pizza=22.3 donut=20.1 cake=16.6 chair=10.5 couch=24.2 potted plant=10.0 bed=26.2 dining table=16.9 toilet=33.0 tv=32.1 laptop=33.2 mouse=31.2 remote=5.5 keyboard=21.3 cell phone=14.3 microwave=28.1 oven=18.6 toaster=0.0 sink=18.9 refrigerator=28.2 book=3.8 clock=29.5 vase=17.9 scissors=9.1 teddy bear=23.7 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.2 [Epoch 97][Batch 99], LR: 1.00E-03, Speed: 135.260 samples/sec, ObjLoss=22.339, BoxCenterLoss=14.661, BoxScaleLoss=5.108, ClassLoss=9.023 [Epoch 97][Batch 199], LR: 1.00E-03, Speed: 77.184 samples/sec, ObjLoss=22.337, BoxCenterLoss=14.661, BoxScaleLoss=5.107, ClassLoss=9.022 [Epoch 97][Batch 299], LR: 1.00E-03, Speed: 128.242 samples/sec, ObjLoss=22.336, BoxCenterLoss=14.661, BoxScaleLoss=5.107, ClassLoss=9.020 [Epoch 97][Batch 399], LR: 1.00E-03, Speed: 107.475 samples/sec, ObjLoss=22.335, BoxCenterLoss=14.661, BoxScaleLoss=5.107, ClassLoss=9.019 [Epoch 97][Batch 499], LR: 1.00E-03, Speed: 112.097 samples/sec, ObjLoss=22.333, BoxCenterLoss=14.660, BoxScaleLoss=5.106, ClassLoss=9.018 [Epoch 97][Batch 599], LR: 1.00E-03, Speed: 95.160 samples/sec, ObjLoss=22.332, BoxCenterLoss=14.660, BoxScaleLoss=5.106, ClassLoss=9.017 [Epoch 97][Batch 699], LR: 1.00E-03, Speed: 147.492 samples/sec, ObjLoss=22.331, BoxCenterLoss=14.660, BoxScaleLoss=5.106, ClassLoss=9.016 [Epoch 97][Batch 799], LR: 1.00E-03, Speed: 89.490 samples/sec, ObjLoss=22.329, BoxCenterLoss=14.660, BoxScaleLoss=5.106, ClassLoss=9.014 [Epoch 97][Batch 899], LR: 1.00E-03, Speed: 134.000 samples/sec, ObjLoss=22.328, BoxCenterLoss=14.660, BoxScaleLoss=5.105, ClassLoss=9.013 [Epoch 97][Batch 999], LR: 1.00E-03, Speed: 139.911 samples/sec, ObjLoss=22.327, BoxCenterLoss=14.660, BoxScaleLoss=5.105, ClassLoss=9.012 [Epoch 97][Batch 1099], LR: 1.00E-03, Speed: 146.769 samples/sec, ObjLoss=22.325, BoxCenterLoss=14.660, BoxScaleLoss=5.105, ClassLoss=9.011 [Epoch 97][Batch 1199], LR: 1.00E-03, Speed: 127.444 samples/sec, ObjLoss=22.324, BoxCenterLoss=14.660, BoxScaleLoss=5.105, ClassLoss=9.010 [Epoch 97][Batch 1299], LR: 1.00E-03, Speed: 135.530 samples/sec, ObjLoss=22.323, BoxCenterLoss=14.660, BoxScaleLoss=5.104, ClassLoss=9.009 [Epoch 97][Batch 1399], LR: 1.00E-03, Speed: 142.044 samples/sec, ObjLoss=22.321, BoxCenterLoss=14.659, BoxScaleLoss=5.104, ClassLoss=9.007 [Epoch 97][Batch 1499], LR: 1.00E-03, Speed: 122.880 samples/sec, ObjLoss=22.320, BoxCenterLoss=14.659, BoxScaleLoss=5.104, ClassLoss=9.006 [Epoch 97][Batch 1599], LR: 1.00E-03, Speed: 117.959 samples/sec, ObjLoss=22.319, BoxCenterLoss=14.659, BoxScaleLoss=5.103, ClassLoss=9.005 [Epoch 97][Batch 1699], LR: 1.00E-03, Speed: 96.519 samples/sec, ObjLoss=22.317, BoxCenterLoss=14.659, BoxScaleLoss=5.103, ClassLoss=9.003 [Epoch 97][Batch 1799], LR: 1.00E-03, Speed: 130.251 samples/sec, ObjLoss=22.317, BoxCenterLoss=14.659, BoxScaleLoss=5.103, ClassLoss=9.002 [Epoch 97] Training cost: 1309.227, ObjLoss=22.317, BoxCenterLoss=14.659, BoxScaleLoss=5.103, ClassLoss=9.002 [Epoch 97] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.190 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.406 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.195 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 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.116 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386 person=31.5 bicycle=13.8 car=19.7 motorcycle=19.9 airplane=37.4 bus=39.5 train=41.3 truck=17.2 boat=9.6 traffic light=10.9 fire hydrant=36.6 stop sign=38.8 parking meter=23.6 bench=9.8 bird=16.0 cat=37.7 dog=32.7 horse=26.7 sheep=25.0 cow=28.8 elephant=35.8 bear=40.3 zebra=38.6 giraffe=37.2 backpack=4.0 umbrella=15.1 handbag=3.2 tie=12.7 suitcase=14.0 frisbee=31.0 skis=6.8 snowboard=10.3 sports ball=23.5 kite=18.0 baseball bat=10.5 baseball glove=16.2 skateboard=16.4 surfboard=11.4 tennis racket=20.5 bottle=15.4 wine glass=13.7 cup=18.8 fork=7.0 knife=3.3 spoon=2.2 bowl=17.4 banana=8.2 apple=4.4 sandwich=18.2 orange=11.8 broccoli=10.0 carrot=7.0 hot dog=15.4 pizza=27.8 donut=18.1 cake=16.0 chair=11.0 couch=24.4 potted plant=10.1 bed=23.7 dining table=10.9 toilet=33.4 tv=26.2 laptop=30.4 mouse=29.8 remote=8.0 keyboard=28.3 cell phone=15.2 microwave=24.6 oven=17.9 toaster=0.0 sink=12.4 refrigerator=26.4 book=5.0 clock=27.6 vase=18.1 scissors=14.2 teddy bear=22.8 hair drier=0.0 toothbrush=2.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.0 [Epoch 98][Batch 99], LR: 1.00E-03, Speed: 136.722 samples/sec, ObjLoss=22.315, BoxCenterLoss=14.659, BoxScaleLoss=5.103, ClassLoss=9.001 [Epoch 98][Batch 199], LR: 1.00E-03, Speed: 133.419 samples/sec, ObjLoss=22.314, BoxCenterLoss=14.659, BoxScaleLoss=5.102, ClassLoss=9.000 [Epoch 98][Batch 299], LR: 1.00E-03, Speed: 70.867 samples/sec, ObjLoss=22.313, BoxCenterLoss=14.659, BoxScaleLoss=5.102, ClassLoss=8.998 [Epoch 98][Batch 399], LR: 1.00E-03, Speed: 117.308 samples/sec, ObjLoss=22.311, BoxCenterLoss=14.659, BoxScaleLoss=5.102, ClassLoss=8.997 [Epoch 98][Batch 499], LR: 1.00E-03, Speed: 76.643 samples/sec, ObjLoss=22.310, BoxCenterLoss=14.659, BoxScaleLoss=5.102, ClassLoss=8.996 [Epoch 98][Batch 599], LR: 1.00E-03, Speed: 131.163 samples/sec, ObjLoss=22.309, BoxCenterLoss=14.659, BoxScaleLoss=5.102, ClassLoss=8.995 [Epoch 98][Batch 699], LR: 1.00E-03, Speed: 145.085 samples/sec, ObjLoss=22.308, BoxCenterLoss=14.659, BoxScaleLoss=5.101, ClassLoss=8.994 [Epoch 98][Batch 799], LR: 1.00E-03, Speed: 134.551 samples/sec, ObjLoss=22.306, BoxCenterLoss=14.658, BoxScaleLoss=5.101, ClassLoss=8.993 [Epoch 98][Batch 899], LR: 1.00E-03, Speed: 114.742 samples/sec, ObjLoss=22.305, BoxCenterLoss=14.658, BoxScaleLoss=5.101, ClassLoss=8.992 [Epoch 98][Batch 999], LR: 1.00E-03, Speed: 161.809 samples/sec, ObjLoss=22.303, BoxCenterLoss=14.658, BoxScaleLoss=5.101, ClassLoss=8.991 [Epoch 98][Batch 1099], LR: 1.00E-03, Speed: 72.723 samples/sec, ObjLoss=22.302, BoxCenterLoss=14.658, BoxScaleLoss=5.100, ClassLoss=8.990 [Epoch 98][Batch 1199], LR: 1.00E-03, Speed: 89.715 samples/sec, ObjLoss=22.301, BoxCenterLoss=14.658, BoxScaleLoss=5.100, ClassLoss=8.989 [Epoch 98][Batch 1299], LR: 1.00E-03, Speed: 147.586 samples/sec, ObjLoss=22.300, BoxCenterLoss=14.658, BoxScaleLoss=5.100, ClassLoss=8.987 [Epoch 98][Batch 1399], LR: 1.00E-03, Speed: 108.126 samples/sec, ObjLoss=22.299, BoxCenterLoss=14.658, BoxScaleLoss=5.100, ClassLoss=8.986 [Epoch 98][Batch 1499], LR: 1.00E-03, Speed: 123.505 samples/sec, ObjLoss=22.298, BoxCenterLoss=14.658, BoxScaleLoss=5.099, ClassLoss=8.985 [Epoch 98][Batch 1599], LR: 1.00E-03, Speed: 159.922 samples/sec, ObjLoss=22.296, BoxCenterLoss=14.657, BoxScaleLoss=5.099, ClassLoss=8.984 [Epoch 98][Batch 1699], LR: 1.00E-03, Speed: 120.532 samples/sec, ObjLoss=22.295, BoxCenterLoss=14.657, BoxScaleLoss=5.099, ClassLoss=8.983 [Epoch 98][Batch 1799], LR: 1.00E-03, Speed: 114.588 samples/sec, ObjLoss=22.293, BoxCenterLoss=14.657, BoxScaleLoss=5.099, ClassLoss=8.982 [Epoch 98] Training cost: 1250.877, ObjLoss=22.293, BoxCenterLoss=14.657, BoxScaleLoss=5.099, ClassLoss=8.982 [Epoch 98] 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 ] = 0.409 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.199 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 ] = 0.190 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 ] = 0.287 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 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422 person=30.5 bicycle=14.2 car=20.6 motorcycle=25.5 airplane=38.8 bus=41.0 train=43.0 truck=18.0 boat=9.9 traffic light=9.6 fire hydrant=31.7 stop sign=36.6 parking meter=21.1 bench=9.5 bird=17.4 cat=43.5 dog=35.2 horse=31.0 sheep=22.9 cow=29.4 elephant=38.8 bear=42.7 zebra=38.7 giraffe=40.3 backpack=4.9 umbrella=20.7 handbag=3.8 tie=13.4 suitcase=16.2 frisbee=31.3 skis=6.7 snowboard=10.2 sports ball=17.4 kite=22.4 baseball bat=8.9 baseball glove=18.4 skateboard=20.9 surfboard=15.9 tennis racket=21.1 bottle=12.6 wine glass=11.9 cup=18.5 fork=8.8 knife=2.7 spoon=2.1 bowl=18.3 banana=10.1 apple=6.2 sandwich=18.3 orange=14.9 broccoli=8.9 carrot=9.5 hot dog=15.9 pizza=29.5 donut=19.9 cake=15.5 chair=11.3 couch=26.6 potted plant=8.8 bed=28.1 dining table=14.0 toilet=28.9 tv=34.8 laptop=34.8 mouse=28.7 remote=7.0 keyboard=27.0 cell phone=16.6 microwave=22.9 oven=17.7 toaster=0.0 sink=19.5 refrigerator=22.1 book=5.6 clock=28.7 vase=16.6 scissors=15.0 teddy bear=23.3 hair drier=0.0 toothbrush=3.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.8 [Epoch 99][Batch 99], LR: 1.00E-03, Speed: 134.211 samples/sec, ObjLoss=22.291, BoxCenterLoss=14.657, BoxScaleLoss=5.098, ClassLoss=8.980 [Epoch 99][Batch 199], LR: 1.00E-03, Speed: 161.398 samples/sec, ObjLoss=22.290, BoxCenterLoss=14.656, BoxScaleLoss=5.098, ClassLoss=8.979 [Epoch 99][Batch 299], LR: 1.00E-03, Speed: 151.047 samples/sec, ObjLoss=22.288, BoxCenterLoss=14.656, BoxScaleLoss=5.098, ClassLoss=8.978 [Epoch 99][Batch 399], LR: 1.00E-03, Speed: 106.622 samples/sec, ObjLoss=22.287, BoxCenterLoss=14.656, BoxScaleLoss=5.098, ClassLoss=8.977 [Epoch 99][Batch 499], LR: 1.00E-03, Speed: 126.237 samples/sec, ObjLoss=22.286, BoxCenterLoss=14.656, BoxScaleLoss=5.097, ClassLoss=8.976 [Epoch 99][Batch 599], LR: 1.00E-03, Speed: 110.301 samples/sec, ObjLoss=22.284, BoxCenterLoss=14.656, BoxScaleLoss=5.097, ClassLoss=8.975 [Epoch 99][Batch 699], LR: 1.00E-03, Speed: 149.043 samples/sec, ObjLoss=22.283, BoxCenterLoss=14.656, BoxScaleLoss=5.097, ClassLoss=8.974 [Epoch 99][Batch 799], LR: 1.00E-03, Speed: 140.778 samples/sec, ObjLoss=22.282, BoxCenterLoss=14.656, BoxScaleLoss=5.097, ClassLoss=8.973 [Epoch 99][Batch 899], LR: 1.00E-03, Speed: 144.557 samples/sec, ObjLoss=22.280, BoxCenterLoss=14.655, BoxScaleLoss=5.097, ClassLoss=8.972 [Epoch 99][Batch 999], LR: 1.00E-03, Speed: 140.873 samples/sec, ObjLoss=22.279, BoxCenterLoss=14.655, BoxScaleLoss=5.096, ClassLoss=8.970 [Epoch 99][Batch 1099], LR: 1.00E-03, Speed: 73.086 samples/sec, ObjLoss=22.278, BoxCenterLoss=14.655, BoxScaleLoss=5.096, ClassLoss=8.969 [Epoch 99][Batch 1199], LR: 1.00E-03, Speed: 77.969 samples/sec, ObjLoss=22.277, BoxCenterLoss=14.655, BoxScaleLoss=5.096, ClassLoss=8.968 [Epoch 99][Batch 1299], LR: 1.00E-03, Speed: 150.621 samples/sec, ObjLoss=22.276, BoxCenterLoss=14.655, BoxScaleLoss=5.096, ClassLoss=8.967 [Epoch 99][Batch 1399], LR: 1.00E-03, Speed: 131.800 samples/sec, ObjLoss=22.274, BoxCenterLoss=14.655, BoxScaleLoss=5.096, ClassLoss=8.966 [Epoch 99][Batch 1499], LR: 1.00E-03, Speed: 96.075 samples/sec, ObjLoss=22.273, BoxCenterLoss=14.655, BoxScaleLoss=5.096, ClassLoss=8.965 [Epoch 99][Batch 1599], LR: 1.00E-03, Speed: 121.428 samples/sec, ObjLoss=22.272, BoxCenterLoss=14.655, BoxScaleLoss=5.095, ClassLoss=8.964 [Epoch 99][Batch 1699], LR: 1.00E-03, Speed: 110.289 samples/sec, ObjLoss=22.271, BoxCenterLoss=14.655, BoxScaleLoss=5.095, ClassLoss=8.963 [Epoch 99][Batch 1799], LR: 1.00E-03, Speed: 140.173 samples/sec, ObjLoss=22.270, BoxCenterLoss=14.655, BoxScaleLoss=5.095, ClassLoss=8.962 [Epoch 99] Training cost: 1216.055, ObjLoss=22.269, BoxCenterLoss=14.655, BoxScaleLoss=5.095, ClassLoss=8.962 [Epoch 99] 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 ] = 0.403 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.074 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.197 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 ] = 0.191 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.278 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 ] = 0.119 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413 person=31.7 bicycle=14.9 car=19.4 motorcycle=22.1 airplane=39.5 bus=41.4 train=40.4 truck=18.4 boat=11.4 traffic light=11.3 fire hydrant=41.3 stop sign=36.4 parking meter=24.4 bench=10.7 bird=16.0 cat=42.6 dog=31.6 horse=27.7 sheep=24.6 cow=29.4 elephant=37.4 bear=42.2 zebra=40.6 giraffe=40.8 backpack=4.6 umbrella=18.8 handbag=3.4 tie=13.4 suitcase=14.7 frisbee=32.2 skis=7.5 snowboard=13.8 sports ball=23.5 kite=23.3 baseball bat=9.5 baseball glove=17.0 skateboard=20.6 surfboard=15.7 tennis racket=20.8 bottle=14.4 wine glass=13.0 cup=18.4 fork=6.4 knife=3.1 spoon=1.2 bowl=16.8 banana=11.3 apple=7.8 sandwich=17.2 orange=15.9 broccoli=9.0 carrot=8.9 hot dog=14.9 pizza=26.0 donut=22.2 cake=14.3 chair=12.1 couch=27.0 potted plant=9.7 bed=28.8 dining table=17.9 toilet=29.5 tv=35.5 laptop=33.1 mouse=31.3 remote=6.2 keyboard=22.2 cell phone=14.5 microwave=23.8 oven=18.9 toaster=0.0 sink=18.5 refrigerator=30.4 book=4.4 clock=27.4 vase=18.0 scissors=13.8 teddy bear=20.2 hair drier=0.0 toothbrush=2.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.0 [Epoch 100][Batch 99], LR: 1.00E-03, Speed: 147.393 samples/sec, ObjLoss=22.268, BoxCenterLoss=14.655, BoxScaleLoss=5.094, ClassLoss=8.960 [Epoch 100][Batch 199], LR: 1.00E-03, Speed: 129.890 samples/sec, ObjLoss=22.267, BoxCenterLoss=14.654, BoxScaleLoss=5.094, ClassLoss=8.959 [Epoch 100][Batch 299], LR: 1.00E-03, Speed: 142.227 samples/sec, ObjLoss=22.266, BoxCenterLoss=14.655, BoxScaleLoss=5.094, ClassLoss=8.958 [Epoch 100][Batch 399], LR: 1.00E-03, Speed: 158.175 samples/sec, ObjLoss=22.264, BoxCenterLoss=14.654, BoxScaleLoss=5.094, ClassLoss=8.957 [Epoch 100][Batch 499], LR: 1.00E-03, Speed: 129.332 samples/sec, ObjLoss=22.263, BoxCenterLoss=14.654, BoxScaleLoss=5.093, ClassLoss=8.956 [Epoch 100][Batch 599], LR: 1.00E-03, Speed: 65.473 samples/sec, ObjLoss=22.262, BoxCenterLoss=14.654, BoxScaleLoss=5.093, ClassLoss=8.954 [Epoch 100][Batch 699], LR: 1.00E-03, Speed: 131.743 samples/sec, ObjLoss=22.261, BoxCenterLoss=14.654, BoxScaleLoss=5.093, ClassLoss=8.953 [Epoch 100][Batch 799], LR: 1.00E-03, Speed: 95.550 samples/sec, ObjLoss=22.260, BoxCenterLoss=14.654, BoxScaleLoss=5.092, ClassLoss=8.952 [Epoch 100][Batch 899], LR: 1.00E-03, Speed: 152.671 samples/sec, ObjLoss=22.258, BoxCenterLoss=14.654, BoxScaleLoss=5.092, ClassLoss=8.951 [Epoch 100][Batch 999], LR: 1.00E-03, Speed: 143.978 samples/sec, ObjLoss=22.257, BoxCenterLoss=14.654, BoxScaleLoss=5.092, ClassLoss=8.950 [Epoch 100][Batch 1099], LR: 1.00E-03, Speed: 102.894 samples/sec, ObjLoss=22.256, BoxCenterLoss=14.653, BoxScaleLoss=5.092, ClassLoss=8.949 [Epoch 100][Batch 1199], LR: 1.00E-03, Speed: 112.151 samples/sec, ObjLoss=22.255, BoxCenterLoss=14.653, BoxScaleLoss=5.091, ClassLoss=8.948 [Epoch 100][Batch 1299], LR: 1.00E-03, Speed: 112.878 samples/sec, ObjLoss=22.253, BoxCenterLoss=14.653, BoxScaleLoss=5.091, ClassLoss=8.947 [Epoch 100][Batch 1399], LR: 1.00E-03, Speed: 128.157 samples/sec, ObjLoss=22.252, BoxCenterLoss=14.653, BoxScaleLoss=5.091, ClassLoss=8.946 [Epoch 100][Batch 1499], LR: 1.00E-03, Speed: 63.629 samples/sec, ObjLoss=22.251, BoxCenterLoss=14.653, BoxScaleLoss=5.091, ClassLoss=8.945 [Epoch 100][Batch 1599], LR: 1.00E-03, Speed: 97.366 samples/sec, ObjLoss=22.250, BoxCenterLoss=14.653, BoxScaleLoss=5.090, ClassLoss=8.944 [Epoch 100][Batch 1699], LR: 1.00E-03, Speed: 145.053 samples/sec, ObjLoss=22.249, BoxCenterLoss=14.653, BoxScaleLoss=5.090, ClassLoss=8.943 [Epoch 100][Batch 1799], LR: 1.00E-03, Speed: 131.120 samples/sec, ObjLoss=22.248, BoxCenterLoss=14.653, BoxScaleLoss=5.090, ClassLoss=8.942 [Epoch 100] Training cost: 1275.162, ObjLoss=22.247, BoxCenterLoss=14.653, BoxScaleLoss=5.090, ClassLoss=8.941 [Epoch 100] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.189 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.395 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.159 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 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.285 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 ] = 0.266 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.116 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 ] = 0.396 person=31.7 bicycle=13.1 car=19.4 motorcycle=24.5 airplane=35.5 bus=38.0 train=42.9 truck=17.8 boat=10.9 traffic light=10.0 fire hydrant=29.6 stop sign=39.1 parking meter=10.5 bench=8.7 bird=14.5 cat=37.9 dog=31.6 horse=31.5 sheep=24.8 cow=28.4 elephant=35.5 bear=34.0 zebra=38.8 giraffe=40.8 backpack=4.2 umbrella=17.4 handbag=3.3 tie=12.4 suitcase=11.4 frisbee=32.7 skis=5.9 snowboard=11.5 sports ball=21.1 kite=15.8 baseball bat=10.3 baseball glove=17.7 skateboard=21.2 surfboard=15.2 tennis racket=22.6 bottle=13.8 wine glass=12.2 cup=18.9 fork=8.4 knife=2.8 spoon=2.2 bowl=18.0 banana=9.1 apple=6.3 sandwich=12.9 orange=13.1 broccoli=9.0 carrot=7.6 hot dog=15.7 pizza=26.4 donut=18.6 cake=12.6 chair=11.8 couch=25.6 potted plant=9.2 bed=25.4 dining table=14.0 toilet=32.9 tv=30.5 laptop=32.8 mouse=34.2 remote=7.8 keyboard=26.6 cell phone=14.0 microwave=26.1 oven=17.8 toaster=0.0 sink=14.6 refrigerator=27.6 book=3.9 clock=27.9 vase=16.0 scissors=13.3 teddy bear=21.3 hair drier=0.0 toothbrush=2.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.9 [Epoch 101][Batch 99], LR: 1.00E-03, Speed: 125.627 samples/sec, ObjLoss=22.246, BoxCenterLoss=14.652, BoxScaleLoss=5.090, ClassLoss=8.940 [Epoch 101][Batch 199], LR: 1.00E-03, Speed: 132.669 samples/sec, ObjLoss=22.244, BoxCenterLoss=14.652, BoxScaleLoss=5.089, ClassLoss=8.939 [Epoch 101][Batch 299], LR: 1.00E-03, Speed: 146.630 samples/sec, ObjLoss=22.243, BoxCenterLoss=14.652, BoxScaleLoss=5.089, ClassLoss=8.938 [Epoch 101][Batch 399], LR: 1.00E-03, Speed: 83.173 samples/sec, ObjLoss=22.241, BoxCenterLoss=14.652, BoxScaleLoss=5.089, ClassLoss=8.937 [Epoch 101][Batch 499], LR: 1.00E-03, Speed: 134.620 samples/sec, ObjLoss=22.240, BoxCenterLoss=14.652, BoxScaleLoss=5.089, ClassLoss=8.936 [Epoch 101][Batch 599], LR: 1.00E-03, Speed: 153.140 samples/sec, ObjLoss=22.239, BoxCenterLoss=14.651, BoxScaleLoss=5.089, ClassLoss=8.935 [Epoch 101][Batch 699], LR: 1.00E-03, Speed: 79.352 samples/sec, ObjLoss=22.238, BoxCenterLoss=14.651, BoxScaleLoss=5.089, ClassLoss=8.934 [Epoch 101][Batch 799], LR: 1.00E-03, Speed: 119.493 samples/sec, ObjLoss=22.236, BoxCenterLoss=14.651, BoxScaleLoss=5.088, ClassLoss=8.933 [Epoch 101][Batch 899], LR: 1.00E-03, Speed: 86.312 samples/sec, ObjLoss=22.235, BoxCenterLoss=14.651, BoxScaleLoss=5.088, ClassLoss=8.932 [Epoch 101][Batch 999], LR: 1.00E-03, Speed: 128.982 samples/sec, ObjLoss=22.234, BoxCenterLoss=14.651, BoxScaleLoss=5.088, ClassLoss=8.931 [Epoch 101][Batch 1099], LR: 1.00E-03, Speed: 140.592 samples/sec, ObjLoss=22.232, BoxCenterLoss=14.651, BoxScaleLoss=5.088, ClassLoss=8.930 [Epoch 101][Batch 1199], LR: 1.00E-03, Speed: 80.951 samples/sec, ObjLoss=22.231, BoxCenterLoss=14.650, BoxScaleLoss=5.087, ClassLoss=8.929 [Epoch 101][Batch 1299], LR: 1.00E-03, Speed: 81.334 samples/sec, ObjLoss=22.229, BoxCenterLoss=14.650, BoxScaleLoss=5.087, ClassLoss=8.928 [Epoch 101][Batch 1399], LR: 1.00E-03, Speed: 102.871 samples/sec, ObjLoss=22.228, BoxCenterLoss=14.650, BoxScaleLoss=5.087, ClassLoss=8.927 [Epoch 101][Batch 1499], LR: 1.00E-03, Speed: 142.380 samples/sec, ObjLoss=22.227, BoxCenterLoss=14.650, BoxScaleLoss=5.087, ClassLoss=8.926 [Epoch 101][Batch 1599], LR: 1.00E-03, Speed: 155.977 samples/sec, ObjLoss=22.226, BoxCenterLoss=14.650, BoxScaleLoss=5.087, ClassLoss=8.925 [Epoch 101][Batch 1699], LR: 1.00E-03, Speed: 54.673 samples/sec, ObjLoss=22.224, BoxCenterLoss=14.650, BoxScaleLoss=5.086, ClassLoss=8.924 [Epoch 101][Batch 1799], LR: 1.00E-03, Speed: 139.696 samples/sec, ObjLoss=22.223, BoxCenterLoss=14.650, BoxScaleLoss=5.086, ClassLoss=8.923 [Epoch 101] Training cost: 1179.133, ObjLoss=22.223, BoxCenterLoss=14.650, BoxScaleLoss=5.086, ClassLoss=8.923 [Epoch 101] 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 ] = 0.403 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.171 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 ] = 0.203 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.302 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 ] = 0.284 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 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416 person=32.7 bicycle=12.8 car=20.8 motorcycle=23.2 airplane=33.7 bus=41.4 train=37.8 truck=19.3 boat=10.0 traffic light=11.2 fire hydrant=37.6 stop sign=35.1 parking meter=22.6 bench=11.0 bird=17.2 cat=38.9 dog=34.2 horse=27.5 sheep=25.5 cow=29.9 elephant=38.3 bear=45.8 zebra=38.8 giraffe=42.0 backpack=5.8 umbrella=18.8 handbag=3.6 tie=13.1 suitcase=15.5 frisbee=35.2 skis=8.4 snowboard=12.0 sports ball=22.7 kite=20.0 baseball bat=10.1 baseball glove=14.7 skateboard=20.1 surfboard=14.3 tennis racket=21.2 bottle=14.8 wine glass=13.4 cup=18.7 fork=9.1 knife=3.6 spoon=2.1 bowl=18.5 banana=9.9 apple=6.1 sandwich=19.5 orange=13.0 broccoli=8.6 carrot=7.0 hot dog=13.5 pizza=31.8 donut=24.6 cake=16.4 chair=11.4 couch=24.6 potted plant=9.8 bed=23.1 dining table=12.4 toilet=29.1 tv=30.2 laptop=31.4 mouse=24.1 remote=7.9 keyboard=30.3 cell phone=15.1 microwave=27.6 oven=17.3 toaster=0.0 sink=18.4 refrigerator=23.8 book=4.4 clock=28.9 vase=17.9 scissors=14.3 teddy bear=22.5 hair drier=0.0 toothbrush=2.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.8 [Epoch 102][Batch 99], LR: 1.00E-03, Speed: 163.047 samples/sec, ObjLoss=22.222, BoxCenterLoss=14.649, BoxScaleLoss=5.086, ClassLoss=8.922 [Epoch 102][Batch 199], LR: 1.00E-03, Speed: 148.399 samples/sec, ObjLoss=22.220, BoxCenterLoss=14.649, BoxScaleLoss=5.086, ClassLoss=8.921 [Epoch 102][Batch 299], LR: 1.00E-03, Speed: 81.323 samples/sec, ObjLoss=22.219, BoxCenterLoss=14.649, BoxScaleLoss=5.085, ClassLoss=8.920 [Epoch 102][Batch 399], LR: 1.00E-03, Speed: 86.138 samples/sec, ObjLoss=22.218, BoxCenterLoss=14.649, BoxScaleLoss=5.085, ClassLoss=8.918 [Epoch 102][Batch 499], LR: 1.00E-03, Speed: 154.339 samples/sec, ObjLoss=22.217, BoxCenterLoss=14.649, BoxScaleLoss=5.085, ClassLoss=8.917 [Epoch 102][Batch 599], LR: 1.00E-03, Speed: 114.115 samples/sec, ObjLoss=22.215, BoxCenterLoss=14.649, BoxScaleLoss=5.085, ClassLoss=8.916 [Epoch 102][Batch 699], LR: 1.00E-03, Speed: 112.601 samples/sec, ObjLoss=22.214, BoxCenterLoss=14.649, BoxScaleLoss=5.085, ClassLoss=8.915 [Epoch 102][Batch 799], LR: 1.00E-03, Speed: 134.211 samples/sec, ObjLoss=22.214, BoxCenterLoss=14.649, BoxScaleLoss=5.084, ClassLoss=8.914 [Epoch 102][Batch 899], LR: 1.00E-03, Speed: 95.770 samples/sec, ObjLoss=22.212, BoxCenterLoss=14.649, BoxScaleLoss=5.084, ClassLoss=8.913 [Epoch 102][Batch 999], LR: 1.00E-03, Speed: 101.242 samples/sec, ObjLoss=22.211, BoxCenterLoss=14.649, BoxScaleLoss=5.084, ClassLoss=8.912 [Epoch 102][Batch 1099], LR: 1.00E-03, Speed: 75.793 samples/sec, ObjLoss=22.209, BoxCenterLoss=14.648, BoxScaleLoss=5.084, ClassLoss=8.911 [Epoch 102][Batch 1199], LR: 1.00E-03, Speed: 63.542 samples/sec, ObjLoss=22.208, BoxCenterLoss=14.648, BoxScaleLoss=5.083, ClassLoss=8.910 [Epoch 102][Batch 1299], LR: 1.00E-03, Speed: 128.865 samples/sec, ObjLoss=22.207, BoxCenterLoss=14.648, BoxScaleLoss=5.083, ClassLoss=8.909 [Epoch 102][Batch 1399], LR: 1.00E-03, Speed: 96.255 samples/sec, ObjLoss=22.206, BoxCenterLoss=14.648, BoxScaleLoss=5.083, ClassLoss=8.908 [Epoch 102][Batch 1499], LR: 1.00E-03, Speed: 112.128 samples/sec, ObjLoss=22.204, BoxCenterLoss=14.648, BoxScaleLoss=5.083, ClassLoss=8.907 [Epoch 102][Batch 1599], LR: 1.00E-03, Speed: 130.529 samples/sec, ObjLoss=22.203, BoxCenterLoss=14.648, BoxScaleLoss=5.083, ClassLoss=8.906 [Epoch 102][Batch 1699], LR: 1.00E-03, Speed: 129.176 samples/sec, ObjLoss=22.202, BoxCenterLoss=14.647, BoxScaleLoss=5.082, ClassLoss=8.905 [Epoch 102][Batch 1799], LR: 1.00E-03, Speed: 121.603 samples/sec, ObjLoss=22.201, BoxCenterLoss=14.647, BoxScaleLoss=5.082, ClassLoss=8.904 [Epoch 102] Training cost: 1250.726, ObjLoss=22.200, BoxCenterLoss=14.647, BoxScaleLoss=5.082, ClassLoss=8.904 [Epoch 102] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.193 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.403 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.161 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 ] = 0.195 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 ] = 0.277 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 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 person=29.4 bicycle=14.5 car=20.1 motorcycle=23.1 airplane=37.2 bus=36.9 train=40.3 truck=16.3 boat=9.3 traffic light=10.8 fire hydrant=37.4 stop sign=36.6 parking meter=17.8 bench=8.6 bird=17.4 cat=38.6 dog=28.6 horse=29.3 sheep=22.3 cow=25.5 elephant=35.9 bear=39.7 zebra=38.5 giraffe=41.7 backpack=3.6 umbrella=18.8 handbag=3.2 tie=12.6 suitcase=13.3 frisbee=33.2 skis=7.1 snowboard=9.5 sports ball=24.1 kite=21.0 baseball bat=12.8 baseball glove=14.7 skateboard=22.8 surfboard=15.1 tennis racket=22.7 bottle=11.6 wine glass=14.4 cup=16.9 fork=7.6 knife=3.8 spoon=1.8 bowl=18.3 banana=9.3 apple=6.5 sandwich=17.6 orange=12.8 broccoli=8.1 carrot=6.9 hot dog=15.0 pizza=29.2 donut=16.8 cake=18.4 chair=9.8 couch=25.0 potted plant=10.4 bed=27.5 dining table=16.7 toilet=28.9 tv=35.1 laptop=34.3 mouse=32.4 remote=8.6 keyboard=25.4 cell phone=13.6 microwave=28.0 oven=17.3 toaster=0.0 sink=15.3 refrigerator=25.2 book=5.2 clock=29.3 vase=16.4 scissors=12.3 teddy bear=20.2 hair drier=0.0 toothbrush=2.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.3 [Epoch 103][Batch 99], LR: 1.00E-03, Speed: 131.169 samples/sec, ObjLoss=22.199, BoxCenterLoss=14.647, BoxScaleLoss=5.082, ClassLoss=8.903 [Epoch 103][Batch 199], LR: 1.00E-03, Speed: 157.097 samples/sec, ObjLoss=22.198, BoxCenterLoss=14.647, BoxScaleLoss=5.081, ClassLoss=8.901 [Epoch 103][Batch 299], LR: 1.00E-03, Speed: 153.688 samples/sec, ObjLoss=22.196, BoxCenterLoss=14.646, BoxScaleLoss=5.081, ClassLoss=8.900 [Epoch 103][Batch 399], LR: 1.00E-03, Speed: 138.890 samples/sec, ObjLoss=22.195, BoxCenterLoss=14.646, BoxScaleLoss=5.081, ClassLoss=8.899 [Epoch 103][Batch 499], LR: 1.00E-03, Speed: 134.294 samples/sec, ObjLoss=22.194, BoxCenterLoss=14.646, BoxScaleLoss=5.081, ClassLoss=8.898 [Epoch 103][Batch 599], LR: 1.00E-03, Speed: 145.491 samples/sec, ObjLoss=22.192, BoxCenterLoss=14.646, BoxScaleLoss=5.080, ClassLoss=8.897 [Epoch 103][Batch 699], LR: 1.00E-03, Speed: 75.237 samples/sec, ObjLoss=22.191, BoxCenterLoss=14.646, BoxScaleLoss=5.080, ClassLoss=8.896 [Epoch 103][Batch 799], LR: 1.00E-03, Speed: 85.440 samples/sec, ObjLoss=22.190, BoxCenterLoss=14.646, BoxScaleLoss=5.080, ClassLoss=8.895 [Epoch 103][Batch 899], LR: 1.00E-03, Speed: 134.213 samples/sec, ObjLoss=22.189, BoxCenterLoss=14.646, BoxScaleLoss=5.080, ClassLoss=8.894 [Epoch 103][Batch 999], LR: 1.00E-03, Speed: 114.708 samples/sec, ObjLoss=22.188, BoxCenterLoss=14.646, BoxScaleLoss=5.080, ClassLoss=8.893 [Epoch 103][Batch 1099], LR: 1.00E-03, Speed: 83.361 samples/sec, ObjLoss=22.186, BoxCenterLoss=14.646, BoxScaleLoss=5.079, ClassLoss=8.892 [Epoch 103][Batch 1199], LR: 1.00E-03, Speed: 145.339 samples/sec, ObjLoss=22.185, BoxCenterLoss=14.646, BoxScaleLoss=5.079, ClassLoss=8.891 [Epoch 103][Batch 1299], LR: 1.00E-03, Speed: 117.280 samples/sec, ObjLoss=22.184, BoxCenterLoss=14.645, BoxScaleLoss=5.079, ClassLoss=8.890 [Epoch 103][Batch 1399], LR: 1.00E-03, Speed: 102.985 samples/sec, ObjLoss=22.182, BoxCenterLoss=14.645, BoxScaleLoss=5.079, ClassLoss=8.889 [Epoch 103][Batch 1499], LR: 1.00E-03, Speed: 59.101 samples/sec, ObjLoss=22.181, BoxCenterLoss=14.645, BoxScaleLoss=5.079, ClassLoss=8.888 [Epoch 103][Batch 1599], LR: 1.00E-03, Speed: 133.950 samples/sec, ObjLoss=22.180, BoxCenterLoss=14.645, BoxScaleLoss=5.078, ClassLoss=8.887 [Epoch 103][Batch 1699], LR: 1.00E-03, Speed: 155.387 samples/sec, ObjLoss=22.179, BoxCenterLoss=14.645, BoxScaleLoss=5.078, ClassLoss=8.886 [Epoch 103][Batch 1799], LR: 1.00E-03, Speed: 103.707 samples/sec, ObjLoss=22.178, BoxCenterLoss=14.645, BoxScaleLoss=5.078, ClassLoss=8.885 [Epoch 103] Training cost: 1254.570, ObjLoss=22.178, BoxCenterLoss=14.645, BoxScaleLoss=5.078, ClassLoss=8.884 [Epoch 103] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.190 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.398 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.161 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 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.281 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 ] = 0.264 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 ] = 0.114 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=31.5 bicycle=12.6 car=18.0 motorcycle=22.3 airplane=40.3 bus=33.2 train=40.1 truck=16.5 boat=8.1 traffic light=11.0 fire hydrant=39.0 stop sign=38.2 parking meter=21.9 bench=8.9 bird=16.6 cat=44.1 dog=33.2 horse=29.3 sheep=23.0 cow=29.1 elephant=36.4 bear=40.1 zebra=40.2 giraffe=40.6 backpack=3.4 umbrella=19.2 handbag=3.0 tie=12.1 suitcase=12.5 frisbee=29.7 skis=6.1 snowboard=9.3 sports ball=19.9 kite=16.6 baseball bat=10.6 baseball glove=15.4 skateboard=21.7 surfboard=13.1 tennis racket=20.1 bottle=13.3 wine glass=13.0 cup=17.9 fork=6.9 knife=3.0 spoon=1.3 bowl=15.4 banana=9.0 apple=7.0 sandwich=15.9 orange=12.6 broccoli=9.0 carrot=7.2 hot dog=14.5 pizza=30.3 donut=18.3 cake=15.5 chair=12.1 couch=22.3 potted plant=11.1 bed=23.5 dining table=10.4 toilet=34.1 tv=31.7 laptop=32.5 mouse=29.2 remote=7.6 keyboard=19.0 cell phone=15.1 microwave=26.7 oven=18.0 toaster=0.0 sink=18.2 refrigerator=25.6 book=5.2 clock=28.0 vase=18.2 scissors=10.9 teddy bear=19.1 hair drier=0.0 toothbrush=3.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.0 [Epoch 104][Batch 99], LR: 1.00E-03, Speed: 146.659 samples/sec, ObjLoss=22.176, BoxCenterLoss=14.645, BoxScaleLoss=5.078, ClassLoss=8.883 [Epoch 104][Batch 199], LR: 1.00E-03, Speed: 130.775 samples/sec, ObjLoss=22.175, BoxCenterLoss=14.645, BoxScaleLoss=5.078, ClassLoss=8.882 [Epoch 104][Batch 299], LR: 1.00E-03, Speed: 151.158 samples/sec, ObjLoss=22.174, BoxCenterLoss=14.645, BoxScaleLoss=5.078, ClassLoss=8.882 [Epoch 104][Batch 399], LR: 1.00E-03, Speed: 149.849 samples/sec, ObjLoss=22.173, BoxCenterLoss=14.645, BoxScaleLoss=5.077, ClassLoss=8.880 [Epoch 104][Batch 499], LR: 1.00E-03, Speed: 169.642 samples/sec, ObjLoss=22.172, BoxCenterLoss=14.645, BoxScaleLoss=5.077, ClassLoss=8.880 [Epoch 104][Batch 599], LR: 1.00E-03, Speed: 53.914 samples/sec, ObjLoss=22.171, BoxCenterLoss=14.645, BoxScaleLoss=5.077, ClassLoss=8.879 [Epoch 104][Batch 699], LR: 1.00E-03, Speed: 104.380 samples/sec, ObjLoss=22.169, BoxCenterLoss=14.644, BoxScaleLoss=5.077, ClassLoss=8.877 [Epoch 104][Batch 799], LR: 1.00E-03, Speed: 81.186 samples/sec, ObjLoss=22.168, BoxCenterLoss=14.644, BoxScaleLoss=5.076, ClassLoss=8.876 [Epoch 104][Batch 899], LR: 1.00E-03, Speed: 136.760 samples/sec, ObjLoss=22.167, BoxCenterLoss=14.644, BoxScaleLoss=5.076, ClassLoss=8.875 [Epoch 104][Batch 999], LR: 1.00E-03, Speed: 78.006 samples/sec, ObjLoss=22.166, BoxCenterLoss=14.644, BoxScaleLoss=5.076, ClassLoss=8.874 [Epoch 104][Batch 1099], LR: 1.00E-03, Speed: 172.600 samples/sec, ObjLoss=22.165, BoxCenterLoss=14.644, BoxScaleLoss=5.076, ClassLoss=8.873 [Epoch 104][Batch 1199], LR: 1.00E-03, Speed: 135.297 samples/sec, ObjLoss=22.163, BoxCenterLoss=14.644, BoxScaleLoss=5.075, ClassLoss=8.872 [Epoch 104][Batch 1299], LR: 1.00E-03, Speed: 150.020 samples/sec, ObjLoss=22.162, BoxCenterLoss=14.643, BoxScaleLoss=5.075, ClassLoss=8.871 [Epoch 104][Batch 1399], LR: 1.00E-03, Speed: 153.392 samples/sec, ObjLoss=22.161, BoxCenterLoss=14.643, BoxScaleLoss=5.075, ClassLoss=8.870 [Epoch 104][Batch 1499], LR: 1.00E-03, Speed: 139.646 samples/sec, ObjLoss=22.160, BoxCenterLoss=14.643, BoxScaleLoss=5.075, ClassLoss=8.869 [Epoch 104][Batch 1599], LR: 1.00E-03, Speed: 125.475 samples/sec, ObjLoss=22.159, BoxCenterLoss=14.643, BoxScaleLoss=5.075, ClassLoss=8.868 [Epoch 104][Batch 1699], LR: 1.00E-03, Speed: 95.867 samples/sec, ObjLoss=22.158, BoxCenterLoss=14.643, BoxScaleLoss=5.074, ClassLoss=8.867 [Epoch 104][Batch 1799], LR: 1.00E-03, Speed: 133.508 samples/sec, ObjLoss=22.156, BoxCenterLoss=14.643, BoxScaleLoss=5.074, ClassLoss=8.866 [Epoch 104] Training cost: 1274.000, ObjLoss=22.156, BoxCenterLoss=14.643, BoxScaleLoss=5.074, ClassLoss=8.865 [Epoch 104] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.412 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.161 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 ] = 0.189 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 ] = 0.187 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 ] = 0.287 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 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416 person=32.3 bicycle=13.1 car=18.6 motorcycle=22.5 airplane=40.4 bus=43.6 train=37.7 truck=18.3 boat=10.7 traffic light=8.7 fire hydrant=36.7 stop sign=34.4 parking meter=21.9 bench=10.2 bird=15.0 cat=38.3 dog=33.6 horse=30.4 sheep=24.6 cow=26.5 elephant=37.7 bear=41.5 zebra=38.4 giraffe=37.9 backpack=5.1 umbrella=18.7 handbag=3.3 tie=11.8 suitcase=15.8 frisbee=30.6 skis=6.6 snowboard=10.9 sports ball=25.4 kite=19.8 baseball bat=10.8 baseball glove=18.2 skateboard=17.7 surfboard=14.4 tennis racket=21.0 bottle=15.3 wine glass=13.9 cup=18.8 fork=6.2 knife=2.9 spoon=1.5 bowl=16.8 banana=9.3 apple=8.7 sandwich=18.8 orange=12.5 broccoli=9.7 carrot=8.4 hot dog=15.8 pizza=27.3 donut=18.5 cake=16.6 chair=10.8 couch=24.9 potted plant=9.8 bed=27.7 dining table=16.4 toilet=30.4 tv=36.1 laptop=34.9 mouse=31.5 remote=7.8 keyboard=23.3 cell phone=14.2 microwave=22.6 oven=17.2 toaster=0.0 sink=14.9 refrigerator=23.2 book=5.5 clock=29.0 vase=16.7 scissors=16.1 teddy bear=22.3 hair drier=0.0 toothbrush=2.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.5 [Epoch 105][Batch 99], LR: 1.00E-03, Speed: 131.382 samples/sec, ObjLoss=22.155, BoxCenterLoss=14.643, BoxScaleLoss=5.074, ClassLoss=8.864 [Epoch 105][Batch 199], LR: 1.00E-03, Speed: 129.634 samples/sec, ObjLoss=22.154, BoxCenterLoss=14.643, BoxScaleLoss=5.074, ClassLoss=8.863 [Epoch 105][Batch 299], LR: 1.00E-03, Speed: 189.719 samples/sec, ObjLoss=22.153, BoxCenterLoss=14.643, BoxScaleLoss=5.073, ClassLoss=8.862 [Epoch 105][Batch 399], LR: 1.00E-03, Speed: 145.391 samples/sec, ObjLoss=22.152, BoxCenterLoss=14.643, BoxScaleLoss=5.073, ClassLoss=8.861 [Epoch 105][Batch 499], LR: 1.00E-03, Speed: 155.505 samples/sec, ObjLoss=22.151, BoxCenterLoss=14.643, BoxScaleLoss=5.073, ClassLoss=8.860 [Epoch 105][Batch 599], LR: 1.00E-03, Speed: 126.101 samples/sec, ObjLoss=22.150, BoxCenterLoss=14.643, BoxScaleLoss=5.073, ClassLoss=8.859 [Epoch 105][Batch 699], LR: 1.00E-03, Speed: 141.693 samples/sec, ObjLoss=22.149, BoxCenterLoss=14.643, BoxScaleLoss=5.072, ClassLoss=8.858 [Epoch 105][Batch 799], LR: 1.00E-03, Speed: 142.912 samples/sec, ObjLoss=22.148, BoxCenterLoss=14.642, BoxScaleLoss=5.072, ClassLoss=8.857 [Epoch 105][Batch 899], LR: 1.00E-03, Speed: 128.416 samples/sec, ObjLoss=22.146, BoxCenterLoss=14.642, BoxScaleLoss=5.072, ClassLoss=8.856 [Epoch 105][Batch 999], LR: 1.00E-03, Speed: 146.764 samples/sec, ObjLoss=22.145, BoxCenterLoss=14.642, BoxScaleLoss=5.072, ClassLoss=8.855 [Epoch 105][Batch 1099], LR: 1.00E-03, Speed: 144.400 samples/sec, ObjLoss=22.144, BoxCenterLoss=14.642, BoxScaleLoss=5.072, ClassLoss=8.854 [Epoch 105][Batch 1199], LR: 1.00E-03, Speed: 135.712 samples/sec, ObjLoss=22.143, BoxCenterLoss=14.642, BoxScaleLoss=5.071, ClassLoss=8.853 [Epoch 105][Batch 1299], LR: 1.00E-03, Speed: 118.476 samples/sec, ObjLoss=22.141, BoxCenterLoss=14.642, BoxScaleLoss=5.071, ClassLoss=8.852 [Epoch 105][Batch 1399], LR: 1.00E-03, Speed: 108.257 samples/sec, ObjLoss=22.140, BoxCenterLoss=14.641, BoxScaleLoss=5.071, ClassLoss=8.851 [Epoch 105][Batch 1499], LR: 1.00E-03, Speed: 128.566 samples/sec, ObjLoss=22.139, BoxCenterLoss=14.641, BoxScaleLoss=5.071, ClassLoss=8.850 [Epoch 105][Batch 1599], LR: 1.00E-03, Speed: 50.456 samples/sec, ObjLoss=22.138, BoxCenterLoss=14.641, BoxScaleLoss=5.070, ClassLoss=8.849 [Epoch 105][Batch 1699], LR: 1.00E-03, Speed: 129.943 samples/sec, ObjLoss=22.137, BoxCenterLoss=14.641, BoxScaleLoss=5.070, ClassLoss=8.848 [Epoch 105][Batch 1799], LR: 1.00E-03, Speed: 127.252 samples/sec, ObjLoss=22.135, BoxCenterLoss=14.641, BoxScaleLoss=5.070, ClassLoss=8.847 [Epoch 105] Training cost: 1213.858, ObjLoss=22.135, BoxCenterLoss=14.641, BoxScaleLoss=5.070, ClassLoss=8.846 [Epoch 105] 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 ] = 0.402 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068 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 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.184 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 person=30.0 bicycle=12.3 car=18.5 motorcycle=22.9 airplane=37.8 bus=42.4 train=34.2 truck=17.3 boat=10.2 traffic light=11.1 fire hydrant=37.2 stop sign=33.8 parking meter=20.7 bench=10.3 bird=17.6 cat=42.0 dog=34.1 horse=30.9 sheep=24.1 cow=27.0 elephant=37.9 bear=38.6 zebra=41.8 giraffe=40.3 backpack=4.0 umbrella=20.0 handbag=2.5 tie=11.4 suitcase=14.1 frisbee=21.2 skis=7.5 snowboard=13.0 sports ball=21.6 kite=21.1 baseball bat=10.4 baseball glove=12.9 skateboard=22.2 surfboard=14.8 tennis racket=17.8 bottle=13.2 wine glass=13.3 cup=19.5 fork=8.9 knife=3.8 spoon=1.8 bowl=17.1 banana=10.5 apple=6.9 sandwich=16.5 orange=14.0 broccoli=8.6 carrot=6.9 hot dog=12.6 pizza=26.7 donut=15.5 cake=16.3 chair=10.6 couch=22.9 potted plant=11.4 bed=25.2 dining table=14.0 toilet=34.5 tv=29.2 laptop=27.4 mouse=30.7 remote=6.6 keyboard=21.2 cell phone=15.4 microwave=24.6 oven=17.0 toaster=0.0 sink=17.5 refrigerator=28.2 book=4.3 clock=26.2 vase=19.0 scissors=14.3 teddy bear=24.9 hair drier=0.0 toothbrush=2.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.1 [Epoch 106][Batch 99], LR: 1.00E-03, Speed: 160.232 samples/sec, ObjLoss=22.134, BoxCenterLoss=14.641, BoxScaleLoss=5.070, ClassLoss=8.845 [Epoch 106][Batch 199], LR: 1.00E-03, Speed: 111.650 samples/sec, ObjLoss=22.133, BoxCenterLoss=14.641, BoxScaleLoss=5.069, ClassLoss=8.844 [Epoch 106][Batch 299], LR: 1.00E-03, Speed: 120.145 samples/sec, ObjLoss=22.132, BoxCenterLoss=14.641, BoxScaleLoss=5.069, ClassLoss=8.843 [Epoch 106][Batch 399], LR: 1.00E-03, Speed: 115.559 samples/sec, ObjLoss=22.130, BoxCenterLoss=14.640, BoxScaleLoss=5.069, ClassLoss=8.842 [Epoch 106][Batch 499], LR: 1.00E-03, Speed: 162.337 samples/sec, ObjLoss=22.129, BoxCenterLoss=14.640, BoxScaleLoss=5.069, ClassLoss=8.841 [Epoch 106][Batch 599], LR: 1.00E-03, Speed: 118.146 samples/sec, ObjLoss=22.128, BoxCenterLoss=14.640, BoxScaleLoss=5.069, ClassLoss=8.840 [Epoch 106][Batch 699], LR: 1.00E-03, Speed: 120.868 samples/sec, ObjLoss=22.127, BoxCenterLoss=14.640, BoxScaleLoss=5.068, ClassLoss=8.839 [Epoch 106][Batch 799], LR: 1.00E-03, Speed: 130.020 samples/sec, ObjLoss=22.126, BoxCenterLoss=14.640, BoxScaleLoss=5.068, ClassLoss=8.838 [Epoch 106][Batch 899], LR: 1.00E-03, Speed: 142.298 samples/sec, ObjLoss=22.124, BoxCenterLoss=14.640, BoxScaleLoss=5.068, ClassLoss=8.837 [Epoch 106][Batch 999], LR: 1.00E-03, Speed: 123.336 samples/sec, ObjLoss=22.123, BoxCenterLoss=14.640, BoxScaleLoss=5.068, ClassLoss=8.836 [Epoch 106][Batch 1099], LR: 1.00E-03, Speed: 141.613 samples/sec, ObjLoss=22.122, BoxCenterLoss=14.640, BoxScaleLoss=5.068, ClassLoss=8.835 [Epoch 106][Batch 1199], LR: 1.00E-03, Speed: 134.179 samples/sec, ObjLoss=22.121, BoxCenterLoss=14.640, BoxScaleLoss=5.067, ClassLoss=8.834 [Epoch 106][Batch 1299], LR: 1.00E-03, Speed: 151.207 samples/sec, ObjLoss=22.119, BoxCenterLoss=14.640, BoxScaleLoss=5.067, ClassLoss=8.833 [Epoch 106][Batch 1399], LR: 1.00E-03, Speed: 129.608 samples/sec, ObjLoss=22.118, BoxCenterLoss=14.639, BoxScaleLoss=5.067, ClassLoss=8.832 [Epoch 106][Batch 1499], LR: 1.00E-03, Speed: 91.444 samples/sec, ObjLoss=22.117, BoxCenterLoss=14.639, BoxScaleLoss=5.067, ClassLoss=8.831 [Epoch 106][Batch 1599], LR: 1.00E-03, Speed: 150.330 samples/sec, ObjLoss=22.116, BoxCenterLoss=14.639, BoxScaleLoss=5.067, ClassLoss=8.830 [Epoch 106][Batch 1699], LR: 1.00E-03, Speed: 147.809 samples/sec, ObjLoss=22.115, BoxCenterLoss=14.639, BoxScaleLoss=5.067, ClassLoss=8.829 [Epoch 106][Batch 1799], LR: 1.00E-03, Speed: 145.741 samples/sec, ObjLoss=22.114, BoxCenterLoss=14.639, BoxScaleLoss=5.066, ClassLoss=8.828 [Epoch 106] Training cost: 1240.280, ObjLoss=22.114, BoxCenterLoss=14.639, BoxScaleLoss=5.066, ClassLoss=8.828 [Epoch 106] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.193 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.084 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.187 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.293 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 ] = 0.272 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 ] = 0.130 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 ] = 0.409 person=29.8 bicycle=13.5 car=19.4 motorcycle=22.6 airplane=40.8 bus=40.8 train=42.3 truck=18.1 boat=10.7 traffic light=8.7 fire hydrant=35.2 stop sign=35.3 parking meter=21.9 bench=11.4 bird=13.7 cat=38.6 dog=30.2 horse=27.4 sheep=18.5 cow=26.4 elephant=36.2 bear=33.7 zebra=38.7 giraffe=37.5 backpack=3.1 umbrella=17.5 handbag=2.7 tie=10.7 suitcase=15.0 frisbee=28.3 skis=7.2 snowboard=13.1 sports ball=14.7 kite=19.4 baseball bat=11.9 baseball glove=13.7 skateboard=24.9 surfboard=15.4 tennis racket=22.0 bottle=14.2 wine glass=10.8 cup=18.9 fork=8.6 knife=3.9 spoon=2.4 bowl=18.5 banana=10.0 apple=6.3 sandwich=17.9 orange=13.0 broccoli=9.1 carrot=8.3 hot dog=14.4 pizza=29.1 donut=22.0 cake=17.3 chair=11.7 couch=26.3 potted plant=9.3 bed=27.1 dining table=15.4 toilet=31.7 tv=34.0 laptop=33.3 mouse=28.8 remote=7.4 keyboard=24.4 cell phone=13.4 microwave=26.6 oven=19.1 toaster=0.0 sink=16.1 refrigerator=26.9 book=6.1 clock=31.3 vase=15.9 scissors=15.0 teddy bear=23.2 hair drier=0.0 toothbrush=3.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.3 [Epoch 107][Batch 99], LR: 1.00E-03, Speed: 147.210 samples/sec, ObjLoss=22.113, BoxCenterLoss=14.639, BoxScaleLoss=5.066, ClassLoss=8.827 [Epoch 107][Batch 199], LR: 1.00E-03, Speed: 123.923 samples/sec, ObjLoss=22.112, BoxCenterLoss=14.639, BoxScaleLoss=5.066, ClassLoss=8.826 [Epoch 107][Batch 299], LR: 1.00E-03, Speed: 127.452 samples/sec, ObjLoss=22.111, BoxCenterLoss=14.639, BoxScaleLoss=5.066, ClassLoss=8.825 [Epoch 107][Batch 399], LR: 1.00E-03, Speed: 142.808 samples/sec, ObjLoss=22.110, BoxCenterLoss=14.639, BoxScaleLoss=5.065, ClassLoss=8.824 [Epoch 107][Batch 499], LR: 1.00E-03, Speed: 161.643 samples/sec, ObjLoss=22.108, BoxCenterLoss=14.639, BoxScaleLoss=5.065, ClassLoss=8.823 [Epoch 107][Batch 599], LR: 1.00E-03, Speed: 129.127 samples/sec, ObjLoss=22.108, BoxCenterLoss=14.639, BoxScaleLoss=5.065, ClassLoss=8.822 [Epoch 107][Batch 699], LR: 1.00E-03, Speed: 155.471 samples/sec, ObjLoss=22.107, BoxCenterLoss=14.639, BoxScaleLoss=5.065, ClassLoss=8.821 [Epoch 107][Batch 799], LR: 1.00E-03, Speed: 130.092 samples/sec, ObjLoss=22.106, BoxCenterLoss=14.638, BoxScaleLoss=5.064, ClassLoss=8.820 [Epoch 107][Batch 899], LR: 1.00E-03, Speed: 104.298 samples/sec, ObjLoss=22.105, BoxCenterLoss=14.638, BoxScaleLoss=5.064, ClassLoss=8.819 [Epoch 107][Batch 999], LR: 1.00E-03, Speed: 112.668 samples/sec, ObjLoss=22.104, BoxCenterLoss=14.639, BoxScaleLoss=5.064, ClassLoss=8.818 [Epoch 107][Batch 1099], LR: 1.00E-03, Speed: 137.590 samples/sec, ObjLoss=22.103, BoxCenterLoss=14.638, BoxScaleLoss=5.064, ClassLoss=8.817 [Epoch 107][Batch 1199], LR: 1.00E-03, Speed: 121.319 samples/sec, ObjLoss=22.101, BoxCenterLoss=14.638, BoxScaleLoss=5.064, ClassLoss=8.816 [Epoch 107][Batch 1299], LR: 1.00E-03, Speed: 134.303 samples/sec, ObjLoss=22.100, BoxCenterLoss=14.638, BoxScaleLoss=5.063, ClassLoss=8.815 [Epoch 107][Batch 1399], LR: 1.00E-03, Speed: 142.071 samples/sec, ObjLoss=22.099, BoxCenterLoss=14.638, BoxScaleLoss=5.063, ClassLoss=8.814 [Epoch 107][Batch 1499], LR: 1.00E-03, Speed: 95.267 samples/sec, ObjLoss=22.098, BoxCenterLoss=14.638, BoxScaleLoss=5.063, ClassLoss=8.813 [Epoch 107][Batch 1599], LR: 1.00E-03, Speed: 79.288 samples/sec, ObjLoss=22.097, BoxCenterLoss=14.638, BoxScaleLoss=5.063, ClassLoss=8.812 [Epoch 107][Batch 1699], LR: 1.00E-03, Speed: 153.672 samples/sec, ObjLoss=22.095, BoxCenterLoss=14.638, BoxScaleLoss=5.063, ClassLoss=8.811 [Epoch 107][Batch 1799], LR: 1.00E-03, Speed: 160.256 samples/sec, ObjLoss=22.094, BoxCenterLoss=14.638, BoxScaleLoss=5.063, ClassLoss=8.810 [Epoch 107] Training cost: 1286.884, ObjLoss=22.094, BoxCenterLoss=14.638, BoxScaleLoss=5.062, ClassLoss=8.810 [Epoch 107] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.189 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.178 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 ] = 0.183 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 ] = 0.272 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 ] = 0.396 person=28.9 bicycle=14.1 car=21.3 motorcycle=21.7 airplane=38.0 bus=41.8 train=42.7 truck=16.4 boat=11.4 traffic light=10.6 fire hydrant=33.2 stop sign=34.1 parking meter=16.3 bench=10.3 bird=15.7 cat=42.2 dog=37.1 horse=29.0 sheep=20.7 cow=25.4 elephant=36.2 bear=38.4 zebra=33.5 giraffe=36.7 backpack=3.6 umbrella=18.9 handbag=2.1 tie=12.4 suitcase=13.1 frisbee=32.1 skis=7.5 snowboard=12.2 sports ball=17.5 kite=20.9 baseball bat=10.8 baseball glove=13.2 skateboard=21.7 surfboard=14.6 tennis racket=17.8 bottle=11.7 wine glass=10.7 cup=15.5 fork=7.4 knife=3.0 spoon=2.6 bowl=18.6 banana=9.2 apple=6.8 sandwich=16.8 orange=14.8 broccoli=7.7 carrot=7.3 hot dog=14.3 pizza=27.0 donut=20.2 cake=16.4 chair=10.0 couch=21.3 potted plant=8.0 bed=24.1 dining table=11.4 toilet=32.9 tv=34.5 laptop=35.0 mouse=30.0 remote=6.2 keyboard=25.5 cell phone=14.0 microwave=26.8 oven=17.8 toaster=0.0 sink=19.6 refrigerator=23.2 book=4.4 clock=27.8 vase=16.0 scissors=11.4 teddy bear=22.3 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.9 [Epoch 108][Batch 99], LR: 1.00E-03, Speed: 143.034 samples/sec, ObjLoss=22.093, BoxCenterLoss=14.637, BoxScaleLoss=5.062, ClassLoss=8.809 [Epoch 108][Batch 199], LR: 1.00E-03, Speed: 108.134 samples/sec, ObjLoss=22.091, BoxCenterLoss=14.637, BoxScaleLoss=5.062, ClassLoss=8.808 [Epoch 108][Batch 299], LR: 1.00E-03, Speed: 123.633 samples/sec, ObjLoss=22.090, BoxCenterLoss=14.637, BoxScaleLoss=5.062, ClassLoss=8.807 [Epoch 108][Batch 399], LR: 1.00E-03, Speed: 82.342 samples/sec, ObjLoss=22.089, BoxCenterLoss=14.637, BoxScaleLoss=5.062, ClassLoss=8.806 [Epoch 108][Batch 499], LR: 1.00E-03, Speed: 113.845 samples/sec, ObjLoss=22.088, BoxCenterLoss=14.637, BoxScaleLoss=5.061, ClassLoss=8.805 [Epoch 108][Batch 599], LR: 1.00E-03, Speed: 88.976 samples/sec, ObjLoss=22.087, BoxCenterLoss=14.637, BoxScaleLoss=5.061, ClassLoss=8.804 [Epoch 108][Batch 699], LR: 1.00E-03, Speed: 96.575 samples/sec, ObjLoss=22.086, BoxCenterLoss=14.637, BoxScaleLoss=5.061, ClassLoss=8.803 [Epoch 108][Batch 799], LR: 1.00E-03, Speed: 155.875 samples/sec, ObjLoss=22.085, BoxCenterLoss=14.637, BoxScaleLoss=5.061, ClassLoss=8.802 [Epoch 108][Batch 899], LR: 1.00E-03, Speed: 96.622 samples/sec, ObjLoss=22.084, BoxCenterLoss=14.637, BoxScaleLoss=5.061, ClassLoss=8.801 [Epoch 108][Batch 999], LR: 1.00E-03, Speed: 157.992 samples/sec, ObjLoss=22.083, BoxCenterLoss=14.637, BoxScaleLoss=5.060, ClassLoss=8.800 [Epoch 108][Batch 1099], LR: 1.00E-03, Speed: 78.265 samples/sec, ObjLoss=22.082, BoxCenterLoss=14.637, BoxScaleLoss=5.060, ClassLoss=8.799 [Epoch 108][Batch 1199], LR: 1.00E-03, Speed: 164.299 samples/sec, ObjLoss=22.080, BoxCenterLoss=14.636, BoxScaleLoss=5.060, ClassLoss=8.798 [Epoch 108][Batch 1299], LR: 1.00E-03, Speed: 123.174 samples/sec, ObjLoss=22.079, BoxCenterLoss=14.636, BoxScaleLoss=5.060, ClassLoss=8.797 [Epoch 108][Batch 1399], LR: 1.00E-03, Speed: 121.254 samples/sec, ObjLoss=22.078, BoxCenterLoss=14.636, BoxScaleLoss=5.059, ClassLoss=8.796 [Epoch 108][Batch 1499], LR: 1.00E-03, Speed: 94.750 samples/sec, ObjLoss=22.077, BoxCenterLoss=14.636, BoxScaleLoss=5.059, ClassLoss=8.795 [Epoch 108][Batch 1599], LR: 1.00E-03, Speed: 108.764 samples/sec, ObjLoss=22.076, BoxCenterLoss=14.636, BoxScaleLoss=5.059, ClassLoss=8.794 [Epoch 108][Batch 1699], LR: 1.00E-03, Speed: 171.573 samples/sec, ObjLoss=22.075, BoxCenterLoss=14.636, BoxScaleLoss=5.059, ClassLoss=8.793 [Epoch 108][Batch 1799], LR: 1.00E-03, Speed: 96.296 samples/sec, ObjLoss=22.074, BoxCenterLoss=14.636, BoxScaleLoss=5.059, ClassLoss=8.792 [Epoch 108] Training cost: 1221.665, ObjLoss=22.074, BoxCenterLoss=14.636, BoxScaleLoss=5.059, ClassLoss=8.792 [Epoch 108] 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 ] = 0.413 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.075 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 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.191 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 ] = 0.293 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 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 person=31.1 bicycle=14.4 car=20.8 motorcycle=25.9 airplane=39.3 bus=38.7 train=43.1 truck=18.2 boat=11.2 traffic light=12.3 fire hydrant=38.7 stop sign=37.0 parking meter=22.9 bench=11.4 bird=15.0 cat=40.7 dog=34.6 horse=31.3 sheep=22.3 cow=27.2 elephant=41.0 bear=41.2 zebra=39.0 giraffe=41.3 backpack=4.9 umbrella=17.0 handbag=3.2 tie=12.0 suitcase=17.2 frisbee=30.2 skis=8.3 snowboard=14.4 sports ball=18.2 kite=21.2 baseball bat=10.5 baseball glove=14.9 skateboard=21.5 surfboard=12.3 tennis racket=19.2 bottle=14.4 wine glass=14.8 cup=18.0 fork=8.6 knife=3.2 spoon=2.2 bowl=19.5 banana=11.1 apple=9.4 sandwich=17.7 orange=16.3 broccoli=10.6 carrot=8.4 hot dog=11.6 pizza=28.8 donut=20.9 cake=20.3 chair=11.8 couch=24.2 potted plant=11.0 bed=28.7 dining table=15.1 toilet=31.3 tv=35.2 laptop=32.3 mouse=28.6 remote=7.6 keyboard=27.1 cell phone=13.0 microwave=21.2 oven=18.2 toaster=0.0 sink=17.1 refrigerator=28.6 book=4.3 clock=29.6 vase=18.8 scissors=12.6 teddy bear=23.4 hair drier=0.0 toothbrush=2.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.0 [Epoch 109][Batch 99], LR: 1.00E-03, Speed: 151.294 samples/sec, ObjLoss=22.072, BoxCenterLoss=14.636, BoxScaleLoss=5.058, ClassLoss=8.791 [Epoch 109][Batch 199], LR: 1.00E-03, Speed: 141.150 samples/sec, ObjLoss=22.071, BoxCenterLoss=14.635, BoxScaleLoss=5.058, ClassLoss=8.790 [Epoch 109][Batch 299], LR: 1.00E-03, Speed: 101.791 samples/sec, ObjLoss=22.070, BoxCenterLoss=14.635, BoxScaleLoss=5.058, ClassLoss=8.789 [Epoch 109][Batch 399], LR: 1.00E-03, Speed: 80.173 samples/sec, ObjLoss=22.069, BoxCenterLoss=14.635, BoxScaleLoss=5.058, ClassLoss=8.788 [Epoch 109][Batch 499], LR: 1.00E-03, Speed: 90.580 samples/sec, ObjLoss=22.068, BoxCenterLoss=14.635, BoxScaleLoss=5.057, ClassLoss=8.787 [Epoch 109][Batch 599], LR: 1.00E-03, Speed: 151.393 samples/sec, ObjLoss=22.067, BoxCenterLoss=14.635, BoxScaleLoss=5.057, ClassLoss=8.786 [Epoch 109][Batch 699], LR: 1.00E-03, Speed: 128.978 samples/sec, ObjLoss=22.065, BoxCenterLoss=14.635, BoxScaleLoss=5.057, ClassLoss=8.785 [Epoch 109][Batch 799], LR: 1.00E-03, Speed: 113.828 samples/sec, ObjLoss=22.064, BoxCenterLoss=14.635, BoxScaleLoss=5.057, ClassLoss=8.784 [Epoch 109][Batch 899], LR: 1.00E-03, Speed: 140.099 samples/sec, ObjLoss=22.063, BoxCenterLoss=14.635, BoxScaleLoss=5.057, ClassLoss=8.783 [Epoch 109][Batch 999], LR: 1.00E-03, Speed: 95.267 samples/sec, ObjLoss=22.062, BoxCenterLoss=14.635, BoxScaleLoss=5.056, ClassLoss=8.782 [Epoch 109][Batch 1099], LR: 1.00E-03, Speed: 123.889 samples/sec, ObjLoss=22.061, BoxCenterLoss=14.635, BoxScaleLoss=5.056, ClassLoss=8.781 [Epoch 109][Batch 1199], LR: 1.00E-03, Speed: 154.314 samples/sec, ObjLoss=22.060, BoxCenterLoss=14.635, BoxScaleLoss=5.056, ClassLoss=8.780 [Epoch 109][Batch 1299], LR: 1.00E-03, Speed: 110.390 samples/sec, ObjLoss=22.059, BoxCenterLoss=14.634, BoxScaleLoss=5.056, ClassLoss=8.779 [Epoch 109][Batch 1399], LR: 1.00E-03, Speed: 83.907 samples/sec, ObjLoss=22.058, BoxCenterLoss=14.634, BoxScaleLoss=5.056, ClassLoss=8.778 [Epoch 109][Batch 1499], LR: 1.00E-03, Speed: 95.056 samples/sec, ObjLoss=22.057, BoxCenterLoss=14.634, BoxScaleLoss=5.056, ClassLoss=8.777 [Epoch 109][Batch 1599], LR: 1.00E-03, Speed: 79.650 samples/sec, ObjLoss=22.056, BoxCenterLoss=14.634, BoxScaleLoss=5.056, ClassLoss=8.777 [Epoch 109][Batch 1699], LR: 1.00E-03, Speed: 111.137 samples/sec, ObjLoss=22.055, BoxCenterLoss=14.634, BoxScaleLoss=5.055, ClassLoss=8.776 [Epoch 109][Batch 1799], LR: 1.00E-03, Speed: 143.559 samples/sec, ObjLoss=22.054, BoxCenterLoss=14.634, BoxScaleLoss=5.055, ClassLoss=8.775 [Epoch 109] Training cost: 1264.040, ObjLoss=22.053, BoxCenterLoss=14.634, BoxScaleLoss=5.055, ClassLoss=8.775 [Epoch 109] 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 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.169 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083 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 ] = 0.302 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 ] = 0.277 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 ] = 0.130 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.409 person=32.2 bicycle=14.4 car=18.0 motorcycle=23.2 airplane=37.2 bus=39.1 train=41.5 truck=16.6 boat=8.9 traffic light=7.4 fire hydrant=37.5 stop sign=40.1 parking meter=26.9 bench=8.8 bird=15.7 cat=40.9 dog=36.2 horse=28.3 sheep=26.4 cow=28.1 elephant=34.9 bear=41.1 zebra=41.3 giraffe=38.6 backpack=5.0 umbrella=17.6 handbag=3.2 tie=13.8 suitcase=13.2 frisbee=35.0 skis=7.0 snowboard=11.6 sports ball=17.6 kite=18.4 baseball bat=9.4 baseball glove=17.2 skateboard=19.0 surfboard=14.7 tennis racket=20.8 bottle=15.2 wine glass=13.8 cup=19.2 fork=9.0 knife=3.2 spoon=2.0 bowl=19.3 banana=11.3 apple=6.9 sandwich=11.7 orange=12.6 broccoli=11.3 carrot=7.5 hot dog=11.1 pizza=28.1 donut=19.6 cake=15.2 chair=11.8 couch=24.9 potted plant=11.3 bed=27.3 dining table=15.4 toilet=33.7 tv=34.7 laptop=30.3 mouse=32.9 remote=8.5 keyboard=26.0 cell phone=14.3 microwave=28.9 oven=19.1 toaster=0.0 sink=16.6 refrigerator=29.4 book=3.7 clock=32.5 vase=17.8 scissors=13.4 teddy bear=21.5 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.8 [Epoch 110][Batch 99], LR: 1.00E-03, Speed: 116.067 samples/sec, ObjLoss=22.052, BoxCenterLoss=14.634, BoxScaleLoss=5.055, ClassLoss=8.774 [Epoch 110][Batch 199], LR: 1.00E-03, Speed: 170.897 samples/sec, ObjLoss=22.051, BoxCenterLoss=14.634, BoxScaleLoss=5.055, ClassLoss=8.773 [Epoch 110][Batch 299], LR: 1.00E-03, Speed: 140.590 samples/sec, ObjLoss=22.050, BoxCenterLoss=14.634, BoxScaleLoss=5.055, ClassLoss=8.772 [Epoch 110][Batch 399], LR: 1.00E-03, Speed: 156.873 samples/sec, ObjLoss=22.049, BoxCenterLoss=14.634, BoxScaleLoss=5.054, ClassLoss=8.771 [Epoch 110][Batch 499], LR: 1.00E-03, Speed: 145.711 samples/sec, ObjLoss=22.048, BoxCenterLoss=14.634, BoxScaleLoss=5.054, ClassLoss=8.770 [Epoch 110][Batch 599], LR: 1.00E-03, Speed: 101.666 samples/sec, ObjLoss=22.047, BoxCenterLoss=14.634, BoxScaleLoss=5.054, ClassLoss=8.769 [Epoch 110][Batch 699], LR: 1.00E-03, Speed: 66.346 samples/sec, ObjLoss=22.046, BoxCenterLoss=14.634, BoxScaleLoss=5.054, ClassLoss=8.768 [Epoch 110][Batch 799], LR: 1.00E-03, Speed: 141.575 samples/sec, ObjLoss=22.045, BoxCenterLoss=14.634, BoxScaleLoss=5.054, ClassLoss=8.767 [Epoch 110][Batch 899], LR: 1.00E-03, Speed: 132.541 samples/sec, ObjLoss=22.044, BoxCenterLoss=14.634, BoxScaleLoss=5.053, ClassLoss=8.766 [Epoch 110][Batch 999], LR: 1.00E-03, Speed: 143.470 samples/sec, ObjLoss=22.043, BoxCenterLoss=14.633, BoxScaleLoss=5.053, ClassLoss=8.765 [Epoch 110][Batch 1099], LR: 1.00E-03, Speed: 86.395 samples/sec, ObjLoss=22.041, BoxCenterLoss=14.633, BoxScaleLoss=5.053, ClassLoss=8.764 [Epoch 110][Batch 1199], LR: 1.00E-03, Speed: 157.565 samples/sec, ObjLoss=22.040, BoxCenterLoss=14.633, BoxScaleLoss=5.053, ClassLoss=8.763 [Epoch 110][Batch 1299], LR: 1.00E-03, Speed: 148.721 samples/sec, ObjLoss=22.039, BoxCenterLoss=14.633, BoxScaleLoss=5.053, ClassLoss=8.762 [Epoch 110][Batch 1399], LR: 1.00E-03, Speed: 140.041 samples/sec, ObjLoss=22.038, BoxCenterLoss=14.633, BoxScaleLoss=5.052, ClassLoss=8.761 [Epoch 110][Batch 1499], LR: 1.00E-03, Speed: 75.572 samples/sec, ObjLoss=22.037, BoxCenterLoss=14.633, BoxScaleLoss=5.052, ClassLoss=8.760 [Epoch 110][Batch 1599], LR: 1.00E-03, Speed: 128.981 samples/sec, ObjLoss=22.035, BoxCenterLoss=14.633, BoxScaleLoss=5.052, ClassLoss=8.759 [Epoch 110][Batch 1699], LR: 1.00E-03, Speed: 152.902 samples/sec, ObjLoss=22.034, BoxCenterLoss=14.633, BoxScaleLoss=5.052, ClassLoss=8.758 [Epoch 110][Batch 1799], LR: 1.00E-03, Speed: 92.233 samples/sec, ObjLoss=22.033, BoxCenterLoss=14.632, BoxScaleLoss=5.052, ClassLoss=8.757 [Epoch 110] Training cost: 1217.024, ObjLoss=22.033, BoxCenterLoss=14.632, BoxScaleLoss=5.052, ClassLoss=8.757 [Epoch 110] 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 ] = 0.394 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.126 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 ] = 0.186 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 ] = 0.173 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.256 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.264 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.385 person=28.3 bicycle=14.6 car=19.4 motorcycle=19.6 airplane=38.6 bus=36.0 train=34.5 truck=16.5 boat=9.1 traffic light=6.4 fire hydrant=36.5 stop sign=31.1 parking meter=19.7 bench=10.2 bird=15.7 cat=32.7 dog=28.8 horse=25.1 sheep=23.6 cow=27.6 elephant=37.9 bear=29.2 zebra=40.3 giraffe=42.8 backpack=3.4 umbrella=19.2 handbag=3.1 tie=8.7 suitcase=14.1 frisbee=27.9 skis=5.4 snowboard=9.9 sports ball=13.4 kite=17.1 baseball bat=8.9 baseball glove=10.4 skateboard=18.3 surfboard=12.8 tennis racket=20.4 bottle=13.2 wine glass=12.9 cup=18.5 fork=7.0 knife=1.9 spoon=2.2 bowl=19.4 banana=10.7 apple=4.7 sandwich=13.9 orange=11.2 broccoli=9.2 carrot=6.1 hot dog=11.2 pizza=24.6 donut=16.9 cake=14.9 chair=10.5 couch=23.4 potted plant=9.7 bed=25.8 dining table=12.6 toilet=27.9 tv=30.6 laptop=28.1 mouse=23.3 remote=5.6 keyboard=22.0 cell phone=10.1 microwave=27.8 oven=14.5 toaster=0.0 sink=17.2 refrigerator=22.8 book=3.6 clock=23.9 vase=13.3 scissors=13.8 teddy bear=22.2 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =17.6 [Epoch 111][Batch 99], LR: 1.00E-03, Speed: 141.332 samples/sec, ObjLoss=22.032, BoxCenterLoss=14.632, BoxScaleLoss=5.051, ClassLoss=8.756 [Epoch 111][Batch 199], LR: 1.00E-03, Speed: 73.448 samples/sec, ObjLoss=22.030, BoxCenterLoss=14.632, BoxScaleLoss=5.051, ClassLoss=8.755 [Epoch 111][Batch 299], LR: 1.00E-03, Speed: 139.301 samples/sec, ObjLoss=22.029, BoxCenterLoss=14.632, BoxScaleLoss=5.051, ClassLoss=8.754 [Epoch 111][Batch 399], LR: 1.00E-03, Speed: 100.331 samples/sec, ObjLoss=22.028, BoxCenterLoss=14.631, BoxScaleLoss=5.050, ClassLoss=8.753 [Epoch 111][Batch 499], LR: 1.00E-03, Speed: 114.928 samples/sec, ObjLoss=22.027, BoxCenterLoss=14.631, BoxScaleLoss=5.050, ClassLoss=8.751 [Epoch 111][Batch 599], LR: 1.00E-03, Speed: 124.916 samples/sec, ObjLoss=22.026, BoxCenterLoss=14.631, BoxScaleLoss=5.050, ClassLoss=8.751 [Epoch 111][Batch 699], LR: 1.00E-03, Speed: 86.279 samples/sec, ObjLoss=22.024, BoxCenterLoss=14.631, BoxScaleLoss=5.050, ClassLoss=8.750 [Epoch 111][Batch 799], LR: 1.00E-03, Speed: 143.459 samples/sec, ObjLoss=22.023, BoxCenterLoss=14.631, BoxScaleLoss=5.050, ClassLoss=8.749 [Epoch 111][Batch 899], LR: 1.00E-03, Speed: 127.468 samples/sec, ObjLoss=22.022, BoxCenterLoss=14.631, BoxScaleLoss=5.049, ClassLoss=8.748 [Epoch 111][Batch 999], LR: 1.00E-03, Speed: 125.196 samples/sec, ObjLoss=22.021, BoxCenterLoss=14.631, BoxScaleLoss=5.049, ClassLoss=8.747 [Epoch 111][Batch 1099], LR: 1.00E-03, Speed: 122.447 samples/sec, ObjLoss=22.020, BoxCenterLoss=14.631, BoxScaleLoss=5.049, ClassLoss=8.746 [Epoch 111][Batch 1199], LR: 1.00E-03, Speed: 97.748 samples/sec, ObjLoss=22.019, BoxCenterLoss=14.630, BoxScaleLoss=5.049, ClassLoss=8.745 [Epoch 111][Batch 1299], LR: 1.00E-03, Speed: 148.627 samples/sec, ObjLoss=22.018, BoxCenterLoss=14.631, BoxScaleLoss=5.049, ClassLoss=8.745 [Epoch 111][Batch 1399], LR: 1.00E-03, Speed: 137.608 samples/sec, ObjLoss=22.017, BoxCenterLoss=14.630, BoxScaleLoss=5.049, ClassLoss=8.744 [Epoch 111][Batch 1499], LR: 1.00E-03, Speed: 130.991 samples/sec, ObjLoss=22.016, BoxCenterLoss=14.630, BoxScaleLoss=5.048, ClassLoss=8.743 [Epoch 111][Batch 1599], LR: 1.00E-03, Speed: 73.057 samples/sec, ObjLoss=22.015, BoxCenterLoss=14.630, BoxScaleLoss=5.048, ClassLoss=8.742 [Epoch 111][Batch 1699], LR: 1.00E-03, Speed: 147.926 samples/sec, ObjLoss=22.014, BoxCenterLoss=14.630, BoxScaleLoss=5.048, ClassLoss=8.741 [Epoch 111][Batch 1799], LR: 1.00E-03, Speed: 107.113 samples/sec, ObjLoss=22.013, BoxCenterLoss=14.630, BoxScaleLoss=5.048, ClassLoss=8.740 [Epoch 111] Training cost: 1231.970, ObjLoss=22.013, BoxCenterLoss=14.630, BoxScaleLoss=5.048, ClassLoss=8.740 [Epoch 111] 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 ] = 0.408 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.144 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 ] = 0.197 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 ] = 0.182 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 ] = 0.121 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 ] = 0.392 person=31.2 bicycle=13.9 car=19.7 motorcycle=24.8 airplane=35.0 bus=38.3 train=40.9 truck=17.3 boat=10.5 traffic light=9.9 fire hydrant=36.2 stop sign=29.8 parking meter=28.1 bench=10.0 bird=12.9 cat=38.9 dog=32.9 horse=28.6 sheep=21.5 cow=25.9 elephant=37.6 bear=42.3 zebra=34.6 giraffe=44.0 backpack=4.4 umbrella=17.8 handbag=3.6 tie=13.8 suitcase=15.1 frisbee=31.2 skis=5.1 snowboard=11.1 sports ball=19.7 kite=19.4 baseball bat=10.0 baseball glove=15.3 skateboard=16.8 surfboard=12.1 tennis racket=20.0 bottle=15.1 wine glass=14.3 cup=18.7 fork=8.6 knife=3.5 spoon=2.2 bowl=15.4 banana=11.6 apple=7.5 sandwich=14.5 orange=8.8 broccoli=8.7 carrot=7.8 hot dog=16.2 pizza=19.8 donut=18.5 cake=13.9 chair=11.4 couch=24.0 potted plant=10.3 bed=25.3 dining table=12.2 toilet=32.7 tv=29.6 laptop=27.6 mouse=25.2 remote=9.7 keyboard=18.6 cell phone=14.4 microwave=19.1 oven=18.4 toaster=0.0 sink=17.2 refrigerator=23.9 book=4.3 clock=25.9 vase=16.6 scissors=14.1 teddy bear=25.4 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.7 [Epoch 112][Batch 99], LR: 1.00E-03, Speed: 156.328 samples/sec, ObjLoss=22.012, BoxCenterLoss=14.630, BoxScaleLoss=5.048, ClassLoss=8.739 [Epoch 112][Batch 199], LR: 1.00E-03, Speed: 164.733 samples/sec, ObjLoss=22.011, BoxCenterLoss=14.630, BoxScaleLoss=5.047, ClassLoss=8.738 [Epoch 112][Batch 299], LR: 1.00E-03, Speed: 150.978 samples/sec, ObjLoss=22.010, BoxCenterLoss=14.630, BoxScaleLoss=5.047, ClassLoss=8.737 [Epoch 112][Batch 399], LR: 1.00E-03, Speed: 74.442 samples/sec, ObjLoss=22.009, BoxCenterLoss=14.630, BoxScaleLoss=5.047, ClassLoss=8.736 [Epoch 112][Batch 499], LR: 1.00E-03, Speed: 80.974 samples/sec, ObjLoss=22.008, BoxCenterLoss=14.630, BoxScaleLoss=5.047, ClassLoss=8.735 [Epoch 112][Batch 599], LR: 1.00E-03, Speed: 153.250 samples/sec, ObjLoss=22.007, BoxCenterLoss=14.630, BoxScaleLoss=5.047, ClassLoss=8.734 [Epoch 112][Batch 699], LR: 1.00E-03, Speed: 117.815 samples/sec, ObjLoss=22.006, BoxCenterLoss=14.630, BoxScaleLoss=5.047, ClassLoss=8.733 [Epoch 112][Batch 799], LR: 1.00E-03, Speed: 133.950 samples/sec, ObjLoss=22.005, BoxCenterLoss=14.630, BoxScaleLoss=5.046, ClassLoss=8.732 [Epoch 112][Batch 899], LR: 1.00E-03, Speed: 138.302 samples/sec, ObjLoss=22.004, BoxCenterLoss=14.630, BoxScaleLoss=5.046, ClassLoss=8.731 [Epoch 112][Batch 999], LR: 1.00E-03, Speed: 144.694 samples/sec, ObjLoss=22.003, BoxCenterLoss=14.630, BoxScaleLoss=5.046, ClassLoss=8.731 [Epoch 112][Batch 1099], LR: 1.00E-03, Speed: 75.876 samples/sec, ObjLoss=22.002, BoxCenterLoss=14.630, BoxScaleLoss=5.046, ClassLoss=8.730 [Epoch 112][Batch 1199], LR: 1.00E-03, Speed: 136.010 samples/sec, ObjLoss=22.001, BoxCenterLoss=14.630, BoxScaleLoss=5.046, ClassLoss=8.729 [Epoch 112][Batch 1299], LR: 1.00E-03, Speed: 153.569 samples/sec, ObjLoss=22.000, BoxCenterLoss=14.630, BoxScaleLoss=5.045, ClassLoss=8.728 [Epoch 112][Batch 1399], LR: 1.00E-03, Speed: 77.828 samples/sec, ObjLoss=21.999, BoxCenterLoss=14.629, BoxScaleLoss=5.045, ClassLoss=8.727 [Epoch 112][Batch 1499], LR: 1.00E-03, Speed: 129.360 samples/sec, ObjLoss=21.998, BoxCenterLoss=14.629, BoxScaleLoss=5.045, ClassLoss=8.726 [Epoch 112][Batch 1599], LR: 1.00E-03, Speed: 163.387 samples/sec, ObjLoss=21.997, BoxCenterLoss=14.629, BoxScaleLoss=5.045, ClassLoss=8.725 [Epoch 112][Batch 1699], LR: 1.00E-03, Speed: 101.757 samples/sec, ObjLoss=21.995, BoxCenterLoss=14.629, BoxScaleLoss=5.044, ClassLoss=8.724 [Epoch 112][Batch 1799], LR: 1.00E-03, Speed: 144.064 samples/sec, ObjLoss=21.994, BoxCenterLoss=14.629, BoxScaleLoss=5.044, ClassLoss=8.723 [Epoch 112] Training cost: 1250.713, ObjLoss=21.994, BoxCenterLoss=14.629, BoxScaleLoss=5.044, ClassLoss=8.723 [Epoch 112] 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 ] = 0.414 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.174 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 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 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 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124 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 ] = 0.416 person=31.9 bicycle=13.9 car=20.6 motorcycle=25.1 airplane=38.1 bus=41.6 train=41.2 truck=16.8 boat=11.4 traffic light=10.1 fire hydrant=38.9 stop sign=32.5 parking meter=25.5 bench=10.8 bird=16.1 cat=38.3 dog=38.2 horse=33.1 sheep=26.1 cow=29.2 elephant=41.0 bear=41.0 zebra=37.8 giraffe=39.6 backpack=5.1 umbrella=19.7 handbag=4.2 tie=13.5 suitcase=16.1 frisbee=33.7 skis=8.5 snowboard=11.5 sports ball=22.7 kite=20.7 baseball bat=8.5 baseball glove=11.6 skateboard=22.0 surfboard=17.8 tennis racket=21.6 bottle=14.7 wine glass=14.5 cup=19.9 fork=8.7 knife=4.6 spoon=2.2 bowl=18.9 banana=10.1 apple=6.6 sandwich=19.3 orange=16.3 broccoli=10.5 carrot=9.7 hot dog=17.0 pizza=25.8 donut=23.5 cake=17.4 chair=12.6 couch=27.0 potted plant=8.9 bed=26.5 dining table=17.8 toilet=34.1 tv=33.8 laptop=30.2 mouse=31.1 remote=9.1 keyboard=20.2 cell phone=15.3 microwave=17.3 oven=17.0 toaster=0.0 sink=18.8 refrigerator=24.0 book=4.5 clock=28.7 vase=17.8 scissors=14.8 teddy bear=25.7 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.2 [Epoch 113][Batch 99], LR: 1.00E-03, Speed: 133.539 samples/sec, ObjLoss=21.993, BoxCenterLoss=14.629, BoxScaleLoss=5.044, ClassLoss=8.722 [Epoch 113][Batch 199], LR: 1.00E-03, Speed: 134.712 samples/sec, ObjLoss=21.992, BoxCenterLoss=14.629, BoxScaleLoss=5.044, ClassLoss=8.721 [Epoch 113][Batch 299], LR: 1.00E-03, Speed: 93.578 samples/sec, ObjLoss=21.991, BoxCenterLoss=14.629, BoxScaleLoss=5.044, ClassLoss=8.720 [Epoch 113][Batch 399], LR: 1.00E-03, Speed: 141.797 samples/sec, ObjLoss=21.990, BoxCenterLoss=14.629, BoxScaleLoss=5.043, ClassLoss=8.719 [Epoch 113][Batch 499], LR: 1.00E-03, Speed: 97.965 samples/sec, ObjLoss=21.989, BoxCenterLoss=14.629, BoxScaleLoss=5.043, ClassLoss=8.718 [Epoch 113][Batch 599], LR: 1.00E-03, Speed: 151.665 samples/sec, ObjLoss=21.988, BoxCenterLoss=14.629, BoxScaleLoss=5.043, ClassLoss=8.717 [Epoch 113][Batch 699], LR: 1.00E-03, Speed: 139.213 samples/sec, ObjLoss=21.987, BoxCenterLoss=14.629, BoxScaleLoss=5.043, ClassLoss=8.716 [Epoch 113][Batch 799], LR: 1.00E-03, Speed: 138.438 samples/sec, ObjLoss=21.986, BoxCenterLoss=14.628, BoxScaleLoss=5.043, ClassLoss=8.715 [Epoch 113][Batch 899], LR: 1.00E-03, Speed: 108.973 samples/sec, ObjLoss=21.985, BoxCenterLoss=14.628, BoxScaleLoss=5.042, ClassLoss=8.714 [Epoch 113][Batch 999], LR: 1.00E-03, Speed: 125.864 samples/sec, ObjLoss=21.983, BoxCenterLoss=14.628, BoxScaleLoss=5.042, ClassLoss=8.713 [Epoch 113][Batch 1099], LR: 1.00E-03, Speed: 75.160 samples/sec, ObjLoss=21.982, BoxCenterLoss=14.628, BoxScaleLoss=5.042, ClassLoss=8.713 [Epoch 113][Batch 1199], LR: 1.00E-03, Speed: 143.455 samples/sec, ObjLoss=21.981, BoxCenterLoss=14.628, BoxScaleLoss=5.042, ClassLoss=8.712 [Epoch 113][Batch 1299], LR: 1.00E-03, Speed: 86.308 samples/sec, ObjLoss=21.980, BoxCenterLoss=14.628, BoxScaleLoss=5.042, ClassLoss=8.711 [Epoch 113][Batch 1399], LR: 1.00E-03, Speed: 123.907 samples/sec, ObjLoss=21.980, BoxCenterLoss=14.628, BoxScaleLoss=5.042, ClassLoss=8.710 [Epoch 113][Batch 1499], LR: 1.00E-03, Speed: 81.004 samples/sec, ObjLoss=21.978, BoxCenterLoss=14.628, BoxScaleLoss=5.041, ClassLoss=8.709 [Epoch 113][Batch 1599], LR: 1.00E-03, Speed: 190.015 samples/sec, ObjLoss=21.977, BoxCenterLoss=14.628, BoxScaleLoss=5.041, ClassLoss=8.708 [Epoch 113][Batch 1699], LR: 1.00E-03, Speed: 161.962 samples/sec, ObjLoss=21.976, BoxCenterLoss=14.628, BoxScaleLoss=5.041, ClassLoss=8.707 [Epoch 113][Batch 1799], LR: 1.00E-03, Speed: 174.886 samples/sec, ObjLoss=21.975, BoxCenterLoss=14.627, BoxScaleLoss=5.041, ClassLoss=8.706 [Epoch 113] Training cost: 1255.030, ObjLoss=21.974, BoxCenterLoss=14.627, BoxScaleLoss=5.041, ClassLoss=8.706 [Epoch 113] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.409 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076 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 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 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 ] = 0.287 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 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 person=33.0 bicycle=13.8 car=21.0 motorcycle=26.7 airplane=41.8 bus=41.6 train=42.4 truck=16.8 boat=9.1 traffic light=12.2 fire hydrant=43.1 stop sign=35.4 parking meter=24.9 bench=11.0 bird=18.0 cat=42.5 dog=39.2 horse=33.6 sheep=29.0 cow=28.8 elephant=38.9 bear=51.2 zebra=38.5 giraffe=43.3 backpack=4.4 umbrella=17.9 handbag=3.1 tie=11.6 suitcase=14.7 frisbee=31.9 skis=8.1 snowboard=11.4 sports ball=21.4 kite=22.1 baseball bat=10.2 baseball glove=18.1 skateboard=21.6 surfboard=14.1 tennis racket=20.3 bottle=15.2 wine glass=14.1 cup=18.5 fork=10.3 knife=3.1 spoon=1.7 bowl=18.4 banana=9.4 apple=7.7 sandwich=20.7 orange=12.4 broccoli=10.6 carrot=8.6 hot dog=12.0 pizza=30.5 donut=17.4 cake=15.5 chair=13.0 couch=27.3 potted plant=11.1 bed=28.0 dining table=14.0 toilet=34.1 tv=37.1 laptop=34.3 mouse=34.8 remote=7.7 keyboard=17.5 cell phone=16.1 microwave=19.5 oven=18.5 toaster=0.0 sink=13.7 refrigerator=26.2 book=4.1 clock=26.6 vase=18.7 scissors=16.3 teddy bear=22.4 hair drier=0.0 toothbrush=3.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 114][Batch 99], LR: 1.00E-03, Speed: 133.694 samples/sec, ObjLoss=21.973, BoxCenterLoss=14.627, BoxScaleLoss=5.040, ClassLoss=8.705 [Epoch 114][Batch 199], LR: 1.00E-03, Speed: 124.512 samples/sec, ObjLoss=21.972, BoxCenterLoss=14.627, BoxScaleLoss=5.040, ClassLoss=8.704 [Epoch 114][Batch 299], LR: 1.00E-03, Speed: 119.068 samples/sec, ObjLoss=21.971, BoxCenterLoss=14.627, BoxScaleLoss=5.040, ClassLoss=8.703 [Epoch 114][Batch 399], LR: 1.00E-03, Speed: 140.289 samples/sec, ObjLoss=21.970, BoxCenterLoss=14.627, BoxScaleLoss=5.040, ClassLoss=8.702 [Epoch 114][Batch 499], LR: 1.00E-03, Speed: 149.832 samples/sec, ObjLoss=21.969, BoxCenterLoss=14.626, BoxScaleLoss=5.039, ClassLoss=8.701 [Epoch 114][Batch 599], LR: 1.00E-03, Speed: 120.809 samples/sec, ObjLoss=21.968, BoxCenterLoss=14.626, BoxScaleLoss=5.039, ClassLoss=8.700 [Epoch 114][Batch 699], LR: 1.00E-03, Speed: 60.227 samples/sec, ObjLoss=21.967, BoxCenterLoss=14.626, BoxScaleLoss=5.039, ClassLoss=8.699 [Epoch 114][Batch 799], LR: 1.00E-03, Speed: 144.741 samples/sec, ObjLoss=21.965, BoxCenterLoss=14.626, BoxScaleLoss=5.039, ClassLoss=8.698 [Epoch 114][Batch 899], LR: 1.00E-03, Speed: 115.901 samples/sec, ObjLoss=21.965, BoxCenterLoss=14.626, BoxScaleLoss=5.039, ClassLoss=8.697 [Epoch 114][Batch 999], LR: 1.00E-03, Speed: 148.787 samples/sec, ObjLoss=21.964, BoxCenterLoss=14.626, BoxScaleLoss=5.038, ClassLoss=8.696 [Epoch 114][Batch 1099], LR: 1.00E-03, Speed: 108.958 samples/sec, ObjLoss=21.963, BoxCenterLoss=14.626, BoxScaleLoss=5.038, ClassLoss=8.696 [Epoch 114][Batch 1199], LR: 1.00E-03, Speed: 160.771 samples/sec, ObjLoss=21.962, BoxCenterLoss=14.626, BoxScaleLoss=5.038, ClassLoss=8.695 [Epoch 114][Batch 1299], LR: 1.00E-03, Speed: 114.828 samples/sec, ObjLoss=21.961, BoxCenterLoss=14.626, BoxScaleLoss=5.038, ClassLoss=8.694 [Epoch 114][Batch 1399], LR: 1.00E-03, Speed: 122.643 samples/sec, ObjLoss=21.960, BoxCenterLoss=14.626, BoxScaleLoss=5.038, ClassLoss=8.693 [Epoch 114][Batch 1499], LR: 1.00E-03, Speed: 145.144 samples/sec, ObjLoss=21.959, BoxCenterLoss=14.626, BoxScaleLoss=5.037, ClassLoss=8.692 [Epoch 114][Batch 1599], LR: 1.00E-03, Speed: 147.938 samples/sec, ObjLoss=21.958, BoxCenterLoss=14.626, BoxScaleLoss=5.037, ClassLoss=8.691 [Epoch 114][Batch 1699], LR: 1.00E-03, Speed: 123.409 samples/sec, ObjLoss=21.957, BoxCenterLoss=14.625, BoxScaleLoss=5.037, ClassLoss=8.690 [Epoch 114][Batch 1799], LR: 1.00E-03, Speed: 186.022 samples/sec, ObjLoss=21.956, BoxCenterLoss=14.625, BoxScaleLoss=5.037, ClassLoss=8.689 [Epoch 114] Training cost: 1261.281, ObjLoss=21.955, BoxCenterLoss=14.625, BoxScaleLoss=5.037, ClassLoss=8.689 [Epoch 114] 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 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.079 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 ] = 0.306 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 ] = 0.282 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 ] = 0.124 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418 person=32.7 bicycle=13.3 car=20.6 motorcycle=23.5 airplane=37.2 bus=42.3 train=43.0 truck=17.4 boat=10.1 traffic light=9.5 fire hydrant=41.0 stop sign=38.6 parking meter=25.2 bench=10.4 bird=18.1 cat=41.3 dog=35.2 horse=31.9 sheep=29.0 cow=30.1 elephant=41.0 bear=38.6 zebra=40.3 giraffe=39.8 backpack=5.0 umbrella=18.3 handbag=3.0 tie=11.6 suitcase=16.3 frisbee=32.0 skis=8.5 snowboard=14.2 sports ball=16.4 kite=23.3 baseball bat=9.5 baseball glove=16.5 skateboard=21.3 surfboard=15.7 tennis racket=22.6 bottle=14.8 wine glass=13.3 cup=17.5 fork=7.8 knife=2.9 spoon=2.2 bowl=18.1 banana=9.7 apple=5.5 sandwich=16.6 orange=14.2 broccoli=10.8 carrot=7.6 hot dog=7.2 pizza=26.7 donut=17.8 cake=15.6 chair=12.1 couch=25.4 potted plant=11.4 bed=26.8 dining table=17.8 toilet=35.1 tv=35.3 laptop=32.0 mouse=27.4 remote=8.2 keyboard=28.1 cell phone=14.7 microwave=28.2 oven=18.8 toaster=0.0 sink=17.9 refrigerator=28.5 book=3.7 clock=27.9 vase=20.1 scissors=16.2 teddy bear=22.5 hair drier=0.0 toothbrush=1.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.2 [Epoch 115][Batch 99], LR: 1.00E-03, Speed: 160.439 samples/sec, ObjLoss=21.954, BoxCenterLoss=14.625, BoxScaleLoss=5.037, ClassLoss=8.688 [Epoch 115][Batch 199], LR: 1.00E-03, Speed: 138.001 samples/sec, ObjLoss=21.953, BoxCenterLoss=14.625, BoxScaleLoss=5.036, ClassLoss=8.687 [Epoch 115][Batch 299], LR: 1.00E-03, Speed: 74.726 samples/sec, ObjLoss=21.952, BoxCenterLoss=14.625, BoxScaleLoss=5.036, ClassLoss=8.686 [Epoch 115][Batch 399], LR: 1.00E-03, Speed: 114.864 samples/sec, ObjLoss=21.951, BoxCenterLoss=14.625, BoxScaleLoss=5.036, ClassLoss=8.685 [Epoch 115][Batch 499], LR: 1.00E-03, Speed: 114.222 samples/sec, ObjLoss=21.950, BoxCenterLoss=14.624, BoxScaleLoss=5.036, ClassLoss=8.684 [Epoch 115][Batch 599], LR: 1.00E-03, Speed: 123.733 samples/sec, ObjLoss=21.949, BoxCenterLoss=14.624, BoxScaleLoss=5.035, ClassLoss=8.683 [Epoch 115][Batch 699], LR: 1.00E-03, Speed: 97.619 samples/sec, ObjLoss=21.948, BoxCenterLoss=14.624, BoxScaleLoss=5.035, ClassLoss=8.682 [Epoch 115][Batch 799], LR: 1.00E-03, Speed: 101.566 samples/sec, ObjLoss=21.947, BoxCenterLoss=14.624, BoxScaleLoss=5.035, ClassLoss=8.681 [Epoch 115][Batch 899], LR: 1.00E-03, Speed: 97.142 samples/sec, ObjLoss=21.946, BoxCenterLoss=14.624, BoxScaleLoss=5.035, ClassLoss=8.681 [Epoch 115][Batch 999], LR: 1.00E-03, Speed: 151.682 samples/sec, ObjLoss=21.945, BoxCenterLoss=14.624, BoxScaleLoss=5.035, ClassLoss=8.680 [Epoch 115][Batch 1099], LR: 1.00E-03, Speed: 134.292 samples/sec, ObjLoss=21.944, BoxCenterLoss=14.624, BoxScaleLoss=5.035, ClassLoss=8.679 [Epoch 115][Batch 1199], LR: 1.00E-03, Speed: 153.207 samples/sec, ObjLoss=21.943, BoxCenterLoss=14.624, BoxScaleLoss=5.034, ClassLoss=8.678 [Epoch 115][Batch 1299], LR: 1.00E-03, Speed: 121.853 samples/sec, ObjLoss=21.942, BoxCenterLoss=14.624, BoxScaleLoss=5.034, ClassLoss=8.677 [Epoch 115][Batch 1399], LR: 1.00E-03, Speed: 138.943 samples/sec, ObjLoss=21.941, BoxCenterLoss=14.624, BoxScaleLoss=5.034, ClassLoss=8.676 [Epoch 115][Batch 1499], LR: 1.00E-03, Speed: 122.278 samples/sec, ObjLoss=21.940, BoxCenterLoss=14.623, BoxScaleLoss=5.034, ClassLoss=8.675 [Epoch 115][Batch 1599], LR: 1.00E-03, Speed: 72.490 samples/sec, ObjLoss=21.939, BoxCenterLoss=14.623, BoxScaleLoss=5.034, ClassLoss=8.675 [Epoch 115][Batch 1699], LR: 1.00E-03, Speed: 131.137 samples/sec, ObjLoss=21.938, BoxCenterLoss=14.623, BoxScaleLoss=5.033, ClassLoss=8.674 [Epoch 115][Batch 1799], LR: 1.00E-03, Speed: 172.041 samples/sec, ObjLoss=21.936, BoxCenterLoss=14.623, BoxScaleLoss=5.033, ClassLoss=8.673 [Epoch 115] Training cost: 1218.743, ObjLoss=21.936, BoxCenterLoss=14.623, BoxScaleLoss=5.033, ClassLoss=8.672 [Epoch 115] 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 ] = 0.408 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.177 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.078 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.203 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 ] = 0.192 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 ] = 0.287 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 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420 person=32.1 bicycle=15.8 car=21.7 motorcycle=24.8 airplane=40.7 bus=43.4 train=43.7 truck=19.7 boat=10.7 traffic light=13.0 fire hydrant=38.7 stop sign=35.7 parking meter=24.6 bench=10.8 bird=17.0 cat=41.7 dog=36.4 horse=30.3 sheep=25.3 cow=26.5 elephant=38.5 bear=38.9 zebra=36.6 giraffe=41.0 backpack=5.1 umbrella=18.7 handbag=3.2 tie=11.6 suitcase=16.2 frisbee=30.4 skis=7.6 snowboard=8.7 sports ball=18.0 kite=17.1 baseball bat=10.2 baseball glove=19.9 skateboard=21.0 surfboard=15.8 tennis racket=22.2 bottle=16.1 wine glass=15.2 cup=18.8 fork=8.5 knife=2.6 spoon=2.6 bowl=19.0 banana=10.6 apple=7.1 sandwich=17.9 orange=12.7 broccoli=8.7 carrot=7.7 hot dog=14.3 pizza=27.3 donut=15.7 cake=14.0 chair=10.9 couch=25.5 potted plant=9.3 bed=32.0 dining table=18.4 toilet=33.7 tv=32.7 laptop=33.1 mouse=36.8 remote=8.0 keyboard=24.3 cell phone=14.1 microwave=30.4 oven=18.7 toaster=0.0 sink=19.2 refrigerator=27.5 book=5.2 clock=28.4 vase=17.5 scissors=12.6 teddy bear=22.6 hair drier=0.0 toothbrush=2.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.2 [Epoch 116][Batch 99], LR: 1.00E-03, Speed: 170.193 samples/sec, ObjLoss=21.935, BoxCenterLoss=14.623, BoxScaleLoss=5.033, ClassLoss=8.671 [Epoch 116][Batch 199], LR: 1.00E-03, Speed: 160.496 samples/sec, ObjLoss=21.933, BoxCenterLoss=14.622, BoxScaleLoss=5.033, ClassLoss=8.670 [Epoch 116][Batch 299], LR: 1.00E-03, Speed: 77.518 samples/sec, ObjLoss=21.932, BoxCenterLoss=14.622, BoxScaleLoss=5.032, ClassLoss=8.669 [Epoch 116][Batch 399], LR: 1.00E-03, Speed: 83.082 samples/sec, ObjLoss=21.932, BoxCenterLoss=14.622, BoxScaleLoss=5.032, ClassLoss=8.669 [Epoch 116][Batch 499], LR: 1.00E-03, Speed: 83.670 samples/sec, ObjLoss=21.930, BoxCenterLoss=14.622, BoxScaleLoss=5.032, ClassLoss=8.668 [Epoch 116][Batch 599], LR: 1.00E-03, Speed: 140.034 samples/sec, ObjLoss=21.929, BoxCenterLoss=14.622, BoxScaleLoss=5.032, ClassLoss=8.666 [Epoch 116][Batch 699], LR: 1.00E-03, Speed: 137.142 samples/sec, ObjLoss=21.928, BoxCenterLoss=14.622, BoxScaleLoss=5.032, ClassLoss=8.666 [Epoch 116][Batch 799], LR: 1.00E-03, Speed: 116.307 samples/sec, ObjLoss=21.927, BoxCenterLoss=14.621, BoxScaleLoss=5.031, ClassLoss=8.665 [Epoch 116][Batch 899], LR: 1.00E-03, Speed: 152.010 samples/sec, ObjLoss=21.926, BoxCenterLoss=14.621, BoxScaleLoss=5.031, ClassLoss=8.664 [Epoch 116][Batch 999], LR: 1.00E-03, Speed: 98.925 samples/sec, ObjLoss=21.925, BoxCenterLoss=14.621, BoxScaleLoss=5.031, ClassLoss=8.663 [Epoch 116][Batch 1099], LR: 1.00E-03, Speed: 103.875 samples/sec, ObjLoss=21.924, BoxCenterLoss=14.621, BoxScaleLoss=5.031, ClassLoss=8.662 [Epoch 116][Batch 1199], LR: 1.00E-03, Speed: 124.842 samples/sec, ObjLoss=21.923, BoxCenterLoss=14.621, BoxScaleLoss=5.030, ClassLoss=8.661 [Epoch 116][Batch 1299], LR: 1.00E-03, Speed: 84.019 samples/sec, ObjLoss=21.922, BoxCenterLoss=14.621, BoxScaleLoss=5.030, ClassLoss=8.660 [Epoch 116][Batch 1399], LR: 1.00E-03, Speed: 62.159 samples/sec, ObjLoss=21.920, BoxCenterLoss=14.621, BoxScaleLoss=5.030, ClassLoss=8.660 [Epoch 116][Batch 1499], LR: 1.00E-03, Speed: 105.910 samples/sec, ObjLoss=21.919, BoxCenterLoss=14.621, BoxScaleLoss=5.030, ClassLoss=8.659 [Epoch 116][Batch 1599], LR: 1.00E-03, Speed: 87.514 samples/sec, ObjLoss=21.918, BoxCenterLoss=14.620, BoxScaleLoss=5.030, ClassLoss=8.658 [Epoch 116][Batch 1699], LR: 1.00E-03, Speed: 161.454 samples/sec, ObjLoss=21.917, BoxCenterLoss=14.620, BoxScaleLoss=5.030, ClassLoss=8.657 [Epoch 116][Batch 1799], LR: 1.00E-03, Speed: 131.069 samples/sec, ObjLoss=21.916, BoxCenterLoss=14.620, BoxScaleLoss=5.030, ClassLoss=8.656 [Epoch 116] Training cost: 1235.871, ObjLoss=21.916, BoxCenterLoss=14.620, BoxScaleLoss=5.029, ClassLoss=8.656 [Epoch 116] 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 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.209 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 ] = 0.191 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 ] = 0.288 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 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 person=31.7 bicycle=14.0 car=21.4 motorcycle=23.9 airplane=36.8 bus=37.9 train=42.5 truck=18.3 boat=10.6 traffic light=9.0 fire hydrant=36.0 stop sign=35.9 parking meter=27.6 bench=12.0 bird=15.2 cat=41.8 dog=35.1 horse=33.0 sheep=24.2 cow=30.5 elephant=41.5 bear=34.1 zebra=43.8 giraffe=43.1 backpack=4.4 umbrella=17.6 handbag=3.1 tie=13.1 suitcase=14.5 frisbee=32.4 skis=8.3 snowboard=10.1 sports ball=22.4 kite=16.8 baseball bat=10.4 baseball glove=17.8 skateboard=18.1 surfboard=14.7 tennis racket=22.1 bottle=13.7 wine glass=13.3 cup=20.5 fork=8.5 knife=2.8 spoon=2.1 bowl=18.2 banana=12.9 apple=5.9 sandwich=18.9 orange=15.8 broccoli=9.8 carrot=8.8 hot dog=17.9 pizza=31.4 donut=20.2 cake=14.5 chair=12.1 couch=26.7 potted plant=10.1 bed=25.4 dining table=12.5 toilet=35.4 tv=34.1 laptop=32.5 mouse=30.7 remote=7.3 keyboard=24.7 cell phone=12.5 microwave=30.4 oven=18.7 toaster=0.0 sink=18.3 refrigerator=28.2 book=4.8 clock=31.2 vase=18.3 scissors=16.5 teddy bear=23.6 hair drier=0.0 toothbrush=2.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.2 [Epoch 117][Batch 99], LR: 1.00E-03, Speed: 106.718 samples/sec, ObjLoss=21.915, BoxCenterLoss=14.620, BoxScaleLoss=5.029, ClassLoss=8.655 [Epoch 117][Batch 199], LR: 1.00E-03, Speed: 157.180 samples/sec, ObjLoss=21.914, BoxCenterLoss=14.620, BoxScaleLoss=5.029, ClassLoss=8.654 [Epoch 117][Batch 299], LR: 1.00E-03, Speed: 114.068 samples/sec, ObjLoss=21.913, BoxCenterLoss=14.620, BoxScaleLoss=5.029, ClassLoss=8.653 [Epoch 117][Batch 399], LR: 1.00E-03, Speed: 129.578 samples/sec, ObjLoss=21.912, BoxCenterLoss=14.620, BoxScaleLoss=5.029, ClassLoss=8.652 [Epoch 117][Batch 499], LR: 1.00E-03, Speed: 143.781 samples/sec, ObjLoss=21.911, BoxCenterLoss=14.620, BoxScaleLoss=5.028, ClassLoss=8.651 [Epoch 117][Batch 599], LR: 1.00E-03, Speed: 91.537 samples/sec, ObjLoss=21.910, BoxCenterLoss=14.619, BoxScaleLoss=5.028, ClassLoss=8.650 [Epoch 117][Batch 699], LR: 1.00E-03, Speed: 104.418 samples/sec, ObjLoss=21.909, BoxCenterLoss=14.619, BoxScaleLoss=5.028, ClassLoss=8.649 [Epoch 117][Batch 799], LR: 1.00E-03, Speed: 118.922 samples/sec, ObjLoss=21.908, BoxCenterLoss=14.619, BoxScaleLoss=5.028, ClassLoss=8.649 [Epoch 117][Batch 899], LR: 1.00E-03, Speed: 128.563 samples/sec, ObjLoss=21.907, BoxCenterLoss=14.619, BoxScaleLoss=5.028, ClassLoss=8.648 [Epoch 117][Batch 999], LR: 1.00E-03, Speed: 143.894 samples/sec, ObjLoss=21.906, BoxCenterLoss=14.619, BoxScaleLoss=5.028, ClassLoss=8.647 [Epoch 117][Batch 1099], LR: 1.00E-03, Speed: 103.791 samples/sec, ObjLoss=21.905, BoxCenterLoss=14.619, BoxScaleLoss=5.027, ClassLoss=8.646 [Epoch 117][Batch 1199], LR: 1.00E-03, Speed: 105.882 samples/sec, ObjLoss=21.904, BoxCenterLoss=14.619, BoxScaleLoss=5.027, ClassLoss=8.646 [Epoch 117][Batch 1299], LR: 1.00E-03, Speed: 149.876 samples/sec, ObjLoss=21.903, BoxCenterLoss=14.619, BoxScaleLoss=5.027, ClassLoss=8.645 [Epoch 117][Batch 1399], LR: 1.00E-03, Speed: 114.445 samples/sec, ObjLoss=21.902, BoxCenterLoss=14.619, BoxScaleLoss=5.027, ClassLoss=8.644 [Epoch 117][Batch 1499], LR: 1.00E-03, Speed: 145.624 samples/sec, ObjLoss=21.901, BoxCenterLoss=14.619, BoxScaleLoss=5.027, ClassLoss=8.643 [Epoch 117][Batch 1599], LR: 1.00E-03, Speed: 124.114 samples/sec, ObjLoss=21.900, BoxCenterLoss=14.619, BoxScaleLoss=5.027, ClassLoss=8.642 [Epoch 117][Batch 1699], LR: 1.00E-03, Speed: 140.125 samples/sec, ObjLoss=21.899, BoxCenterLoss=14.619, BoxScaleLoss=5.026, ClassLoss=8.641 [Epoch 117][Batch 1799], LR: 1.00E-03, Speed: 136.039 samples/sec, ObjLoss=21.898, BoxCenterLoss=14.619, BoxScaleLoss=5.026, ClassLoss=8.641 [Epoch 117] Training cost: 1275.498, ObjLoss=21.898, BoxCenterLoss=14.619, BoxScaleLoss=5.026, ClassLoss=8.640 [Epoch 117] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.405 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.136 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 ] = 0.182 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 ] = 0.178 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 ] = 0.118 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 ] = 0.389 person=29.8 bicycle=11.8 car=19.8 motorcycle=21.9 airplane=36.5 bus=38.8 train=30.0 truck=15.9 boat=10.2 traffic light=12.6 fire hydrant=24.6 stop sign=36.8 parking meter=19.3 bench=9.4 bird=13.9 cat=35.7 dog=29.3 horse=27.5 sheep=22.6 cow=23.3 elephant=31.5 bear=35.0 zebra=36.2 giraffe=39.7 backpack=4.2 umbrella=16.2 handbag=3.6 tie=12.3 suitcase=10.5 frisbee=29.9 skis=7.3 snowboard=11.7 sports ball=23.8 kite=19.7 baseball bat=8.5 baseball glove=15.5 skateboard=18.4 surfboard=14.3 tennis racket=21.8 bottle=13.9 wine glass=12.2 cup=18.7 fork=9.4 knife=3.3 spoon=1.9 bowl=19.2 banana=9.4 apple=6.5 sandwich=17.6 orange=15.5 broccoli=8.6 carrot=7.3 hot dog=17.3 pizza=28.0 donut=19.5 cake=16.3 chair=11.7 couch=22.2 potted plant=9.1 bed=22.8 dining table=12.0 toilet=37.0 tv=27.8 laptop=33.8 mouse=28.7 remote=6.8 keyboard=24.5 cell phone=13.4 microwave=27.8 oven=13.9 toaster=0.0 sink=17.7 refrigerator=17.9 book=4.9 clock=27.5 vase=15.1 scissors=17.5 teddy bear=22.5 hair drier=0.0 toothbrush=0.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.4 [Epoch 118][Batch 99], LR: 1.00E-03, Speed: 174.251 samples/sec, ObjLoss=21.897, BoxCenterLoss=14.618, BoxScaleLoss=5.026, ClassLoss=8.639 [Epoch 118][Batch 199], LR: 1.00E-03, Speed: 106.798 samples/sec, ObjLoss=21.896, BoxCenterLoss=14.619, BoxScaleLoss=5.026, ClassLoss=8.638 [Epoch 118][Batch 299], LR: 1.00E-03, Speed: 139.210 samples/sec, ObjLoss=21.895, BoxCenterLoss=14.618, BoxScaleLoss=5.026, ClassLoss=8.638 [Epoch 118][Batch 399], LR: 1.00E-03, Speed: 91.375 samples/sec, ObjLoss=21.894, BoxCenterLoss=14.619, BoxScaleLoss=5.026, ClassLoss=8.637 [Epoch 118][Batch 499], LR: 1.00E-03, Speed: 124.850 samples/sec, ObjLoss=21.893, BoxCenterLoss=14.618, BoxScaleLoss=5.025, ClassLoss=8.636 [Epoch 118][Batch 599], LR: 1.00E-03, Speed: 110.947 samples/sec, ObjLoss=21.892, BoxCenterLoss=14.618, BoxScaleLoss=5.025, ClassLoss=8.635 [Epoch 118][Batch 699], LR: 1.00E-03, Speed: 77.039 samples/sec, ObjLoss=21.891, BoxCenterLoss=14.618, BoxScaleLoss=5.025, ClassLoss=8.634 [Epoch 118][Batch 799], LR: 1.00E-03, Speed: 133.250 samples/sec, ObjLoss=21.890, BoxCenterLoss=14.618, BoxScaleLoss=5.025, ClassLoss=8.633 [Epoch 118][Batch 899], LR: 1.00E-03, Speed: 69.175 samples/sec, ObjLoss=21.889, BoxCenterLoss=14.618, BoxScaleLoss=5.025, ClassLoss=8.632 [Epoch 118][Batch 999], LR: 1.00E-03, Speed: 102.660 samples/sec, ObjLoss=21.888, BoxCenterLoss=14.618, BoxScaleLoss=5.024, ClassLoss=8.631 [Epoch 118][Batch 1099], LR: 1.00E-03, Speed: 79.125 samples/sec, ObjLoss=21.887, BoxCenterLoss=14.618, BoxScaleLoss=5.024, ClassLoss=8.630 [Epoch 118][Batch 1199], LR: 1.00E-03, Speed: 144.202 samples/sec, ObjLoss=21.886, BoxCenterLoss=14.618, BoxScaleLoss=5.024, ClassLoss=8.630 [Epoch 118][Batch 1299], LR: 1.00E-03, Speed: 127.392 samples/sec, ObjLoss=21.885, BoxCenterLoss=14.618, BoxScaleLoss=5.024, ClassLoss=8.629 [Epoch 118][Batch 1399], LR: 1.00E-03, Speed: 103.370 samples/sec, ObjLoss=21.884, BoxCenterLoss=14.618, BoxScaleLoss=5.024, ClassLoss=8.628 [Epoch 118][Batch 1499], LR: 1.00E-03, Speed: 144.316 samples/sec, ObjLoss=21.883, BoxCenterLoss=14.617, BoxScaleLoss=5.023, ClassLoss=8.627 [Epoch 118][Batch 1599], LR: 1.00E-03, Speed: 184.431 samples/sec, ObjLoss=21.882, BoxCenterLoss=14.617, BoxScaleLoss=5.023, ClassLoss=8.626 [Epoch 118][Batch 1699], LR: 1.00E-03, Speed: 120.907 samples/sec, ObjLoss=21.881, BoxCenterLoss=14.617, BoxScaleLoss=5.023, ClassLoss=8.625 [Epoch 118][Batch 1799], LR: 1.00E-03, Speed: 169.086 samples/sec, ObjLoss=21.880, BoxCenterLoss=14.617, BoxScaleLoss=5.023, ClassLoss=8.625 [Epoch 118] Training cost: 1245.486, ObjLoss=21.880, BoxCenterLoss=14.617, BoxScaleLoss=5.023, ClassLoss=8.624 [Epoch 118] 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 ] = 0.409 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.178 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 ] = 0.204 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 ] = 0.188 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 ] = 0.285 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 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 person=32.7 bicycle=14.0 car=21.5 motorcycle=24.9 airplane=38.6 bus=39.0 train=38.2 truck=18.4 boat=9.9 traffic light=12.6 fire hydrant=39.8 stop sign=41.7 parking meter=28.0 bench=11.1 bird=18.2 cat=35.4 dog=35.6 horse=31.8 sheep=26.4 cow=30.6 elephant=39.2 bear=38.8 zebra=39.4 giraffe=42.3 backpack=5.2 umbrella=21.2 handbag=3.4 tie=12.6 suitcase=15.1 frisbee=34.7 skis=7.0 snowboard=14.0 sports ball=18.3 kite=22.0 baseball bat=9.0 baseball glove=18.2 skateboard=23.5 surfboard=16.0 tennis racket=22.2 bottle=13.6 wine glass=15.4 cup=19.9 fork=8.4 knife=3.0 spoon=1.9 bowl=19.4 banana=11.9 apple=5.1 sandwich=15.5 orange=13.6 broccoli=10.7 carrot=8.7 hot dog=16.1 pizza=27.8 donut=19.6 cake=15.5 chair=12.3 couch=25.1 potted plant=11.4 bed=20.7 dining table=10.2 toilet=32.8 tv=32.7 laptop=33.4 mouse=32.8 remote=7.3 keyboard=28.6 cell phone=14.5 microwave=24.0 oven=17.2 toaster=0.0 sink=13.2 refrigerator=24.8 book=4.9 clock=29.5 vase=17.3 scissors=11.4 teddy bear=24.1 hair drier=0.0 toothbrush=2.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.1 [Epoch 119][Batch 99], LR: 1.00E-03, Speed: 139.011 samples/sec, ObjLoss=21.879, BoxCenterLoss=14.617, BoxScaleLoss=5.023, ClassLoss=8.623 [Epoch 119][Batch 199], LR: 1.00E-03, Speed: 154.538 samples/sec, ObjLoss=21.878, BoxCenterLoss=14.617, BoxScaleLoss=5.022, ClassLoss=8.622 [Epoch 119][Batch 299], LR: 1.00E-03, Speed: 127.113 samples/sec, ObjLoss=21.877, BoxCenterLoss=14.617, BoxScaleLoss=5.022, ClassLoss=8.622 [Epoch 119][Batch 399], LR: 1.00E-03, Speed: 151.639 samples/sec, ObjLoss=21.876, BoxCenterLoss=14.617, BoxScaleLoss=5.022, ClassLoss=8.621 [Epoch 119][Batch 499], LR: 1.00E-03, Speed: 134.722 samples/sec, ObjLoss=21.875, BoxCenterLoss=14.617, BoxScaleLoss=5.022, ClassLoss=8.620 [Epoch 119][Batch 599], LR: 1.00E-03, Speed: 160.996 samples/sec, ObjLoss=21.874, BoxCenterLoss=14.617, BoxScaleLoss=5.022, ClassLoss=8.619 [Epoch 119][Batch 699], LR: 1.00E-03, Speed: 106.368 samples/sec, ObjLoss=21.873, BoxCenterLoss=14.617, BoxScaleLoss=5.022, ClassLoss=8.618 [Epoch 119][Batch 799], LR: 1.00E-03, Speed: 118.514 samples/sec, ObjLoss=21.872, BoxCenterLoss=14.616, BoxScaleLoss=5.022, ClassLoss=8.618 [Epoch 119][Batch 899], LR: 1.00E-03, Speed: 127.487 samples/sec, ObjLoss=21.871, BoxCenterLoss=14.616, BoxScaleLoss=5.021, ClassLoss=8.617 [Epoch 119][Batch 999], LR: 1.00E-03, Speed: 118.281 samples/sec, ObjLoss=21.870, BoxCenterLoss=14.616, BoxScaleLoss=5.021, ClassLoss=8.616 [Epoch 119][Batch 1099], LR: 1.00E-03, Speed: 86.582 samples/sec, ObjLoss=21.869, BoxCenterLoss=14.616, BoxScaleLoss=5.021, ClassLoss=8.615 [Epoch 119][Batch 1199], LR: 1.00E-03, Speed: 126.882 samples/sec, ObjLoss=21.868, BoxCenterLoss=14.616, BoxScaleLoss=5.021, ClassLoss=8.614 [Epoch 119][Batch 1299], LR: 1.00E-03, Speed: 158.110 samples/sec, ObjLoss=21.867, BoxCenterLoss=14.615, BoxScaleLoss=5.021, ClassLoss=8.613 [Epoch 119][Batch 1399], LR: 1.00E-03, Speed: 152.712 samples/sec, ObjLoss=21.866, BoxCenterLoss=14.616, BoxScaleLoss=5.020, ClassLoss=8.612 [Epoch 119][Batch 1499], LR: 1.00E-03, Speed: 128.695 samples/sec, ObjLoss=21.865, BoxCenterLoss=14.615, BoxScaleLoss=5.020, ClassLoss=8.611 [Epoch 119][Batch 1599], LR: 1.00E-03, Speed: 126.836 samples/sec, ObjLoss=21.865, BoxCenterLoss=14.615, BoxScaleLoss=5.020, ClassLoss=8.610 [Epoch 119][Batch 1699], LR: 1.00E-03, Speed: 142.042 samples/sec, ObjLoss=21.864, BoxCenterLoss=14.615, BoxScaleLoss=5.020, ClassLoss=8.609 [Epoch 119][Batch 1799], LR: 1.00E-03, Speed: 138.838 samples/sec, ObjLoss=21.863, BoxCenterLoss=14.615, BoxScaleLoss=5.019, ClassLoss=8.608 [Epoch 119] Training cost: 1216.595, ObjLoss=21.863, BoxCenterLoss=14.616, BoxScaleLoss=5.019, ClassLoss=8.608 [Epoch 119] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.193 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.203 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 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279 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 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 person=32.4 bicycle=14.5 car=18.5 motorcycle=25.2 airplane=41.8 bus=33.3 train=34.3 truck=16.9 boat=9.0 traffic light=13.0 fire hydrant=36.4 stop sign=28.0 parking meter=20.7 bench=10.9 bird=16.5 cat=39.1 dog=34.3 horse=30.8 sheep=26.7 cow=25.7 elephant=28.7 bear=38.8 zebra=39.5 giraffe=42.9 backpack=4.1 umbrella=17.1 handbag=3.5 tie=12.3 suitcase=15.3 frisbee=32.8 skis=7.7 snowboard=10.9 sports ball=23.0 kite=19.2 baseball bat=10.6 baseball glove=16.3 skateboard=23.2 surfboard=16.5 tennis racket=23.5 bottle=14.8 wine glass=14.2 cup=19.1 fork=9.9 knife=4.6 spoon=2.3 bowl=19.6 banana=10.8 apple=4.3 sandwich=17.0 orange=13.4 broccoli=11.0 carrot=7.9 hot dog=16.4 pizza=27.7 donut=18.6 cake=14.9 chair=12.2 couch=20.7 potted plant=9.9 bed=22.7 dining table=13.5 toilet=30.4 tv=30.5 laptop=33.9 mouse=29.6 remote=7.7 keyboard=27.7 cell phone=12.7 microwave=27.1 oven=20.1 toaster=0.0 sink=11.7 refrigerator=23.0 book=4.0 clock=22.9 vase=16.6 scissors=17.1 teddy bear=26.2 hair drier=0.0 toothbrush=2.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.3 [Epoch 120][Batch 99], LR: 1.00E-03, Speed: 142.091 samples/sec, ObjLoss=21.862, BoxCenterLoss=14.615, BoxScaleLoss=5.019, ClassLoss=8.607 [Epoch 120][Batch 199], LR: 1.00E-03, Speed: 150.646 samples/sec, ObjLoss=21.861, BoxCenterLoss=14.615, BoxScaleLoss=5.019, ClassLoss=8.606 [Epoch 120][Batch 299], LR: 1.00E-03, Speed: 151.156 samples/sec, ObjLoss=21.860, BoxCenterLoss=14.615, BoxScaleLoss=5.019, ClassLoss=8.606 [Epoch 120][Batch 399], LR: 1.00E-03, Speed: 133.546 samples/sec, ObjLoss=21.859, BoxCenterLoss=14.615, BoxScaleLoss=5.018, ClassLoss=8.604 [Epoch 120][Batch 499], LR: 1.00E-03, Speed: 147.591 samples/sec, ObjLoss=21.858, BoxCenterLoss=14.615, BoxScaleLoss=5.018, ClassLoss=8.604 [Epoch 120][Batch 599], LR: 1.00E-03, Speed: 128.659 samples/sec, ObjLoss=21.857, BoxCenterLoss=14.615, BoxScaleLoss=5.018, ClassLoss=8.603 [Epoch 120][Batch 699], LR: 1.00E-03, Speed: 137.707 samples/sec, ObjLoss=21.856, BoxCenterLoss=14.615, BoxScaleLoss=5.018, ClassLoss=8.602 [Epoch 120][Batch 799], LR: 1.00E-03, Speed: 133.871 samples/sec, ObjLoss=21.855, BoxCenterLoss=14.615, BoxScaleLoss=5.017, ClassLoss=8.601 [Epoch 120][Batch 899], LR: 1.00E-03, Speed: 130.830 samples/sec, ObjLoss=21.854, BoxCenterLoss=14.614, BoxScaleLoss=5.017, ClassLoss=8.600 [Epoch 120][Batch 999], LR: 1.00E-03, Speed: 150.547 samples/sec, ObjLoss=21.853, BoxCenterLoss=14.614, BoxScaleLoss=5.017, ClassLoss=8.599 [Epoch 120][Batch 1099], LR: 1.00E-03, Speed: 144.872 samples/sec, ObjLoss=21.852, BoxCenterLoss=14.614, BoxScaleLoss=5.017, ClassLoss=8.598 [Epoch 120][Batch 1199], LR: 1.00E-03, Speed: 147.946 samples/sec, ObjLoss=21.851, BoxCenterLoss=14.614, BoxScaleLoss=5.017, ClassLoss=8.597 [Epoch 120][Batch 1299], LR: 1.00E-03, Speed: 131.154 samples/sec, ObjLoss=21.850, BoxCenterLoss=14.614, BoxScaleLoss=5.017, ClassLoss=8.597 [Epoch 120][Batch 1399], LR: 1.00E-03, Speed: 119.174 samples/sec, ObjLoss=21.849, BoxCenterLoss=14.614, BoxScaleLoss=5.016, ClassLoss=8.596 [Epoch 120][Batch 1499], LR: 1.00E-03, Speed: 115.496 samples/sec, ObjLoss=21.848, BoxCenterLoss=14.614, BoxScaleLoss=5.016, ClassLoss=8.595 [Epoch 120][Batch 1599], LR: 1.00E-03, Speed: 107.400 samples/sec, ObjLoss=21.847, BoxCenterLoss=14.614, BoxScaleLoss=5.016, ClassLoss=8.594 [Epoch 120][Batch 1699], LR: 1.00E-03, Speed: 130.919 samples/sec, ObjLoss=21.846, BoxCenterLoss=14.614, BoxScaleLoss=5.016, ClassLoss=8.593 [Epoch 120][Batch 1799], LR: 1.00E-03, Speed: 171.523 samples/sec, ObjLoss=21.845, BoxCenterLoss=14.614, BoxScaleLoss=5.016, ClassLoss=8.592 [Epoch 120] Training cost: 1144.860, ObjLoss=21.845, BoxCenterLoss=14.614, BoxScaleLoss=5.016, ClassLoss=8.592 [Epoch 120] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.408 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.190 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 ] = 0.207 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 ] = 0.281 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.289 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 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416 person=29.4 bicycle=14.6 car=20.3 motorcycle=23.7 airplane=41.8 bus=39.4 train=41.0 truck=17.2 boat=7.9 traffic light=10.1 fire hydrant=40.0 stop sign=36.5 parking meter=25.7 bench=11.3 bird=14.5 cat=37.9 dog=35.4 horse=29.7 sheep=25.7 cow=28.8 elephant=37.9 bear=40.7 zebra=37.5 giraffe=44.0 backpack=4.3 umbrella=16.7 handbag=3.1 tie=15.4 suitcase=13.6 frisbee=33.5 skis=6.6 snowboard=10.0 sports ball=24.3 kite=21.9 baseball bat=11.2 baseball glove=13.7 skateboard=19.8 surfboard=16.1 tennis racket=23.7 bottle=15.0 wine glass=13.0 cup=18.8 fork=9.2 knife=3.5 spoon=2.4 bowl=20.5 banana=11.8 apple=7.6 sandwich=18.0 orange=14.3 broccoli=9.5 carrot=6.9 hot dog=16.0 pizza=27.2 donut=18.6 cake=16.7 chair=12.0 couch=26.4 potted plant=9.5 bed=31.0 dining table=15.9 toilet=36.5 tv=31.5 laptop=35.6 mouse=33.6 remote=7.3 keyboard=27.6 cell phone=14.5 microwave=30.5 oven=19.5 toaster=8.3 sink=19.7 refrigerator=30.1 book=5.3 clock=32.0 vase=19.5 scissors=18.0 teddy bear=22.2 hair drier=0.0 toothbrush=3.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 121][Batch 99], LR: 1.00E-03, Speed: 150.757 samples/sec, ObjLoss=21.844, BoxCenterLoss=14.614, BoxScaleLoss=5.016, ClassLoss=8.592 [Epoch 121][Batch 199], LR: 1.00E-03, Speed: 151.782 samples/sec, ObjLoss=21.843, BoxCenterLoss=14.614, BoxScaleLoss=5.015, ClassLoss=8.591 [Epoch 121][Batch 299], LR: 1.00E-03, Speed: 150.991 samples/sec, ObjLoss=21.842, BoxCenterLoss=14.613, BoxScaleLoss=5.015, ClassLoss=8.590 [Epoch 121][Batch 399], LR: 1.00E-03, Speed: 158.763 samples/sec, ObjLoss=21.841, BoxCenterLoss=14.613, BoxScaleLoss=5.015, ClassLoss=8.589 [Epoch 121][Batch 499], LR: 1.00E-03, Speed: 108.537 samples/sec, ObjLoss=21.840, BoxCenterLoss=14.613, BoxScaleLoss=5.015, ClassLoss=8.588 [Epoch 121][Batch 599], LR: 1.00E-03, Speed: 116.329 samples/sec, ObjLoss=21.839, BoxCenterLoss=14.613, BoxScaleLoss=5.015, ClassLoss=8.587 [Epoch 121][Batch 699], LR: 1.00E-03, Speed: 104.238 samples/sec, ObjLoss=21.838, BoxCenterLoss=14.613, BoxScaleLoss=5.014, ClassLoss=8.586 [Epoch 121][Batch 799], LR: 1.00E-03, Speed: 140.906 samples/sec, ObjLoss=21.837, BoxCenterLoss=14.613, BoxScaleLoss=5.014, ClassLoss=8.586 [Epoch 121][Batch 899], LR: 1.00E-03, Speed: 126.070 samples/sec, ObjLoss=21.836, BoxCenterLoss=14.613, BoxScaleLoss=5.014, ClassLoss=8.585 [Epoch 121][Batch 999], LR: 1.00E-03, Speed: 135.318 samples/sec, ObjLoss=21.835, BoxCenterLoss=14.613, BoxScaleLoss=5.014, ClassLoss=8.584 [Epoch 121][Batch 1099], LR: 1.00E-03, Speed: 128.312 samples/sec, ObjLoss=21.834, BoxCenterLoss=14.612, BoxScaleLoss=5.014, ClassLoss=8.583 [Epoch 121][Batch 1199], LR: 1.00E-03, Speed: 80.926 samples/sec, ObjLoss=21.833, BoxCenterLoss=14.612, BoxScaleLoss=5.014, ClassLoss=8.582 [Epoch 121][Batch 1299], LR: 1.00E-03, Speed: 140.220 samples/sec, ObjLoss=21.832, BoxCenterLoss=14.612, BoxScaleLoss=5.013, ClassLoss=8.581 [Epoch 121][Batch 1399], LR: 1.00E-03, Speed: 119.608 samples/sec, ObjLoss=21.831, BoxCenterLoss=14.612, BoxScaleLoss=5.013, ClassLoss=8.581 [Epoch 121][Batch 1499], LR: 1.00E-03, Speed: 139.463 samples/sec, ObjLoss=21.831, BoxCenterLoss=14.612, BoxScaleLoss=5.013, ClassLoss=8.580 [Epoch 121][Batch 1599], LR: 1.00E-03, Speed: 148.260 samples/sec, ObjLoss=21.830, BoxCenterLoss=14.612, BoxScaleLoss=5.013, ClassLoss=8.579 [Epoch 121][Batch 1699], LR: 1.00E-03, Speed: 100.329 samples/sec, ObjLoss=21.829, BoxCenterLoss=14.612, BoxScaleLoss=5.013, ClassLoss=8.578 [Epoch 121][Batch 1799], LR: 1.00E-03, Speed: 141.359 samples/sec, ObjLoss=21.827, BoxCenterLoss=14.612, BoxScaleLoss=5.012, ClassLoss=8.578 [Epoch 121] Training cost: 1203.956, ObjLoss=21.827, BoxCenterLoss=14.612, BoxScaleLoss=5.012, ClassLoss=8.577 [Epoch 121] 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 ] = 0.409 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.140 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.075 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 ] = 0.284 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 ] = 0.265 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.274 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 ] = 0.401 person=31.6 bicycle=14.2 car=18.6 motorcycle=24.0 airplane=38.1 bus=32.9 train=31.8 truck=15.9 boat=10.5 traffic light=11.7 fire hydrant=36.7 stop sign=30.5 parking meter=20.3 bench=10.9 bird=17.0 cat=35.6 dog=33.2 horse=28.0 sheep=25.2 cow=26.5 elephant=36.4 bear=35.2 zebra=38.9 giraffe=42.0 backpack=4.7 umbrella=17.6 handbag=3.2 tie=11.2 suitcase=14.3 frisbee=32.5 skis=7.2 snowboard=7.1 sports ball=20.3 kite=22.4 baseball bat=7.6 baseball glove=15.7 skateboard=16.9 surfboard=14.5 tennis racket=18.4 bottle=12.5 wine glass=14.1 cup=18.0 fork=8.0 knife=3.4 spoon=1.9 bowl=19.0 banana=10.9 apple=5.8 sandwich=20.2 orange=12.3 broccoli=11.2 carrot=8.2 hot dog=15.0 pizza=29.0 donut=16.6 cake=16.4 chair=11.6 couch=22.2 potted plant=9.5 bed=18.6 dining table=10.9 toilet=34.6 tv=30.5 laptop=32.4 mouse=28.4 remote=7.4 keyboard=21.7 cell phone=14.8 microwave=21.5 oven=14.5 toaster=2.4 sink=17.8 refrigerator=26.5 book=5.0 clock=26.0 vase=13.8 scissors=11.5 teddy bear=22.9 hair drier=0.0 toothbrush=2.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.6 [Epoch 122][Batch 99], LR: 1.00E-03, Speed: 142.942 samples/sec, ObjLoss=21.826, BoxCenterLoss=14.612, BoxScaleLoss=5.012, ClassLoss=8.576 [Epoch 122][Batch 199], LR: 1.00E-03, Speed: 133.764 samples/sec, ObjLoss=21.825, BoxCenterLoss=14.612, BoxScaleLoss=5.012, ClassLoss=8.576 [Epoch 122][Batch 299], LR: 1.00E-03, Speed: 100.535 samples/sec, ObjLoss=21.824, BoxCenterLoss=14.611, BoxScaleLoss=5.012, ClassLoss=8.575 [Epoch 122][Batch 399], LR: 1.00E-03, Speed: 85.094 samples/sec, ObjLoss=21.823, BoxCenterLoss=14.611, BoxScaleLoss=5.012, ClassLoss=8.574 [Epoch 122][Batch 499], LR: 1.00E-03, Speed: 134.596 samples/sec, ObjLoss=21.822, BoxCenterLoss=14.611, BoxScaleLoss=5.011, ClassLoss=8.573 [Epoch 122][Batch 599], LR: 1.00E-03, Speed: 143.720 samples/sec, ObjLoss=21.821, BoxCenterLoss=14.611, BoxScaleLoss=5.011, ClassLoss=8.572 [Epoch 122][Batch 699], LR: 1.00E-03, Speed: 149.149 samples/sec, ObjLoss=21.820, BoxCenterLoss=14.611, BoxScaleLoss=5.011, ClassLoss=8.571 [Epoch 122][Batch 799], LR: 1.00E-03, Speed: 105.273 samples/sec, ObjLoss=21.819, BoxCenterLoss=14.611, BoxScaleLoss=5.011, ClassLoss=8.570 [Epoch 122][Batch 899], LR: 1.00E-03, Speed: 61.867 samples/sec, ObjLoss=21.818, BoxCenterLoss=14.611, BoxScaleLoss=5.011, ClassLoss=8.569 [Epoch 122][Batch 999], LR: 1.00E-03, Speed: 77.530 samples/sec, ObjLoss=21.817, BoxCenterLoss=14.611, BoxScaleLoss=5.010, ClassLoss=8.569 [Epoch 122][Batch 1099], LR: 1.00E-03, Speed: 80.077 samples/sec, ObjLoss=21.816, BoxCenterLoss=14.610, BoxScaleLoss=5.010, ClassLoss=8.568 [Epoch 122][Batch 1199], LR: 1.00E-03, Speed: 105.947 samples/sec, ObjLoss=21.815, BoxCenterLoss=14.610, BoxScaleLoss=5.010, ClassLoss=8.567 [Epoch 122][Batch 1299], LR: 1.00E-03, Speed: 151.696 samples/sec, ObjLoss=21.814, BoxCenterLoss=14.610, BoxScaleLoss=5.010, ClassLoss=8.566 [Epoch 122][Batch 1399], LR: 1.00E-03, Speed: 133.388 samples/sec, ObjLoss=21.813, BoxCenterLoss=14.610, BoxScaleLoss=5.010, ClassLoss=8.566 [Epoch 122][Batch 1499], LR: 1.00E-03, Speed: 64.298 samples/sec, ObjLoss=21.812, BoxCenterLoss=14.610, BoxScaleLoss=5.010, ClassLoss=8.565 [Epoch 122][Batch 1599], LR: 1.00E-03, Speed: 93.802 samples/sec, ObjLoss=21.812, BoxCenterLoss=14.610, BoxScaleLoss=5.009, ClassLoss=8.564 [Epoch 122][Batch 1699], LR: 1.00E-03, Speed: 145.091 samples/sec, ObjLoss=21.811, BoxCenterLoss=14.610, BoxScaleLoss=5.009, ClassLoss=8.563 [Epoch 122][Batch 1799], LR: 1.00E-03, Speed: 139.006 samples/sec, ObjLoss=21.810, BoxCenterLoss=14.610, BoxScaleLoss=5.009, ClassLoss=8.562 [Epoch 122] Training cost: 1254.288, ObjLoss=21.809, BoxCenterLoss=14.610, BoxScaleLoss=5.009, ClassLoss=8.562 [Epoch 122] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.403 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.189 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 ] = 0.189 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 ] = 0.290 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 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418 person=32.3 bicycle=12.1 car=19.0 motorcycle=23.7 airplane=35.2 bus=39.6 train=42.9 truck=18.9 boat=10.3 traffic light=9.0 fire hydrant=37.1 stop sign=40.2 parking meter=22.4 bench=11.0 bird=16.0 cat=40.4 dog=33.1 horse=33.6 sheep=23.2 cow=29.3 elephant=38.0 bear=45.0 zebra=35.8 giraffe=43.2 backpack=4.2 umbrella=18.5 handbag=2.6 tie=9.6 suitcase=13.7 frisbee=33.4 skis=7.9 snowboard=10.7 sports ball=23.7 kite=20.5 baseball bat=8.7 baseball glove=17.4 skateboard=19.5 surfboard=15.6 tennis racket=20.1 bottle=15.1 wine glass=16.8 cup=18.9 fork=10.1 knife=3.6 spoon=2.9 bowl=17.5 banana=11.5 apple=7.5 sandwich=15.5 orange=12.2 broccoli=10.0 carrot=7.9 hot dog=13.2 pizza=23.3 donut=15.8 cake=17.7 chair=10.8 couch=25.7 potted plant=9.2 bed=27.3 dining table=15.2 toilet=31.1 tv=32.0 laptop=32.2 mouse=27.7 remote=6.9 keyboard=22.3 cell phone=14.8 microwave=22.0 oven=16.3 toaster=0.0 sink=15.3 refrigerator=21.9 book=4.9 clock=27.6 vase=18.9 scissors=15.2 teddy bear=23.2 hair drier=0.0 toothbrush=1.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.5 [Epoch 123][Batch 99], LR: 1.00E-03, Speed: 146.601 samples/sec, ObjLoss=21.809, BoxCenterLoss=14.610, BoxScaleLoss=5.009, ClassLoss=8.561 [Epoch 123][Batch 199], LR: 1.00E-03, Speed: 159.994 samples/sec, ObjLoss=21.808, BoxCenterLoss=14.610, BoxScaleLoss=5.009, ClassLoss=8.560 [Epoch 123][Batch 299], LR: 1.00E-03, Speed: 134.183 samples/sec, ObjLoss=21.807, BoxCenterLoss=14.610, BoxScaleLoss=5.008, ClassLoss=8.560 [Epoch 123][Batch 399], LR: 1.00E-03, Speed: 154.223 samples/sec, ObjLoss=21.806, BoxCenterLoss=14.609, BoxScaleLoss=5.008, ClassLoss=8.559 [Epoch 123][Batch 499], LR: 1.00E-03, Speed: 134.757 samples/sec, ObjLoss=21.805, BoxCenterLoss=14.609, BoxScaleLoss=5.008, ClassLoss=8.558 [Epoch 123][Batch 599], LR: 1.00E-03, Speed: 118.334 samples/sec, ObjLoss=21.803, BoxCenterLoss=14.609, BoxScaleLoss=5.008, ClassLoss=8.557 [Epoch 123][Batch 699], LR: 1.00E-03, Speed: 148.252 samples/sec, ObjLoss=21.803, BoxCenterLoss=14.609, BoxScaleLoss=5.008, ClassLoss=8.556 [Epoch 123][Batch 799], LR: 1.00E-03, Speed: 149.025 samples/sec, ObjLoss=21.802, BoxCenterLoss=14.609, BoxScaleLoss=5.008, ClassLoss=8.556 [Epoch 123][Batch 899], LR: 1.00E-03, Speed: 146.906 samples/sec, ObjLoss=21.801, BoxCenterLoss=14.609, BoxScaleLoss=5.007, ClassLoss=8.555 [Epoch 123][Batch 999], LR: 1.00E-03, Speed: 120.755 samples/sec, ObjLoss=21.800, BoxCenterLoss=14.609, BoxScaleLoss=5.007, ClassLoss=8.554 [Epoch 123][Batch 1099], LR: 1.00E-03, Speed: 104.076 samples/sec, ObjLoss=21.799, BoxCenterLoss=14.609, BoxScaleLoss=5.007, ClassLoss=8.553 [Epoch 123][Batch 1199], LR: 1.00E-03, Speed: 115.634 samples/sec, ObjLoss=21.799, BoxCenterLoss=14.609, BoxScaleLoss=5.007, ClassLoss=8.552 [Epoch 123][Batch 1299], LR: 1.00E-03, Speed: 136.402 samples/sec, ObjLoss=21.797, BoxCenterLoss=14.609, BoxScaleLoss=5.007, ClassLoss=8.552 [Epoch 123][Batch 1399], LR: 1.00E-03, Speed: 141.979 samples/sec, ObjLoss=21.796, BoxCenterLoss=14.608, BoxScaleLoss=5.007, ClassLoss=8.551 [Epoch 123][Batch 1499], LR: 1.00E-03, Speed: 127.272 samples/sec, ObjLoss=21.796, BoxCenterLoss=14.608, BoxScaleLoss=5.006, ClassLoss=8.550 [Epoch 123][Batch 1599], LR: 1.00E-03, Speed: 144.123 samples/sec, ObjLoss=21.795, BoxCenterLoss=14.608, BoxScaleLoss=5.006, ClassLoss=8.549 [Epoch 123][Batch 1699], LR: 1.00E-03, Speed: 75.472 samples/sec, ObjLoss=21.794, BoxCenterLoss=14.608, BoxScaleLoss=5.006, ClassLoss=8.548 [Epoch 123][Batch 1799], LR: 1.00E-03, Speed: 175.322 samples/sec, ObjLoss=21.794, BoxCenterLoss=14.608, BoxScaleLoss=5.006, ClassLoss=8.547 [Epoch 123] Training cost: 1259.375, ObjLoss=21.793, BoxCenterLoss=14.608, BoxScaleLoss=5.006, ClassLoss=8.547 [Epoch 123] 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 ] = 0.413 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.178 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 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 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 ] = 0.290 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 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 person=32.7 bicycle=13.4 car=20.2 motorcycle=24.9 airplane=40.1 bus=40.9 train=34.2 truck=17.7 boat=11.3 traffic light=10.0 fire hydrant=36.3 stop sign=35.5 parking meter=24.5 bench=12.6 bird=13.8 cat=43.9 dog=35.1 horse=27.5 sheep=21.2 cow=28.3 elephant=40.9 bear=49.7 zebra=39.8 giraffe=39.0 backpack=5.2 umbrella=18.6 handbag=3.9 tie=12.3 suitcase=15.4 frisbee=36.3 skis=7.0 snowboard=11.6 sports ball=22.3 kite=14.2 baseball bat=10.1 baseball glove=18.8 skateboard=22.3 surfboard=16.3 tennis racket=20.2 bottle=15.9 wine glass=15.4 cup=21.6 fork=7.8 knife=4.0 spoon=2.3 bowl=21.4 banana=9.9 apple=7.2 sandwich=17.6 orange=16.2 broccoli=10.4 carrot=8.3 hot dog=12.8 pizza=31.2 donut=22.4 cake=15.9 chair=12.6 couch=25.2 potted plant=10.9 bed=25.2 dining table=13.7 toilet=35.2 tv=36.9 laptop=36.2 mouse=34.1 remote=7.3 keyboard=25.6 cell phone=14.1 microwave=26.3 oven=20.0 toaster=0.0 sink=18.2 refrigerator=25.8 book=5.4 clock=27.3 vase=15.9 scissors=15.8 teddy bear=23.5 hair drier=0.0 toothbrush=2.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.3 [Epoch 124][Batch 99], LR: 1.00E-03, Speed: 167.055 samples/sec, ObjLoss=21.793, BoxCenterLoss=14.608, BoxScaleLoss=5.005, ClassLoss=8.546 [Epoch 124][Batch 199], LR: 1.00E-03, Speed: 161.793 samples/sec, ObjLoss=21.792, BoxCenterLoss=14.608, BoxScaleLoss=5.005, ClassLoss=8.545 [Epoch 124][Batch 299], LR: 1.00E-03, Speed: 110.654 samples/sec, ObjLoss=21.790, BoxCenterLoss=14.608, BoxScaleLoss=5.005, ClassLoss=8.544 [Epoch 124][Batch 399], LR: 1.00E-03, Speed: 128.139 samples/sec, ObjLoss=21.790, BoxCenterLoss=14.608, BoxScaleLoss=5.005, ClassLoss=8.544 [Epoch 124][Batch 499], LR: 1.00E-03, Speed: 166.768 samples/sec, ObjLoss=21.789, BoxCenterLoss=14.608, BoxScaleLoss=5.005, ClassLoss=8.543 [Epoch 124][Batch 599], LR: 1.00E-03, Speed: 91.936 samples/sec, ObjLoss=21.788, BoxCenterLoss=14.608, BoxScaleLoss=5.004, ClassLoss=8.542 [Epoch 124][Batch 699], LR: 1.00E-03, Speed: 157.876 samples/sec, ObjLoss=21.787, BoxCenterLoss=14.608, BoxScaleLoss=5.004, ClassLoss=8.541 [Epoch 124][Batch 799], LR: 1.00E-03, Speed: 69.717 samples/sec, ObjLoss=21.786, BoxCenterLoss=14.607, BoxScaleLoss=5.004, ClassLoss=8.540 [Epoch 124][Batch 899], LR: 1.00E-03, Speed: 129.600 samples/sec, ObjLoss=21.785, BoxCenterLoss=14.607, BoxScaleLoss=5.004, ClassLoss=8.539 [Epoch 124][Batch 999], LR: 1.00E-03, Speed: 139.003 samples/sec, ObjLoss=21.784, BoxCenterLoss=14.607, BoxScaleLoss=5.004, ClassLoss=8.539 [Epoch 124][Batch 1099], LR: 1.00E-03, Speed: 135.870 samples/sec, ObjLoss=21.783, BoxCenterLoss=14.607, BoxScaleLoss=5.004, ClassLoss=8.538 [Epoch 124][Batch 1199], LR: 1.00E-03, Speed: 80.475 samples/sec, ObjLoss=21.782, BoxCenterLoss=14.607, BoxScaleLoss=5.004, ClassLoss=8.537 [Epoch 124][Batch 1299], LR: 1.00E-03, Speed: 146.619 samples/sec, ObjLoss=21.781, BoxCenterLoss=14.607, BoxScaleLoss=5.003, ClassLoss=8.536 [Epoch 124][Batch 1399], LR: 1.00E-03, Speed: 111.725 samples/sec, ObjLoss=21.781, BoxCenterLoss=14.607, BoxScaleLoss=5.003, ClassLoss=8.535 [Epoch 124][Batch 1499], LR: 1.00E-03, Speed: 135.174 samples/sec, ObjLoss=21.780, BoxCenterLoss=14.607, BoxScaleLoss=5.003, ClassLoss=8.535 [Epoch 124][Batch 1599], LR: 1.00E-03, Speed: 173.286 samples/sec, ObjLoss=21.779, BoxCenterLoss=14.607, BoxScaleLoss=5.003, ClassLoss=8.534 [Epoch 124][Batch 1699], LR: 1.00E-03, Speed: 86.822 samples/sec, ObjLoss=21.778, BoxCenterLoss=14.607, BoxScaleLoss=5.003, ClassLoss=8.533 [Epoch 124][Batch 1799], LR: 1.00E-03, Speed: 215.369 samples/sec, ObjLoss=21.777, BoxCenterLoss=14.607, BoxScaleLoss=5.003, ClassLoss=8.532 [Epoch 124] Training cost: 1185.080, ObjLoss=21.777, BoxCenterLoss=14.607, BoxScaleLoss=5.002, ClassLoss=8.532 [Epoch 124] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.419 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.191 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 ] = 0.289 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 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.421 person=32.7 bicycle=14.3 car=20.0 motorcycle=24.7 airplane=35.0 bus=39.8 train=35.8 truck=19.3 boat=9.6 traffic light=11.6 fire hydrant=42.1 stop sign=34.1 parking meter=22.2 bench=11.5 bird=14.4 cat=42.9 dog=35.0 horse=33.2 sheep=26.1 cow=28.8 elephant=39.3 bear=47.2 zebra=42.7 giraffe=42.7 backpack=5.4 umbrella=18.4 handbag=4.1 tie=13.1 suitcase=14.4 frisbee=37.2 skis=8.8 snowboard=12.8 sports ball=23.6 kite=21.0 baseball bat=9.2 baseball glove=19.2 skateboard=21.3 surfboard=15.5 tennis racket=21.1 bottle=14.0 wine glass=15.5 cup=19.4 fork=9.5 knife=3.8 spoon=2.5 bowl=19.4 banana=11.4 apple=7.7 sandwich=18.1 orange=12.2 broccoli=9.5 carrot=9.4 hot dog=13.8 pizza=27.7 donut=24.6 cake=16.8 chair=12.6 couch=26.1 potted plant=10.5 bed=25.6 dining table=12.5 toilet=35.5 tv=31.8 laptop=32.5 mouse=35.2 remote=7.0 keyboard=27.8 cell phone=15.1 microwave=21.4 oven=21.4 toaster=0.0 sink=17.6 refrigerator=27.9 book=5.3 clock=31.5 vase=17.3 scissors=16.0 teddy bear=23.4 hair drier=0.0 toothbrush=1.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 125][Batch 99], LR: 1.00E-03, Speed: 125.223 samples/sec, ObjLoss=21.776, BoxCenterLoss=14.607, BoxScaleLoss=5.002, ClassLoss=8.531 [Epoch 125][Batch 199], LR: 1.00E-03, Speed: 151.581 samples/sec, ObjLoss=21.775, BoxCenterLoss=14.607, BoxScaleLoss=5.002, ClassLoss=8.530 [Epoch 125][Batch 299], LR: 1.00E-03, Speed: 149.852 samples/sec, ObjLoss=21.774, BoxCenterLoss=14.607, BoxScaleLoss=5.002, ClassLoss=8.529 [Epoch 125][Batch 399], LR: 1.00E-03, Speed: 119.684 samples/sec, ObjLoss=21.773, BoxCenterLoss=14.606, BoxScaleLoss=5.002, ClassLoss=8.529 [Epoch 125][Batch 499], LR: 1.00E-03, Speed: 88.395 samples/sec, ObjLoss=21.772, BoxCenterLoss=14.606, BoxScaleLoss=5.002, ClassLoss=8.528 [Epoch 125][Batch 599], LR: 1.00E-03, Speed: 137.754 samples/sec, ObjLoss=21.771, BoxCenterLoss=14.606, BoxScaleLoss=5.001, ClassLoss=8.527 [Epoch 125][Batch 699], LR: 1.00E-03, Speed: 159.917 samples/sec, ObjLoss=21.770, BoxCenterLoss=14.606, BoxScaleLoss=5.001, ClassLoss=8.526 [Epoch 125][Batch 799], LR: 1.00E-03, Speed: 88.737 samples/sec, ObjLoss=21.769, BoxCenterLoss=14.606, BoxScaleLoss=5.001, ClassLoss=8.525 [Epoch 125][Batch 899], LR: 1.00E-03, Speed: 145.205 samples/sec, ObjLoss=21.768, BoxCenterLoss=14.606, BoxScaleLoss=5.001, ClassLoss=8.524 [Epoch 125][Batch 999], LR: 1.00E-03, Speed: 70.425 samples/sec, ObjLoss=21.767, BoxCenterLoss=14.606, BoxScaleLoss=5.001, ClassLoss=8.524 [Epoch 125][Batch 1099], LR: 1.00E-03, Speed: 77.583 samples/sec, ObjLoss=21.766, BoxCenterLoss=14.606, BoxScaleLoss=5.001, ClassLoss=8.523 [Epoch 125][Batch 1199], LR: 1.00E-03, Speed: 121.672 samples/sec, ObjLoss=21.766, BoxCenterLoss=14.606, BoxScaleLoss=5.000, ClassLoss=8.522 [Epoch 125][Batch 1299], LR: 1.00E-03, Speed: 139.712 samples/sec, ObjLoss=21.765, BoxCenterLoss=14.606, BoxScaleLoss=5.000, ClassLoss=8.521 [Epoch 125][Batch 1399], LR: 1.00E-03, Speed: 112.949 samples/sec, ObjLoss=21.763, BoxCenterLoss=14.605, BoxScaleLoss=5.000, ClassLoss=8.520 [Epoch 125][Batch 1499], LR: 1.00E-03, Speed: 90.903 samples/sec, ObjLoss=21.763, BoxCenterLoss=14.605, BoxScaleLoss=5.000, ClassLoss=8.520 [Epoch 125][Batch 1599], LR: 1.00E-03, Speed: 84.621 samples/sec, ObjLoss=21.762, BoxCenterLoss=14.605, BoxScaleLoss=5.000, ClassLoss=8.519 [Epoch 125][Batch 1699], LR: 1.00E-03, Speed: 119.747 samples/sec, ObjLoss=21.761, BoxCenterLoss=14.605, BoxScaleLoss=4.999, ClassLoss=8.518 [Epoch 125][Batch 1799], LR: 1.00E-03, Speed: 151.551 samples/sec, ObjLoss=21.760, BoxCenterLoss=14.605, BoxScaleLoss=4.999, ClassLoss=8.517 [Epoch 125] Training cost: 1255.893, ObjLoss=21.760, BoxCenterLoss=14.605, BoxScaleLoss=4.999, ClassLoss=8.517 [Epoch 125] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.409 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.084 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 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.191 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 ] = 0.291 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 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 person=31.5 bicycle=14.3 car=21.0 motorcycle=22.9 airplane=34.7 bus=38.0 train=35.5 truck=18.5 boat=10.8 traffic light=13.3 fire hydrant=33.9 stop sign=37.3 parking meter=20.8 bench=9.8 bird=15.5 cat=39.2 dog=30.9 horse=32.2 sheep=22.5 cow=25.7 elephant=37.2 bear=43.1 zebra=40.0 giraffe=43.2 backpack=4.5 umbrella=19.1 handbag=2.8 tie=13.3 suitcase=13.1 frisbee=28.3 skis=8.7 snowboard=11.7 sports ball=20.9 kite=19.0 baseball bat=11.6 baseball glove=19.2 skateboard=20.0 surfboard=16.0 tennis racket=20.3 bottle=15.0 wine glass=11.8 cup=19.8 fork=6.5 knife=3.7 spoon=1.8 bowl=18.8 banana=10.9 apple=9.1 sandwich=17.4 orange=15.1 broccoli=10.3 carrot=10.4 hot dog=16.6 pizza=26.2 donut=20.0 cake=17.7 chair=11.9 couch=23.3 potted plant=10.7 bed=26.5 dining table=16.5 toilet=30.9 tv=36.1 laptop=30.8 mouse=26.5 remote=8.2 keyboard=27.2 cell phone=14.4 microwave=24.7 oven=18.3 toaster=0.0 sink=19.8 refrigerator=29.8 book=5.6 clock=28.3 vase=16.9 scissors=16.5 teddy bear=20.5 hair drier=0.0 toothbrush=2.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.7 [Epoch 126][Batch 99], LR: 1.00E-03, Speed: 111.208 samples/sec, ObjLoss=21.759, BoxCenterLoss=14.605, BoxScaleLoss=4.999, ClassLoss=8.516 [Epoch 126][Batch 199], LR: 1.00E-03, Speed: 132.292 samples/sec, ObjLoss=21.758, BoxCenterLoss=14.605, BoxScaleLoss=4.999, ClassLoss=8.515 [Epoch 126][Batch 299], LR: 1.00E-03, Speed: 144.873 samples/sec, ObjLoss=21.757, BoxCenterLoss=14.605, BoxScaleLoss=4.999, ClassLoss=8.515 [Epoch 126][Batch 399], LR: 1.00E-03, Speed: 86.725 samples/sec, ObjLoss=21.757, BoxCenterLoss=14.605, BoxScaleLoss=4.999, ClassLoss=8.514 [Epoch 126][Batch 499], LR: 1.00E-03, Speed: 144.609 samples/sec, ObjLoss=21.756, BoxCenterLoss=14.605, BoxScaleLoss=4.998, ClassLoss=8.513 [Epoch 126][Batch 599], LR: 1.00E-03, Speed: 132.253 samples/sec, ObjLoss=21.755, BoxCenterLoss=14.605, BoxScaleLoss=4.998, ClassLoss=8.512 [Epoch 126][Batch 699], LR: 1.00E-03, Speed: 81.049 samples/sec, ObjLoss=21.754, BoxCenterLoss=14.605, BoxScaleLoss=4.998, ClassLoss=8.512 [Epoch 126][Batch 799], LR: 1.00E-03, Speed: 92.670 samples/sec, ObjLoss=21.753, BoxCenterLoss=14.604, BoxScaleLoss=4.998, ClassLoss=8.511 [Epoch 126][Batch 899], LR: 1.00E-03, Speed: 125.034 samples/sec, ObjLoss=21.752, BoxCenterLoss=14.604, BoxScaleLoss=4.998, ClassLoss=8.510 [Epoch 126][Batch 999], LR: 1.00E-03, Speed: 114.823 samples/sec, ObjLoss=21.751, BoxCenterLoss=14.604, BoxScaleLoss=4.998, ClassLoss=8.509 [Epoch 126][Batch 1099], LR: 1.00E-03, Speed: 177.917 samples/sec, ObjLoss=21.750, BoxCenterLoss=14.604, BoxScaleLoss=4.997, ClassLoss=8.508 [Epoch 126][Batch 1199], LR: 1.00E-03, Speed: 141.100 samples/sec, ObjLoss=21.749, BoxCenterLoss=14.604, BoxScaleLoss=4.997, ClassLoss=8.507 [Epoch 126][Batch 1299], LR: 1.00E-03, Speed: 71.457 samples/sec, ObjLoss=21.748, BoxCenterLoss=14.604, BoxScaleLoss=4.997, ClassLoss=8.506 [Epoch 126][Batch 1399], LR: 1.00E-03, Speed: 60.746 samples/sec, ObjLoss=21.747, BoxCenterLoss=14.604, BoxScaleLoss=4.997, ClassLoss=8.506 [Epoch 126][Batch 1499], LR: 1.00E-03, Speed: 137.573 samples/sec, ObjLoss=21.746, BoxCenterLoss=14.604, BoxScaleLoss=4.997, ClassLoss=8.505 [Epoch 126][Batch 1599], LR: 1.00E-03, Speed: 65.482 samples/sec, ObjLoss=21.745, BoxCenterLoss=14.604, BoxScaleLoss=4.997, ClassLoss=8.504 [Epoch 126][Batch 1699], LR: 1.00E-03, Speed: 111.751 samples/sec, ObjLoss=21.745, BoxCenterLoss=14.604, BoxScaleLoss=4.997, ClassLoss=8.504 [Epoch 126][Batch 1799], LR: 1.00E-03, Speed: 151.703 samples/sec, ObjLoss=21.744, BoxCenterLoss=14.604, BoxScaleLoss=4.996, ClassLoss=8.503 [Epoch 126] Training cost: 1260.092, ObjLoss=21.744, BoxCenterLoss=14.604, BoxScaleLoss=4.996, ClassLoss=8.503 [Epoch 126] 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 ] = 0.401 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.174 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 ] = 0.195 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 ] = 0.191 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 ] = 0.284 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 ] = 0.278 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 person=32.3 bicycle=15.2 car=20.8 motorcycle=25.3 airplane=38.5 bus=37.3 train=41.3 truck=19.2 boat=11.3 traffic light=8.0 fire hydrant=38.2 stop sign=37.7 parking meter=23.9 bench=9.5 bird=11.1 cat=42.5 dog=33.3 horse=33.8 sheep=24.5 cow=28.3 elephant=37.6 bear=36.6 zebra=41.3 giraffe=39.8 backpack=4.0 umbrella=18.5 handbag=3.1 tie=11.9 suitcase=12.3 frisbee=26.0 skis=6.4 snowboard=8.8 sports ball=20.9 kite=11.4 baseball bat=9.7 baseball glove=18.8 skateboard=22.2 surfboard=13.7 tennis racket=22.9 bottle=15.8 wine glass=13.1 cup=19.6 fork=7.9 knife=2.6 spoon=3.1 bowl=19.4 banana=10.9 apple=7.2 sandwich=17.6 orange=13.8 broccoli=9.7 carrot=7.1 hot dog=11.6 pizza=28.9 donut=21.6 cake=16.7 chair=11.5 couch=25.0 potted plant=11.9 bed=28.2 dining table=17.3 toilet=36.1 tv=33.6 laptop=34.6 mouse=29.8 remote=7.5 keyboard=25.3 cell phone=13.9 microwave=27.2 oven=21.1 toaster=0.0 sink=17.6 refrigerator=22.7 book=4.4 clock=29.2 vase=17.2 scissors=17.3 teddy bear=23.0 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.8 [Epoch 127][Batch 99], LR: 1.00E-03, Speed: 164.216 samples/sec, ObjLoss=21.743, BoxCenterLoss=14.604, BoxScaleLoss=4.996, ClassLoss=8.502 [Epoch 127][Batch 199], LR: 1.00E-03, Speed: 144.251 samples/sec, ObjLoss=21.742, BoxCenterLoss=14.604, BoxScaleLoss=4.996, ClassLoss=8.501 [Epoch 127][Batch 299], LR: 1.00E-03, Speed: 95.591 samples/sec, ObjLoss=21.741, BoxCenterLoss=14.603, BoxScaleLoss=4.996, ClassLoss=8.500 [Epoch 127][Batch 399], LR: 1.00E-03, Speed: 102.633 samples/sec, ObjLoss=21.740, BoxCenterLoss=14.603, BoxScaleLoss=4.995, ClassLoss=8.499 [Epoch 127][Batch 499], LR: 1.00E-03, Speed: 129.317 samples/sec, ObjLoss=21.739, BoxCenterLoss=14.603, BoxScaleLoss=4.995, ClassLoss=8.498 [Epoch 127][Batch 599], LR: 1.00E-03, Speed: 83.473 samples/sec, ObjLoss=21.738, BoxCenterLoss=14.603, BoxScaleLoss=4.995, ClassLoss=8.498 [Epoch 127][Batch 699], LR: 1.00E-03, Speed: 88.363 samples/sec, ObjLoss=21.737, BoxCenterLoss=14.603, BoxScaleLoss=4.995, ClassLoss=8.497 [Epoch 127][Batch 799], LR: 1.00E-03, Speed: 161.390 samples/sec, ObjLoss=21.736, BoxCenterLoss=14.603, BoxScaleLoss=4.995, ClassLoss=8.496 [Epoch 127][Batch 899], LR: 1.00E-03, Speed: 109.635 samples/sec, ObjLoss=21.736, BoxCenterLoss=14.603, BoxScaleLoss=4.995, ClassLoss=8.495 [Epoch 127][Batch 999], LR: 1.00E-03, Speed: 97.035 samples/sec, ObjLoss=21.735, BoxCenterLoss=14.603, BoxScaleLoss=4.994, ClassLoss=8.494 [Epoch 127][Batch 1099], LR: 1.00E-03, Speed: 121.433 samples/sec, ObjLoss=21.734, BoxCenterLoss=14.603, BoxScaleLoss=4.994, ClassLoss=8.493 [Epoch 127][Batch 1199], LR: 1.00E-03, Speed: 138.268 samples/sec, ObjLoss=21.733, BoxCenterLoss=14.603, BoxScaleLoss=4.994, ClassLoss=8.493 [Epoch 127][Batch 1299], LR: 1.00E-03, Speed: 146.512 samples/sec, ObjLoss=21.732, BoxCenterLoss=14.603, BoxScaleLoss=4.994, ClassLoss=8.492 [Epoch 127][Batch 1399], LR: 1.00E-03, Speed: 168.225 samples/sec, ObjLoss=21.731, BoxCenterLoss=14.603, BoxScaleLoss=4.994, ClassLoss=8.491 [Epoch 127][Batch 1499], LR: 1.00E-03, Speed: 145.985 samples/sec, ObjLoss=21.730, BoxCenterLoss=14.603, BoxScaleLoss=4.994, ClassLoss=8.490 [Epoch 127][Batch 1599], LR: 1.00E-03, Speed: 151.242 samples/sec, ObjLoss=21.729, BoxCenterLoss=14.603, BoxScaleLoss=4.993, ClassLoss=8.490 [Epoch 127][Batch 1699], LR: 1.00E-03, Speed: 125.732 samples/sec, ObjLoss=21.729, BoxCenterLoss=14.602, BoxScaleLoss=4.993, ClassLoss=8.489 [Epoch 127][Batch 1799], LR: 1.00E-03, Speed: 139.113 samples/sec, ObjLoss=21.728, BoxCenterLoss=14.603, BoxScaleLoss=4.993, ClassLoss=8.488 [Epoch 127] Training cost: 1288.107, ObjLoss=21.728, BoxCenterLoss=14.603, BoxScaleLoss=4.993, ClassLoss=8.488 [Epoch 127] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172 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 ] = 0.220 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 ] = 0.195 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 ] = 0.295 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 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416 person=32.6 bicycle=15.1 car=21.5 motorcycle=22.2 airplane=40.4 bus=40.9 train=47.0 truck=16.7 boat=11.7 traffic light=12.0 fire hydrant=31.4 stop sign=36.6 parking meter=24.4 bench=11.1 bird=14.3 cat=36.5 dog=38.5 horse=34.6 sheep=26.8 cow=28.9 elephant=37.6 bear=35.3 zebra=42.6 giraffe=41.7 backpack=5.3 umbrella=20.4 handbag=3.5 tie=13.4 suitcase=14.5 frisbee=32.7 skis=8.3 snowboard=14.0 sports ball=19.1 kite=19.4 baseball bat=9.9 baseball glove=16.2 skateboard=21.3 surfboard=16.1 tennis racket=24.7 bottle=14.2 wine glass=12.3 cup=19.6 fork=8.6 knife=3.1 spoon=2.5 bowl=20.7 banana=12.0 apple=7.6 sandwich=16.2 orange=14.6 broccoli=11.1 carrot=7.6 hot dog=12.5 pizza=23.8 donut=20.5 cake=13.6 chair=12.7 couch=29.0 potted plant=11.8 bed=31.1 dining table=18.4 toilet=35.8 tv=35.6 laptop=27.4 mouse=31.3 remote=10.4 keyboard=27.3 cell phone=16.6 microwave=27.0 oven=20.0 toaster=0.0 sink=20.2 refrigerator=26.6 book=5.1 clock=30.8 vase=17.7 scissors=17.0 teddy bear=20.5 hair drier=0.0 toothbrush=4.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 128][Batch 99], LR: 1.00E-03, Speed: 140.906 samples/sec, ObjLoss=21.727, BoxCenterLoss=14.602, BoxScaleLoss=4.993, ClassLoss=8.487 [Epoch 128][Batch 199], LR: 1.00E-03, Speed: 150.691 samples/sec, ObjLoss=21.726, BoxCenterLoss=14.602, BoxScaleLoss=4.993, ClassLoss=8.487 [Epoch 128][Batch 299], LR: 1.00E-03, Speed: 158.674 samples/sec, ObjLoss=21.725, BoxCenterLoss=14.602, BoxScaleLoss=4.993, ClassLoss=8.486 [Epoch 128][Batch 399], LR: 1.00E-03, Speed: 119.795 samples/sec, ObjLoss=21.724, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.485 [Epoch 128][Batch 499], LR: 1.00E-03, Speed: 82.648 samples/sec, ObjLoss=21.723, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.484 [Epoch 128][Batch 599], LR: 1.00E-03, Speed: 161.512 samples/sec, ObjLoss=21.723, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.484 [Epoch 128][Batch 699], LR: 1.00E-03, Speed: 142.251 samples/sec, ObjLoss=21.722, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.483 [Epoch 128][Batch 799], LR: 1.00E-03, Speed: 89.801 samples/sec, ObjLoss=21.721, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.482 [Epoch 128][Batch 899], LR: 1.00E-03, Speed: 131.299 samples/sec, ObjLoss=21.720, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.482 [Epoch 128][Batch 999], LR: 1.00E-03, Speed: 167.790 samples/sec, ObjLoss=21.719, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.481 [Epoch 128][Batch 1099], LR: 1.00E-03, Speed: 188.063 samples/sec, ObjLoss=21.718, BoxCenterLoss=14.602, BoxScaleLoss=4.992, ClassLoss=8.480 [Epoch 128][Batch 1199], LR: 1.00E-03, Speed: 127.087 samples/sec, ObjLoss=21.718, BoxCenterLoss=14.602, BoxScaleLoss=4.991, ClassLoss=8.479 [Epoch 128][Batch 1299], LR: 1.00E-03, Speed: 95.624 samples/sec, ObjLoss=21.717, BoxCenterLoss=14.602, BoxScaleLoss=4.991, ClassLoss=8.479 [Epoch 128][Batch 1399], LR: 1.00E-03, Speed: 166.018 samples/sec, ObjLoss=21.716, BoxCenterLoss=14.602, BoxScaleLoss=4.991, ClassLoss=8.478 [Epoch 128][Batch 1499], LR: 1.00E-03, Speed: 132.451 samples/sec, ObjLoss=21.715, BoxCenterLoss=14.602, BoxScaleLoss=4.991, ClassLoss=8.477 [Epoch 128][Batch 1599], LR: 1.00E-03, Speed: 84.364 samples/sec, ObjLoss=21.714, BoxCenterLoss=14.601, BoxScaleLoss=4.991, ClassLoss=8.476 [Epoch 128][Batch 1699], LR: 1.00E-03, Speed: 77.280 samples/sec, ObjLoss=21.713, BoxCenterLoss=14.601, BoxScaleLoss=4.991, ClassLoss=8.475 [Epoch 128][Batch 1799], LR: 1.00E-03, Speed: 148.274 samples/sec, ObjLoss=21.713, BoxCenterLoss=14.601, BoxScaleLoss=4.990, ClassLoss=8.475 [Epoch 128] Training cost: 1239.222, ObjLoss=21.713, BoxCenterLoss=14.601, BoxScaleLoss=4.990, ClassLoss=8.474 [Epoch 128] 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 ] = 0.191 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 ] = 0.206 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 ] = 0.195 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 ] = 0.293 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 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430 person=33.5 bicycle=14.1 car=22.0 motorcycle=24.6 airplane=40.9 bus=39.9 train=44.4 truck=17.6 boat=11.2 traffic light=13.1 fire hydrant=38.4 stop sign=37.2 parking meter=22.3 bench=11.5 bird=15.6 cat=40.7 dog=35.3 horse=35.4 sheep=27.1 cow=33.5 elephant=41.5 bear=47.2 zebra=43.8 giraffe=45.8 backpack=4.7 umbrella=18.2 handbag=3.5 tie=13.5 suitcase=16.6 frisbee=29.2 skis=7.6 snowboard=11.2 sports ball=22.8 kite=19.8 baseball bat=11.1 baseball glove=19.1 skateboard=22.6 surfboard=15.2 tennis racket=22.5 bottle=16.3 wine glass=15.1 cup=20.5 fork=9.1 knife=3.2 spoon=2.8 bowl=19.8 banana=9.5 apple=9.0 sandwich=19.2 orange=13.8 broccoli=10.3 carrot=8.7 hot dog=14.2 pizza=29.3 donut=20.1 cake=17.0 chair=13.3 couch=24.1 potted plant=9.8 bed=25.2 dining table=13.5 toilet=35.8 tv=34.0 laptop=32.8 mouse=30.2 remote=7.6 keyboard=23.6 cell phone=15.1 microwave=22.6 oven=21.4 toaster=0.0 sink=16.4 refrigerator=25.7 book=5.7 clock=29.7 vase=18.1 scissors=19.0 teddy bear=23.8 hair drier=0.0 toothbrush=1.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 129][Batch 99], LR: 1.00E-03, Speed: 169.390 samples/sec, ObjLoss=21.712, BoxCenterLoss=14.601, BoxScaleLoss=4.990, ClassLoss=8.474 [Epoch 129][Batch 199], LR: 1.00E-03, Speed: 65.609 samples/sec, ObjLoss=21.711, BoxCenterLoss=14.601, BoxScaleLoss=4.990, ClassLoss=8.473 [Epoch 129][Batch 299], LR: 1.00E-03, Speed: 160.599 samples/sec, ObjLoss=21.710, BoxCenterLoss=14.601, BoxScaleLoss=4.990, ClassLoss=8.472 [Epoch 129][Batch 399], LR: 1.00E-03, Speed: 149.521 samples/sec, ObjLoss=21.709, BoxCenterLoss=14.601, BoxScaleLoss=4.990, ClassLoss=8.471 [Epoch 129][Batch 499], LR: 1.00E-03, Speed: 129.158 samples/sec, ObjLoss=21.708, BoxCenterLoss=14.601, BoxScaleLoss=4.989, ClassLoss=8.470 [Epoch 129][Batch 599], LR: 1.00E-03, Speed: 132.890 samples/sec, ObjLoss=21.707, BoxCenterLoss=14.601, BoxScaleLoss=4.989, ClassLoss=8.470 [Epoch 129][Batch 699], LR: 1.00E-03, Speed: 144.155 samples/sec, ObjLoss=21.706, BoxCenterLoss=14.600, BoxScaleLoss=4.989, ClassLoss=8.469 [Epoch 129][Batch 799], LR: 1.00E-03, Speed: 71.145 samples/sec, ObjLoss=21.705, BoxCenterLoss=14.600, BoxScaleLoss=4.989, ClassLoss=8.468 [Epoch 129][Batch 899], LR: 1.00E-03, Speed: 124.159 samples/sec, ObjLoss=21.703, BoxCenterLoss=14.600, BoxScaleLoss=4.989, ClassLoss=8.468 [Epoch 129][Batch 999], LR: 1.00E-03, Speed: 155.884 samples/sec, ObjLoss=21.703, BoxCenterLoss=14.600, BoxScaleLoss=4.989, ClassLoss=8.467 [Epoch 129][Batch 1099], LR: 1.00E-03, Speed: 135.967 samples/sec, ObjLoss=21.702, BoxCenterLoss=14.600, BoxScaleLoss=4.989, ClassLoss=8.466 [Epoch 129][Batch 1199], LR: 1.00E-03, Speed: 132.242 samples/sec, ObjLoss=21.701, BoxCenterLoss=14.600, BoxScaleLoss=4.988, ClassLoss=8.465 [Epoch 129][Batch 1299], LR: 1.00E-03, Speed: 127.747 samples/sec, ObjLoss=21.700, BoxCenterLoss=14.600, BoxScaleLoss=4.988, ClassLoss=8.465 [Epoch 129][Batch 1399], LR: 1.00E-03, Speed: 119.804 samples/sec, ObjLoss=21.699, BoxCenterLoss=14.600, BoxScaleLoss=4.988, ClassLoss=8.464 [Epoch 129][Batch 1499], LR: 1.00E-03, Speed: 143.295 samples/sec, ObjLoss=21.699, BoxCenterLoss=14.600, BoxScaleLoss=4.988, ClassLoss=8.463 [Epoch 129][Batch 1599], LR: 1.00E-03, Speed: 88.275 samples/sec, ObjLoss=21.698, BoxCenterLoss=14.600, BoxScaleLoss=4.988, ClassLoss=8.463 [Epoch 129][Batch 1699], LR: 1.00E-03, Speed: 59.107 samples/sec, ObjLoss=21.697, BoxCenterLoss=14.600, BoxScaleLoss=4.988, ClassLoss=8.462 [Epoch 129][Batch 1799], LR: 1.00E-03, Speed: 150.138 samples/sec, ObjLoss=21.696, BoxCenterLoss=14.599, BoxScaleLoss=4.988, ClassLoss=8.461 [Epoch 129] Training cost: 1276.128, ObjLoss=21.696, BoxCenterLoss=14.599, BoxScaleLoss=4.987, ClassLoss=8.461 [Epoch 129] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.189 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.404 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.149 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 ] = 0.269 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 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.270 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 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.385 person=30.2 bicycle=12.6 car=20.6 motorcycle=22.3 airplane=39.6 bus=38.2 train=42.6 truck=18.1 boat=11.0 traffic light=11.4 fire hydrant=32.6 stop sign=29.7 parking meter=21.0 bench=11.3 bird=15.2 cat=41.4 dog=31.5 horse=33.7 sheep=24.0 cow=30.2 elephant=33.8 bear=26.8 zebra=40.1 giraffe=41.6 backpack=4.5 umbrella=17.1 handbag=2.4 tie=13.6 suitcase=12.1 frisbee=27.6 skis=7.3 snowboard=11.6 sports ball=20.9 kite=21.7 baseball bat=10.1 baseball glove=14.5 skateboard=16.9 surfboard=14.2 tennis racket=20.3 bottle=12.8 wine glass=11.1 cup=17.7 fork=9.2 knife=3.1 spoon=1.9 bowl=18.1 banana=8.3 apple=5.6 sandwich=16.2 orange=10.2 broccoli=8.8 carrot=5.8 hot dog=13.3 pizza=28.4 donut=16.1 cake=14.5 chair=11.8 couch=25.8 potted plant=10.6 bed=24.2 dining table=12.4 toilet=30.1 tv=30.1 laptop=29.9 mouse=23.8 remote=8.5 keyboard=29.7 cell phone=12.7 microwave=33.3 oven=19.5 toaster=0.0 sink=16.6 refrigerator=23.2 book=5.1 clock=30.3 vase=16.2 scissors=13.8 teddy bear=22.2 hair drier=0.0 toothbrush=3.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.9 [Epoch 130][Batch 99], LR: 1.00E-03, Speed: 142.956 samples/sec, ObjLoss=21.695, BoxCenterLoss=14.599, BoxScaleLoss=4.987, ClassLoss=8.460 [Epoch 130][Batch 199], LR: 1.00E-03, Speed: 128.984 samples/sec, ObjLoss=21.694, BoxCenterLoss=14.599, BoxScaleLoss=4.987, ClassLoss=8.459 [Epoch 130][Batch 299], LR: 1.00E-03, Speed: 66.957 samples/sec, ObjLoss=21.693, BoxCenterLoss=14.599, BoxScaleLoss=4.987, ClassLoss=8.458 [Epoch 130][Batch 399], LR: 1.00E-03, Speed: 95.215 samples/sec, ObjLoss=21.692, BoxCenterLoss=14.599, BoxScaleLoss=4.987, ClassLoss=8.458 [Epoch 130][Batch 499], LR: 1.00E-03, Speed: 131.060 samples/sec, ObjLoss=21.691, BoxCenterLoss=14.599, BoxScaleLoss=4.987, ClassLoss=8.457 [Epoch 130][Batch 599], LR: 1.00E-03, Speed: 133.980 samples/sec, ObjLoss=21.690, BoxCenterLoss=14.599, BoxScaleLoss=4.986, ClassLoss=8.456 [Epoch 130][Batch 699], LR: 1.00E-03, Speed: 122.876 samples/sec, ObjLoss=21.689, BoxCenterLoss=14.599, BoxScaleLoss=4.986, ClassLoss=8.455 [Epoch 130][Batch 799], LR: 1.00E-03, Speed: 124.614 samples/sec, ObjLoss=21.689, BoxCenterLoss=14.599, BoxScaleLoss=4.986, ClassLoss=8.455 [Epoch 130][Batch 899], LR: 1.00E-03, Speed: 121.524 samples/sec, ObjLoss=21.688, BoxCenterLoss=14.599, BoxScaleLoss=4.986, ClassLoss=8.454 [Epoch 130][Batch 999], LR: 1.00E-03, Speed: 112.439 samples/sec, ObjLoss=21.687, BoxCenterLoss=14.598, BoxScaleLoss=4.986, ClassLoss=8.453 [Epoch 130][Batch 1099], LR: 1.00E-03, Speed: 157.932 samples/sec, ObjLoss=21.686, BoxCenterLoss=14.598, BoxScaleLoss=4.986, ClassLoss=8.453 [Epoch 130][Batch 1199], LR: 1.00E-03, Speed: 61.281 samples/sec, ObjLoss=21.685, BoxCenterLoss=14.598, BoxScaleLoss=4.986, ClassLoss=8.452 [Epoch 130][Batch 1299], LR: 1.00E-03, Speed: 159.616 samples/sec, ObjLoss=21.684, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.451 [Epoch 130][Batch 1399], LR: 1.00E-03, Speed: 97.576 samples/sec, ObjLoss=21.683, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.451 [Epoch 130][Batch 1499], LR: 1.00E-03, Speed: 127.075 samples/sec, ObjLoss=21.682, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.450 [Epoch 130][Batch 1599], LR: 1.00E-03, Speed: 88.938 samples/sec, ObjLoss=21.682, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.449 [Epoch 130][Batch 1699], LR: 1.00E-03, Speed: 82.102 samples/sec, ObjLoss=21.681, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.448 [Epoch 130][Batch 1799], LR: 1.00E-03, Speed: 125.812 samples/sec, ObjLoss=21.680, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.448 [Epoch 130] Training cost: 1258.806, ObjLoss=21.680, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.447 [Epoch 130] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.411 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.161 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 ] = 0.209 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 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.274 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 ] = 0.136 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 ] = 0.382 person=31.6 bicycle=13.2 car=19.1 motorcycle=23.1 airplane=36.7 bus=40.9 train=39.2 truck=17.3 boat=9.2 traffic light=14.2 fire hydrant=39.9 stop sign=37.4 parking meter=22.1 bench=9.3 bird=16.2 cat=37.5 dog=28.2 horse=27.6 sheep=23.9 cow=25.2 elephant=30.5 bear=37.6 zebra=40.4 giraffe=43.1 backpack=5.1 umbrella=16.1 handbag=3.3 tie=13.6 suitcase=17.3 frisbee=28.7 skis=8.9 snowboard=9.1 sports ball=25.6 kite=24.8 baseball bat=9.6 baseball glove=16.6 skateboard=20.9 surfboard=16.8 tennis racket=19.4 bottle=14.0 wine glass=14.2 cup=18.2 fork=7.5 knife=3.8 spoon=2.4 bowl=20.1 banana=12.3 apple=6.3 sandwich=11.7 orange=11.7 broccoli=10.6 carrot=7.5 hot dog=14.3 pizza=24.7 donut=20.0 cake=16.4 chair=12.1 couch=24.0 potted plant=10.0 bed=27.5 dining table=15.6 toilet=33.5 tv=33.0 laptop=30.3 mouse=29.8 remote=8.3 keyboard=27.2 cell phone=15.5 microwave=27.6 oven=18.7 toaster=0.0 sink=18.9 refrigerator=26.4 book=5.3 clock=29.3 vase=17.9 scissors=13.4 teddy bear=18.8 hair drier=0.0 toothbrush=4.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.5 [Epoch 131][Batch 99], LR: 1.00E-03, Speed: 152.915 samples/sec, ObjLoss=21.679, BoxCenterLoss=14.598, BoxScaleLoss=4.985, ClassLoss=8.447 [Epoch 131][Batch 199], LR: 1.00E-03, Speed: 145.844 samples/sec, ObjLoss=21.678, BoxCenterLoss=14.598, BoxScaleLoss=4.984, ClassLoss=8.446 [Epoch 131][Batch 299], LR: 1.00E-03, Speed: 91.067 samples/sec, ObjLoss=21.678, BoxCenterLoss=14.598, BoxScaleLoss=4.984, ClassLoss=8.446 [Epoch 131][Batch 399], LR: 1.00E-03, Speed: 96.435 samples/sec, ObjLoss=21.677, BoxCenterLoss=14.598, BoxScaleLoss=4.984, ClassLoss=8.445 [Epoch 131][Batch 499], LR: 1.00E-03, Speed: 106.378 samples/sec, ObjLoss=21.676, BoxCenterLoss=14.598, BoxScaleLoss=4.984, ClassLoss=8.444 [Epoch 131][Batch 599], LR: 1.00E-03, Speed: 138.694 samples/sec, ObjLoss=21.676, BoxCenterLoss=14.598, BoxScaleLoss=4.984, ClassLoss=8.443 [Epoch 131][Batch 699], LR: 1.00E-03, Speed: 134.387 samples/sec, ObjLoss=21.675, BoxCenterLoss=14.598, BoxScaleLoss=4.984, ClassLoss=8.443 [Epoch 131][Batch 799], LR: 1.00E-03, Speed: 145.047 samples/sec, ObjLoss=21.674, BoxCenterLoss=14.598, BoxScaleLoss=4.984, ClassLoss=8.442 [Epoch 131][Batch 899], LR: 1.00E-03, Speed: 75.881 samples/sec, ObjLoss=21.673, BoxCenterLoss=14.597, BoxScaleLoss=4.984, ClassLoss=8.441 [Epoch 131][Batch 999], LR: 1.00E-03, Speed: 96.046 samples/sec, ObjLoss=21.672, BoxCenterLoss=14.597, BoxScaleLoss=4.983, ClassLoss=8.441 [Epoch 131][Batch 1099], LR: 1.00E-03, Speed: 126.825 samples/sec, ObjLoss=21.671, BoxCenterLoss=14.597, BoxScaleLoss=4.983, ClassLoss=8.440 [Epoch 131][Batch 1199], LR: 1.00E-03, Speed: 139.794 samples/sec, ObjLoss=21.670, BoxCenterLoss=14.597, BoxScaleLoss=4.983, ClassLoss=8.439 [Epoch 131][Batch 1299], LR: 1.00E-03, Speed: 86.886 samples/sec, ObjLoss=21.669, BoxCenterLoss=14.597, BoxScaleLoss=4.983, ClassLoss=8.439 [Epoch 131][Batch 1399], LR: 1.00E-03, Speed: 128.818 samples/sec, ObjLoss=21.668, BoxCenterLoss=14.597, BoxScaleLoss=4.983, ClassLoss=8.438 [Epoch 131][Batch 1499], LR: 1.00E-03, Speed: 58.358 samples/sec, ObjLoss=21.667, BoxCenterLoss=14.597, BoxScaleLoss=4.983, ClassLoss=8.437 [Epoch 131][Batch 1599], LR: 1.00E-03, Speed: 91.495 samples/sec, ObjLoss=21.666, BoxCenterLoss=14.597, BoxScaleLoss=4.982, ClassLoss=8.436 [Epoch 131][Batch 1699], LR: 1.00E-03, Speed: 91.627 samples/sec, ObjLoss=21.665, BoxCenterLoss=14.596, BoxScaleLoss=4.982, ClassLoss=8.435 [Epoch 131][Batch 1799], LR: 1.00E-03, Speed: 146.554 samples/sec, ObjLoss=21.664, BoxCenterLoss=14.596, BoxScaleLoss=4.982, ClassLoss=8.435 [Epoch 131] Training cost: 1245.582, ObjLoss=21.664, BoxCenterLoss=14.596, BoxScaleLoss=4.982, ClassLoss=8.434 [Epoch 131] 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 ] = 0.415 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.171 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 ] = 0.201 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 ] = 0.192 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 ] = 0.291 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 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430 person=30.7 bicycle=13.2 car=18.2 motorcycle=23.1 airplane=38.9 bus=38.3 train=41.8 truck=16.9 boat=10.6 traffic light=12.5 fire hydrant=39.8 stop sign=40.3 parking meter=24.6 bench=11.8 bird=16.1 cat=39.4 dog=36.4 horse=28.7 sheep=24.2 cow=27.5 elephant=35.1 bear=40.4 zebra=37.2 giraffe=47.4 backpack=4.8 umbrella=18.8 handbag=3.9 tie=11.9 suitcase=16.5 frisbee=34.4 skis=6.5 snowboard=11.8 sports ball=18.7 kite=22.5 baseball bat=9.9 baseball glove=14.2 skateboard=21.7 surfboard=18.0 tennis racket=17.6 bottle=14.8 wine glass=15.0 cup=18.6 fork=7.9 knife=4.2 spoon=2.5 bowl=21.6 banana=10.2 apple=7.2 sandwich=17.6 orange=13.9 broccoli=10.7 carrot=8.8 hot dog=13.3 pizza=24.5 donut=20.9 cake=12.3 chair=12.0 couch=27.4 potted plant=11.9 bed=25.6 dining table=13.2 toilet=33.6 tv=33.1 laptop=31.3 mouse=29.8 remote=9.5 keyboard=26.4 cell phone=13.7 microwave=27.9 oven=15.8 toaster=0.0 sink=19.4 refrigerator=27.0 book=4.6 clock=29.6 vase=18.0 scissors=19.9 teddy bear=18.7 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.0 [Epoch 132][Batch 99], LR: 1.00E-03, Speed: 163.191 samples/sec, ObjLoss=21.663, BoxCenterLoss=14.596, BoxScaleLoss=4.982, ClassLoss=8.434 [Epoch 132][Batch 199], LR: 1.00E-03, Speed: 132.538 samples/sec, ObjLoss=21.662, BoxCenterLoss=14.596, BoxScaleLoss=4.982, ClassLoss=8.433 [Epoch 132][Batch 299], LR: 1.00E-03, Speed: 127.344 samples/sec, ObjLoss=21.661, BoxCenterLoss=14.596, BoxScaleLoss=4.982, ClassLoss=8.432 [Epoch 132][Batch 399], LR: 1.00E-03, Speed: 63.039 samples/sec, ObjLoss=21.660, BoxCenterLoss=14.596, BoxScaleLoss=4.982, ClassLoss=8.432 [Epoch 132][Batch 499], LR: 1.00E-03, Speed: 131.964 samples/sec, ObjLoss=21.659, BoxCenterLoss=14.596, BoxScaleLoss=4.981, ClassLoss=8.431 [Epoch 132][Batch 599], LR: 1.00E-03, Speed: 108.564 samples/sec, ObjLoss=21.658, BoxCenterLoss=14.596, BoxScaleLoss=4.981, ClassLoss=8.430 [Epoch 132][Batch 699], LR: 1.00E-03, Speed: 101.936 samples/sec, ObjLoss=21.658, BoxCenterLoss=14.596, BoxScaleLoss=4.981, ClassLoss=8.429 [Epoch 132][Batch 799], LR: 1.00E-03, Speed: 51.545 samples/sec, ObjLoss=21.657, BoxCenterLoss=14.595, BoxScaleLoss=4.981, ClassLoss=8.429 [Epoch 132][Batch 899], LR: 1.00E-03, Speed: 83.039 samples/sec, ObjLoss=21.656, BoxCenterLoss=14.595, BoxScaleLoss=4.981, ClassLoss=8.428 [Epoch 132][Batch 999], LR: 1.00E-03, Speed: 153.494 samples/sec, ObjLoss=21.655, BoxCenterLoss=14.595, BoxScaleLoss=4.980, ClassLoss=8.427 [Epoch 132][Batch 1099], LR: 1.00E-03, Speed: 129.314 samples/sec, ObjLoss=21.655, BoxCenterLoss=14.595, BoxScaleLoss=4.980, ClassLoss=8.426 [Epoch 132][Batch 1199], LR: 1.00E-03, Speed: 126.796 samples/sec, ObjLoss=21.653, BoxCenterLoss=14.595, BoxScaleLoss=4.980, ClassLoss=8.426 [Epoch 132][Batch 1299], LR: 1.00E-03, Speed: 100.278 samples/sec, ObjLoss=21.652, BoxCenterLoss=14.595, BoxScaleLoss=4.980, ClassLoss=8.425 [Epoch 132][Batch 1399], LR: 1.00E-03, Speed: 130.705 samples/sec, ObjLoss=21.652, BoxCenterLoss=14.595, BoxScaleLoss=4.980, ClassLoss=8.424 [Epoch 132][Batch 1499], LR: 1.00E-03, Speed: 87.741 samples/sec, ObjLoss=21.651, BoxCenterLoss=14.595, BoxScaleLoss=4.980, ClassLoss=8.423 [Epoch 132][Batch 1599], LR: 1.00E-03, Speed: 124.402 samples/sec, ObjLoss=21.650, BoxCenterLoss=14.595, BoxScaleLoss=4.979, ClassLoss=8.423 [Epoch 132][Batch 1699], LR: 1.00E-03, Speed: 135.848 samples/sec, ObjLoss=21.649, BoxCenterLoss=14.595, BoxScaleLoss=4.979, ClassLoss=8.422 [Epoch 132][Batch 1799], LR: 1.00E-03, Speed: 140.164 samples/sec, ObjLoss=21.648, BoxCenterLoss=14.595, BoxScaleLoss=4.979, ClassLoss=8.421 [Epoch 132] Training cost: 1251.775, ObjLoss=21.648, BoxCenterLoss=14.595, BoxScaleLoss=4.979, ClassLoss=8.421 [Epoch 132] 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 ] = 0.415 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172 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 ] = 0.206 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 ] = 0.188 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 ] = 0.286 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 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.409 person=31.5 bicycle=14.1 car=21.0 motorcycle=24.6 airplane=35.5 bus=39.8 train=38.1 truck=17.8 boat=10.9 traffic light=10.3 fire hydrant=34.6 stop sign=37.3 parking meter=21.2 bench=11.5 bird=16.4 cat=36.5 dog=33.9 horse=29.0 sheep=28.7 cow=29.4 elephant=38.1 bear=41.0 zebra=40.9 giraffe=36.0 backpack=4.9 umbrella=19.8 handbag=3.8 tie=12.8 suitcase=14.6 frisbee=30.5 skis=6.6 snowboard=10.3 sports ball=24.0 kite=22.5 baseball bat=8.4 baseball glove=20.6 skateboard=23.0 surfboard=14.7 tennis racket=23.0 bottle=16.4 wine glass=14.7 cup=19.8 fork=9.2 knife=3.1 spoon=2.7 bowl=19.7 banana=11.3 apple=7.9 sandwich=21.6 orange=14.0 broccoli=10.0 carrot=9.1 hot dog=15.0 pizza=23.7 donut=22.5 cake=18.0 chair=12.0 couch=26.5 potted plant=10.9 bed=18.4 dining table=9.4 toilet=33.8 tv=31.9 laptop=33.1 mouse=31.8 remote=9.1 keyboard=25.7 cell phone=15.7 microwave=29.4 oven=14.7 toaster=0.0 sink=17.5 refrigerator=28.5 book=4.1 clock=29.0 vase=19.2 scissors=15.3 teddy bear=24.0 hair drier=0.0 toothbrush=6.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.0 [Epoch 133][Batch 99], LR: 1.00E-03, Speed: 144.929 samples/sec, ObjLoss=21.647, BoxCenterLoss=14.595, BoxScaleLoss=4.979, ClassLoss=8.420 [Epoch 133][Batch 199], LR: 1.00E-03, Speed: 145.412 samples/sec, ObjLoss=21.647, BoxCenterLoss=14.594, BoxScaleLoss=4.979, ClassLoss=8.419 [Epoch 133][Batch 299], LR: 1.00E-03, Speed: 100.909 samples/sec, ObjLoss=21.646, BoxCenterLoss=14.594, BoxScaleLoss=4.979, ClassLoss=8.419 [Epoch 133][Batch 399], LR: 1.00E-03, Speed: 151.645 samples/sec, ObjLoss=21.645, BoxCenterLoss=14.594, BoxScaleLoss=4.979, ClassLoss=8.418 [Epoch 133][Batch 499], LR: 1.00E-03, Speed: 88.097 samples/sec, ObjLoss=21.644, BoxCenterLoss=14.594, BoxScaleLoss=4.978, ClassLoss=8.417 [Epoch 133][Batch 599], LR: 1.00E-03, Speed: 127.313 samples/sec, ObjLoss=21.643, BoxCenterLoss=14.594, BoxScaleLoss=4.978, ClassLoss=8.416 [Epoch 133][Batch 699], LR: 1.00E-03, Speed: 107.089 samples/sec, ObjLoss=21.642, BoxCenterLoss=14.594, BoxScaleLoss=4.978, ClassLoss=8.416 [Epoch 133][Batch 799], LR: 1.00E-03, Speed: 119.914 samples/sec, ObjLoss=21.642, BoxCenterLoss=14.594, BoxScaleLoss=4.978, ClassLoss=8.415 [Epoch 133][Batch 899], LR: 1.00E-03, Speed: 107.052 samples/sec, ObjLoss=21.641, BoxCenterLoss=14.594, BoxScaleLoss=4.978, ClassLoss=8.414 [Epoch 133][Batch 999], LR: 1.00E-03, Speed: 80.032 samples/sec, ObjLoss=21.640, BoxCenterLoss=14.594, BoxScaleLoss=4.978, ClassLoss=8.414 [Epoch 133][Batch 1099], LR: 1.00E-03, Speed: 106.138 samples/sec, ObjLoss=21.639, BoxCenterLoss=14.594, BoxScaleLoss=4.978, ClassLoss=8.413 [Epoch 133][Batch 1199], LR: 1.00E-03, Speed: 98.812 samples/sec, ObjLoss=21.638, BoxCenterLoss=14.594, BoxScaleLoss=4.977, ClassLoss=8.412 [Epoch 133][Batch 1299], LR: 1.00E-03, Speed: 115.081 samples/sec, ObjLoss=21.638, BoxCenterLoss=14.594, BoxScaleLoss=4.977, ClassLoss=8.411 [Epoch 133][Batch 1399], LR: 1.00E-03, Speed: 134.293 samples/sec, ObjLoss=21.637, BoxCenterLoss=14.594, BoxScaleLoss=4.977, ClassLoss=8.411 [Epoch 133][Batch 1499], LR: 1.00E-03, Speed: 78.196 samples/sec, ObjLoss=21.636, BoxCenterLoss=14.594, BoxScaleLoss=4.977, ClassLoss=8.410 [Epoch 133][Batch 1599], LR: 1.00E-03, Speed: 54.305 samples/sec, ObjLoss=21.635, BoxCenterLoss=14.594, BoxScaleLoss=4.977, ClassLoss=8.409 [Epoch 133][Batch 1699], LR: 1.00E-03, Speed: 150.982 samples/sec, ObjLoss=21.634, BoxCenterLoss=14.594, BoxScaleLoss=4.977, ClassLoss=8.408 [Epoch 133][Batch 1799], LR: 1.00E-03, Speed: 133.583 samples/sec, ObjLoss=21.633, BoxCenterLoss=14.594, BoxScaleLoss=4.976, ClassLoss=8.408 [Epoch 133] Training cost: 1298.733, ObjLoss=21.633, BoxCenterLoss=14.594, BoxScaleLoss=4.976, ClassLoss=8.407 [Epoch 133] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.190 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.413 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.202 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.280 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 ] = 0.268 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 ] = 0.116 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 ] = 0.392 person=29.7 bicycle=11.3 car=20.6 motorcycle=22.9 airplane=35.4 bus=39.1 train=36.5 truck=16.1 boat=11.8 traffic light=11.0 fire hydrant=34.3 stop sign=41.6 parking meter=19.9 bench=10.6 bird=16.8 cat=41.8 dog=31.0 horse=30.4 sheep=25.9 cow=28.3 elephant=37.2 bear=40.1 zebra=41.2 giraffe=42.6 backpack=4.8 umbrella=19.1 handbag=4.1 tie=11.5 suitcase=14.4 frisbee=30.6 skis=6.2 snowboard=12.2 sports ball=24.0 kite=19.7 baseball bat=10.1 baseball glove=17.0 skateboard=16.2 surfboard=14.9 tennis racket=21.1 bottle=14.9 wine glass=14.5 cup=18.8 fork=8.1 knife=3.0 spoon=3.0 bowl=19.9 banana=9.9 apple=5.2 sandwich=15.8 orange=9.6 broccoli=9.5 carrot=8.2 hot dog=16.4 pizza=28.7 donut=18.1 cake=15.6 chair=11.5 couch=24.5 potted plant=10.0 bed=21.8 dining table=10.3 toilet=20.9 tv=31.2 laptop=28.1 mouse=24.1 remote=8.4 keyboard=20.6 cell phone=15.2 microwave=30.5 oven=15.6 toaster=0.0 sink=18.7 refrigerator=22.0 book=4.4 clock=28.7 vase=18.0 scissors=8.6 teddy bear=23.9 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.0 [Epoch 134][Batch 99], LR: 1.00E-03, Speed: 131.170 samples/sec, ObjLoss=21.633, BoxCenterLoss=14.594, BoxScaleLoss=4.976, ClassLoss=8.407 [Epoch 134][Batch 199], LR: 1.00E-03, Speed: 132.586 samples/sec, ObjLoss=21.632, BoxCenterLoss=14.594, BoxScaleLoss=4.976, ClassLoss=8.406 [Epoch 134][Batch 299], LR: 1.00E-03, Speed: 149.696 samples/sec, ObjLoss=21.631, BoxCenterLoss=14.593, BoxScaleLoss=4.976, ClassLoss=8.405 [Epoch 134][Batch 399], LR: 1.00E-03, Speed: 108.523 samples/sec, ObjLoss=21.630, BoxCenterLoss=14.593, BoxScaleLoss=4.976, ClassLoss=8.405 [Epoch 134][Batch 499], LR: 1.00E-03, Speed: 130.812 samples/sec, ObjLoss=21.629, BoxCenterLoss=14.593, BoxScaleLoss=4.976, ClassLoss=8.404 [Epoch 134][Batch 599], LR: 1.00E-03, Speed: 160.269 samples/sec, ObjLoss=21.628, BoxCenterLoss=14.593, BoxScaleLoss=4.976, ClassLoss=8.403 [Epoch 134][Batch 699], LR: 1.00E-03, Speed: 86.934 samples/sec, ObjLoss=21.627, BoxCenterLoss=14.593, BoxScaleLoss=4.975, ClassLoss=8.403 [Epoch 134][Batch 799], LR: 1.00E-03, Speed: 136.439 samples/sec, ObjLoss=21.626, BoxCenterLoss=14.593, BoxScaleLoss=4.975, ClassLoss=8.402 [Epoch 134][Batch 899], LR: 1.00E-03, Speed: 78.825 samples/sec, ObjLoss=21.625, BoxCenterLoss=14.593, BoxScaleLoss=4.975, ClassLoss=8.401 [Epoch 134][Batch 999], LR: 1.00E-03, Speed: 95.920 samples/sec, ObjLoss=21.625, BoxCenterLoss=14.593, BoxScaleLoss=4.975, ClassLoss=8.400 [Epoch 134][Batch 1099], LR: 1.00E-03, Speed: 87.359 samples/sec, ObjLoss=21.624, BoxCenterLoss=14.593, BoxScaleLoss=4.975, ClassLoss=8.400 [Epoch 134][Batch 1199], LR: 1.00E-03, Speed: 139.147 samples/sec, ObjLoss=21.623, BoxCenterLoss=14.592, BoxScaleLoss=4.975, ClassLoss=8.399 [Epoch 134][Batch 1299], LR: 1.00E-03, Speed: 150.657 samples/sec, ObjLoss=21.622, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.398 [Epoch 134][Batch 1399], LR: 1.00E-03, Speed: 125.393 samples/sec, ObjLoss=21.621, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.397 [Epoch 134][Batch 1499], LR: 1.00E-03, Speed: 112.197 samples/sec, ObjLoss=21.620, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.397 [Epoch 134][Batch 1599], LR: 1.00E-03, Speed: 116.549 samples/sec, ObjLoss=21.620, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.396 [Epoch 134][Batch 1699], LR: 1.00E-03, Speed: 80.184 samples/sec, ObjLoss=21.619, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.396 [Epoch 134][Batch 1799], LR: 1.00E-03, Speed: 204.548 samples/sec, ObjLoss=21.618, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.395 [Epoch 134] Training cost: 1241.117, ObjLoss=21.618, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.395 [Epoch 134] 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 ] = 0.417 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 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 ] = 0.317 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 ] = 0.291 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 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.431 person=33.0 bicycle=14.5 car=21.3 motorcycle=26.3 airplane=36.4 bus=42.4 train=44.3 truck=20.2 boat=12.4 traffic light=10.1 fire hydrant=37.8 stop sign=33.1 parking meter=25.8 bench=11.2 bird=18.1 cat=41.7 dog=36.4 horse=31.9 sheep=27.8 cow=31.0 elephant=39.1 bear=40.1 zebra=42.8 giraffe=43.4 backpack=4.9 umbrella=19.4 handbag=3.5 tie=13.6 suitcase=14.6 frisbee=33.1 skis=8.7 snowboard=14.0 sports ball=19.7 kite=18.8 baseball bat=11.5 baseball glove=17.7 skateboard=24.1 surfboard=16.8 tennis racket=23.6 bottle=14.7 wine glass=14.9 cup=18.9 fork=10.2 knife=4.5 spoon=2.6 bowl=20.5 banana=10.9 apple=5.4 sandwich=18.2 orange=11.7 broccoli=8.9 carrot=8.3 hot dog=14.3 pizza=29.2 donut=21.1 cake=18.7 chair=12.7 couch=26.4 potted plant=11.5 bed=26.2 dining table=10.6 toilet=36.3 tv=36.2 laptop=29.2 mouse=25.8 remote=9.0 keyboard=29.2 cell phone=15.3 microwave=29.8 oven=18.8 toaster=0.0 sink=18.6 refrigerator=28.7 book=5.2 clock=28.0 vase=18.8 scissors=15.7 teddy bear=27.2 hair drier=0.0 toothbrush=4.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 135][Batch 99], LR: 1.00E-03, Speed: 159.084 samples/sec, ObjLoss=21.617, BoxCenterLoss=14.592, BoxScaleLoss=4.974, ClassLoss=8.394 [Epoch 135][Batch 199], LR: 1.00E-03, Speed: 70.382 samples/sec, ObjLoss=21.616, BoxCenterLoss=14.592, BoxScaleLoss=4.973, ClassLoss=8.393 [Epoch 135][Batch 299], LR: 1.00E-03, Speed: 120.794 samples/sec, ObjLoss=21.615, BoxCenterLoss=14.592, BoxScaleLoss=4.973, ClassLoss=8.393 [Epoch 135][Batch 399], LR: 1.00E-03, Speed: 107.320 samples/sec, ObjLoss=21.614, BoxCenterLoss=14.592, BoxScaleLoss=4.973, ClassLoss=8.392 [Epoch 135][Batch 499], LR: 1.00E-03, Speed: 144.004 samples/sec, ObjLoss=21.613, BoxCenterLoss=14.591, BoxScaleLoss=4.973, ClassLoss=8.391 [Epoch 135][Batch 599], LR: 1.00E-03, Speed: 107.915 samples/sec, ObjLoss=21.613, BoxCenterLoss=14.591, BoxScaleLoss=4.973, ClassLoss=8.390 [Epoch 135][Batch 699], LR: 1.00E-03, Speed: 123.139 samples/sec, ObjLoss=21.612, BoxCenterLoss=14.591, BoxScaleLoss=4.973, ClassLoss=8.390 [Epoch 135][Batch 799], LR: 1.00E-03, Speed: 107.867 samples/sec, ObjLoss=21.611, BoxCenterLoss=14.591, BoxScaleLoss=4.973, ClassLoss=8.389 [Epoch 135][Batch 899], LR: 1.00E-03, Speed: 70.900 samples/sec, ObjLoss=21.610, BoxCenterLoss=14.591, BoxScaleLoss=4.972, ClassLoss=8.388 [Epoch 135][Batch 999], LR: 1.00E-03, Speed: 83.209 samples/sec, ObjLoss=21.609, BoxCenterLoss=14.591, BoxScaleLoss=4.972, ClassLoss=8.388 [Epoch 135][Batch 1099], LR: 1.00E-03, Speed: 144.726 samples/sec, ObjLoss=21.608, BoxCenterLoss=14.591, BoxScaleLoss=4.972, ClassLoss=8.387 [Epoch 135][Batch 1199], LR: 1.00E-03, Speed: 92.322 samples/sec, ObjLoss=21.607, BoxCenterLoss=14.591, BoxScaleLoss=4.972, ClassLoss=8.386 [Epoch 135][Batch 1299], LR: 1.00E-03, Speed: 131.507 samples/sec, ObjLoss=21.606, BoxCenterLoss=14.591, BoxScaleLoss=4.972, ClassLoss=8.385 [Epoch 135][Batch 1399], LR: 1.00E-03, Speed: 132.139 samples/sec, ObjLoss=21.606, BoxCenterLoss=14.591, BoxScaleLoss=4.972, ClassLoss=8.385 [Epoch 135][Batch 1499], LR: 1.00E-03, Speed: 143.073 samples/sec, ObjLoss=21.605, BoxCenterLoss=14.591, BoxScaleLoss=4.971, ClassLoss=8.384 [Epoch 135][Batch 1599], LR: 1.00E-03, Speed: 83.676 samples/sec, ObjLoss=21.604, BoxCenterLoss=14.591, BoxScaleLoss=4.971, ClassLoss=8.383 [Epoch 135][Batch 1699], LR: 1.00E-03, Speed: 72.797 samples/sec, ObjLoss=21.603, BoxCenterLoss=14.590, BoxScaleLoss=4.971, ClassLoss=8.382 [Epoch 135][Batch 1799], LR: 1.00E-03, Speed: 177.908 samples/sec, ObjLoss=21.602, BoxCenterLoss=14.590, BoxScaleLoss=4.971, ClassLoss=8.381 [Epoch 135] Training cost: 1336.993, ObjLoss=21.602, BoxCenterLoss=14.590, BoxScaleLoss=4.971, ClassLoss=8.381 [Epoch 135] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.196 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.160 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 ] = 0.208 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 ] = 0.274 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 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.398 person=32.5 bicycle=12.8 car=20.8 motorcycle=22.6 airplane=35.6 bus=35.0 train=36.9 truck=16.7 boat=7.8 traffic light=13.2 fire hydrant=36.7 stop sign=38.1 parking meter=22.5 bench=11.0 bird=16.0 cat=38.4 dog=29.4 horse=32.7 sheep=26.5 cow=26.8 elephant=38.1 bear=39.3 zebra=36.1 giraffe=42.7 backpack=5.4 umbrella=18.2 handbag=3.3 tie=14.4 suitcase=17.3 frisbee=33.2 skis=6.4 snowboard=10.4 sports ball=21.5 kite=20.9 baseball bat=11.1 baseball glove=18.7 skateboard=21.4 surfboard=17.0 tennis racket=22.6 bottle=12.9 wine glass=13.8 cup=17.7 fork=6.9 knife=3.1 spoon=1.9 bowl=17.9 banana=10.6 apple=7.4 sandwich=14.9 orange=12.6 broccoli=9.6 carrot=8.2 hot dog=10.9 pizza=23.8 donut=21.7 cake=17.7 chair=12.8 couch=25.5 potted plant=10.4 bed=28.0 dining table=16.7 toilet=32.0 tv=34.2 laptop=29.6 mouse=31.3 remote=7.8 keyboard=22.6 cell phone=14.8 microwave=23.2 oven=20.2 toaster=0.0 sink=16.1 refrigerator=29.2 book=4.9 clock=31.8 vase=16.0 scissors=10.7 teddy bear=22.8 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.6 [Epoch 136][Batch 99], LR: 1.00E-03, Speed: 187.986 samples/sec, ObjLoss=21.601, BoxCenterLoss=14.590, BoxScaleLoss=4.971, ClassLoss=8.381 [Epoch 136][Batch 199], LR: 1.00E-03, Speed: 130.705 samples/sec, ObjLoss=21.601, BoxCenterLoss=14.590, BoxScaleLoss=4.971, ClassLoss=8.380 [Epoch 136][Batch 299], LR: 1.00E-03, Speed: 154.567 samples/sec, ObjLoss=21.600, BoxCenterLoss=14.591, BoxScaleLoss=4.970, ClassLoss=8.379 [Epoch 136][Batch 399], LR: 1.00E-03, Speed: 144.836 samples/sec, ObjLoss=21.599, BoxCenterLoss=14.590, BoxScaleLoss=4.970, ClassLoss=8.379 [Epoch 136][Batch 499], LR: 1.00E-03, Speed: 128.506 samples/sec, ObjLoss=21.598, BoxCenterLoss=14.590, BoxScaleLoss=4.970, ClassLoss=8.378 [Epoch 136][Batch 599], LR: 1.00E-03, Speed: 109.229 samples/sec, ObjLoss=21.598, BoxCenterLoss=14.590, BoxScaleLoss=4.970, ClassLoss=8.377 [Epoch 136][Batch 699], LR: 1.00E-03, Speed: 95.643 samples/sec, ObjLoss=21.597, BoxCenterLoss=14.590, BoxScaleLoss=4.970, ClassLoss=8.376 [Epoch 136][Batch 799], LR: 1.00E-03, Speed: 83.937 samples/sec, ObjLoss=21.596, BoxCenterLoss=14.590, BoxScaleLoss=4.970, ClassLoss=8.376 [Epoch 136][Batch 899], LR: 1.00E-03, Speed: 131.924 samples/sec, ObjLoss=21.595, BoxCenterLoss=14.590, BoxScaleLoss=4.969, ClassLoss=8.375 [Epoch 136][Batch 999], LR: 1.00E-03, Speed: 110.223 samples/sec, ObjLoss=21.594, BoxCenterLoss=14.590, BoxScaleLoss=4.969, ClassLoss=8.374 [Epoch 136][Batch 1099], LR: 1.00E-03, Speed: 106.726 samples/sec, ObjLoss=21.594, BoxCenterLoss=14.590, BoxScaleLoss=4.969, ClassLoss=8.373 [Epoch 136][Batch 1199], LR: 1.00E-03, Speed: 133.796 samples/sec, ObjLoss=21.593, BoxCenterLoss=14.590, BoxScaleLoss=4.969, ClassLoss=8.373 [Epoch 136][Batch 1299], LR: 1.00E-03, Speed: 94.473 samples/sec, ObjLoss=21.592, BoxCenterLoss=14.590, BoxScaleLoss=4.969, ClassLoss=8.372 [Epoch 136][Batch 1399], LR: 1.00E-03, Speed: 157.508 samples/sec, ObjLoss=21.591, BoxCenterLoss=14.589, BoxScaleLoss=4.969, ClassLoss=8.371 [Epoch 136][Batch 1499], LR: 1.00E-03, Speed: 140.720 samples/sec, ObjLoss=21.590, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.371 [Epoch 136][Batch 1599], LR: 1.00E-03, Speed: 162.264 samples/sec, ObjLoss=21.589, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.370 [Epoch 136][Batch 1699], LR: 1.00E-03, Speed: 148.435 samples/sec, ObjLoss=21.589, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.369 [Epoch 136][Batch 1799], LR: 1.00E-03, Speed: 130.629 samples/sec, ObjLoss=21.588, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.369 [Epoch 136] Training cost: 1277.392, ObjLoss=21.587, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.368 [Epoch 136] 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 ] = 0.417 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.087 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 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.299 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 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.431 person=33.1 bicycle=13.9 car=21.0 motorcycle=24.6 airplane=37.9 bus=41.5 train=42.4 truck=18.9 boat=9.9 traffic light=12.2 fire hydrant=38.8 stop sign=40.0 parking meter=25.3 bench=11.7 bird=15.1 cat=41.8 dog=35.1 horse=33.9 sheep=24.0 cow=31.3 elephant=41.9 bear=41.6 zebra=44.4 giraffe=41.0 backpack=5.8 umbrella=17.4 handbag=4.0 tie=13.1 suitcase=17.6 frisbee=32.4 skis=8.6 snowboard=11.6 sports ball=24.5 kite=23.7 baseball bat=11.1 baseball glove=17.3 skateboard=22.8 surfboard=17.5 tennis racket=21.2 bottle=15.3 wine glass=14.2 cup=19.2 fork=9.9 knife=2.8 spoon=2.7 bowl=21.2 banana=12.5 apple=7.0 sandwich=19.4 orange=15.9 broccoli=10.0 carrot=8.7 hot dog=16.7 pizza=30.4 donut=22.5 cake=15.2 chair=12.3 couch=28.2 potted plant=9.6 bed=27.7 dining table=16.7 toilet=36.8 tv=34.3 laptop=36.8 mouse=34.0 remote=6.6 keyboard=27.8 cell phone=15.5 microwave=26.9 oven=18.7 toaster=0.0 sink=20.3 refrigerator=31.0 book=4.3 clock=31.6 vase=17.2 scissors=12.7 teddy bear=23.1 hair drier=0.0 toothbrush=3.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.1 [Epoch 137][Batch 99], LR: 1.00E-03, Speed: 140.111 samples/sec, ObjLoss=21.587, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.368 [Epoch 137][Batch 199], LR: 1.00E-03, Speed: 124.866 samples/sec, ObjLoss=21.586, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.367 [Epoch 137][Batch 299], LR: 1.00E-03, Speed: 132.212 samples/sec, ObjLoss=21.585, BoxCenterLoss=14.589, BoxScaleLoss=4.968, ClassLoss=8.366 [Epoch 137][Batch 399], LR: 1.00E-03, Speed: 135.525 samples/sec, ObjLoss=21.584, BoxCenterLoss=14.589, BoxScaleLoss=4.967, ClassLoss=8.365 [Epoch 137][Batch 499], LR: 1.00E-03, Speed: 112.852 samples/sec, ObjLoss=21.583, BoxCenterLoss=14.589, BoxScaleLoss=4.967, ClassLoss=8.365 [Epoch 137][Batch 599], LR: 1.00E-03, Speed: 129.469 samples/sec, ObjLoss=21.583, BoxCenterLoss=14.589, BoxScaleLoss=4.967, ClassLoss=8.364 [Epoch 137][Batch 699], LR: 1.00E-03, Speed: 140.285 samples/sec, ObjLoss=21.582, BoxCenterLoss=14.589, BoxScaleLoss=4.967, ClassLoss=8.363 [Epoch 137][Batch 799], LR: 1.00E-03, Speed: 152.697 samples/sec, ObjLoss=21.581, BoxCenterLoss=14.589, BoxScaleLoss=4.967, ClassLoss=8.362 [Epoch 137][Batch 899], LR: 1.00E-03, Speed: 106.404 samples/sec, ObjLoss=21.580, BoxCenterLoss=14.589, BoxScaleLoss=4.967, ClassLoss=8.362 [Epoch 137][Batch 999], LR: 1.00E-03, Speed: 64.615 samples/sec, ObjLoss=21.580, BoxCenterLoss=14.589, BoxScaleLoss=4.967, ClassLoss=8.361 [Epoch 137][Batch 1099], LR: 1.00E-03, Speed: 130.056 samples/sec, ObjLoss=21.579, BoxCenterLoss=14.588, BoxScaleLoss=4.966, ClassLoss=8.361 [Epoch 137][Batch 1199], LR: 1.00E-03, Speed: 90.697 samples/sec, ObjLoss=21.578, BoxCenterLoss=14.588, BoxScaleLoss=4.966, ClassLoss=8.360 [Epoch 137][Batch 1299], LR: 1.00E-03, Speed: 80.121 samples/sec, ObjLoss=21.577, BoxCenterLoss=14.588, BoxScaleLoss=4.966, ClassLoss=8.359 [Epoch 137][Batch 1399], LR: 1.00E-03, Speed: 88.827 samples/sec, ObjLoss=21.576, BoxCenterLoss=14.588, BoxScaleLoss=4.966, ClassLoss=8.358 [Epoch 137][Batch 1499], LR: 1.00E-03, Speed: 159.371 samples/sec, ObjLoss=21.575, BoxCenterLoss=14.588, BoxScaleLoss=4.966, ClassLoss=8.358 [Epoch 137][Batch 1599], LR: 1.00E-03, Speed: 92.805 samples/sec, ObjLoss=21.574, BoxCenterLoss=14.588, BoxScaleLoss=4.966, ClassLoss=8.357 [Epoch 137][Batch 1699], LR: 1.00E-03, Speed: 68.642 samples/sec, ObjLoss=21.574, BoxCenterLoss=14.588, BoxScaleLoss=4.966, ClassLoss=8.357 [Epoch 137][Batch 1799], LR: 1.00E-03, Speed: 160.937 samples/sec, ObjLoss=21.573, BoxCenterLoss=14.588, BoxScaleLoss=4.965, ClassLoss=8.356 [Epoch 137] Training cost: 1382.470, ObjLoss=21.572, BoxCenterLoss=14.588, BoxScaleLoss=4.965, ClassLoss=8.356 [Epoch 137] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.413 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.087 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.187 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 ] = 0.188 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 ] = 0.286 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 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418 person=32.2 bicycle=11.8 car=21.3 motorcycle=24.9 airplane=35.0 bus=42.1 train=45.9 truck=18.1 boat=11.8 traffic light=12.4 fire hydrant=30.1 stop sign=32.5 parking meter=26.8 bench=11.4 bird=15.4 cat=41.8 dog=34.7 horse=30.4 sheep=24.2 cow=30.9 elephant=38.6 bear=41.9 zebra=40.7 giraffe=43.0 backpack=4.9 umbrella=16.2 handbag=3.6 tie=12.6 suitcase=15.0 frisbee=26.6 skis=6.9 snowboard=13.0 sports ball=20.9 kite=20.3 baseball bat=9.3 baseball glove=17.3 skateboard=19.8 surfboard=11.7 tennis racket=17.3 bottle=14.7 wine glass=12.0 cup=17.7 fork=7.8 knife=3.1 spoon=2.3 bowl=17.6 banana=10.7 apple=7.6 sandwich=18.9 orange=13.8 broccoli=9.8 carrot=7.8 hot dog=15.6 pizza=25.6 donut=21.0 cake=16.1 chair=12.1 couch=25.9 potted plant=9.6 bed=27.1 dining table=13.1 toilet=30.5 tv=30.1 laptop=35.5 mouse=29.6 remote=7.4 keyboard=22.1 cell phone=14.1 microwave=29.6 oven=18.0 toaster=0.0 sink=14.3 refrigerator=24.7 book=5.6 clock=30.9 vase=19.2 scissors=14.7 teddy bear=23.6 hair drier=0.0 toothbrush=2.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.7 [Epoch 138][Batch 99], LR: 1.00E-03, Speed: 157.257 samples/sec, ObjLoss=21.571, BoxCenterLoss=14.588, BoxScaleLoss=4.965, ClassLoss=8.355 [Epoch 138][Batch 199], LR: 1.00E-03, Speed: 157.192 samples/sec, ObjLoss=21.570, BoxCenterLoss=14.587, BoxScaleLoss=4.965, ClassLoss=8.354 [Epoch 138][Batch 299], LR: 1.00E-03, Speed: 133.231 samples/sec, ObjLoss=21.570, BoxCenterLoss=14.587, BoxScaleLoss=4.965, ClassLoss=8.353 [Epoch 138][Batch 399], LR: 1.00E-03, Speed: 110.110 samples/sec, ObjLoss=21.569, BoxCenterLoss=14.587, BoxScaleLoss=4.965, ClassLoss=8.352 [Epoch 138][Batch 499], LR: 1.00E-03, Speed: 182.570 samples/sec, ObjLoss=21.568, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.352 [Epoch 138][Batch 599], LR: 1.00E-03, Speed: 99.940 samples/sec, ObjLoss=21.567, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.351 [Epoch 138][Batch 699], LR: 1.00E-03, Speed: 117.370 samples/sec, ObjLoss=21.566, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.351 [Epoch 138][Batch 799], LR: 1.00E-03, Speed: 130.813 samples/sec, ObjLoss=21.566, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.350 [Epoch 138][Batch 899], LR: 1.00E-03, Speed: 144.946 samples/sec, ObjLoss=21.565, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.349 [Epoch 138][Batch 999], LR: 1.00E-03, Speed: 149.400 samples/sec, ObjLoss=21.564, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.349 [Epoch 138][Batch 1099], LR: 1.00E-03, Speed: 156.778 samples/sec, ObjLoss=21.563, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.348 [Epoch 138][Batch 1199], LR: 1.00E-03, Speed: 67.122 samples/sec, ObjLoss=21.562, BoxCenterLoss=14.587, BoxScaleLoss=4.964, ClassLoss=8.347 [Epoch 138][Batch 1299], LR: 1.00E-03, Speed: 130.939 samples/sec, ObjLoss=21.561, BoxCenterLoss=14.586, BoxScaleLoss=4.963, ClassLoss=8.347 [Epoch 138][Batch 1399], LR: 1.00E-03, Speed: 49.152 samples/sec, ObjLoss=21.561, BoxCenterLoss=14.587, BoxScaleLoss=4.963, ClassLoss=8.346 [Epoch 138][Batch 1499], LR: 1.00E-03, Speed: 78.258 samples/sec, ObjLoss=21.560, BoxCenterLoss=14.586, BoxScaleLoss=4.963, ClassLoss=8.345 [Epoch 138][Batch 1599], LR: 1.00E-03, Speed: 137.755 samples/sec, ObjLoss=21.559, BoxCenterLoss=14.586, BoxScaleLoss=4.963, ClassLoss=8.344 [Epoch 138][Batch 1699], LR: 1.00E-03, Speed: 79.491 samples/sec, ObjLoss=21.558, BoxCenterLoss=14.586, BoxScaleLoss=4.963, ClassLoss=8.344 [Epoch 138][Batch 1799], LR: 1.00E-03, Speed: 142.082 samples/sec, ObjLoss=21.558, BoxCenterLoss=14.586, BoxScaleLoss=4.963, ClassLoss=8.343 [Epoch 138] Training cost: 1382.183, ObjLoss=21.558, BoxCenterLoss=14.586, BoxScaleLoss=4.963, ClassLoss=8.343 [Epoch 138] 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 ] = 0.411 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.075 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 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 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 ] = 0.290 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 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.437 person=30.9 bicycle=15.0 car=21.0 motorcycle=27.2 airplane=42.7 bus=39.5 train=47.2 truck=19.2 boat=10.2 traffic light=9.1 fire hydrant=35.5 stop sign=31.1 parking meter=22.3 bench=10.6 bird=14.2 cat=42.3 dog=34.7 horse=32.9 sheep=26.5 cow=30.5 elephant=39.5 bear=44.3 zebra=40.1 giraffe=46.1 backpack=4.7 umbrella=18.6 handbag=3.9 tie=11.5 suitcase=16.0 frisbee=29.8 skis=7.3 snowboard=10.0 sports ball=17.8 kite=21.1 baseball bat=10.2 baseball glove=14.8 skateboard=20.3 surfboard=14.6 tennis racket=20.0 bottle=14.9 wine glass=14.0 cup=20.0 fork=9.7 knife=3.0 spoon=2.0 bowl=22.3 banana=10.7 apple=8.2 sandwich=19.2 orange=15.8 broccoli=10.5 carrot=8.4 hot dog=11.9 pizza=23.7 donut=22.2 cake=14.7 chair=11.7 couch=23.1 potted plant=10.3 bed=29.2 dining table=15.0 toilet=33.5 tv=34.5 laptop=34.5 mouse=30.1 remote=7.0 keyboard=25.6 cell phone=13.7 microwave=31.8 oven=19.8 toaster=0.0 sink=20.6 refrigerator=30.0 book=4.9 clock=27.7 vase=18.4 scissors=12.3 teddy bear=24.5 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.3 [Epoch 139][Batch 99], LR: 1.00E-03, Speed: 135.378 samples/sec, ObjLoss=21.557, BoxCenterLoss=14.586, BoxScaleLoss=4.962, ClassLoss=8.342 [Epoch 139][Batch 199], LR: 1.00E-03, Speed: 149.119 samples/sec, ObjLoss=21.556, BoxCenterLoss=14.586, BoxScaleLoss=4.962, ClassLoss=8.341 [Epoch 139][Batch 299], LR: 1.00E-03, Speed: 147.587 samples/sec, ObjLoss=21.555, BoxCenterLoss=14.586, BoxScaleLoss=4.962, ClassLoss=8.340 [Epoch 139][Batch 399], LR: 1.00E-03, Speed: 60.434 samples/sec, ObjLoss=21.554, BoxCenterLoss=14.586, BoxScaleLoss=4.962, ClassLoss=8.340 [Epoch 139][Batch 499], LR: 1.00E-03, Speed: 135.671 samples/sec, ObjLoss=21.554, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.339 [Epoch 139][Batch 599], LR: 1.00E-03, Speed: 63.278 samples/sec, ObjLoss=21.553, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.338 [Epoch 139][Batch 699], LR: 1.00E-03, Speed: 146.184 samples/sec, ObjLoss=21.552, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.337 [Epoch 139][Batch 799], LR: 1.00E-03, Speed: 158.326 samples/sec, ObjLoss=21.552, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.337 [Epoch 139][Batch 899], LR: 1.00E-03, Speed: 138.697 samples/sec, ObjLoss=21.551, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.336 [Epoch 139][Batch 999], LR: 1.00E-03, Speed: 72.645 samples/sec, ObjLoss=21.550, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.336 [Epoch 139][Batch 1099], LR: 1.00E-03, Speed: 148.547 samples/sec, ObjLoss=21.549, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.335 [Epoch 139][Batch 1199], LR: 1.00E-03, Speed: 70.938 samples/sec, ObjLoss=21.549, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.334 [Epoch 139][Batch 1299], LR: 1.00E-03, Speed: 77.417 samples/sec, ObjLoss=21.548, BoxCenterLoss=14.586, BoxScaleLoss=4.961, ClassLoss=8.334 [Epoch 139][Batch 1399], LR: 1.00E-03, Speed: 62.160 samples/sec, ObjLoss=21.547, BoxCenterLoss=14.586, BoxScaleLoss=4.960, ClassLoss=8.333 [Epoch 139][Batch 1499], LR: 1.00E-03, Speed: 122.217 samples/sec, ObjLoss=21.546, BoxCenterLoss=14.586, BoxScaleLoss=4.960, ClassLoss=8.332 [Epoch 139][Batch 1599], LR: 1.00E-03, Speed: 97.952 samples/sec, ObjLoss=21.545, BoxCenterLoss=14.585, BoxScaleLoss=4.960, ClassLoss=8.332 [Epoch 139][Batch 1699], LR: 1.00E-03, Speed: 48.130 samples/sec, ObjLoss=21.544, BoxCenterLoss=14.585, BoxScaleLoss=4.960, ClassLoss=8.331 [Epoch 139][Batch 1799], LR: 1.00E-03, Speed: 117.302 samples/sec, ObjLoss=21.544, BoxCenterLoss=14.585, BoxScaleLoss=4.960, ClassLoss=8.331 [Epoch 139] Training cost: 1361.668, ObjLoss=21.543, BoxCenterLoss=14.585, BoxScaleLoss=4.960, ClassLoss=8.330 [Epoch 139] 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 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.187 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 ] = 0.210 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.316 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 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124 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 ] = 0.436 person=32.7 bicycle=14.0 car=22.5 motorcycle=24.0 airplane=35.5 bus=42.5 train=47.6 truck=17.4 boat=10.1 traffic light=9.9 fire hydrant=39.1 stop sign=32.4 parking meter=22.3 bench=10.9 bird=18.5 cat=40.6 dog=31.2 horse=32.0 sheep=28.7 cow=30.8 elephant=37.0 bear=45.8 zebra=44.2 giraffe=42.0 backpack=5.1 umbrella=20.7 handbag=3.5 tie=11.4 suitcase=15.8 frisbee=35.1 skis=8.3 snowboard=12.2 sports ball=19.5 kite=21.5 baseball bat=13.1 baseball glove=19.0 skateboard=23.6 surfboard=14.9 tennis racket=21.9 bottle=16.0 wine glass=13.2 cup=20.8 fork=11.7 knife=3.8 spoon=2.0 bowl=21.1 banana=12.1 apple=8.0 sandwich=18.6 orange=16.7 broccoli=11.8 carrot=8.2 hot dog=19.2 pizza=27.0 donut=24.0 cake=16.5 chair=12.9 couch=26.0 potted plant=10.3 bed=28.7 dining table=15.1 toilet=36.5 tv=36.0 laptop=33.6 mouse=32.1 remote=9.2 keyboard=26.5 cell phone=15.3 microwave=31.0 oven=18.3 toaster=0.0 sink=17.0 refrigerator=28.1 book=4.9 clock=32.2 vase=19.9 scissors=14.9 teddy bear=23.8 hair drier=0.0 toothbrush=3.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.0 [Epoch 140][Batch 99], LR: 1.00E-03, Speed: 155.342 samples/sec, ObjLoss=21.542, BoxCenterLoss=14.585, BoxScaleLoss=4.960, ClassLoss=8.330 [Epoch 140][Batch 199], LR: 1.00E-03, Speed: 142.900 samples/sec, ObjLoss=21.542, BoxCenterLoss=14.585, BoxScaleLoss=4.959, ClassLoss=8.329 [Epoch 140][Batch 299], LR: 1.00E-03, Speed: 60.849 samples/sec, ObjLoss=21.541, BoxCenterLoss=14.585, BoxScaleLoss=4.959, ClassLoss=8.328 [Epoch 140][Batch 399], LR: 1.00E-03, Speed: 141.887 samples/sec, ObjLoss=21.540, BoxCenterLoss=14.585, BoxScaleLoss=4.959, ClassLoss=8.327 [Epoch 140][Batch 499], LR: 1.00E-03, Speed: 145.115 samples/sec, ObjLoss=21.539, BoxCenterLoss=14.585, BoxScaleLoss=4.959, ClassLoss=8.327 [Epoch 140][Batch 599], LR: 1.00E-03, Speed: 80.469 samples/sec, ObjLoss=21.538, BoxCenterLoss=14.584, BoxScaleLoss=4.959, ClassLoss=8.326 [Epoch 140][Batch 699], LR: 1.00E-03, Speed: 110.174 samples/sec, ObjLoss=21.537, BoxCenterLoss=14.584, BoxScaleLoss=4.959, ClassLoss=8.325 [Epoch 140][Batch 799], LR: 1.00E-03, Speed: 170.689 samples/sec, ObjLoss=21.536, BoxCenterLoss=14.584, BoxScaleLoss=4.959, ClassLoss=8.325 [Epoch 140][Batch 899], LR: 1.00E-03, Speed: 113.377 samples/sec, ObjLoss=21.535, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.324 [Epoch 140][Batch 999], LR: 1.00E-03, Speed: 139.309 samples/sec, ObjLoss=21.535, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.323 [Epoch 140][Batch 1099], LR: 1.00E-03, Speed: 118.287 samples/sec, ObjLoss=21.534, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.323 [Epoch 140][Batch 1199], LR: 1.00E-03, Speed: 143.532 samples/sec, ObjLoss=21.533, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.322 [Epoch 140][Batch 1299], LR: 1.00E-03, Speed: 141.769 samples/sec, ObjLoss=21.532, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.322 [Epoch 140][Batch 1399], LR: 1.00E-03, Speed: 112.453 samples/sec, ObjLoss=21.532, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.321 [Epoch 140][Batch 1499], LR: 1.00E-03, Speed: 74.049 samples/sec, ObjLoss=21.531, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.320 [Epoch 140][Batch 1599], LR: 1.00E-03, Speed: 151.053 samples/sec, ObjLoss=21.530, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.320 [Epoch 140][Batch 1699], LR: 1.00E-03, Speed: 79.689 samples/sec, ObjLoss=21.529, BoxCenterLoss=14.584, BoxScaleLoss=4.958, ClassLoss=8.319 [Epoch 140][Batch 1799], LR: 1.00E-03, Speed: 151.585 samples/sec, ObjLoss=21.529, BoxCenterLoss=14.584, BoxScaleLoss=4.957, ClassLoss=8.319 [Epoch 140] Training cost: 1273.864, ObjLoss=21.528, BoxCenterLoss=14.584, BoxScaleLoss=4.957, ClassLoss=8.318 [Epoch 140] 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 ] = 0.191 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 ] = 0.210 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 ] = 0.200 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 ] = 0.299 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 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442 person=32.0 bicycle=14.0 car=21.5 motorcycle=25.9 airplane=37.1 bus=45.9 train=42.6 truck=18.9 boat=10.5 traffic light=8.1 fire hydrant=41.9 stop sign=40.1 parking meter=25.9 bench=11.8 bird=15.4 cat=46.1 dog=36.8 horse=32.9 sheep=28.7 cow=28.9 elephant=40.3 bear=44.2 zebra=45.5 giraffe=45.5 backpack=6.0 umbrella=18.9 handbag=2.9 tie=13.4 suitcase=17.2 frisbee=34.0 skis=8.7 snowboard=12.3 sports ball=20.3 kite=20.5 baseball bat=10.4 baseball glove=17.8 skateboard=24.4 surfboard=16.1 tennis racket=21.8 bottle=15.9 wine glass=14.0 cup=21.2 fork=11.0 knife=4.1 spoon=1.6 bowl=20.9 banana=10.8 apple=5.3 sandwich=18.7 orange=14.1 broccoli=12.2 carrot=9.7 hot dog=15.9 pizza=29.4 donut=20.1 cake=17.9 chair=12.5 couch=26.9 potted plant=11.4 bed=31.1 dining table=17.5 toilet=35.8 tv=34.3 laptop=34.2 mouse=27.2 remote=7.2 keyboard=22.6 cell phone=13.6 microwave=29.6 oven=21.1 toaster=0.0 sink=20.4 refrigerator=31.1 book=3.8 clock=28.4 vase=19.7 scissors=12.9 teddy bear=26.3 hair drier=0.0 toothbrush=4.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.3 [Epoch 141][Batch 99], LR: 1.00E-03, Speed: 146.716 samples/sec, ObjLoss=21.528, BoxCenterLoss=14.584, BoxScaleLoss=4.957, ClassLoss=8.318 [Epoch 141][Batch 199], LR: 1.00E-03, Speed: 141.684 samples/sec, ObjLoss=21.527, BoxCenterLoss=14.583, BoxScaleLoss=4.957, ClassLoss=8.317 [Epoch 141][Batch 299], LR: 1.00E-03, Speed: 130.419 samples/sec, ObjLoss=21.526, BoxCenterLoss=14.583, BoxScaleLoss=4.957, ClassLoss=8.316 [Epoch 141][Batch 399], LR: 1.00E-03, Speed: 126.261 samples/sec, ObjLoss=21.525, BoxCenterLoss=14.583, BoxScaleLoss=4.957, ClassLoss=8.315 [Epoch 141][Batch 499], LR: 1.00E-03, Speed: 130.641 samples/sec, ObjLoss=21.525, BoxCenterLoss=14.583, BoxScaleLoss=4.957, ClassLoss=8.315 [Epoch 141][Batch 599], LR: 1.00E-03, Speed: 40.999 samples/sec, ObjLoss=21.524, BoxCenterLoss=14.583, BoxScaleLoss=4.957, ClassLoss=8.314 [Epoch 141][Batch 699], LR: 1.00E-03, Speed: 160.389 samples/sec, ObjLoss=21.523, BoxCenterLoss=14.583, BoxScaleLoss=4.956, ClassLoss=8.314 [Epoch 141][Batch 799], LR: 1.00E-03, Speed: 56.328 samples/sec, ObjLoss=21.522, BoxCenterLoss=14.583, BoxScaleLoss=4.956, ClassLoss=8.313 [Epoch 141][Batch 899], LR: 1.00E-03, Speed: 51.354 samples/sec, ObjLoss=21.522, BoxCenterLoss=14.583, BoxScaleLoss=4.956, ClassLoss=8.312 [Epoch 141][Batch 999], LR: 1.00E-03, Speed: 58.375 samples/sec, ObjLoss=21.521, BoxCenterLoss=14.583, BoxScaleLoss=4.956, ClassLoss=8.312 [Epoch 141][Batch 1099], LR: 1.00E-03, Speed: 80.091 samples/sec, ObjLoss=21.520, BoxCenterLoss=14.583, BoxScaleLoss=4.956, ClassLoss=8.311 [Epoch 141][Batch 1199], LR: 1.00E-03, Speed: 72.384 samples/sec, ObjLoss=21.520, BoxCenterLoss=14.583, BoxScaleLoss=4.956, ClassLoss=8.310 [Epoch 141][Batch 1299], LR: 1.00E-03, Speed: 130.620 samples/sec, ObjLoss=21.519, BoxCenterLoss=14.583, BoxScaleLoss=4.956, ClassLoss=8.310 [Epoch 141][Batch 1399], LR: 1.00E-03, Speed: 157.668 samples/sec, ObjLoss=21.518, BoxCenterLoss=14.583, BoxScaleLoss=4.955, ClassLoss=8.309 [Epoch 141][Batch 1499], LR: 1.00E-03, Speed: 95.779 samples/sec, ObjLoss=21.517, BoxCenterLoss=14.583, BoxScaleLoss=4.955, ClassLoss=8.308 [Epoch 141][Batch 1599], LR: 1.00E-03, Speed: 88.399 samples/sec, ObjLoss=21.517, BoxCenterLoss=14.583, BoxScaleLoss=4.955, ClassLoss=8.308 [Epoch 141][Batch 1699], LR: 1.00E-03, Speed: 130.176 samples/sec, ObjLoss=21.516, BoxCenterLoss=14.582, BoxScaleLoss=4.955, ClassLoss=8.307 [Epoch 141][Batch 1799], LR: 1.00E-03, Speed: 146.317 samples/sec, ObjLoss=21.515, BoxCenterLoss=14.582, BoxScaleLoss=4.955, ClassLoss=8.306 [Epoch 141] Training cost: 1440.872, ObjLoss=21.515, BoxCenterLoss=14.582, BoxScaleLoss=4.955, ClassLoss=8.306 [Epoch 141] 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 ] = 0.412 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.207 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 ] = 0.188 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.273 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 ] = 0.119 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 ] = 0.415 person=32.0 bicycle=14.3 car=19.5 motorcycle=22.8 airplane=30.9 bus=36.8 train=38.9 truck=16.8 boat=10.0 traffic light=12.7 fire hydrant=34.3 stop sign=37.1 parking meter=19.4 bench=9.3 bird=15.4 cat=46.3 dog=35.1 horse=29.6 sheep=21.8 cow=27.1 elephant=36.8 bear=44.9 zebra=35.6 giraffe=38.3 backpack=4.4 umbrella=21.2 handbag=3.0 tie=13.0 suitcase=17.5 frisbee=31.1 skis=7.4 snowboard=11.6 sports ball=22.0 kite=16.9 baseball bat=10.0 baseball glove=13.7 skateboard=21.5 surfboard=16.3 tennis racket=21.4 bottle=15.2 wine glass=13.0 cup=20.7 fork=9.0 knife=4.4 spoon=2.2 bowl=21.1 banana=11.0 apple=7.3 sandwich=17.9 orange=15.2 broccoli=11.1 carrot=8.4 hot dog=16.3 pizza=29.2 donut=17.3 cake=16.2 chair=11.3 couch=28.2 potted plant=10.3 bed=23.0 dining table=10.7 toilet=29.6 tv=38.2 laptop=34.0 mouse=25.2 remote=7.5 keyboard=29.0 cell phone=14.5 microwave=28.9 oven=20.4 toaster=0.0 sink=20.8 refrigerator=29.3 book=4.8 clock=30.1 vase=17.8 scissors=16.3 teddy bear=21.4 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.8 [Epoch 142][Batch 99], LR: 1.00E-03, Speed: 143.732 samples/sec, ObjLoss=21.514, BoxCenterLoss=14.582, BoxScaleLoss=4.955, ClassLoss=8.306 [Epoch 142][Batch 199], LR: 1.00E-03, Speed: 159.608 samples/sec, ObjLoss=21.513, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.305 [Epoch 142][Batch 299], LR: 1.00E-03, Speed: 73.762 samples/sec, ObjLoss=21.513, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.304 [Epoch 142][Batch 399], LR: 1.00E-03, Speed: 136.725 samples/sec, ObjLoss=21.512, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.303 [Epoch 142][Batch 499], LR: 1.00E-03, Speed: 126.391 samples/sec, ObjLoss=21.511, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.303 [Epoch 142][Batch 599], LR: 1.00E-03, Speed: 78.537 samples/sec, ObjLoss=21.510, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.302 [Epoch 142][Batch 699], LR: 1.00E-03, Speed: 137.598 samples/sec, ObjLoss=21.509, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.301 [Epoch 142][Batch 799], LR: 1.00E-03, Speed: 96.715 samples/sec, ObjLoss=21.509, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.301 [Epoch 142][Batch 899], LR: 1.00E-03, Speed: 154.925 samples/sec, ObjLoss=21.508, BoxCenterLoss=14.582, BoxScaleLoss=4.954, ClassLoss=8.300 [Epoch 142][Batch 999], LR: 1.00E-03, Speed: 77.590 samples/sec, ObjLoss=21.507, BoxCenterLoss=14.582, BoxScaleLoss=4.953, ClassLoss=8.300 [Epoch 142][Batch 1099], LR: 1.00E-03, Speed: 53.576 samples/sec, ObjLoss=21.506, BoxCenterLoss=14.582, BoxScaleLoss=4.953, ClassLoss=8.299 [Epoch 142][Batch 1199], LR: 1.00E-03, Speed: 126.011 samples/sec, ObjLoss=21.506, BoxCenterLoss=14.581, BoxScaleLoss=4.953, ClassLoss=8.299 [Epoch 142][Batch 1299], LR: 1.00E-03, Speed: 93.491 samples/sec, ObjLoss=21.505, BoxCenterLoss=14.581, BoxScaleLoss=4.953, ClassLoss=8.298 [Epoch 142][Batch 1399], LR: 1.00E-03, Speed: 80.699 samples/sec, ObjLoss=21.504, BoxCenterLoss=14.581, BoxScaleLoss=4.953, ClassLoss=8.297 [Epoch 142][Batch 1499], LR: 1.00E-03, Speed: 64.555 samples/sec, ObjLoss=21.503, BoxCenterLoss=14.581, BoxScaleLoss=4.953, ClassLoss=8.297 [Epoch 142][Batch 1599], LR: 1.00E-03, Speed: 143.923 samples/sec, ObjLoss=21.502, BoxCenterLoss=14.581, BoxScaleLoss=4.953, ClassLoss=8.296 [Epoch 142][Batch 1699], LR: 1.00E-03, Speed: 56.508 samples/sec, ObjLoss=21.502, BoxCenterLoss=14.581, BoxScaleLoss=4.952, ClassLoss=8.295 [Epoch 142][Batch 1799], LR: 1.00E-03, Speed: 105.361 samples/sec, ObjLoss=21.501, BoxCenterLoss=14.581, BoxScaleLoss=4.952, ClassLoss=8.295 [Epoch 142] Training cost: 1375.143, ObjLoss=21.501, BoxCenterLoss=14.581, BoxScaleLoss=4.952, ClassLoss=8.295 [Epoch 142] 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 ] = 0.184 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 ] = 0.211 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315 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 ] = 0.287 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 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426 person=30.5 bicycle=14.5 car=20.0 motorcycle=24.1 airplane=37.8 bus=34.8 train=45.3 truck=17.3 boat=9.2 traffic light=7.6 fire hydrant=36.9 stop sign=35.3 parking meter=24.6 bench=11.0 bird=15.9 cat=43.9 dog=31.7 horse=28.6 sheep=25.4 cow=31.1 elephant=36.7 bear=38.2 zebra=43.3 giraffe=43.4 backpack=5.4 umbrella=20.2 handbag=3.9 tie=14.0 suitcase=17.1 frisbee=31.8 skis=7.8 snowboard=12.4 sports ball=17.0 kite=21.0 baseball bat=11.3 baseball glove=16.9 skateboard=21.5 surfboard=13.9 tennis racket=22.3 bottle=14.0 wine glass=16.6 cup=21.9 fork=9.8 knife=4.5 spoon=1.8 bowl=22.8 banana=11.2 apple=6.5 sandwich=21.1 orange=18.3 broccoli=8.7 carrot=7.3 hot dog=17.3 pizza=30.3 donut=24.3 cake=17.7 chair=12.2 couch=26.2 potted plant=9.9 bed=27.1 dining table=15.9 toilet=34.9 tv=33.9 laptop=29.5 mouse=32.5 remote=9.2 keyboard=29.4 cell phone=16.7 microwave=28.0 oven=19.5 toaster=0.0 sink=19.9 refrigerator=32.8 book=4.7 clock=28.3 vase=19.7 scissors=12.6 teddy bear=25.8 hair drier=0.0 toothbrush=5.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.7 [Epoch 143][Batch 99], LR: 1.00E-03, Speed: 138.212 samples/sec, ObjLoss=21.500, BoxCenterLoss=14.581, BoxScaleLoss=4.952, ClassLoss=8.294 [Epoch 143][Batch 199], LR: 1.00E-03, Speed: 152.732 samples/sec, ObjLoss=21.500, BoxCenterLoss=14.581, BoxScaleLoss=4.952, ClassLoss=8.293 [Epoch 143][Batch 299], LR: 1.00E-03, Speed: 82.272 samples/sec, ObjLoss=21.499, BoxCenterLoss=14.581, BoxScaleLoss=4.952, ClassLoss=8.292 [Epoch 143][Batch 399], LR: 1.00E-03, Speed: 101.827 samples/sec, ObjLoss=21.498, BoxCenterLoss=14.581, BoxScaleLoss=4.952, ClassLoss=8.292 [Epoch 143][Batch 499], LR: 1.00E-03, Speed: 132.558 samples/sec, ObjLoss=21.497, BoxCenterLoss=14.580, BoxScaleLoss=4.951, ClassLoss=8.291 [Epoch 143][Batch 599], LR: 1.00E-03, Speed: 142.376 samples/sec, ObjLoss=21.496, BoxCenterLoss=14.580, BoxScaleLoss=4.951, ClassLoss=8.290 [Epoch 143][Batch 699], LR: 1.00E-03, Speed: 119.332 samples/sec, ObjLoss=21.495, BoxCenterLoss=14.580, BoxScaleLoss=4.951, ClassLoss=8.290 [Epoch 143][Batch 799], LR: 1.00E-03, Speed: 85.843 samples/sec, ObjLoss=21.495, BoxCenterLoss=14.580, BoxScaleLoss=4.951, ClassLoss=8.289 [Epoch 143][Batch 899], LR: 1.00E-03, Speed: 129.271 samples/sec, ObjLoss=21.494, BoxCenterLoss=14.580, BoxScaleLoss=4.951, ClassLoss=8.288 [Epoch 143][Batch 999], LR: 1.00E-03, Speed: 148.654 samples/sec, ObjLoss=21.493, BoxCenterLoss=14.580, BoxScaleLoss=4.950, ClassLoss=8.287 [Epoch 143][Batch 1099], LR: 1.00E-03, Speed: 70.202 samples/sec, ObjLoss=21.492, BoxCenterLoss=14.580, BoxScaleLoss=4.950, ClassLoss=8.287 [Epoch 143][Batch 1199], LR: 1.00E-03, Speed: 60.034 samples/sec, ObjLoss=21.492, BoxCenterLoss=14.580, BoxScaleLoss=4.950, ClassLoss=8.286 [Epoch 143][Batch 1299], LR: 1.00E-03, Speed: 119.565 samples/sec, ObjLoss=21.491, BoxCenterLoss=14.580, BoxScaleLoss=4.950, ClassLoss=8.285 [Epoch 143][Batch 1399], LR: 1.00E-03, Speed: 90.237 samples/sec, ObjLoss=21.490, BoxCenterLoss=14.580, BoxScaleLoss=4.950, ClassLoss=8.285 [Epoch 143][Batch 1499], LR: 1.00E-03, Speed: 139.817 samples/sec, ObjLoss=21.489, BoxCenterLoss=14.580, BoxScaleLoss=4.950, ClassLoss=8.284 [Epoch 143][Batch 1599], LR: 1.00E-03, Speed: 101.808 samples/sec, ObjLoss=21.489, BoxCenterLoss=14.580, BoxScaleLoss=4.950, ClassLoss=8.283 [Epoch 143][Batch 1699], LR: 1.00E-03, Speed: 64.761 samples/sec, ObjLoss=21.488, BoxCenterLoss=14.579, BoxScaleLoss=4.950, ClassLoss=8.283 [Epoch 143][Batch 1799], LR: 1.00E-03, Speed: 152.962 samples/sec, ObjLoss=21.487, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.282 [Epoch 143] Training cost: 1441.500, ObjLoss=21.487, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.282 [Epoch 143] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.413 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.187 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 ] = 0.212 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.318 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 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.289 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 ] = 0.293 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433 person=31.8 bicycle=12.8 car=20.6 motorcycle=24.4 airplane=36.7 bus=42.9 train=44.9 truck=18.2 boat=9.9 traffic light=12.5 fire hydrant=37.4 stop sign=39.0 parking meter=29.1 bench=10.0 bird=18.0 cat=42.9 dog=35.5 horse=32.4 sheep=29.6 cow=29.9 elephant=34.8 bear=43.8 zebra=39.4 giraffe=42.9 backpack=4.3 umbrella=19.2 handbag=2.5 tie=12.3 suitcase=14.0 frisbee=32.7 skis=7.9 snowboard=9.8 sports ball=24.0 kite=21.4 baseball bat=8.3 baseball glove=11.3 skateboard=17.8 surfboard=15.4 tennis racket=21.7 bottle=14.8 wine glass=14.7 cup=21.2 fork=9.3 knife=3.2 spoon=2.5 bowl=21.9 banana=10.5 apple=5.6 sandwich=18.1 orange=13.4 broccoli=11.7 carrot=6.7 hot dog=15.4 pizza=32.0 donut=20.6 cake=16.7 chair=12.7 couch=24.1 potted plant=11.6 bed=23.6 dining table=11.9 toilet=37.1 tv=32.8 laptop=36.9 mouse=25.1 remote=8.5 keyboard=29.0 cell phone=16.4 microwave=29.7 oven=19.0 toaster=0.0 sink=17.6 refrigerator=28.7 book=4.8 clock=31.7 vase=17.9 scissors=15.3 teddy bear=25.5 hair drier=0.0 toothbrush=3.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 144][Batch 99], LR: 1.00E-03, Speed: 132.878 samples/sec, ObjLoss=21.486, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.281 [Epoch 144][Batch 199], LR: 1.00E-03, Speed: 164.915 samples/sec, ObjLoss=21.485, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.280 [Epoch 144][Batch 299], LR: 1.00E-03, Speed: 57.448 samples/sec, ObjLoss=21.484, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.280 [Epoch 144][Batch 399], LR: 1.00E-03, Speed: 107.079 samples/sec, ObjLoss=21.484, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.279 [Epoch 144][Batch 499], LR: 1.00E-03, Speed: 123.094 samples/sec, ObjLoss=21.483, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.279 [Epoch 144][Batch 599], LR: 1.00E-03, Speed: 99.150 samples/sec, ObjLoss=21.482, BoxCenterLoss=14.579, BoxScaleLoss=4.949, ClassLoss=8.278 [Epoch 144][Batch 699], LR: 1.00E-03, Speed: 109.516 samples/sec, ObjLoss=21.481, BoxCenterLoss=14.579, BoxScaleLoss=4.948, ClassLoss=8.277 [Epoch 144][Batch 799], LR: 1.00E-03, Speed: 135.043 samples/sec, ObjLoss=21.480, BoxCenterLoss=14.578, BoxScaleLoss=4.948, ClassLoss=8.276 [Epoch 144][Batch 899], LR: 1.00E-03, Speed: 86.706 samples/sec, ObjLoss=21.480, BoxCenterLoss=14.579, BoxScaleLoss=4.948, ClassLoss=8.276 [Epoch 144][Batch 999], LR: 1.00E-03, Speed: 66.405 samples/sec, ObjLoss=21.479, BoxCenterLoss=14.578, BoxScaleLoss=4.948, ClassLoss=8.275 [Epoch 144][Batch 1099], LR: 1.00E-03, Speed: 70.915 samples/sec, ObjLoss=21.478, BoxCenterLoss=14.578, BoxScaleLoss=4.948, ClassLoss=8.274 [Epoch 144][Batch 1199], LR: 1.00E-03, Speed: 72.039 samples/sec, ObjLoss=21.478, BoxCenterLoss=14.578, BoxScaleLoss=4.948, ClassLoss=8.274 [Epoch 144][Batch 1299], LR: 1.00E-03, Speed: 131.843 samples/sec, ObjLoss=21.477, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.273 [Epoch 144][Batch 1399], LR: 1.00E-03, Speed: 144.504 samples/sec, ObjLoss=21.476, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.273 [Epoch 144][Batch 1499], LR: 1.00E-03, Speed: 144.095 samples/sec, ObjLoss=21.476, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.272 [Epoch 144][Batch 1599], LR: 1.00E-03, Speed: 157.929 samples/sec, ObjLoss=21.475, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.271 [Epoch 144][Batch 1699], LR: 1.00E-03, Speed: 145.013 samples/sec, ObjLoss=21.474, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.271 [Epoch 144][Batch 1799], LR: 1.00E-03, Speed: 106.221 samples/sec, ObjLoss=21.474, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.270 [Epoch 144] Training cost: 1435.585, ObjLoss=21.473, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.270 [Epoch 144] 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 ] = 0.404 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.190 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 ] = 0.183 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 ] = 0.272 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 ] = 0.271 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.400 person=29.7 bicycle=14.0 car=21.0 motorcycle=24.1 airplane=37.0 bus=36.9 train=40.8 truck=16.4 boat=10.2 traffic light=10.8 fire hydrant=34.7 stop sign=33.4 parking meter=22.5 bench=10.0 bird=15.5 cat=38.8 dog=33.7 horse=27.3 sheep=23.5 cow=28.5 elephant=34.7 bear=41.0 zebra=37.6 giraffe=41.9 backpack=3.3 umbrella=19.6 handbag=3.1 tie=13.6 suitcase=14.0 frisbee=33.5 skis=7.9 snowboard=13.4 sports ball=24.3 kite=21.8 baseball bat=9.9 baseball glove=17.3 skateboard=22.0 surfboard=14.7 tennis racket=17.5 bottle=15.2 wine glass=13.0 cup=19.2 fork=10.8 knife=4.0 spoon=2.3 bowl=17.7 banana=10.5 apple=6.0 sandwich=17.2 orange=11.4 broccoli=10.4 carrot=7.7 hot dog=13.3 pizza=26.2 donut=20.2 cake=16.7 chair=11.5 couch=23.3 potted plant=9.6 bed=23.3 dining table=14.3 toilet=34.1 tv=31.5 laptop=32.4 mouse=29.8 remote=5.9 keyboard=27.8 cell phone=14.9 microwave=26.8 oven=16.1 toaster=0.0 sink=12.9 refrigerator=27.0 book=4.7 clock=27.1 vase=16.3 scissors=15.6 teddy bear=22.8 hair drier=0.0 toothbrush=3.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.4 [Epoch 145][Batch 99], LR: 1.00E-03, Speed: 133.324 samples/sec, ObjLoss=21.472, BoxCenterLoss=14.578, BoxScaleLoss=4.947, ClassLoss=8.269 [Epoch 145][Batch 199], LR: 1.00E-03, Speed: 131.029 samples/sec, ObjLoss=21.472, BoxCenterLoss=14.578, BoxScaleLoss=4.946, ClassLoss=8.268 [Epoch 145][Batch 299], LR: 1.00E-03, Speed: 141.735 samples/sec, ObjLoss=21.471, BoxCenterLoss=14.578, BoxScaleLoss=4.946, ClassLoss=8.268 [Epoch 145][Batch 399], LR: 1.00E-03, Speed: 102.934 samples/sec, ObjLoss=21.470, BoxCenterLoss=14.578, BoxScaleLoss=4.946, ClassLoss=8.267 [Epoch 145][Batch 499], LR: 1.00E-03, Speed: 86.745 samples/sec, ObjLoss=21.469, BoxCenterLoss=14.578, BoxScaleLoss=4.946, ClassLoss=8.266 [Epoch 145][Batch 599], LR: 1.00E-03, Speed: 44.928 samples/sec, ObjLoss=21.469, BoxCenterLoss=14.578, BoxScaleLoss=4.946, ClassLoss=8.266 [Epoch 145][Batch 699], LR: 1.00E-03, Speed: 68.565 samples/sec, ObjLoss=21.468, BoxCenterLoss=14.578, BoxScaleLoss=4.946, ClassLoss=8.265 [Epoch 145][Batch 799], LR: 1.00E-03, Speed: 63.937 samples/sec, ObjLoss=21.467, BoxCenterLoss=14.578, BoxScaleLoss=4.946, ClassLoss=8.264 [Epoch 145][Batch 899], LR: 1.00E-03, Speed: 68.807 samples/sec, ObjLoss=21.467, BoxCenterLoss=14.577, BoxScaleLoss=4.945, ClassLoss=8.264 [Epoch 145][Batch 999], LR: 1.00E-03, Speed: 148.930 samples/sec, ObjLoss=21.466, BoxCenterLoss=14.577, BoxScaleLoss=4.945, ClassLoss=8.263 [Epoch 145][Batch 1099], LR: 1.00E-03, Speed: 61.787 samples/sec, ObjLoss=21.465, BoxCenterLoss=14.577, BoxScaleLoss=4.945, ClassLoss=8.262 [Epoch 145][Batch 1199], LR: 1.00E-03, Speed: 59.967 samples/sec, ObjLoss=21.464, BoxCenterLoss=14.577, BoxScaleLoss=4.945, ClassLoss=8.262 [Epoch 145][Batch 1299], LR: 1.00E-03, Speed: 158.596 samples/sec, ObjLoss=21.464, BoxCenterLoss=14.577, BoxScaleLoss=4.945, ClassLoss=8.261 [Epoch 145][Batch 1399], LR: 1.00E-03, Speed: 76.818 samples/sec, ObjLoss=21.463, BoxCenterLoss=14.577, BoxScaleLoss=4.945, ClassLoss=8.261 [Epoch 145][Batch 1499], LR: 1.00E-03, Speed: 143.383 samples/sec, ObjLoss=21.462, BoxCenterLoss=14.577, BoxScaleLoss=4.945, ClassLoss=8.260 [Epoch 145][Batch 1599], LR: 1.00E-03, Speed: 110.741 samples/sec, ObjLoss=21.461, BoxCenterLoss=14.577, BoxScaleLoss=4.944, ClassLoss=8.259 [Epoch 145][Batch 1699], LR: 1.00E-03, Speed: 130.244 samples/sec, ObjLoss=21.461, BoxCenterLoss=14.577, BoxScaleLoss=4.944, ClassLoss=8.259 [Epoch 145][Batch 1799], LR: 1.00E-03, Speed: 149.151 samples/sec, ObjLoss=21.460, BoxCenterLoss=14.577, BoxScaleLoss=4.944, ClassLoss=8.258 [Epoch 145] Training cost: 1458.918, ObjLoss=21.460, BoxCenterLoss=14.577, BoxScaleLoss=4.944, ClassLoss=8.258 [Epoch 145] 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 ] = 0.414 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.175 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 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.307 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 ] = 0.275 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 ] = 0.123 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 ] = 0.412 person=32.3 bicycle=13.7 car=22.0 motorcycle=24.2 airplane=39.4 bus=41.2 train=41.8 truck=20.7 boat=10.6 traffic light=13.1 fire hydrant=37.3 stop sign=38.1 parking meter=25.3 bench=11.2 bird=14.7 cat=39.0 dog=33.6 horse=32.6 sheep=24.3 cow=31.8 elephant=37.0 bear=40.0 zebra=37.4 giraffe=43.4 backpack=4.6 umbrella=19.5 handbag=3.0 tie=11.3 suitcase=14.6 frisbee=32.6 skis=8.3 snowboard=14.9 sports ball=22.8 kite=22.5 baseball bat=10.5 baseball glove=18.2 skateboard=23.1 surfboard=14.7 tennis racket=20.0 bottle=15.0 wine glass=13.3 cup=19.3 fork=11.1 knife=3.4 spoon=2.4 bowl=17.8 banana=12.0 apple=6.2 sandwich=20.8 orange=13.3 broccoli=9.0 carrot=8.0 hot dog=13.5 pizza=27.3 donut=21.5 cake=17.2 chair=13.1 couch=25.5 potted plant=10.7 bed=19.9 dining table=13.8 toilet=33.3 tv=38.4 laptop=33.0 mouse=23.1 remote=9.3 keyboard=25.3 cell phone=14.4 microwave=28.9 oven=19.3 toaster=0.0 sink=15.8 refrigerator=23.5 book=5.4 clock=29.9 vase=19.3 scissors=12.6 teddy bear=25.6 hair drier=0.0 toothbrush=3.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.3 [Epoch 146][Batch 99], LR: 1.00E-03, Speed: 142.207 samples/sec, ObjLoss=21.459, BoxCenterLoss=14.577, BoxScaleLoss=4.944, ClassLoss=8.257 [Epoch 146][Batch 199], LR: 1.00E-03, Speed: 147.459 samples/sec, ObjLoss=21.458, BoxCenterLoss=14.577, BoxScaleLoss=4.944, ClassLoss=8.256 [Epoch 146][Batch 299], LR: 1.00E-03, Speed: 98.419 samples/sec, ObjLoss=21.457, BoxCenterLoss=14.577, BoxScaleLoss=4.944, ClassLoss=8.256 [Epoch 146][Batch 399], LR: 1.00E-03, Speed: 87.191 samples/sec, ObjLoss=21.457, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.255 [Epoch 146][Batch 499], LR: 1.00E-03, Speed: 107.067 samples/sec, ObjLoss=21.456, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.254 [Epoch 146][Batch 599], LR: 1.00E-03, Speed: 81.654 samples/sec, ObjLoss=21.455, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.254 [Epoch 146][Batch 699], LR: 1.00E-03, Speed: 120.691 samples/sec, ObjLoss=21.454, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.253 [Epoch 146][Batch 799], LR: 1.00E-03, Speed: 137.715 samples/sec, ObjLoss=21.454, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.253 [Epoch 146][Batch 899], LR: 1.00E-03, Speed: 96.328 samples/sec, ObjLoss=21.453, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.252 [Epoch 146][Batch 999], LR: 1.00E-03, Speed: 75.114 samples/sec, ObjLoss=21.452, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.252 [Epoch 146][Batch 1099], LR: 1.00E-03, Speed: 53.319 samples/sec, ObjLoss=21.452, BoxCenterLoss=14.576, BoxScaleLoss=4.943, ClassLoss=8.251 [Epoch 146][Batch 1199], LR: 1.00E-03, Speed: 98.377 samples/sec, ObjLoss=21.451, BoxCenterLoss=14.576, BoxScaleLoss=4.942, ClassLoss=8.250 [Epoch 146][Batch 1299], LR: 1.00E-03, Speed: 71.683 samples/sec, ObjLoss=21.451, BoxCenterLoss=14.576, BoxScaleLoss=4.942, ClassLoss=8.250 [Epoch 146][Batch 1399], LR: 1.00E-03, Speed: 68.126 samples/sec, ObjLoss=21.450, BoxCenterLoss=14.576, BoxScaleLoss=4.942, ClassLoss=8.249 [Epoch 146][Batch 1499], LR: 1.00E-03, Speed: 96.165 samples/sec, ObjLoss=21.449, BoxCenterLoss=14.576, BoxScaleLoss=4.942, ClassLoss=8.249 [Epoch 146][Batch 1599], LR: 1.00E-03, Speed: 155.912 samples/sec, ObjLoss=21.448, BoxCenterLoss=14.576, BoxScaleLoss=4.942, ClassLoss=8.248 [Epoch 146][Batch 1699], LR: 1.00E-03, Speed: 87.689 samples/sec, ObjLoss=21.448, BoxCenterLoss=14.575, BoxScaleLoss=4.942, ClassLoss=8.248 [Epoch 146][Batch 1799], LR: 1.00E-03, Speed: 152.563 samples/sec, ObjLoss=21.447, BoxCenterLoss=14.575, BoxScaleLoss=4.942, ClassLoss=8.247 [Epoch 146] Training cost: 1454.768, ObjLoss=21.447, BoxCenterLoss=14.575, BoxScaleLoss=4.942, ClassLoss=8.247 [Epoch 146] 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 ] = 0.186 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 ] = 0.213 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 ] = 0.197 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 ] = 0.295 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 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 person=33.2 bicycle=14.5 car=20.6 motorcycle=24.7 airplane=36.2 bus=45.4 train=42.7 truck=20.3 boat=10.7 traffic light=12.4 fire hydrant=37.4 stop sign=37.7 parking meter=24.2 bench=11.2 bird=12.5 cat=40.8 dog=33.2 horse=32.9 sheep=25.6 cow=32.0 elephant=33.6 bear=44.4 zebra=44.5 giraffe=42.3 backpack=5.2 umbrella=20.1 handbag=3.6 tie=12.6 suitcase=16.1 frisbee=34.4 skis=8.5 snowboard=14.4 sports ball=20.9 kite=19.1 baseball bat=10.7 baseball glove=17.0 skateboard=20.6 surfboard=15.4 tennis racket=21.7 bottle=17.0 wine glass=14.2 cup=19.5 fork=10.1 knife=3.5 spoon=3.2 bowl=20.0 banana=10.5 apple=5.5 sandwich=17.4 orange=17.0 broccoli=11.5 carrot=6.7 hot dog=16.4 pizza=29.8 donut=19.2 cake=15.2 chair=12.3 couch=24.2 potted plant=11.2 bed=29.1 dining table=18.0 toilet=34.7 tv=37.6 laptop=34.8 mouse=32.2 remote=7.1 keyboard=22.4 cell phone=16.3 microwave=33.0 oven=21.4 toaster=0.0 sink=17.7 refrigerator=24.6 book=4.4 clock=28.9 vase=17.3 scissors=15.0 teddy bear=20.0 hair drier=0.0 toothbrush=3.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.7 [Epoch 147][Batch 99], LR: 1.00E-03, Speed: 147.005 samples/sec, ObjLoss=21.446, BoxCenterLoss=14.575, BoxScaleLoss=4.942, ClassLoss=8.246 [Epoch 147][Batch 199], LR: 1.00E-03, Speed: 145.212 samples/sec, ObjLoss=21.445, BoxCenterLoss=14.575, BoxScaleLoss=4.941, ClassLoss=8.245 [Epoch 147][Batch 299], LR: 1.00E-03, Speed: 140.203 samples/sec, ObjLoss=21.444, BoxCenterLoss=14.575, BoxScaleLoss=4.941, ClassLoss=8.245 [Epoch 147][Batch 399], LR: 1.00E-03, Speed: 149.477 samples/sec, ObjLoss=21.443, BoxCenterLoss=14.575, BoxScaleLoss=4.941, ClassLoss=8.244 [Epoch 147][Batch 499], LR: 1.00E-03, Speed: 125.817 samples/sec, ObjLoss=21.443, BoxCenterLoss=14.575, BoxScaleLoss=4.941, ClassLoss=8.243 [Epoch 147][Batch 599], LR: 1.00E-03, Speed: 137.106 samples/sec, ObjLoss=21.442, BoxCenterLoss=14.575, BoxScaleLoss=4.941, ClassLoss=8.243 [Epoch 147][Batch 699], LR: 1.00E-03, Speed: 80.261 samples/sec, ObjLoss=21.441, BoxCenterLoss=14.575, BoxScaleLoss=4.941, ClassLoss=8.242 [Epoch 147][Batch 799], LR: 1.00E-03, Speed: 71.668 samples/sec, ObjLoss=21.440, BoxCenterLoss=14.574, BoxScaleLoss=4.940, ClassLoss=8.241 [Epoch 147][Batch 899], LR: 1.00E-03, Speed: 76.720 samples/sec, ObjLoss=21.440, BoxCenterLoss=14.575, BoxScaleLoss=4.940, ClassLoss=8.241 [Epoch 147][Batch 999], LR: 1.00E-03, Speed: 81.163 samples/sec, ObjLoss=21.439, BoxCenterLoss=14.575, BoxScaleLoss=4.940, ClassLoss=8.240 [Epoch 147][Batch 1099], LR: 1.00E-03, Speed: 71.277 samples/sec, ObjLoss=21.438, BoxCenterLoss=14.575, BoxScaleLoss=4.940, ClassLoss=8.240 [Epoch 147][Batch 1199], LR: 1.00E-03, Speed: 99.007 samples/sec, ObjLoss=21.438, BoxCenterLoss=14.575, BoxScaleLoss=4.940, ClassLoss=8.239 [Epoch 147][Batch 1299], LR: 1.00E-03, Speed: 147.538 samples/sec, ObjLoss=21.437, BoxCenterLoss=14.574, BoxScaleLoss=4.940, ClassLoss=8.238 [Epoch 147][Batch 1399], LR: 1.00E-03, Speed: 82.054 samples/sec, ObjLoss=21.436, BoxCenterLoss=14.574, BoxScaleLoss=4.940, ClassLoss=8.238 [Epoch 147][Batch 1499], LR: 1.00E-03, Speed: 76.670 samples/sec, ObjLoss=21.435, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.237 [Epoch 147][Batch 1599], LR: 1.00E-03, Speed: 86.108 samples/sec, ObjLoss=21.435, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.236 [Epoch 147][Batch 1699], LR: 1.00E-03, Speed: 95.114 samples/sec, ObjLoss=21.434, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.236 [Epoch 147][Batch 1799], LR: 1.00E-03, Speed: 107.780 samples/sec, ObjLoss=21.433, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.235 [Epoch 147] Training cost: 1452.190, ObjLoss=21.433, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.235 [Epoch 147] 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 ] = 0.427 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.196 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 ] = 0.223 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 ] = 0.202 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 ] = 0.305 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 ] = 0.318 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.439 person=34.1 bicycle=15.1 car=21.5 motorcycle=24.9 airplane=40.3 bus=45.2 train=48.4 truck=18.7 boat=12.0 traffic light=13.1 fire hydrant=41.9 stop sign=41.5 parking meter=23.1 bench=12.5 bird=18.0 cat=43.9 dog=36.3 horse=36.0 sheep=29.5 cow=30.4 elephant=41.4 bear=46.4 zebra=44.0 giraffe=46.0 backpack=4.3 umbrella=20.3 handbag=3.7 tie=13.9 suitcase=15.4 frisbee=31.0 skis=8.1 snowboard=12.0 sports ball=24.3 kite=19.6 baseball bat=12.5 baseball glove=16.1 skateboard=21.5 surfboard=15.7 tennis racket=23.8 bottle=16.5 wine glass=15.5 cup=21.4 fork=12.2 knife=3.7 spoon=3.2 bowl=22.2 banana=12.1 apple=8.6 sandwich=20.3 orange=15.9 broccoli=10.0 carrot=6.5 hot dog=18.0 pizza=31.7 donut=18.9 cake=17.1 chair=12.8 couch=28.2 potted plant=11.4 bed=28.6 dining table=16.1 toilet=35.8 tv=35.2 laptop=32.6 mouse=31.2 remote=9.1 keyboard=29.9 cell phone=14.7 microwave=28.9 oven=19.1 toaster=0.0 sink=19.6 refrigerator=29.5 book=4.8 clock=28.1 vase=19.1 scissors=16.9 teddy bear=20.7 hair drier=0.0 toothbrush=3.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.7 [Epoch 148][Batch 99], LR: 1.00E-03, Speed: 150.859 samples/sec, ObjLoss=21.432, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.234 [Epoch 148][Batch 199], LR: 1.00E-03, Speed: 139.922 samples/sec, ObjLoss=21.431, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.234 [Epoch 148][Batch 299], LR: 1.00E-03, Speed: 159.287 samples/sec, ObjLoss=21.430, BoxCenterLoss=14.574, BoxScaleLoss=4.939, ClassLoss=8.233 [Epoch 148][Batch 399], LR: 1.00E-03, Speed: 110.830 samples/sec, ObjLoss=21.429, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.232 [Epoch 148][Batch 499], LR: 1.00E-03, Speed: 91.714 samples/sec, ObjLoss=21.428, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.231 [Epoch 148][Batch 599], LR: 1.00E-03, Speed: 146.883 samples/sec, ObjLoss=21.428, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.231 [Epoch 148][Batch 699], LR: 1.00E-03, Speed: 63.447 samples/sec, ObjLoss=21.427, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.230 [Epoch 148][Batch 799], LR: 1.00E-03, Speed: 133.491 samples/sec, ObjLoss=21.426, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.230 [Epoch 148][Batch 899], LR: 1.00E-03, Speed: 87.017 samples/sec, ObjLoss=21.426, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.229 [Epoch 148][Batch 999], LR: 1.00E-03, Speed: 115.749 samples/sec, ObjLoss=21.425, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.229 [Epoch 148][Batch 1099], LR: 1.00E-03, Speed: 83.049 samples/sec, ObjLoss=21.424, BoxCenterLoss=14.573, BoxScaleLoss=4.938, ClassLoss=8.228 [Epoch 148][Batch 1199], LR: 1.00E-03, Speed: 71.017 samples/sec, ObjLoss=21.423, BoxCenterLoss=14.573, BoxScaleLoss=4.937, ClassLoss=8.227 [Epoch 148][Batch 1299], LR: 1.00E-03, Speed: 52.990 samples/sec, ObjLoss=21.423, BoxCenterLoss=14.573, BoxScaleLoss=4.937, ClassLoss=8.227 [Epoch 148][Batch 1399], LR: 1.00E-03, Speed: 68.700 samples/sec, ObjLoss=21.422, BoxCenterLoss=14.573, BoxScaleLoss=4.937, ClassLoss=8.226 [Epoch 148][Batch 1499], LR: 1.00E-03, Speed: 91.069 samples/sec, ObjLoss=21.421, BoxCenterLoss=14.573, BoxScaleLoss=4.937, ClassLoss=8.226 [Epoch 148][Batch 1599], LR: 1.00E-03, Speed: 44.950 samples/sec, ObjLoss=21.420, BoxCenterLoss=14.572, BoxScaleLoss=4.937, ClassLoss=8.225 [Epoch 148][Batch 1699], LR: 1.00E-03, Speed: 76.431 samples/sec, ObjLoss=21.420, BoxCenterLoss=14.572, BoxScaleLoss=4.937, ClassLoss=8.224 [Epoch 148][Batch 1799], LR: 1.00E-03, Speed: 114.517 samples/sec, ObjLoss=21.419, BoxCenterLoss=14.572, BoxScaleLoss=4.937, ClassLoss=8.224 [Epoch 148] Training cost: 1492.294, ObjLoss=21.419, BoxCenterLoss=14.572, BoxScaleLoss=4.937, ClassLoss=8.224 [Epoch 148] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.174 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 ] = 0.212 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 ] = 0.285 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 ] = 0.150 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 ] = 0.400 person=31.6 bicycle=14.6 car=22.0 motorcycle=25.5 airplane=38.2 bus=38.1 train=43.7 truck=17.9 boat=10.6 traffic light=11.7 fire hydrant=37.9 stop sign=33.0 parking meter=23.9 bench=10.7 bird=16.6 cat=37.8 dog=35.0 horse=33.7 sheep=25.6 cow=31.8 elephant=36.4 bear=34.9 zebra=40.7 giraffe=42.2 backpack=5.3 umbrella=19.3 handbag=4.5 tie=11.8 suitcase=15.1 frisbee=29.6 skis=7.6 snowboard=11.4 sports ball=27.0 kite=19.1 baseball bat=10.6 baseball glove=19.4 skateboard=23.1 surfboard=17.6 tennis racket=23.1 bottle=15.9 wine glass=15.2 cup=21.0 fork=8.3 knife=4.1 spoon=2.4 bowl=19.6 banana=9.7 apple=7.2 sandwich=19.3 orange=15.8 broccoli=11.4 carrot=8.2 hot dog=16.1 pizza=25.6 donut=21.9 cake=14.3 chair=12.5 couch=22.1 potted plant=9.5 bed=23.0 dining table=16.2 toilet=38.3 tv=33.5 laptop=29.6 mouse=33.8 remote=8.9 keyboard=26.8 cell phone=16.6 microwave=30.5 oven=22.0 toaster=0.0 sink=20.3 refrigerator=26.0 book=5.2 clock=31.3 vase=16.8 scissors=13.5 teddy bear=21.6 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 149][Batch 99], LR: 1.00E-03, Speed: 167.828 samples/sec, ObjLoss=21.418, BoxCenterLoss=14.572, BoxScaleLoss=4.937, ClassLoss=8.223 [Epoch 149][Batch 199], LR: 1.00E-03, Speed: 143.672 samples/sec, ObjLoss=21.417, BoxCenterLoss=14.572, BoxScaleLoss=4.936, ClassLoss=8.222 [Epoch 149][Batch 299], LR: 1.00E-03, Speed: 114.358 samples/sec, ObjLoss=21.416, BoxCenterLoss=14.572, BoxScaleLoss=4.936, ClassLoss=8.222 [Epoch 149][Batch 399], LR: 1.00E-03, Speed: 129.463 samples/sec, ObjLoss=21.415, BoxCenterLoss=14.572, BoxScaleLoss=4.936, ClassLoss=8.221 [Epoch 149][Batch 499], LR: 1.00E-03, Speed: 107.744 samples/sec, ObjLoss=21.415, BoxCenterLoss=14.572, BoxScaleLoss=4.936, ClassLoss=8.220 [Epoch 149][Batch 599], LR: 1.00E-03, Speed: 81.505 samples/sec, ObjLoss=21.414, BoxCenterLoss=14.572, BoxScaleLoss=4.936, ClassLoss=8.220 [Epoch 149][Batch 699], LR: 1.00E-03, Speed: 74.831 samples/sec, ObjLoss=21.413, BoxCenterLoss=14.571, BoxScaleLoss=4.936, ClassLoss=8.219 [Epoch 149][Batch 799], LR: 1.00E-03, Speed: 161.950 samples/sec, ObjLoss=21.412, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.218 [Epoch 149][Batch 899], LR: 1.00E-03, Speed: 56.170 samples/sec, ObjLoss=21.411, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.218 [Epoch 149][Batch 999], LR: 1.00E-03, Speed: 72.351 samples/sec, ObjLoss=21.410, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.217 [Epoch 149][Batch 1099], LR: 1.00E-03, Speed: 68.029 samples/sec, ObjLoss=21.410, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.217 [Epoch 149][Batch 1199], LR: 1.00E-03, Speed: 52.913 samples/sec, ObjLoss=21.409, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.216 [Epoch 149][Batch 1299], LR: 1.00E-03, Speed: 120.406 samples/sec, ObjLoss=21.409, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.215 [Epoch 149][Batch 1399], LR: 1.00E-03, Speed: 90.655 samples/sec, ObjLoss=21.408, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.215 [Epoch 149][Batch 1499], LR: 1.00E-03, Speed: 85.829 samples/sec, ObjLoss=21.407, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.214 [Epoch 149][Batch 1599], LR: 1.00E-03, Speed: 128.658 samples/sec, ObjLoss=21.407, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.214 [Epoch 149][Batch 1699], LR: 1.00E-03, Speed: 126.010 samples/sec, ObjLoss=21.406, BoxCenterLoss=14.571, BoxScaleLoss=4.935, ClassLoss=8.213 [Epoch 149][Batch 1799], LR: 1.00E-03, Speed: 94.805 samples/sec, ObjLoss=21.405, BoxCenterLoss=14.571, BoxScaleLoss=4.934, ClassLoss=8.213 [Epoch 149] Training cost: 1498.735, ObjLoss=21.405, BoxCenterLoss=14.571, BoxScaleLoss=4.934, ClassLoss=8.212 [Epoch 149] 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 ] = 0.414 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.177 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 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 ] = 0.304 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 ] = 0.283 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 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412 person=31.5 bicycle=13.6 car=20.8 motorcycle=23.7 airplane=39.9 bus=40.5 train=42.8 truck=20.0 boat=10.9 traffic light=12.7 fire hydrant=34.2 stop sign=36.8 parking meter=21.3 bench=10.7 bird=17.3 cat=37.5 dog=28.6 horse=31.3 sheep=29.2 cow=27.1 elephant=35.1 bear=45.3 zebra=36.7 giraffe=42.9 backpack=4.0 umbrella=18.6 handbag=3.9 tie=12.5 suitcase=12.7 frisbee=33.5 skis=5.7 snowboard=11.5 sports ball=22.4 kite=19.7 baseball bat=11.3 baseball glove=19.0 skateboard=25.1 surfboard=16.1 tennis racket=18.1 bottle=16.2 wine glass=13.3 cup=19.8 fork=8.2 knife=3.3 spoon=3.1 bowl=21.4 banana=9.3 apple=7.0 sandwich=17.4 orange=16.2 broccoli=11.0 carrot=7.7 hot dog=15.9 pizza=26.8 donut=20.6 cake=16.3 chair=12.3 couch=26.5 potted plant=11.1 bed=26.6 dining table=16.7 toilet=36.2 tv=32.5 laptop=34.8 mouse=36.4 remote=8.9 keyboard=29.8 cell phone=14.6 microwave=31.0 oven=18.6 toaster=0.0 sink=19.1 refrigerator=24.3 book=4.8 clock=30.5 vase=20.6 scissors=12.1 teddy bear=24.6 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 150][Batch 99], LR: 1.00E-03, Speed: 126.040 samples/sec, ObjLoss=21.404, BoxCenterLoss=14.571, BoxScaleLoss=4.934, ClassLoss=8.212 [Epoch 150][Batch 199], LR: 1.00E-03, Speed: 144.061 samples/sec, ObjLoss=21.403, BoxCenterLoss=14.571, BoxScaleLoss=4.934, ClassLoss=8.211 [Epoch 150][Batch 299], LR: 1.00E-03, Speed: 66.122 samples/sec, ObjLoss=21.403, BoxCenterLoss=14.570, BoxScaleLoss=4.934, ClassLoss=8.211 [Epoch 150][Batch 399], LR: 1.00E-03, Speed: 128.353 samples/sec, ObjLoss=21.402, BoxCenterLoss=14.570, BoxScaleLoss=4.934, ClassLoss=8.210 [Epoch 150][Batch 499], LR: 1.00E-03, Speed: 104.627 samples/sec, ObjLoss=21.401, BoxCenterLoss=14.570, BoxScaleLoss=4.934, ClassLoss=8.210 [Epoch 150][Batch 599], LR: 1.00E-03, Speed: 98.185 samples/sec, ObjLoss=21.401, BoxCenterLoss=14.570, BoxScaleLoss=4.933, ClassLoss=8.209 [Epoch 150][Batch 699], LR: 1.00E-03, Speed: 100.036 samples/sec, ObjLoss=21.400, BoxCenterLoss=14.570, BoxScaleLoss=4.933, ClassLoss=8.208 [Epoch 150][Batch 799], LR: 1.00E-03, Speed: 95.218 samples/sec, ObjLoss=21.399, BoxCenterLoss=14.570, BoxScaleLoss=4.933, ClassLoss=8.208 [Epoch 150][Batch 899], LR: 1.00E-03, Speed: 66.108 samples/sec, ObjLoss=21.398, BoxCenterLoss=14.570, BoxScaleLoss=4.933, ClassLoss=8.207 [Epoch 150][Batch 999], LR: 1.00E-03, Speed: 124.159 samples/sec, ObjLoss=21.397, BoxCenterLoss=14.570, BoxScaleLoss=4.933, ClassLoss=8.206 [Epoch 150][Batch 1099], LR: 1.00E-03, Speed: 69.789 samples/sec, ObjLoss=21.397, BoxCenterLoss=14.570, BoxScaleLoss=4.933, ClassLoss=8.206 [Epoch 150][Batch 1199], LR: 1.00E-03, Speed: 126.700 samples/sec, ObjLoss=21.396, BoxCenterLoss=14.570, BoxScaleLoss=4.932, ClassLoss=8.205 [Epoch 150][Batch 1299], LR: 1.00E-03, Speed: 84.901 samples/sec, ObjLoss=21.395, BoxCenterLoss=14.570, BoxScaleLoss=4.932, ClassLoss=8.204 [Epoch 150][Batch 1399], LR: 1.00E-03, Speed: 54.123 samples/sec, ObjLoss=21.395, BoxCenterLoss=14.570, BoxScaleLoss=4.932, ClassLoss=8.204 [Epoch 150][Batch 1499], LR: 1.00E-03, Speed: 151.407 samples/sec, ObjLoss=21.394, BoxCenterLoss=14.569, BoxScaleLoss=4.932, ClassLoss=8.203 [Epoch 150][Batch 1599], LR: 1.00E-03, Speed: 78.225 samples/sec, ObjLoss=21.393, BoxCenterLoss=14.569, BoxScaleLoss=4.932, ClassLoss=8.203 [Epoch 150][Batch 1699], LR: 1.00E-03, Speed: 66.766 samples/sec, ObjLoss=21.392, BoxCenterLoss=14.569, BoxScaleLoss=4.932, ClassLoss=8.202 [Epoch 150][Batch 1799], LR: 1.00E-03, Speed: 139.600 samples/sec, ObjLoss=21.392, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.201 [Epoch 150] Training cost: 1584.899, ObjLoss=21.392, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.201 [Epoch 150] 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 ] = 0.411 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.081 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 ] = 0.321 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 ] = 0.287 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 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.435 person=33.5 bicycle=14.1 car=21.5 motorcycle=21.6 airplane=41.5 bus=41.1 train=43.7 truck=21.2 boat=11.0 traffic light=10.0 fire hydrant=38.2 stop sign=41.2 parking meter=23.1 bench=10.7 bird=14.7 cat=41.4 dog=35.1 horse=34.9 sheep=26.0 cow=31.4 elephant=38.9 bear=46.3 zebra=43.6 giraffe=45.1 backpack=4.3 umbrella=18.8 handbag=3.3 tie=13.2 suitcase=15.1 frisbee=33.8 skis=8.7 snowboard=14.6 sports ball=18.2 kite=11.3 baseball bat=10.3 baseball glove=18.8 skateboard=22.4 surfboard=16.0 tennis racket=23.2 bottle=15.0 wine glass=14.4 cup=20.6 fork=11.2 knife=4.5 spoon=2.2 bowl=18.6 banana=11.8 apple=6.8 sandwich=19.0 orange=13.5 broccoli=10.6 carrot=7.5 hot dog=13.3 pizza=27.5 donut=23.4 cake=15.8 chair=12.6 couch=24.2 potted plant=10.9 bed=27.5 dining table=17.5 toilet=35.3 tv=32.0 laptop=35.4 mouse=31.1 remote=8.1 keyboard=29.0 cell phone=15.1 microwave=28.6 oven=20.1 toaster=0.0 sink=18.0 refrigerator=31.6 book=4.8 clock=31.7 vase=17.1 scissors=17.0 teddy bear=26.6 hair drier=0.0 toothbrush=3.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 151][Batch 99], LR: 1.00E-03, Speed: 115.153 samples/sec, ObjLoss=21.391, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.201 [Epoch 151][Batch 199], LR: 1.00E-03, Speed: 132.697 samples/sec, ObjLoss=21.390, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.200 [Epoch 151][Batch 299], LR: 1.00E-03, Speed: 126.525 samples/sec, ObjLoss=21.390, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.200 [Epoch 151][Batch 399], LR: 1.00E-03, Speed: 154.928 samples/sec, ObjLoss=21.389, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.199 [Epoch 151][Batch 499], LR: 1.00E-03, Speed: 66.904 samples/sec, ObjLoss=21.388, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.198 [Epoch 151][Batch 599], LR: 1.00E-03, Speed: 127.513 samples/sec, ObjLoss=21.388, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.198 [Epoch 151][Batch 699], LR: 1.00E-03, Speed: 86.985 samples/sec, ObjLoss=21.387, BoxCenterLoss=14.569, BoxScaleLoss=4.931, ClassLoss=8.197 [Epoch 151][Batch 799], LR: 1.00E-03, Speed: 112.497 samples/sec, ObjLoss=21.386, BoxCenterLoss=14.568, BoxScaleLoss=4.930, ClassLoss=8.196 [Epoch 151][Batch 899], LR: 1.00E-03, Speed: 79.210 samples/sec, ObjLoss=21.385, BoxCenterLoss=14.568, BoxScaleLoss=4.930, ClassLoss=8.196 [Epoch 151][Batch 999], LR: 1.00E-03, Speed: 73.966 samples/sec, ObjLoss=21.385, BoxCenterLoss=14.568, BoxScaleLoss=4.930, ClassLoss=8.195 [Epoch 151][Batch 1099], LR: 1.00E-03, Speed: 106.165 samples/sec, ObjLoss=21.384, BoxCenterLoss=14.568, BoxScaleLoss=4.930, ClassLoss=8.194 [Epoch 151][Batch 1199], LR: 1.00E-03, Speed: 129.205 samples/sec, ObjLoss=21.383, BoxCenterLoss=14.568, BoxScaleLoss=4.930, ClassLoss=8.194 [Epoch 151][Batch 1299], LR: 1.00E-03, Speed: 61.059 samples/sec, ObjLoss=21.383, BoxCenterLoss=14.568, BoxScaleLoss=4.930, ClassLoss=8.193 [Epoch 151][Batch 1399], LR: 1.00E-03, Speed: 118.539 samples/sec, ObjLoss=21.382, BoxCenterLoss=14.568, BoxScaleLoss=4.930, ClassLoss=8.193 [Epoch 151][Batch 1499], LR: 1.00E-03, Speed: 76.388 samples/sec, ObjLoss=21.381, BoxCenterLoss=14.568, BoxScaleLoss=4.929, ClassLoss=8.192 [Epoch 151][Batch 1599], LR: 1.00E-03, Speed: 103.113 samples/sec, ObjLoss=21.381, BoxCenterLoss=14.568, BoxScaleLoss=4.929, ClassLoss=8.191 [Epoch 151][Batch 1699], LR: 1.00E-03, Speed: 70.761 samples/sec, ObjLoss=21.380, BoxCenterLoss=14.568, BoxScaleLoss=4.929, ClassLoss=8.191 [Epoch 151][Batch 1799], LR: 1.00E-03, Speed: 138.706 samples/sec, ObjLoss=21.379, BoxCenterLoss=14.568, BoxScaleLoss=4.929, ClassLoss=8.190 [Epoch 151] Training cost: 1341.202, ObjLoss=21.379, BoxCenterLoss=14.568, BoxScaleLoss=4.929, ClassLoss=8.190 [Epoch 151] 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 ] = 0.414 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.169 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.080 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 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.189 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 ] = 0.286 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 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420 person=30.4 bicycle=13.6 car=21.4 motorcycle=23.1 airplane=37.8 bus=38.9 train=43.3 truck=17.9 boat=9.6 traffic light=12.6 fire hydrant=39.8 stop sign=37.1 parking meter=21.0 bench=11.5 bird=15.8 cat=42.0 dog=33.1 horse=30.8 sheep=24.3 cow=27.5 elephant=36.0 bear=36.5 zebra=38.7 giraffe=42.8 backpack=4.6 umbrella=14.3 handbag=3.3 tie=13.3 suitcase=15.7 frisbee=29.9 skis=6.6 snowboard=12.6 sports ball=21.7 kite=23.3 baseball bat=9.6 baseball glove=12.9 skateboard=21.4 surfboard=13.9 tennis racket=20.5 bottle=16.0 wine glass=16.3 cup=20.0 fork=9.3 knife=3.7 spoon=2.9 bowl=19.6 banana=12.0 apple=6.6 sandwich=18.7 orange=11.4 broccoli=10.1 carrot=10.0 hot dog=14.0 pizza=30.5 donut=16.8 cake=14.2 chair=11.6 couch=28.7 potted plant=10.7 bed=28.6 dining table=14.2 toilet=32.1 tv=32.7 laptop=35.1 mouse=25.8 remote=6.8 keyboard=27.6 cell phone=11.6 microwave=24.1 oven=18.5 toaster=0.0 sink=18.2 refrigerator=30.1 book=5.0 clock=29.0 vase=18.9 scissors=11.9 teddy bear=23.9 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.9 [Epoch 152][Batch 99], LR: 1.00E-03, Speed: 149.836 samples/sec, ObjLoss=21.378, BoxCenterLoss=14.567, BoxScaleLoss=4.929, ClassLoss=8.189 [Epoch 152][Batch 199], LR: 1.00E-03, Speed: 149.212 samples/sec, ObjLoss=21.377, BoxCenterLoss=14.567, BoxScaleLoss=4.929, ClassLoss=8.189 [Epoch 152][Batch 299], LR: 1.00E-03, Speed: 146.279 samples/sec, ObjLoss=21.376, BoxCenterLoss=14.567, BoxScaleLoss=4.929, ClassLoss=8.188 [Epoch 152][Batch 399], LR: 1.00E-03, Speed: 169.628 samples/sec, ObjLoss=21.376, BoxCenterLoss=14.567, BoxScaleLoss=4.929, ClassLoss=8.188 [Epoch 152][Batch 499], LR: 1.00E-03, Speed: 143.820 samples/sec, ObjLoss=21.375, BoxCenterLoss=14.567, BoxScaleLoss=4.928, ClassLoss=8.187 [Epoch 152][Batch 599], LR: 1.00E-03, Speed: 10.093 samples/sec, ObjLoss=21.374, BoxCenterLoss=14.567, BoxScaleLoss=4.928, ClassLoss=8.186 [Epoch 152][Batch 699], LR: 1.00E-03, Speed: 98.056 samples/sec, ObjLoss=21.374, BoxCenterLoss=14.567, BoxScaleLoss=4.928, ClassLoss=8.186 [Epoch 152][Batch 799], LR: 1.00E-03, Speed: 98.490 samples/sec, ObjLoss=21.373, BoxCenterLoss=14.567, BoxScaleLoss=4.928, ClassLoss=8.185 [Epoch 152][Batch 899], LR: 1.00E-03, Speed: 126.433 samples/sec, ObjLoss=21.372, BoxCenterLoss=14.567, BoxScaleLoss=4.928, ClassLoss=8.184 [Epoch 152][Batch 999], LR: 1.00E-03, Speed: 75.514 samples/sec, ObjLoss=21.372, BoxCenterLoss=14.567, BoxScaleLoss=4.928, ClassLoss=8.184 [Epoch 152][Batch 1099], LR: 1.00E-03, Speed: 139.151 samples/sec, ObjLoss=21.371, BoxCenterLoss=14.567, BoxScaleLoss=4.928, ClassLoss=8.183 [Epoch 152][Batch 1199], LR: 1.00E-03, Speed: 128.235 samples/sec, ObjLoss=21.371, BoxCenterLoss=14.567, BoxScaleLoss=4.927, ClassLoss=8.182 [Epoch 152][Batch 1299], LR: 1.00E-03, Speed: 148.680 samples/sec, ObjLoss=21.370, BoxCenterLoss=14.567, BoxScaleLoss=4.927, ClassLoss=8.182 [Epoch 152][Batch 1399], LR: 1.00E-03, Speed: 127.532 samples/sec, ObjLoss=21.369, BoxCenterLoss=14.567, BoxScaleLoss=4.927, ClassLoss=8.181 [Epoch 152][Batch 1499], LR: 1.00E-03, Speed: 105.420 samples/sec, ObjLoss=21.369, BoxCenterLoss=14.567, BoxScaleLoss=4.927, ClassLoss=8.181 [Epoch 152][Batch 1599], LR: 1.00E-03, Speed: 126.077 samples/sec, ObjLoss=21.368, BoxCenterLoss=14.567, BoxScaleLoss=4.927, ClassLoss=8.180 [Epoch 152][Batch 1699], LR: 1.00E-03, Speed: 130.738 samples/sec, ObjLoss=21.368, BoxCenterLoss=14.567, BoxScaleLoss=4.927, ClassLoss=8.179 [Epoch 152][Batch 1799], LR: 1.00E-03, Speed: 131.553 samples/sec, ObjLoss=21.367, BoxCenterLoss=14.567, BoxScaleLoss=4.926, ClassLoss=8.179 [Epoch 152] Training cost: 1281.442, ObjLoss=21.367, BoxCenterLoss=14.567, BoxScaleLoss=4.926, ClassLoss=8.179 [Epoch 152] 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 ] = 0.415 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077 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 ] = 0.289 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 ] = 0.277 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 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 person=32.2 bicycle=12.1 car=20.6 motorcycle=19.8 airplane=35.7 bus=38.3 train=41.4 truck=18.5 boat=11.0 traffic light=11.9 fire hydrant=36.7 stop sign=41.4 parking meter=30.3 bench=9.2 bird=15.6 cat=36.3 dog=26.9 horse=28.8 sheep=25.7 cow=30.0 elephant=38.3 bear=45.1 zebra=39.6 giraffe=41.5 backpack=4.7 umbrella=18.8 handbag=3.6 tie=13.0 suitcase=15.4 frisbee=30.5 skis=8.0 snowboard=13.4 sports ball=19.3 kite=17.7 baseball bat=10.4 baseball glove=15.8 skateboard=19.2 surfboard=15.1 tennis racket=22.5 bottle=14.9 wine glass=15.7 cup=21.5 fork=9.2 knife=4.3 spoon=2.2 bowl=20.1 banana=12.5 apple=7.7 sandwich=16.8 orange=17.3 broccoli=10.1 carrot=9.7 hot dog=17.2 pizza=26.9 donut=19.9 cake=18.3 chair=12.0 couch=27.8 potted plant=11.6 bed=19.7 dining table=10.8 toilet=33.7 tv=33.7 laptop=30.3 mouse=34.2 remote=8.4 keyboard=20.1 cell phone=15.3 microwave=29.5 oven=19.7 toaster=0.0 sink=19.3 refrigerator=22.9 book=5.3 clock=30.4 vase=20.4 scissors=16.2 teddy bear=22.0 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.1 [Epoch 153][Batch 99], LR: 1.00E-03, Speed: 138.747 samples/sec, ObjLoss=21.366, BoxCenterLoss=14.567, BoxScaleLoss=4.926, ClassLoss=8.178 [Epoch 153][Batch 199], LR: 1.00E-03, Speed: 130.075 samples/sec, ObjLoss=21.366, BoxCenterLoss=14.567, BoxScaleLoss=4.926, ClassLoss=8.177 [Epoch 153][Batch 299], LR: 1.00E-03, Speed: 132.373 samples/sec, ObjLoss=21.365, BoxCenterLoss=14.566, BoxScaleLoss=4.926, ClassLoss=8.177 [Epoch 153][Batch 399], LR: 1.00E-03, Speed: 125.310 samples/sec, ObjLoss=21.364, BoxCenterLoss=14.566, BoxScaleLoss=4.926, ClassLoss=8.176 [Epoch 153][Batch 499], LR: 1.00E-03, Speed: 116.910 samples/sec, ObjLoss=21.363, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.175 [Epoch 153][Batch 599], LR: 1.00E-03, Speed: 91.383 samples/sec, ObjLoss=21.362, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.175 [Epoch 153][Batch 699], LR: 1.00E-03, Speed: 89.841 samples/sec, ObjLoss=21.362, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.174 [Epoch 153][Batch 799], LR: 1.00E-03, Speed: 83.010 samples/sec, ObjLoss=21.361, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.174 [Epoch 153][Batch 899], LR: 1.00E-03, Speed: 135.520 samples/sec, ObjLoss=21.361, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.173 [Epoch 153][Batch 999], LR: 1.00E-03, Speed: 155.370 samples/sec, ObjLoss=21.360, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.172 [Epoch 153][Batch 1099], LR: 1.00E-03, Speed: 122.035 samples/sec, ObjLoss=21.359, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.172 [Epoch 153][Batch 1199], LR: 1.00E-03, Speed: 94.406 samples/sec, ObjLoss=21.358, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.171 [Epoch 153][Batch 1299], LR: 1.00E-03, Speed: 53.025 samples/sec, ObjLoss=21.358, BoxCenterLoss=14.566, BoxScaleLoss=4.925, ClassLoss=8.171 [Epoch 153][Batch 1399], LR: 1.00E-03, Speed: 103.805 samples/sec, ObjLoss=21.357, BoxCenterLoss=14.566, BoxScaleLoss=4.924, ClassLoss=8.170 [Epoch 153][Batch 1499], LR: 1.00E-03, Speed: 62.711 samples/sec, ObjLoss=21.356, BoxCenterLoss=14.566, BoxScaleLoss=4.924, ClassLoss=8.169 [Epoch 153][Batch 1599], LR: 1.00E-03, Speed: 85.998 samples/sec, ObjLoss=21.356, BoxCenterLoss=14.566, BoxScaleLoss=4.924, ClassLoss=8.169 [Epoch 153][Batch 1699], LR: 1.00E-03, Speed: 58.687 samples/sec, ObjLoss=21.355, BoxCenterLoss=14.566, BoxScaleLoss=4.924, ClassLoss=8.168 [Epoch 153][Batch 1799], LR: 1.00E-03, Speed: 163.137 samples/sec, ObjLoss=21.354, BoxCenterLoss=14.565, BoxScaleLoss=4.924, ClassLoss=8.168 [Epoch 153] Training cost: 1376.901, ObjLoss=21.354, BoxCenterLoss=14.565, BoxScaleLoss=4.924, ClassLoss=8.168 [Epoch 153] 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 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.144 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 ] = 0.212 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 ] = 0.181 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 ] = 0.278 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 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372 person=31.9 bicycle=12.5 car=22.2 motorcycle=23.6 airplane=36.9 bus=33.9 train=34.5 truck=19.0 boat=9.7 traffic light=13.8 fire hydrant=32.6 stop sign=34.5 parking meter=24.4 bench=10.5 bird=11.9 cat=31.8 dog=29.3 horse=27.9 sheep=22.4 cow=25.9 elephant=36.7 bear=27.7 zebra=40.1 giraffe=43.2 backpack=4.5 umbrella=16.6 handbag=4.3 tie=13.7 suitcase=16.1 frisbee=31.5 skis=9.3 snowboard=13.6 sports ball=20.3 kite=20.5 baseball bat=9.6 baseball glove=15.0 skateboard=22.2 surfboard=15.0 tennis racket=22.6 bottle=14.2 wine glass=14.1 cup=17.9 fork=8.0 knife=3.0 spoon=1.0 bowl=17.5 banana=11.1 apple=3.9 sandwich=11.4 orange=11.6 broccoli=9.0 carrot=8.0 hot dog=10.6 pizza=21.8 donut=16.9 cake=12.2 chair=13.7 couch=22.7 potted plant=9.0 bed=25.4 dining table=14.5 toilet=28.1 tv=33.3 laptop=34.4 mouse=34.0 remote=7.0 keyboard=26.0 cell phone=13.8 microwave=32.7 oven=15.6 toaster=0.0 sink=16.7 refrigerator=23.4 book=4.5 clock=28.9 vase=17.7 scissors=12.9 teddy bear=20.2 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =18.8 [Epoch 154][Batch 99], LR: 1.00E-03, Speed: 141.571 samples/sec, ObjLoss=21.354, BoxCenterLoss=14.565, BoxScaleLoss=4.924, ClassLoss=8.167 [Epoch 154][Batch 199], LR: 1.00E-03, Speed: 148.354 samples/sec, ObjLoss=21.353, BoxCenterLoss=14.565, BoxScaleLoss=4.924, ClassLoss=8.166 [Epoch 154][Batch 299], LR: 1.00E-03, Speed: 73.467 samples/sec, ObjLoss=21.352, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.166 [Epoch 154][Batch 399], LR: 1.00E-03, Speed: 110.049 samples/sec, ObjLoss=21.351, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.165 [Epoch 154][Batch 499], LR: 1.00E-03, Speed: 127.812 samples/sec, ObjLoss=21.350, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.164 [Epoch 154][Batch 599], LR: 1.00E-03, Speed: 86.259 samples/sec, ObjLoss=21.350, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.164 [Epoch 154][Batch 699], LR: 1.00E-03, Speed: 102.704 samples/sec, ObjLoss=21.349, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.163 [Epoch 154][Batch 799], LR: 1.00E-03, Speed: 156.521 samples/sec, ObjLoss=21.348, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.163 [Epoch 154][Batch 899], LR: 1.00E-03, Speed: 84.960 samples/sec, ObjLoss=21.348, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.162 [Epoch 154][Batch 999], LR: 1.00E-03, Speed: 69.312 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.162 [Epoch 154][Batch 1099], LR: 1.00E-03, Speed: 77.119 samples/sec, ObjLoss=21.347, BoxCenterLoss=14.565, BoxScaleLoss=4.923, ClassLoss=8.161 [Epoch 154][Batch 1199], LR: 1.00E-03, Speed: 69.043 samples/sec, ObjLoss=21.346, BoxCenterLoss=14.565, BoxScaleLoss=4.922, ClassLoss=8.161 [Epoch 154][Batch 1299], LR: 1.00E-03, Speed: 122.494 samples/sec, ObjLoss=21.345, BoxCenterLoss=14.565, BoxScaleLoss=4.922, ClassLoss=8.160 [Epoch 154][Batch 1399], LR: 1.00E-03, Speed: 79.802 samples/sec, ObjLoss=21.345, BoxCenterLoss=14.565, BoxScaleLoss=4.922, ClassLoss=8.160 [Epoch 154][Batch 1499], LR: 1.00E-03, Speed: 57.621 samples/sec, ObjLoss=21.344, BoxCenterLoss=14.564, BoxScaleLoss=4.922, ClassLoss=8.159 [Epoch 154][Batch 1599], LR: 1.00E-03, Speed: 50.972 samples/sec, ObjLoss=21.343, BoxCenterLoss=14.564, BoxScaleLoss=4.922, ClassLoss=8.158 [Epoch 154][Batch 1699], LR: 1.00E-03, Speed: 47.348 samples/sec, ObjLoss=21.342, BoxCenterLoss=14.564, BoxScaleLoss=4.922, ClassLoss=8.158 [Epoch 154][Batch 1799], LR: 1.00E-03, Speed: 120.917 samples/sec, ObjLoss=21.342, BoxCenterLoss=14.564, BoxScaleLoss=4.922, ClassLoss=8.157 [Epoch 154] Training cost: 1547.020, ObjLoss=21.342, BoxCenterLoss=14.564, BoxScaleLoss=4.922, ClassLoss=8.157 [Epoch 154] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.178 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 ] = 0.213 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.302 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 ] = 0.289 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299 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 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426 person=31.3 bicycle=12.3 car=22.7 motorcycle=26.2 airplane=41.3 bus=40.0 train=42.1 truck=18.3 boat=10.4 traffic light=12.2 fire hydrant=42.4 stop sign=29.7 parking meter=15.8 bench=12.6 bird=15.8 cat=41.3 dog=34.5 horse=28.0 sheep=24.5 cow=30.2 elephant=35.0 bear=37.3 zebra=37.7 giraffe=39.9 backpack=5.5 umbrella=20.6 handbag=4.4 tie=13.7 suitcase=16.8 frisbee=34.3 skis=7.0 snowboard=13.6 sports ball=25.2 kite=20.7 baseball bat=9.7 baseball glove=18.5 skateboard=22.9 surfboard=14.9 tennis racket=20.8 bottle=15.5 wine glass=14.5 cup=18.4 fork=9.8 knife=3.2 spoon=2.3 bowl=20.5 banana=13.5 apple=8.5 sandwich=20.6 orange=13.0 broccoli=9.9 carrot=10.0 hot dog=17.9 pizza=29.1 donut=25.1 cake=18.8 chair=11.4 couch=27.0 potted plant=10.1 bed=29.9 dining table=13.0 toilet=38.1 tv=34.7 laptop=34.2 mouse=27.2 remote=8.7 keyboard=26.8 cell phone=13.4 microwave=23.9 oven=18.3 toaster=0.0 sink=17.0 refrigerator=30.6 book=4.5 clock=29.3 vase=19.6 scissors=16.1 teddy bear=24.4 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 155][Batch 99], LR: 1.00E-03, Speed: 145.889 samples/sec, ObjLoss=21.341, BoxCenterLoss=14.564, BoxScaleLoss=4.922, ClassLoss=8.156 [Epoch 155][Batch 199], LR: 1.00E-03, Speed: 138.034 samples/sec, ObjLoss=21.340, BoxCenterLoss=14.564, BoxScaleLoss=4.922, ClassLoss=8.156 [Epoch 155][Batch 299], LR: 1.00E-03, Speed: 131.663 samples/sec, ObjLoss=21.340, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.155 [Epoch 155][Batch 399], LR: 1.00E-03, Speed: 77.350 samples/sec, ObjLoss=21.339, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.155 [Epoch 155][Batch 499], LR: 1.00E-03, Speed: 89.663 samples/sec, ObjLoss=21.339, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.154 [Epoch 155][Batch 599], LR: 1.00E-03, Speed: 73.580 samples/sec, ObjLoss=21.338, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.153 [Epoch 155][Batch 699], LR: 1.00E-03, Speed: 61.785 samples/sec, ObjLoss=21.337, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.153 [Epoch 155][Batch 799], LR: 1.00E-03, Speed: 72.026 samples/sec, ObjLoss=21.337, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.152 [Epoch 155][Batch 899], LR: 1.00E-03, Speed: 135.284 samples/sec, ObjLoss=21.336, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.152 [Epoch 155][Batch 999], LR: 1.00E-03, Speed: 67.635 samples/sec, ObjLoss=21.335, BoxCenterLoss=14.564, BoxScaleLoss=4.921, ClassLoss=8.151 [Epoch 155][Batch 1099], LR: 1.00E-03, Speed: 68.297 samples/sec, ObjLoss=21.335, BoxCenterLoss=14.564, BoxScaleLoss=4.920, ClassLoss=8.150 [Epoch 155][Batch 1199], LR: 1.00E-03, Speed: 79.655 samples/sec, ObjLoss=21.334, BoxCenterLoss=14.564, BoxScaleLoss=4.920, ClassLoss=8.150 [Epoch 155][Batch 1299], LR: 1.00E-03, Speed: 74.794 samples/sec, ObjLoss=21.333, BoxCenterLoss=14.563, BoxScaleLoss=4.920, ClassLoss=8.149 [Epoch 155][Batch 1399], LR: 1.00E-03, Speed: 88.274 samples/sec, ObjLoss=21.332, BoxCenterLoss=14.563, BoxScaleLoss=4.920, ClassLoss=8.149 [Epoch 155][Batch 1499], LR: 1.00E-03, Speed: 134.172 samples/sec, ObjLoss=21.332, BoxCenterLoss=14.563, BoxScaleLoss=4.920, ClassLoss=8.148 [Epoch 155][Batch 1599], LR: 1.00E-03, Speed: 59.900 samples/sec, ObjLoss=21.331, BoxCenterLoss=14.563, BoxScaleLoss=4.920, ClassLoss=8.148 [Epoch 155][Batch 1699], LR: 1.00E-03, Speed: 50.460 samples/sec, ObjLoss=21.330, BoxCenterLoss=14.563, BoxScaleLoss=4.920, ClassLoss=8.147 [Epoch 155][Batch 1799], LR: 1.00E-03, Speed: 97.484 samples/sec, ObjLoss=21.330, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.147 [Epoch 155] Training cost: 1535.152, ObjLoss=21.329, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.146 [Epoch 155] 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 ] = 0.419 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 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321 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 ] = 0.297 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 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429 person=30.6 bicycle=14.9 car=19.3 motorcycle=25.4 airplane=38.9 bus=42.1 train=42.2 truck=19.3 boat=11.6 traffic light=11.4 fire hydrant=35.1 stop sign=39.3 parking meter=22.1 bench=11.8 bird=15.0 cat=40.8 dog=33.3 horse=28.1 sheep=24.6 cow=28.9 elephant=38.0 bear=49.9 zebra=42.7 giraffe=43.2 backpack=5.1 umbrella=19.6 handbag=3.9 tie=12.5 suitcase=14.5 frisbee=31.6 skis=7.4 snowboard=12.5 sports ball=22.3 kite=18.0 baseball bat=10.3 baseball glove=16.8 skateboard=21.9 surfboard=17.0 tennis racket=17.9 bottle=16.1 wine glass=14.0 cup=20.5 fork=9.8 knife=4.1 spoon=2.6 bowl=21.2 banana=9.3 apple=6.3 sandwich=21.5 orange=15.3 broccoli=9.3 carrot=7.7 hot dog=16.3 pizza=31.7 donut=23.6 cake=19.0 chair=12.6 couch=27.5 potted plant=11.0 bed=30.2 dining table=17.0 toilet=38.1 tv=34.5 laptop=31.2 mouse=31.4 remote=7.4 keyboard=28.1 cell phone=14.3 microwave=30.2 oven=18.1 toaster=0.0 sink=21.5 refrigerator=29.3 book=3.1 clock=26.3 vase=20.2 scissors=15.5 teddy bear=22.8 hair drier=0.0 toothbrush=2.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 156][Batch 99], LR: 1.00E-03, Speed: 164.087 samples/sec, ObjLoss=21.329, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.146 [Epoch 156][Batch 199], LR: 1.00E-03, Speed: 144.988 samples/sec, ObjLoss=21.328, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.145 [Epoch 156][Batch 299], LR: 1.00E-03, Speed: 148.683 samples/sec, ObjLoss=21.327, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.145 [Epoch 156][Batch 399], LR: 1.00E-03, Speed: 133.137 samples/sec, ObjLoss=21.327, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.144 [Epoch 156][Batch 499], LR: 1.00E-03, Speed: 126.922 samples/sec, ObjLoss=21.326, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.144 [Epoch 156][Batch 599], LR: 1.00E-03, Speed: 134.945 samples/sec, ObjLoss=21.325, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.143 [Epoch 156][Batch 699], LR: 1.00E-03, Speed: 154.067 samples/sec, ObjLoss=21.325, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.142 [Epoch 156][Batch 799], LR: 1.00E-03, Speed: 50.585 samples/sec, ObjLoss=21.324, BoxCenterLoss=14.563, BoxScaleLoss=4.919, ClassLoss=8.142 [Epoch 156][Batch 899], LR: 1.00E-03, Speed: 105.176 samples/sec, ObjLoss=21.323, BoxCenterLoss=14.562, BoxScaleLoss=4.919, ClassLoss=8.141 [Epoch 156][Batch 999], LR: 1.00E-03, Speed: 98.360 samples/sec, ObjLoss=21.322, BoxCenterLoss=14.562, BoxScaleLoss=4.919, ClassLoss=8.141 [Epoch 156][Batch 1099], LR: 1.00E-03, Speed: 75.231 samples/sec, ObjLoss=21.322, BoxCenterLoss=14.563, BoxScaleLoss=4.918, ClassLoss=8.140 [Epoch 156][Batch 1199], LR: 1.00E-03, Speed: 50.790 samples/sec, ObjLoss=21.321, BoxCenterLoss=14.562, BoxScaleLoss=4.918, ClassLoss=8.140 [Epoch 156][Batch 1299], LR: 1.00E-03, Speed: 93.725 samples/sec, ObjLoss=21.321, BoxCenterLoss=14.562, BoxScaleLoss=4.918, ClassLoss=8.139 [Epoch 156][Batch 1399], LR: 1.00E-03, Speed: 105.926 samples/sec, ObjLoss=21.320, BoxCenterLoss=14.562, BoxScaleLoss=4.918, ClassLoss=8.139 [Epoch 156][Batch 1499], LR: 1.00E-03, Speed: 107.882 samples/sec, ObjLoss=21.319, BoxCenterLoss=14.562, BoxScaleLoss=4.918, ClassLoss=8.138 [Epoch 156][Batch 1599], LR: 1.00E-03, Speed: 80.939 samples/sec, ObjLoss=21.318, BoxCenterLoss=14.562, BoxScaleLoss=4.918, ClassLoss=8.137 [Epoch 156][Batch 1699], LR: 1.00E-03, Speed: 94.392 samples/sec, ObjLoss=21.318, BoxCenterLoss=14.562, BoxScaleLoss=4.917, ClassLoss=8.137 [Epoch 156][Batch 1799], LR: 1.00E-03, Speed: 141.876 samples/sec, ObjLoss=21.317, BoxCenterLoss=14.562, BoxScaleLoss=4.917, ClassLoss=8.136 [Epoch 156] Training cost: 1548.390, ObjLoss=21.317, BoxCenterLoss=14.561, BoxScaleLoss=4.917, ClassLoss=8.136 [Epoch 156] 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 ] = 0.417 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.309 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 ] = 0.285 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 ] = 0.140 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 ] = 0.428 person=32.6 bicycle=13.2 car=18.9 motorcycle=24.2 airplane=33.3 bus=39.8 train=43.9 truck=17.7 boat=9.7 traffic light=13.4 fire hydrant=41.4 stop sign=36.9 parking meter=18.8 bench=10.4 bird=14.3 cat=43.4 dog=31.5 horse=33.2 sheep=28.8 cow=28.6 elephant=34.6 bear=42.9 zebra=39.6 giraffe=45.7 backpack=4.8 umbrella=18.4 handbag=3.9 tie=15.7 suitcase=14.1 frisbee=32.1 skis=6.4 snowboard=8.8 sports ball=22.7 kite=18.8 baseball bat=9.7 baseball glove=15.9 skateboard=21.4 surfboard=12.2 tennis racket=19.0 bottle=14.6 wine glass=14.2 cup=18.6 fork=11.6 knife=3.1 spoon=2.6 bowl=15.3 banana=11.3 apple=6.1 sandwich=18.5 orange=9.8 broccoli=11.3 carrot=9.5 hot dog=14.8 pizza=21.9 donut=20.1 cake=16.0 chair=12.7 couch=28.5 potted plant=12.1 bed=26.2 dining table=18.1 toilet=33.8 tv=33.8 laptop=36.3 mouse=31.3 remote=8.6 keyboard=23.5 cell phone=14.7 microwave=24.1 oven=19.0 toaster=1.2 sink=14.1 refrigerator=29.5 book=4.9 clock=27.6 vase=20.0 scissors=15.1 teddy bear=24.7 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.0 [Epoch 157][Batch 99], LR: 1.00E-03, Speed: 126.786 samples/sec, ObjLoss=21.316, BoxCenterLoss=14.561, BoxScaleLoss=4.917, ClassLoss=8.135 [Epoch 157][Batch 199], LR: 1.00E-03, Speed: 139.503 samples/sec, ObjLoss=21.315, BoxCenterLoss=14.561, BoxScaleLoss=4.917, ClassLoss=8.135 [Epoch 157][Batch 299], LR: 1.00E-03, Speed: 92.319 samples/sec, ObjLoss=21.314, BoxCenterLoss=14.561, BoxScaleLoss=4.917, ClassLoss=8.134 [Epoch 157][Batch 399], LR: 1.00E-03, Speed: 110.768 samples/sec, ObjLoss=21.314, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.133 [Epoch 157][Batch 499], LR: 1.00E-03, Speed: 103.563 samples/sec, ObjLoss=21.313, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.133 [Epoch 157][Batch 599], LR: 1.00E-03, Speed: 90.906 samples/sec, ObjLoss=21.312, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.132 [Epoch 157][Batch 699], LR: 1.00E-03, Speed: 69.319 samples/sec, ObjLoss=21.312, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.132 [Epoch 157][Batch 799], LR: 1.00E-03, Speed: 108.687 samples/sec, ObjLoss=21.311, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.131 [Epoch 157][Batch 899], LR: 1.00E-03, Speed: 68.257 samples/sec, ObjLoss=21.310, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.131 [Epoch 157][Batch 999], LR: 1.00E-03, Speed: 88.704 samples/sec, ObjLoss=21.310, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.130 [Epoch 157][Batch 1099], LR: 1.00E-03, Speed: 89.097 samples/sec, ObjLoss=21.309, BoxCenterLoss=14.561, BoxScaleLoss=4.916, ClassLoss=8.130 [Epoch 157][Batch 1199], LR: 1.00E-03, Speed: 70.561 samples/sec, ObjLoss=21.308, BoxCenterLoss=14.560, BoxScaleLoss=4.916, ClassLoss=8.129 [Epoch 157][Batch 1299], LR: 1.00E-03, Speed: 46.476 samples/sec, ObjLoss=21.308, BoxCenterLoss=14.560, BoxScaleLoss=4.915, ClassLoss=8.128 [Epoch 157][Batch 1399], LR: 1.00E-03, Speed: 69.514 samples/sec, ObjLoss=21.307, BoxCenterLoss=14.560, BoxScaleLoss=4.915, ClassLoss=8.128 [Epoch 157][Batch 1499], LR: 1.00E-03, Speed: 45.249 samples/sec, ObjLoss=21.306, BoxCenterLoss=14.560, BoxScaleLoss=4.915, ClassLoss=8.127 [Epoch 157][Batch 1599], LR: 1.00E-03, Speed: 92.353 samples/sec, ObjLoss=21.306, BoxCenterLoss=14.560, BoxScaleLoss=4.915, ClassLoss=8.127 [Epoch 157][Batch 1699], LR: 1.00E-03, Speed: 132.140 samples/sec, ObjLoss=21.305, BoxCenterLoss=14.560, BoxScaleLoss=4.915, ClassLoss=8.126 [Epoch 157][Batch 1799], LR: 1.00E-03, Speed: 149.812 samples/sec, ObjLoss=21.304, BoxCenterLoss=14.560, BoxScaleLoss=4.915, ClassLoss=8.126 [Epoch 157] Training cost: 1597.994, ObjLoss=21.304, BoxCenterLoss=14.560, BoxScaleLoss=4.915, ClassLoss=8.126 [Epoch 157] 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 ] = 0.420 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.177 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 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 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 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 ] = 0.291 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 ] = 0.281 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.432 person=32.0 bicycle=12.5 car=20.7 motorcycle=23.0 airplane=30.5 bus=40.1 train=42.6 truck=19.4 boat=11.0 traffic light=11.0 fire hydrant=34.5 stop sign=35.6 parking meter=20.2 bench=12.3 bird=15.0 cat=42.6 dog=33.8 horse=31.3 sheep=24.3 cow=28.4 elephant=38.2 bear=38.8 zebra=43.0 giraffe=41.0 backpack=4.0 umbrella=18.2 handbag=3.7 tie=12.6 suitcase=14.2 frisbee=33.4 skis=7.8 snowboard=10.1 sports ball=22.9 kite=23.0 baseball bat=7.9 baseball glove=15.5 skateboard=22.7 surfboard=16.6 tennis racket=22.6 bottle=12.6 wine glass=12.7 cup=19.1 fork=10.3 knife=4.1 spoon=1.6 bowl=21.6 banana=12.1 apple=9.4 sandwich=20.9 orange=18.5 broccoli=10.4 carrot=9.1 hot dog=15.4 pizza=31.5 donut=19.3 cake=17.9 chair=12.3 couch=25.9 potted plant=9.4 bed=27.7 dining table=14.3 toilet=35.3 tv=32.5 laptop=35.7 mouse=28.6 remote=7.6 keyboard=29.7 cell phone=14.0 microwave=33.9 oven=19.6 toaster=0.0 sink=18.2 refrigerator=28.3 book=6.5 clock=28.3 vase=19.0 scissors=13.7 teddy bear=24.1 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 158][Batch 99], LR: 1.00E-03, Speed: 153.323 samples/sec, ObjLoss=21.303, BoxCenterLoss=14.559, BoxScaleLoss=4.915, ClassLoss=8.125 [Epoch 158][Batch 199], LR: 1.00E-03, Speed: 140.369 samples/sec, ObjLoss=21.302, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.124 [Epoch 158][Batch 299], LR: 1.00E-03, Speed: 124.467 samples/sec, ObjLoss=21.301, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.124 [Epoch 158][Batch 399], LR: 1.00E-03, Speed: 103.126 samples/sec, ObjLoss=21.300, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.123 [Epoch 158][Batch 499], LR: 1.00E-03, Speed: 60.378 samples/sec, ObjLoss=21.300, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.122 [Epoch 158][Batch 599], LR: 1.00E-03, Speed: 100.098 samples/sec, ObjLoss=21.299, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.122 [Epoch 158][Batch 699], LR: 1.00E-03, Speed: 137.242 samples/sec, ObjLoss=21.298, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.121 [Epoch 158][Batch 799], LR: 1.00E-03, Speed: 71.952 samples/sec, ObjLoss=21.298, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.121 [Epoch 158][Batch 899], LR: 1.00E-03, Speed: 86.869 samples/sec, ObjLoss=21.297, BoxCenterLoss=14.559, BoxScaleLoss=4.914, ClassLoss=8.120 [Epoch 158][Batch 999], LR: 1.00E-03, Speed: 67.864 samples/sec, ObjLoss=21.296, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.119 [Epoch 158][Batch 1099], LR: 1.00E-03, Speed: 82.968 samples/sec, ObjLoss=21.296, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.119 [Epoch 158][Batch 1199], LR: 1.00E-03, Speed: 109.562 samples/sec, ObjLoss=21.295, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.118 [Epoch 158][Batch 1299], LR: 1.00E-03, Speed: 68.623 samples/sec, ObjLoss=21.295, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.118 [Epoch 158][Batch 1399], LR: 1.00E-03, Speed: 55.044 samples/sec, ObjLoss=21.294, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.117 [Epoch 158][Batch 1499], LR: 1.00E-03, Speed: 57.232 samples/sec, ObjLoss=21.293, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.116 [Epoch 158][Batch 1599], LR: 1.00E-03, Speed: 133.597 samples/sec, ObjLoss=21.293, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.116 [Epoch 158][Batch 1699], LR: 1.00E-03, Speed: 95.680 samples/sec, ObjLoss=21.292, BoxCenterLoss=14.559, BoxScaleLoss=4.913, ClassLoss=8.115 [Epoch 158][Batch 1799], LR: 1.00E-03, Speed: 133.541 samples/sec, ObjLoss=21.292, BoxCenterLoss=14.559, BoxScaleLoss=4.912, ClassLoss=8.115 [Epoch 158] Training cost: 1546.090, ObjLoss=21.291, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.115 [Epoch 158] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.214 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.305 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 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430 person=32.7 bicycle=13.7 car=22.6 motorcycle=24.8 airplane=36.4 bus=43.2 train=47.7 truck=18.6 boat=11.8 traffic light=12.0 fire hydrant=34.2 stop sign=42.5 parking meter=21.3 bench=11.7 bird=15.1 cat=39.7 dog=33.0 horse=31.4 sheep=26.5 cow=33.6 elephant=41.5 bear=50.0 zebra=41.4 giraffe=44.4 backpack=5.7 umbrella=19.5 handbag=3.9 tie=12.5 suitcase=15.1 frisbee=29.5 skis=6.5 snowboard=13.0 sports ball=19.5 kite=20.5 baseball bat=9.7 baseball glove=15.7 skateboard=20.1 surfboard=17.0 tennis racket=20.4 bottle=17.1 wine glass=15.4 cup=21.2 fork=9.5 knife=4.7 spoon=2.8 bowl=20.9 banana=11.8 apple=8.5 sandwich=19.2 orange=12.8 broccoli=9.8 carrot=8.8 hot dog=14.5 pizza=28.0 donut=23.1 cake=20.4 chair=13.7 couch=27.9 potted plant=11.2 bed=27.7 dining table=16.7 toilet=37.0 tv=33.8 laptop=35.5 mouse=27.0 remote=9.5 keyboard=26.8 cell phone=15.4 microwave=30.0 oven=21.2 toaster=3.0 sink=18.2 refrigerator=32.8 book=5.1 clock=27.8 vase=18.0 scissors=15.9 teddy bear=27.7 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 159][Batch 99], LR: 1.00E-03, Speed: 158.895 samples/sec, ObjLoss=21.290, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.114 [Epoch 159][Batch 199], LR: 1.00E-03, Speed: 143.796 samples/sec, ObjLoss=21.290, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.114 [Epoch 159][Batch 299], LR: 1.00E-03, Speed: 66.452 samples/sec, ObjLoss=21.289, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.113 [Epoch 159][Batch 399], LR: 1.00E-03, Speed: 93.622 samples/sec, ObjLoss=21.289, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.112 [Epoch 159][Batch 499], LR: 1.00E-03, Speed: 88.917 samples/sec, ObjLoss=21.288, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.112 [Epoch 159][Batch 599], LR: 1.00E-03, Speed: 96.679 samples/sec, ObjLoss=21.287, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.111 [Epoch 159][Batch 699], LR: 1.00E-03, Speed: 155.291 samples/sec, ObjLoss=21.287, BoxCenterLoss=14.558, BoxScaleLoss=4.912, ClassLoss=8.111 [Epoch 159][Batch 799], LR: 1.00E-03, Speed: 81.812 samples/sec, ObjLoss=21.286, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.110 [Epoch 159][Batch 899], LR: 1.00E-03, Speed: 79.764 samples/sec, ObjLoss=21.285, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.110 [Epoch 159][Batch 999], LR: 1.00E-03, Speed: 60.433 samples/sec, ObjLoss=21.284, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.109 [Epoch 159][Batch 1099], LR: 1.00E-03, Speed: 62.869 samples/sec, ObjLoss=21.284, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.109 [Epoch 159][Batch 1199], LR: 1.00E-03, Speed: 75.144 samples/sec, ObjLoss=21.283, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.108 [Epoch 159][Batch 1299], LR: 1.00E-03, Speed: 112.263 samples/sec, ObjLoss=21.283, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.107 [Epoch 159][Batch 1399], LR: 1.00E-03, Speed: 78.700 samples/sec, ObjLoss=21.282, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.107 [Epoch 159][Batch 1499], LR: 1.00E-03, Speed: 70.349 samples/sec, ObjLoss=21.281, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.106 [Epoch 159][Batch 1599], LR: 1.00E-03, Speed: 81.742 samples/sec, ObjLoss=21.281, BoxCenterLoss=14.558, BoxScaleLoss=4.911, ClassLoss=8.106 [Epoch 159][Batch 1699], LR: 1.00E-03, Speed: 109.107 samples/sec, ObjLoss=21.280, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.105 [Epoch 159][Batch 1799], LR: 1.00E-03, Speed: 72.171 samples/sec, ObjLoss=21.279, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.105 [Epoch 159] Training cost: 1649.638, ObjLoss=21.279, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.105 [Epoch 159] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.424 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.080 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 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.185 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 ] = 0.286 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 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.397 person=30.1 bicycle=13.1 car=19.5 motorcycle=22.8 airplane=41.1 bus=33.7 train=38.0 truck=18.6 boat=12.5 traffic light=13.1 fire hydrant=41.4 stop sign=40.0 parking meter=18.3 bench=12.0 bird=15.5 cat=33.8 dog=31.5 horse=32.6 sheep=22.9 cow=30.3 elephant=35.3 bear=45.5 zebra=32.3 giraffe=37.4 backpack=5.4 umbrella=21.1 handbag=3.4 tie=12.1 suitcase=14.4 frisbee=30.0 skis=7.0 snowboard=15.5 sports ball=24.0 kite=19.0 baseball bat=11.3 baseball glove=17.7 skateboard=22.9 surfboard=18.1 tennis racket=18.9 bottle=14.1 wine glass=12.4 cup=18.3 fork=9.4 knife=3.4 spoon=3.9 bowl=16.5 banana=11.1 apple=8.1 sandwich=18.2 orange=14.7 broccoli=10.3 carrot=10.4 hot dog=12.5 pizza=29.7 donut=16.2 cake=16.9 chair=11.2 couch=23.0 potted plant=11.0 bed=25.6 dining table=13.4 toilet=27.0 tv=33.4 laptop=24.0 mouse=29.5 remote=8.8 keyboard=25.7 cell phone=13.2 microwave=26.8 oven=14.4 toaster=0.0 sink=19.5 refrigerator=28.7 book=4.4 clock=27.6 vase=16.6 scissors=15.3 teddy bear=19.2 hair drier=0.0 toothbrush=4.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.5 [Epoch 160][Batch 99], LR: 1.00E-03, Speed: 137.405 samples/sec, ObjLoss=21.278, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.104 [Epoch 160][Batch 199], LR: 1.00E-03, Speed: 140.874 samples/sec, ObjLoss=21.278, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.104 [Epoch 160][Batch 299], LR: 1.00E-03, Speed: 118.975 samples/sec, ObjLoss=21.277, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.103 [Epoch 160][Batch 399], LR: 1.00E-03, Speed: 65.459 samples/sec, ObjLoss=21.276, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.102 [Epoch 160][Batch 499], LR: 1.00E-03, Speed: 80.075 samples/sec, ObjLoss=21.276, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.102 [Epoch 160][Batch 599], LR: 1.00E-03, Speed: 48.135 samples/sec, ObjLoss=21.275, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.101 [Epoch 160][Batch 699], LR: 1.00E-03, Speed: 91.869 samples/sec, ObjLoss=21.274, BoxCenterLoss=14.557, BoxScaleLoss=4.910, ClassLoss=8.101 [Epoch 160][Batch 799], LR: 1.00E-03, Speed: 90.421 samples/sec, ObjLoss=21.273, BoxCenterLoss=14.557, BoxScaleLoss=4.909, ClassLoss=8.100 [Epoch 160][Batch 899], LR: 1.00E-03, Speed: 64.342 samples/sec, ObjLoss=21.273, BoxCenterLoss=14.556, BoxScaleLoss=4.909, ClassLoss=8.100 [Epoch 160][Batch 999], LR: 1.00E-03, Speed: 63.601 samples/sec, ObjLoss=21.272, BoxCenterLoss=14.556, BoxScaleLoss=4.909, ClassLoss=8.099 [Epoch 160][Batch 1099], LR: 1.00E-03, Speed: 73.863 samples/sec, ObjLoss=21.271, BoxCenterLoss=14.556, BoxScaleLoss=4.909, ClassLoss=8.098 [Epoch 160][Batch 1199], LR: 1.00E-03, Speed: 61.074 samples/sec, ObjLoss=21.271, BoxCenterLoss=14.556, BoxScaleLoss=4.909, ClassLoss=8.098 [Epoch 160][Batch 1299], LR: 1.00E-03, Speed: 80.760 samples/sec, ObjLoss=21.270, BoxCenterLoss=14.556, BoxScaleLoss=4.909, ClassLoss=8.097 [Epoch 160][Batch 1399], LR: 1.00E-03, Speed: 84.856 samples/sec, ObjLoss=21.270, BoxCenterLoss=14.556, BoxScaleLoss=4.909, ClassLoss=8.097 [Epoch 160][Batch 1499], LR: 1.00E-03, Speed: 72.712 samples/sec, ObjLoss=21.269, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.096 [Epoch 160][Batch 1599], LR: 1.00E-03, Speed: 78.329 samples/sec, ObjLoss=21.269, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.096 [Epoch 160][Batch 1699], LR: 1.00E-03, Speed: 86.619 samples/sec, ObjLoss=21.268, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.095 [Epoch 160][Batch 1799], LR: 1.00E-03, Speed: 133.975 samples/sec, ObjLoss=21.267, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.095 [Epoch 160] Training cost: 1590.760, ObjLoss=21.267, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.094 [Epoch 160] 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 ] = 0.180 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 ] = 0.215 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 ] = 0.196 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 ] = 0.294 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 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410 person=32.0 bicycle=13.2 car=21.4 motorcycle=24.7 airplane=39.2 bus=38.1 train=44.0 truck=19.0 boat=10.9 traffic light=11.9 fire hydrant=36.2 stop sign=35.2 parking meter=24.6 bench=10.7 bird=17.8 cat=41.5 dog=34.9 horse=35.2 sheep=28.0 cow=30.8 elephant=31.0 bear=35.9 zebra=38.5 giraffe=44.4 backpack=4.5 umbrella=17.0 handbag=3.4 tie=14.4 suitcase=14.8 frisbee=34.8 skis=7.7 snowboard=13.1 sports ball=22.9 kite=22.1 baseball bat=9.5 baseball glove=19.3 skateboard=21.7 surfboard=15.6 tennis racket=19.1 bottle=16.7 wine glass=14.1 cup=20.3 fork=9.5 knife=3.6 spoon=3.2 bowl=20.0 banana=13.6 apple=4.5 sandwich=19.1 orange=12.4 broccoli=12.4 carrot=10.6 hot dog=14.5 pizza=27.0 donut=22.9 cake=17.5 chair=11.3 couch=23.2 potted plant=11.0 bed=26.9 dining table=17.1 toilet=33.3 tv=28.5 laptop=33.2 mouse=28.2 remote=8.6 keyboard=24.3 cell phone=12.9 microwave=37.0 oven=20.2 toaster=3.0 sink=20.9 refrigerator=32.0 book=3.1 clock=31.5 vase=20.2 scissors=13.7 teddy bear=24.1 hair drier=0.0 toothbrush=2.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.6 [Epoch 161][Batch 99], LR: 1.00E-03, Speed: 126.499 samples/sec, ObjLoss=21.266, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.094 [Epoch 161][Batch 199], LR: 1.00E-03, Speed: 104.232 samples/sec, ObjLoss=21.266, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.093 [Epoch 161][Batch 299], LR: 1.00E-03, Speed: 127.134 samples/sec, ObjLoss=21.265, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.093 [Epoch 161][Batch 399], LR: 1.00E-03, Speed: 123.357 samples/sec, ObjLoss=21.264, BoxCenterLoss=14.556, BoxScaleLoss=4.908, ClassLoss=8.092 [Epoch 161][Batch 499], LR: 1.00E-03, Speed: 74.224 samples/sec, ObjLoss=21.263, BoxCenterLoss=14.555, BoxScaleLoss=4.908, ClassLoss=8.092 [Epoch 161][Batch 599], LR: 1.00E-03, Speed: 83.309 samples/sec, ObjLoss=21.263, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.091 [Epoch 161][Batch 699], LR: 1.00E-03, Speed: 55.802 samples/sec, ObjLoss=21.262, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.091 [Epoch 161][Batch 799], LR: 1.00E-03, Speed: 161.289 samples/sec, ObjLoss=21.262, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.090 [Epoch 161][Batch 899], LR: 1.00E-03, Speed: 128.671 samples/sec, ObjLoss=21.261, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.090 [Epoch 161][Batch 999], LR: 1.00E-03, Speed: 98.609 samples/sec, ObjLoss=21.260, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.089 [Epoch 161][Batch 1099], LR: 1.00E-03, Speed: 110.062 samples/sec, ObjLoss=21.259, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.089 [Epoch 161][Batch 1199], LR: 1.00E-03, Speed: 63.241 samples/sec, ObjLoss=21.258, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.088 [Epoch 161][Batch 1299], LR: 1.00E-03, Speed: 86.858 samples/sec, ObjLoss=21.258, BoxCenterLoss=14.555, BoxScaleLoss=4.907, ClassLoss=8.088 [Epoch 161][Batch 1399], LR: 1.00E-03, Speed: 74.128 samples/sec, ObjLoss=21.257, BoxCenterLoss=14.554, BoxScaleLoss=4.907, ClassLoss=8.087 [Epoch 161][Batch 1499], LR: 1.00E-03, Speed: 113.937 samples/sec, ObjLoss=21.256, BoxCenterLoss=14.554, BoxScaleLoss=4.907, ClassLoss=8.087 [Epoch 161][Batch 1599], LR: 1.00E-03, Speed: 77.589 samples/sec, ObjLoss=21.256, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.086 [Epoch 161][Batch 1699], LR: 1.00E-03, Speed: 55.484 samples/sec, ObjLoss=21.255, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.086 [Epoch 161][Batch 1799], LR: 1.00E-03, Speed: 80.160 samples/sec, ObjLoss=21.255, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.085 [Epoch 161] Training cost: 1480.701, ObjLoss=21.255, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.085 [Epoch 161] 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 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.201 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 ] = 0.193 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 ] = 0.291 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 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.421 person=33.3 bicycle=13.9 car=19.9 motorcycle=24.7 airplane=35.5 bus=41.3 train=44.9 truck=19.4 boat=9.9 traffic light=12.6 fire hydrant=34.6 stop sign=36.4 parking meter=25.8 bench=10.7 bird=15.9 cat=43.5 dog=36.0 horse=30.6 sheep=23.2 cow=29.0 elephant=39.6 bear=37.0 zebra=36.5 giraffe=42.0 backpack=4.4 umbrella=18.4 handbag=4.0 tie=14.2 suitcase=13.9 frisbee=34.9 skis=7.5 snowboard=12.8 sports ball=21.6 kite=21.9 baseball bat=11.0 baseball glove=18.6 skateboard=19.9 surfboard=16.6 tennis racket=21.3 bottle=15.0 wine glass=16.2 cup=18.0 fork=8.8 knife=4.2 spoon=4.0 bowl=18.9 banana=11.2 apple=5.8 sandwich=18.3 orange=16.4 broccoli=10.1 carrot=7.9 hot dog=14.4 pizza=24.4 donut=18.4 cake=16.7 chair=10.9 couch=27.4 potted plant=10.8 bed=26.6 dining table=12.5 toilet=35.5 tv=33.4 laptop=35.5 mouse=28.1 remote=7.1 keyboard=24.1 cell phone=14.6 microwave=25.4 oven=19.2 toaster=0.0 sink=21.3 refrigerator=24.9 book=4.7 clock=25.2 vase=17.2 scissors=17.3 teddy bear=24.0 hair drier=0.0 toothbrush=5.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.2 [Epoch 162][Batch 99], LR: 1.00E-03, Speed: 167.112 samples/sec, ObjLoss=21.254, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.084 [Epoch 162][Batch 199], LR: 1.00E-03, Speed: 133.254 samples/sec, ObjLoss=21.253, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.084 [Epoch 162][Batch 299], LR: 1.00E-03, Speed: 74.993 samples/sec, ObjLoss=21.252, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.083 [Epoch 162][Batch 399], LR: 1.00E-03, Speed: 114.879 samples/sec, ObjLoss=21.252, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.083 [Epoch 162][Batch 499], LR: 1.00E-03, Speed: 64.102 samples/sec, ObjLoss=21.251, BoxCenterLoss=14.554, BoxScaleLoss=4.906, ClassLoss=8.082 [Epoch 162][Batch 599], LR: 1.00E-03, Speed: 64.308 samples/sec, ObjLoss=21.250, BoxCenterLoss=14.554, BoxScaleLoss=4.905, ClassLoss=8.082 [Epoch 162][Batch 699], LR: 1.00E-03, Speed: 116.996 samples/sec, ObjLoss=21.249, BoxCenterLoss=14.554, BoxScaleLoss=4.905, ClassLoss=8.081 [Epoch 162][Batch 799], LR: 1.00E-03, Speed: 137.503 samples/sec, ObjLoss=21.249, BoxCenterLoss=14.554, BoxScaleLoss=4.905, ClassLoss=8.081 [Epoch 162][Batch 899], LR: 1.00E-03, Speed: 88.556 samples/sec, ObjLoss=21.248, BoxCenterLoss=14.554, BoxScaleLoss=4.905, ClassLoss=8.080 [Epoch 162][Batch 999], LR: 1.00E-03, Speed: 107.878 samples/sec, ObjLoss=21.248, BoxCenterLoss=14.554, BoxScaleLoss=4.905, ClassLoss=8.080 [Epoch 162][Batch 1099], LR: 1.00E-03, Speed: 142.184 samples/sec, ObjLoss=21.247, BoxCenterLoss=14.554, BoxScaleLoss=4.905, ClassLoss=8.079 [Epoch 162][Batch 1199], LR: 1.00E-03, Speed: 86.745 samples/sec, ObjLoss=21.246, BoxCenterLoss=14.554, BoxScaleLoss=4.905, ClassLoss=8.079 [Epoch 162][Batch 1299], LR: 1.00E-03, Speed: 82.858 samples/sec, ObjLoss=21.246, BoxCenterLoss=14.553, BoxScaleLoss=4.905, ClassLoss=8.078 [Epoch 162][Batch 1399], LR: 1.00E-03, Speed: 84.515 samples/sec, ObjLoss=21.245, BoxCenterLoss=14.553, BoxScaleLoss=4.905, ClassLoss=8.078 [Epoch 162][Batch 1499], LR: 1.00E-03, Speed: 144.237 samples/sec, ObjLoss=21.245, BoxCenterLoss=14.553, BoxScaleLoss=4.905, ClassLoss=8.077 [Epoch 162][Batch 1599], LR: 1.00E-03, Speed: 133.068 samples/sec, ObjLoss=21.244, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.076 [Epoch 162][Batch 1699], LR: 1.00E-03, Speed: 84.300 samples/sec, ObjLoss=21.243, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.076 [Epoch 162][Batch 1799], LR: 1.00E-03, Speed: 104.414 samples/sec, ObjLoss=21.243, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.076 [Epoch 162] Training cost: 1633.349, ObjLoss=21.242, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.075 [Epoch 162] 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 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.189 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 ] = 0.220 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321 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 ] = 0.290 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299 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 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.439 person=32.6 bicycle=12.9 car=21.4 motorcycle=25.3 airplane=40.6 bus=39.0 train=41.8 truck=20.3 boat=10.9 traffic light=11.2 fire hydrant=35.9 stop sign=33.2 parking meter=21.2 bench=11.7 bird=16.1 cat=42.0 dog=37.0 horse=32.9 sheep=26.3 cow=31.4 elephant=38.6 bear=45.2 zebra=36.8 giraffe=43.2 backpack=5.1 umbrella=20.7 handbag=3.5 tie=12.6 suitcase=16.8 frisbee=31.2 skis=8.1 snowboard=13.1 sports ball=25.4 kite=19.5 baseball bat=10.4 baseball glove=14.8 skateboard=20.5 surfboard=14.5 tennis racket=22.9 bottle=16.1 wine glass=14.6 cup=20.7 fork=11.6 knife=4.6 spoon=2.6 bowl=19.1 banana=10.6 apple=8.3 sandwich=19.8 orange=16.7 broccoli=10.7 carrot=9.7 hot dog=14.9 pizza=24.6 donut=24.6 cake=17.7 chair=13.1 couch=27.8 potted plant=9.8 bed=26.1 dining table=16.5 toilet=34.5 tv=36.9 laptop=34.6 mouse=33.9 remote=9.9 keyboard=31.1 cell phone=16.4 microwave=27.2 oven=18.9 toaster=0.0 sink=18.1 refrigerator=27.9 book=4.7 clock=28.6 vase=20.6 scissors=12.6 teddy bear=22.9 hair drier=0.0 toothbrush=5.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 163][Batch 99], LR: 1.00E-03, Speed: 132.746 samples/sec, ObjLoss=21.242, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.075 [Epoch 163][Batch 199], LR: 1.00E-03, Speed: 124.671 samples/sec, ObjLoss=21.241, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.074 [Epoch 163][Batch 299], LR: 1.00E-03, Speed: 122.538 samples/sec, ObjLoss=21.240, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.074 [Epoch 163][Batch 399], LR: 1.00E-03, Speed: 82.626 samples/sec, ObjLoss=21.240, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.073 [Epoch 163][Batch 499], LR: 1.00E-03, Speed: 81.188 samples/sec, ObjLoss=21.239, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.073 [Epoch 163][Batch 599], LR: 1.00E-03, Speed: 89.561 samples/sec, ObjLoss=21.238, BoxCenterLoss=14.553, BoxScaleLoss=4.904, ClassLoss=8.072 [Epoch 163][Batch 699], LR: 1.00E-03, Speed: 81.434 samples/sec, ObjLoss=21.238, BoxCenterLoss=14.553, BoxScaleLoss=4.903, ClassLoss=8.072 [Epoch 163][Batch 799], LR: 1.00E-03, Speed: 44.434 samples/sec, ObjLoss=21.237, BoxCenterLoss=14.553, BoxScaleLoss=4.903, ClassLoss=8.071 [Epoch 163][Batch 899], LR: 1.00E-03, Speed: 55.877 samples/sec, ObjLoss=21.237, BoxCenterLoss=14.552, BoxScaleLoss=4.903, ClassLoss=8.071 [Epoch 163][Batch 999], LR: 1.00E-03, Speed: 104.891 samples/sec, ObjLoss=21.236, BoxCenterLoss=14.552, BoxScaleLoss=4.903, ClassLoss=8.070 [Epoch 163][Batch 1099], LR: 1.00E-03, Speed: 113.602 samples/sec, ObjLoss=21.235, BoxCenterLoss=14.552, BoxScaleLoss=4.903, ClassLoss=8.070 [Epoch 163][Batch 1199], LR: 1.00E-03, Speed: 47.624 samples/sec, ObjLoss=21.235, BoxCenterLoss=14.552, BoxScaleLoss=4.903, ClassLoss=8.069 [Epoch 163][Batch 1299], LR: 1.00E-03, Speed: 66.359 samples/sec, ObjLoss=21.234, BoxCenterLoss=14.552, BoxScaleLoss=4.903, ClassLoss=8.069 [Epoch 163][Batch 1399], LR: 1.00E-03, Speed: 70.098 samples/sec, ObjLoss=21.233, BoxCenterLoss=14.552, BoxScaleLoss=4.903, ClassLoss=8.068 [Epoch 163][Batch 1499], LR: 1.00E-03, Speed: 68.933 samples/sec, ObjLoss=21.233, BoxCenterLoss=14.552, BoxScaleLoss=4.903, ClassLoss=8.068 [Epoch 163][Batch 1599], LR: 1.00E-03, Speed: 101.407 samples/sec, ObjLoss=21.232, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.067 [Epoch 163][Batch 1699], LR: 1.00E-03, Speed: 58.552 samples/sec, ObjLoss=21.232, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.067 [Epoch 163][Batch 1799], LR: 1.00E-03, Speed: 100.214 samples/sec, ObjLoss=21.231, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.066 [Epoch 163] Training cost: 1587.811, ObjLoss=21.231, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.066 [Epoch 163] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.420 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.174 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 ] = 0.220 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.302 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 ] = 0.287 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 ] = 0.134 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 ] = 0.418 person=31.9 bicycle=12.0 car=22.1 motorcycle=22.0 airplane=40.4 bus=39.1 train=47.4 truck=20.6 boat=11.1 traffic light=11.4 fire hydrant=39.7 stop sign=32.4 parking meter=14.3 bench=12.4 bird=16.3 cat=41.6 dog=34.7 horse=33.3 sheep=26.5 cow=29.1 elephant=40.6 bear=42.3 zebra=41.7 giraffe=35.9 backpack=4.9 umbrella=20.3 handbag=3.8 tie=13.0 suitcase=17.8 frisbee=35.2 skis=6.2 snowboard=14.1 sports ball=19.1 kite=19.8 baseball bat=9.3 baseball glove=17.4 skateboard=23.4 surfboard=16.1 tennis racket=21.6 bottle=14.2 wine glass=13.1 cup=18.6 fork=10.0 knife=3.4 spoon=3.0 bowl=21.2 banana=12.4 apple=7.6 sandwich=16.3 orange=16.9 broccoli=9.7 carrot=8.8 hot dog=12.2 pizza=25.5 donut=19.4 cake=14.1 chair=12.4 couch=26.1 potted plant=10.6 bed=28.7 dining table=13.8 toilet=32.1 tv=35.4 laptop=29.5 mouse=30.1 remote=8.0 keyboard=28.0 cell phone=14.4 microwave=34.0 oven=21.1 toaster=2.0 sink=22.1 refrigerator=32.8 book=4.8 clock=29.0 vase=17.8 scissors=12.3 teddy bear=23.7 hair drier=0.0 toothbrush=3.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 164][Batch 99], LR: 1.00E-03, Speed: 143.122 samples/sec, ObjLoss=21.230, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.065 [Epoch 164][Batch 199], LR: 1.00E-03, Speed: 114.313 samples/sec, ObjLoss=21.230, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.065 [Epoch 164][Batch 299], LR: 1.00E-03, Speed: 147.012 samples/sec, ObjLoss=21.229, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.064 [Epoch 164][Batch 399], LR: 1.00E-03, Speed: 56.271 samples/sec, ObjLoss=21.228, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.064 [Epoch 164][Batch 499], LR: 1.00E-03, Speed: 56.800 samples/sec, ObjLoss=21.228, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.063 [Epoch 164][Batch 599], LR: 1.00E-03, Speed: 55.259 samples/sec, ObjLoss=21.227, BoxCenterLoss=14.552, BoxScaleLoss=4.902, ClassLoss=8.063 [Epoch 164][Batch 699], LR: 1.00E-03, Speed: 62.006 samples/sec, ObjLoss=21.227, BoxCenterLoss=14.551, BoxScaleLoss=4.901, ClassLoss=8.062 [Epoch 164][Batch 799], LR: 1.00E-03, Speed: 71.515 samples/sec, ObjLoss=21.226, BoxCenterLoss=14.551, BoxScaleLoss=4.901, ClassLoss=8.062 [Epoch 164][Batch 899], LR: 1.00E-03, Speed: 77.841 samples/sec, ObjLoss=21.225, BoxCenterLoss=14.551, BoxScaleLoss=4.901, ClassLoss=8.061 [Epoch 164][Batch 999], LR: 1.00E-03, Speed: 68.233 samples/sec, ObjLoss=21.225, BoxCenterLoss=14.551, BoxScaleLoss=4.901, ClassLoss=8.061 [Epoch 164][Batch 1099], LR: 1.00E-03, Speed: 77.758 samples/sec, ObjLoss=21.224, BoxCenterLoss=14.551, BoxScaleLoss=4.901, ClassLoss=8.060 [Epoch 164][Batch 1199], LR: 1.00E-03, Speed: 64.154 samples/sec, ObjLoss=21.223, BoxCenterLoss=14.551, BoxScaleLoss=4.901, ClassLoss=8.059 [Epoch 164][Batch 1299], LR: 1.00E-03, Speed: 80.812 samples/sec, ObjLoss=21.223, BoxCenterLoss=14.551, BoxScaleLoss=4.901, ClassLoss=8.059 [Epoch 164][Batch 1399], LR: 1.00E-03, Speed: 118.389 samples/sec, ObjLoss=21.222, BoxCenterLoss=14.551, BoxScaleLoss=4.900, ClassLoss=8.058 [Epoch 164][Batch 1499], LR: 1.00E-03, Speed: 114.066 samples/sec, ObjLoss=21.221, BoxCenterLoss=14.551, BoxScaleLoss=4.900, ClassLoss=8.058 [Epoch 164][Batch 1599], LR: 1.00E-03, Speed: 59.210 samples/sec, ObjLoss=21.221, BoxCenterLoss=14.551, BoxScaleLoss=4.900, ClassLoss=8.057 [Epoch 164][Batch 1699], LR: 1.00E-03, Speed: 68.225 samples/sec, ObjLoss=21.220, BoxCenterLoss=14.551, BoxScaleLoss=4.900, ClassLoss=8.057 [Epoch 164][Batch 1799], LR: 1.00E-03, Speed: 132.577 samples/sec, ObjLoss=21.219, BoxCenterLoss=14.550, BoxScaleLoss=4.900, ClassLoss=8.056 [Epoch 164] Training cost: 1549.607, ObjLoss=21.219, BoxCenterLoss=14.550, BoxScaleLoss=4.900, ClassLoss=8.056 [Epoch 164] 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 ] = 0.414 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.164 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.209 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 ] = 0.187 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 ] = 0.286 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 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415 person=31.6 bicycle=13.9 car=21.8 motorcycle=20.8 airplane=37.6 bus=38.0 train=34.7 truck=19.0 boat=12.0 traffic light=12.2 fire hydrant=36.2 stop sign=36.2 parking meter=18.3 bench=11.1 bird=17.6 cat=39.6 dog=34.1 horse=27.7 sheep=27.0 cow=28.2 elephant=34.0 bear=43.2 zebra=37.3 giraffe=42.1 backpack=5.1 umbrella=16.1 handbag=3.7 tie=14.3 suitcase=17.6 frisbee=34.5 skis=6.2 snowboard=10.2 sports ball=18.7 kite=20.1 baseball bat=8.9 baseball glove=17.1 skateboard=21.1 surfboard=15.2 tennis racket=20.0 bottle=14.0 wine glass=15.6 cup=17.3 fork=9.3 knife=4.2 spoon=2.6 bowl=18.2 banana=12.4 apple=7.7 sandwich=18.9 orange=13.3 broccoli=10.6 carrot=9.2 hot dog=15.6 pizza=31.4 donut=23.5 cake=16.6 chair=12.5 couch=26.7 potted plant=8.8 bed=18.7 dining table=11.8 toilet=33.5 tv=34.4 laptop=33.2 mouse=29.1 remote=8.2 keyboard=25.8 cell phone=14.7 microwave=30.8 oven=19.6 toaster=0.0 sink=20.5 refrigerator=25.9 book=5.0 clock=28.0 vase=17.6 scissors=10.7 teddy bear=23.4 hair drier=0.0 toothbrush=2.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.8 [Epoch 165][Batch 99], LR: 1.00E-03, Speed: 179.543 samples/sec, ObjLoss=21.219, BoxCenterLoss=14.550, BoxScaleLoss=4.900, ClassLoss=8.055 [Epoch 165][Batch 199], LR: 1.00E-03, Speed: 125.286 samples/sec, ObjLoss=21.218, BoxCenterLoss=14.550, BoxScaleLoss=4.900, ClassLoss=8.055 [Epoch 165][Batch 299], LR: 1.00E-03, Speed: 79.087 samples/sec, ObjLoss=21.217, BoxCenterLoss=14.550, BoxScaleLoss=4.900, ClassLoss=8.054 [Epoch 165][Batch 399], LR: 1.00E-03, Speed: 93.927 samples/sec, ObjLoss=21.217, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.054 [Epoch 165][Batch 499], LR: 1.00E-03, Speed: 109.903 samples/sec, ObjLoss=21.216, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.053 [Epoch 165][Batch 599], LR: 1.00E-03, Speed: 104.919 samples/sec, ObjLoss=21.215, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.053 [Epoch 165][Batch 699], LR: 1.00E-03, Speed: 128.046 samples/sec, ObjLoss=21.214, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.052 [Epoch 165][Batch 799], LR: 1.00E-03, Speed: 57.418 samples/sec, ObjLoss=21.214, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.052 [Epoch 165][Batch 899], LR: 1.00E-03, Speed: 35.550 samples/sec, ObjLoss=21.214, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.051 [Epoch 165][Batch 999], LR: 1.00E-03, Speed: 98.264 samples/sec, ObjLoss=21.213, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.051 [Epoch 165][Batch 1099], LR: 1.00E-03, Speed: 71.223 samples/sec, ObjLoss=21.212, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.050 [Epoch 165][Batch 1199], LR: 1.00E-03, Speed: 89.726 samples/sec, ObjLoss=21.212, BoxCenterLoss=14.550, BoxScaleLoss=4.899, ClassLoss=8.050 [Epoch 165][Batch 1299], LR: 1.00E-03, Speed: 90.197 samples/sec, ObjLoss=21.211, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.049 [Epoch 165][Batch 1399], LR: 1.00E-03, Speed: 92.771 samples/sec, ObjLoss=21.210, BoxCenterLoss=14.550, BoxScaleLoss=4.898, ClassLoss=8.049 [Epoch 165][Batch 1499], LR: 1.00E-03, Speed: 71.087 samples/sec, ObjLoss=21.210, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.048 [Epoch 165][Batch 1599], LR: 1.00E-03, Speed: 93.357 samples/sec, ObjLoss=21.209, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.048 [Epoch 165][Batch 1699], LR: 1.00E-03, Speed: 101.967 samples/sec, ObjLoss=21.209, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.047 [Epoch 165][Batch 1799], LR: 1.00E-03, Speed: 147.128 samples/sec, ObjLoss=21.208, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.047 [Epoch 165] Training cost: 1612.805, ObjLoss=21.208, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.046 [Epoch 165] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.196 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.162 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.290 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 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 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 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396 person=32.4 bicycle=13.9 car=20.1 motorcycle=26.6 airplane=32.2 bus=40.5 train=40.0 truck=14.5 boat=11.0 traffic light=11.2 fire hydrant=26.5 stop sign=40.7 parking meter=23.3 bench=10.7 bird=15.1 cat=36.9 dog=30.6 horse=33.3 sheep=21.4 cow=28.7 elephant=38.1 bear=32.0 zebra=41.4 giraffe=43.1 backpack=4.5 umbrella=18.6 handbag=4.0 tie=10.9 suitcase=11.7 frisbee=35.1 skis=5.5 snowboard=13.0 sports ball=19.6 kite=20.0 baseball bat=9.7 baseball glove=20.0 skateboard=23.0 surfboard=15.8 tennis racket=23.0 bottle=13.9 wine glass=15.2 cup=19.1 fork=9.0 knife=4.6 spoon=2.6 bowl=20.5 banana=9.8 apple=5.9 sandwich=16.4 orange=13.4 broccoli=10.8 carrot=9.0 hot dog=15.4 pizza=25.6 donut=17.3 cake=15.0 chair=12.9 couch=26.7 potted plant=10.3 bed=28.7 dining table=13.2 toilet=31.1 tv=29.6 laptop=34.4 mouse=27.6 remote=8.3 keyboard=21.2 cell phone=15.3 microwave=32.8 oven=17.2 toaster=0.0 sink=14.9 refrigerator=29.6 book=4.7 clock=30.3 vase=18.2 scissors=12.4 teddy bear=22.2 hair drier=0.0 toothbrush=4.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.6 [Epoch 166][Batch 99], LR: 1.00E-03, Speed: 164.861 samples/sec, ObjLoss=21.207, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.046 [Epoch 166][Batch 199], LR: 1.00E-03, Speed: 60.209 samples/sec, ObjLoss=21.206, BoxCenterLoss=14.549, BoxScaleLoss=4.898, ClassLoss=8.045 [Epoch 166][Batch 299], LR: 1.00E-03, Speed: 85.900 samples/sec, ObjLoss=21.206, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.045 [Epoch 166][Batch 399], LR: 1.00E-03, Speed: 78.828 samples/sec, ObjLoss=21.205, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.044 [Epoch 166][Batch 499], LR: 1.00E-03, Speed: 117.514 samples/sec, ObjLoss=21.205, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.043 [Epoch 166][Batch 599], LR: 1.00E-03, Speed: 50.252 samples/sec, ObjLoss=21.204, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.043 [Epoch 166][Batch 699], LR: 1.00E-03, Speed: 143.370 samples/sec, ObjLoss=21.203, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.042 [Epoch 166][Batch 799], LR: 1.00E-03, Speed: 70.400 samples/sec, ObjLoss=21.203, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.042 [Epoch 166][Batch 899], LR: 1.00E-03, Speed: 73.915 samples/sec, ObjLoss=21.202, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.042 [Epoch 166][Batch 999], LR: 1.00E-03, Speed: 34.787 samples/sec, ObjLoss=21.201, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.041 [Epoch 166][Batch 1099], LR: 1.00E-03, Speed: 81.868 samples/sec, ObjLoss=21.201, BoxCenterLoss=14.549, BoxScaleLoss=4.897, ClassLoss=8.041 [Epoch 166][Batch 1199], LR: 1.00E-03, Speed: 108.156 samples/sec, ObjLoss=21.200, BoxCenterLoss=14.549, BoxScaleLoss=4.896, ClassLoss=8.040 [Epoch 166][Batch 1299], LR: 1.00E-03, Speed: 85.468 samples/sec, ObjLoss=21.200, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.040 [Epoch 166][Batch 1399], LR: 1.00E-03, Speed: 128.647 samples/sec, ObjLoss=21.199, BoxCenterLoss=14.549, BoxScaleLoss=4.896, ClassLoss=8.039 [Epoch 166][Batch 1499], LR: 1.00E-03, Speed: 141.951 samples/sec, ObjLoss=21.198, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.039 [Epoch 166][Batch 1599], LR: 1.00E-03, Speed: 76.855 samples/sec, ObjLoss=21.198, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.038 [Epoch 166][Batch 1699], LR: 1.00E-03, Speed: 56.750 samples/sec, ObjLoss=21.197, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.038 [Epoch 166][Batch 1799], LR: 1.00E-03, Speed: 124.947 samples/sec, ObjLoss=21.196, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.037 [Epoch 166] Training cost: 1613.883, ObjLoss=21.196, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.037 [Epoch 166] 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 ] = 0.425 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 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 ] = 0.302 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 ] = 0.290 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 ] = 0.145 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 ] = 0.421 person=32.3 bicycle=13.8 car=20.4 motorcycle=24.6 airplane=42.1 bus=38.5 train=38.6 truck=18.3 boat=11.8 traffic light=11.6 fire hydrant=37.7 stop sign=37.8 parking meter=23.5 bench=11.1 bird=16.6 cat=39.9 dog=33.2 horse=31.0 sheep=26.8 cow=28.4 elephant=36.4 bear=46.2 zebra=39.6 giraffe=42.9 backpack=5.3 umbrella=20.5 handbag=4.4 tie=12.4 suitcase=17.0 frisbee=32.3 skis=6.3 snowboard=11.4 sports ball=27.3 kite=23.1 baseball bat=11.5 baseball glove=17.9 skateboard=21.9 surfboard=15.7 tennis racket=19.8 bottle=15.2 wine glass=14.8 cup=19.6 fork=10.4 knife=3.9 spoon=2.7 bowl=18.9 banana=10.6 apple=7.9 sandwich=21.3 orange=14.9 broccoli=11.7 carrot=10.0 hot dog=18.1 pizza=27.4 donut=19.9 cake=17.1 chair=12.9 couch=25.7 potted plant=8.8 bed=29.8 dining table=17.4 toilet=34.8 tv=35.9 laptop=37.4 mouse=34.4 remote=8.5 keyboard=24.4 cell phone=16.0 microwave=26.1 oven=18.8 toaster=0.0 sink=18.2 refrigerator=25.6 book=4.9 clock=30.3 vase=18.5 scissors=13.9 teddy bear=23.8 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 167][Batch 99], LR: 1.00E-03, Speed: 144.915 samples/sec, ObjLoss=21.196, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.036 [Epoch 167][Batch 199], LR: 1.00E-03, Speed: 136.689 samples/sec, ObjLoss=21.195, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.036 [Epoch 167][Batch 299], LR: 1.00E-03, Speed: 102.877 samples/sec, ObjLoss=21.194, BoxCenterLoss=14.548, BoxScaleLoss=4.896, ClassLoss=8.035 [Epoch 167][Batch 399], LR: 1.00E-03, Speed: 131.270 samples/sec, ObjLoss=21.194, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.035 [Epoch 167][Batch 499], LR: 1.00E-03, Speed: 138.485 samples/sec, ObjLoss=21.193, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.034 [Epoch 167][Batch 599], LR: 1.00E-03, Speed: 79.498 samples/sec, ObjLoss=21.193, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.034 [Epoch 167][Batch 699], LR: 1.00E-03, Speed: 69.488 samples/sec, ObjLoss=21.192, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.033 [Epoch 167][Batch 799], LR: 1.00E-03, Speed: 68.059 samples/sec, ObjLoss=21.192, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.033 [Epoch 167][Batch 899], LR: 1.00E-03, Speed: 81.650 samples/sec, ObjLoss=21.191, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.033 [Epoch 167][Batch 999], LR: 1.00E-03, Speed: 80.218 samples/sec, ObjLoss=21.191, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.032 [Epoch 167][Batch 1099], LR: 1.00E-03, Speed: 153.394 samples/sec, ObjLoss=21.190, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.032 [Epoch 167][Batch 1199], LR: 1.00E-03, Speed: 70.169 samples/sec, ObjLoss=21.189, BoxCenterLoss=14.548, BoxScaleLoss=4.895, ClassLoss=8.031 [Epoch 167][Batch 1299], LR: 1.00E-03, Speed: 69.111 samples/sec, ObjLoss=21.189, BoxCenterLoss=14.548, BoxScaleLoss=4.894, ClassLoss=8.031 [Epoch 167][Batch 1399], LR: 1.00E-03, Speed: 43.626 samples/sec, ObjLoss=21.188, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.030 [Epoch 167][Batch 1499], LR: 1.00E-03, Speed: 61.378 samples/sec, ObjLoss=21.187, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.030 [Epoch 167][Batch 1599], LR: 1.00E-03, Speed: 65.712 samples/sec, ObjLoss=21.187, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.029 [Epoch 167][Batch 1699], LR: 1.00E-03, Speed: 60.847 samples/sec, ObjLoss=21.186, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.029 [Epoch 167][Batch 1799], LR: 1.00E-03, Speed: 109.812 samples/sec, ObjLoss=21.186, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.028 [Epoch 167] Training cost: 1648.448, ObjLoss=21.185, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.028 [Epoch 167] 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 ] = 0.420 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 ] = 0.219 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 ] = 0.198 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 ] = 0.298 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 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436 person=31.7 bicycle=14.8 car=21.7 motorcycle=26.0 airplane=32.4 bus=40.9 train=42.6 truck=19.1 boat=10.0 traffic light=12.9 fire hydrant=37.7 stop sign=40.3 parking meter=23.3 bench=11.8 bird=14.9 cat=45.1 dog=36.0 horse=30.4 sheep=25.4 cow=30.3 elephant=39.8 bear=44.1 zebra=41.8 giraffe=38.8 backpack=5.6 umbrella=20.2 handbag=4.0 tie=14.0 suitcase=15.1 frisbee=35.6 skis=6.5 snowboard=14.9 sports ball=21.2 kite=20.4 baseball bat=10.6 baseball glove=18.7 skateboard=23.5 surfboard=18.2 tennis racket=25.7 bottle=15.6 wine glass=14.0 cup=19.4 fork=12.0 knife=3.9 spoon=3.9 bowl=22.0 banana=10.6 apple=7.8 sandwich=16.9 orange=16.2 broccoli=9.5 carrot=9.1 hot dog=14.7 pizza=27.5 donut=18.7 cake=15.7 chair=11.8 couch=26.7 potted plant=11.3 bed=25.3 dining table=14.4 toilet=37.4 tv=34.4 laptop=33.6 mouse=34.8 remote=10.8 keyboard=28.0 cell phone=14.9 microwave=31.4 oven=21.1 toaster=0.0 sink=19.0 refrigerator=29.3 book=5.2 clock=28.7 vase=20.1 scissors=17.5 teddy bear=26.2 hair drier=0.0 toothbrush=5.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.1 [Epoch 168][Batch 99], LR: 1.00E-03, Speed: 147.939 samples/sec, ObjLoss=21.185, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.027 [Epoch 168][Batch 199], LR: 1.00E-03, Speed: 152.572 samples/sec, ObjLoss=21.184, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.027 [Epoch 168][Batch 299], LR: 1.00E-03, Speed: 123.309 samples/sec, ObjLoss=21.183, BoxCenterLoss=14.547, BoxScaleLoss=4.894, ClassLoss=8.026 [Epoch 168][Batch 399], LR: 1.00E-03, Speed: 54.390 samples/sec, ObjLoss=21.183, BoxCenterLoss=14.547, BoxScaleLoss=4.893, ClassLoss=8.026 [Epoch 168][Batch 499], LR: 1.00E-03, Speed: 72.318 samples/sec, ObjLoss=21.182, BoxCenterLoss=14.547, BoxScaleLoss=4.893, ClassLoss=8.025 [Epoch 168][Batch 599], LR: 1.00E-03, Speed: 97.054 samples/sec, ObjLoss=21.182, BoxCenterLoss=14.547, BoxScaleLoss=4.893, ClassLoss=8.025 [Epoch 168][Batch 699], LR: 1.00E-03, Speed: 103.047 samples/sec, ObjLoss=21.181, BoxCenterLoss=14.547, BoxScaleLoss=4.893, ClassLoss=8.024 [Epoch 168][Batch 799], LR: 1.00E-03, Speed: 65.405 samples/sec, ObjLoss=21.181, BoxCenterLoss=14.547, BoxScaleLoss=4.893, ClassLoss=8.023 [Epoch 168][Batch 899], LR: 1.00E-03, Speed: 94.446 samples/sec, ObjLoss=21.180, BoxCenterLoss=14.547, BoxScaleLoss=4.893, ClassLoss=8.023 [Epoch 168][Batch 999], LR: 1.00E-03, Speed: 80.207 samples/sec, ObjLoss=21.179, BoxCenterLoss=14.547, BoxScaleLoss=4.893, ClassLoss=8.022 [Epoch 168][Batch 1099], LR: 1.00E-03, Speed: 86.825 samples/sec, ObjLoss=21.179, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.022 [Epoch 168][Batch 1199], LR: 1.00E-03, Speed: 50.008 samples/sec, ObjLoss=21.178, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.021 [Epoch 168][Batch 1299], LR: 1.00E-03, Speed: 52.224 samples/sec, ObjLoss=21.177, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.021 [Epoch 168][Batch 1399], LR: 1.00E-03, Speed: 108.396 samples/sec, ObjLoss=21.177, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.020 [Epoch 168][Batch 1499], LR: 1.00E-03, Speed: 83.130 samples/sec, ObjLoss=21.176, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.020 [Epoch 168][Batch 1599], LR: 1.00E-03, Speed: 120.049 samples/sec, ObjLoss=21.176, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.019 [Epoch 168][Batch 1699], LR: 1.00E-03, Speed: 58.351 samples/sec, ObjLoss=21.175, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.019 [Epoch 168][Batch 1799], LR: 1.00E-03, Speed: 125.902 samples/sec, ObjLoss=21.175, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.018 [Epoch 168] Training cost: 1656.316, ObjLoss=21.174, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.018 [Epoch 168] 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 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.186 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 ] = 0.201 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.327 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.296 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 ] = 0.288 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445 person=33.0 bicycle=14.3 car=20.5 motorcycle=25.2 airplane=42.1 bus=41.1 train=43.9 truck=17.2 boat=11.9 traffic light=12.0 fire hydrant=38.9 stop sign=40.4 parking meter=24.7 bench=11.9 bird=17.3 cat=43.4 dog=33.6 horse=30.3 sheep=25.0 cow=28.5 elephant=38.9 bear=38.8 zebra=37.3 giraffe=40.9 backpack=6.0 umbrella=21.7 handbag=3.9 tie=10.7 suitcase=13.9 frisbee=29.5 skis=7.1 snowboard=15.4 sports ball=23.1 kite=22.8 baseball bat=9.1 baseball glove=17.6 skateboard=22.3 surfboard=16.8 tennis racket=23.3 bottle=15.7 wine glass=14.6 cup=21.6 fork=8.1 knife=3.4 spoon=3.3 bowl=20.4 banana=12.7 apple=7.2 sandwich=16.0 orange=14.5 broccoli=10.8 carrot=6.6 hot dog=15.1 pizza=29.5 donut=16.6 cake=17.5 chair=12.8 couch=26.7 potted plant=9.6 bed=29.4 dining table=16.9 toilet=34.6 tv=33.4 laptop=33.3 mouse=30.8 remote=7.6 keyboard=28.8 cell phone=15.4 microwave=30.9 oven=21.0 toaster=3.6 sink=19.4 refrigerator=30.0 book=4.0 clock=29.9 vase=20.0 scissors=15.5 teddy bear=24.0 hair drier=0.0 toothbrush=3.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 169][Batch 99], LR: 1.00E-03, Speed: 136.450 samples/sec, ObjLoss=21.174, BoxCenterLoss=14.546, BoxScaleLoss=4.892, ClassLoss=8.018 [Epoch 169][Batch 199], LR: 1.00E-03, Speed: 128.178 samples/sec, ObjLoss=21.173, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.017 [Epoch 169][Batch 299], LR: 1.00E-03, Speed: 121.871 samples/sec, ObjLoss=21.173, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.017 [Epoch 169][Batch 399], LR: 1.00E-03, Speed: 105.219 samples/sec, ObjLoss=21.172, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.016 [Epoch 169][Batch 499], LR: 1.00E-03, Speed: 89.086 samples/sec, ObjLoss=21.172, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.015 [Epoch 169][Batch 599], LR: 1.00E-03, Speed: 76.598 samples/sec, ObjLoss=21.171, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.015 [Epoch 169][Batch 699], LR: 1.00E-03, Speed: 70.532 samples/sec, ObjLoss=21.171, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.015 [Epoch 169][Batch 799], LR: 1.00E-03, Speed: 85.199 samples/sec, ObjLoss=21.170, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.014 [Epoch 169][Batch 899], LR: 1.00E-03, Speed: 75.408 samples/sec, ObjLoss=21.169, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.013 [Epoch 169][Batch 999], LR: 1.00E-03, Speed: 82.066 samples/sec, ObjLoss=21.169, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.013 [Epoch 169][Batch 1099], LR: 1.00E-03, Speed: 75.603 samples/sec, ObjLoss=21.168, BoxCenterLoss=14.546, BoxScaleLoss=4.891, ClassLoss=8.012 [Epoch 169][Batch 1199], LR: 1.00E-03, Speed: 59.007 samples/sec, ObjLoss=21.168, BoxCenterLoss=14.546, BoxScaleLoss=4.890, ClassLoss=8.012 [Epoch 169][Batch 1299], LR: 1.00E-03, Speed: 69.997 samples/sec, ObjLoss=21.167, BoxCenterLoss=14.546, BoxScaleLoss=4.890, ClassLoss=8.011 [Epoch 169][Batch 1399], LR: 1.00E-03, Speed: 91.957 samples/sec, ObjLoss=21.166, BoxCenterLoss=14.545, BoxScaleLoss=4.890, ClassLoss=8.011 [Epoch 169][Batch 1499], LR: 1.00E-03, Speed: 110.938 samples/sec, ObjLoss=21.166, BoxCenterLoss=14.545, BoxScaleLoss=4.890, ClassLoss=8.010 [Epoch 169][Batch 1599], LR: 1.00E-03, Speed: 144.199 samples/sec, ObjLoss=21.165, BoxCenterLoss=14.545, BoxScaleLoss=4.890, ClassLoss=8.010 [Epoch 169][Batch 1699], LR: 1.00E-03, Speed: 113.213 samples/sec, ObjLoss=21.164, BoxCenterLoss=14.545, BoxScaleLoss=4.890, ClassLoss=8.009 [Epoch 169][Batch 1799], LR: 1.00E-03, Speed: 190.657 samples/sec, ObjLoss=21.164, BoxCenterLoss=14.545, BoxScaleLoss=4.890, ClassLoss=8.009 [Epoch 169] Training cost: 1658.867, ObjLoss=21.164, BoxCenterLoss=14.545, BoxScaleLoss=4.890, ClassLoss=8.009 [Epoch 169] 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 ] = 0.419 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.183 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 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 ] = 0.304 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 ] = 0.298 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 ] = 0.307 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415 person=32.2 bicycle=14.7 car=20.6 motorcycle=24.5 airplane=38.6 bus=42.4 train=39.0 truck=19.3 boat=10.0 traffic light=11.4 fire hydrant=35.7 stop sign=37.5 parking meter=24.1 bench=10.6 bird=14.8 cat=40.5 dog=33.9 horse=33.0 sheep=27.4 cow=30.3 elephant=42.2 bear=40.8 zebra=41.6 giraffe=40.0 backpack=6.2 umbrella=15.9 handbag=3.9 tie=14.0 suitcase=16.7 frisbee=35.8 skis=3.9 snowboard=11.2 sports ball=20.8 kite=19.1 baseball bat=9.7 baseball glove=18.4 skateboard=22.4 surfboard=14.6 tennis racket=23.7 bottle=15.3 wine glass=13.8 cup=20.2 fork=10.4 knife=4.6 spoon=3.0 bowl=21.3 banana=11.8 apple=8.8 sandwich=18.9 orange=13.4 broccoli=11.3 carrot=9.3 hot dog=21.1 pizza=27.9 donut=25.8 cake=18.1 chair=12.9 couch=26.9 potted plant=10.3 bed=28.3 dining table=16.6 toilet=36.2 tv=33.8 laptop=34.2 mouse=30.5 remote=7.9 keyboard=20.7 cell phone=15.9 microwave=33.7 oven=19.4 toaster=0.0 sink=22.9 refrigerator=30.3 book=4.1 clock=29.2 vase=18.8 scissors=9.8 teddy bear=22.3 hair drier=0.0 toothbrush=5.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 170][Batch 99], LR: 1.00E-03, Speed: 130.739 samples/sec, ObjLoss=21.163, BoxCenterLoss=14.545, BoxScaleLoss=4.890, ClassLoss=8.008 [Epoch 170][Batch 199], LR: 1.00E-03, Speed: 69.527 samples/sec, ObjLoss=21.163, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.008 [Epoch 170][Batch 299], LR: 1.00E-03, Speed: 93.708 samples/sec, ObjLoss=21.162, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.007 [Epoch 170][Batch 399], LR: 1.00E-03, Speed: 79.213 samples/sec, ObjLoss=21.162, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.007 [Epoch 170][Batch 499], LR: 1.00E-03, Speed: 52.692 samples/sec, ObjLoss=21.161, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.006 [Epoch 170][Batch 599], LR: 1.00E-03, Speed: 104.405 samples/sec, ObjLoss=21.161, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.005 [Epoch 170][Batch 699], LR: 1.00E-03, Speed: 134.745 samples/sec, ObjLoss=21.160, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.005 [Epoch 170][Batch 799], LR: 1.00E-03, Speed: 137.694 samples/sec, ObjLoss=21.159, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.004 [Epoch 170][Batch 899], LR: 1.00E-03, Speed: 72.154 samples/sec, ObjLoss=21.159, BoxCenterLoss=14.545, BoxScaleLoss=4.889, ClassLoss=8.004 [Epoch 170][Batch 999], LR: 1.00E-03, Speed: 66.588 samples/sec, ObjLoss=21.158, BoxCenterLoss=14.545, BoxScaleLoss=4.888, ClassLoss=8.003 [Epoch 170][Batch 1099], LR: 1.00E-03, Speed: 79.472 samples/sec, ObjLoss=21.158, BoxCenterLoss=14.545, BoxScaleLoss=4.888, ClassLoss=8.003 [Epoch 170][Batch 1199], LR: 1.00E-03, Speed: 56.204 samples/sec, ObjLoss=21.157, BoxCenterLoss=14.545, BoxScaleLoss=4.888, ClassLoss=8.002 [Epoch 170][Batch 1299], LR: 1.00E-03, Speed: 107.334 samples/sec, ObjLoss=21.156, BoxCenterLoss=14.545, BoxScaleLoss=4.888, ClassLoss=8.002 [Epoch 170][Batch 1399], LR: 1.00E-03, Speed: 63.085 samples/sec, ObjLoss=21.156, BoxCenterLoss=14.545, BoxScaleLoss=4.888, ClassLoss=8.001 [Epoch 170][Batch 1499], LR: 1.00E-03, Speed: 90.986 samples/sec, ObjLoss=21.155, BoxCenterLoss=14.545, BoxScaleLoss=4.888, ClassLoss=8.001 [Epoch 170][Batch 1599], LR: 1.00E-03, Speed: 96.285 samples/sec, ObjLoss=21.155, BoxCenterLoss=14.545, BoxScaleLoss=4.888, ClassLoss=8.001 [Epoch 170][Batch 1699], LR: 1.00E-03, Speed: 85.705 samples/sec, ObjLoss=21.154, BoxCenterLoss=14.544, BoxScaleLoss=4.888, ClassLoss=8.000 [Epoch 170][Batch 1799], LR: 1.00E-03, Speed: 133.887 samples/sec, ObjLoss=21.154, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.999 [Epoch 170] Training cost: 1667.038, ObjLoss=21.153, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.999 [Epoch 170] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.416 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.203 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 ] = 0.195 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 ] = 0.293 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 ] = 0.294 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 person=33.3 bicycle=13.5 car=21.3 motorcycle=22.5 airplane=40.2 bus=39.6 train=42.1 truck=19.8 boat=10.6 traffic light=12.7 fire hydrant=34.4 stop sign=38.9 parking meter=23.9 bench=12.0 bird=14.8 cat=42.4 dog=30.7 horse=30.3 sheep=23.5 cow=27.4 elephant=41.1 bear=44.2 zebra=42.5 giraffe=34.8 backpack=5.3 umbrella=18.5 handbag=3.9 tie=12.3 suitcase=13.3 frisbee=34.8 skis=7.1 snowboard=10.5 sports ball=23.2 kite=21.0 baseball bat=9.8 baseball glove=17.0 skateboard=21.3 surfboard=15.5 tennis racket=22.3 bottle=15.9 wine glass=15.8 cup=20.3 fork=9.7 knife=4.0 spoon=3.0 bowl=20.6 banana=10.9 apple=7.7 sandwich=14.3 orange=14.8 broccoli=10.1 carrot=8.3 hot dog=19.5 pizza=28.7 donut=18.3 cake=13.1 chair=12.0 couch=26.6 potted plant=8.9 bed=29.0 dining table=16.3 toilet=33.8 tv=36.2 laptop=33.9 mouse=29.2 remote=8.7 keyboard=26.8 cell phone=14.4 microwave=29.0 oven=19.7 toaster=0.0 sink=19.4 refrigerator=29.8 book=4.5 clock=29.9 vase=16.9 scissors=16.5 teddy bear=24.8 hair drier=0.0 toothbrush=2.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 171][Batch 99], LR: 1.00E-03, Speed: 127.179 samples/sec, ObjLoss=21.153, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.999 [Epoch 171][Batch 199], LR: 1.00E-03, Speed: 103.639 samples/sec, ObjLoss=21.152, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.998 [Epoch 171][Batch 299], LR: 1.00E-03, Speed: 73.142 samples/sec, ObjLoss=21.151, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.998 [Epoch 171][Batch 399], LR: 1.00E-03, Speed: 109.100 samples/sec, ObjLoss=21.151, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.997 [Epoch 171][Batch 499], LR: 1.00E-03, Speed: 130.029 samples/sec, ObjLoss=21.150, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.996 [Epoch 171][Batch 599], LR: 1.00E-03, Speed: 71.969 samples/sec, ObjLoss=21.149, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.996 [Epoch 171][Batch 699], LR: 1.00E-03, Speed: 106.218 samples/sec, ObjLoss=21.149, BoxCenterLoss=14.544, BoxScaleLoss=4.887, ClassLoss=7.995 [Epoch 171][Batch 799], LR: 1.00E-03, Speed: 48.166 samples/sec, ObjLoss=21.149, BoxCenterLoss=14.544, BoxScaleLoss=4.886, ClassLoss=7.995 [Epoch 171][Batch 899], LR: 1.00E-03, Speed: 100.720 samples/sec, ObjLoss=21.148, BoxCenterLoss=14.544, BoxScaleLoss=4.886, ClassLoss=7.995 [Epoch 171][Batch 999], LR: 1.00E-03, Speed: 140.805 samples/sec, ObjLoss=21.147, BoxCenterLoss=14.544, BoxScaleLoss=4.886, ClassLoss=7.994 [Epoch 171][Batch 1099], LR: 1.00E-03, Speed: 49.677 samples/sec, ObjLoss=21.147, BoxCenterLoss=14.544, BoxScaleLoss=4.886, ClassLoss=7.994 [Epoch 171][Batch 1199], LR: 1.00E-03, Speed: 77.254 samples/sec, ObjLoss=21.146, BoxCenterLoss=14.544, BoxScaleLoss=4.886, ClassLoss=7.993 [Epoch 171][Batch 1299], LR: 1.00E-03, Speed: 50.826 samples/sec, ObjLoss=21.146, BoxCenterLoss=14.543, BoxScaleLoss=4.886, ClassLoss=7.993 [Epoch 171][Batch 1399], LR: 1.00E-03, Speed: 72.105 samples/sec, ObjLoss=21.145, BoxCenterLoss=14.543, BoxScaleLoss=4.886, ClassLoss=7.992 [Epoch 171][Batch 1499], LR: 1.00E-03, Speed: 46.679 samples/sec, ObjLoss=21.144, BoxCenterLoss=14.543, BoxScaleLoss=4.886, ClassLoss=7.992 [Epoch 171][Batch 1599], LR: 1.00E-03, Speed: 66.782 samples/sec, ObjLoss=21.144, BoxCenterLoss=14.543, BoxScaleLoss=4.886, ClassLoss=7.991 [Epoch 171][Batch 1699], LR: 1.00E-03, Speed: 65.438 samples/sec, ObjLoss=21.143, BoxCenterLoss=14.543, BoxScaleLoss=4.886, ClassLoss=7.991 [Epoch 171][Batch 1799], LR: 1.00E-03, Speed: 119.270 samples/sec, ObjLoss=21.142, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.990 [Epoch 171] Training cost: 1698.066, ObjLoss=21.142, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.990 [Epoch 171] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.433 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.217 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 ] = 0.198 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 ] = 0.300 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 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441 person=33.1 bicycle=13.6 car=21.2 motorcycle=24.3 airplane=36.2 bus=40.4 train=44.8 truck=19.2 boat=12.0 traffic light=12.5 fire hydrant=37.3 stop sign=36.7 parking meter=27.9 bench=10.9 bird=18.0 cat=42.2 dog=33.1 horse=30.9 sheep=26.7 cow=28.4 elephant=41.8 bear=46.3 zebra=41.9 giraffe=42.5 backpack=4.8 umbrella=19.6 handbag=4.4 tie=14.5 suitcase=16.2 frisbee=28.9 skis=7.1 snowboard=11.7 sports ball=23.6 kite=22.5 baseball bat=12.9 baseball glove=16.9 skateboard=21.9 surfboard=16.4 tennis racket=20.6 bottle=15.2 wine glass=15.0 cup=21.4 fork=11.2 knife=3.1 spoon=2.6 bowl=21.3 banana=11.1 apple=6.0 sandwich=17.9 orange=15.3 broccoli=11.4 carrot=10.0 hot dog=18.9 pizza=29.0 donut=22.4 cake=19.2 chair=13.4 couch=25.8 potted plant=11.5 bed=29.5 dining table=15.4 toilet=29.6 tv=36.0 laptop=36.6 mouse=32.6 remote=10.0 keyboard=23.7 cell phone=16.7 microwave=31.0 oven=18.4 toaster=0.0 sink=19.5 refrigerator=31.8 book=5.9 clock=30.5 vase=21.3 scissors=14.7 teddy bear=21.5 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 172][Batch 99], LR: 1.00E-03, Speed: 130.322 samples/sec, ObjLoss=21.142, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.989 [Epoch 172][Batch 199], LR: 1.00E-03, Speed: 107.654 samples/sec, ObjLoss=21.141, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.989 [Epoch 172][Batch 299], LR: 1.00E-03, Speed: 85.184 samples/sec, ObjLoss=21.141, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.988 [Epoch 172][Batch 399], LR: 1.00E-03, Speed: 65.043 samples/sec, ObjLoss=21.140, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.988 [Epoch 172][Batch 499], LR: 1.00E-03, Speed: 80.124 samples/sec, ObjLoss=21.139, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.987 [Epoch 172][Batch 599], LR: 1.00E-03, Speed: 94.719 samples/sec, ObjLoss=21.139, BoxCenterLoss=14.543, BoxScaleLoss=4.885, ClassLoss=7.987 [Epoch 172][Batch 699], LR: 1.00E-03, Speed: 126.127 samples/sec, ObjLoss=21.138, BoxCenterLoss=14.543, BoxScaleLoss=4.884, ClassLoss=7.986 [Epoch 172][Batch 799], LR: 1.00E-03, Speed: 47.808 samples/sec, ObjLoss=21.137, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.986 [Epoch 172][Batch 899], LR: 1.00E-03, Speed: 61.849 samples/sec, ObjLoss=21.137, BoxCenterLoss=14.543, BoxScaleLoss=4.884, ClassLoss=7.985 [Epoch 172][Batch 999], LR: 1.00E-03, Speed: 53.954 samples/sec, ObjLoss=21.137, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.985 [Epoch 172][Batch 1099], LR: 1.00E-03, Speed: 142.336 samples/sec, ObjLoss=21.136, BoxCenterLoss=14.543, BoxScaleLoss=4.884, ClassLoss=7.984 [Epoch 172][Batch 1199], LR: 1.00E-03, Speed: 62.532 samples/sec, ObjLoss=21.135, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.984 [Epoch 172][Batch 1299], LR: 1.00E-03, Speed: 70.355 samples/sec, ObjLoss=21.135, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.983 [Epoch 172][Batch 1399], LR: 1.00E-03, Speed: 148.451 samples/sec, ObjLoss=21.134, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.983 [Epoch 172][Batch 1499], LR: 1.00E-03, Speed: 46.861 samples/sec, ObjLoss=21.133, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.982 [Epoch 172][Batch 1599], LR: 1.00E-03, Speed: 110.893 samples/sec, ObjLoss=21.133, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.982 [Epoch 172][Batch 1699], LR: 1.00E-03, Speed: 59.382 samples/sec, ObjLoss=21.132, BoxCenterLoss=14.542, BoxScaleLoss=4.884, ClassLoss=7.981 [Epoch 172][Batch 1799], LR: 1.00E-03, Speed: 113.345 samples/sec, ObjLoss=21.132, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.981 [Epoch 172] Training cost: 1681.338, ObjLoss=21.132, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.981 [Epoch 172] 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 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.193 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 ] = 0.211 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 ] = 0.199 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 ] = 0.298 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 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433 person=32.6 bicycle=15.8 car=20.6 motorcycle=24.3 airplane=38.5 bus=40.1 train=47.0 truck=19.2 boat=10.3 traffic light=11.2 fire hydrant=41.3 stop sign=32.3 parking meter=28.7 bench=11.1 bird=18.8 cat=45.1 dog=34.6 horse=34.8 sheep=26.9 cow=33.2 elephant=41.0 bear=44.5 zebra=41.9 giraffe=42.6 backpack=6.0 umbrella=20.0 handbag=4.1 tie=15.3 suitcase=15.6 frisbee=35.6 skis=7.4 snowboard=11.4 sports ball=21.5 kite=20.7 baseball bat=11.6 baseball glove=14.2 skateboard=21.7 surfboard=16.3 tennis racket=23.8 bottle=15.3 wine glass=16.1 cup=20.2 fork=8.6 knife=4.4 spoon=2.7 bowl=20.6 banana=12.2 apple=3.6 sandwich=18.9 orange=11.7 broccoli=10.1 carrot=7.6 hot dog=15.9 pizza=30.6 donut=25.3 cake=14.8 chair=13.4 couch=27.7 potted plant=10.1 bed=29.9 dining table=15.5 toilet=37.6 tv=37.0 laptop=35.3 mouse=32.4 remote=9.1 keyboard=26.9 cell phone=16.4 microwave=30.8 oven=17.4 toaster=0.0 sink=18.7 refrigerator=28.5 book=5.2 clock=30.2 vase=19.1 scissors=14.3 teddy bear=22.5 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.3 [Epoch 173][Batch 99], LR: 1.00E-03, Speed: 136.352 samples/sec, ObjLoss=21.131, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.980 [Epoch 173][Batch 199], LR: 1.00E-03, Speed: 75.219 samples/sec, ObjLoss=21.131, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.980 [Epoch 173][Batch 299], LR: 1.00E-03, Speed: 68.480 samples/sec, ObjLoss=21.130, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.979 [Epoch 173][Batch 399], LR: 1.00E-03, Speed: 82.224 samples/sec, ObjLoss=21.130, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.979 [Epoch 173][Batch 499], LR: 1.00E-03, Speed: 74.104 samples/sec, ObjLoss=21.129, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.978 [Epoch 173][Batch 599], LR: 1.00E-03, Speed: 67.661 samples/sec, ObjLoss=21.128, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.977 [Epoch 173][Batch 699], LR: 1.00E-03, Speed: 49.873 samples/sec, ObjLoss=21.128, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.977 [Epoch 173][Batch 799], LR: 1.00E-03, Speed: 83.361 samples/sec, ObjLoss=21.127, BoxCenterLoss=14.542, BoxScaleLoss=4.883, ClassLoss=7.976 [Epoch 173][Batch 899], LR: 1.00E-03, Speed: 42.590 samples/sec, ObjLoss=21.127, BoxCenterLoss=14.542, BoxScaleLoss=4.882, ClassLoss=7.976 [Epoch 173][Batch 999], LR: 1.00E-03, Speed: 68.987 samples/sec, ObjLoss=21.126, BoxCenterLoss=14.542, BoxScaleLoss=4.882, ClassLoss=7.975 [Epoch 173][Batch 1099], LR: 1.00E-03, Speed: 70.563 samples/sec, ObjLoss=21.126, BoxCenterLoss=14.542, BoxScaleLoss=4.882, ClassLoss=7.975 [Epoch 173][Batch 1199], LR: 1.00E-03, Speed: 76.153 samples/sec, ObjLoss=21.125, BoxCenterLoss=14.542, BoxScaleLoss=4.882, ClassLoss=7.974 [Epoch 173][Batch 1299], LR: 1.00E-03, Speed: 145.292 samples/sec, ObjLoss=21.124, BoxCenterLoss=14.541, BoxScaleLoss=4.882, ClassLoss=7.974 [Epoch 173][Batch 1399], LR: 1.00E-03, Speed: 84.757 samples/sec, ObjLoss=21.124, BoxCenterLoss=14.541, BoxScaleLoss=4.882, ClassLoss=7.974 [Epoch 173][Batch 1499], LR: 1.00E-03, Speed: 76.299 samples/sec, ObjLoss=21.123, BoxCenterLoss=14.541, BoxScaleLoss=4.882, ClassLoss=7.973 [Epoch 173][Batch 1599], LR: 1.00E-03, Speed: 74.762 samples/sec, ObjLoss=21.122, BoxCenterLoss=14.541, BoxScaleLoss=4.882, ClassLoss=7.973 [Epoch 173][Batch 1699], LR: 1.00E-03, Speed: 73.550 samples/sec, ObjLoss=21.122, BoxCenterLoss=14.541, BoxScaleLoss=4.882, ClassLoss=7.972 [Epoch 173][Batch 1799], LR: 1.00E-03, Speed: 75.047 samples/sec, ObjLoss=21.121, BoxCenterLoss=14.541, BoxScaleLoss=4.882, ClassLoss=7.972 [Epoch 173] Training cost: 1716.115, ObjLoss=21.121, BoxCenterLoss=14.541, BoxScaleLoss=4.882, ClassLoss=7.972 [Epoch 173] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.427 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.189 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 ] = 0.311 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 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.305 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 ] = 0.318 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430 person=34.4 bicycle=15.4 car=22.9 motorcycle=22.7 airplane=40.0 bus=40.3 train=41.7 truck=18.3 boat=11.1 traffic light=12.2 fire hydrant=33.9 stop sign=35.1 parking meter=19.7 bench=10.6 bird=17.0 cat=38.7 dog=36.0 horse=32.8 sheep=28.9 cow=33.8 elephant=42.2 bear=39.6 zebra=39.4 giraffe=40.3 backpack=6.4 umbrella=20.9 handbag=4.2 tie=14.6 suitcase=18.1 frisbee=34.7 skis=6.7 snowboard=11.3 sports ball=22.2 kite=21.6 baseball bat=10.6 baseball glove=17.6 skateboard=19.0 surfboard=17.4 tennis racket=22.7 bottle=16.4 wine glass=15.4 cup=20.3 fork=10.0 knife=4.2 spoon=2.6 bowl=20.0 banana=10.3 apple=8.1 sandwich=19.2 orange=15.0 broccoli=9.4 carrot=7.7 hot dog=19.7 pizza=29.7 donut=20.3 cake=16.4 chair=14.0 couch=24.0 potted plant=10.6 bed=30.4 dining table=18.6 toilet=37.1 tv=34.5 laptop=35.9 mouse=36.5 remote=6.8 keyboard=27.5 cell phone=15.0 microwave=31.1 oven=19.0 toaster=2.4 sink=19.0 refrigerator=30.6 book=5.5 clock=31.2 vase=20.5 scissors=17.7 teddy bear=27.1 hair drier=0.0 toothbrush=2.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 174][Batch 99], LR: 1.00E-03, Speed: 133.772 samples/sec, ObjLoss=21.121, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.971 [Epoch 174][Batch 199], LR: 1.00E-03, Speed: 73.233 samples/sec, ObjLoss=21.120, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.970 [Epoch 174][Batch 299], LR: 1.00E-03, Speed: 69.420 samples/sec, ObjLoss=21.119, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.970 [Epoch 174][Batch 399], LR: 1.00E-03, Speed: 62.271 samples/sec, ObjLoss=21.119, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.969 [Epoch 174][Batch 499], LR: 1.00E-03, Speed: 134.442 samples/sec, ObjLoss=21.118, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.969 [Epoch 174][Batch 599], LR: 1.00E-03, Speed: 52.609 samples/sec, ObjLoss=21.118, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.968 [Epoch 174][Batch 699], LR: 1.00E-03, Speed: 68.113 samples/sec, ObjLoss=21.117, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.968 [Epoch 174][Batch 799], LR: 1.00E-03, Speed: 140.970 samples/sec, ObjLoss=21.116, BoxCenterLoss=14.541, BoxScaleLoss=4.881, ClassLoss=7.967 [Epoch 174][Batch 899], LR: 1.00E-03, Speed: 78.644 samples/sec, ObjLoss=21.115, BoxCenterLoss=14.540, BoxScaleLoss=4.881, ClassLoss=7.967 [Epoch 174][Batch 999], LR: 1.00E-03, Speed: 77.807 samples/sec, ObjLoss=21.115, BoxCenterLoss=14.540, BoxScaleLoss=4.881, ClassLoss=7.967 [Epoch 174][Batch 1099], LR: 1.00E-03, Speed: 120.923 samples/sec, ObjLoss=21.114, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.966 [Epoch 174][Batch 1199], LR: 1.00E-03, Speed: 45.995 samples/sec, ObjLoss=21.114, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.965 [Epoch 174][Batch 1299], LR: 1.00E-03, Speed: 64.210 samples/sec, ObjLoss=21.113, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.965 [Epoch 174][Batch 1399], LR: 1.00E-03, Speed: 69.404 samples/sec, ObjLoss=21.113, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.965 [Epoch 174][Batch 1499], LR: 1.00E-03, Speed: 83.000 samples/sec, ObjLoss=21.112, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.964 [Epoch 174][Batch 1599], LR: 1.00E-03, Speed: 72.975 samples/sec, ObjLoss=21.111, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.964 [Epoch 174][Batch 1699], LR: 1.00E-03, Speed: 64.791 samples/sec, ObjLoss=21.111, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.963 [Epoch 174][Batch 1799], LR: 1.00E-03, Speed: 83.201 samples/sec, ObjLoss=21.110, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.963 [Epoch 174] Training cost: 1607.455, ObjLoss=21.110, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.963 [Epoch 174] 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 ] = 0.424 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.187 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 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.307 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 ] = 0.287 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 ] = 0.144 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 ] = 0.415 person=33.6 bicycle=14.2 car=22.3 motorcycle=23.7 airplane=40.7 bus=42.0 train=43.9 truck=18.1 boat=12.2 traffic light=13.1 fire hydrant=38.3 stop sign=38.2 parking meter=18.2 bench=11.1 bird=17.5 cat=41.1 dog=33.6 horse=30.5 sheep=27.2 cow=27.0 elephant=33.1 bear=41.8 zebra=38.8 giraffe=42.4 backpack=5.4 umbrella=19.3 handbag=3.8 tie=13.8 suitcase=17.5 frisbee=35.7 skis=7.2 snowboard=12.6 sports ball=25.2 kite=23.4 baseball bat=8.0 baseball glove=20.4 skateboard=23.5 surfboard=17.6 tennis racket=22.9 bottle=15.4 wine glass=15.7 cup=19.0 fork=10.6 knife=4.3 spoon=3.5 bowl=20.2 banana=11.6 apple=6.2 sandwich=18.9 orange=11.2 broccoli=11.5 carrot=5.7 hot dog=16.6 pizza=27.1 donut=20.4 cake=15.8 chair=12.8 couch=27.1 potted plant=11.4 bed=28.5 dining table=15.8 toilet=35.2 tv=34.1 laptop=31.6 mouse=35.4 remote=9.3 keyboard=28.1 cell phone=15.5 microwave=32.6 oven=18.7 toaster=0.0 sink=20.9 refrigerator=27.9 book=5.0 clock=30.9 vase=19.0 scissors=17.0 teddy bear=24.9 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.0 [Epoch 175][Batch 99], LR: 1.00E-03, Speed: 120.324 samples/sec, ObjLoss=21.109, BoxCenterLoss=14.540, BoxScaleLoss=4.880, ClassLoss=7.962 [Epoch 175][Batch 199], LR: 1.00E-03, Speed: 86.395 samples/sec, ObjLoss=21.109, BoxCenterLoss=14.540, BoxScaleLoss=4.879, ClassLoss=7.961 [Epoch 175][Batch 299], LR: 1.00E-03, Speed: 78.759 samples/sec, ObjLoss=21.108, BoxCenterLoss=14.540, BoxScaleLoss=4.879, ClassLoss=7.961 [Epoch 175][Batch 399], LR: 1.00E-03, Speed: 52.339 samples/sec, ObjLoss=21.107, BoxCenterLoss=14.539, BoxScaleLoss=4.879, ClassLoss=7.960 [Epoch 175][Batch 499], LR: 1.00E-03, Speed: 90.856 samples/sec, ObjLoss=21.107, BoxCenterLoss=14.539, BoxScaleLoss=4.879, ClassLoss=7.960 [Epoch 175][Batch 599], LR: 1.00E-03, Speed: 51.817 samples/sec, ObjLoss=21.106, BoxCenterLoss=14.539, BoxScaleLoss=4.879, ClassLoss=7.959 [Epoch 175][Batch 699], LR: 1.00E-03, Speed: 160.622 samples/sec, ObjLoss=21.106, BoxCenterLoss=14.539, BoxScaleLoss=4.879, ClassLoss=7.959 [Epoch 175][Batch 799], LR: 1.00E-03, Speed: 72.834 samples/sec, ObjLoss=21.105, BoxCenterLoss=14.539, BoxScaleLoss=4.879, ClassLoss=7.958 [Epoch 175][Batch 899], LR: 1.00E-03, Speed: 110.848 samples/sec, ObjLoss=21.104, BoxCenterLoss=14.539, BoxScaleLoss=4.879, ClassLoss=7.958 [Epoch 175][Batch 999], LR: 1.00E-03, Speed: 111.553 samples/sec, ObjLoss=21.104, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.957 [Epoch 175][Batch 1099], LR: 1.00E-03, Speed: 59.423 samples/sec, ObjLoss=21.103, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.957 [Epoch 175][Batch 1199], LR: 1.00E-03, Speed: 125.236 samples/sec, ObjLoss=21.103, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.956 [Epoch 175][Batch 1299], LR: 1.00E-03, Speed: 89.045 samples/sec, ObjLoss=21.102, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.956 [Epoch 175][Batch 1399], LR: 1.00E-03, Speed: 78.708 samples/sec, ObjLoss=21.101, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.955 [Epoch 175][Batch 1499], LR: 1.00E-03, Speed: 88.788 samples/sec, ObjLoss=21.101, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.955 [Epoch 175][Batch 1599], LR: 1.00E-03, Speed: 79.499 samples/sec, ObjLoss=21.100, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.955 [Epoch 175][Batch 1699], LR: 1.00E-03, Speed: 80.648 samples/sec, ObjLoss=21.100, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.954 [Epoch 175][Batch 1799], LR: 1.00E-03, Speed: 150.393 samples/sec, ObjLoss=21.099, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.954 [Epoch 175] Training cost: 1641.818, ObjLoss=21.099, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.953 [Epoch 175] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.417 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.179 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 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.311 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 ] = 0.284 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 ] = 0.135 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 ] = 0.422 person=33.2 bicycle=14.0 car=21.6 motorcycle=22.8 airplane=39.0 bus=43.0 train=40.8 truck=21.2 boat=10.4 traffic light=14.0 fire hydrant=37.7 stop sign=37.9 parking meter=24.5 bench=11.6 bird=15.0 cat=44.1 dog=36.6 horse=28.4 sheep=27.0 cow=26.5 elephant=39.7 bear=43.3 zebra=43.0 giraffe=44.0 backpack=5.0 umbrella=20.0 handbag=3.6 tie=10.6 suitcase=15.5 frisbee=33.3 skis=6.8 snowboard=15.5 sports ball=23.1 kite=22.5 baseball bat=7.7 baseball glove=18.0 skateboard=18.2 surfboard=17.4 tennis racket=20.6 bottle=16.4 wine glass=15.9 cup=19.3 fork=10.1 knife=3.0 spoon=2.5 bowl=19.4 banana=9.6 apple=6.5 sandwich=14.3 orange=16.8 broccoli=11.3 carrot=7.8 hot dog=14.3 pizza=25.2 donut=22.6 cake=17.5 chair=12.3 couch=28.2 potted plant=11.1 bed=26.9 dining table=17.0 toilet=36.3 tv=33.6 laptop=33.9 mouse=25.8 remote=5.7 keyboard=25.9 cell phone=12.3 microwave=28.8 oven=19.7 toaster=0.0 sink=17.7 refrigerator=30.0 book=4.2 clock=31.5 vase=17.0 scissors=10.5 teddy bear=22.2 hair drier=0.0 toothbrush=2.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 176][Batch 99], LR: 1.00E-03, Speed: 150.985 samples/sec, ObjLoss=21.098, BoxCenterLoss=14.539, BoxScaleLoss=4.878, ClassLoss=7.953 [Epoch 176][Batch 199], LR: 1.00E-03, Speed: 109.134 samples/sec, ObjLoss=21.098, BoxCenterLoss=14.539, BoxScaleLoss=4.877, ClassLoss=7.952 [Epoch 176][Batch 299], LR: 1.00E-03, Speed: 80.511 samples/sec, ObjLoss=21.097, BoxCenterLoss=14.539, BoxScaleLoss=4.877, ClassLoss=7.952 [Epoch 176][Batch 399], LR: 1.00E-03, Speed: 65.440 samples/sec, ObjLoss=21.097, BoxCenterLoss=14.538, BoxScaleLoss=4.877, ClassLoss=7.951 [Epoch 176][Batch 499], LR: 1.00E-03, Speed: 54.050 samples/sec, ObjLoss=21.096, BoxCenterLoss=14.538, BoxScaleLoss=4.877, ClassLoss=7.951 [Epoch 176][Batch 599], LR: 1.00E-03, Speed: 76.570 samples/sec, ObjLoss=21.096, BoxCenterLoss=14.538, BoxScaleLoss=4.877, ClassLoss=7.951 [Epoch 176][Batch 699], LR: 1.00E-03, Speed: 57.562 samples/sec, ObjLoss=21.095, BoxCenterLoss=14.538, BoxScaleLoss=4.877, ClassLoss=7.950 [Epoch 176][Batch 799], LR: 1.00E-03, Speed: 74.260 samples/sec, ObjLoss=21.094, BoxCenterLoss=14.538, BoxScaleLoss=4.877, ClassLoss=7.950 [Epoch 176][Batch 899], LR: 1.00E-03, Speed: 66.733 samples/sec, ObjLoss=21.094, BoxCenterLoss=14.538, BoxScaleLoss=4.877, ClassLoss=7.949 [Epoch 176][Batch 999], LR: 1.00E-03, Speed: 52.909 samples/sec, ObjLoss=21.093, BoxCenterLoss=14.538, BoxScaleLoss=4.877, ClassLoss=7.949 [Epoch 176][Batch 1099], LR: 1.00E-03, Speed: 98.690 samples/sec, ObjLoss=21.093, BoxCenterLoss=14.538, BoxScaleLoss=4.876, ClassLoss=7.948 [Epoch 176][Batch 1199], LR: 1.00E-03, Speed: 86.398 samples/sec, ObjLoss=21.092, BoxCenterLoss=14.538, BoxScaleLoss=4.876, ClassLoss=7.948 [Epoch 176][Batch 1299], LR: 1.00E-03, Speed: 69.125 samples/sec, ObjLoss=21.091, BoxCenterLoss=14.538, BoxScaleLoss=4.876, ClassLoss=7.947 [Epoch 176][Batch 1399], LR: 1.00E-03, Speed: 148.599 samples/sec, ObjLoss=21.091, BoxCenterLoss=14.538, BoxScaleLoss=4.876, ClassLoss=7.947 [Epoch 176][Batch 1499], LR: 1.00E-03, Speed: 48.660 samples/sec, ObjLoss=21.091, BoxCenterLoss=14.538, BoxScaleLoss=4.876, ClassLoss=7.946 [Epoch 176][Batch 1599], LR: 1.00E-03, Speed: 131.605 samples/sec, ObjLoss=21.090, BoxCenterLoss=14.538, BoxScaleLoss=4.876, ClassLoss=7.946 [Epoch 176][Batch 1699], LR: 1.00E-03, Speed: 111.870 samples/sec, ObjLoss=21.089, BoxCenterLoss=14.537, BoxScaleLoss=4.876, ClassLoss=7.945 [Epoch 176][Batch 1799], LR: 1.00E-03, Speed: 153.966 samples/sec, ObjLoss=21.089, BoxCenterLoss=14.537, BoxScaleLoss=4.876, ClassLoss=7.945 [Epoch 176] Training cost: 1659.021, ObjLoss=21.089, BoxCenterLoss=14.537, BoxScaleLoss=4.876, ClassLoss=7.945 [Epoch 176] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.413 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.160 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 ] = 0.203 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.293 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.273 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 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.403 person=32.8 bicycle=14.2 car=21.1 motorcycle=23.5 airplane=35.4 bus=41.1 train=39.9 truck=18.1 boat=11.4 traffic light=11.2 fire hydrant=41.1 stop sign=34.9 parking meter=19.0 bench=9.8 bird=15.7 cat=37.3 dog=26.7 horse=30.8 sheep=24.7 cow=29.1 elephant=35.4 bear=35.7 zebra=38.7 giraffe=40.9 backpack=5.3 umbrella=17.6 handbag=3.6 tie=14.2 suitcase=12.9 frisbee=30.6 skis=6.4 snowboard=13.4 sports ball=14.4 kite=21.5 baseball bat=12.0 baseball glove=15.0 skateboard=17.9 surfboard=17.3 tennis racket=18.9 bottle=15.3 wine glass=13.3 cup=20.7 fork=7.9 knife=4.0 spoon=2.4 bowl=21.4 banana=12.9 apple=7.6 sandwich=17.5 orange=16.0 broccoli=9.6 carrot=9.1 hot dog=17.7 pizza=25.7 donut=18.2 cake=15.9 chair=11.4 couch=22.5 potted plant=13.3 bed=26.1 dining table=12.9 toilet=31.0 tv=29.9 laptop=32.2 mouse=35.3 remote=7.8 keyboard=28.0 cell phone=12.9 microwave=23.8 oven=17.8 toaster=0.0 sink=17.4 refrigerator=29.0 book=3.8 clock=26.7 vase=19.2 scissors=10.1 teddy bear=22.4 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.5 [Epoch 177][Batch 99], LR: 1.00E-03, Speed: 103.704 samples/sec, ObjLoss=21.088, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.944 [Epoch 177][Batch 199], LR: 1.00E-03, Speed: 115.401 samples/sec, ObjLoss=21.087, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.944 [Epoch 177][Batch 299], LR: 1.00E-03, Speed: 63.931 samples/sec, ObjLoss=21.087, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.943 [Epoch 177][Batch 399], LR: 1.00E-03, Speed: 61.919 samples/sec, ObjLoss=21.086, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.943 [Epoch 177][Batch 499], LR: 1.00E-03, Speed: 71.592 samples/sec, ObjLoss=21.085, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.942 [Epoch 177][Batch 599], LR: 1.00E-03, Speed: 91.479 samples/sec, ObjLoss=21.085, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.942 [Epoch 177][Batch 699], LR: 1.00E-03, Speed: 65.865 samples/sec, ObjLoss=21.084, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.941 [Epoch 177][Batch 799], LR: 1.00E-03, Speed: 73.060 samples/sec, ObjLoss=21.084, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.941 [Epoch 177][Batch 899], LR: 1.00E-03, Speed: 60.439 samples/sec, ObjLoss=21.083, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.940 [Epoch 177][Batch 999], LR: 1.00E-03, Speed: 65.979 samples/sec, ObjLoss=21.083, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.940 [Epoch 177][Batch 1099], LR: 1.00E-03, Speed: 83.872 samples/sec, ObjLoss=21.082, BoxCenterLoss=14.537, BoxScaleLoss=4.875, ClassLoss=7.939 [Epoch 177][Batch 1199], LR: 1.00E-03, Speed: 81.343 samples/sec, ObjLoss=21.081, BoxCenterLoss=14.536, BoxScaleLoss=4.875, ClassLoss=7.939 [Epoch 177][Batch 1299], LR: 1.00E-03, Speed: 61.156 samples/sec, ObjLoss=21.080, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.939 [Epoch 177][Batch 1399], LR: 1.00E-03, Speed: 130.295 samples/sec, ObjLoss=21.080, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.938 [Epoch 177][Batch 1499], LR: 1.00E-03, Speed: 74.151 samples/sec, ObjLoss=21.079, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.938 [Epoch 177][Batch 1599], LR: 1.00E-03, Speed: 81.286 samples/sec, ObjLoss=21.079, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.937 [Epoch 177][Batch 1699], LR: 1.00E-03, Speed: 71.303 samples/sec, ObjLoss=21.078, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.937 [Epoch 177][Batch 1799], LR: 1.00E-03, Speed: 131.263 samples/sec, ObjLoss=21.078, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.936 [Epoch 177] Training cost: 1636.979, ObjLoss=21.078, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.936 [Epoch 177] 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 ] = 0.415 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.175 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 ] = 0.199 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 ] = 0.190 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 ] = 0.288 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 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423 person=32.8 bicycle=14.8 car=19.9 motorcycle=24.0 airplane=37.9 bus=40.5 train=32.9 truck=18.2 boat=10.0 traffic light=12.1 fire hydrant=39.4 stop sign=37.5 parking meter=24.6 bench=11.5 bird=15.4 cat=42.3 dog=36.6 horse=32.3 sheep=26.3 cow=29.9 elephant=40.1 bear=43.9 zebra=38.6 giraffe=39.0 backpack=4.2 umbrella=15.9 handbag=3.4 tie=13.6 suitcase=11.8 frisbee=28.3 skis=7.8 snowboard=12.0 sports ball=24.3 kite=20.8 baseball bat=7.4 baseball glove=14.8 skateboard=17.1 surfboard=16.8 tennis racket=18.8 bottle=13.6 wine glass=14.9 cup=20.6 fork=10.9 knife=4.9 spoon=3.2 bowl=18.3 banana=12.5 apple=7.0 sandwich=20.8 orange=15.5 broccoli=9.3 carrot=8.8 hot dog=15.0 pizza=28.9 donut=21.1 cake=17.3 chair=12.0 couch=26.7 potted plant=11.6 bed=23.7 dining table=11.9 toilet=36.5 tv=35.2 laptop=34.4 mouse=35.9 remote=8.5 keyboard=20.5 cell phone=15.1 microwave=24.5 oven=18.8 toaster=3.6 sink=17.6 refrigerator=28.8 book=5.2 clock=30.8 vase=19.0 scissors=15.7 teddy bear=23.3 hair drier=0.0 toothbrush=4.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.3 [Epoch 178][Batch 99], LR: 1.00E-03, Speed: 128.683 samples/sec, ObjLoss=21.077, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.936 [Epoch 178][Batch 199], LR: 1.00E-03, Speed: 92.355 samples/sec, ObjLoss=21.076, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.935 [Epoch 178][Batch 299], LR: 1.00E-03, Speed: 84.704 samples/sec, ObjLoss=21.076, BoxCenterLoss=14.536, BoxScaleLoss=4.874, ClassLoss=7.935 [Epoch 178][Batch 399], LR: 1.00E-03, Speed: 78.525 samples/sec, ObjLoss=21.075, BoxCenterLoss=14.536, BoxScaleLoss=4.873, ClassLoss=7.934 [Epoch 178][Batch 499], LR: 1.00E-03, Speed: 79.655 samples/sec, ObjLoss=21.074, BoxCenterLoss=14.536, BoxScaleLoss=4.873, ClassLoss=7.934 [Epoch 178][Batch 599], LR: 1.00E-03, Speed: 107.582 samples/sec, ObjLoss=21.074, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.933 [Epoch 178][Batch 699], LR: 1.00E-03, Speed: 93.530 samples/sec, ObjLoss=21.073, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.933 [Epoch 178][Batch 799], LR: 1.00E-03, Speed: 73.334 samples/sec, ObjLoss=21.073, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.932 [Epoch 178][Batch 899], LR: 1.00E-03, Speed: 92.493 samples/sec, ObjLoss=21.072, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.932 [Epoch 178][Batch 999], LR: 1.00E-03, Speed: 62.642 samples/sec, ObjLoss=21.072, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.931 [Epoch 178][Batch 1099], LR: 1.00E-03, Speed: 54.846 samples/sec, ObjLoss=21.071, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.931 [Epoch 178][Batch 1199], LR: 1.00E-03, Speed: 74.988 samples/sec, ObjLoss=21.071, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.930 [Epoch 178][Batch 1299], LR: 1.00E-03, Speed: 111.604 samples/sec, ObjLoss=21.070, BoxCenterLoss=14.535, BoxScaleLoss=4.873, ClassLoss=7.930 [Epoch 178][Batch 1399], LR: 1.00E-03, Speed: 56.483 samples/sec, ObjLoss=21.069, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.929 [Epoch 178][Batch 1499], LR: 1.00E-03, Speed: 60.689 samples/sec, ObjLoss=21.069, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.929 [Epoch 178][Batch 1599], LR: 1.00E-03, Speed: 68.387 samples/sec, ObjLoss=21.068, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.929 [Epoch 178][Batch 1699], LR: 1.00E-03, Speed: 63.150 samples/sec, ObjLoss=21.067, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.928 [Epoch 178][Batch 1799], LR: 1.00E-03, Speed: 90.972 samples/sec, ObjLoss=21.067, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.928 [Epoch 178] Training cost: 1669.748, ObjLoss=21.067, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.928 [Epoch 178] 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 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.165 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 ] = 0.197 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 ] = 0.190 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.278 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 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 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418 person=32.5 bicycle=14.9 car=22.2 motorcycle=24.9 airplane=40.2 bus=45.1 train=43.7 truck=18.7 boat=10.9 traffic light=12.4 fire hydrant=38.5 stop sign=37.2 parking meter=22.0 bench=12.2 bird=15.8 cat=38.5 dog=25.5 horse=32.1 sheep=27.0 cow=28.2 elephant=35.7 bear=37.1 zebra=38.5 giraffe=41.3 backpack=4.9 umbrella=19.2 handbag=3.8 tie=15.3 suitcase=15.3 frisbee=31.6 skis=7.9 snowboard=13.2 sports ball=19.5 kite=22.4 baseball bat=11.3 baseball glove=18.6 skateboard=22.6 surfboard=16.3 tennis racket=22.5 bottle=13.0 wine glass=16.0 cup=20.3 fork=8.0 knife=4.2 spoon=2.3 bowl=17.2 banana=10.5 apple=5.5 sandwich=15.4 orange=12.1 broccoli=10.1 carrot=8.6 hot dog=12.2 pizza=27.3 donut=21.9 cake=15.0 chair=12.7 couch=21.2 potted plant=9.8 bed=28.8 dining table=13.6 toilet=35.6 tv=31.2 laptop=31.7 mouse=30.3 remote=9.5 keyboard=24.4 cell phone=14.2 microwave=29.5 oven=19.9 toaster=0.0 sink=19.4 refrigerator=28.6 book=5.1 clock=26.5 vase=15.8 scissors=14.1 teddy bear=23.3 hair drier=0.0 toothbrush=2.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.1 [Epoch 179][Batch 99], LR: 1.00E-03, Speed: 127.908 samples/sec, ObjLoss=21.066, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.927 [Epoch 179][Batch 199], LR: 1.00E-03, Speed: 131.789 samples/sec, ObjLoss=21.066, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.926 [Epoch 179][Batch 299], LR: 1.00E-03, Speed: 125.910 samples/sec, ObjLoss=21.065, BoxCenterLoss=14.535, BoxScaleLoss=4.872, ClassLoss=7.926 [Epoch 179][Batch 399], LR: 1.00E-03, Speed: 102.595 samples/sec, ObjLoss=21.064, BoxCenterLoss=14.534, BoxScaleLoss=4.872, ClassLoss=7.925 [Epoch 179][Batch 499], LR: 1.00E-03, Speed: 115.386 samples/sec, ObjLoss=21.064, BoxCenterLoss=14.534, BoxScaleLoss=4.872, ClassLoss=7.925 [Epoch 179][Batch 599], LR: 1.00E-03, Speed: 70.997 samples/sec, ObjLoss=21.063, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.924 [Epoch 179][Batch 699], LR: 1.00E-03, Speed: 75.739 samples/sec, ObjLoss=21.062, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.924 [Epoch 179][Batch 799], LR: 1.00E-03, Speed: 55.905 samples/sec, ObjLoss=21.062, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.923 [Epoch 179][Batch 899], LR: 1.00E-03, Speed: 79.505 samples/sec, ObjLoss=21.061, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.923 [Epoch 179][Batch 999], LR: 1.00E-03, Speed: 61.482 samples/sec, ObjLoss=21.061, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.923 [Epoch 179][Batch 1099], LR: 1.00E-03, Speed: 80.880 samples/sec, ObjLoss=21.060, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.922 [Epoch 179][Batch 1199], LR: 1.00E-03, Speed: 58.031 samples/sec, ObjLoss=21.060, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.922 [Epoch 179][Batch 1299], LR: 1.00E-03, Speed: 100.684 samples/sec, ObjLoss=21.059, BoxCenterLoss=14.534, BoxScaleLoss=4.871, ClassLoss=7.921 [Epoch 179][Batch 1399], LR: 1.00E-03, Speed: 75.397 samples/sec, ObjLoss=21.059, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.921 [Epoch 179][Batch 1499], LR: 1.00E-03, Speed: 62.693 samples/sec, ObjLoss=21.058, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.920 [Epoch 179][Batch 1599], LR: 1.00E-03, Speed: 60.009 samples/sec, ObjLoss=21.058, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.920 [Epoch 179][Batch 1699], LR: 1.00E-03, Speed: 81.077 samples/sec, ObjLoss=21.057, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.919 [Epoch 179][Batch 1799], LR: 1.00E-03, Speed: 134.987 samples/sec, ObjLoss=21.057, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.918 [Epoch 179] Training cost: 1659.123, ObjLoss=21.056, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.918 [Epoch 179] 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 ] = 0.432 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.225 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 ] = 0.208 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 ] = 0.312 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 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.459 person=35.4 bicycle=16.1 car=21.4 motorcycle=27.7 airplane=41.9 bus=44.5 train=44.5 truck=20.3 boat=11.9 traffic light=9.6 fire hydrant=40.8 stop sign=40.1 parking meter=26.7 bench=12.6 bird=18.9 cat=46.9 dog=34.6 horse=30.7 sheep=30.3 cow=30.9 elephant=42.6 bear=46.2 zebra=39.7 giraffe=46.2 backpack=5.4 umbrella=20.0 handbag=4.4 tie=14.8 suitcase=16.7 frisbee=31.0 skis=8.9 snowboard=10.6 sports ball=22.6 kite=22.6 baseball bat=13.0 baseball glove=20.7 skateboard=24.3 surfboard=17.6 tennis racket=23.1 bottle=15.0 wine glass=16.2 cup=20.7 fork=10.4 knife=5.2 spoon=3.4 bowl=19.6 banana=12.9 apple=5.7 sandwich=16.8 orange=14.5 broccoli=11.3 carrot=9.8 hot dog=18.2 pizza=30.5 donut=25.5 cake=16.5 chair=13.8 couch=29.5 potted plant=12.1 bed=28.6 dining table=18.2 toilet=41.1 tv=38.5 laptop=36.1 mouse=32.2 remote=10.2 keyboard=26.9 cell phone=16.5 microwave=27.4 oven=18.7 toaster=0.0 sink=19.1 refrigerator=31.3 book=6.0 clock=28.8 vase=18.8 scissors=16.1 teddy bear=27.2 hair drier=0.0 toothbrush=6.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.1 [Epoch 180][Batch 99], LR: 1.00E-03, Speed: 154.887 samples/sec, ObjLoss=21.056, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.918 [Epoch 180][Batch 199], LR: 1.00E-03, Speed: 159.110 samples/sec, ObjLoss=21.055, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.917 [Epoch 180][Batch 299], LR: 1.00E-03, Speed: 75.741 samples/sec, ObjLoss=21.055, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.917 [Epoch 180][Batch 399], LR: 1.00E-03, Speed: 69.904 samples/sec, ObjLoss=21.054, BoxCenterLoss=14.534, BoxScaleLoss=4.870, ClassLoss=7.916 [Epoch 180][Batch 499], LR: 1.00E-03, Speed: 74.185 samples/sec, ObjLoss=21.054, BoxCenterLoss=14.533, BoxScaleLoss=4.870, ClassLoss=7.916 [Epoch 180][Batch 599], LR: 1.00E-03, Speed: 104.227 samples/sec, ObjLoss=21.053, BoxCenterLoss=14.533, BoxScaleLoss=4.870, ClassLoss=7.916 [Epoch 180][Batch 699], LR: 1.00E-03, Speed: 82.408 samples/sec, ObjLoss=21.052, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.915 [Epoch 180][Batch 799], LR: 1.00E-03, Speed: 71.901 samples/sec, ObjLoss=21.052, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.915 [Epoch 180][Batch 899], LR: 1.00E-03, Speed: 66.554 samples/sec, ObjLoss=21.051, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.914 [Epoch 180][Batch 999], LR: 1.00E-03, Speed: 89.200 samples/sec, ObjLoss=21.051, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.914 [Epoch 180][Batch 1099], LR: 1.00E-03, Speed: 98.539 samples/sec, ObjLoss=21.050, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.913 [Epoch 180][Batch 1199], LR: 1.00E-03, Speed: 84.629 samples/sec, ObjLoss=21.050, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.913 [Epoch 180][Batch 1299], LR: 1.00E-03, Speed: 68.841 samples/sec, ObjLoss=21.049, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.912 [Epoch 180][Batch 1399], LR: 1.00E-03, Speed: 91.906 samples/sec, ObjLoss=21.049, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.912 [Epoch 180][Batch 1499], LR: 1.00E-03, Speed: 66.867 samples/sec, ObjLoss=21.048, BoxCenterLoss=14.533, BoxScaleLoss=4.869, ClassLoss=7.911 [Epoch 180][Batch 1599], LR: 1.00E-03, Speed: 77.484 samples/sec, ObjLoss=21.048, BoxCenterLoss=14.533, BoxScaleLoss=4.868, ClassLoss=7.911 [Epoch 180][Batch 1699], LR: 1.00E-03, Speed: 101.556 samples/sec, ObjLoss=21.047, BoxCenterLoss=14.533, BoxScaleLoss=4.868, ClassLoss=7.910 [Epoch 180][Batch 1799], LR: 1.00E-03, Speed: 69.646 samples/sec, ObjLoss=21.046, BoxCenterLoss=14.533, BoxScaleLoss=4.868, ClassLoss=7.910 [Epoch 180] Training cost: 1694.328, ObjLoss=21.046, BoxCenterLoss=14.533, BoxScaleLoss=4.868, ClassLoss=7.910 [Epoch 180] 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 ] = 0.417 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.177 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 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 ] = 0.314 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 ] = 0.285 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 ] = 0.140 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 ] = 0.426 person=33.4 bicycle=15.5 car=20.5 motorcycle=24.6 airplane=34.0 bus=40.8 train=42.5 truck=19.3 boat=10.3 traffic light=11.8 fire hydrant=34.7 stop sign=40.9 parking meter=22.5 bench=11.0 bird=18.2 cat=42.6 dog=28.4 horse=34.2 sheep=26.5 cow=28.1 elephant=41.0 bear=42.8 zebra=37.7 giraffe=42.6 backpack=5.4 umbrella=18.7 handbag=4.0 tie=13.6 suitcase=16.0 frisbee=29.4 skis=9.2 snowboard=9.8 sports ball=19.2 kite=18.5 baseball bat=12.2 baseball glove=17.6 skateboard=24.0 surfboard=15.2 tennis racket=21.7 bottle=15.8 wine glass=15.0 cup=19.3 fork=10.1 knife=4.7 spoon=2.7 bowl=20.0 banana=9.9 apple=7.7 sandwich=19.1 orange=14.4 broccoli=10.0 carrot=9.5 hot dog=13.5 pizza=24.2 donut=20.5 cake=16.8 chair=12.2 couch=23.4 potted plant=14.1 bed=26.1 dining table=14.5 toilet=38.3 tv=33.3 laptop=36.0 mouse=29.4 remote=5.0 keyboard=21.4 cell phone=12.2 microwave=31.5 oven=19.0 toaster=0.0 sink=19.9 refrigerator=27.5 book=5.1 clock=31.7 vase=20.4 scissors=14.2 teddy bear=24.1 hair drier=0.0 toothbrush=3.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 181][Batch 99], LR: 1.00E-03, Speed: 162.839 samples/sec, ObjLoss=21.046, BoxCenterLoss=14.533, BoxScaleLoss=4.868, ClassLoss=7.909 [Epoch 181][Batch 199], LR: 1.00E-03, Speed: 117.509 samples/sec, ObjLoss=21.045, BoxCenterLoss=14.532, BoxScaleLoss=4.868, ClassLoss=7.909 [Epoch 181][Batch 299], LR: 1.00E-03, Speed: 81.388 samples/sec, ObjLoss=21.044, BoxCenterLoss=14.532, BoxScaleLoss=4.868, ClassLoss=7.908 [Epoch 181][Batch 399], LR: 1.00E-03, Speed: 53.053 samples/sec, ObjLoss=21.044, BoxCenterLoss=14.532, BoxScaleLoss=4.868, ClassLoss=7.908 [Epoch 181][Batch 499], LR: 1.00E-03, Speed: 110.666 samples/sec, ObjLoss=21.043, BoxCenterLoss=14.532, BoxScaleLoss=4.868, ClassLoss=7.907 [Epoch 181][Batch 599], LR: 1.00E-03, Speed: 70.565 samples/sec, ObjLoss=21.042, BoxCenterLoss=14.532, BoxScaleLoss=4.868, ClassLoss=7.907 [Epoch 181][Batch 699], LR: 1.00E-03, Speed: 57.095 samples/sec, ObjLoss=21.042, BoxCenterLoss=14.532, BoxScaleLoss=4.867, ClassLoss=7.907 [Epoch 181][Batch 799], LR: 1.00E-03, Speed: 91.578 samples/sec, ObjLoss=21.041, BoxCenterLoss=14.532, BoxScaleLoss=4.867, ClassLoss=7.906 [Epoch 181][Batch 899], LR: 1.00E-03, Speed: 82.748 samples/sec, ObjLoss=21.041, BoxCenterLoss=14.532, BoxScaleLoss=4.867, ClassLoss=7.906 [Epoch 181][Batch 999], LR: 1.00E-03, Speed: 118.555 samples/sec, ObjLoss=21.040, BoxCenterLoss=14.532, BoxScaleLoss=4.867, ClassLoss=7.905 [Epoch 181][Batch 1099], LR: 1.00E-03, Speed: 117.727 samples/sec, ObjLoss=21.040, BoxCenterLoss=14.532, BoxScaleLoss=4.867, ClassLoss=7.905 [Epoch 181][Batch 1199], LR: 1.00E-03, Speed: 77.813 samples/sec, ObjLoss=21.039, BoxCenterLoss=14.532, BoxScaleLoss=4.867, ClassLoss=7.904 [Epoch 181][Batch 1299], LR: 1.00E-03, Speed: 69.072 samples/sec, ObjLoss=21.038, BoxCenterLoss=14.531, BoxScaleLoss=4.867, ClassLoss=7.904 [Epoch 181][Batch 1399], LR: 1.00E-03, Speed: 90.477 samples/sec, ObjLoss=21.038, BoxCenterLoss=14.531, BoxScaleLoss=4.867, ClassLoss=7.903 [Epoch 181][Batch 1499], LR: 1.00E-03, Speed: 171.239 samples/sec, ObjLoss=21.037, BoxCenterLoss=14.531, BoxScaleLoss=4.867, ClassLoss=7.903 [Epoch 181][Batch 1599], LR: 1.00E-03, Speed: 57.553 samples/sec, ObjLoss=21.037, BoxCenterLoss=14.531, BoxScaleLoss=4.867, ClassLoss=7.903 [Epoch 181][Batch 1699], LR: 1.00E-03, Speed: 153.393 samples/sec, ObjLoss=21.036, BoxCenterLoss=14.531, BoxScaleLoss=4.867, ClassLoss=7.902 [Epoch 181][Batch 1799], LR: 1.00E-03, Speed: 156.120 samples/sec, ObjLoss=21.035, BoxCenterLoss=14.531, BoxScaleLoss=4.867, ClassLoss=7.902 [Epoch 181] Training cost: 1579.854, ObjLoss=21.035, BoxCenterLoss=14.531, BoxScaleLoss=4.867, ClassLoss=7.902 [Epoch 181] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.427 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.222 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.302 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 ] = 0.285 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 ] = 0.145 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415 person=31.2 bicycle=13.7 car=21.9 motorcycle=21.3 airplane=40.7 bus=37.4 train=46.8 truck=19.4 boat=13.4 traffic light=10.1 fire hydrant=32.8 stop sign=39.1 parking meter=19.7 bench=10.3 bird=15.0 cat=39.8 dog=33.6 horse=30.5 sheep=27.5 cow=30.4 elephant=39.9 bear=42.2 zebra=41.3 giraffe=27.6 backpack=4.3 umbrella=19.7 handbag=4.3 tie=14.3 suitcase=15.7 frisbee=33.2 skis=7.3 snowboard=12.9 sports ball=24.1 kite=23.1 baseball bat=11.7 baseball glove=17.4 skateboard=22.6 surfboard=15.8 tennis racket=22.0 bottle=16.4 wine glass=15.1 cup=19.7 fork=9.7 knife=4.2 spoon=2.8 bowl=21.1 banana=12.5 apple=6.0 sandwich=18.0 orange=14.7 broccoli=11.3 carrot=9.6 hot dog=13.6 pizza=32.5 donut=20.9 cake=17.7 chair=11.9 couch=22.6 potted plant=12.7 bed=28.2 dining table=14.2 toilet=28.9 tv=36.4 laptop=30.5 mouse=30.9 remote=8.1 keyboard=27.1 cell phone=17.2 microwave=28.9 oven=19.2 toaster=7.1 sink=17.9 refrigerator=23.1 book=5.8 clock=30.5 vase=18.6 scissors=13.0 teddy bear=19.0 hair drier=0.0 toothbrush=6.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 182][Batch 99], LR: 1.00E-03, Speed: 138.209 samples/sec, ObjLoss=21.035, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.901 [Epoch 182][Batch 199], LR: 1.00E-03, Speed: 114.973 samples/sec, ObjLoss=21.034, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.901 [Epoch 182][Batch 299], LR: 1.00E-03, Speed: 61.201 samples/sec, ObjLoss=21.034, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.900 [Epoch 182][Batch 399], LR: 1.00E-03, Speed: 71.412 samples/sec, ObjLoss=21.033, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.900 [Epoch 182][Batch 499], LR: 1.00E-03, Speed: 117.929 samples/sec, ObjLoss=21.033, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.899 [Epoch 182][Batch 599], LR: 1.00E-03, Speed: 92.630 samples/sec, ObjLoss=21.032, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.899 [Epoch 182][Batch 699], LR: 1.00E-03, Speed: 95.259 samples/sec, ObjLoss=21.032, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.898 [Epoch 182][Batch 799], LR: 1.00E-03, Speed: 62.863 samples/sec, ObjLoss=21.031, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.898 [Epoch 182][Batch 899], LR: 1.00E-03, Speed: 40.154 samples/sec, ObjLoss=21.031, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.897 [Epoch 182][Batch 999], LR: 1.00E-03, Speed: 116.962 samples/sec, ObjLoss=21.030, BoxCenterLoss=14.531, BoxScaleLoss=4.866, ClassLoss=7.897 [Epoch 182][Batch 1099], LR: 1.00E-03, Speed: 72.358 samples/sec, ObjLoss=21.029, BoxCenterLoss=14.531, BoxScaleLoss=4.865, ClassLoss=7.896 [Epoch 182][Batch 1199], LR: 1.00E-03, Speed: 119.597 samples/sec, ObjLoss=21.029, BoxCenterLoss=14.531, BoxScaleLoss=4.865, ClassLoss=7.896 [Epoch 182][Batch 1299], LR: 1.00E-03, Speed: 82.163 samples/sec, ObjLoss=21.029, BoxCenterLoss=14.531, BoxScaleLoss=4.865, ClassLoss=7.895 [Epoch 182][Batch 1399], LR: 1.00E-03, Speed: 90.805 samples/sec, ObjLoss=21.028, BoxCenterLoss=14.531, BoxScaleLoss=4.865, ClassLoss=7.895 [Epoch 182][Batch 1499], LR: 1.00E-03, Speed: 107.041 samples/sec, ObjLoss=21.027, BoxCenterLoss=14.531, BoxScaleLoss=4.865, ClassLoss=7.894 [Epoch 182][Batch 1599], LR: 1.00E-03, Speed: 64.045 samples/sec, ObjLoss=21.027, BoxCenterLoss=14.531, BoxScaleLoss=4.865, ClassLoss=7.894 [Epoch 182][Batch 1699], LR: 1.00E-03, Speed: 134.429 samples/sec, ObjLoss=21.026, BoxCenterLoss=14.530, BoxScaleLoss=4.865, ClassLoss=7.893 [Epoch 182][Batch 1799], LR: 1.00E-03, Speed: 150.710 samples/sec, ObjLoss=21.026, BoxCenterLoss=14.530, BoxScaleLoss=4.865, ClassLoss=7.893 [Epoch 182] Training cost: 1571.136, ObjLoss=21.025, BoxCenterLoss=14.530, BoxScaleLoss=4.865, ClassLoss=7.893 [Epoch 182] 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 ] = 0.416 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.187 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 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.317 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 ] = 0.284 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 ] = 0.117 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430 person=32.3 bicycle=14.1 car=18.4 motorcycle=25.5 airplane=32.2 bus=38.6 train=42.5 truck=18.1 boat=12.0 traffic light=10.8 fire hydrant=38.1 stop sign=36.9 parking meter=18.6 bench=11.2 bird=16.7 cat=43.0 dog=35.7 horse=32.1 sheep=27.0 cow=26.2 elephant=44.0 bear=41.5 zebra=40.5 giraffe=45.2 backpack=4.7 umbrella=20.7 handbag=4.7 tie=15.2 suitcase=14.5 frisbee=31.2 skis=8.6 snowboard=9.4 sports ball=16.9 kite=20.8 baseball bat=10.3 baseball glove=14.1 skateboard=23.0 surfboard=13.5 tennis racket=21.6 bottle=13.0 wine glass=15.0 cup=19.1 fork=8.5 knife=4.7 spoon=2.5 bowl=19.1 banana=12.7 apple=5.3 sandwich=20.3 orange=12.0 broccoli=11.5 carrot=8.3 hot dog=19.1 pizza=25.4 donut=22.8 cake=18.7 chair=12.0 couch=23.3 potted plant=13.2 bed=30.0 dining table=15.1 toilet=35.4 tv=36.6 laptop=34.1 mouse=33.2 remote=7.7 keyboard=26.0 cell phone=16.1 microwave=32.6 oven=20.8 toaster=7.1 sink=20.5 refrigerator=28.7 book=4.3 clock=28.1 vase=18.7 scissors=17.2 teddy bear=24.7 hair drier=0.0 toothbrush=1.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.7 [Epoch 183][Batch 99], LR: 1.00E-03, Speed: 146.046 samples/sec, ObjLoss=21.025, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.892 [Epoch 183][Batch 199], LR: 1.00E-03, Speed: 132.182 samples/sec, ObjLoss=21.024, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.892 [Epoch 183][Batch 299], LR: 1.00E-03, Speed: 133.386 samples/sec, ObjLoss=21.023, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.891 [Epoch 183][Batch 399], LR: 1.00E-03, Speed: 86.563 samples/sec, ObjLoss=21.023, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.891 [Epoch 183][Batch 499], LR: 1.00E-03, Speed: 88.437 samples/sec, ObjLoss=21.022, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.890 [Epoch 183][Batch 599], LR: 1.00E-03, Speed: 100.224 samples/sec, ObjLoss=21.022, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.890 [Epoch 183][Batch 699], LR: 1.00E-03, Speed: 69.645 samples/sec, ObjLoss=21.021, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.889 [Epoch 183][Batch 799], LR: 1.00E-03, Speed: 54.132 samples/sec, ObjLoss=21.021, BoxCenterLoss=14.530, BoxScaleLoss=4.864, ClassLoss=7.889 [Epoch 183][Batch 899], LR: 1.00E-03, Speed: 151.505 samples/sec, ObjLoss=21.020, BoxCenterLoss=14.530, BoxScaleLoss=4.863, ClassLoss=7.888 [Epoch 183][Batch 999], LR: 1.00E-03, Speed: 85.591 samples/sec, ObjLoss=21.020, BoxCenterLoss=14.530, BoxScaleLoss=4.863, ClassLoss=7.888 [Epoch 183][Batch 1099], LR: 1.00E-03, Speed: 94.704 samples/sec, ObjLoss=21.019, BoxCenterLoss=14.530, BoxScaleLoss=4.863, ClassLoss=7.887 [Epoch 183][Batch 1199], LR: 1.00E-03, Speed: 78.499 samples/sec, ObjLoss=21.019, BoxCenterLoss=14.529, BoxScaleLoss=4.863, ClassLoss=7.887 [Epoch 183][Batch 1299], LR: 1.00E-03, Speed: 133.921 samples/sec, ObjLoss=21.018, BoxCenterLoss=14.529, BoxScaleLoss=4.863, ClassLoss=7.886 [Epoch 183][Batch 1399], LR: 1.00E-03, Speed: 109.596 samples/sec, ObjLoss=21.017, BoxCenterLoss=14.529, BoxScaleLoss=4.863, ClassLoss=7.886 [Epoch 183][Batch 1499], LR: 1.00E-03, Speed: 73.370 samples/sec, ObjLoss=21.017, BoxCenterLoss=14.529, BoxScaleLoss=4.863, ClassLoss=7.885 [Epoch 183][Batch 1599], LR: 1.00E-03, Speed: 74.581 samples/sec, ObjLoss=21.016, BoxCenterLoss=14.529, BoxScaleLoss=4.863, ClassLoss=7.885 [Epoch 183][Batch 1699], LR: 1.00E-03, Speed: 71.722 samples/sec, ObjLoss=21.016, BoxCenterLoss=14.529, BoxScaleLoss=4.863, ClassLoss=7.885 [Epoch 183][Batch 1799], LR: 1.00E-03, Speed: 71.889 samples/sec, ObjLoss=21.015, BoxCenterLoss=14.529, BoxScaleLoss=4.863, ClassLoss=7.884 [Epoch 183] Training cost: 1656.891, ObjLoss=21.015, BoxCenterLoss=14.529, BoxScaleLoss=4.862, ClassLoss=7.884 [Epoch 183] 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 ] = 0.423 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.087 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 ] = 0.312 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 ] = 0.288 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 ] = 0.135 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 ] = 0.422 person=31.2 bicycle=14.2 car=21.1 motorcycle=23.3 airplane=40.7 bus=41.2 train=46.3 truck=19.9 boat=11.5 traffic light=9.4 fire hydrant=34.0 stop sign=35.7 parking meter=23.5 bench=11.3 bird=17.4 cat=41.5 dog=35.8 horse=29.9 sheep=26.9 cow=26.5 elephant=40.6 bear=40.7 zebra=44.2 giraffe=49.0 backpack=4.0 umbrella=18.9 handbag=3.1 tie=13.5 suitcase=12.7 frisbee=36.8 skis=7.3 snowboard=14.7 sports ball=22.8 kite=22.8 baseball bat=11.7 baseball glove=16.2 skateboard=22.6 surfboard=16.3 tennis racket=21.4 bottle=14.1 wine glass=16.8 cup=18.9 fork=11.2 knife=4.1 spoon=3.0 bowl=22.5 banana=13.0 apple=6.6 sandwich=21.1 orange=15.1 broccoli=10.5 carrot=8.7 hot dog=18.6 pizza=30.6 donut=19.6 cake=20.0 chair=12.6 couch=21.6 potted plant=10.0 bed=26.9 dining table=13.1 toilet=38.2 tv=32.3 laptop=32.0 mouse=30.2 remote=10.3 keyboard=30.3 cell phone=15.2 microwave=26.0 oven=17.6 toaster=0.0 sink=20.1 refrigerator=28.4 book=4.7 clock=27.5 vase=19.3 scissors=16.5 teddy bear=20.8 hair drier=0.0 toothbrush=6.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 184][Batch 99], LR: 1.00E-03, Speed: 141.023 samples/sec, ObjLoss=21.015, BoxCenterLoss=14.529, BoxScaleLoss=4.862, ClassLoss=7.883 [Epoch 184][Batch 199], LR: 1.00E-03, Speed: 73.513 samples/sec, ObjLoss=21.014, BoxCenterLoss=14.529, BoxScaleLoss=4.862, ClassLoss=7.883 [Epoch 184][Batch 299], LR: 1.00E-03, Speed: 149.992 samples/sec, ObjLoss=21.014, BoxCenterLoss=14.529, BoxScaleLoss=4.862, ClassLoss=7.883 [Epoch 184][Batch 399], LR: 1.00E-03, Speed: 56.717 samples/sec, ObjLoss=21.013, BoxCenterLoss=14.529, BoxScaleLoss=4.862, ClassLoss=7.882 [Epoch 184][Batch 499], LR: 1.00E-03, Speed: 81.754 samples/sec, ObjLoss=21.013, BoxCenterLoss=14.529, BoxScaleLoss=4.862, ClassLoss=7.882 [Epoch 184][Batch 599], LR: 1.00E-03, Speed: 108.242 samples/sec, ObjLoss=21.012, BoxCenterLoss=14.529, BoxScaleLoss=4.862, ClassLoss=7.881 [Epoch 184][Batch 699], LR: 1.00E-03, Speed: 150.601 samples/sec, ObjLoss=21.011, BoxCenterLoss=14.528, BoxScaleLoss=4.862, ClassLoss=7.881 [Epoch 184][Batch 799], LR: 1.00E-03, Speed: 89.897 samples/sec, ObjLoss=21.011, BoxCenterLoss=14.528, BoxScaleLoss=4.862, ClassLoss=7.880 [Epoch 184][Batch 899], LR: 1.00E-03, Speed: 79.936 samples/sec, ObjLoss=21.010, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.880 [Epoch 184][Batch 999], LR: 1.00E-03, Speed: 140.531 samples/sec, ObjLoss=21.009, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.879 [Epoch 184][Batch 1099], LR: 1.00E-03, Speed: 86.291 samples/sec, ObjLoss=21.009, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.879 [Epoch 184][Batch 1199], LR: 1.00E-03, Speed: 126.270 samples/sec, ObjLoss=21.008, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.878 [Epoch 184][Batch 1299], LR: 1.00E-03, Speed: 122.660 samples/sec, ObjLoss=21.008, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.878 [Epoch 184][Batch 1399], LR: 1.00E-03, Speed: 58.425 samples/sec, ObjLoss=21.007, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.877 [Epoch 184][Batch 1499], LR: 1.00E-03, Speed: 44.673 samples/sec, ObjLoss=21.007, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.877 [Epoch 184][Batch 1599], LR: 1.00E-03, Speed: 50.289 samples/sec, ObjLoss=21.006, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.877 [Epoch 184][Batch 1699], LR: 1.00E-03, Speed: 125.646 samples/sec, ObjLoss=21.006, BoxCenterLoss=14.528, BoxScaleLoss=4.861, ClassLoss=7.876 [Epoch 184][Batch 1799], LR: 1.00E-03, Speed: 76.253 samples/sec, ObjLoss=21.005, BoxCenterLoss=14.528, BoxScaleLoss=4.860, ClassLoss=7.876 [Epoch 184] Training cost: 1523.463, ObjLoss=21.005, BoxCenterLoss=14.528, BoxScaleLoss=4.860, ClassLoss=7.875 [Epoch 184] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.190 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.409 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.148 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.084 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 ] = 0.263 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.182 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 ] = 0.278 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 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367 person=33.2 bicycle=11.8 car=21.3 motorcycle=21.3 airplane=34.1 bus=40.0 train=40.2 truck=15.6 boat=10.8 traffic light=12.8 fire hydrant=26.2 stop sign=33.5 parking meter=21.6 bench=9.9 bird=16.2 cat=31.5 dog=30.2 horse=27.9 sheep=22.4 cow=25.1 elephant=40.1 bear=41.5 zebra=37.1 giraffe=36.2 backpack=4.7 umbrella=13.7 handbag=4.7 tie=13.8 suitcase=12.8 frisbee=32.1 skis=5.6 snowboard=12.4 sports ball=21.7 kite=19.2 baseball bat=9.3 baseball glove=18.1 skateboard=20.4 surfboard=13.5 tennis racket=22.0 bottle=15.3 wine glass=13.0 cup=19.9 fork=8.1 knife=3.3 spoon=2.5 bowl=21.4 banana=10.0 apple=7.4 sandwich=13.9 orange=13.0 broccoli=10.4 carrot=9.0 hot dog=15.7 pizza=24.0 donut=19.5 cake=15.0 chair=12.1 couch=23.9 potted plant=8.2 bed=25.4 dining table=12.0 toilet=26.4 tv=32.7 laptop=29.9 mouse=35.2 remote=9.6 keyboard=25.0 cell phone=16.0 microwave=31.7 oven=19.3 toaster=0.0 sink=16.2 refrigerator=25.8 book=4.7 clock=30.4 vase=16.8 scissors=14.6 teddy bear=14.6 hair drier=0.0 toothbrush=4.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.0 [Epoch 185][Batch 99], LR: 1.00E-03, Speed: 142.411 samples/sec, ObjLoss=21.005, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.875 [Epoch 185][Batch 199], LR: 1.00E-03, Speed: 153.494 samples/sec, ObjLoss=21.004, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.874 [Epoch 185][Batch 299], LR: 1.00E-03, Speed: 105.040 samples/sec, ObjLoss=21.003, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.874 [Epoch 185][Batch 399], LR: 1.00E-03, Speed: 61.158 samples/sec, ObjLoss=21.003, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.874 [Epoch 185][Batch 499], LR: 1.00E-03, Speed: 80.410 samples/sec, ObjLoss=21.002, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.873 [Epoch 185][Batch 599], LR: 1.00E-03, Speed: 42.948 samples/sec, ObjLoss=21.002, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.872 [Epoch 185][Batch 699], LR: 1.00E-03, Speed: 142.444 samples/sec, ObjLoss=21.001, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.872 [Epoch 185][Batch 799], LR: 1.00E-03, Speed: 112.790 samples/sec, ObjLoss=21.001, BoxCenterLoss=14.527, BoxScaleLoss=4.860, ClassLoss=7.872 [Epoch 185][Batch 899], LR: 1.00E-03, Speed: 68.162 samples/sec, ObjLoss=21.000, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.871 [Epoch 185][Batch 999], LR: 1.00E-03, Speed: 69.896 samples/sec, ObjLoss=21.000, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.871 [Epoch 185][Batch 1099], LR: 1.00E-03, Speed: 53.736 samples/sec, ObjLoss=20.999, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.870 [Epoch 185][Batch 1199], LR: 1.00E-03, Speed: 86.897 samples/sec, ObjLoss=20.999, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.870 [Epoch 185][Batch 1299], LR: 1.00E-03, Speed: 138.822 samples/sec, ObjLoss=20.998, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.869 [Epoch 185][Batch 1399], LR: 1.00E-03, Speed: 104.458 samples/sec, ObjLoss=20.998, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.869 [Epoch 185][Batch 1499], LR: 1.00E-03, Speed: 111.205 samples/sec, ObjLoss=20.997, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.868 [Epoch 185][Batch 1599], LR: 1.00E-03, Speed: 61.821 samples/sec, ObjLoss=20.997, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.868 [Epoch 185][Batch 1699], LR: 1.00E-03, Speed: 52.557 samples/sec, ObjLoss=20.996, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.868 [Epoch 185][Batch 1799], LR: 1.00E-03, Speed: 146.790 samples/sec, ObjLoss=20.996, BoxCenterLoss=14.526, BoxScaleLoss=4.859, ClassLoss=7.867 [Epoch 185] Training cost: 1575.998, ObjLoss=20.996, BoxCenterLoss=14.527, BoxScaleLoss=4.859, ClassLoss=7.867 [Epoch 185] 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 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 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 ] = 0.315 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 ] = 0.287 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 ] = 0.135 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 ] = 0.431 person=34.5 bicycle=14.7 car=22.3 motorcycle=24.5 airplane=34.5 bus=41.6 train=41.7 truck=19.7 boat=13.5 traffic light=13.6 fire hydrant=39.2 stop sign=35.8 parking meter=21.0 bench=9.9 bird=17.8 cat=42.4 dog=36.3 horse=32.9 sheep=27.4 cow=34.0 elephant=35.6 bear=39.0 zebra=40.1 giraffe=45.3 backpack=4.8 umbrella=17.7 handbag=5.4 tie=14.4 suitcase=15.4 frisbee=34.6 skis=6.3 snowboard=10.0 sports ball=23.3 kite=23.4 baseball bat=10.0 baseball glove=18.5 skateboard=21.0 surfboard=14.5 tennis racket=24.5 bottle=14.6 wine glass=16.5 cup=18.2 fork=10.8 knife=5.0 spoon=3.3 bowl=18.5 banana=11.3 apple=5.1 sandwich=20.8 orange=17.4 broccoli=10.5 carrot=9.1 hot dog=15.4 pizza=24.4 donut=20.3 cake=16.9 chair=13.1 couch=25.3 potted plant=12.6 bed=23.2 dining table=13.8 toilet=38.5 tv=35.5 laptop=32.4 mouse=30.3 remote=8.7 keyboard=25.7 cell phone=16.5 microwave=26.7 oven=17.7 toaster=0.0 sink=16.2 refrigerator=28.1 book=5.9 clock=25.9 vase=16.8 scissors=14.1 teddy bear=26.5 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.7 [Epoch 186][Batch 99], LR: 1.00E-03, Speed: 149.733 samples/sec, ObjLoss=20.995, BoxCenterLoss=14.526, BoxScaleLoss=4.859, ClassLoss=7.867 [Epoch 186][Batch 199], LR: 1.00E-03, Speed: 153.829 samples/sec, ObjLoss=20.994, BoxCenterLoss=14.526, BoxScaleLoss=4.859, ClassLoss=7.866 [Epoch 186][Batch 299], LR: 1.00E-03, Speed: 58.400 samples/sec, ObjLoss=20.994, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.866 [Epoch 186][Batch 399], LR: 1.00E-03, Speed: 67.217 samples/sec, ObjLoss=20.994, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.865 [Epoch 186][Batch 499], LR: 1.00E-03, Speed: 68.792 samples/sec, ObjLoss=20.993, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.865 [Epoch 186][Batch 599], LR: 1.00E-03, Speed: 54.504 samples/sec, ObjLoss=20.992, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.864 [Epoch 186][Batch 699], LR: 1.00E-03, Speed: 145.807 samples/sec, ObjLoss=20.992, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.864 [Epoch 186][Batch 799], LR: 1.00E-03, Speed: 54.574 samples/sec, ObjLoss=20.991, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.864 [Epoch 186][Batch 899], LR: 1.00E-03, Speed: 78.358 samples/sec, ObjLoss=20.991, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.863 [Epoch 186][Batch 999], LR: 1.00E-03, Speed: 100.746 samples/sec, ObjLoss=20.990, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.863 [Epoch 186][Batch 1099], LR: 1.00E-03, Speed: 92.894 samples/sec, ObjLoss=20.990, BoxCenterLoss=14.526, BoxScaleLoss=4.858, ClassLoss=7.862 [Epoch 186][Batch 1199], LR: 1.00E-03, Speed: 146.868 samples/sec, ObjLoss=20.989, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.862 [Epoch 186][Batch 1299], LR: 1.00E-03, Speed: 109.355 samples/sec, ObjLoss=20.989, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.861 [Epoch 186][Batch 1399], LR: 1.00E-03, Speed: 88.859 samples/sec, ObjLoss=20.988, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.861 [Epoch 186][Batch 1499], LR: 1.00E-03, Speed: 92.854 samples/sec, ObjLoss=20.988, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.860 [Epoch 186][Batch 1599], LR: 1.00E-03, Speed: 96.946 samples/sec, ObjLoss=20.987, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.860 [Epoch 186][Batch 1699], LR: 1.00E-03, Speed: 85.331 samples/sec, ObjLoss=20.987, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.860 [Epoch 186][Batch 1799], LR: 1.00E-03, Speed: 147.057 samples/sec, ObjLoss=20.986, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.859 [Epoch 186] Training cost: 1685.107, ObjLoss=20.986, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.859 [Epoch 186] 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 ] = 0.424 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.186 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 ] = 0.219 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.427 person=33.0 bicycle=16.7 car=20.3 motorcycle=25.6 airplane=37.6 bus=37.1 train=44.0 truck=18.9 boat=12.0 traffic light=13.0 fire hydrant=40.8 stop sign=37.3 parking meter=26.2 bench=11.4 bird=16.2 cat=40.3 dog=32.6 horse=31.7 sheep=25.4 cow=28.9 elephant=39.2 bear=45.7 zebra=42.4 giraffe=40.7 backpack=5.0 umbrella=20.1 handbag=4.9 tie=12.5 suitcase=13.2 frisbee=36.6 skis=7.3 snowboard=12.4 sports ball=13.8 kite=23.9 baseball bat=9.3 baseball glove=15.1 skateboard=23.4 surfboard=17.1 tennis racket=18.7 bottle=15.8 wine glass=16.6 cup=21.5 fork=10.1 knife=3.5 spoon=3.7 bowl=20.2 banana=11.7 apple=5.6 sandwich=19.9 orange=15.4 broccoli=11.7 carrot=10.0 hot dog=13.6 pizza=30.4 donut=20.5 cake=17.9 chair=13.2 couch=27.5 potted plant=11.8 bed=28.1 dining table=14.5 toilet=36.6 tv=29.3 laptop=32.6 mouse=37.7 remote=9.5 keyboard=28.7 cell phone=15.6 microwave=27.9 oven=18.8 toaster=5.9 sink=19.4 refrigerator=29.8 book=4.3 clock=27.4 vase=18.7 scissors=17.2 teddy bear=24.5 hair drier=0.0 toothbrush=2.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.0 [Epoch 187][Batch 99], LR: 1.00E-03, Speed: 140.598 samples/sec, ObjLoss=20.986, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.859 [Epoch 187][Batch 199], LR: 1.00E-03, Speed: 75.270 samples/sec, ObjLoss=20.985, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.858 [Epoch 187][Batch 299], LR: 1.00E-03, Speed: 71.521 samples/sec, ObjLoss=20.985, BoxCenterLoss=14.526, BoxScaleLoss=4.857, ClassLoss=7.858 [Epoch 187][Batch 399], LR: 1.00E-03, Speed: 139.423 samples/sec, ObjLoss=20.984, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.857 [Epoch 187][Batch 499], LR: 1.00E-03, Speed: 134.891 samples/sec, ObjLoss=20.983, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.857 [Epoch 187][Batch 599], LR: 1.00E-03, Speed: 74.151 samples/sec, ObjLoss=20.983, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.856 [Epoch 187][Batch 699], LR: 1.00E-03, Speed: 93.527 samples/sec, ObjLoss=20.982, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.856 [Epoch 187][Batch 799], LR: 1.00E-03, Speed: 67.521 samples/sec, ObjLoss=20.982, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.855 [Epoch 187][Batch 899], LR: 1.00E-03, Speed: 67.576 samples/sec, ObjLoss=20.981, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.855 [Epoch 187][Batch 999], LR: 1.00E-03, Speed: 68.485 samples/sec, ObjLoss=20.981, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.854 [Epoch 187][Batch 1099], LR: 1.00E-03, Speed: 93.792 samples/sec, ObjLoss=20.980, BoxCenterLoss=14.525, BoxScaleLoss=4.856, ClassLoss=7.854 [Epoch 187][Batch 1199], LR: 1.00E-03, Speed: 127.414 samples/sec, ObjLoss=20.980, BoxCenterLoss=14.525, BoxScaleLoss=4.855, ClassLoss=7.853 [Epoch 187][Batch 1299], LR: 1.00E-03, Speed: 57.709 samples/sec, ObjLoss=20.979, BoxCenterLoss=14.525, BoxScaleLoss=4.855, ClassLoss=7.853 [Epoch 187][Batch 1399], LR: 1.00E-03, Speed: 76.691 samples/sec, ObjLoss=20.978, BoxCenterLoss=14.525, BoxScaleLoss=4.855, ClassLoss=7.853 [Epoch 187][Batch 1499], LR: 1.00E-03, Speed: 64.843 samples/sec, ObjLoss=20.978, BoxCenterLoss=14.525, BoxScaleLoss=4.855, ClassLoss=7.852 [Epoch 187][Batch 1599], LR: 1.00E-03, Speed: 74.048 samples/sec, ObjLoss=20.977, BoxCenterLoss=14.525, BoxScaleLoss=4.855, ClassLoss=7.852 [Epoch 187][Batch 1699], LR: 1.00E-03, Speed: 85.771 samples/sec, ObjLoss=20.977, BoxCenterLoss=14.525, BoxScaleLoss=4.855, ClassLoss=7.851 [Epoch 187][Batch 1799], LR: 1.00E-03, Speed: 67.610 samples/sec, ObjLoss=20.976, BoxCenterLoss=14.524, BoxScaleLoss=4.855, ClassLoss=7.851 [Epoch 187] Training cost: 1673.046, ObjLoss=20.976, BoxCenterLoss=14.524, BoxScaleLoss=4.855, ClassLoss=7.851 [Epoch 187] 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 ] = 0.419 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.207 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 ] = 0.195 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 ] = 0.295 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 ] = 0.297 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 person=32.8 bicycle=14.1 car=23.1 motorcycle=27.5 airplane=40.1 bus=43.1 train=40.3 truck=19.6 boat=11.8 traffic light=12.0 fire hydrant=30.4 stop sign=35.3 parking meter=16.8 bench=11.0 bird=18.2 cat=44.6 dog=33.4 horse=31.3 sheep=25.2 cow=28.6 elephant=39.0 bear=43.1 zebra=38.9 giraffe=43.7 backpack=4.8 umbrella=19.9 handbag=4.3 tie=11.1 suitcase=16.7 frisbee=33.3 skis=7.6 snowboard=11.8 sports ball=23.2 kite=21.5 baseball bat=10.6 baseball glove=18.0 skateboard=22.7 surfboard=17.4 tennis racket=20.8 bottle=17.2 wine glass=15.8 cup=19.3 fork=11.9 knife=3.8 spoon=3.9 bowl=20.8 banana=11.7 apple=6.7 sandwich=17.8 orange=18.0 broccoli=8.5 carrot=9.0 hot dog=15.0 pizza=26.2 donut=19.5 cake=16.9 chair=12.7 couch=28.1 potted plant=11.1 bed=27.3 dining table=13.1 toilet=38.3 tv=37.2 laptop=36.5 mouse=33.6 remote=8.1 keyboard=20.7 cell phone=15.8 microwave=26.3 oven=19.6 toaster=0.0 sink=16.0 refrigerator=29.2 book=4.1 clock=30.0 vase=19.3 scissors=16.7 teddy bear=27.5 hair drier=0.0 toothbrush=2.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 188][Batch 99], LR: 1.00E-03, Speed: 141.383 samples/sec, ObjLoss=20.976, BoxCenterLoss=14.524, BoxScaleLoss=4.855, ClassLoss=7.850 [Epoch 188][Batch 199], LR: 1.00E-03, Speed: 122.194 samples/sec, ObjLoss=20.975, BoxCenterLoss=14.524, BoxScaleLoss=4.855, ClassLoss=7.850 [Epoch 188][Batch 299], LR: 1.00E-03, Speed: 94.406 samples/sec, ObjLoss=20.975, BoxCenterLoss=14.524, BoxScaleLoss=4.855, ClassLoss=7.849 [Epoch 188][Batch 399], LR: 1.00E-03, Speed: 72.668 samples/sec, ObjLoss=20.974, BoxCenterLoss=14.524, BoxScaleLoss=4.855, ClassLoss=7.849 [Epoch 188][Batch 499], LR: 1.00E-03, Speed: 70.752 samples/sec, ObjLoss=20.974, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.849 [Epoch 188][Batch 599], LR: 1.00E-03, Speed: 53.941 samples/sec, ObjLoss=20.973, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.848 [Epoch 188][Batch 699], LR: 1.00E-03, Speed: 76.991 samples/sec, ObjLoss=20.973, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.848 [Epoch 188][Batch 799], LR: 1.00E-03, Speed: 48.766 samples/sec, ObjLoss=20.972, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.847 [Epoch 188][Batch 899], LR: 1.00E-03, Speed: 81.422 samples/sec, ObjLoss=20.971, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.847 [Epoch 188][Batch 999], LR: 1.00E-03, Speed: 76.294 samples/sec, ObjLoss=20.971, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.847 [Epoch 188][Batch 1099], LR: 1.00E-03, Speed: 156.386 samples/sec, ObjLoss=20.970, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.846 [Epoch 188][Batch 1199], LR: 1.00E-03, Speed: 62.227 samples/sec, ObjLoss=20.969, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.846 [Epoch 188][Batch 1299], LR: 1.00E-03, Speed: 65.800 samples/sec, ObjLoss=20.969, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.846 [Epoch 188][Batch 1399], LR: 1.00E-03, Speed: 62.814 samples/sec, ObjLoss=20.968, BoxCenterLoss=14.523, BoxScaleLoss=4.854, ClassLoss=7.845 [Epoch 188][Batch 1499], LR: 1.00E-03, Speed: 101.660 samples/sec, ObjLoss=20.968, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.845 [Epoch 188][Batch 1599], LR: 1.00E-03, Speed: 117.050 samples/sec, ObjLoss=20.967, BoxCenterLoss=14.524, BoxScaleLoss=4.854, ClassLoss=7.844 [Epoch 188][Batch 1699], LR: 1.00E-03, Speed: 44.899 samples/sec, ObjLoss=20.967, BoxCenterLoss=14.523, BoxScaleLoss=4.854, ClassLoss=7.844 [Epoch 188][Batch 1799], LR: 1.00E-03, Speed: 86.138 samples/sec, ObjLoss=20.966, BoxCenterLoss=14.523, BoxScaleLoss=4.854, ClassLoss=7.843 [Epoch 188] Training cost: 1614.042, ObjLoss=20.966, BoxCenterLoss=14.523, BoxScaleLoss=4.854, ClassLoss=7.843 [Epoch 188] 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 ] = 0.185 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 ] = 0.206 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 ] = 0.199 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 ] = 0.295 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 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426 person=31.1 bicycle=13.7 car=22.8 motorcycle=23.9 airplane=36.5 bus=39.3 train=41.8 truck=20.5 boat=11.5 traffic light=12.2 fire hydrant=34.1 stop sign=39.1 parking meter=20.1 bench=10.3 bird=14.7 cat=44.6 dog=31.5 horse=32.1 sheep=27.8 cow=29.9 elephant=37.2 bear=43.1 zebra=44.5 giraffe=41.5 backpack=4.6 umbrella=17.5 handbag=4.2 tie=14.4 suitcase=14.8 frisbee=32.0 skis=8.6 snowboard=13.5 sports ball=21.4 kite=23.2 baseball bat=13.3 baseball glove=16.5 skateboard=22.9 surfboard=15.0 tennis racket=19.8 bottle=14.2 wine glass=14.7 cup=20.6 fork=10.1 knife=4.5 spoon=3.5 bowl=19.2 banana=13.1 apple=4.7 sandwich=16.9 orange=11.8 broccoli=11.9 carrot=9.5 hot dog=15.1 pizza=28.9 donut=20.1 cake=16.9 chair=12.9 couch=27.7 potted plant=10.3 bed=29.1 dining table=17.6 toilet=36.0 tv=36.4 laptop=34.5 mouse=33.2 remote=9.7 keyboard=27.1 cell phone=13.8 microwave=27.5 oven=22.7 toaster=0.0 sink=18.8 refrigerator=30.2 book=5.6 clock=28.6 vase=17.0 scissors=18.8 teddy bear=24.8 hair drier=0.0 toothbrush=3.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.8 [Epoch 189][Batch 99], LR: 1.00E-03, Speed: 112.319 samples/sec, ObjLoss=20.966, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.843 [Epoch 189][Batch 199], LR: 1.00E-03, Speed: 72.080 samples/sec, ObjLoss=20.965, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.842 [Epoch 189][Batch 299], LR: 1.00E-03, Speed: 62.239 samples/sec, ObjLoss=20.965, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.842 [Epoch 189][Batch 399], LR: 1.00E-03, Speed: 60.498 samples/sec, ObjLoss=20.964, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.841 [Epoch 189][Batch 499], LR: 1.00E-03, Speed: 130.748 samples/sec, ObjLoss=20.963, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.841 [Epoch 189][Batch 599], LR: 1.00E-03, Speed: 69.658 samples/sec, ObjLoss=20.963, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.841 [Epoch 189][Batch 699], LR: 1.00E-03, Speed: 41.566 samples/sec, ObjLoss=20.962, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.840 [Epoch 189][Batch 799], LR: 1.00E-03, Speed: 85.428 samples/sec, ObjLoss=20.962, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.840 [Epoch 189][Batch 899], LR: 1.00E-03, Speed: 109.414 samples/sec, ObjLoss=20.961, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.839 [Epoch 189][Batch 999], LR: 1.00E-03, Speed: 80.561 samples/sec, ObjLoss=20.961, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.839 [Epoch 189][Batch 1099], LR: 1.00E-03, Speed: 96.309 samples/sec, ObjLoss=20.960, BoxCenterLoss=14.523, BoxScaleLoss=4.853, ClassLoss=7.839 [Epoch 189][Batch 1199], LR: 1.00E-03, Speed: 64.586 samples/sec, ObjLoss=20.960, BoxCenterLoss=14.523, BoxScaleLoss=4.852, ClassLoss=7.838 [Epoch 189][Batch 1299], LR: 1.00E-03, Speed: 85.037 samples/sec, ObjLoss=20.959, BoxCenterLoss=14.523, BoxScaleLoss=4.852, ClassLoss=7.838 [Epoch 189][Batch 1399], LR: 1.00E-03, Speed: 46.747 samples/sec, ObjLoss=20.959, BoxCenterLoss=14.523, BoxScaleLoss=4.852, ClassLoss=7.837 [Epoch 189][Batch 1499], LR: 1.00E-03, Speed: 86.793 samples/sec, ObjLoss=20.958, BoxCenterLoss=14.522, BoxScaleLoss=4.852, ClassLoss=7.837 [Epoch 189][Batch 1599], LR: 1.00E-03, Speed: 80.381 samples/sec, ObjLoss=20.958, BoxCenterLoss=14.523, BoxScaleLoss=4.852, ClassLoss=7.836 [Epoch 189][Batch 1699], LR: 1.00E-03, Speed: 68.302 samples/sec, ObjLoss=20.957, BoxCenterLoss=14.522, BoxScaleLoss=4.852, ClassLoss=7.836 [Epoch 189][Batch 1799], LR: 1.00E-03, Speed: 49.512 samples/sec, ObjLoss=20.957, BoxCenterLoss=14.522, BoxScaleLoss=4.852, ClassLoss=7.835 [Epoch 189] Training cost: 1693.931, ObjLoss=20.957, BoxCenterLoss=14.522, BoxScaleLoss=4.852, ClassLoss=7.835 [Epoch 189] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 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 ] = 0.317 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 ] = 0.290 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 ] = 0.130 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 ] = 0.435 person=34.4 bicycle=15.0 car=23.2 motorcycle=24.8 airplane=34.2 bus=41.8 train=44.7 truck=20.8 boat=11.2 traffic light=12.2 fire hydrant=38.6 stop sign=39.4 parking meter=18.7 bench=13.1 bird=16.9 cat=43.6 dog=35.3 horse=30.9 sheep=29.9 cow=31.4 elephant=40.5 bear=44.1 zebra=43.4 giraffe=44.9 backpack=5.2 umbrella=17.0 handbag=4.4 tie=14.6 suitcase=15.3 frisbee=29.9 skis=8.2 snowboard=12.8 sports ball=18.7 kite=20.1 baseball bat=10.6 baseball glove=20.7 skateboard=22.8 surfboard=17.7 tennis racket=23.1 bottle=17.7 wine glass=15.4 cup=21.2 fork=10.4 knife=4.8 spoon=3.3 bowl=21.1 banana=11.9 apple=6.1 sandwich=19.3 orange=15.9 broccoli=9.7 carrot=7.7 hot dog=17.7 pizza=26.1 donut=21.1 cake=17.4 chair=13.1 couch=25.9 potted plant=10.7 bed=24.1 dining table=13.1 toilet=39.6 tv=33.3 laptop=32.1 mouse=31.1 remote=9.1 keyboard=25.0 cell phone=15.4 microwave=29.4 oven=18.3 toaster=1.7 sink=22.1 refrigerator=32.3 book=4.6 clock=31.0 vase=17.2 scissors=16.7 teddy bear=22.1 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 190][Batch 99], LR: 1.00E-03, Speed: 130.629 samples/sec, ObjLoss=20.956, BoxCenterLoss=14.522, BoxScaleLoss=4.852, ClassLoss=7.835 [Epoch 190][Batch 199], LR: 1.00E-03, Speed: 78.775 samples/sec, ObjLoss=20.955, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.834 [Epoch 190][Batch 299], LR: 1.00E-03, Speed: 92.705 samples/sec, ObjLoss=20.955, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.834 [Epoch 190][Batch 399], LR: 1.00E-03, Speed: 79.362 samples/sec, ObjLoss=20.954, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.833 [Epoch 190][Batch 499], LR: 1.00E-03, Speed: 82.153 samples/sec, ObjLoss=20.954, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.833 [Epoch 190][Batch 599], LR: 1.00E-03, Speed: 72.845 samples/sec, ObjLoss=20.953, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.832 [Epoch 190][Batch 699], LR: 1.00E-03, Speed: 72.494 samples/sec, ObjLoss=20.953, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.832 [Epoch 190][Batch 799], LR: 1.00E-03, Speed: 75.407 samples/sec, ObjLoss=20.952, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.832 [Epoch 190][Batch 899], LR: 1.00E-03, Speed: 59.929 samples/sec, ObjLoss=20.952, BoxCenterLoss=14.522, BoxScaleLoss=4.851, ClassLoss=7.831 [Epoch 190][Batch 999], LR: 1.00E-03, Speed: 122.790 samples/sec, ObjLoss=20.951, BoxCenterLoss=14.522, BoxScaleLoss=4.850, ClassLoss=7.831 [Epoch 190][Batch 1099], LR: 1.00E-03, Speed: 54.637 samples/sec, ObjLoss=20.951, BoxCenterLoss=14.522, BoxScaleLoss=4.850, ClassLoss=7.830 [Epoch 190][Batch 1199], LR: 1.00E-03, Speed: 93.745 samples/sec, ObjLoss=20.950, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.830 [Epoch 190][Batch 1299], LR: 1.00E-03, Speed: 94.134 samples/sec, ObjLoss=20.949, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.829 [Epoch 190][Batch 1399], LR: 1.00E-03, Speed: 75.587 samples/sec, ObjLoss=20.949, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.829 [Epoch 190][Batch 1499], LR: 1.00E-03, Speed: 47.203 samples/sec, ObjLoss=20.948, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.829 [Epoch 190][Batch 1599], LR: 1.00E-03, Speed: 102.732 samples/sec, ObjLoss=20.948, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.828 [Epoch 190][Batch 1699], LR: 1.00E-03, Speed: 76.503 samples/sec, ObjLoss=20.947, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.828 [Epoch 190][Batch 1799], LR: 1.00E-03, Speed: 168.819 samples/sec, ObjLoss=20.947, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.827 [Epoch 190] Training cost: 1638.768, ObjLoss=20.947, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.827 [Epoch 190] 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 ] = 0.415 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.170 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 ] = 0.197 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 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 ] = 0.293 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 ] = 0.282 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 person=33.4 bicycle=15.6 car=20.8 motorcycle=23.4 airplane=35.4 bus=41.2 train=44.2 truck=16.8 boat=9.6 traffic light=13.8 fire hydrant=40.2 stop sign=32.3 parking meter=22.6 bench=10.8 bird=16.0 cat=43.2 dog=32.9 horse=30.5 sheep=21.0 cow=29.5 elephant=37.6 bear=38.3 zebra=38.7 giraffe=41.4 backpack=4.2 umbrella=17.3 handbag=3.9 tie=15.4 suitcase=17.8 frisbee=28.2 skis=6.8 snowboard=8.0 sports ball=18.6 kite=23.7 baseball bat=9.1 baseball glove=15.7 skateboard=23.5 surfboard=15.4 tennis racket=19.8 bottle=14.7 wine glass=15.2 cup=19.8 fork=10.1 knife=3.5 spoon=2.4 bowl=20.2 banana=10.7 apple=7.0 sandwich=20.8 orange=17.1 broccoli=8.9 carrot=8.1 hot dog=13.8 pizza=22.0 donut=24.8 cake=15.1 chair=12.6 couch=27.7 potted plant=9.9 bed=32.5 dining table=17.4 toilet=35.8 tv=32.2 laptop=34.4 mouse=32.8 remote=7.8 keyboard=22.5 cell phone=13.7 microwave=17.5 oven=20.3 toaster=1.8 sink=17.2 refrigerator=29.3 book=5.3 clock=28.1 vase=17.8 scissors=11.2 teddy bear=23.5 hair drier=0.0 toothbrush=1.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.0 [Epoch 191][Batch 99], LR: 1.00E-03, Speed: 156.847 samples/sec, ObjLoss=20.946, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.827 [Epoch 191][Batch 199], LR: 1.00E-03, Speed: 80.772 samples/sec, ObjLoss=20.946, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.826 [Epoch 191][Batch 299], LR: 1.00E-03, Speed: 97.034 samples/sec, ObjLoss=20.945, BoxCenterLoss=14.521, BoxScaleLoss=4.850, ClassLoss=7.826 [Epoch 191][Batch 399], LR: 1.00E-03, Speed: 74.337 samples/sec, ObjLoss=20.945, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.825 [Epoch 191][Batch 499], LR: 1.00E-03, Speed: 67.940 samples/sec, ObjLoss=20.944, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.825 [Epoch 191][Batch 599], LR: 1.00E-03, Speed: 88.171 samples/sec, ObjLoss=20.944, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.824 [Epoch 191][Batch 699], LR: 1.00E-03, Speed: 74.128 samples/sec, ObjLoss=20.943, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.824 [Epoch 191][Batch 799], LR: 1.00E-03, Speed: 48.713 samples/sec, ObjLoss=20.943, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.824 [Epoch 191][Batch 899], LR: 1.00E-03, Speed: 37.086 samples/sec, ObjLoss=20.942, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.823 [Epoch 191][Batch 999], LR: 1.00E-03, Speed: 62.899 samples/sec, ObjLoss=20.942, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.823 [Epoch 191][Batch 1099], LR: 1.00E-03, Speed: 53.129 samples/sec, ObjLoss=20.941, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.822 [Epoch 191][Batch 1199], LR: 1.00E-03, Speed: 91.866 samples/sec, ObjLoss=20.941, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.822 [Epoch 191][Batch 1299], LR: 1.00E-03, Speed: 72.792 samples/sec, ObjLoss=20.940, BoxCenterLoss=14.521, BoxScaleLoss=4.849, ClassLoss=7.821 [Epoch 191][Batch 1399], LR: 1.00E-03, Speed: 84.917 samples/sec, ObjLoss=20.940, BoxCenterLoss=14.520, BoxScaleLoss=4.849, ClassLoss=7.821 [Epoch 191][Batch 1499], LR: 1.00E-03, Speed: 122.750 samples/sec, ObjLoss=20.939, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.820 [Epoch 191][Batch 1599], LR: 1.00E-03, Speed: 57.775 samples/sec, ObjLoss=20.938, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.820 [Epoch 191][Batch 1699], LR: 1.00E-03, Speed: 68.802 samples/sec, ObjLoss=20.938, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.820 [Epoch 191][Batch 1799], LR: 1.00E-03, Speed: 94.100 samples/sec, ObjLoss=20.937, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.819 [Epoch 191] Training cost: 1624.458, ObjLoss=20.937, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.819 [Epoch 191] 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 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.320 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 ] = 0.289 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 ] = 0.139 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 ] = 0.431 person=34.0 bicycle=12.7 car=23.5 motorcycle=26.5 airplane=39.6 bus=38.8 train=43.9 truck=18.3 boat=11.6 traffic light=13.9 fire hydrant=37.9 stop sign=35.1 parking meter=24.1 bench=11.9 bird=19.9 cat=43.0 dog=33.3 horse=29.7 sheep=26.0 cow=31.0 elephant=39.0 bear=41.4 zebra=43.6 giraffe=44.7 backpack=5.9 umbrella=18.8 handbag=4.4 tie=14.4 suitcase=15.7 frisbee=31.4 skis=8.4 snowboard=13.9 sports ball=22.7 kite=23.1 baseball bat=10.0 baseball glove=18.5 skateboard=23.3 surfboard=17.9 tennis racket=23.4 bottle=17.0 wine glass=16.5 cup=20.4 fork=10.4 knife=4.6 spoon=2.3 bowl=17.6 banana=12.9 apple=7.2 sandwich=16.7 orange=14.7 broccoli=9.9 carrot=8.7 hot dog=17.5 pizza=28.6 donut=16.6 cake=18.3 chair=12.7 couch=27.6 potted plant=10.8 bed=30.4 dining table=15.2 toilet=34.9 tv=32.6 laptop=34.6 mouse=31.0 remote=9.3 keyboard=24.6 cell phone=16.1 microwave=30.4 oven=20.1 toaster=7.1 sink=18.1 refrigerator=30.2 book=4.9 clock=29.6 vase=19.9 scissors=16.2 teddy bear=25.5 hair drier=0.0 toothbrush=4.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.3 [Epoch 192][Batch 99], LR: 1.00E-03, Speed: 142.871 samples/sec, ObjLoss=20.936, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.819 [Epoch 192][Batch 199], LR: 1.00E-03, Speed: 47.852 samples/sec, ObjLoss=20.936, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.818 [Epoch 192][Batch 299], LR: 1.00E-03, Speed: 60.040 samples/sec, ObjLoss=20.935, BoxCenterLoss=14.520, BoxScaleLoss=4.848, ClassLoss=7.818 [Epoch 192][Batch 399], LR: 1.00E-03, Speed: 101.999 samples/sec, ObjLoss=20.935, BoxCenterLoss=14.519, BoxScaleLoss=4.848, ClassLoss=7.817 [Epoch 192][Batch 499], LR: 1.00E-03, Speed: 87.107 samples/sec, ObjLoss=20.934, BoxCenterLoss=14.519, BoxScaleLoss=4.848, ClassLoss=7.817 [Epoch 192][Batch 599], LR: 1.00E-03, Speed: 123.006 samples/sec, ObjLoss=20.933, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.816 [Epoch 192][Batch 699], LR: 1.00E-03, Speed: 79.898 samples/sec, ObjLoss=20.933, BoxCenterLoss=14.519, BoxScaleLoss=4.848, ClassLoss=7.816 [Epoch 192][Batch 799], LR: 1.00E-03, Speed: 74.335 samples/sec, ObjLoss=20.932, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.816 [Epoch 192][Batch 899], LR: 1.00E-03, Speed: 98.618 samples/sec, ObjLoss=20.932, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.815 [Epoch 192][Batch 999], LR: 1.00E-03, Speed: 56.938 samples/sec, ObjLoss=20.931, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.815 [Epoch 192][Batch 1099], LR: 1.00E-03, Speed: 67.448 samples/sec, ObjLoss=20.931, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.815 [Epoch 192][Batch 1199], LR: 1.00E-03, Speed: 73.614 samples/sec, ObjLoss=20.930, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.814 [Epoch 192][Batch 1299], LR: 1.00E-03, Speed: 55.931 samples/sec, ObjLoss=20.930, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.814 [Epoch 192][Batch 1399], LR: 1.00E-03, Speed: 87.702 samples/sec, ObjLoss=20.929, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.813 [Epoch 192][Batch 1499], LR: 1.00E-03, Speed: 90.454 samples/sec, ObjLoss=20.929, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.813 [Epoch 192][Batch 1599], LR: 1.00E-03, Speed: 84.091 samples/sec, ObjLoss=20.929, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.812 [Epoch 192][Batch 1699], LR: 1.00E-03, Speed: 56.413 samples/sec, ObjLoss=20.928, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.812 [Epoch 192][Batch 1799], LR: 1.00E-03, Speed: 115.870 samples/sec, ObjLoss=20.927, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.812 [Epoch 192] Training cost: 1679.275, ObjLoss=20.927, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.811 [Epoch 192] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.419 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 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 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.297 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 ] = 0.301 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.444 person=34.6 bicycle=15.3 car=21.5 motorcycle=27.3 airplane=37.0 bus=43.3 train=42.7 truck=20.2 boat=12.0 traffic light=11.7 fire hydrant=40.2 stop sign=36.6 parking meter=18.8 bench=12.9 bird=18.2 cat=46.6 dog=39.1 horse=34.4 sheep=26.4 cow=32.3 elephant=42.3 bear=46.0 zebra=45.2 giraffe=45.3 backpack=5.6 umbrella=20.8 handbag=4.6 tie=12.5 suitcase=16.9 frisbee=32.9 skis=7.9 snowboard=9.9 sports ball=21.5 kite=21.8 baseball bat=10.8 baseball glove=20.3 skateboard=23.5 surfboard=14.9 tennis racket=21.7 bottle=15.4 wine glass=14.3 cup=18.4 fork=10.6 knife=4.4 spoon=2.7 bowl=21.4 banana=11.2 apple=5.0 sandwich=17.7 orange=12.1 broccoli=9.5 carrot=8.4 hot dog=15.4 pizza=25.7 donut=17.3 cake=16.6 chair=12.5 couch=26.1 potted plant=12.9 bed=25.7 dining table=13.0 toilet=37.6 tv=29.7 laptop=35.6 mouse=32.9 remote=7.9 keyboard=29.8 cell phone=12.1 microwave=27.8 oven=19.0 toaster=2.4 sink=20.9 refrigerator=30.4 book=4.5 clock=30.5 vase=18.8 scissors=16.0 teddy bear=24.6 hair drier=0.0 toothbrush=3.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 193][Batch 99], LR: 1.00E-03, Speed: 140.234 samples/sec, ObjLoss=20.927, BoxCenterLoss=14.519, BoxScaleLoss=4.847, ClassLoss=7.811 [Epoch 193][Batch 199], LR: 1.00E-03, Speed: 77.038 samples/sec, ObjLoss=20.926, BoxCenterLoss=14.519, BoxScaleLoss=4.846, ClassLoss=7.811 [Epoch 193][Batch 299], LR: 1.00E-03, Speed: 82.025 samples/sec, ObjLoss=20.926, BoxCenterLoss=14.519, BoxScaleLoss=4.846, ClassLoss=7.810 [Epoch 193][Batch 399], LR: 1.00E-03, Speed: 83.304 samples/sec, ObjLoss=20.925, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.810 [Epoch 193][Batch 499], LR: 1.00E-03, Speed: 96.815 samples/sec, ObjLoss=20.925, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.809 [Epoch 193][Batch 599], LR: 1.00E-03, Speed: 143.764 samples/sec, ObjLoss=20.924, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.809 [Epoch 193][Batch 699], LR: 1.00E-03, Speed: 57.951 samples/sec, ObjLoss=20.924, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.808 [Epoch 193][Batch 799], LR: 1.00E-03, Speed: 98.411 samples/sec, ObjLoss=20.923, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.808 [Epoch 193][Batch 899], LR: 1.00E-03, Speed: 54.727 samples/sec, ObjLoss=20.923, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.808 [Epoch 193][Batch 999], LR: 1.00E-03, Speed: 86.230 samples/sec, ObjLoss=20.922, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.807 [Epoch 193][Batch 1099], LR: 1.00E-03, Speed: 71.990 samples/sec, ObjLoss=20.921, BoxCenterLoss=14.518, BoxScaleLoss=4.846, ClassLoss=7.807 [Epoch 193][Batch 1199], LR: 1.00E-03, Speed: 87.371 samples/sec, ObjLoss=20.921, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.806 [Epoch 193][Batch 1299], LR: 1.00E-03, Speed: 71.721 samples/sec, ObjLoss=20.921, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.806 [Epoch 193][Batch 1399], LR: 1.00E-03, Speed: 79.812 samples/sec, ObjLoss=20.920, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.806 [Epoch 193][Batch 1499], LR: 1.00E-03, Speed: 79.566 samples/sec, ObjLoss=20.920, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.805 [Epoch 193][Batch 1599], LR: 1.00E-03, Speed: 57.727 samples/sec, ObjLoss=20.920, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.805 [Epoch 193][Batch 1699], LR: 1.00E-03, Speed: 92.646 samples/sec, ObjLoss=20.919, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.804 [Epoch 193][Batch 1799], LR: 1.00E-03, Speed: 134.270 samples/sec, ObjLoss=20.919, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.804 [Epoch 193] Training cost: 1727.694, ObjLoss=20.919, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.804 [Epoch 193] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.434 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.097 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 ] = 0.329 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 ] = 0.302 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 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.450 person=32.5 bicycle=15.5 car=19.6 motorcycle=27.5 airplane=38.9 bus=42.1 train=41.9 truck=18.6 boat=10.4 traffic light=10.9 fire hydrant=31.5 stop sign=38.4 parking meter=20.5 bench=10.4 bird=17.5 cat=44.5 dog=35.2 horse=31.7 sheep=26.6 cow=31.6 elephant=41.2 bear=47.8 zebra=42.7 giraffe=46.2 backpack=5.1 umbrella=19.6 handbag=4.4 tie=14.0 suitcase=18.2 frisbee=34.3 skis=8.4 snowboard=12.5 sports ball=22.4 kite=20.3 baseball bat=10.0 baseball glove=21.7 skateboard=24.6 surfboard=18.5 tennis racket=21.1 bottle=15.6 wine glass=15.5 cup=20.7 fork=11.0 knife=4.5 spoon=4.2 bowl=22.1 banana=13.0 apple=8.3 sandwich=19.1 orange=15.3 broccoli=10.4 carrot=9.7 hot dog=18.9 pizza=29.9 donut=25.9 cake=20.7 chair=13.2 couch=26.6 potted plant=12.3 bed=29.5 dining table=17.9 toilet=34.4 tv=38.6 laptop=38.3 mouse=31.6 remote=9.5 keyboard=30.2 cell phone=16.5 microwave=24.7 oven=20.8 toaster=1.6 sink=19.6 refrigerator=31.1 book=5.0 clock=29.1 vase=19.5 scissors=15.8 teddy bear=26.4 hair drier=0.0 toothbrush=5.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.8 [Epoch 194][Batch 99], LR: 1.00E-03, Speed: 142.677 samples/sec, ObjLoss=20.918, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.803 [Epoch 194][Batch 199], LR: 1.00E-03, Speed: 81.417 samples/sec, ObjLoss=20.917, BoxCenterLoss=14.518, BoxScaleLoss=4.845, ClassLoss=7.803 [Epoch 194][Batch 299], LR: 1.00E-03, Speed: 77.520 samples/sec, ObjLoss=20.917, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.802 [Epoch 194][Batch 399], LR: 1.00E-03, Speed: 91.543 samples/sec, ObjLoss=20.917, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.802 [Epoch 194][Batch 499], LR: 1.00E-03, Speed: 61.913 samples/sec, ObjLoss=20.916, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.801 [Epoch 194][Batch 599], LR: 1.00E-03, Speed: 68.191 samples/sec, ObjLoss=20.915, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.801 [Epoch 194][Batch 699], LR: 1.00E-03, Speed: 85.869 samples/sec, ObjLoss=20.915, BoxCenterLoss=14.517, BoxScaleLoss=4.844, ClassLoss=7.800 [Epoch 194][Batch 799], LR: 1.00E-03, Speed: 149.910 samples/sec, ObjLoss=20.915, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.800 [Epoch 194][Batch 899], LR: 1.00E-03, Speed: 80.276 samples/sec, ObjLoss=20.914, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.800 [Epoch 194][Batch 999], LR: 1.00E-03, Speed: 78.114 samples/sec, ObjLoss=20.914, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.799 [Epoch 194][Batch 1099], LR: 1.00E-03, Speed: 128.533 samples/sec, ObjLoss=20.914, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.799 [Epoch 194][Batch 1199], LR: 1.00E-03, Speed: 72.385 samples/sec, ObjLoss=20.913, BoxCenterLoss=14.518, BoxScaleLoss=4.844, ClassLoss=7.798 [Epoch 194][Batch 1299], LR: 1.00E-03, Speed: 77.550 samples/sec, ObjLoss=20.913, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.798 [Epoch 194][Batch 1399], LR: 1.00E-03, Speed: 71.016 samples/sec, ObjLoss=20.912, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.798 [Epoch 194][Batch 1499], LR: 1.00E-03, Speed: 61.605 samples/sec, ObjLoss=20.912, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.797 [Epoch 194][Batch 1599], LR: 1.00E-03, Speed: 63.058 samples/sec, ObjLoss=20.911, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.797 [Epoch 194][Batch 1699], LR: 1.00E-03, Speed: 56.374 samples/sec, ObjLoss=20.911, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.796 [Epoch 194][Batch 1799], LR: 1.00E-03, Speed: 80.175 samples/sec, ObjLoss=20.910, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.796 [Epoch 194] Training cost: 1683.053, ObjLoss=20.910, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.795 [Epoch 194] 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 ] = 0.430 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.213 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.191 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 ] = 0.294 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 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420 person=31.3 bicycle=15.6 car=20.8 motorcycle=24.3 airplane=38.1 bus=33.5 train=45.9 truck=15.4 boat=10.9 traffic light=9.7 fire hydrant=32.1 stop sign=34.4 parking meter=22.3 bench=10.9 bird=16.2 cat=38.5 dog=34.4 horse=28.9 sheep=25.4 cow=30.6 elephant=41.3 bear=43.1 zebra=41.2 giraffe=36.7 backpack=4.7 umbrella=19.3 handbag=3.9 tie=14.3 suitcase=14.1 frisbee=36.3 skis=7.7 snowboard=11.2 sports ball=22.5 kite=22.0 baseball bat=13.4 baseball glove=18.3 skateboard=15.4 surfboard=14.8 tennis racket=23.4 bottle=14.9 wine glass=15.8 cup=18.8 fork=10.9 knife=3.6 spoon=3.5 bowl=18.8 banana=11.8 apple=8.2 sandwich=17.5 orange=15.7 broccoli=9.4 carrot=8.1 hot dog=16.4 pizza=28.4 donut=24.0 cake=17.1 chair=12.7 couch=24.5 potted plant=9.8 bed=23.9 dining table=13.3 toilet=35.8 tv=35.4 laptop=35.2 mouse=32.5 remote=8.5 keyboard=26.3 cell phone=14.7 microwave=24.8 oven=19.9 toaster=3.0 sink=21.9 refrigerator=33.0 book=4.6 clock=26.6 vase=16.6 scissors=14.1 teddy bear=24.8 hair drier=0.0 toothbrush=2.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 195][Batch 99], LR: 1.00E-03, Speed: 153.599 samples/sec, ObjLoss=20.909, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.795 [Epoch 195][Batch 199], LR: 1.00E-03, Speed: 71.899 samples/sec, ObjLoss=20.909, BoxCenterLoss=14.517, BoxScaleLoss=4.843, ClassLoss=7.794 [Epoch 195][Batch 299], LR: 1.00E-03, Speed: 80.203 samples/sec, ObjLoss=20.908, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.794 [Epoch 195][Batch 399], LR: 1.00E-03, Speed: 79.492 samples/sec, ObjLoss=20.908, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.794 [Epoch 195][Batch 499], LR: 1.00E-03, Speed: 105.177 samples/sec, ObjLoss=20.907, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.793 [Epoch 195][Batch 599], LR: 1.00E-03, Speed: 84.523 samples/sec, ObjLoss=20.907, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.793 [Epoch 195][Batch 699], LR: 1.00E-03, Speed: 83.912 samples/sec, ObjLoss=20.906, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.792 [Epoch 195][Batch 799], LR: 1.00E-03, Speed: 57.981 samples/sec, ObjLoss=20.906, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.792 [Epoch 195][Batch 899], LR: 1.00E-03, Speed: 67.085 samples/sec, ObjLoss=20.905, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.792 [Epoch 195][Batch 999], LR: 1.00E-03, Speed: 103.876 samples/sec, ObjLoss=20.905, BoxCenterLoss=14.517, BoxScaleLoss=4.842, ClassLoss=7.791 [Epoch 195][Batch 1099], LR: 1.00E-03, Speed: 66.853 samples/sec, ObjLoss=20.904, BoxCenterLoss=14.516, BoxScaleLoss=4.842, ClassLoss=7.791 [Epoch 195][Batch 1199], LR: 1.00E-03, Speed: 73.510 samples/sec, ObjLoss=20.904, BoxCenterLoss=14.516, BoxScaleLoss=4.842, ClassLoss=7.790 [Epoch 195][Batch 1299], LR: 1.00E-03, Speed: 90.119 samples/sec, ObjLoss=20.903, BoxCenterLoss=14.516, BoxScaleLoss=4.842, ClassLoss=7.790 [Epoch 195][Batch 1399], LR: 1.00E-03, Speed: 68.967 samples/sec, ObjLoss=20.903, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.789 [Epoch 195][Batch 1499], LR: 1.00E-03, Speed: 64.227 samples/sec, ObjLoss=20.902, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.789 [Epoch 195][Batch 1599], LR: 1.00E-03, Speed: 118.858 samples/sec, ObjLoss=20.902, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.788 [Epoch 195][Batch 1699], LR: 1.00E-03, Speed: 166.082 samples/sec, ObjLoss=20.901, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.788 [Epoch 195][Batch 1799], LR: 1.00E-03, Speed: 162.509 samples/sec, ObjLoss=20.901, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.788 [Epoch 195] Training cost: 1628.497, ObjLoss=20.900, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.788 [Epoch 195] 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 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.317 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 ] = 0.291 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 ] = 0.138 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 ] = 0.435 person=32.5 bicycle=14.9 car=22.7 motorcycle=24.1 airplane=38.5 bus=41.6 train=40.8 truck=19.3 boat=11.2 traffic light=13.3 fire hydrant=33.6 stop sign=41.7 parking meter=24.8 bench=9.6 bird=16.9 cat=37.9 dog=29.5 horse=29.4 sheep=25.3 cow=28.8 elephant=37.6 bear=39.0 zebra=43.2 giraffe=44.8 backpack=4.8 umbrella=19.0 handbag=3.6 tie=15.4 suitcase=18.2 frisbee=36.1 skis=8.7 snowboard=15.2 sports ball=24.3 kite=21.4 baseball bat=10.5 baseball glove=13.5 skateboard=23.1 surfboard=16.2 tennis racket=21.1 bottle=14.7 wine glass=14.5 cup=20.8 fork=10.4 knife=3.6 spoon=2.0 bowl=21.5 banana=12.0 apple=7.9 sandwich=16.4 orange=13.3 broccoli=10.4 carrot=8.0 hot dog=14.6 pizza=24.3 donut=23.6 cake=16.4 chair=11.9 couch=28.6 potted plant=9.8 bed=31.4 dining table=19.0 toilet=36.9 tv=36.7 laptop=35.4 mouse=31.2 remote=7.6 keyboard=23.2 cell phone=13.9 microwave=28.6 oven=20.5 toaster=0.0 sink=17.1 refrigerator=31.0 book=5.2 clock=31.7 vase=19.9 scissors=16.9 teddy bear=23.9 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 196][Batch 99], LR: 1.00E-03, Speed: 137.284 samples/sec, ObjLoss=20.900, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.787 [Epoch 196][Batch 199], LR: 1.00E-03, Speed: 63.915 samples/sec, ObjLoss=20.899, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.787 [Epoch 196][Batch 299], LR: 1.00E-03, Speed: 117.258 samples/sec, ObjLoss=20.899, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.786 [Epoch 196][Batch 399], LR: 1.00E-03, Speed: 68.992 samples/sec, ObjLoss=20.898, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.786 [Epoch 196][Batch 499], LR: 1.00E-03, Speed: 99.499 samples/sec, ObjLoss=20.898, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.785 [Epoch 196][Batch 599], LR: 1.00E-03, Speed: 62.553 samples/sec, ObjLoss=20.897, BoxCenterLoss=14.516, BoxScaleLoss=4.841, ClassLoss=7.785 [Epoch 196][Batch 699], LR: 1.00E-03, Speed: 48.197 samples/sec, ObjLoss=20.897, BoxCenterLoss=14.516, BoxScaleLoss=4.840, ClassLoss=7.785 [Epoch 196][Batch 799], LR: 1.00E-03, Speed: 37.262 samples/sec, ObjLoss=20.897, BoxCenterLoss=14.516, BoxScaleLoss=4.840, ClassLoss=7.784 [Epoch 196][Batch 899], LR: 1.00E-03, Speed: 98.334 samples/sec, ObjLoss=20.896, BoxCenterLoss=14.516, BoxScaleLoss=4.840, ClassLoss=7.784 [Epoch 196][Batch 999], LR: 1.00E-03, Speed: 125.540 samples/sec, ObjLoss=20.896, BoxCenterLoss=14.516, BoxScaleLoss=4.840, ClassLoss=7.783 [Epoch 196][Batch 1099], LR: 1.00E-03, Speed: 135.353 samples/sec, ObjLoss=20.895, BoxCenterLoss=14.515, BoxScaleLoss=4.840, ClassLoss=7.783 [Epoch 196][Batch 1199], LR: 1.00E-03, Speed: 84.177 samples/sec, ObjLoss=20.895, BoxCenterLoss=14.515, BoxScaleLoss=4.840, ClassLoss=7.782 [Epoch 196][Batch 1299], LR: 1.00E-03, Speed: 122.948 samples/sec, ObjLoss=20.894, BoxCenterLoss=14.515, BoxScaleLoss=4.840, ClassLoss=7.782 [Epoch 196][Batch 1399], LR: 1.00E-03, Speed: 82.834 samples/sec, ObjLoss=20.894, BoxCenterLoss=14.515, BoxScaleLoss=4.840, ClassLoss=7.781 [Epoch 196][Batch 1499], LR: 1.00E-03, Speed: 53.813 samples/sec, ObjLoss=20.893, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.781 [Epoch 196][Batch 1599], LR: 1.00E-03, Speed: 72.312 samples/sec, ObjLoss=20.893, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.780 [Epoch 196][Batch 1699], LR: 1.00E-03, Speed: 109.711 samples/sec, ObjLoss=20.892, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.780 [Epoch 196][Batch 1799], LR: 1.00E-03, Speed: 71.552 samples/sec, ObjLoss=20.892, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.780 [Epoch 196] Training cost: 1693.522, ObjLoss=20.892, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.780 [Epoch 196] 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 ] = 0.424 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172 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 ] = 0.303 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 ] = 0.284 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 ] = 0.133 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 ] = 0.414 person=32.4 bicycle=15.0 car=19.3 motorcycle=25.1 airplane=37.4 bus=40.4 train=44.4 truck=19.2 boat=13.1 traffic light=11.8 fire hydrant=37.8 stop sign=38.6 parking meter=23.7 bench=10.4 bird=16.6 cat=40.9 dog=30.8 horse=31.5 sheep=26.0 cow=29.2 elephant=41.2 bear=42.1 zebra=39.2 giraffe=43.0 backpack=4.3 umbrella=19.4 handbag=4.5 tie=13.1 suitcase=14.0 frisbee=35.0 skis=7.9 snowboard=13.0 sports ball=22.1 kite=21.6 baseball bat=10.8 baseball glove=19.4 skateboard=22.4 surfboard=15.5 tennis racket=21.0 bottle=16.9 wine glass=14.6 cup=20.9 fork=11.0 knife=3.6 spoon=2.6 bowl=17.0 banana=12.3 apple=6.8 sandwich=14.6 orange=15.3 broccoli=10.7 carrot=9.1 hot dog=13.5 pizza=25.2 donut=21.6 cake=17.7 chair=11.5 couch=23.5 potted plant=10.5 bed=26.2 dining table=14.9 toilet=31.8 tv=35.1 laptop=35.5 mouse=29.3 remote=8.5 keyboard=30.0 cell phone=15.8 microwave=26.1 oven=19.9 toaster=0.0 sink=19.0 refrigerator=27.7 book=4.7 clock=31.4 vase=15.9 scissors=17.0 teddy bear=21.0 hair drier=0.0 toothbrush=5.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.6 [Epoch 197][Batch 99], LR: 1.00E-03, Speed: 137.158 samples/sec, ObjLoss=20.891, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.779 [Epoch 197][Batch 199], LR: 1.00E-03, Speed: 79.650 samples/sec, ObjLoss=20.890, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.779 [Epoch 197][Batch 299], LR: 1.00E-03, Speed: 47.308 samples/sec, ObjLoss=20.890, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.778 [Epoch 197][Batch 399], LR: 1.00E-03, Speed: 79.395 samples/sec, ObjLoss=20.889, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.778 [Epoch 197][Batch 499], LR: 1.00E-03, Speed: 60.358 samples/sec, ObjLoss=20.889, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.777 [Epoch 197][Batch 599], LR: 1.00E-03, Speed: 96.646 samples/sec, ObjLoss=20.888, BoxCenterLoss=14.515, BoxScaleLoss=4.839, ClassLoss=7.777 [Epoch 197][Batch 699], LR: 1.00E-03, Speed: 63.510 samples/sec, ObjLoss=20.888, BoxCenterLoss=14.514, BoxScaleLoss=4.839, ClassLoss=7.777 [Epoch 197][Batch 799], LR: 1.00E-03, Speed: 53.796 samples/sec, ObjLoss=20.887, BoxCenterLoss=14.514, BoxScaleLoss=4.839, ClassLoss=7.776 [Epoch 197][Batch 899], LR: 1.00E-03, Speed: 67.114 samples/sec, ObjLoss=20.887, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.776 [Epoch 197][Batch 999], LR: 1.00E-03, Speed: 95.120 samples/sec, ObjLoss=20.886, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.775 [Epoch 197][Batch 1099], LR: 1.00E-03, Speed: 83.378 samples/sec, ObjLoss=20.886, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.775 [Epoch 197][Batch 1199], LR: 1.00E-03, Speed: 60.640 samples/sec, ObjLoss=20.886, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.774 [Epoch 197][Batch 1299], LR: 1.00E-03, Speed: 86.570 samples/sec, ObjLoss=20.885, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.774 [Epoch 197][Batch 1399], LR: 1.00E-03, Speed: 61.334 samples/sec, ObjLoss=20.884, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.774 [Epoch 197][Batch 1499], LR: 1.00E-03, Speed: 84.163 samples/sec, ObjLoss=20.884, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.773 [Epoch 197][Batch 1599], LR: 1.00E-03, Speed: 144.533 samples/sec, ObjLoss=20.884, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.773 [Epoch 197][Batch 1699], LR: 1.00E-03, Speed: 87.377 samples/sec, ObjLoss=20.883, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.773 [Epoch 197][Batch 1799], LR: 1.00E-03, Speed: 126.666 samples/sec, ObjLoss=20.883, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.772 [Epoch 197] Training cost: 1716.305, ObjLoss=20.882, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.772 [Epoch 197] 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 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.188 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.081 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 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.302 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 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445 person=32.3 bicycle=13.5 car=20.2 motorcycle=24.5 airplane=41.1 bus=41.9 train=43.6 truck=17.3 boat=11.2 traffic light=11.4 fire hydrant=37.8 stop sign=38.1 parking meter=20.6 bench=12.4 bird=18.3 cat=40.3 dog=37.4 horse=30.9 sheep=26.6 cow=28.1 elephant=39.9 bear=42.6 zebra=43.5 giraffe=44.4 backpack=5.2 umbrella=19.1 handbag=3.7 tie=13.4 suitcase=16.0 frisbee=33.1 skis=8.1 snowboard=11.8 sports ball=17.9 kite=19.6 baseball bat=9.3 baseball glove=18.4 skateboard=21.2 surfboard=16.2 tennis racket=23.1 bottle=14.6 wine glass=13.4 cup=19.9 fork=10.4 knife=3.8 spoon=3.2 bowl=21.9 banana=12.7 apple=6.8 sandwich=16.4 orange=15.9 broccoli=10.8 carrot=9.5 hot dog=17.7 pizza=28.5 donut=19.1 cake=14.9 chair=12.1 couch=27.4 potted plant=9.5 bed=31.2 dining table=17.3 toilet=33.1 tv=37.4 laptop=35.1 mouse=31.1 remote=9.3 keyboard=26.6 cell phone=14.5 microwave=31.5 oven=21.6 toaster=0.0 sink=19.6 refrigerator=31.4 book=3.9 clock=29.1 vase=17.3 scissors=13.3 teddy bear=23.7 hair drier=0.0 toothbrush=2.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 198][Batch 99], LR: 1.00E-03, Speed: 141.871 samples/sec, ObjLoss=20.882, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.772 [Epoch 198][Batch 199], LR: 1.00E-03, Speed: 60.554 samples/sec, ObjLoss=20.881, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.771 [Epoch 198][Batch 299], LR: 1.00E-03, Speed: 83.252 samples/sec, ObjLoss=20.881, BoxCenterLoss=14.514, BoxScaleLoss=4.838, ClassLoss=7.771 [Epoch 198][Batch 399], LR: 1.00E-03, Speed: 124.395 samples/sec, ObjLoss=20.880, BoxCenterLoss=14.514, BoxScaleLoss=4.837, ClassLoss=7.771 [Epoch 198][Batch 499], LR: 1.00E-03, Speed: 82.712 samples/sec, ObjLoss=20.880, BoxCenterLoss=14.514, BoxScaleLoss=4.837, ClassLoss=7.770 [Epoch 198][Batch 599], LR: 1.00E-03, Speed: 104.768 samples/sec, ObjLoss=20.879, BoxCenterLoss=14.514, BoxScaleLoss=4.837, ClassLoss=7.770 [Epoch 198][Batch 699], LR: 1.00E-03, Speed: 111.780 samples/sec, ObjLoss=20.878, BoxCenterLoss=14.513, BoxScaleLoss=4.837, ClassLoss=7.769 [Epoch 198][Batch 799], LR: 1.00E-03, Speed: 56.315 samples/sec, ObjLoss=20.878, BoxCenterLoss=14.513, BoxScaleLoss=4.837, ClassLoss=7.769 [Epoch 198][Batch 899], LR: 1.00E-03, Speed: 91.382 samples/sec, ObjLoss=20.877, BoxCenterLoss=14.513, BoxScaleLoss=4.837, ClassLoss=7.768 [Epoch 198][Batch 999], LR: 1.00E-03, Speed: 48.993 samples/sec, ObjLoss=20.877, BoxCenterLoss=14.513, BoxScaleLoss=4.837, ClassLoss=7.768 [Epoch 198][Batch 1099], LR: 1.00E-03, Speed: 55.939 samples/sec, ObjLoss=20.876, BoxCenterLoss=14.513, BoxScaleLoss=4.837, ClassLoss=7.768 [Epoch 198][Batch 1199], LR: 1.00E-03, Speed: 134.122 samples/sec, ObjLoss=20.876, BoxCenterLoss=14.513, BoxScaleLoss=4.837, ClassLoss=7.767 [Epoch 198][Batch 1299], LR: 1.00E-03, Speed: 70.354 samples/sec, ObjLoss=20.876, BoxCenterLoss=14.513, BoxScaleLoss=4.837, ClassLoss=7.767 [Epoch 198][Batch 1399], LR: 1.00E-03, Speed: 67.530 samples/sec, ObjLoss=20.875, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.767 [Epoch 198][Batch 1499], LR: 1.00E-03, Speed: 72.915 samples/sec, ObjLoss=20.875, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.766 [Epoch 198][Batch 1599], LR: 1.00E-03, Speed: 48.084 samples/sec, ObjLoss=20.874, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.766 [Epoch 198][Batch 1699], LR: 1.00E-03, Speed: 90.036 samples/sec, ObjLoss=20.874, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.765 [Epoch 198][Batch 1799], LR: 1.00E-03, Speed: 122.165 samples/sec, ObjLoss=20.873, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.765 [Epoch 198] Training cost: 1722.700, ObjLoss=20.873, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.765 [Epoch 198] 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 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.179 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 ] = 0.210 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.307 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 ] = 0.285 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 ] = 0.139 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 person=33.9 bicycle=15.9 car=21.2 motorcycle=24.8 airplane=39.5 bus=39.0 train=42.6 truck=18.2 boat=11.2 traffic light=12.5 fire hydrant=37.7 stop sign=37.3 parking meter=15.2 bench=10.3 bird=16.3 cat=44.0 dog=32.7 horse=30.7 sheep=22.9 cow=29.0 elephant=41.4 bear=42.8 zebra=41.9 giraffe=40.9 backpack=3.9 umbrella=21.5 handbag=4.3 tie=14.2 suitcase=14.7 frisbee=36.7 skis=7.8 snowboard=10.5 sports ball=23.7 kite=19.8 baseball bat=10.4 baseball glove=18.4 skateboard=20.2 surfboard=15.3 tennis racket=19.9 bottle=15.7 wine glass=12.9 cup=19.1 fork=7.8 knife=3.4 spoon=2.7 bowl=21.1 banana=12.6 apple=7.9 sandwich=19.6 orange=14.5 broccoli=10.0 carrot=9.3 hot dog=17.8 pizza=28.6 donut=17.5 cake=16.2 chair=12.3 couch=26.2 potted plant=11.2 bed=24.3 dining table=13.7 toilet=32.3 tv=34.9 laptop=32.6 mouse=32.8 remote=9.6 keyboard=27.4 cell phone=16.0 microwave=28.3 oven=16.2 toaster=8.3 sink=20.4 refrigerator=26.4 book=5.1 clock=30.6 vase=19.0 scissors=14.6 teddy bear=24.0 hair drier=0.0 toothbrush=4.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.6 [Epoch 199][Batch 99], LR: 1.00E-03, Speed: 137.487 samples/sec, ObjLoss=20.873, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.764 [Epoch 199][Batch 199], LR: 1.00E-03, Speed: 65.256 samples/sec, ObjLoss=20.872, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.764 [Epoch 199][Batch 299], LR: 1.00E-03, Speed: 78.226 samples/sec, ObjLoss=20.872, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.763 [Epoch 199][Batch 399], LR: 1.00E-03, Speed: 73.281 samples/sec, ObjLoss=20.871, BoxCenterLoss=14.513, BoxScaleLoss=4.836, ClassLoss=7.763 [Epoch 199][Batch 499], LR: 1.00E-03, Speed: 70.344 samples/sec, ObjLoss=20.871, BoxCenterLoss=14.512, BoxScaleLoss=4.836, ClassLoss=7.763 [Epoch 199][Batch 599], LR: 1.00E-03, Speed: 69.301 samples/sec, ObjLoss=20.870, BoxCenterLoss=14.512, BoxScaleLoss=4.836, ClassLoss=7.762 [Epoch 199][Batch 699], LR: 1.00E-03, Speed: 95.722 samples/sec, ObjLoss=20.870, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.762 [Epoch 199][Batch 799], LR: 1.00E-03, Speed: 98.000 samples/sec, ObjLoss=20.869, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.761 [Epoch 199][Batch 899], LR: 1.00E-03, Speed: 81.528 samples/sec, ObjLoss=20.869, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.761 [Epoch 199][Batch 999], LR: 1.00E-03, Speed: 59.597 samples/sec, ObjLoss=20.868, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.760 [Epoch 199][Batch 1099], LR: 1.00E-03, Speed: 62.613 samples/sec, ObjLoss=20.868, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.760 [Epoch 199][Batch 1199], LR: 1.00E-03, Speed: 125.834 samples/sec, ObjLoss=20.867, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.760 [Epoch 199][Batch 1299], LR: 1.00E-03, Speed: 48.732 samples/sec, ObjLoss=20.867, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.759 [Epoch 199][Batch 1399], LR: 1.00E-03, Speed: 67.561 samples/sec, ObjLoss=20.866, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.759 [Epoch 199][Batch 1499], LR: 1.00E-03, Speed: 80.420 samples/sec, ObjLoss=20.866, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.758 [Epoch 199][Batch 1599], LR: 1.00E-03, Speed: 104.732 samples/sec, ObjLoss=20.866, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.758 [Epoch 199][Batch 1699], LR: 1.00E-03, Speed: 73.307 samples/sec, ObjLoss=20.865, BoxCenterLoss=14.512, BoxScaleLoss=4.835, ClassLoss=7.758 [Epoch 199][Batch 1799], LR: 1.00E-03, Speed: 143.481 samples/sec, ObjLoss=20.864, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.757 [Epoch 199] Training cost: 1721.917, ObjLoss=20.864, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.757 [Epoch 199] 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 ] = 0.425 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.193 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 ] = 0.226 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.316 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.300 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 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429 person=31.5 bicycle=16.3 car=22.2 motorcycle=25.5 airplane=40.4 bus=42.2 train=45.8 truck=19.6 boat=11.8 traffic light=14.5 fire hydrant=39.2 stop sign=37.2 parking meter=26.5 bench=12.4 bird=19.1 cat=38.5 dog=33.7 horse=33.3 sheep=25.9 cow=27.8 elephant=38.6 bear=38.2 zebra=40.5 giraffe=41.5 backpack=5.7 umbrella=19.8 handbag=4.1 tie=14.0 suitcase=14.7 frisbee=36.4 skis=7.7 snowboard=13.0 sports ball=22.8 kite=23.4 baseball bat=11.0 baseball glove=15.9 skateboard=24.2 surfboard=16.9 tennis racket=22.9 bottle=14.7 wine glass=16.1 cup=20.9 fork=7.8 knife=2.6 spoon=3.2 bowl=20.1 banana=13.3 apple=6.6 sandwich=15.3 orange=15.7 broccoli=10.3 carrot=9.2 hot dog=14.5 pizza=27.2 donut=22.1 cake=17.7 chair=13.1 couch=21.6 potted plant=12.4 bed=29.8 dining table=19.2 toilet=34.5 tv=30.7 laptop=30.5 mouse=37.7 remote=9.6 keyboard=26.9 cell phone=14.8 microwave=29.8 oven=17.8 toaster=3.6 sink=21.3 refrigerator=30.1 book=4.1 clock=32.3 vase=21.0 scissors=17.2 teddy bear=26.9 hair drier=0.0 toothbrush=4.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.3 [Epoch 200][Batch 99], LR: 1.00E-03, Speed: 145.934 samples/sec, ObjLoss=20.864, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.757 [Epoch 200][Batch 199], LR: 1.00E-03, Speed: 103.473 samples/sec, ObjLoss=20.863, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.756 [Epoch 200][Batch 299], LR: 1.00E-03, Speed: 81.985 samples/sec, ObjLoss=20.863, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.756 [Epoch 200][Batch 399], LR: 1.00E-03, Speed: 94.869 samples/sec, ObjLoss=20.863, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.755 [Epoch 200][Batch 499], LR: 1.00E-03, Speed: 64.806 samples/sec, ObjLoss=20.862, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.755 [Epoch 200][Batch 599], LR: 1.00E-03, Speed: 104.704 samples/sec, ObjLoss=20.861, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.755 [Epoch 200][Batch 699], LR: 1.00E-03, Speed: 106.524 samples/sec, ObjLoss=20.861, BoxCenterLoss=14.512, BoxScaleLoss=4.834, ClassLoss=7.754 [Epoch 200][Batch 799], LR: 1.00E-03, Speed: 83.631 samples/sec, ObjLoss=20.860, BoxCenterLoss=14.511, BoxScaleLoss=4.834, ClassLoss=7.754 [Epoch 200][Batch 899], LR: 1.00E-03, Speed: 74.036 samples/sec, ObjLoss=20.860, BoxCenterLoss=14.511, BoxScaleLoss=4.834, ClassLoss=7.754 [Epoch 200][Batch 999], LR: 1.00E-03, Speed: 105.419 samples/sec, ObjLoss=20.859, BoxCenterLoss=14.511, BoxScaleLoss=4.834, ClassLoss=7.753 [Epoch 200][Batch 1099], LR: 1.00E-03, Speed: 75.488 samples/sec, ObjLoss=20.859, BoxCenterLoss=14.511, BoxScaleLoss=4.834, ClassLoss=7.753 [Epoch 200][Batch 1199], LR: 1.00E-03, Speed: 105.721 samples/sec, ObjLoss=20.858, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.752 [Epoch 200][Batch 1299], LR: 1.00E-03, Speed: 128.070 samples/sec, ObjLoss=20.858, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.752 [Epoch 200][Batch 1399], LR: 1.00E-03, Speed: 49.777 samples/sec, ObjLoss=20.857, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.752 [Epoch 200][Batch 1499], LR: 1.00E-03, Speed: 80.134 samples/sec, ObjLoss=20.857, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.751 [Epoch 200][Batch 1599], LR: 1.00E-03, Speed: 144.616 samples/sec, ObjLoss=20.857, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.751 [Epoch 200][Batch 1699], LR: 1.00E-03, Speed: 54.468 samples/sec, ObjLoss=20.856, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.750 [Epoch 200][Batch 1799], LR: 1.00E-03, Speed: 106.991 samples/sec, ObjLoss=20.855, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.750 [Epoch 200] Training cost: 1696.305, ObjLoss=20.855, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.750 [Epoch 200] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.216 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.426 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.196 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 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.299 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 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420 person=34.2 bicycle=14.9 car=23.5 motorcycle=25.6 airplane=40.3 bus=42.5 train=45.8 truck=19.8 boat=11.7 traffic light=13.2 fire hydrant=37.3 stop sign=44.7 parking meter=32.8 bench=11.0 bird=15.7 cat=40.4 dog=36.3 horse=33.2 sheep=29.2 cow=31.7 elephant=43.1 bear=45.3 zebra=41.5 giraffe=44.5 backpack=5.9 umbrella=20.5 handbag=4.0 tie=15.7 suitcase=15.7 frisbee=34.3 skis=8.5 snowboard=11.9 sports ball=24.7 kite=21.1 baseball bat=10.1 baseball glove=17.8 skateboard=23.5 surfboard=15.6 tennis racket=24.7 bottle=15.9 wine glass=16.4 cup=21.6 fork=10.5 knife=4.1 spoon=3.2 bowl=20.0 banana=11.0 apple=7.7 sandwich=19.7 orange=14.5 broccoli=9.9 carrot=9.6 hot dog=17.6 pizza=26.0 donut=21.5 cake=17.1 chair=13.0 couch=26.2 potted plant=10.8 bed=22.1 dining table=14.1 toilet=37.9 tv=36.0 laptop=33.9 mouse=30.4 remote=8.6 keyboard=28.3 cell phone=14.8 microwave=27.0 oven=20.3 toaster=0.0 sink=20.0 refrigerator=28.6 book=5.5 clock=27.8 vase=19.6 scissors=16.0 teddy bear=24.7 hair drier=0.0 toothbrush=3.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.6 [Epoch 201][Batch 99], LR: 1.00E-03, Speed: 135.887 samples/sec, ObjLoss=20.855, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.749 [Epoch 201][Batch 199], LR: 1.00E-03, Speed: 86.575 samples/sec, ObjLoss=20.854, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.749 [Epoch 201][Batch 299], LR: 1.00E-03, Speed: 95.855 samples/sec, ObjLoss=20.854, BoxCenterLoss=14.511, BoxScaleLoss=4.833, ClassLoss=7.748 [Epoch 201][Batch 399], LR: 1.00E-03, Speed: 68.313 samples/sec, ObjLoss=20.853, BoxCenterLoss=14.511, BoxScaleLoss=4.832, ClassLoss=7.748 [Epoch 201][Batch 499], LR: 1.00E-03, Speed: 72.956 samples/sec, ObjLoss=20.853, BoxCenterLoss=14.511, BoxScaleLoss=4.832, ClassLoss=7.747 [Epoch 201][Batch 599], LR: 1.00E-03, Speed: 65.928 samples/sec, ObjLoss=20.852, BoxCenterLoss=14.511, BoxScaleLoss=4.832, ClassLoss=7.747 [Epoch 201][Batch 699], LR: 1.00E-03, Speed: 98.351 samples/sec, ObjLoss=20.852, BoxCenterLoss=14.511, BoxScaleLoss=4.832, ClassLoss=7.747 [Epoch 201][Batch 799], LR: 1.00E-03, Speed: 88.734 samples/sec, ObjLoss=20.851, BoxCenterLoss=14.511, BoxScaleLoss=4.832, ClassLoss=7.746 [Epoch 201][Batch 899], LR: 1.00E-03, Speed: 77.469 samples/sec, ObjLoss=20.851, BoxCenterLoss=14.510, BoxScaleLoss=4.832, ClassLoss=7.746 [Epoch 201][Batch 999], LR: 1.00E-03, Speed: 72.925 samples/sec, ObjLoss=20.850, BoxCenterLoss=14.510, BoxScaleLoss=4.832, ClassLoss=7.746 [Epoch 201][Batch 1099], LR: 1.00E-03, Speed: 78.651 samples/sec, ObjLoss=20.850, BoxCenterLoss=14.511, BoxScaleLoss=4.832, ClassLoss=7.745 [Epoch 201][Batch 1199], LR: 1.00E-03, Speed: 67.662 samples/sec, ObjLoss=20.850, BoxCenterLoss=14.511, BoxScaleLoss=4.832, ClassLoss=7.745 [Epoch 201][Batch 1299], LR: 1.00E-03, Speed: 96.659 samples/sec, ObjLoss=20.849, BoxCenterLoss=14.510, BoxScaleLoss=4.832, ClassLoss=7.744 [Epoch 201][Batch 1399], LR: 1.00E-03, Speed: 87.447 samples/sec, ObjLoss=20.849, BoxCenterLoss=14.510, BoxScaleLoss=4.832, ClassLoss=7.744 [Epoch 201][Batch 1499], LR: 1.00E-03, Speed: 63.311 samples/sec, ObjLoss=20.848, BoxCenterLoss=14.510, BoxScaleLoss=4.832, ClassLoss=7.744 [Epoch 201][Batch 1599], LR: 1.00E-03, Speed: 82.943 samples/sec, ObjLoss=20.848, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.743 [Epoch 201][Batch 1699], LR: 1.00E-03, Speed: 66.051 samples/sec, ObjLoss=20.847, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.743 [Epoch 201][Batch 1799], LR: 1.00E-03, Speed: 87.517 samples/sec, ObjLoss=20.847, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.742 [Epoch 201] Training cost: 1645.919, ObjLoss=20.846, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.742 [Epoch 201] 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 ] = 0.423 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.171 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 ] = 0.207 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 ] = 0.195 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 ] = 0.296 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 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427 person=32.3 bicycle=15.7 car=21.5 motorcycle=24.1 airplane=35.7 bus=43.4 train=45.1 truck=18.6 boat=10.5 traffic light=11.5 fire hydrant=35.4 stop sign=34.7 parking meter=26.3 bench=10.9 bird=16.2 cat=39.0 dog=36.6 horse=34.5 sheep=25.5 cow=27.0 elephant=38.4 bear=36.7 zebra=37.2 giraffe=44.6 backpack=4.2 umbrella=19.0 handbag=3.6 tie=13.9 suitcase=14.1 frisbee=31.4 skis=7.5 snowboard=12.7 sports ball=17.1 kite=19.6 baseball bat=9.6 baseball glove=11.2 skateboard=23.6 surfboard=16.5 tennis racket=21.0 bottle=14.9 wine glass=13.7 cup=19.4 fork=10.6 knife=3.0 spoon=2.8 bowl=17.0 banana=10.8 apple=6.3 sandwich=18.8 orange=13.3 broccoli=9.4 carrot=7.8 hot dog=16.3 pizza=27.4 donut=22.2 cake=19.0 chair=12.7 couch=25.8 potted plant=9.9 bed=30.6 dining table=14.8 toilet=32.0 tv=33.6 laptop=35.0 mouse=32.6 remote=11.0 keyboard=26.5 cell phone=16.7 microwave=22.5 oven=16.7 toaster=8.3 sink=18.2 refrigerator=26.0 book=4.6 clock=32.1 vase=15.7 scissors=14.5 teddy bear=24.6 hair drier=0.0 toothbrush=4.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.3 [Epoch 202][Batch 99], LR: 1.00E-03, Speed: 101.438 samples/sec, ObjLoss=20.846, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.742 [Epoch 202][Batch 199], LR: 1.00E-03, Speed: 59.428 samples/sec, ObjLoss=20.845, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.742 [Epoch 202][Batch 299], LR: 1.00E-03, Speed: 73.181 samples/sec, ObjLoss=20.845, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.741 [Epoch 202][Batch 399], LR: 1.00E-03, Speed: 55.074 samples/sec, ObjLoss=20.844, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.741 [Epoch 202][Batch 499], LR: 1.00E-03, Speed: 108.386 samples/sec, ObjLoss=20.844, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.740 [Epoch 202][Batch 599], LR: 1.00E-03, Speed: 61.624 samples/sec, ObjLoss=20.844, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.740 [Epoch 202][Batch 699], LR: 1.00E-03, Speed: 133.877 samples/sec, ObjLoss=20.843, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.739 [Epoch 202][Batch 799], LR: 1.00E-03, Speed: 72.194 samples/sec, ObjLoss=20.843, BoxCenterLoss=14.510, BoxScaleLoss=4.831, ClassLoss=7.739 [Epoch 202][Batch 899], LR: 1.00E-03, Speed: 91.144 samples/sec, ObjLoss=20.842, BoxCenterLoss=14.510, BoxScaleLoss=4.830, ClassLoss=7.739 [Epoch 202][Batch 999], LR: 1.00E-03, Speed: 130.209 samples/sec, ObjLoss=20.842, BoxCenterLoss=14.510, BoxScaleLoss=4.830, ClassLoss=7.738 [Epoch 202][Batch 1099], LR: 1.00E-03, Speed: 74.912 samples/sec, ObjLoss=20.841, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.738 [Epoch 202][Batch 1199], LR: 1.00E-03, Speed: 76.153 samples/sec, ObjLoss=20.841, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.737 [Epoch 202][Batch 1299], LR: 1.00E-03, Speed: 63.954 samples/sec, ObjLoss=20.840, BoxCenterLoss=14.510, BoxScaleLoss=4.830, ClassLoss=7.737 [Epoch 202][Batch 1399], LR: 1.00E-03, Speed: 81.693 samples/sec, ObjLoss=20.840, BoxCenterLoss=14.510, BoxScaleLoss=4.830, ClassLoss=7.737 [Epoch 202][Batch 1499], LR: 1.00E-03, Speed: 72.426 samples/sec, ObjLoss=20.840, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.736 [Epoch 202][Batch 1599], LR: 1.00E-03, Speed: 82.253 samples/sec, ObjLoss=20.839, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.736 [Epoch 202][Batch 1699], LR: 1.00E-03, Speed: 92.260 samples/sec, ObjLoss=20.839, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.735 [Epoch 202][Batch 1799], LR: 1.00E-03, Speed: 92.550 samples/sec, ObjLoss=20.838, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.735 [Epoch 202] Training cost: 1694.527, ObjLoss=20.838, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.735 [Epoch 202] 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 ] = 0.420 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.175 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 ] = 0.222 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 ] = 0.192 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 ] = 0.289 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 ] = 0.308 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419 person=32.0 bicycle=14.8 car=20.8 motorcycle=24.1 airplane=31.7 bus=35.8 train=41.7 truck=17.5 boat=11.0 traffic light=12.5 fire hydrant=41.4 stop sign=35.8 parking meter=18.5 bench=10.9 bird=15.3 cat=43.3 dog=36.5 horse=32.4 sheep=27.3 cow=27.4 elephant=28.5 bear=33.7 zebra=42.0 giraffe=40.9 backpack=5.0 umbrella=19.1 handbag=4.9 tie=12.5 suitcase=14.9 frisbee=27.3 skis=8.5 snowboard=15.4 sports ball=21.1 kite=22.0 baseball bat=8.6 baseball glove=17.3 skateboard=23.6 surfboard=16.6 tennis racket=21.7 bottle=16.5 wine glass=16.0 cup=19.7 fork=10.7 knife=4.1 spoon=3.5 bowl=16.5 banana=12.1 apple=8.3 sandwich=20.6 orange=14.5 broccoli=10.5 carrot=11.0 hot dog=16.4 pizza=23.5 donut=20.4 cake=18.9 chair=11.9 couch=25.6 potted plant=11.4 bed=25.1 dining table=11.3 toilet=38.5 tv=33.6 laptop=38.4 mouse=36.1 remote=9.4 keyboard=27.4 cell phone=13.6 microwave=30.7 oven=17.2 toaster=0.0 sink=19.3 refrigerator=27.1 book=5.0 clock=32.1 vase=19.4 scissors=13.0 teddy bear=22.9 hair drier=0.0 toothbrush=6.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.4 [Epoch 203][Batch 99], LR: 1.00E-03, Speed: 159.193 samples/sec, ObjLoss=20.837, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.734 [Epoch 203][Batch 199], LR: 1.00E-03, Speed: 122.417 samples/sec, ObjLoss=20.837, BoxCenterLoss=14.509, BoxScaleLoss=4.830, ClassLoss=7.734 [Epoch 203][Batch 299], LR: 1.00E-03, Speed: 68.367 samples/sec, ObjLoss=20.836, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.734 [Epoch 203][Batch 399], LR: 1.00E-03, Speed: 47.968 samples/sec, ObjLoss=20.836, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.733 [Epoch 203][Batch 499], LR: 1.00E-03, Speed: 75.548 samples/sec, ObjLoss=20.835, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.733 [Epoch 203][Batch 599], LR: 1.00E-03, Speed: 50.615 samples/sec, ObjLoss=20.835, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.732 [Epoch 203][Batch 699], LR: 1.00E-03, Speed: 106.439 samples/sec, ObjLoss=20.834, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.732 [Epoch 203][Batch 799], LR: 1.00E-03, Speed: 51.018 samples/sec, ObjLoss=20.834, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.732 [Epoch 203][Batch 899], LR: 1.00E-03, Speed: 75.968 samples/sec, ObjLoss=20.833, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.731 [Epoch 203][Batch 999], LR: 1.00E-03, Speed: 71.954 samples/sec, ObjLoss=20.833, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.731 [Epoch 203][Batch 1099], LR: 1.00E-03, Speed: 83.249 samples/sec, ObjLoss=20.832, BoxCenterLoss=14.509, BoxScaleLoss=4.829, ClassLoss=7.730 [Epoch 203][Batch 1199], LR: 1.00E-03, Speed: 72.346 samples/sec, ObjLoss=20.832, BoxCenterLoss=14.508, BoxScaleLoss=4.829, ClassLoss=7.730 [Epoch 203][Batch 1299], LR: 1.00E-03, Speed: 97.202 samples/sec, ObjLoss=20.831, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.730 [Epoch 203][Batch 1399], LR: 1.00E-03, Speed: 73.641 samples/sec, ObjLoss=20.831, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.729 [Epoch 203][Batch 1499], LR: 1.00E-03, Speed: 107.592 samples/sec, ObjLoss=20.830, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.729 [Epoch 203][Batch 1599], LR: 1.00E-03, Speed: 93.894 samples/sec, ObjLoss=20.830, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.728 [Epoch 203][Batch 1699], LR: 1.00E-03, Speed: 67.359 samples/sec, ObjLoss=20.829, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.728 [Epoch 203][Batch 1799], LR: 1.00E-03, Speed: 109.189 samples/sec, ObjLoss=20.829, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.727 [Epoch 203] Training cost: 1695.413, ObjLoss=20.829, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.727 [Epoch 203] 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 ] = 0.428 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 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 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.414 person=34.1 bicycle=14.1 car=21.8 motorcycle=24.9 airplane=42.5 bus=46.0 train=46.0 truck=19.9 boat=10.4 traffic light=13.6 fire hydrant=41.4 stop sign=38.9 parking meter=25.9 bench=11.0 bird=15.4 cat=37.7 dog=34.5 horse=35.8 sheep=26.1 cow=30.6 elephant=43.3 bear=45.0 zebra=44.9 giraffe=42.7 backpack=4.9 umbrella=19.3 handbag=3.8 tie=14.1 suitcase=15.9 frisbee=37.9 skis=9.0 snowboard=12.7 sports ball=22.0 kite=23.5 baseball bat=10.1 baseball glove=19.1 skateboard=23.8 surfboard=16.4 tennis racket=22.4 bottle=16.9 wine glass=16.3 cup=23.6 fork=10.3 knife=4.3 spoon=2.6 bowl=19.6 banana=12.6 apple=7.8 sandwich=15.2 orange=12.7 broccoli=9.8 carrot=9.7 hot dog=16.0 pizza=28.2 donut=16.2 cake=15.0 chair=12.8 couch=26.5 potted plant=11.5 bed=29.7 dining table=13.3 toilet=37.0 tv=35.0 laptop=34.8 mouse=29.2 remote=10.2 keyboard=23.8 cell phone=14.6 microwave=26.5 oven=17.4 toaster=0.0 sink=22.6 refrigerator=33.7 book=6.1 clock=27.4 vase=19.6 scissors=16.6 teddy bear=22.5 hair drier=0.0 toothbrush=5.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.4 [Epoch 204][Batch 99], LR: 1.00E-03, Speed: 126.943 samples/sec, ObjLoss=20.828, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.727 [Epoch 204][Batch 199], LR: 1.00E-03, Speed: 83.840 samples/sec, ObjLoss=20.828, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.726 [Epoch 204][Batch 299], LR: 1.00E-03, Speed: 60.164 samples/sec, ObjLoss=20.828, BoxCenterLoss=14.508, BoxScaleLoss=4.828, ClassLoss=7.726 [Epoch 204][Batch 399], LR: 1.00E-03, Speed: 78.682 samples/sec, ObjLoss=20.827, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.725 [Epoch 204][Batch 499], LR: 1.00E-03, Speed: 114.737 samples/sec, ObjLoss=20.827, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.725 [Epoch 204][Batch 599], LR: 1.00E-03, Speed: 144.900 samples/sec, ObjLoss=20.826, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.725 [Epoch 204][Batch 699], LR: 1.00E-03, Speed: 85.764 samples/sec, ObjLoss=20.826, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.724 [Epoch 204][Batch 799], LR: 1.00E-03, Speed: 81.508 samples/sec, ObjLoss=20.825, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.724 [Epoch 204][Batch 899], LR: 1.00E-03, Speed: 77.789 samples/sec, ObjLoss=20.825, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.724 [Epoch 204][Batch 999], LR: 1.00E-03, Speed: 67.594 samples/sec, ObjLoss=20.824, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.723 [Epoch 204][Batch 1099], LR: 1.00E-03, Speed: 119.272 samples/sec, ObjLoss=20.824, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.723 [Epoch 204][Batch 1199], LR: 1.00E-03, Speed: 77.530 samples/sec, ObjLoss=20.823, BoxCenterLoss=14.508, BoxScaleLoss=4.827, ClassLoss=7.722 [Epoch 204][Batch 1299], LR: 1.00E-03, Speed: 65.294 samples/sec, ObjLoss=20.822, BoxCenterLoss=14.507, BoxScaleLoss=4.827, ClassLoss=7.722 [Epoch 204][Batch 1399], LR: 1.00E-03, Speed: 153.580 samples/sec, ObjLoss=20.822, BoxCenterLoss=14.507, BoxScaleLoss=4.827, ClassLoss=7.722 [Epoch 204][Batch 1499], LR: 1.00E-03, Speed: 138.574 samples/sec, ObjLoss=20.822, BoxCenterLoss=14.507, BoxScaleLoss=4.827, ClassLoss=7.721 [Epoch 204][Batch 1599], LR: 1.00E-03, Speed: 71.154 samples/sec, ObjLoss=20.821, BoxCenterLoss=14.507, BoxScaleLoss=4.827, ClassLoss=7.721 [Epoch 204][Batch 1699], LR: 1.00E-03, Speed: 60.861 samples/sec, ObjLoss=20.821, BoxCenterLoss=14.507, BoxScaleLoss=4.827, ClassLoss=7.720 [Epoch 204][Batch 1799], LR: 1.00E-03, Speed: 130.702 samples/sec, ObjLoss=20.820, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.720 [Epoch 204] Training cost: 1690.165, ObjLoss=20.820, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.720 [Epoch 204] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.189 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 ] = 0.213 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321 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 ] = 0.291 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 ] = 0.139 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 ] = 0.427 person=33.8 bicycle=15.5 car=21.6 motorcycle=25.2 airplane=38.8 bus=41.9 train=42.2 truck=19.4 boat=10.9 traffic light=12.8 fire hydrant=38.4 stop sign=42.5 parking meter=21.9 bench=11.1 bird=15.8 cat=39.5 dog=37.4 horse=29.2 sheep=27.2 cow=30.1 elephant=40.3 bear=43.3 zebra=42.6 giraffe=45.5 backpack=6.2 umbrella=20.6 handbag=4.3 tie=14.7 suitcase=16.4 frisbee=32.4 skis=6.1 snowboard=12.6 sports ball=20.4 kite=20.7 baseball bat=10.8 baseball glove=17.6 skateboard=23.5 surfboard=14.5 tennis racket=20.5 bottle=15.0 wine glass=15.3 cup=19.9 fork=9.9 knife=3.8 spoon=2.8 bowl=20.9 banana=9.7 apple=7.7 sandwich=20.7 orange=16.9 broccoli=11.0 carrot=9.0 hot dog=16.1 pizza=26.3 donut=24.7 cake=18.7 chair=12.2 couch=26.8 potted plant=12.5 bed=28.4 dining table=14.2 toilet=36.9 tv=33.5 laptop=34.2 mouse=35.0 remote=10.5 keyboard=24.8 cell phone=12.0 microwave=26.6 oven=20.7 toaster=0.0 sink=19.7 refrigerator=27.7 book=4.8 clock=30.0 vase=17.3 scissors=16.7 teddy bear=25.0 hair drier=0.0 toothbrush=6.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 205][Batch 99], LR: 1.00E-03, Speed: 121.833 samples/sec, ObjLoss=20.820, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.720 [Epoch 205][Batch 199], LR: 1.00E-03, Speed: 72.848 samples/sec, ObjLoss=20.819, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.719 [Epoch 205][Batch 299], LR: 1.00E-03, Speed: 66.839 samples/sec, ObjLoss=20.818, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.719 [Epoch 205][Batch 399], LR: 1.00E-03, Speed: 131.273 samples/sec, ObjLoss=20.818, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.718 [Epoch 205][Batch 499], LR: 1.00E-03, Speed: 73.087 samples/sec, ObjLoss=20.817, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.718 [Epoch 205][Batch 599], LR: 1.00E-03, Speed: 93.916 samples/sec, ObjLoss=20.817, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.718 [Epoch 205][Batch 699], LR: 1.00E-03, Speed: 63.222 samples/sec, ObjLoss=20.816, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.717 [Epoch 205][Batch 799], LR: 1.00E-03, Speed: 105.887 samples/sec, ObjLoss=20.816, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.717 [Epoch 205][Batch 899], LR: 1.00E-03, Speed: 135.079 samples/sec, ObjLoss=20.815, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.716 [Epoch 205][Batch 999], LR: 1.00E-03, Speed: 69.739 samples/sec, ObjLoss=20.815, BoxCenterLoss=14.507, BoxScaleLoss=4.826, ClassLoss=7.716 [Epoch 205][Batch 1099], LR: 1.00E-03, Speed: 96.700 samples/sec, ObjLoss=20.814, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.716 [Epoch 205][Batch 1199], LR: 1.00E-03, Speed: 78.713 samples/sec, ObjLoss=20.814, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.715 [Epoch 205][Batch 1299], LR: 1.00E-03, Speed: 95.754 samples/sec, ObjLoss=20.814, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.715 [Epoch 205][Batch 1399], LR: 1.00E-03, Speed: 103.995 samples/sec, ObjLoss=20.813, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.715 [Epoch 205][Batch 1499], LR: 1.00E-03, Speed: 89.123 samples/sec, ObjLoss=20.813, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.714 [Epoch 205][Batch 1599], LR: 1.00E-03, Speed: 90.229 samples/sec, ObjLoss=20.812, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.714 [Epoch 205][Batch 1699], LR: 1.00E-03, Speed: 58.971 samples/sec, ObjLoss=20.812, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.713 [Epoch 205][Batch 1799], LR: 1.00E-03, Speed: 103.835 samples/sec, ObjLoss=20.811, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.713 [Epoch 205] Training cost: 1647.458, ObjLoss=20.811, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.713 [Epoch 205] 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 ] = 0.418 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.202 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.217 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 ] = 0.203 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 ] = 0.305 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 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441 person=32.9 bicycle=15.3 car=22.0 motorcycle=25.4 airplane=38.6 bus=43.7 train=45.9 truck=20.2 boat=12.1 traffic light=14.1 fire hydrant=40.9 stop sign=42.1 parking meter=25.8 bench=11.2 bird=17.4 cat=43.9 dog=33.6 horse=33.8 sheep=24.1 cow=31.1 elephant=39.7 bear=42.9 zebra=42.7 giraffe=43.0 backpack=5.3 umbrella=20.4 handbag=4.4 tie=11.5 suitcase=16.0 frisbee=26.4 skis=6.8 snowboard=13.0 sports ball=24.6 kite=20.6 baseball bat=8.5 baseball glove=16.6 skateboard=24.6 surfboard=16.0 tennis racket=22.9 bottle=12.8 wine glass=15.8 cup=21.7 fork=10.0 knife=2.2 spoon=2.2 bowl=21.6 banana=9.7 apple=7.0 sandwich=18.7 orange=15.0 broccoli=9.8 carrot=8.9 hot dog=16.1 pizza=25.0 donut=21.2 cake=17.0 chair=12.6 couch=27.3 potted plant=12.8 bed=32.4 dining table=20.2 toilet=38.1 tv=36.9 laptop=38.5 mouse=32.6 remote=7.1 keyboard=28.8 cell phone=15.1 microwave=32.0 oven=21.2 toaster=1.2 sink=17.1 refrigerator=31.6 book=4.8 clock=30.8 vase=17.7 scissors=17.6 teddy bear=26.6 hair drier=0.0 toothbrush=2.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.5 [Epoch 206][Batch 99], LR: 1.00E-03, Speed: 117.796 samples/sec, ObjLoss=20.811, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.712 [Epoch 206][Batch 199], LR: 1.00E-03, Speed: 63.046 samples/sec, ObjLoss=20.810, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.712 [Epoch 206][Batch 299], LR: 1.00E-03, Speed: 59.059 samples/sec, ObjLoss=20.809, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.712 [Epoch 206][Batch 399], LR: 1.00E-03, Speed: 100.948 samples/sec, ObjLoss=20.809, BoxCenterLoss=14.506, BoxScaleLoss=4.825, ClassLoss=7.711 [Epoch 206][Batch 499], LR: 1.00E-03, Speed: 104.811 samples/sec, ObjLoss=20.809, BoxCenterLoss=14.506, BoxScaleLoss=4.824, ClassLoss=7.711 [Epoch 206][Batch 599], LR: 1.00E-03, Speed: 90.991 samples/sec, ObjLoss=20.808, BoxCenterLoss=14.506, BoxScaleLoss=4.824, ClassLoss=7.711 [Epoch 206][Batch 699], LR: 1.00E-03, Speed: 72.500 samples/sec, ObjLoss=20.807, BoxCenterLoss=14.506, BoxScaleLoss=4.824, ClassLoss=7.710 [Epoch 206][Batch 799], LR: 1.00E-03, Speed: 64.554 samples/sec, ObjLoss=20.807, BoxCenterLoss=14.506, BoxScaleLoss=4.824, ClassLoss=7.710 [Epoch 206][Batch 899], LR: 1.00E-03, Speed: 71.280 samples/sec, ObjLoss=20.806, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.709 [Epoch 206][Batch 999], LR: 1.00E-03, Speed: 111.456 samples/sec, ObjLoss=20.806, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.709 [Epoch 206][Batch 1099], LR: 1.00E-03, Speed: 87.468 samples/sec, ObjLoss=20.806, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.709 [Epoch 206][Batch 1199], LR: 1.00E-03, Speed: 66.089 samples/sec, ObjLoss=20.805, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.708 [Epoch 206][Batch 1299], LR: 1.00E-03, Speed: 78.860 samples/sec, ObjLoss=20.805, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.708 [Epoch 206][Batch 1399], LR: 1.00E-03, Speed: 90.788 samples/sec, ObjLoss=20.804, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.707 [Epoch 206][Batch 1499], LR: 1.00E-03, Speed: 78.584 samples/sec, ObjLoss=20.804, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.707 [Epoch 206][Batch 1599], LR: 1.00E-03, Speed: 60.342 samples/sec, ObjLoss=20.803, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.707 [Epoch 206][Batch 1699], LR: 1.00E-03, Speed: 40.661 samples/sec, ObjLoss=20.803, BoxCenterLoss=14.505, BoxScaleLoss=4.824, ClassLoss=7.706 [Epoch 206][Batch 1799], LR: 1.00E-03, Speed: 113.877 samples/sec, ObjLoss=20.802, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.706 [Epoch 206] Training cost: 1690.668, ObjLoss=20.802, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.706 [Epoch 206] 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 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167 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 ] = 0.220 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.287 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.192 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 ] = 0.295 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 ] = 0.310 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.400 person=33.5 bicycle=13.7 car=22.1 motorcycle=22.4 airplane=37.7 bus=33.9 train=38.3 truck=18.2 boat=10.8 traffic light=14.0 fire hydrant=38.4 stop sign=35.0 parking meter=24.2 bench=10.2 bird=14.3 cat=33.7 dog=30.2 horse=31.3 sheep=25.1 cow=29.0 elephant=39.6 bear=33.1 zebra=38.2 giraffe=38.6 backpack=6.3 umbrella=19.3 handbag=4.6 tie=14.7 suitcase=15.1 frisbee=34.3 skis=7.1 snowboard=14.0 sports ball=23.0 kite=22.7 baseball bat=10.2 baseball glove=18.7 skateboard=27.0 surfboard=14.8 tennis racket=23.6 bottle=18.1 wine glass=14.6 cup=21.5 fork=8.8 knife=2.1 spoon=3.1 bowl=20.1 banana=9.7 apple=6.0 sandwich=13.1 orange=13.3 broccoli=9.5 carrot=9.0 hot dog=16.5 pizza=26.0 donut=16.2 cake=15.9 chair=12.5 couch=27.5 potted plant=11.4 bed=25.8 dining table=17.1 toilet=30.9 tv=35.0 laptop=35.1 mouse=33.6 remote=8.7 keyboard=31.1 cell phone=11.9 microwave=27.8 oven=19.9 toaster=0.0 sink=19.9 refrigerator=28.8 book=5.1 clock=30.8 vase=20.2 scissors=15.4 teddy bear=26.0 hair drier=0.0 toothbrush=3.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.3 [Epoch 207][Batch 99], LR: 1.00E-03, Speed: 94.796 samples/sec, ObjLoss=20.802, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.705 [Epoch 207][Batch 199], LR: 1.00E-03, Speed: 75.502 samples/sec, ObjLoss=20.801, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.705 [Epoch 207][Batch 299], LR: 1.00E-03, Speed: 95.144 samples/sec, ObjLoss=20.801, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.705 [Epoch 207][Batch 399], LR: 1.00E-03, Speed: 81.254 samples/sec, ObjLoss=20.800, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.704 [Epoch 207][Batch 499], LR: 1.00E-03, Speed: 148.069 samples/sec, ObjLoss=20.800, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.704 [Epoch 207][Batch 599], LR: 1.00E-03, Speed: 59.680 samples/sec, ObjLoss=20.799, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.703 [Epoch 207][Batch 699], LR: 1.00E-03, Speed: 80.087 samples/sec, ObjLoss=20.799, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.703 [Epoch 207][Batch 799], LR: 1.00E-03, Speed: 41.662 samples/sec, ObjLoss=20.798, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.703 [Epoch 207][Batch 899], LR: 1.00E-03, Speed: 56.577 samples/sec, ObjLoss=20.798, BoxCenterLoss=14.505, BoxScaleLoss=4.823, ClassLoss=7.702 [Epoch 207][Batch 999], LR: 1.00E-03, Speed: 57.392 samples/sec, ObjLoss=20.798, BoxCenterLoss=14.505, BoxScaleLoss=4.822, ClassLoss=7.702 [Epoch 207][Batch 1099], LR: 1.00E-03, Speed: 53.158 samples/sec, ObjLoss=20.797, BoxCenterLoss=14.505, BoxScaleLoss=4.822, ClassLoss=7.701 [Epoch 207][Batch 1199], LR: 1.00E-03, Speed: 87.344 samples/sec, ObjLoss=20.797, BoxCenterLoss=14.505, BoxScaleLoss=4.822, ClassLoss=7.701 [Epoch 207][Batch 1299], LR: 1.00E-03, Speed: 123.903 samples/sec, ObjLoss=20.796, BoxCenterLoss=14.505, BoxScaleLoss=4.822, ClassLoss=7.701 [Epoch 207][Batch 1399], LR: 1.00E-03, Speed: 125.666 samples/sec, ObjLoss=20.796, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.700 [Epoch 207][Batch 1499], LR: 1.00E-03, Speed: 99.414 samples/sec, ObjLoss=20.796, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.700 [Epoch 207][Batch 1599], LR: 1.00E-03, Speed: 56.926 samples/sec, ObjLoss=20.795, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.700 [Epoch 207][Batch 1699], LR: 1.00E-03, Speed: 91.139 samples/sec, ObjLoss=20.795, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.699 [Epoch 207][Batch 1799], LR: 1.00E-03, Speed: 102.505 samples/sec, ObjLoss=20.794, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.699 [Epoch 207] Training cost: 1730.238, ObjLoss=20.794, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.699 [Epoch 207] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.420 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.174 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 ] = 0.218 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 ] = 0.192 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 ] = 0.289 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 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.405 person=30.6 bicycle=14.4 car=19.5 motorcycle=26.4 airplane=37.5 bus=39.6 train=37.1 truck=19.8 boat=9.1 traffic light=8.9 fire hydrant=37.7 stop sign=35.6 parking meter=22.7 bench=11.2 bird=15.1 cat=40.3 dog=36.5 horse=29.2 sheep=26.7 cow=32.0 elephant=39.4 bear=45.4 zebra=42.8 giraffe=40.2 backpack=4.2 umbrella=20.3 handbag=3.5 tie=11.9 suitcase=15.0 frisbee=33.0 skis=7.0 snowboard=14.7 sports ball=23.5 kite=18.7 baseball bat=10.1 baseball glove=14.9 skateboard=24.2 surfboard=13.7 tennis racket=23.2 bottle=14.8 wine glass=15.8 cup=19.0 fork=8.7 knife=3.3 spoon=3.7 bowl=19.7 banana=10.6 apple=5.4 sandwich=18.9 orange=17.2 broccoli=10.6 carrot=8.6 hot dog=16.6 pizza=30.4 donut=16.6 cake=18.1 chair=13.5 couch=24.6 potted plant=11.1 bed=25.5 dining table=15.3 toilet=28.6 tv=34.3 laptop=34.6 mouse=32.0 remote=7.8 keyboard=28.9 cell phone=13.2 microwave=27.7 oven=17.8 toaster=0.0 sink=20.1 refrigerator=31.5 book=5.6 clock=30.0 vase=17.2 scissors=16.4 teddy bear=24.7 hair drier=0.0 toothbrush=3.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.5 [Epoch 208][Batch 99], LR: 1.00E-03, Speed: 141.648 samples/sec, ObjLoss=20.794, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.698 [Epoch 208][Batch 199], LR: 1.00E-03, Speed: 99.431 samples/sec, ObjLoss=20.793, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.698 [Epoch 208][Batch 299], LR: 1.00E-03, Speed: 82.833 samples/sec, ObjLoss=20.793, BoxCenterLoss=14.504, BoxScaleLoss=4.822, ClassLoss=7.697 [Epoch 208][Batch 399], LR: 1.00E-03, Speed: 85.839 samples/sec, ObjLoss=20.792, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.697 [Epoch 208][Batch 499], LR: 1.00E-03, Speed: 95.310 samples/sec, ObjLoss=20.792, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.696 [Epoch 208][Batch 599], LR: 1.00E-03, Speed: 88.264 samples/sec, ObjLoss=20.791, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.696 [Epoch 208][Batch 699], LR: 1.00E-03, Speed: 77.004 samples/sec, ObjLoss=20.791, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.696 [Epoch 208][Batch 799], LR: 1.00E-03, Speed: 66.840 samples/sec, ObjLoss=20.790, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.695 [Epoch 208][Batch 899], LR: 1.00E-03, Speed: 76.633 samples/sec, ObjLoss=20.790, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.695 [Epoch 208][Batch 999], LR: 1.00E-03, Speed: 59.625 samples/sec, ObjLoss=20.790, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.695 [Epoch 208][Batch 1099], LR: 1.00E-03, Speed: 111.656 samples/sec, ObjLoss=20.789, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.694 [Epoch 208][Batch 1199], LR: 1.00E-03, Speed: 53.059 samples/sec, ObjLoss=20.789, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.694 [Epoch 208][Batch 1299], LR: 1.00E-03, Speed: 77.658 samples/sec, ObjLoss=20.788, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.693 [Epoch 208][Batch 1399], LR: 1.00E-03, Speed: 68.628 samples/sec, ObjLoss=20.788, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.693 [Epoch 208][Batch 1499], LR: 1.00E-03, Speed: 118.081 samples/sec, ObjLoss=20.788, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.693 [Epoch 208][Batch 1599], LR: 1.00E-03, Speed: 78.787 samples/sec, ObjLoss=20.787, BoxCenterLoss=14.504, BoxScaleLoss=4.821, ClassLoss=7.692 [Epoch 208][Batch 1699], LR: 1.00E-03, Speed: 55.487 samples/sec, ObjLoss=20.787, BoxCenterLoss=14.504, BoxScaleLoss=4.820, ClassLoss=7.692 [Epoch 208][Batch 1799], LR: 1.00E-03, Speed: 160.902 samples/sec, ObjLoss=20.786, BoxCenterLoss=14.504, BoxScaleLoss=4.820, ClassLoss=7.692 [Epoch 208] Training cost: 1749.374, ObjLoss=20.786, BoxCenterLoss=14.504, BoxScaleLoss=4.820, ClassLoss=7.691 [Epoch 208] 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 ] = 0.431 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 ] = 0.220 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 ] = 0.196 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 ] = 0.296 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 ] = 0.304 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 person=34.6 bicycle=16.2 car=22.0 motorcycle=26.4 airplane=38.8 bus=40.9 train=39.6 truck=16.8 boat=11.1 traffic light=11.6 fire hydrant=35.5 stop sign=34.5 parking meter=18.8 bench=10.3 bird=17.0 cat=42.7 dog=34.9 horse=30.6 sheep=27.4 cow=32.6 elephant=40.1 bear=39.5 zebra=43.5 giraffe=44.0 backpack=4.6 umbrella=20.4 handbag=3.9 tie=13.6 suitcase=14.0 frisbee=31.5 skis=8.8 snowboard=11.6 sports ball=21.0 kite=25.1 baseball bat=11.8 baseball glove=14.4 skateboard=20.1 surfboard=17.1 tennis racket=24.8 bottle=17.5 wine glass=16.0 cup=19.4 fork=7.8 knife=2.6 spoon=3.4 bowl=19.1 banana=12.2 apple=6.8 sandwich=19.6 orange=13.5 broccoli=11.2 carrot=7.9 hot dog=17.6 pizza=32.5 donut=15.3 cake=16.5 chair=12.5 couch=28.2 potted plant=11.2 bed=27.2 dining table=14.6 toilet=31.6 tv=32.5 laptop=36.1 mouse=29.2 remote=8.0 keyboard=28.3 cell phone=13.6 microwave=30.2 oven=20.6 toaster=3.0 sink=18.9 refrigerator=31.5 book=5.6 clock=32.3 vase=18.7 scissors=19.2 teddy bear=25.4 hair drier=0.0 toothbrush=4.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 209][Batch 99], LR: 1.00E-03, Speed: 160.570 samples/sec, ObjLoss=20.786, BoxCenterLoss=14.504, BoxScaleLoss=4.820, ClassLoss=7.691 [Epoch 209][Batch 199], LR: 1.00E-03, Speed: 70.821 samples/sec, ObjLoss=20.785, BoxCenterLoss=14.504, BoxScaleLoss=4.820, ClassLoss=7.691 [Epoch 209][Batch 299], LR: 1.00E-03, Speed: 145.676 samples/sec, ObjLoss=20.785, BoxCenterLoss=14.504, BoxScaleLoss=4.820, ClassLoss=7.690 [Epoch 209][Batch 399], LR: 1.00E-03, Speed: 69.467 samples/sec, ObjLoss=20.784, BoxCenterLoss=14.503, BoxScaleLoss=4.820, ClassLoss=7.690 [Epoch 209][Batch 499], LR: 1.00E-03, Speed: 80.619 samples/sec, ObjLoss=20.784, BoxCenterLoss=14.503, BoxScaleLoss=4.820, ClassLoss=7.690 [Epoch 209][Batch 599], LR: 1.00E-03, Speed: 101.250 samples/sec, ObjLoss=20.783, BoxCenterLoss=14.503, BoxScaleLoss=4.820, ClassLoss=7.689 [Epoch 209][Batch 699], LR: 1.00E-03, Speed: 86.024 samples/sec, ObjLoss=20.783, BoxCenterLoss=14.503, BoxScaleLoss=4.820, ClassLoss=7.689 [Epoch 209][Batch 799], LR: 1.00E-03, Speed: 96.374 samples/sec, ObjLoss=20.782, BoxCenterLoss=14.503, BoxScaleLoss=4.820, ClassLoss=7.688 [Epoch 209][Batch 899], LR: 1.00E-03, Speed: 60.096 samples/sec, ObjLoss=20.782, BoxCenterLoss=14.503, BoxScaleLoss=4.820, ClassLoss=7.688 [Epoch 209][Batch 999], LR: 1.00E-03, Speed: 84.816 samples/sec, ObjLoss=20.781, BoxCenterLoss=14.503, BoxScaleLoss=4.820, ClassLoss=7.688 [Epoch 209][Batch 1099], LR: 1.00E-03, Speed: 77.384 samples/sec, ObjLoss=20.781, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.687 [Epoch 209][Batch 1199], LR: 1.00E-03, Speed: 118.425 samples/sec, ObjLoss=20.780, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.687 [Epoch 209][Batch 1299], LR: 1.00E-03, Speed: 69.281 samples/sec, ObjLoss=20.780, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.687 [Epoch 209][Batch 1399], LR: 1.00E-03, Speed: 123.109 samples/sec, ObjLoss=20.779, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.686 [Epoch 209][Batch 1499], LR: 1.00E-03, Speed: 75.268 samples/sec, ObjLoss=20.779, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.686 [Epoch 209][Batch 1599], LR: 1.00E-03, Speed: 117.880 samples/sec, ObjLoss=20.779, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.686 [Epoch 209][Batch 1699], LR: 1.00E-03, Speed: 69.303 samples/sec, ObjLoss=20.778, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.685 [Epoch 209][Batch 1799], LR: 1.00E-03, Speed: 54.873 samples/sec, ObjLoss=20.778, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.685 [Epoch 209] Training cost: 1727.018, ObjLoss=20.778, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.685 [Epoch 209] 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 ] = 0.420 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.079 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 ] = 0.312 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 ] = 0.285 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 ] = 0.133 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=32.6 bicycle=14.1 car=21.1 motorcycle=25.4 airplane=40.3 bus=39.6 train=47.7 truck=18.6 boat=11.5 traffic light=12.5 fire hydrant=36.0 stop sign=36.0 parking meter=20.5 bench=11.6 bird=17.4 cat=43.1 dog=34.4 horse=32.0 sheep=25.0 cow=30.4 elephant=39.0 bear=35.2 zebra=44.3 giraffe=46.0 backpack=3.2 umbrella=19.0 handbag=4.0 tie=16.6 suitcase=14.2 frisbee=33.4 skis=7.0 snowboard=10.3 sports ball=22.3 kite=23.0 baseball bat=10.4 baseball glove=17.2 skateboard=22.6 surfboard=16.8 tennis racket=24.3 bottle=15.7 wine glass=15.7 cup=20.7 fork=9.9 knife=4.1 spoon=2.3 bowl=18.5 banana=10.4 apple=7.1 sandwich=16.5 orange=15.2 broccoli=9.5 carrot=10.7 hot dog=11.7 pizza=23.7 donut=14.1 cake=18.0 chair=12.1 couch=24.3 potted plant=11.4 bed=27.4 dining table=14.2 toilet=35.8 tv=34.9 laptop=33.1 mouse=31.7 remote=7.3 keyboard=28.2 cell phone=16.4 microwave=23.3 oven=20.9 toaster=1.5 sink=17.8 refrigerator=35.9 book=4.7 clock=31.1 vase=16.6 scissors=15.2 teddy bear=24.2 hair drier=0.0 toothbrush=7.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.7 [Epoch 210][Batch 99], LR: 1.00E-03, Speed: 127.507 samples/sec, ObjLoss=20.777, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.684 [Epoch 210][Batch 199], LR: 1.00E-03, Speed: 80.373 samples/sec, ObjLoss=20.777, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.684 [Epoch 210][Batch 299], LR: 1.00E-03, Speed: 61.117 samples/sec, ObjLoss=20.776, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.684 [Epoch 210][Batch 399], LR: 1.00E-03, Speed: 83.912 samples/sec, ObjLoss=20.776, BoxCenterLoss=14.503, BoxScaleLoss=4.819, ClassLoss=7.683 [Epoch 210][Batch 499], LR: 1.00E-03, Speed: 77.471 samples/sec, ObjLoss=20.776, BoxCenterLoss=14.503, BoxScaleLoss=4.818, ClassLoss=7.683 [Epoch 210][Batch 599], LR: 1.00E-03, Speed: 56.028 samples/sec, ObjLoss=20.775, BoxCenterLoss=14.503, BoxScaleLoss=4.818, ClassLoss=7.682 [Epoch 210][Batch 699], LR: 1.00E-03, Speed: 47.706 samples/sec, ObjLoss=20.775, BoxCenterLoss=14.503, BoxScaleLoss=4.818, ClassLoss=7.682 [Epoch 210][Batch 799], LR: 1.00E-03, Speed: 60.863 samples/sec, ObjLoss=20.775, BoxCenterLoss=14.503, BoxScaleLoss=4.818, ClassLoss=7.682 [Epoch 210][Batch 899], LR: 1.00E-03, Speed: 66.304 samples/sec, ObjLoss=20.774, BoxCenterLoss=14.503, BoxScaleLoss=4.818, ClassLoss=7.681 [Epoch 210][Batch 999], LR: 1.00E-03, Speed: 77.015 samples/sec, ObjLoss=20.774, BoxCenterLoss=14.503, BoxScaleLoss=4.818, ClassLoss=7.681 [Epoch 210][Batch 1099], LR: 1.00E-03, Speed: 67.662 samples/sec, ObjLoss=20.773, BoxCenterLoss=14.503, BoxScaleLoss=4.818, ClassLoss=7.681 [Epoch 210][Batch 1199], LR: 1.00E-03, Speed: 93.418 samples/sec, ObjLoss=20.773, BoxCenterLoss=14.502, BoxScaleLoss=4.818, ClassLoss=7.680 [Epoch 210][Batch 1299], LR: 1.00E-03, Speed: 92.460 samples/sec, ObjLoss=20.772, BoxCenterLoss=14.502, BoxScaleLoss=4.818, ClassLoss=7.680 [Epoch 210][Batch 1399], LR: 1.00E-03, Speed: 48.334 samples/sec, ObjLoss=20.772, BoxCenterLoss=14.502, BoxScaleLoss=4.818, ClassLoss=7.679 [Epoch 210][Batch 1499], LR: 1.00E-03, Speed: 64.729 samples/sec, ObjLoss=20.771, BoxCenterLoss=14.502, BoxScaleLoss=4.818, ClassLoss=7.679 [Epoch 210][Batch 1599], LR: 1.00E-03, Speed: 69.342 samples/sec, ObjLoss=20.771, BoxCenterLoss=14.502, BoxScaleLoss=4.818, ClassLoss=7.679 [Epoch 210][Batch 1699], LR: 1.00E-03, Speed: 71.173 samples/sec, ObjLoss=20.770, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.678 [Epoch 210][Batch 1799], LR: 1.00E-03, Speed: 141.017 samples/sec, ObjLoss=20.770, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.678 [Epoch 210] Training cost: 1716.786, ObjLoss=20.770, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.678 [Epoch 210] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.422 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.155 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.079 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 ] = 0.293 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.189 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 ] = 0.285 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 ] = 0.298 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 person=31.0 bicycle=14.1 car=19.7 motorcycle=23.0 airplane=35.4 bus=38.9 train=40.6 truck=19.7 boat=8.1 traffic light=10.0 fire hydrant=33.7 stop sign=31.5 parking meter=24.5 bench=10.1 bird=12.6 cat=39.6 dog=33.8 horse=25.4 sheep=20.5 cow=26.9 elephant=36.1 bear=35.9 zebra=39.7 giraffe=43.3 backpack=5.5 umbrella=18.0 handbag=4.6 tie=14.5 suitcase=15.0 frisbee=30.4 skis=7.8 snowboard=11.3 sports ball=13.2 kite=19.6 baseball bat=10.8 baseball glove=13.3 skateboard=23.1 surfboard=14.5 tennis racket=22.9 bottle=15.9 wine glass=12.9 cup=20.5 fork=9.3 knife=3.6 spoon=3.2 bowl=19.1 banana=11.4 apple=6.4 sandwich=17.0 orange=14.1 broccoli=10.8 carrot=8.4 hot dog=16.9 pizza=28.1 donut=19.8 cake=16.1 chair=11.2 couch=23.8 potted plant=10.9 bed=25.4 dining table=14.7 toilet=35.7 tv=31.3 laptop=34.7 mouse=30.3 remote=7.4 keyboard=24.1 cell phone=15.5 microwave=27.8 oven=18.8 toaster=8.3 sink=18.5 refrigerator=26.4 book=3.3 clock=27.5 vase=17.3 scissors=15.7 teddy bear=24.6 hair drier=0.0 toothbrush=7.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =19.7 [Epoch 211][Batch 99], LR: 1.00E-03, Speed: 135.488 samples/sec, ObjLoss=20.769, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.677 [Epoch 211][Batch 199], LR: 1.00E-03, Speed: 82.100 samples/sec, ObjLoss=20.769, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.677 [Epoch 211][Batch 299], LR: 1.00E-03, Speed: 82.107 samples/sec, ObjLoss=20.768, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.677 [Epoch 211][Batch 399], LR: 1.00E-03, Speed: 65.460 samples/sec, ObjLoss=20.768, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.677 [Epoch 211][Batch 499], LR: 1.00E-03, Speed: 66.645 samples/sec, ObjLoss=20.767, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.676 [Epoch 211][Batch 599], LR: 1.00E-03, Speed: 94.552 samples/sec, ObjLoss=20.767, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.676 [Epoch 211][Batch 699], LR: 1.00E-03, Speed: 59.788 samples/sec, ObjLoss=20.766, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.675 [Epoch 211][Batch 799], LR: 1.00E-03, Speed: 52.924 samples/sec, ObjLoss=20.766, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.675 [Epoch 211][Batch 899], LR: 1.00E-03, Speed: 65.348 samples/sec, ObjLoss=20.766, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.675 [Epoch 211][Batch 999], LR: 1.00E-03, Speed: 74.984 samples/sec, ObjLoss=20.765, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.674 [Epoch 211][Batch 1099], LR: 1.00E-03, Speed: 81.360 samples/sec, ObjLoss=20.765, BoxCenterLoss=14.502, BoxScaleLoss=4.817, ClassLoss=7.674 [Epoch 211][Batch 1199], LR: 1.00E-03, Speed: 65.584 samples/sec, ObjLoss=20.764, BoxCenterLoss=14.502, BoxScaleLoss=4.816, ClassLoss=7.673 [Epoch 211][Batch 1299], LR: 1.00E-03, Speed: 110.286 samples/sec, ObjLoss=20.764, BoxCenterLoss=14.502, BoxScaleLoss=4.816, ClassLoss=7.673 [Epoch 211][Batch 1399], LR: 1.00E-03, Speed: 50.363 samples/sec, ObjLoss=20.763, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.673 [Epoch 211][Batch 1499], LR: 1.00E-03, Speed: 74.479 samples/sec, ObjLoss=20.763, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.672 [Epoch 211][Batch 1599], LR: 1.00E-03, Speed: 62.538 samples/sec, ObjLoss=20.763, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.672 [Epoch 211][Batch 1699], LR: 1.00E-03, Speed: 75.026 samples/sec, ObjLoss=20.762, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.671 [Epoch 211][Batch 1799], LR: 1.00E-03, Speed: 164.111 samples/sec, ObjLoss=20.761, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.671 [Epoch 211] Training cost: 1670.457, ObjLoss=20.761, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.671 [Epoch 211] 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 ] = 0.421 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.084 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 ] = 0.319 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 ] = 0.292 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 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430 person=33.1 bicycle=13.6 car=20.8 motorcycle=23.2 airplane=36.9 bus=36.2 train=34.4 truck=17.7 boat=12.3 traffic light=11.1 fire hydrant=33.3 stop sign=36.0 parking meter=22.2 bench=11.3 bird=15.6 cat=39.8 dog=31.5 horse=29.4 sheep=24.1 cow=32.3 elephant=37.1 bear=40.5 zebra=39.1 giraffe=41.4 backpack=3.7 umbrella=19.6 handbag=4.5 tie=13.2 suitcase=15.9 frisbee=31.9 skis=8.8 snowboard=11.5 sports ball=21.7 kite=21.1 baseball bat=11.0 baseball glove=16.5 skateboard=23.5 surfboard=16.4 tennis racket=23.5 bottle=14.9 wine glass=16.0 cup=20.2 fork=10.3 knife=1.8 spoon=3.6 bowl=18.3 banana=12.7 apple=7.8 sandwich=20.5 orange=14.7 broccoli=10.9 carrot=8.9 hot dog=16.1 pizza=27.4 donut=23.2 cake=18.8 chair=11.7 couch=26.0 potted plant=11.8 bed=23.8 dining table=15.2 toilet=37.7 tv=38.6 laptop=35.9 mouse=32.7 remote=9.2 keyboard=28.9 cell phone=15.4 microwave=34.7 oven=17.9 toaster=0.0 sink=20.7 refrigerator=27.4 book=4.3 clock=32.0 vase=19.7 scissors=16.2 teddy bear=24.5 hair drier=0.0 toothbrush=2.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.6 [Epoch 212][Batch 99], LR: 1.00E-03, Speed: 66.020 samples/sec, ObjLoss=20.761, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.671 [Epoch 212][Batch 199], LR: 1.00E-03, Speed: 98.688 samples/sec, ObjLoss=20.760, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.670 [Epoch 212][Batch 299], LR: 1.00E-03, Speed: 86.005 samples/sec, ObjLoss=20.760, BoxCenterLoss=14.501, BoxScaleLoss=4.816, ClassLoss=7.670 [Epoch 212][Batch 399], LR: 1.00E-03, Speed: 72.578 samples/sec, ObjLoss=20.759, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.669 [Epoch 212][Batch 499], LR: 1.00E-03, Speed: 57.439 samples/sec, ObjLoss=20.759, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.669 [Epoch 212][Batch 599], LR: 1.00E-03, Speed: 92.896 samples/sec, ObjLoss=20.758, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.669 [Epoch 212][Batch 699], LR: 1.00E-03, Speed: 73.747 samples/sec, ObjLoss=20.758, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.668 [Epoch 212][Batch 799], LR: 1.00E-03, Speed: 89.071 samples/sec, ObjLoss=20.757, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.668 [Epoch 212][Batch 899], LR: 1.00E-03, Speed: 75.408 samples/sec, ObjLoss=20.757, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.667 [Epoch 212][Batch 999], LR: 1.00E-03, Speed: 84.063 samples/sec, ObjLoss=20.756, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.667 [Epoch 212][Batch 1099], LR: 1.00E-03, Speed: 77.558 samples/sec, ObjLoss=20.756, BoxCenterLoss=14.501, BoxScaleLoss=4.815, ClassLoss=7.667 [Epoch 212][Batch 1199], LR: 1.00E-03, Speed: 51.823 samples/sec, ObjLoss=20.756, BoxCenterLoss=14.500, BoxScaleLoss=4.815, ClassLoss=7.666 [Epoch 212][Batch 1299], LR: 1.00E-03, Speed: 56.674 samples/sec, ObjLoss=20.755, BoxCenterLoss=14.500, BoxScaleLoss=4.815, ClassLoss=7.666 [Epoch 212][Batch 1399], LR: 1.00E-03, Speed: 84.566 samples/sec, ObjLoss=20.755, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.665 [Epoch 212][Batch 1499], LR: 1.00E-03, Speed: 80.650 samples/sec, ObjLoss=20.754, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.665 [Epoch 212][Batch 1599], LR: 1.00E-03, Speed: 72.207 samples/sec, ObjLoss=20.754, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.665 [Epoch 212][Batch 1699], LR: 1.00E-03, Speed: 92.955 samples/sec, ObjLoss=20.753, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.664 [Epoch 212][Batch 1799], LR: 1.00E-03, Speed: 170.534 samples/sec, ObjLoss=20.753, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.664 [Epoch 212] Training cost: 1660.736, ObjLoss=20.753, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.664 [Epoch 212] 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 ] = 0.431 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.090 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 ] = 0.331 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 ] = 0.292 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.302 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 ] = 0.306 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445 person=33.7 bicycle=13.8 car=21.5 motorcycle=22.1 airplane=42.6 bus=38.1 train=41.6 truck=18.0 boat=10.3 traffic light=13.2 fire hydrant=36.5 stop sign=40.3 parking meter=27.5 bench=10.8 bird=18.8 cat=42.1 dog=37.3 horse=31.0 sheep=27.2 cow=32.5 elephant=38.3 bear=48.5 zebra=40.2 giraffe=43.9 backpack=4.7 umbrella=22.2 handbag=3.7 tie=15.2 suitcase=15.3 frisbee=29.2 skis=7.7 snowboard=14.9 sports ball=25.2 kite=24.4 baseball bat=9.4 baseball glove=16.9 skateboard=23.7 surfboard=16.2 tennis racket=25.1 bottle=15.6 wine glass=15.6 cup=19.7 fork=10.8 knife=4.8 spoon=3.0 bowl=20.6 banana=11.1 apple=6.8 sandwich=18.3 orange=17.4 broccoli=11.2 carrot=9.2 hot dog=15.6 pizza=31.1 donut=23.3 cake=15.8 chair=12.8 couch=25.2 potted plant=12.0 bed=26.9 dining table=15.5 toilet=36.8 tv=35.1 laptop=30.8 mouse=32.8 remote=9.2 keyboard=22.5 cell phone=14.1 microwave=30.3 oven=20.2 toaster=0.0 sink=20.4 refrigerator=29.9 book=4.6 clock=27.9 vase=20.7 scissors=14.1 teddy bear=26.7 hair drier=0.0 toothbrush=5.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.3 [Epoch 213][Batch 99], LR: 1.00E-03, Speed: 132.299 samples/sec, ObjLoss=20.752, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.663 [Epoch 213][Batch 199], LR: 1.00E-03, Speed: 92.379 samples/sec, ObjLoss=20.752, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.663 [Epoch 213][Batch 299], LR: 1.00E-03, Speed: 84.165 samples/sec, ObjLoss=20.751, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.662 [Epoch 213][Batch 399], LR: 1.00E-03, Speed: 60.979 samples/sec, ObjLoss=20.751, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.662 [Epoch 213][Batch 499], LR: 1.00E-03, Speed: 138.192 samples/sec, ObjLoss=20.750, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.662 [Epoch 213][Batch 599], LR: 1.00E-03, Speed: 92.511 samples/sec, ObjLoss=20.750, BoxCenterLoss=14.500, BoxScaleLoss=4.814, ClassLoss=7.661 [Epoch 213][Batch 699], LR: 1.00E-03, Speed: 45.109 samples/sec, ObjLoss=20.749, BoxCenterLoss=14.500, BoxScaleLoss=4.813, ClassLoss=7.661 [Epoch 213][Batch 799], LR: 1.00E-03, Speed: 87.126 samples/sec, ObjLoss=20.749, BoxCenterLoss=14.500, BoxScaleLoss=4.813, ClassLoss=7.660 [Epoch 213][Batch 899], LR: 1.00E-03, Speed: 63.798 samples/sec, ObjLoss=20.749, BoxCenterLoss=14.500, BoxScaleLoss=4.813, ClassLoss=7.660 [Epoch 213][Batch 999], LR: 1.00E-03, Speed: 63.292 samples/sec, ObjLoss=20.748, BoxCenterLoss=14.500, BoxScaleLoss=4.813, ClassLoss=7.660 [Epoch 213][Batch 1099], LR: 1.00E-03, Speed: 66.923 samples/sec, ObjLoss=20.748, BoxCenterLoss=14.499, BoxScaleLoss=4.813, ClassLoss=7.659 [Epoch 213][Batch 1199], LR: 1.00E-03, Speed: 58.231 samples/sec, ObjLoss=20.747, BoxCenterLoss=14.499, BoxScaleLoss=4.813, ClassLoss=7.659 [Epoch 213][Batch 1299], LR: 1.00E-03, Speed: 82.552 samples/sec, ObjLoss=20.747, BoxCenterLoss=14.499, BoxScaleLoss=4.813, ClassLoss=7.659 [Epoch 213][Batch 1399], LR: 1.00E-03, Speed: 69.062 samples/sec, ObjLoss=20.746, BoxCenterLoss=14.499, BoxScaleLoss=4.813, ClassLoss=7.658 [Epoch 213][Batch 1499], LR: 1.00E-03, Speed: 70.514 samples/sec, ObjLoss=20.746, BoxCenterLoss=14.499, BoxScaleLoss=4.813, ClassLoss=7.658 [Epoch 213][Batch 1599], LR: 1.00E-03, Speed: 149.900 samples/sec, ObjLoss=20.746, BoxCenterLoss=14.499, BoxScaleLoss=4.813, ClassLoss=7.657 [Epoch 213][Batch 1699], LR: 1.00E-03, Speed: 72.413 samples/sec, ObjLoss=20.745, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.657 [Epoch 213][Batch 1799], LR: 1.00E-03, Speed: 98.397 samples/sec, ObjLoss=20.745, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.657 [Epoch 213] Training cost: 1682.364, ObjLoss=20.745, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.657 [Epoch 213] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.435 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.092 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.318 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 ] = 0.301 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 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.431 person=33.9 bicycle=14.8 car=19.4 motorcycle=23.5 airplane=40.0 bus=39.4 train=44.1 truck=17.2 boat=10.3 traffic light=13.3 fire hydrant=37.6 stop sign=38.4 parking meter=18.9 bench=11.6 bird=18.0 cat=45.8 dog=37.9 horse=31.9 sheep=26.5 cow=29.4 elephant=40.5 bear=45.7 zebra=44.4 giraffe=45.6 backpack=4.8 umbrella=17.2 handbag=4.2 tie=15.5 suitcase=16.0 frisbee=31.8 skis=8.3 snowboard=12.0 sports ball=17.8 kite=24.5 baseball bat=11.0 baseball glove=17.1 skateboard=21.7 surfboard=14.2 tennis racket=22.8 bottle=14.3 wine glass=17.2 cup=20.7 fork=11.5 knife=4.2 spoon=3.8 bowl=20.9 banana=13.5 apple=5.8 sandwich=17.3 orange=12.2 broccoli=10.5 carrot=8.6 hot dog=16.5 pizza=30.0 donut=20.9 cake=16.2 chair=13.1 couch=25.6 potted plant=12.0 bed=30.1 dining table=17.2 toilet=35.1 tv=37.2 laptop=36.2 mouse=24.8 remote=8.4 keyboard=26.8 cell phone=15.1 microwave=30.7 oven=20.1 toaster=3.6 sink=21.1 refrigerator=28.0 book=5.4 clock=28.8 vase=20.0 scissors=19.8 teddy bear=22.8 hair drier=0.0 toothbrush=3.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 214][Batch 99], LR: 1.00E-03, Speed: 160.231 samples/sec, ObjLoss=20.744, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.656 [Epoch 214][Batch 199], LR: 1.00E-03, Speed: 84.435 samples/sec, ObjLoss=20.744, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.656 [Epoch 214][Batch 299], LR: 1.00E-03, Speed: 46.867 samples/sec, ObjLoss=20.743, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.655 [Epoch 214][Batch 399], LR: 1.00E-03, Speed: 89.128 samples/sec, ObjLoss=20.743, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.655 [Epoch 214][Batch 499], LR: 1.00E-03, Speed: 77.977 samples/sec, ObjLoss=20.742, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.655 [Epoch 214][Batch 599], LR: 1.00E-03, Speed: 115.032 samples/sec, ObjLoss=20.742, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.654 [Epoch 214][Batch 699], LR: 1.00E-03, Speed: 79.148 samples/sec, ObjLoss=20.741, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.654 [Epoch 214][Batch 799], LR: 1.00E-03, Speed: 57.415 samples/sec, ObjLoss=20.741, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.654 [Epoch 214][Batch 899], LR: 1.00E-03, Speed: 71.565 samples/sec, ObjLoss=20.741, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.653 [Epoch 214][Batch 999], LR: 1.00E-03, Speed: 78.687 samples/sec, ObjLoss=20.740, BoxCenterLoss=14.499, BoxScaleLoss=4.812, ClassLoss=7.653 [Epoch 214][Batch 1099], LR: 1.00E-03, Speed: 92.271 samples/sec, ObjLoss=20.740, BoxCenterLoss=14.499, BoxScaleLoss=4.811, ClassLoss=7.652 [Epoch 214][Batch 1199], LR: 1.00E-03, Speed: 98.748 samples/sec, ObjLoss=20.740, BoxCenterLoss=14.499, BoxScaleLoss=4.811, ClassLoss=7.652 [Epoch 214][Batch 1299], LR: 1.00E-03, Speed: 56.239 samples/sec, ObjLoss=20.739, BoxCenterLoss=14.499, BoxScaleLoss=4.811, ClassLoss=7.652 [Epoch 214][Batch 1399], LR: 1.00E-03, Speed: 58.264 samples/sec, ObjLoss=20.739, BoxCenterLoss=14.499, BoxScaleLoss=4.811, ClassLoss=7.651 [Epoch 214][Batch 1499], LR: 1.00E-03, Speed: 75.205 samples/sec, ObjLoss=20.738, BoxCenterLoss=14.499, BoxScaleLoss=4.811, ClassLoss=7.651 [Epoch 214][Batch 1599], LR: 1.00E-03, Speed: 63.920 samples/sec, ObjLoss=20.738, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.651 [Epoch 214][Batch 1699], LR: 1.00E-03, Speed: 82.983 samples/sec, ObjLoss=20.737, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.650 [Epoch 214][Batch 1799], LR: 1.00E-03, Speed: 83.118 samples/sec, ObjLoss=20.737, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.650 [Epoch 214] Training cost: 1622.037, ObjLoss=20.737, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.650 [Epoch 214] 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 ] = 0.426 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.179 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 ] = 0.223 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.302 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.199 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 ] = 0.307 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 ] = 0.320 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.421 person=33.8 bicycle=14.3 car=21.9 motorcycle=24.5 airplane=40.8 bus=43.0 train=41.2 truck=19.1 boat=11.9 traffic light=14.5 fire hydrant=35.0 stop sign=38.5 parking meter=30.3 bench=11.7 bird=17.2 cat=42.2 dog=33.5 horse=31.0 sheep=25.3 cow=29.8 elephant=36.6 bear=41.0 zebra=42.4 giraffe=43.3 backpack=5.6 umbrella=20.3 handbag=5.1 tie=15.0 suitcase=16.7 frisbee=29.9 skis=7.5 snowboard=10.7 sports ball=23.0 kite=19.1 baseball bat=10.5 baseball glove=14.6 skateboard=23.5 surfboard=12.9 tennis racket=23.3 bottle=13.5 wine glass=15.8 cup=20.6 fork=10.3 knife=4.3 spoon=3.4 bowl=21.1 banana=10.8 apple=7.1 sandwich=19.7 orange=15.8 broccoli=11.9 carrot=9.8 hot dog=16.8 pizza=27.1 donut=20.6 cake=18.0 chair=14.1 couch=23.4 potted plant=13.1 bed=26.1 dining table=13.6 toilet=39.0 tv=35.2 laptop=29.6 mouse=33.8 remote=9.4 keyboard=19.3 cell phone=16.1 microwave=31.0 oven=18.5 toaster=1.6 sink=18.2 refrigerator=23.3 book=5.7 clock=30.0 vase=17.2 scissors=17.5 teddy bear=23.9 hair drier=0.0 toothbrush=3.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =20.9 [Epoch 215][Batch 99], LR: 1.00E-03, Speed: 153.512 samples/sec, ObjLoss=20.736, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.650 [Epoch 215][Batch 199], LR: 1.00E-03, Speed: 99.679 samples/sec, ObjLoss=20.736, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.649 [Epoch 215][Batch 299], LR: 1.00E-03, Speed: 128.003 samples/sec, ObjLoss=20.735, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.649 [Epoch 215][Batch 399], LR: 1.00E-03, Speed: 105.576 samples/sec, ObjLoss=20.735, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.648 [Epoch 215][Batch 499], LR: 1.00E-03, Speed: 129.915 samples/sec, ObjLoss=20.734, BoxCenterLoss=14.498, BoxScaleLoss=4.811, ClassLoss=7.648 [Epoch 215][Batch 599], LR: 1.00E-03, Speed: 122.817 samples/sec, ObjLoss=20.734, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.648 [Epoch 215][Batch 699], LR: 1.00E-03, Speed: 59.805 samples/sec, ObjLoss=20.734, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.647 [Epoch 215][Batch 799], LR: 1.00E-03, Speed: 76.045 samples/sec, ObjLoss=20.733, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.647 [Epoch 215][Batch 899], LR: 1.00E-03, Speed: 61.528 samples/sec, ObjLoss=20.732, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.646 [Epoch 215][Batch 999], LR: 1.00E-03, Speed: 81.057 samples/sec, ObjLoss=20.732, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.646 [Epoch 215][Batch 1099], LR: 1.00E-03, Speed: 96.916 samples/sec, ObjLoss=20.732, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.646 [Epoch 215][Batch 1199], LR: 1.00E-03, Speed: 94.566 samples/sec, ObjLoss=20.731, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.646 [Epoch 215][Batch 1299], LR: 1.00E-03, Speed: 55.796 samples/sec, ObjLoss=20.731, BoxCenterLoss=14.498, BoxScaleLoss=4.810, ClassLoss=7.645 [Epoch 215][Batch 1399], LR: 1.00E-03, Speed: 75.748 samples/sec, ObjLoss=20.730, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.645 [Epoch 215][Batch 1499], LR: 1.00E-03, Speed: 77.601 samples/sec, ObjLoss=20.730, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.645 [Epoch 215][Batch 1599], LR: 1.00E-03, Speed: 54.430 samples/sec, ObjLoss=20.729, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.644 [Epoch 215][Batch 1699], LR: 1.00E-03, Speed: 119.413 samples/sec, ObjLoss=20.729, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.644 [Epoch 215][Batch 1799], LR: 1.00E-03, Speed: 171.918 samples/sec, ObjLoss=20.728, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.644 [Epoch 215] Training cost: 1630.581, ObjLoss=20.728, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.644 [Epoch 215] 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 ] = 0.430 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.225 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315 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 ] = 0.302 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 ] = 0.143 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 ] = 0.434 person=33.4 bicycle=16.0 car=23.6 motorcycle=25.4 airplane=36.5 bus=45.1 train=44.7 truck=20.5 boat=12.4 traffic light=11.3 fire hydrant=40.3 stop sign=38.6 parking meter=23.7 bench=12.4 bird=14.4 cat=43.7 dog=34.9 horse=30.5 sheep=28.5 cow=29.5 elephant=40.4 bear=47.4 zebra=45.5 giraffe=43.4 backpack=5.6 umbrella=20.5 handbag=5.1 tie=15.1 suitcase=14.4 frisbee=33.7 skis=8.4 snowboard=10.6 sports ball=24.3 kite=25.4 baseball bat=13.5 baseball glove=18.7 skateboard=24.5 surfboard=17.3 tennis racket=21.2 bottle=15.3 wine glass=14.2 cup=22.3 fork=10.5 knife=5.7 spoon=4.5 bowl=20.8 banana=11.6 apple=7.8 sandwich=16.6 orange=12.1 broccoli=10.0 carrot=8.9 hot dog=15.6 pizza=24.4 donut=21.7 cake=17.6 chair=13.5 couch=30.1 potted plant=11.1 bed=30.3 dining table=18.6 toilet=32.1 tv=33.5 laptop=31.9 mouse=31.8 remote=9.5 keyboard=26.5 cell phone=13.9 microwave=31.6 oven=19.1 toaster=7.1 sink=18.7 refrigerator=33.3 book=5.6 clock=33.2 vase=19.0 scissors=18.7 teddy bear=23.0 hair drier=0.0 toothbrush=4.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.7 [Epoch 216][Batch 99], LR: 1.00E-03, Speed: 126.777 samples/sec, ObjLoss=20.728, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.643 [Epoch 216][Batch 199], LR: 1.00E-03, Speed: 155.839 samples/sec, ObjLoss=20.727, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.643 [Epoch 216][Batch 299], LR: 1.00E-03, Speed: 105.793 samples/sec, ObjLoss=20.727, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.643 [Epoch 216][Batch 399], LR: 1.00E-03, Speed: 102.439 samples/sec, ObjLoss=20.726, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.642 [Epoch 216][Batch 499], LR: 1.00E-03, Speed: 152.463 samples/sec, ObjLoss=20.726, BoxCenterLoss=14.497, BoxScaleLoss=4.810, ClassLoss=7.642 [Epoch 216][Batch 599], LR: 1.00E-03, Speed: 86.254 samples/sec, ObjLoss=20.725, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.642 [Epoch 216][Batch 699], LR: 1.00E-03, Speed: 127.688 samples/sec, ObjLoss=20.725, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.641 [Epoch 216][Batch 799], LR: 1.00E-03, Speed: 151.591 samples/sec, ObjLoss=20.724, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.641 [Epoch 216][Batch 899], LR: 1.00E-03, Speed: 134.730 samples/sec, ObjLoss=20.724, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.641 [Epoch 216][Batch 999], LR: 1.00E-03, Speed: 118.035 samples/sec, ObjLoss=20.723, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.640 [Epoch 216][Batch 1099], LR: 1.00E-03, Speed: 146.466 samples/sec, ObjLoss=20.723, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.640 [Epoch 216][Batch 1199], LR: 1.00E-03, Speed: 135.581 samples/sec, ObjLoss=20.722, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.640 [Epoch 216][Batch 1299], LR: 1.00E-03, Speed: 73.869 samples/sec, ObjLoss=20.722, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.639 [Epoch 216][Batch 1399], LR: 1.00E-03, Speed: 51.123 samples/sec, ObjLoss=20.721, BoxCenterLoss=14.497, BoxScaleLoss=4.809, ClassLoss=7.639 [Epoch 216][Batch 1499], LR: 1.00E-03, Speed: 78.732 samples/sec, ObjLoss=20.721, BoxCenterLoss=14.496, BoxScaleLoss=4.809, ClassLoss=7.639 [Epoch 216][Batch 1599], LR: 1.00E-03, Speed: 69.862 samples/sec, ObjLoss=20.721, BoxCenterLoss=14.496, BoxScaleLoss=4.809, ClassLoss=7.638 [Epoch 216][Batch 1699], LR: 1.00E-03, Speed: 66.490 samples/sec, ObjLoss=20.720, BoxCenterLoss=14.496, BoxScaleLoss=4.809, ClassLoss=7.638 [Epoch 216][Batch 1799], LR: 1.00E-03, Speed: 88.725 samples/sec, ObjLoss=20.720, BoxCenterLoss=14.496, BoxScaleLoss=4.809, ClassLoss=7.638 [Epoch 216] Training cost: 1314.942, ObjLoss=20.720, BoxCenterLoss=14.496, BoxScaleLoss=4.809, ClassLoss=7.637 [Epoch 216] 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 ] = 0.429 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.197 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.081 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 ] = 0.325 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 ] = 0.294 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 ] = 0.127 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 ] = 0.443 person=33.7 bicycle=16.3 car=22.4 motorcycle=25.6 airplane=39.2 bus=42.9 train=42.5 truck=20.8 boat=11.6 traffic light=12.6 fire hydrant=33.4 stop sign=41.5 parking meter=26.1 bench=12.1 bird=18.6 cat=42.8 dog=37.3 horse=33.6 sheep=29.6 cow=32.3 elephant=43.7 bear=46.4 zebra=42.4 giraffe=44.1 backpack=4.1 umbrella=18.4 handbag=3.6 tie=14.9 suitcase=13.6 frisbee=31.8 skis=8.2 snowboard=11.7 sports ball=20.6 kite=24.3 baseball bat=12.6 baseball glove=15.1 skateboard=25.8 surfboard=20.0 tennis racket=24.8 bottle=16.4 wine glass=17.6 cup=20.0 fork=11.2 knife=5.2 spoon=4.0 bowl=18.4 banana=13.8 apple=6.7 sandwich=19.2 orange=16.7 broccoli=11.5 carrot=9.0 hot dog=16.3 pizza=25.9 donut=23.4 cake=13.4 chair=13.3 couch=28.5 potted plant=11.0 bed=30.2 dining table=17.1 toilet=38.7 tv=33.7 laptop=33.4 mouse=33.7 remote=8.6 keyboard=23.8 cell phone=14.5 microwave=26.6 oven=19.7 toaster=4.8 sink=20.1 refrigerator=27.4 book=6.0 clock=30.1 vase=16.8 scissors=16.1 teddy bear=25.9 hair drier=0.0 toothbrush=4.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.7 [Epoch 217][Batch 99], LR: 1.00E-03, Speed: 121.019 samples/sec, ObjLoss=20.719, BoxCenterLoss=14.496, BoxScaleLoss=4.809, ClassLoss=7.637 [Epoch 217][Batch 199], LR: 1.00E-03, Speed: 133.516 samples/sec, ObjLoss=20.719, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.637 [Epoch 217][Batch 299], LR: 1.00E-03, Speed: 79.879 samples/sec, ObjLoss=20.718, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.636 [Epoch 217][Batch 399], LR: 1.00E-03, Speed: 79.398 samples/sec, ObjLoss=20.718, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.636 [Epoch 217][Batch 499], LR: 1.00E-03, Speed: 89.779 samples/sec, ObjLoss=20.717, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.635 [Epoch 217][Batch 599], LR: 1.00E-03, Speed: 62.133 samples/sec, ObjLoss=20.717, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.635 [Epoch 217][Batch 699], LR: 1.00E-03, Speed: 85.629 samples/sec, ObjLoss=20.717, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.635 [Epoch 217][Batch 799], LR: 1.00E-03, Speed: 82.498 samples/sec, ObjLoss=20.716, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.634 [Epoch 217][Batch 899], LR: 1.00E-03, Speed: 77.182 samples/sec, ObjLoss=20.716, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.634 [Epoch 217][Batch 999], LR: 1.00E-03, Speed: 89.043 samples/sec, ObjLoss=20.715, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.634 [Epoch 217][Batch 1099], LR: 1.00E-03, Speed: 74.199 samples/sec, ObjLoss=20.715, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.633 [Epoch 217][Batch 1199], LR: 1.00E-03, Speed: 93.385 samples/sec, ObjLoss=20.714, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.633 [Epoch 217][Batch 1299], LR: 1.00E-03, Speed: 51.995 samples/sec, ObjLoss=20.714, BoxCenterLoss=14.496, BoxScaleLoss=4.808, ClassLoss=7.632 [Epoch 217][Batch 1399], LR: 1.00E-03, Speed: 90.808 samples/sec, ObjLoss=20.714, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.632 [Epoch 217][Batch 1499], LR: 1.00E-03, Speed: 101.601 samples/sec, ObjLoss=20.713, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.632 [Epoch 217][Batch 1599], LR: 1.00E-03, Speed: 95.957 samples/sec, ObjLoss=20.713, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.631 [Epoch 217][Batch 1699], LR: 1.00E-03, Speed: 61.299 samples/sec, ObjLoss=20.712, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.631 [Epoch 217][Batch 1799], LR: 1.00E-03, Speed: 128.282 samples/sec, ObjLoss=20.712, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.631 [Epoch 217] Training cost: 1578.513, ObjLoss=20.712, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.631 [Epoch 217] 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 ] = 0.196 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 ] = 0.211 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 ] = 0.202 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.295 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 ] = 0.146 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 ] = 0.438 person=34.0 bicycle=15.6 car=22.1 motorcycle=24.8 airplane=40.2 bus=42.5 train=48.2 truck=19.7 boat=12.4 traffic light=13.1 fire hydrant=40.8 stop sign=36.4 parking meter=24.2 bench=11.7 bird=17.9 cat=38.6 dog=37.6 horse=32.9 sheep=24.9 cow=29.5 elephant=37.1 bear=45.8 zebra=44.2 giraffe=45.1 backpack=4.7 umbrella=19.7 handbag=4.7 tie=12.8 suitcase=15.5 frisbee=31.3 skis=8.3 snowboard=13.9 sports ball=22.6 kite=24.7 baseball bat=9.6 baseball glove=13.9 skateboard=22.2 surfboard=17.0 tennis racket=23.8 bottle=16.5 wine glass=15.6 cup=20.9 fork=10.9 knife=5.0 spoon=3.3 bowl=19.4 banana=10.9 apple=6.3 sandwich=21.6 orange=15.1 broccoli=10.8 carrot=8.0 hot dog=18.0 pizza=28.9 donut=24.6 cake=17.2 chair=13.3 couch=26.6 potted plant=12.8 bed=30.4 dining table=18.2 toilet=35.3 tv=39.8 laptop=35.6 mouse=27.6 remote=7.6 keyboard=20.3 cell phone=15.3 microwave=30.1 oven=21.5 toaster=0.0 sink=21.7 refrigerator=30.7 book=5.1 clock=29.9 vase=16.5 scissors=15.6 teddy bear=25.2 hair drier=0.0 toothbrush=5.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.5 [Epoch 218][Batch 99], LR: 1.00E-03, Speed: 124.969 samples/sec, ObjLoss=20.711, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.630 [Epoch 218][Batch 199], LR: 1.00E-03, Speed: 148.039 samples/sec, ObjLoss=20.711, BoxCenterLoss=14.496, BoxScaleLoss=4.807, ClassLoss=7.630 [Epoch 218][Batch 299], LR: 1.00E-03, Speed: 47.783 samples/sec, ObjLoss=20.711, BoxCenterLoss=14.495, BoxScaleLoss=4.807, ClassLoss=7.630 [Epoch 218][Batch 399], LR: 1.00E-03, Speed: 91.136 samples/sec, ObjLoss=20.710, BoxCenterLoss=14.495, BoxScaleLoss=4.807, ClassLoss=7.629 [Epoch 218][Batch 499], LR: 1.00E-03, Speed: 89.099 samples/sec, ObjLoss=20.710, BoxCenterLoss=14.495, BoxScaleLoss=4.807, ClassLoss=7.629 [Epoch 218][Batch 599], LR: 1.00E-03, Speed: 143.379 samples/sec, ObjLoss=20.709, BoxCenterLoss=14.495, BoxScaleLoss=4.807, ClassLoss=7.628 [Epoch 218][Batch 699], LR: 1.00E-03, Speed: 133.256 samples/sec, ObjLoss=20.709, BoxCenterLoss=14.495, BoxScaleLoss=4.807, ClassLoss=7.628 [Epoch 218][Batch 799], LR: 1.00E-03, Speed: 122.341 samples/sec, ObjLoss=20.708, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.628 [Epoch 218][Batch 899], LR: 1.00E-03, Speed: 111.357 samples/sec, ObjLoss=20.708, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.627 [Epoch 218][Batch 999], LR: 1.00E-03, Speed: 138.156 samples/sec, ObjLoss=20.708, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.627 [Epoch 218][Batch 1099], LR: 1.00E-03, Speed: 52.765 samples/sec, ObjLoss=20.707, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.627 [Epoch 218][Batch 1199], LR: 1.00E-03, Speed: 65.465 samples/sec, ObjLoss=20.707, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.627 [Epoch 218][Batch 1299], LR: 1.00E-03, Speed: 156.433 samples/sec, ObjLoss=20.706, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.626 [Epoch 218][Batch 1399], LR: 1.00E-03, Speed: 144.193 samples/sec, ObjLoss=20.706, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.626 [Epoch 218][Batch 1499], LR: 1.00E-03, Speed: 81.230 samples/sec, ObjLoss=20.705, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.626 [Epoch 218][Batch 1599], LR: 1.00E-03, Speed: 153.345 samples/sec, ObjLoss=20.705, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.625 [Epoch 218][Batch 1699], LR: 1.00E-03, Speed: 138.700 samples/sec, ObjLoss=20.705, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.625 [Epoch 218][Batch 1799], LR: 1.00E-03, Speed: 111.429 samples/sec, ObjLoss=20.704, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.624 [Epoch 218] Training cost: 1329.463, ObjLoss=20.704, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.624 [Epoch 218] 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 ] = 0.427 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 ] = 0.221 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 ] = 0.202 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 ] = 0.136 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 ] = 0.441 person=35.0 bicycle=14.3 car=23.1 motorcycle=25.7 airplane=41.1 bus=43.3 train=45.4 truck=20.1 boat=12.0 traffic light=14.6 fire hydrant=40.1 stop sign=42.1 parking meter=22.5 bench=12.7 bird=18.0 cat=45.7 dog=38.2 horse=33.3 sheep=29.5 cow=35.0 elephant=41.9 bear=49.7 zebra=38.7 giraffe=46.8 backpack=6.0 umbrella=20.6 handbag=4.8 tie=15.2 suitcase=15.9 frisbee=36.1 skis=7.7 snowboard=11.5 sports ball=27.5 kite=21.2 baseball bat=10.6 baseball glove=20.8 skateboard=23.0 surfboard=17.4 tennis racket=20.3 bottle=14.6 wine glass=15.4 cup=20.0 fork=9.7 knife=3.1 spoon=3.2 bowl=19.8 banana=12.2 apple=7.1 sandwich=17.1 orange=18.0 broccoli=13.2 carrot=8.7 hot dog=15.8 pizza=25.3 donut=21.6 cake=18.7 chair=12.6 couch=26.4 potted plant=11.3 bed=28.5 dining table=13.9 toilet=40.2 tv=36.8 laptop=34.4 mouse=33.5 remote=8.6 keyboard=27.2 cell phone=15.6 microwave=31.8 oven=19.8 toaster=0.0 sink=18.8 refrigerator=33.2 book=4.3 clock=31.7 vase=15.6 scissors=18.2 teddy bear=25.1 hair drier=0.0 toothbrush=3.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =22.0 [Epoch 219][Batch 99], LR: 1.00E-03, Speed: 89.593 samples/sec, ObjLoss=20.704, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.624 [Epoch 219][Batch 199], LR: 1.00E-03, Speed: 129.599 samples/sec, ObjLoss=20.703, BoxCenterLoss=14.495, BoxScaleLoss=4.806, ClassLoss=7.624 [Epoch 219][Batch 299], LR: 1.00E-03, Speed: 135.848 samples/sec, ObjLoss=20.703, BoxCenterLoss=14.494, BoxScaleLoss=4.806, ClassLoss=7.623 [Epoch 219][Batch 399], LR: 1.00E-03, Speed: 107.405 samples/sec, ObjLoss=20.702, BoxCenterLoss=14.494, BoxScaleLoss=4.806, ClassLoss=7.623 [Epoch 219][Batch 499], LR: 1.00E-03, Speed: 110.062 samples/sec, ObjLoss=20.702, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.623 [Epoch 219][Batch 599], LR: 1.00E-03, Speed: 55.051 samples/sec, ObjLoss=20.701, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.622 [Epoch 219][Batch 699], LR: 1.00E-03, Speed: 97.919 samples/sec, ObjLoss=20.701, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.622 [Epoch 219][Batch 799], LR: 1.00E-03, Speed: 111.851 samples/sec, ObjLoss=20.700, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.621 [Epoch 219][Batch 899], LR: 1.00E-03, Speed: 80.335 samples/sec, ObjLoss=20.700, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.621 [Epoch 219][Batch 999], LR: 1.00E-03, Speed: 95.192 samples/sec, ObjLoss=20.700, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.621 [Epoch 219][Batch 1099], LR: 1.00E-03, Speed: 72.283 samples/sec, ObjLoss=20.699, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.620 [Epoch 219][Batch 1199], LR: 1.00E-03, Speed: 79.188 samples/sec, ObjLoss=20.699, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.620 [Epoch 219][Batch 1299], LR: 1.00E-03, Speed: 95.683 samples/sec, ObjLoss=20.698, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.620 [Epoch 219][Batch 1399], LR: 1.00E-03, Speed: 90.945 samples/sec, ObjLoss=20.698, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.619 [Epoch 219][Batch 1499], LR: 1.00E-03, Speed: 120.043 samples/sec, ObjLoss=20.697, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.619 [Epoch 219][Batch 1599], LR: 1.00E-03, Speed: 70.841 samples/sec, ObjLoss=20.697, BoxCenterLoss=14.494, BoxScaleLoss=4.805, ClassLoss=7.618 [Epoch 219][Batch 1699], LR: 1.00E-03, Speed: 84.482 samples/sec, ObjLoss=20.697, BoxCenterLoss=14.494, BoxScaleLoss=4.804, ClassLoss=7.618 [Epoch 219][Batch 1799], LR: 1.00E-03, Speed: 115.766 samples/sec, ObjLoss=20.696, BoxCenterLoss=14.494, BoxScaleLoss=4.804, ClassLoss=7.618 [Epoch 219] Training cost: 1546.361, ObjLoss=20.696, BoxCenterLoss=14.494, BoxScaleLoss=4.804, ClassLoss=7.618 [Epoch 219] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.419 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088 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 ] = 0.322 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 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.305 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 ] = 0.314 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442 person=32.9 bicycle=14.5 car=21.1 motorcycle=25.3 airplane=43.4 bus=38.8 train=40.7 truck=17.9 boat=10.7 traffic light=9.9 fire hydrant=36.5 stop sign=36.5 parking meter=26.3 bench=11.0 bird=13.6 cat=40.5 dog=31.5 horse=34.2 sheep=24.4 cow=31.2 elephant=37.0 bear=45.5 zebra=41.0 giraffe=42.9 backpack=4.2 umbrella=18.8 handbag=4.4 tie=15.6 suitcase=16.5 frisbee=36.7 skis=9.2 snowboard=14.1 sports ball=24.4 kite=22.8 baseball bat=11.2 baseball glove=22.2 skateboard=25.0 surfboard=15.9 tennis racket=23.6 bottle=16.1 wine glass=15.2 cup=21.4 fork=9.7 knife=3.6 spoon=3.7 bowl=19.3 banana=10.4 apple=5.2 sandwich=17.0 orange=14.2 broccoli=11.1 carrot=6.3 hot dog=18.5 pizza=27.4 donut=23.1 cake=15.0 chair=12.6 couch=28.6 potted plant=11.7 bed=30.0 dining table=18.2 toilet=32.8 tv=35.0 laptop=34.8 mouse=34.8 remote=10.5 keyboard=28.4 cell phone=17.0 microwave=25.2 oven=19.7 toaster=0.0 sink=20.7 refrigerator=30.2 book=5.9 clock=30.6 vase=18.1 scissors=15.4 teddy bear=25.8 hair drier=0.0 toothbrush=3.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =21.2 [Epoch 220][Batch 99], LR: 1.00E-04, Speed: 89.892 samples/sec, ObjLoss=20.696, BoxCenterLoss=14.494, BoxScaleLoss=4.804, ClassLoss=7.617 [Epoch 220][Batch 199], LR: 1.00E-04, Speed: 132.692 samples/sec, ObjLoss=20.695, BoxCenterLoss=14.494, BoxScaleLoss=4.804, ClassLoss=7.617 [Epoch 220][Batch 299], LR: 1.00E-04, Speed: 88.178 samples/sec, ObjLoss=20.694, BoxCenterLoss=14.494, BoxScaleLoss=4.804, ClassLoss=7.616 [Epoch 220][Batch 399], LR: 1.00E-04, Speed: 113.661 samples/sec, ObjLoss=20.694, BoxCenterLoss=14.493, BoxScaleLoss=4.804, ClassLoss=7.616 [Epoch 220][Batch 499], LR: 1.00E-04, Speed: 74.602 samples/sec, ObjLoss=20.693, BoxCenterLoss=14.494, BoxScaleLoss=4.804, ClassLoss=7.615 [Epoch 220][Batch 599], LR: 1.00E-04, Speed: 124.031 samples/sec, ObjLoss=20.692, BoxCenterLoss=14.494, BoxScaleLoss=4.803, ClassLoss=7.615 [Epoch 220][Batch 699], LR: 1.00E-04, Speed: 80.554 samples/sec, ObjLoss=20.692, BoxCenterLoss=14.493, BoxScaleLoss=4.803, ClassLoss=7.614 [Epoch 220][Batch 799], LR: 1.00E-04, Speed: 85.050 samples/sec, ObjLoss=20.691, BoxCenterLoss=14.493, BoxScaleLoss=4.803, ClassLoss=7.614 [Epoch 220][Batch 899], LR: 1.00E-04, Speed: 70.079 samples/sec, ObjLoss=20.690, BoxCenterLoss=14.493, BoxScaleLoss=4.803, ClassLoss=7.613 [Epoch 220][Batch 999], LR: 1.00E-04, Speed: 178.831 samples/sec, ObjLoss=20.690, BoxCenterLoss=14.493, BoxScaleLoss=4.803, ClassLoss=7.613 [Epoch 220][Batch 1099], LR: 1.00E-04, Speed: 72.965 samples/sec, ObjLoss=20.689, BoxCenterLoss=14.493, BoxScaleLoss=4.802, ClassLoss=7.612 [Epoch 220][Batch 1199], LR: 1.00E-04, Speed: 58.150 samples/sec, ObjLoss=20.688, BoxCenterLoss=14.493, BoxScaleLoss=4.802, ClassLoss=7.612 [Epoch 220][Batch 1299], LR: 1.00E-04, Speed: 63.419 samples/sec, ObjLoss=20.688, BoxCenterLoss=14.493, BoxScaleLoss=4.802, ClassLoss=7.611 [Epoch 220][Batch 1399], LR: 1.00E-04, Speed: 74.124 samples/sec, ObjLoss=20.687, BoxCenterLoss=14.493, BoxScaleLoss=4.802, ClassLoss=7.611 [Epoch 220][Batch 1499], LR: 1.00E-04, Speed: 88.723 samples/sec, ObjLoss=20.686, BoxCenterLoss=14.493, BoxScaleLoss=4.802, ClassLoss=7.610 [Epoch 220][Batch 1599], LR: 1.00E-04, Speed: 99.247 samples/sec, ObjLoss=20.686, BoxCenterLoss=14.493, BoxScaleLoss=4.801, ClassLoss=7.610 [Epoch 220][Batch 1699], LR: 1.00E-04, Speed: 113.611 samples/sec, ObjLoss=20.685, BoxCenterLoss=14.493, BoxScaleLoss=4.801, ClassLoss=7.609 [Epoch 220][Batch 1799], LR: 1.00E-04, Speed: 143.463 samples/sec, ObjLoss=20.684, BoxCenterLoss=14.493, BoxScaleLoss=4.801, ClassLoss=7.608 [Epoch 220] Training cost: 1579.009, ObjLoss=20.684, BoxCenterLoss=14.493, BoxScaleLoss=4.801, ClassLoss=7.608 [Epoch 220] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.467 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.274 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117 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 ] = 0.394 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 ] = 0.344 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.173 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 ] = 0.511 person=38.0 bicycle=18.1 car=25.9 motorcycle=29.5 airplane=47.8 bus=48.6 train=52.8 truck=24.0 boat=15.1 traffic light=17.0 fire hydrant=46.1 stop sign=47.3 parking meter=28.7 bench=14.5 bird=22.4 cat=49.4 dog=44.4 horse=39.2 sheep=32.9 cow=38.2 elephant=47.7 bear=57.1 zebra=50.1 giraffe=51.5 backpack=6.9 umbrella=24.5 handbag=6.2 tie=18.3 suitcase=21.8 frisbee=45.0 skis=11.0 snowboard=17.4 sports ball=28.3 kite=28.3 baseball bat=15.4 baseball glove=23.4 skateboard=29.8 surfboard=21.5 tennis racket=27.1 bottle=20.0 wine glass=20.0 cup=25.3 fork=14.1 knife=4.9 spoon=5.1 bowl=24.8 banana=13.8 apple=8.9 sandwich=23.1 orange=19.7 broccoli=13.7 carrot=12.9 hot dog=22.6 pizza=33.7 donut=27.6 cake=20.6 chair=16.0 couch=31.4 potted plant=15.2 bed=34.2 dining table=18.2 toilet=43.4 tv=43.4 laptop=41.4 mouse=41.9 remote=12.1 keyboard=34.0 cell phone=19.8 microwave=38.2 oven=23.8 toaster=0.0 sink=23.6 refrigerator=36.6 book=6.1 clock=37.4 vase=23.2 scissors=21.6 teddy bear=29.8 hair drier=0.0 toothbrush=8.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =26.5 [Epoch 221][Batch 99], LR: 1.00E-04, Speed: 128.250 samples/sec, ObjLoss=20.683, BoxCenterLoss=14.493, BoxScaleLoss=4.801, ClassLoss=7.608 [Epoch 221][Batch 199], LR: 1.00E-04, Speed: 138.873 samples/sec, ObjLoss=20.682, BoxCenterLoss=14.493, BoxScaleLoss=4.801, ClassLoss=7.607 [Epoch 221][Batch 299], LR: 1.00E-04, Speed: 131.865 samples/sec, ObjLoss=20.682, BoxCenterLoss=14.493, BoxScaleLoss=4.800, ClassLoss=7.607 [Epoch 221][Batch 399], LR: 1.00E-04, Speed: 96.104 samples/sec, ObjLoss=20.681, BoxCenterLoss=14.493, BoxScaleLoss=4.800, ClassLoss=7.606 [Epoch 221][Batch 499], LR: 1.00E-04, Speed: 84.260 samples/sec, ObjLoss=20.680, BoxCenterLoss=14.492, BoxScaleLoss=4.800, ClassLoss=7.606 [Epoch 221][Batch 599], LR: 1.00E-04, Speed: 137.248 samples/sec, ObjLoss=20.679, BoxCenterLoss=14.492, BoxScaleLoss=4.800, ClassLoss=7.605 [Epoch 221][Batch 699], LR: 1.00E-04, Speed: 115.823 samples/sec, ObjLoss=20.679, BoxCenterLoss=14.492, BoxScaleLoss=4.799, ClassLoss=7.604 [Epoch 221][Batch 799], LR: 1.00E-04, Speed: 145.396 samples/sec, ObjLoss=20.678, BoxCenterLoss=14.492, BoxScaleLoss=4.799, ClassLoss=7.604 [Epoch 221][Batch 899], LR: 1.00E-04, Speed: 67.506 samples/sec, ObjLoss=20.677, BoxCenterLoss=14.492, BoxScaleLoss=4.799, ClassLoss=7.603 [Epoch 221][Batch 999], LR: 1.00E-04, Speed: 134.294 samples/sec, ObjLoss=20.676, BoxCenterLoss=14.492, BoxScaleLoss=4.799, ClassLoss=7.602 [Epoch 221][Batch 1099], LR: 1.00E-04, Speed: 74.361 samples/sec, ObjLoss=20.676, BoxCenterLoss=14.492, BoxScaleLoss=4.798, ClassLoss=7.602 [Epoch 221][Batch 1199], LR: 1.00E-04, Speed: 81.648 samples/sec, ObjLoss=20.675, BoxCenterLoss=14.492, BoxScaleLoss=4.798, ClassLoss=7.601 [Epoch 221][Batch 1299], LR: 1.00E-04, Speed: 80.870 samples/sec, ObjLoss=20.674, BoxCenterLoss=14.492, BoxScaleLoss=4.798, ClassLoss=7.601 [Epoch 221][Batch 1399], LR: 1.00E-04, Speed: 71.727 samples/sec, ObjLoss=20.673, BoxCenterLoss=14.492, BoxScaleLoss=4.798, ClassLoss=7.600 [Epoch 221][Batch 1499], LR: 1.00E-04, Speed: 126.800 samples/sec, ObjLoss=20.673, BoxCenterLoss=14.492, BoxScaleLoss=4.798, ClassLoss=7.600 [Epoch 221][Batch 1599], LR: 1.00E-04, Speed: 88.578 samples/sec, ObjLoss=20.672, BoxCenterLoss=14.492, BoxScaleLoss=4.798, ClassLoss=7.599 [Epoch 221][Batch 1699], LR: 1.00E-04, Speed: 54.256 samples/sec, ObjLoss=20.671, BoxCenterLoss=14.492, BoxScaleLoss=4.797, ClassLoss=7.598 [Epoch 221][Batch 1799], LR: 1.00E-04, Speed: 136.131 samples/sec, ObjLoss=20.670, BoxCenterLoss=14.492, BoxScaleLoss=4.797, ClassLoss=7.598 [Epoch 221] Training cost: 1538.131, ObjLoss=20.670, BoxCenterLoss=14.492, BoxScaleLoss=4.797, ClassLoss=7.598 [Epoch 221] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.269 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.470 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.399 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.174 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 ] = 0.514 person=38.2 bicycle=18.5 car=26.1 motorcycle=29.5 airplane=46.8 bus=48.8 train=53.0 truck=24.3 boat=15.0 traffic light=16.1 fire hydrant=46.6 stop sign=49.5 parking meter=32.8 bench=14.8 bird=22.6 cat=49.9 dog=42.7 horse=39.7 sheep=33.3 cow=37.6 elephant=47.8 bear=55.8 zebra=49.5 giraffe=51.7 backpack=6.9 umbrella=26.0 handbag=6.4 tie=19.0 suitcase=21.1 frisbee=45.4 skis=11.0 snowboard=17.4 sports ball=28.1 kite=28.3 baseball bat=15.4 baseball glove=22.9 skateboard=30.6 surfboard=21.9 tennis racket=26.9 bottle=20.5 wine glass=20.4 cup=25.8 fork=14.0 knife=5.3 spoon=4.9 bowl=25.8 banana=14.2 apple=9.2 sandwich=22.0 orange=20.2 broccoli=13.7 carrot=13.2 hot dog=24.0 pizza=35.6 donut=27.0 cake=21.6 chair=16.0 couch=32.1 potted plant=15.7 bed=34.4 dining table=20.6 toilet=44.9 tv=42.4 laptop=42.9 mouse=42.5 remote=13.0 keyboard=35.3 cell phone=20.1 microwave=38.1 oven=23.5 toaster=2.8 sink=25.8 refrigerator=38.4 book=6.9 clock=37.2 vase=24.6 scissors=22.3 teddy bear=29.8 hair drier=0.0 toothbrush=9.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =26.9 [Epoch 222][Batch 99], LR: 1.00E-04, Speed: 124.680 samples/sec, ObjLoss=20.669, BoxCenterLoss=14.492, BoxScaleLoss=4.797, ClassLoss=7.597 [Epoch 222][Batch 199], LR: 1.00E-04, Speed: 123.409 samples/sec, ObjLoss=20.669, BoxCenterLoss=14.492, BoxScaleLoss=4.797, ClassLoss=7.597 [Epoch 222][Batch 299], LR: 1.00E-04, Speed: 115.410 samples/sec, ObjLoss=20.668, BoxCenterLoss=14.492, BoxScaleLoss=4.796, ClassLoss=7.596 [Epoch 222][Batch 399], LR: 1.00E-04, Speed: 97.731 samples/sec, ObjLoss=20.667, BoxCenterLoss=14.492, BoxScaleLoss=4.796, ClassLoss=7.595 [Epoch 222][Batch 499], LR: 1.00E-04, Speed: 136.638 samples/sec, ObjLoss=20.666, BoxCenterLoss=14.491, BoxScaleLoss=4.796, ClassLoss=7.595 [Epoch 222][Batch 599], LR: 1.00E-04, Speed: 98.705 samples/sec, ObjLoss=20.666, BoxCenterLoss=14.491, BoxScaleLoss=4.796, ClassLoss=7.594 [Epoch 222][Batch 699], LR: 1.00E-04, Speed: 129.270 samples/sec, ObjLoss=20.665, BoxCenterLoss=14.491, BoxScaleLoss=4.795, ClassLoss=7.593 [Epoch 222][Batch 799], LR: 1.00E-04, Speed: 64.837 samples/sec, ObjLoss=20.664, BoxCenterLoss=14.491, BoxScaleLoss=4.795, ClassLoss=7.593 [Epoch 222][Batch 899], LR: 1.00E-04, Speed: 108.238 samples/sec, ObjLoss=20.663, BoxCenterLoss=14.491, BoxScaleLoss=4.795, ClassLoss=7.592 [Epoch 222][Batch 999], LR: 1.00E-04, Speed: 60.119 samples/sec, ObjLoss=20.663, BoxCenterLoss=14.491, BoxScaleLoss=4.795, ClassLoss=7.592 [Epoch 222][Batch 1099], LR: 1.00E-04, Speed: 82.180 samples/sec, ObjLoss=20.662, BoxCenterLoss=14.491, BoxScaleLoss=4.795, ClassLoss=7.591 [Epoch 222][Batch 1199], LR: 1.00E-04, Speed: 102.056 samples/sec, ObjLoss=20.661, BoxCenterLoss=14.491, BoxScaleLoss=4.794, ClassLoss=7.590 [Epoch 222][Batch 1299], LR: 1.00E-04, Speed: 60.560 samples/sec, ObjLoss=20.660, BoxCenterLoss=14.491, BoxScaleLoss=4.794, ClassLoss=7.590 [Epoch 222][Batch 1399], LR: 1.00E-04, Speed: 85.208 samples/sec, ObjLoss=20.659, BoxCenterLoss=14.491, BoxScaleLoss=4.794, ClassLoss=7.589 [Epoch 222][Batch 1499], LR: 1.00E-04, Speed: 65.928 samples/sec, ObjLoss=20.658, BoxCenterLoss=14.491, BoxScaleLoss=4.794, ClassLoss=7.589 [Epoch 222][Batch 1599], LR: 1.00E-04, Speed: 57.472 samples/sec, ObjLoss=20.658, BoxCenterLoss=14.490, BoxScaleLoss=4.793, ClassLoss=7.588 [Epoch 222][Batch 1699], LR: 1.00E-04, Speed: 89.639 samples/sec, ObjLoss=20.657, BoxCenterLoss=14.490, BoxScaleLoss=4.793, ClassLoss=7.587 [Epoch 222][Batch 1799], LR: 1.00E-04, Speed: 112.917 samples/sec, ObjLoss=20.656, BoxCenterLoss=14.490, BoxScaleLoss=4.793, ClassLoss=7.587 [Epoch 222] Training cost: 1583.567, ObjLoss=20.656, BoxCenterLoss=14.490, BoxScaleLoss=4.793, ClassLoss=7.587 [Epoch 222] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.269 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.472 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.118 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.347 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.176 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519 person=38.3 bicycle=19.2 car=26.2 motorcycle=29.6 airplane=47.7 bus=48.9 train=52.6 truck=24.3 boat=15.1 traffic light=16.9 fire hydrant=45.9 stop sign=50.1 parking meter=32.8 bench=14.7 bird=22.7 cat=51.1 dog=42.7 horse=41.0 sheep=32.5 cow=37.9 elephant=48.1 bear=54.6 zebra=50.5 giraffe=51.2 backpack=7.5 umbrella=25.0 handbag=6.6 tie=18.4 suitcase=21.4 frisbee=46.2 skis=10.9 snowboard=18.1 sports ball=29.2 kite=28.5 baseball bat=14.2 baseball glove=23.3 skateboard=30.3 surfboard=22.8 tennis racket=27.7 bottle=19.5 wine glass=19.9 cup=25.4 fork=14.6 knife=5.7 spoon=5.1 bowl=26.2 banana=15.4 apple=9.0 sandwich=22.3 orange=19.3 broccoli=14.5 carrot=12.5 hot dog=23.5 pizza=34.9 donut=27.8 cake=21.7 chair=16.3 couch=31.8 potted plant=15.8 bed=33.4 dining table=17.0 toilet=44.1 tv=42.2 laptop=43.2 mouse=42.2 remote=12.6 keyboard=34.7 cell phone=20.2 microwave=38.2 oven=24.8 toaster=2.4 sink=26.1 refrigerator=37.2 book=6.5 clock=37.6 vase=23.5 scissors=24.6 teddy bear=30.8 hair drier=0.0 toothbrush=8.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =26.9 [Epoch 223][Batch 99], LR: 1.00E-04, Speed: 113.390 samples/sec, ObjLoss=20.655, BoxCenterLoss=14.490, BoxScaleLoss=4.793, ClassLoss=7.586 [Epoch 223][Batch 199], LR: 1.00E-04, Speed: 99.371 samples/sec, ObjLoss=20.655, BoxCenterLoss=14.490, BoxScaleLoss=4.792, ClassLoss=7.585 [Epoch 223][Batch 299], LR: 1.00E-04, Speed: 143.852 samples/sec, ObjLoss=20.654, BoxCenterLoss=14.490, BoxScaleLoss=4.792, ClassLoss=7.585 [Epoch 223][Batch 399], LR: 1.00E-04, Speed: 124.797 samples/sec, ObjLoss=20.653, BoxCenterLoss=14.490, BoxScaleLoss=4.792, ClassLoss=7.584 [Epoch 223][Batch 499], LR: 1.00E-04, Speed: 86.198 samples/sec, ObjLoss=20.652, BoxCenterLoss=14.490, BoxScaleLoss=4.792, ClassLoss=7.584 [Epoch 223][Batch 599], LR: 1.00E-04, Speed: 89.343 samples/sec, ObjLoss=20.651, BoxCenterLoss=14.490, BoxScaleLoss=4.792, ClassLoss=7.583 [Epoch 223][Batch 699], LR: 1.00E-04, Speed: 97.527 samples/sec, ObjLoss=20.651, BoxCenterLoss=14.490, BoxScaleLoss=4.791, ClassLoss=7.582 [Epoch 223][Batch 799], LR: 1.00E-04, Speed: 57.311 samples/sec, ObjLoss=20.650, BoxCenterLoss=14.490, BoxScaleLoss=4.791, ClassLoss=7.582 [Epoch 223][Batch 899], LR: 1.00E-04, Speed: 72.343 samples/sec, ObjLoss=20.649, BoxCenterLoss=14.490, BoxScaleLoss=4.791, ClassLoss=7.581 [Epoch 223][Batch 999], LR: 1.00E-04, Speed: 135.723 samples/sec, ObjLoss=20.648, BoxCenterLoss=14.490, BoxScaleLoss=4.791, ClassLoss=7.580 [Epoch 223][Batch 1099], LR: 1.00E-04, Speed: 78.294 samples/sec, ObjLoss=20.647, BoxCenterLoss=14.490, BoxScaleLoss=4.790, ClassLoss=7.580 [Epoch 223][Batch 1199], LR: 1.00E-04, Speed: 129.126 samples/sec, ObjLoss=20.647, BoxCenterLoss=14.490, BoxScaleLoss=4.790, ClassLoss=7.579 [Epoch 223][Batch 1299], LR: 1.00E-04, Speed: 58.701 samples/sec, ObjLoss=20.646, BoxCenterLoss=14.489, BoxScaleLoss=4.790, ClassLoss=7.579 [Epoch 223][Batch 1399], LR: 1.00E-04, Speed: 134.049 samples/sec, ObjLoss=20.645, BoxCenterLoss=14.489, BoxScaleLoss=4.790, ClassLoss=7.578 [Epoch 223][Batch 1499], LR: 1.00E-04, Speed: 77.634 samples/sec, ObjLoss=20.644, BoxCenterLoss=14.489, BoxScaleLoss=4.790, ClassLoss=7.577 [Epoch 223][Batch 1599], LR: 1.00E-04, Speed: 50.497 samples/sec, ObjLoss=20.643, BoxCenterLoss=14.489, BoxScaleLoss=4.789, ClassLoss=7.577 [Epoch 223][Batch 1699], LR: 1.00E-04, Speed: 60.983 samples/sec, ObjLoss=20.642, BoxCenterLoss=14.489, BoxScaleLoss=4.789, ClassLoss=7.576 [Epoch 223][Batch 1799], LR: 1.00E-04, Speed: 114.393 samples/sec, ObjLoss=20.642, BoxCenterLoss=14.489, BoxScaleLoss=4.789, ClassLoss=7.576 [Epoch 223] Training cost: 1596.274, ObjLoss=20.641, BoxCenterLoss=14.489, BoxScaleLoss=4.789, ClassLoss=7.575 [Epoch 223] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.271 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.277 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117 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 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.173 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.517 person=38.2 bicycle=18.8 car=25.9 motorcycle=30.2 airplane=48.9 bus=48.3 train=50.9 truck=24.6 boat=15.2 traffic light=16.4 fire hydrant=45.2 stop sign=49.5 parking meter=33.7 bench=15.0 bird=22.6 cat=51.4 dog=44.9 horse=39.5 sheep=33.1 cow=37.9 elephant=48.2 bear=56.4 zebra=50.2 giraffe=53.0 backpack=7.2 umbrella=25.9 handbag=6.8 tie=18.7 suitcase=21.0 frisbee=44.4 skis=11.7 snowboard=18.5 sports ball=29.1 kite=28.2 baseball bat=14.6 baseball glove=23.4 skateboard=31.6 surfboard=22.5 tennis racket=27.6 bottle=19.9 wine glass=20.6 cup=25.5 fork=14.3 knife=5.8 spoon=6.0 bowl=25.7 banana=14.9 apple=9.4 sandwich=21.9 orange=19.5 broccoli=14.0 carrot=12.4 hot dog=23.0 pizza=35.2 donut=28.8 cake=21.6 chair=15.8 couch=32.6 potted plant=16.0 bed=31.6 dining table=17.5 toilet=46.1 tv=43.0 laptop=43.2 mouse=43.4 remote=13.1 keyboard=36.4 cell phone=20.6 microwave=38.3 oven=23.8 toaster=4.2 sink=25.8 refrigerator=36.3 book=6.4 clock=37.4 vase=24.4 scissors=21.9 teddy bear=30.7 hair drier=0.0 toothbrush=9.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.1 [Epoch 224][Batch 99], LR: 1.00E-04, Speed: 138.028 samples/sec, ObjLoss=20.641, BoxCenterLoss=14.489, BoxScaleLoss=4.789, ClassLoss=7.575 [Epoch 224][Batch 199], LR: 1.00E-04, Speed: 89.996 samples/sec, ObjLoss=20.640, BoxCenterLoss=14.489, BoxScaleLoss=4.788, ClassLoss=7.574 [Epoch 224][Batch 299], LR: 1.00E-04, Speed: 138.043 samples/sec, ObjLoss=20.639, BoxCenterLoss=14.489, BoxScaleLoss=4.788, ClassLoss=7.573 [Epoch 224][Batch 399], LR: 1.00E-04, Speed: 115.252 samples/sec, ObjLoss=20.638, BoxCenterLoss=14.489, BoxScaleLoss=4.788, ClassLoss=7.573 [Epoch 224][Batch 499], LR: 1.00E-04, Speed: 74.133 samples/sec, ObjLoss=20.637, BoxCenterLoss=14.489, BoxScaleLoss=4.788, ClassLoss=7.572 [Epoch 224][Batch 599], LR: 1.00E-04, Speed: 67.412 samples/sec, ObjLoss=20.637, BoxCenterLoss=14.489, BoxScaleLoss=4.788, ClassLoss=7.572 [Epoch 224][Batch 699], LR: 1.00E-04, Speed: 54.166 samples/sec, ObjLoss=20.636, BoxCenterLoss=14.488, BoxScaleLoss=4.787, ClassLoss=7.571 [Epoch 224][Batch 799], LR: 1.00E-04, Speed: 90.448 samples/sec, ObjLoss=20.635, BoxCenterLoss=14.488, BoxScaleLoss=4.787, ClassLoss=7.570 [Epoch 224][Batch 899], LR: 1.00E-04, Speed: 106.966 samples/sec, ObjLoss=20.634, BoxCenterLoss=14.488, BoxScaleLoss=4.787, ClassLoss=7.570 [Epoch 224][Batch 999], LR: 1.00E-04, Speed: 149.021 samples/sec, ObjLoss=20.634, BoxCenterLoss=14.488, BoxScaleLoss=4.787, ClassLoss=7.569 [Epoch 224][Batch 1099], LR: 1.00E-04, Speed: 117.443 samples/sec, ObjLoss=20.633, BoxCenterLoss=14.488, BoxScaleLoss=4.786, ClassLoss=7.568 [Epoch 224][Batch 1199], LR: 1.00E-04, Speed: 78.985 samples/sec, ObjLoss=20.632, BoxCenterLoss=14.488, BoxScaleLoss=4.786, ClassLoss=7.568 [Epoch 224][Batch 1299], LR: 1.00E-04, Speed: 122.677 samples/sec, ObjLoss=20.631, BoxCenterLoss=14.488, BoxScaleLoss=4.786, ClassLoss=7.567 [Epoch 224][Batch 1399], LR: 1.00E-04, Speed: 54.599 samples/sec, ObjLoss=20.631, BoxCenterLoss=14.488, BoxScaleLoss=4.786, ClassLoss=7.567 [Epoch 224][Batch 1499], LR: 1.00E-04, Speed: 66.255 samples/sec, ObjLoss=20.630, BoxCenterLoss=14.488, BoxScaleLoss=4.786, ClassLoss=7.566 [Epoch 224][Batch 1599], LR: 1.00E-04, Speed: 68.434 samples/sec, ObjLoss=20.629, BoxCenterLoss=14.488, BoxScaleLoss=4.785, ClassLoss=7.565 [Epoch 224][Batch 1699], LR: 1.00E-04, Speed: 57.220 samples/sec, ObjLoss=20.628, BoxCenterLoss=14.488, BoxScaleLoss=4.785, ClassLoss=7.565 [Epoch 224][Batch 1799], LR: 1.00E-04, Speed: 144.121 samples/sec, ObjLoss=20.627, BoxCenterLoss=14.488, BoxScaleLoss=4.785, ClassLoss=7.564 [Epoch 224] Training cost: 1533.511, ObjLoss=20.627, BoxCenterLoss=14.488, BoxScaleLoss=4.785, ClassLoss=7.564 [Epoch 224] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.277 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.398 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.364 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 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.511 person=38.3 bicycle=18.4 car=26.2 motorcycle=30.1 airplane=47.9 bus=49.2 train=53.3 truck=23.7 boat=14.7 traffic light=16.4 fire hydrant=47.8 stop sign=51.1 parking meter=32.1 bench=14.5 bird=22.6 cat=48.4 dog=41.8 horse=39.9 sheep=31.5 cow=38.8 elephant=48.2 bear=55.5 zebra=51.2 giraffe=51.6 backpack=7.2 umbrella=25.0 handbag=6.4 tie=19.2 suitcase=20.8 frisbee=45.5 skis=12.2 snowboard=18.0 sports ball=30.4 kite=27.7 baseball bat=16.1 baseball glove=24.3 skateboard=31.9 surfboard=21.5 tennis racket=28.3 bottle=20.7 wine glass=20.0 cup=25.7 fork=14.3 knife=5.5 spoon=5.3 bowl=25.3 banana=13.5 apple=9.8 sandwich=22.2 orange=20.1 broccoli=13.8 carrot=12.8 hot dog=22.2 pizza=31.5 donut=29.4 cake=21.7 chair=16.2 couch=31.7 potted plant=16.2 bed=32.3 dining table=18.3 toilet=46.4 tv=44.2 laptop=42.2 mouse=43.8 remote=12.0 keyboard=34.8 cell phone=20.3 microwave=38.1 oven=23.9 toaster=2.4 sink=26.3 refrigerator=38.0 book=6.5 clock=37.4 vase=24.3 scissors=22.6 teddy bear=28.7 hair drier=0.0 toothbrush=8.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.0 [Epoch 225][Batch 99], LR: 1.00E-04, Speed: 121.678 samples/sec, ObjLoss=20.626, BoxCenterLoss=14.488, BoxScaleLoss=4.785, ClassLoss=7.563 [Epoch 225][Batch 199], LR: 1.00E-04, Speed: 144.006 samples/sec, ObjLoss=20.625, BoxCenterLoss=14.488, BoxScaleLoss=4.784, ClassLoss=7.563 [Epoch 225][Batch 299], LR: 1.00E-04, Speed: 138.521 samples/sec, ObjLoss=20.624, BoxCenterLoss=14.488, BoxScaleLoss=4.784, ClassLoss=7.562 [Epoch 225][Batch 399], LR: 1.00E-04, Speed: 75.666 samples/sec, ObjLoss=20.624, BoxCenterLoss=14.487, BoxScaleLoss=4.784, ClassLoss=7.561 [Epoch 225][Batch 499], LR: 1.00E-04, Speed: 91.663 samples/sec, ObjLoss=20.623, BoxCenterLoss=14.487, BoxScaleLoss=4.784, ClassLoss=7.561 [Epoch 225][Batch 599], LR: 1.00E-04, Speed: 67.878 samples/sec, ObjLoss=20.622, BoxCenterLoss=14.487, BoxScaleLoss=4.784, ClassLoss=7.560 [Epoch 225][Batch 699], LR: 1.00E-04, Speed: 69.765 samples/sec, ObjLoss=20.621, BoxCenterLoss=14.487, BoxScaleLoss=4.783, ClassLoss=7.560 [Epoch 225][Batch 799], LR: 1.00E-04, Speed: 59.803 samples/sec, ObjLoss=20.621, BoxCenterLoss=14.487, BoxScaleLoss=4.783, ClassLoss=7.559 [Epoch 225][Batch 899], LR: 1.00E-04, Speed: 135.649 samples/sec, ObjLoss=20.620, BoxCenterLoss=14.487, BoxScaleLoss=4.783, ClassLoss=7.558 [Epoch 225][Batch 999], LR: 1.00E-04, Speed: 124.897 samples/sec, ObjLoss=20.619, BoxCenterLoss=14.487, BoxScaleLoss=4.783, ClassLoss=7.558 [Epoch 225][Batch 1099], LR: 1.00E-04, Speed: 45.609 samples/sec, ObjLoss=20.618, BoxCenterLoss=14.487, BoxScaleLoss=4.783, ClassLoss=7.557 [Epoch 225][Batch 1199], LR: 1.00E-04, Speed: 62.151 samples/sec, ObjLoss=20.617, BoxCenterLoss=14.487, BoxScaleLoss=4.782, ClassLoss=7.557 [Epoch 225][Batch 1299], LR: 1.00E-04, Speed: 83.415 samples/sec, ObjLoss=20.616, BoxCenterLoss=14.487, BoxScaleLoss=4.782, ClassLoss=7.556 [Epoch 225][Batch 1399], LR: 1.00E-04, Speed: 75.294 samples/sec, ObjLoss=20.616, BoxCenterLoss=14.487, BoxScaleLoss=4.782, ClassLoss=7.555 [Epoch 225][Batch 1499], LR: 1.00E-04, Speed: 100.695 samples/sec, ObjLoss=20.615, BoxCenterLoss=14.487, BoxScaleLoss=4.782, ClassLoss=7.555 [Epoch 225][Batch 1599], LR: 1.00E-04, Speed: 78.880 samples/sec, ObjLoss=20.614, BoxCenterLoss=14.487, BoxScaleLoss=4.781, ClassLoss=7.554 [Epoch 225][Batch 1699], LR: 1.00E-04, Speed: 90.154 samples/sec, ObjLoss=20.613, BoxCenterLoss=14.487, BoxScaleLoss=4.781, ClassLoss=7.554 [Epoch 225][Batch 1799], LR: 1.00E-04, Speed: 132.037 samples/sec, ObjLoss=20.613, BoxCenterLoss=14.486, BoxScaleLoss=4.781, ClassLoss=7.553 [Epoch 225] Training cost: 1575.353, ObjLoss=20.612, BoxCenterLoss=14.486, BoxScaleLoss=4.781, ClassLoss=7.553 [Epoch 225] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.401 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.514 person=38.6 bicycle=19.4 car=26.9 motorcycle=30.5 airplane=48.5 bus=49.8 train=53.2 truck=25.1 boat=15.3 traffic light=17.0 fire hydrant=44.9 stop sign=49.2 parking meter=31.5 bench=15.0 bird=23.0 cat=51.7 dog=44.5 horse=39.9 sheep=34.2 cow=38.6 elephant=48.2 bear=53.9 zebra=50.0 giraffe=51.5 backpack=7.7 umbrella=26.1 handbag=6.6 tie=19.4 suitcase=22.1 frisbee=45.2 skis=11.6 snowboard=19.0 sports ball=29.4 kite=28.8 baseball bat=14.9 baseball glove=23.1 skateboard=32.2 surfboard=22.6 tennis racket=28.9 bottle=20.6 wine glass=20.5 cup=26.1 fork=14.3 knife=5.2 spoon=5.6 bowl=25.8 banana=15.3 apple=9.9 sandwich=23.9 orange=20.7 broccoli=14.1 carrot=13.0 hot dog=22.5 pizza=35.7 donut=27.8 cake=22.2 chair=16.7 couch=32.4 potted plant=15.8 bed=34.6 dining table=18.7 toilet=45.4 tv=43.7 laptop=41.3 mouse=43.4 remote=12.8 keyboard=35.6 cell phone=20.6 microwave=37.0 oven=24.0 toaster=0.0 sink=26.3 refrigerator=38.7 book=6.7 clock=36.7 vase=23.4 scissors=21.4 teddy bear=29.0 hair drier=0.0 toothbrush=10.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.2 [Epoch 226][Batch 99], LR: 1.00E-04, Speed: 113.342 samples/sec, ObjLoss=20.611, BoxCenterLoss=14.486, BoxScaleLoss=4.781, ClassLoss=7.552 [Epoch 226][Batch 199], LR: 1.00E-04, Speed: 128.858 samples/sec, ObjLoss=20.611, BoxCenterLoss=14.486, BoxScaleLoss=4.780, ClassLoss=7.552 [Epoch 226][Batch 299], LR: 1.00E-04, Speed: 148.295 samples/sec, ObjLoss=20.610, BoxCenterLoss=14.486, BoxScaleLoss=4.780, ClassLoss=7.551 [Epoch 226][Batch 399], LR: 1.00E-04, Speed: 79.215 samples/sec, ObjLoss=20.609, BoxCenterLoss=14.486, BoxScaleLoss=4.780, ClassLoss=7.550 [Epoch 226][Batch 499], LR: 1.00E-04, Speed: 57.794 samples/sec, ObjLoss=20.608, BoxCenterLoss=14.486, BoxScaleLoss=4.780, ClassLoss=7.550 [Epoch 226][Batch 599], LR: 1.00E-04, Speed: 77.631 samples/sec, ObjLoss=20.607, BoxCenterLoss=14.486, BoxScaleLoss=4.780, ClassLoss=7.549 [Epoch 226][Batch 699], LR: 1.00E-04, Speed: 61.924 samples/sec, ObjLoss=20.607, BoxCenterLoss=14.486, BoxScaleLoss=4.779, ClassLoss=7.548 [Epoch 226][Batch 799], LR: 1.00E-04, Speed: 80.730 samples/sec, ObjLoss=20.606, BoxCenterLoss=14.486, BoxScaleLoss=4.779, ClassLoss=7.548 [Epoch 226][Batch 899], LR: 1.00E-04, Speed: 79.752 samples/sec, ObjLoss=20.605, BoxCenterLoss=14.486, BoxScaleLoss=4.779, ClassLoss=7.547 [Epoch 226][Batch 999], LR: 1.00E-04, Speed: 146.429 samples/sec, ObjLoss=20.605, BoxCenterLoss=14.486, BoxScaleLoss=4.779, ClassLoss=7.546 [Epoch 226][Batch 1099], LR: 1.00E-04, Speed: 99.091 samples/sec, ObjLoss=20.604, BoxCenterLoss=14.486, BoxScaleLoss=4.778, ClassLoss=7.546 [Epoch 226][Batch 1199], LR: 1.00E-04, Speed: 59.701 samples/sec, ObjLoss=20.603, BoxCenterLoss=14.486, BoxScaleLoss=4.778, ClassLoss=7.545 [Epoch 226][Batch 1299], LR: 1.00E-04, Speed: 118.945 samples/sec, ObjLoss=20.602, BoxCenterLoss=14.486, BoxScaleLoss=4.778, ClassLoss=7.545 [Epoch 226][Batch 1399], LR: 1.00E-04, Speed: 90.105 samples/sec, ObjLoss=20.601, BoxCenterLoss=14.486, BoxScaleLoss=4.778, ClassLoss=7.544 [Epoch 226][Batch 1499], LR: 1.00E-04, Speed: 50.686 samples/sec, ObjLoss=20.600, BoxCenterLoss=14.485, BoxScaleLoss=4.778, ClassLoss=7.543 [Epoch 226][Batch 1599], LR: 1.00E-04, Speed: 63.387 samples/sec, ObjLoss=20.600, BoxCenterLoss=14.485, BoxScaleLoss=4.777, ClassLoss=7.543 [Epoch 226][Batch 1699], LR: 1.00E-04, Speed: 65.113 samples/sec, ObjLoss=20.599, BoxCenterLoss=14.485, BoxScaleLoss=4.777, ClassLoss=7.542 [Epoch 226][Batch 1799], LR: 1.00E-04, Speed: 139.857 samples/sec, ObjLoss=20.598, BoxCenterLoss=14.485, BoxScaleLoss=4.777, ClassLoss=7.542 [Epoch 226] Training cost: 1582.780, ObjLoss=20.598, BoxCenterLoss=14.485, BoxScaleLoss=4.777, ClassLoss=7.541 [Epoch 226] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.279 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 ] = 0.280 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.177 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.514 person=38.2 bicycle=19.3 car=26.5 motorcycle=28.9 airplane=47.2 bus=49.0 train=53.0 truck=24.4 boat=16.6 traffic light=16.7 fire hydrant=46.7 stop sign=50.8 parking meter=31.2 bench=15.3 bird=22.9 cat=51.8 dog=45.2 horse=40.8 sheep=34.6 cow=38.4 elephant=47.9 bear=56.7 zebra=48.9 giraffe=52.9 backpack=7.7 umbrella=26.3 handbag=6.5 tie=18.8 suitcase=21.3 frisbee=45.6 skis=11.7 snowboard=20.1 sports ball=28.5 kite=29.3 baseball bat=15.1 baseball glove=23.4 skateboard=31.0 surfboard=23.0 tennis racket=26.9 bottle=20.0 wine glass=20.9 cup=26.2 fork=13.3 knife=6.2 spoon=4.9 bowl=26.1 banana=15.0 apple=8.9 sandwich=23.4 orange=20.6 broccoli=13.9 carrot=13.0 hot dog=23.3 pizza=35.0 donut=27.6 cake=21.2 chair=16.4 couch=31.6 potted plant=16.0 bed=32.8 dining table=16.0 toilet=45.7 tv=42.1 laptop=42.9 mouse=42.5 remote=13.4 keyboard=34.3 cell phone=21.2 microwave=39.1 oven=23.7 toaster=2.4 sink=27.1 refrigerator=38.3 book=7.6 clock=36.9 vase=23.7 scissors=21.6 teddy bear=29.4 hair drier=0.0 toothbrush=12.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.2 [Epoch 227][Batch 99], LR: 1.00E-04, Speed: 123.814 samples/sec, ObjLoss=20.597, BoxCenterLoss=14.485, BoxScaleLoss=4.777, ClassLoss=7.541 [Epoch 227][Batch 199], LR: 1.00E-04, Speed: 130.017 samples/sec, ObjLoss=20.596, BoxCenterLoss=14.485, BoxScaleLoss=4.777, ClassLoss=7.540 [Epoch 227][Batch 299], LR: 1.00E-04, Speed: 89.720 samples/sec, ObjLoss=20.595, BoxCenterLoss=14.485, BoxScaleLoss=4.776, ClassLoss=7.539 [Epoch 227][Batch 399], LR: 1.00E-04, Speed: 120.430 samples/sec, ObjLoss=20.594, BoxCenterLoss=14.485, BoxScaleLoss=4.776, ClassLoss=7.539 [Epoch 227][Batch 499], LR: 1.00E-04, Speed: 60.884 samples/sec, ObjLoss=20.594, BoxCenterLoss=14.485, BoxScaleLoss=4.776, ClassLoss=7.538 [Epoch 227][Batch 599], LR: 1.00E-04, Speed: 97.496 samples/sec, ObjLoss=20.593, BoxCenterLoss=14.485, BoxScaleLoss=4.776, ClassLoss=7.538 [Epoch 227][Batch 699], LR: 1.00E-04, Speed: 77.391 samples/sec, ObjLoss=20.592, BoxCenterLoss=14.485, BoxScaleLoss=4.776, ClassLoss=7.537 [Epoch 227][Batch 799], LR: 1.00E-04, Speed: 154.514 samples/sec, ObjLoss=20.591, BoxCenterLoss=14.485, BoxScaleLoss=4.775, ClassLoss=7.536 [Epoch 227][Batch 899], LR: 1.00E-04, Speed: 159.885 samples/sec, ObjLoss=20.590, BoxCenterLoss=14.485, BoxScaleLoss=4.775, ClassLoss=7.536 [Epoch 227][Batch 999], LR: 1.00E-04, Speed: 88.011 samples/sec, ObjLoss=20.589, BoxCenterLoss=14.484, BoxScaleLoss=4.775, ClassLoss=7.535 [Epoch 227][Batch 1099], LR: 1.00E-04, Speed: 69.090 samples/sec, ObjLoss=20.589, BoxCenterLoss=14.484, BoxScaleLoss=4.775, ClassLoss=7.535 [Epoch 227][Batch 1199], LR: 1.00E-04, Speed: 100.044 samples/sec, ObjLoss=20.588, BoxCenterLoss=14.484, BoxScaleLoss=4.775, ClassLoss=7.534 [Epoch 227][Batch 1299], LR: 1.00E-04, Speed: 94.554 samples/sec, ObjLoss=20.587, BoxCenterLoss=14.484, BoxScaleLoss=4.774, ClassLoss=7.534 [Epoch 227][Batch 1399], LR: 1.00E-04, Speed: 64.686 samples/sec, ObjLoss=20.586, BoxCenterLoss=14.484, BoxScaleLoss=4.774, ClassLoss=7.533 [Epoch 227][Batch 1499], LR: 1.00E-04, Speed: 80.820 samples/sec, ObjLoss=20.585, BoxCenterLoss=14.484, BoxScaleLoss=4.774, ClassLoss=7.532 [Epoch 227][Batch 1599], LR: 1.00E-04, Speed: 62.455 samples/sec, ObjLoss=20.584, BoxCenterLoss=14.484, BoxScaleLoss=4.774, ClassLoss=7.532 [Epoch 227][Batch 1699], LR: 1.00E-04, Speed: 86.014 samples/sec, ObjLoss=20.584, BoxCenterLoss=14.484, BoxScaleLoss=4.773, ClassLoss=7.531 [Epoch 227][Batch 1799], LR: 1.00E-04, Speed: 129.176 samples/sec, ObjLoss=20.583, BoxCenterLoss=14.484, BoxScaleLoss=4.773, ClassLoss=7.530 [Epoch 227] Training cost: 1594.569, ObjLoss=20.583, BoxCenterLoss=14.484, BoxScaleLoss=4.773, ClassLoss=7.530 [Epoch 227] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.240 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.361 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 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518 person=38.6 bicycle=19.3 car=26.1 motorcycle=29.4 airplane=49.5 bus=48.6 train=51.1 truck=24.6 boat=15.9 traffic light=16.5 fire hydrant=47.3 stop sign=51.0 parking meter=32.5 bench=16.0 bird=23.0 cat=49.9 dog=45.7 horse=40.7 sheep=33.9 cow=38.9 elephant=48.9 bear=54.3 zebra=49.5 giraffe=52.1 backpack=7.9 umbrella=26.3 handbag=7.0 tie=18.7 suitcase=22.8 frisbee=45.6 skis=11.8 snowboard=17.6 sports ball=27.6 kite=28.6 baseball bat=15.5 baseball glove=23.7 skateboard=31.2 surfboard=24.2 tennis racket=28.6 bottle=20.6 wine glass=21.2 cup=25.9 fork=14.8 knife=6.2 spoon=5.9 bowl=26.3 banana=15.5 apple=9.4 sandwich=21.1 orange=20.8 broccoli=14.4 carrot=12.6 hot dog=23.9 pizza=34.9 donut=26.8 cake=20.9 chair=16.5 couch=32.6 potted plant=16.4 bed=33.9 dining table=18.8 toilet=44.5 tv=43.5 laptop=41.9 mouse=42.3 remote=13.0 keyboard=35.4 cell phone=20.1 microwave=37.8 oven=23.7 toaster=2.4 sink=26.9 refrigerator=37.8 book=7.2 clock=36.7 vase=24.6 scissors=21.7 teddy bear=29.6 hair drier=0.0 toothbrush=10.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.2 [Epoch 228][Batch 99], LR: 1.00E-04, Speed: 142.467 samples/sec, ObjLoss=20.582, BoxCenterLoss=14.484, BoxScaleLoss=4.773, ClassLoss=7.530 [Epoch 228][Batch 199], LR: 1.00E-04, Speed: 121.840 samples/sec, ObjLoss=20.581, BoxCenterLoss=14.484, BoxScaleLoss=4.773, ClassLoss=7.529 [Epoch 228][Batch 299], LR: 1.00E-04, Speed: 110.460 samples/sec, ObjLoss=20.580, BoxCenterLoss=14.484, BoxScaleLoss=4.773, ClassLoss=7.528 [Epoch 228][Batch 399], LR: 1.00E-04, Speed: 121.729 samples/sec, ObjLoss=20.580, BoxCenterLoss=14.484, BoxScaleLoss=4.772, ClassLoss=7.528 [Epoch 228][Batch 499], LR: 1.00E-04, Speed: 72.214 samples/sec, ObjLoss=20.579, BoxCenterLoss=14.484, BoxScaleLoss=4.772, ClassLoss=7.527 [Epoch 228][Batch 599], LR: 1.00E-04, Speed: 80.982 samples/sec, ObjLoss=20.578, BoxCenterLoss=14.484, BoxScaleLoss=4.772, ClassLoss=7.526 [Epoch 228][Batch 699], LR: 1.00E-04, Speed: 137.154 samples/sec, ObjLoss=20.577, BoxCenterLoss=14.484, BoxScaleLoss=4.772, ClassLoss=7.526 [Epoch 228][Batch 799], LR: 1.00E-04, Speed: 108.902 samples/sec, ObjLoss=20.576, BoxCenterLoss=14.483, BoxScaleLoss=4.771, ClassLoss=7.525 [Epoch 228][Batch 899], LR: 1.00E-04, Speed: 61.018 samples/sec, ObjLoss=20.576, BoxCenterLoss=14.483, BoxScaleLoss=4.771, ClassLoss=7.525 [Epoch 228][Batch 999], LR: 1.00E-04, Speed: 88.350 samples/sec, ObjLoss=20.575, BoxCenterLoss=14.483, BoxScaleLoss=4.771, ClassLoss=7.524 [Epoch 228][Batch 1099], LR: 1.00E-04, Speed: 75.804 samples/sec, ObjLoss=20.574, BoxCenterLoss=14.483, BoxScaleLoss=4.771, ClassLoss=7.523 [Epoch 228][Batch 1199], LR: 1.00E-04, Speed: 96.979 samples/sec, ObjLoss=20.573, BoxCenterLoss=14.483, BoxScaleLoss=4.771, ClassLoss=7.523 [Epoch 228][Batch 1299], LR: 1.00E-04, Speed: 59.303 samples/sec, ObjLoss=20.572, BoxCenterLoss=14.483, BoxScaleLoss=4.770, ClassLoss=7.522 [Epoch 228][Batch 1399], LR: 1.00E-04, Speed: 70.047 samples/sec, ObjLoss=20.572, BoxCenterLoss=14.483, BoxScaleLoss=4.770, ClassLoss=7.521 [Epoch 228][Batch 1499], LR: 1.00E-04, Speed: 63.438 samples/sec, ObjLoss=20.571, BoxCenterLoss=14.483, BoxScaleLoss=4.770, ClassLoss=7.521 [Epoch 228][Batch 1599], LR: 1.00E-04, Speed: 57.224 samples/sec, ObjLoss=20.570, BoxCenterLoss=14.483, BoxScaleLoss=4.770, ClassLoss=7.520 [Epoch 228][Batch 1699], LR: 1.00E-04, Speed: 88.114 samples/sec, ObjLoss=20.569, BoxCenterLoss=14.483, BoxScaleLoss=4.770, ClassLoss=7.520 [Epoch 228][Batch 1799], LR: 1.00E-04, Speed: 142.218 samples/sec, ObjLoss=20.569, BoxCenterLoss=14.483, BoxScaleLoss=4.769, ClassLoss=7.519 [Epoch 228] Training cost: 1571.637, ObjLoss=20.568, BoxCenterLoss=14.483, BoxScaleLoss=4.769, ClassLoss=7.519 [Epoch 228] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.271 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.476 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.282 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.399 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.361 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 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 person=38.3 bicycle=18.8 car=26.5 motorcycle=29.4 airplane=48.9 bus=48.7 train=52.2 truck=24.0 boat=16.2 traffic light=16.6 fire hydrant=45.5 stop sign=49.9 parking meter=31.4 bench=14.8 bird=23.1 cat=47.7 dog=43.5 horse=39.5 sheep=33.5 cow=38.3 elephant=47.9 bear=54.0 zebra=49.1 giraffe=51.2 backpack=7.5 umbrella=26.0 handbag=6.8 tie=18.8 suitcase=22.3 frisbee=46.2 skis=11.5 snowboard=17.9 sports ball=29.7 kite=27.9 baseball bat=15.3 baseball glove=23.7 skateboard=31.7 surfboard=23.9 tennis racket=28.3 bottle=20.6 wine glass=20.4 cup=25.8 fork=15.2 knife=5.6 spoon=5.7 bowl=25.6 banana=15.6 apple=9.6 sandwich=22.1 orange=19.9 broccoli=13.4 carrot=12.2 hot dog=22.8 pizza=32.9 donut=27.6 cake=21.7 chair=16.4 couch=31.5 potted plant=17.1 bed=33.0 dining table=19.5 toilet=46.2 tv=42.4 laptop=42.7 mouse=43.3 remote=13.5 keyboard=36.2 cell phone=20.6 microwave=39.0 oven=25.2 toaster=2.8 sink=26.1 refrigerator=37.9 book=7.2 clock=37.3 vase=24.2 scissors=22.8 teddy bear=29.2 hair drier=0.0 toothbrush=10.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.1 [Epoch 229][Batch 99], LR: 1.00E-04, Speed: 157.791 samples/sec, ObjLoss=20.568, BoxCenterLoss=14.483, BoxScaleLoss=4.769, ClassLoss=7.518 [Epoch 229][Batch 199], LR: 1.00E-04, Speed: 143.473 samples/sec, ObjLoss=20.567, BoxCenterLoss=14.483, BoxScaleLoss=4.769, ClassLoss=7.518 [Epoch 229][Batch 299], LR: 1.00E-04, Speed: 140.955 samples/sec, ObjLoss=20.566, BoxCenterLoss=14.483, BoxScaleLoss=4.769, ClassLoss=7.517 [Epoch 229][Batch 399], LR: 1.00E-04, Speed: 132.879 samples/sec, ObjLoss=20.565, BoxCenterLoss=14.482, BoxScaleLoss=4.768, ClassLoss=7.516 [Epoch 229][Batch 499], LR: 1.00E-04, Speed: 89.697 samples/sec, ObjLoss=20.564, BoxCenterLoss=14.482, BoxScaleLoss=4.768, ClassLoss=7.516 [Epoch 229][Batch 599], LR: 1.00E-04, Speed: 84.612 samples/sec, ObjLoss=20.563, BoxCenterLoss=14.482, BoxScaleLoss=4.768, ClassLoss=7.515 [Epoch 229][Batch 699], LR: 1.00E-04, Speed: 95.616 samples/sec, ObjLoss=20.562, BoxCenterLoss=14.482, BoxScaleLoss=4.768, ClassLoss=7.514 [Epoch 229][Batch 799], LR: 1.00E-04, Speed: 73.296 samples/sec, ObjLoss=20.562, BoxCenterLoss=14.482, BoxScaleLoss=4.768, ClassLoss=7.514 [Epoch 229][Batch 899], LR: 1.00E-04, Speed: 73.112 samples/sec, ObjLoss=20.561, BoxCenterLoss=14.482, BoxScaleLoss=4.767, ClassLoss=7.513 [Epoch 229][Batch 999], LR: 1.00E-04, Speed: 82.549 samples/sec, ObjLoss=20.560, BoxCenterLoss=14.482, BoxScaleLoss=4.767, ClassLoss=7.513 [Epoch 229][Batch 1099], LR: 1.00E-04, Speed: 61.017 samples/sec, ObjLoss=20.559, BoxCenterLoss=14.482, BoxScaleLoss=4.767, ClassLoss=7.512 [Epoch 229][Batch 1199], LR: 1.00E-04, Speed: 106.983 samples/sec, ObjLoss=20.558, BoxCenterLoss=14.482, BoxScaleLoss=4.767, ClassLoss=7.511 [Epoch 229][Batch 1299], LR: 1.00E-04, Speed: 63.967 samples/sec, ObjLoss=20.557, BoxCenterLoss=14.482, BoxScaleLoss=4.767, ClassLoss=7.511 [Epoch 229][Batch 1399], LR: 1.00E-04, Speed: 84.188 samples/sec, ObjLoss=20.557, BoxCenterLoss=14.482, BoxScaleLoss=4.766, ClassLoss=7.510 [Epoch 229][Batch 1499], LR: 1.00E-04, Speed: 89.194 samples/sec, ObjLoss=20.556, BoxCenterLoss=14.481, BoxScaleLoss=4.766, ClassLoss=7.510 [Epoch 229][Batch 1599], LR: 1.00E-04, Speed: 62.991 samples/sec, ObjLoss=20.555, BoxCenterLoss=14.481, BoxScaleLoss=4.766, ClassLoss=7.509 [Epoch 229][Batch 1699], LR: 1.00E-04, Speed: 50.566 samples/sec, ObjLoss=20.554, BoxCenterLoss=14.481, BoxScaleLoss=4.766, ClassLoss=7.508 [Epoch 229][Batch 1799], LR: 1.00E-04, Speed: 118.410 samples/sec, ObjLoss=20.553, BoxCenterLoss=14.481, BoxScaleLoss=4.766, ClassLoss=7.508 [Epoch 229] Training cost: 1524.418, ObjLoss=20.553, BoxCenterLoss=14.481, BoxScaleLoss=4.766, ClassLoss=7.508 [Epoch 229] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.273 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.475 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.280 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 ] = 0.284 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.176 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515 person=38.4 bicycle=19.1 car=26.9 motorcycle=29.3 airplane=48.9 bus=49.5 train=52.2 truck=24.3 boat=16.3 traffic light=16.4 fire hydrant=46.8 stop sign=50.3 parking meter=32.0 bench=15.7 bird=23.2 cat=51.9 dog=45.1 horse=40.7 sheep=35.1 cow=37.5 elephant=48.8 bear=56.4 zebra=49.9 giraffe=52.4 backpack=7.4 umbrella=25.9 handbag=7.3 tie=18.5 suitcase=21.0 frisbee=46.4 skis=11.5 snowboard=19.2 sports ball=30.2 kite=29.6 baseball bat=15.1 baseball glove=23.3 skateboard=31.8 surfboard=22.9 tennis racket=28.0 bottle=20.2 wine glass=20.7 cup=26.2 fork=15.0 knife=6.0 spoon=5.6 bowl=25.9 banana=15.2 apple=8.1 sandwich=23.0 orange=21.1 broccoli=13.3 carrot=12.7 hot dog=24.0 pizza=36.7 donut=26.2 cake=22.0 chair=16.6 couch=31.5 potted plant=16.6 bed=33.3 dining table=17.4 toilet=45.6 tv=41.9 laptop=42.8 mouse=43.1 remote=13.4 keyboard=36.1 cell phone=21.2 microwave=37.2 oven=23.0 toaster=3.6 sink=25.9 refrigerator=38.8 book=6.8 clock=37.4 vase=24.2 scissors=21.7 teddy bear=31.6 hair drier=0.0 toothbrush=10.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.3 [Epoch 230][Batch 99], LR: 1.00E-04, Speed: 130.240 samples/sec, ObjLoss=20.552, BoxCenterLoss=14.481, BoxScaleLoss=4.765, ClassLoss=7.507 [Epoch 230][Batch 199], LR: 1.00E-04, Speed: 134.482 samples/sec, ObjLoss=20.552, BoxCenterLoss=14.481, BoxScaleLoss=4.765, ClassLoss=7.506 [Epoch 230][Batch 299], LR: 1.00E-04, Speed: 107.188 samples/sec, ObjLoss=20.551, BoxCenterLoss=14.481, BoxScaleLoss=4.765, ClassLoss=7.506 [Epoch 230][Batch 399], LR: 1.00E-04, Speed: 79.678 samples/sec, ObjLoss=20.550, BoxCenterLoss=14.481, BoxScaleLoss=4.765, ClassLoss=7.505 [Epoch 230][Batch 499], LR: 1.00E-04, Speed: 101.617 samples/sec, ObjLoss=20.549, BoxCenterLoss=14.481, BoxScaleLoss=4.764, ClassLoss=7.505 [Epoch 230][Batch 599], LR: 1.00E-04, Speed: 69.186 samples/sec, ObjLoss=20.549, BoxCenterLoss=14.481, BoxScaleLoss=4.764, ClassLoss=7.504 [Epoch 230][Batch 699], LR: 1.00E-04, Speed: 62.472 samples/sec, ObjLoss=20.548, BoxCenterLoss=14.481, BoxScaleLoss=4.764, ClassLoss=7.503 [Epoch 230][Batch 799], LR: 1.00E-04, Speed: 84.876 samples/sec, ObjLoss=20.547, BoxCenterLoss=14.481, BoxScaleLoss=4.764, ClassLoss=7.503 [Epoch 230][Batch 899], LR: 1.00E-04, Speed: 104.800 samples/sec, ObjLoss=20.546, BoxCenterLoss=14.481, BoxScaleLoss=4.764, ClassLoss=7.502 [Epoch 230][Batch 999], LR: 1.00E-04, Speed: 80.189 samples/sec, ObjLoss=20.545, BoxCenterLoss=14.481, BoxScaleLoss=4.763, ClassLoss=7.501 [Epoch 230][Batch 1099], LR: 1.00E-04, Speed: 73.625 samples/sec, ObjLoss=20.545, BoxCenterLoss=14.481, BoxScaleLoss=4.763, ClassLoss=7.501 [Epoch 230][Batch 1199], LR: 1.00E-04, Speed: 58.668 samples/sec, ObjLoss=20.544, BoxCenterLoss=14.481, BoxScaleLoss=4.763, ClassLoss=7.500 [Epoch 230][Batch 1299], LR: 1.00E-04, Speed: 62.838 samples/sec, ObjLoss=20.543, BoxCenterLoss=14.481, BoxScaleLoss=4.763, ClassLoss=7.500 [Epoch 230][Batch 1399], LR: 1.00E-04, Speed: 84.135 samples/sec, ObjLoss=20.542, BoxCenterLoss=14.480, BoxScaleLoss=4.763, ClassLoss=7.499 [Epoch 230][Batch 1499], LR: 1.00E-04, Speed: 93.000 samples/sec, ObjLoss=20.541, BoxCenterLoss=14.480, BoxScaleLoss=4.762, ClassLoss=7.498 [Epoch 230][Batch 1599], LR: 1.00E-04, Speed: 113.014 samples/sec, ObjLoss=20.540, BoxCenterLoss=14.480, BoxScaleLoss=4.762, ClassLoss=7.498 [Epoch 230][Batch 1699], LR: 1.00E-04, Speed: 77.001 samples/sec, ObjLoss=20.539, BoxCenterLoss=14.480, BoxScaleLoss=4.762, ClassLoss=7.497 [Epoch 230][Batch 1799], LR: 1.00E-04, Speed: 96.480 samples/sec, ObjLoss=20.539, BoxCenterLoss=14.480, BoxScaleLoss=4.762, ClassLoss=7.497 [Epoch 230] Training cost: 1532.079, ObjLoss=20.538, BoxCenterLoss=14.480, BoxScaleLoss=4.762, ClassLoss=7.496 [Epoch 230] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.475 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.406 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.517 person=38.2 bicycle=19.1 car=26.2 motorcycle=30.2 airplane=48.6 bus=49.8 train=52.7 truck=24.4 boat=15.8 traffic light=17.3 fire hydrant=46.4 stop sign=51.1 parking meter=30.5 bench=15.3 bird=23.0 cat=51.9 dog=43.9 horse=40.9 sheep=34.1 cow=38.1 elephant=50.0 bear=54.7 zebra=50.1 giraffe=51.0 backpack=7.5 umbrella=25.8 handbag=7.1 tie=18.8 suitcase=21.4 frisbee=46.7 skis=12.1 snowboard=17.5 sports ball=28.8 kite=29.8 baseball bat=15.2 baseball glove=23.3 skateboard=32.2 surfboard=23.0 tennis racket=27.9 bottle=20.1 wine glass=20.9 cup=25.9 fork=15.0 knife=6.0 spoon=5.6 bowl=26.1 banana=15.4 apple=9.3 sandwich=23.5 orange=18.3 broccoli=13.5 carrot=12.6 hot dog=21.5 pizza=33.5 donut=26.1 cake=22.1 chair=16.4 couch=31.8 potted plant=16.5 bed=33.9 dining table=17.4 toilet=44.7 tv=43.8 laptop=43.7 mouse=43.7 remote=12.5 keyboard=34.7 cell phone=20.8 microwave=39.0 oven=22.1 toaster=7.1 sink=25.8 refrigerator=37.9 book=6.9 clock=36.5 vase=24.9 scissors=20.5 teddy bear=29.2 hair drier=0.0 toothbrush=10.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.2 [Epoch 231][Batch 99], LR: 1.00E-04, Speed: 136.381 samples/sec, ObjLoss=20.538, BoxCenterLoss=14.480, BoxScaleLoss=4.761, ClassLoss=7.496 [Epoch 231][Batch 199], LR: 1.00E-04, Speed: 143.302 samples/sec, ObjLoss=20.537, BoxCenterLoss=14.480, BoxScaleLoss=4.761, ClassLoss=7.495 [Epoch 231][Batch 299], LR: 1.00E-04, Speed: 155.267 samples/sec, ObjLoss=20.536, BoxCenterLoss=14.480, BoxScaleLoss=4.761, ClassLoss=7.494 [Epoch 231][Batch 399], LR: 1.00E-04, Speed: 77.845 samples/sec, ObjLoss=20.535, BoxCenterLoss=14.480, BoxScaleLoss=4.761, ClassLoss=7.494 [Epoch 231][Batch 499], LR: 1.00E-04, Speed: 95.061 samples/sec, ObjLoss=20.534, BoxCenterLoss=14.480, BoxScaleLoss=4.761, ClassLoss=7.493 [Epoch 231][Batch 599], LR: 1.00E-04, Speed: 84.965 samples/sec, ObjLoss=20.534, BoxCenterLoss=14.480, BoxScaleLoss=4.760, ClassLoss=7.492 [Epoch 231][Batch 699], LR: 1.00E-04, Speed: 80.437 samples/sec, ObjLoss=20.533, BoxCenterLoss=14.480, BoxScaleLoss=4.760, ClassLoss=7.492 [Epoch 231][Batch 799], LR: 1.00E-04, Speed: 67.441 samples/sec, ObjLoss=20.532, BoxCenterLoss=14.480, BoxScaleLoss=4.760, ClassLoss=7.491 [Epoch 231][Batch 899], LR: 1.00E-04, Speed: 77.225 samples/sec, ObjLoss=20.531, BoxCenterLoss=14.479, BoxScaleLoss=4.760, ClassLoss=7.491 [Epoch 231][Batch 999], LR: 1.00E-04, Speed: 103.620 samples/sec, ObjLoss=20.530, BoxCenterLoss=14.479, BoxScaleLoss=4.760, ClassLoss=7.490 [Epoch 231][Batch 1099], LR: 1.00E-04, Speed: 102.617 samples/sec, ObjLoss=20.529, BoxCenterLoss=14.479, BoxScaleLoss=4.759, ClassLoss=7.489 [Epoch 231][Batch 1199], LR: 1.00E-04, Speed: 69.896 samples/sec, ObjLoss=20.529, BoxCenterLoss=14.479, BoxScaleLoss=4.759, ClassLoss=7.489 [Epoch 231][Batch 1299], LR: 1.00E-04, Speed: 69.782 samples/sec, ObjLoss=20.528, BoxCenterLoss=14.479, BoxScaleLoss=4.759, ClassLoss=7.488 [Epoch 231][Batch 1399], LR: 1.00E-04, Speed: 72.910 samples/sec, ObjLoss=20.527, BoxCenterLoss=14.479, BoxScaleLoss=4.759, ClassLoss=7.488 [Epoch 231][Batch 1499], LR: 1.00E-04, Speed: 65.725 samples/sec, ObjLoss=20.526, BoxCenterLoss=14.479, BoxScaleLoss=4.759, ClassLoss=7.487 [Epoch 231][Batch 1599], LR: 1.00E-04, Speed: 79.784 samples/sec, ObjLoss=20.525, BoxCenterLoss=14.479, BoxScaleLoss=4.758, ClassLoss=7.486 [Epoch 231][Batch 1699], LR: 1.00E-04, Speed: 83.267 samples/sec, ObjLoss=20.525, BoxCenterLoss=14.479, BoxScaleLoss=4.758, ClassLoss=7.486 [Epoch 231][Batch 1799], LR: 1.00E-04, Speed: 103.740 samples/sec, ObjLoss=20.524, BoxCenterLoss=14.479, BoxScaleLoss=4.758, ClassLoss=7.485 [Epoch 231] Training cost: 1515.390, ObjLoss=20.524, BoxCenterLoss=14.479, BoxScaleLoss=4.758, ClassLoss=7.485 [Epoch 231] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.274 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.282 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.240 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518 person=38.7 bicycle=19.5 car=26.9 motorcycle=31.5 airplane=48.1 bus=50.1 train=52.6 truck=24.8 boat=16.6 traffic light=16.4 fire hydrant=46.7 stop sign=51.4 parking meter=32.7 bench=15.6 bird=23.1 cat=53.2 dog=45.1 horse=40.8 sheep=34.4 cow=38.0 elephant=48.6 bear=54.0 zebra=50.7 giraffe=52.2 backpack=7.8 umbrella=25.6 handbag=7.1 tie=18.9 suitcase=21.9 frisbee=47.5 skis=11.9 snowboard=18.3 sports ball=28.4 kite=29.0 baseball bat=15.7 baseball glove=23.0 skateboard=32.3 surfboard=23.3 tennis racket=28.3 bottle=20.6 wine glass=20.4 cup=26.2 fork=14.5 knife=5.5 spoon=5.7 bowl=26.1 banana=15.1 apple=9.5 sandwich=23.2 orange=20.4 broccoli=14.1 carrot=12.0 hot dog=23.7 pizza=36.6 donut=27.3 cake=22.5 chair=16.7 couch=32.1 potted plant=16.8 bed=32.5 dining table=18.4 toilet=46.2 tv=42.3 laptop=42.0 mouse=43.7 remote=12.6 keyboard=36.3 cell phone=20.9 microwave=37.9 oven=23.2 toaster=3.6 sink=24.3 refrigerator=36.7 book=7.2 clock=38.0 vase=24.3 scissors=24.8 teddy bear=30.9 hair drier=0.0 toothbrush=9.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.4 [Epoch 232][Batch 99], LR: 1.00E-04, Speed: 137.735 samples/sec, ObjLoss=20.523, BoxCenterLoss=14.479, BoxScaleLoss=4.758, ClassLoss=7.484 [Epoch 232][Batch 199], LR: 1.00E-04, Speed: 109.357 samples/sec, ObjLoss=20.522, BoxCenterLoss=14.479, BoxScaleLoss=4.757, ClassLoss=7.484 [Epoch 232][Batch 299], LR: 1.00E-04, Speed: 142.765 samples/sec, ObjLoss=20.521, BoxCenterLoss=14.479, BoxScaleLoss=4.757, ClassLoss=7.483 [Epoch 232][Batch 399], LR: 1.00E-04, Speed: 76.409 samples/sec, ObjLoss=20.520, BoxCenterLoss=14.478, BoxScaleLoss=4.757, ClassLoss=7.482 [Epoch 232][Batch 499], LR: 1.00E-04, Speed: 94.503 samples/sec, ObjLoss=20.520, BoxCenterLoss=14.478, BoxScaleLoss=4.757, ClassLoss=7.482 [Epoch 232][Batch 599], LR: 1.00E-04, Speed: 105.113 samples/sec, ObjLoss=20.519, BoxCenterLoss=14.478, BoxScaleLoss=4.757, ClassLoss=7.481 [Epoch 232][Batch 699], LR: 1.00E-04, Speed: 76.485 samples/sec, ObjLoss=20.518, BoxCenterLoss=14.478, BoxScaleLoss=4.756, ClassLoss=7.480 [Epoch 232][Batch 799], LR: 1.00E-04, Speed: 68.602 samples/sec, ObjLoss=20.517, BoxCenterLoss=14.478, BoxScaleLoss=4.756, ClassLoss=7.480 [Epoch 232][Batch 899], LR: 1.00E-04, Speed: 75.951 samples/sec, ObjLoss=20.516, BoxCenterLoss=14.478, BoxScaleLoss=4.756, ClassLoss=7.479 [Epoch 232][Batch 999], LR: 1.00E-04, Speed: 70.544 samples/sec, ObjLoss=20.516, BoxCenterLoss=14.478, BoxScaleLoss=4.756, ClassLoss=7.479 [Epoch 232][Batch 1099], LR: 1.00E-04, Speed: 126.085 samples/sec, ObjLoss=20.515, BoxCenterLoss=14.478, BoxScaleLoss=4.755, ClassLoss=7.478 [Epoch 232][Batch 1199], LR: 1.00E-04, Speed: 87.448 samples/sec, ObjLoss=20.514, BoxCenterLoss=14.478, BoxScaleLoss=4.755, ClassLoss=7.477 [Epoch 232][Batch 1299], LR: 1.00E-04, Speed: 76.399 samples/sec, ObjLoss=20.513, BoxCenterLoss=14.478, BoxScaleLoss=4.755, ClassLoss=7.477 [Epoch 232][Batch 1399], LR: 1.00E-04, Speed: 125.167 samples/sec, ObjLoss=20.512, BoxCenterLoss=14.478, BoxScaleLoss=4.755, ClassLoss=7.476 [Epoch 232][Batch 1499], LR: 1.00E-04, Speed: 66.810 samples/sec, ObjLoss=20.512, BoxCenterLoss=14.478, BoxScaleLoss=4.755, ClassLoss=7.475 [Epoch 232][Batch 1599], LR: 1.00E-04, Speed: 61.733 samples/sec, ObjLoss=20.511, BoxCenterLoss=14.478, BoxScaleLoss=4.754, ClassLoss=7.475 [Epoch 232][Batch 1699], LR: 1.00E-04, Speed: 93.485 samples/sec, ObjLoss=20.510, BoxCenterLoss=14.477, BoxScaleLoss=4.754, ClassLoss=7.474 [Epoch 232][Batch 1799], LR: 1.00E-04, Speed: 107.820 samples/sec, ObjLoss=20.509, BoxCenterLoss=14.477, BoxScaleLoss=4.754, ClassLoss=7.474 [Epoch 232] Training cost: 1592.182, ObjLoss=20.509, BoxCenterLoss=14.477, BoxScaleLoss=4.754, ClassLoss=7.473 [Epoch 232] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.478 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.406 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.517 person=38.5 bicycle=19.2 car=26.5 motorcycle=29.4 airplane=47.7 bus=48.5 train=53.2 truck=24.6 boat=15.8 traffic light=16.7 fire hydrant=48.0 stop sign=51.0 parking meter=31.7 bench=15.7 bird=23.8 cat=50.4 dog=45.0 horse=39.6 sheep=34.3 cow=37.5 elephant=47.9 bear=55.2 zebra=48.4 giraffe=52.3 backpack=7.2 umbrella=25.8 handbag=7.2 tie=18.9 suitcase=23.3 frisbee=46.1 skis=11.6 snowboard=18.8 sports ball=29.4 kite=28.9 baseball bat=15.9 baseball glove=22.5 skateboard=32.5 surfboard=22.9 tennis racket=28.1 bottle=20.9 wine glass=20.7 cup=26.0 fork=13.9 knife=6.1 spoon=5.9 bowl=26.2 banana=15.0 apple=8.7 sandwich=21.9 orange=19.3 broccoli=13.1 carrot=12.5 hot dog=23.8 pizza=32.5 donut=27.0 cake=22.3 chair=16.8 couch=31.4 potted plant=17.3 bed=32.0 dining table=19.9 toilet=46.0 tv=42.5 laptop=41.0 mouse=44.3 remote=12.5 keyboard=35.2 cell phone=20.0 microwave=40.7 oven=23.6 toaster=2.4 sink=26.8 refrigerator=38.4 book=7.3 clock=36.7 vase=24.4 scissors=24.4 teddy bear=30.2 hair drier=0.0 toothbrush=10.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.2 [Epoch 233][Batch 99], LR: 1.00E-04, Speed: 109.693 samples/sec, ObjLoss=20.508, BoxCenterLoss=14.477, BoxScaleLoss=4.754, ClassLoss=7.473 [Epoch 233][Batch 199], LR: 1.00E-04, Speed: 134.459 samples/sec, ObjLoss=20.507, BoxCenterLoss=14.477, BoxScaleLoss=4.754, ClassLoss=7.472 [Epoch 233][Batch 299], LR: 1.00E-04, Speed: 116.679 samples/sec, ObjLoss=20.506, BoxCenterLoss=14.477, BoxScaleLoss=4.753, ClassLoss=7.472 [Epoch 233][Batch 399], LR: 1.00E-04, Speed: 77.491 samples/sec, ObjLoss=20.506, BoxCenterLoss=14.477, BoxScaleLoss=4.753, ClassLoss=7.471 [Epoch 233][Batch 499], LR: 1.00E-04, Speed: 79.764 samples/sec, ObjLoss=20.505, BoxCenterLoss=14.477, BoxScaleLoss=4.753, ClassLoss=7.470 [Epoch 233][Batch 599], LR: 1.00E-04, Speed: 69.391 samples/sec, ObjLoss=20.504, BoxCenterLoss=14.477, BoxScaleLoss=4.753, ClassLoss=7.470 [Epoch 233][Batch 699], LR: 1.00E-04, Speed: 84.597 samples/sec, ObjLoss=20.503, BoxCenterLoss=14.477, BoxScaleLoss=4.753, ClassLoss=7.469 [Epoch 233][Batch 799], LR: 1.00E-04, Speed: 73.641 samples/sec, ObjLoss=20.502, BoxCenterLoss=14.477, BoxScaleLoss=4.752, ClassLoss=7.469 [Epoch 233][Batch 899], LR: 1.00E-04, Speed: 79.781 samples/sec, ObjLoss=20.502, BoxCenterLoss=14.477, BoxScaleLoss=4.752, ClassLoss=7.468 [Epoch 233][Batch 999], LR: 1.00E-04, Speed: 85.427 samples/sec, ObjLoss=20.501, BoxCenterLoss=14.477, BoxScaleLoss=4.752, ClassLoss=7.467 [Epoch 233][Batch 1099], LR: 1.00E-04, Speed: 101.152 samples/sec, ObjLoss=20.500, BoxCenterLoss=14.477, BoxScaleLoss=4.752, ClassLoss=7.467 [Epoch 233][Batch 1199], LR: 1.00E-04, Speed: 59.632 samples/sec, ObjLoss=20.499, BoxCenterLoss=14.477, BoxScaleLoss=4.751, ClassLoss=7.466 [Epoch 233][Batch 1299], LR: 1.00E-04, Speed: 115.338 samples/sec, ObjLoss=20.498, BoxCenterLoss=14.476, BoxScaleLoss=4.751, ClassLoss=7.465 [Epoch 233][Batch 1399], LR: 1.00E-04, Speed: 77.846 samples/sec, ObjLoss=20.497, BoxCenterLoss=14.476, BoxScaleLoss=4.751, ClassLoss=7.465 [Epoch 233][Batch 1499], LR: 1.00E-04, Speed: 95.139 samples/sec, ObjLoss=20.497, BoxCenterLoss=14.476, BoxScaleLoss=4.751, ClassLoss=7.464 [Epoch 233][Batch 1599], LR: 1.00E-04, Speed: 106.782 samples/sec, ObjLoss=20.496, BoxCenterLoss=14.476, BoxScaleLoss=4.751, ClassLoss=7.463 [Epoch 233][Batch 1699], LR: 1.00E-04, Speed: 134.749 samples/sec, ObjLoss=20.495, BoxCenterLoss=14.476, BoxScaleLoss=4.750, ClassLoss=7.463 [Epoch 233][Batch 1799], LR: 1.00E-04, Speed: 130.790 samples/sec, ObjLoss=20.494, BoxCenterLoss=14.476, BoxScaleLoss=4.750, ClassLoss=7.462 [Epoch 233] Training cost: 1502.742, ObjLoss=20.494, BoxCenterLoss=14.476, BoxScaleLoss=4.750, ClassLoss=7.462 [Epoch 233] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.277 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.126 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 person=38.6 bicycle=19.7 car=26.9 motorcycle=31.6 airplane=49.9 bus=50.4 train=53.1 truck=24.7 boat=16.6 traffic light=17.0 fire hydrant=48.2 stop sign=50.9 parking meter=32.9 bench=15.4 bird=21.7 cat=51.0 dog=44.7 horse=41.4 sheep=33.8 cow=38.5 elephant=48.0 bear=57.4 zebra=50.2 giraffe=53.3 backpack=7.6 umbrella=25.7 handbag=6.8 tie=19.6 suitcase=22.2 frisbee=47.7 skis=11.4 snowboard=19.4 sports ball=30.7 kite=29.2 baseball bat=15.3 baseball glove=24.5 skateboard=32.3 surfboard=23.1 tennis racket=28.9 bottle=20.7 wine glass=21.2 cup=26.9 fork=14.0 knife=5.5 spoon=5.9 bowl=25.8 banana=15.8 apple=9.3 sandwich=23.8 orange=20.5 broccoli=13.5 carrot=13.3 hot dog=23.4 pizza=35.3 donut=28.6 cake=22.0 chair=16.7 couch=34.0 potted plant=17.1 bed=35.2 dining table=20.9 toilet=46.4 tv=43.0 laptop=43.6 mouse=43.8 remote=12.7 keyboard=37.9 cell phone=20.2 microwave=37.5 oven=23.7 toaster=4.2 sink=24.9 refrigerator=38.9 book=7.3 clock=37.9 vase=25.4 scissors=21.4 teddy bear=29.2 hair drier=0.0 toothbrush=10.4 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.7 [Epoch 234][Batch 99], LR: 1.00E-04, Speed: 138.060 samples/sec, ObjLoss=20.493, BoxCenterLoss=14.476, BoxScaleLoss=4.750, ClassLoss=7.461 [Epoch 234][Batch 199], LR: 1.00E-04, Speed: 136.160 samples/sec, ObjLoss=20.492, BoxCenterLoss=14.476, BoxScaleLoss=4.750, ClassLoss=7.461 [Epoch 234][Batch 299], LR: 1.00E-04, Speed: 86.984 samples/sec, ObjLoss=20.491, BoxCenterLoss=14.476, BoxScaleLoss=4.750, ClassLoss=7.460 [Epoch 234][Batch 399], LR: 1.00E-04, Speed: 131.544 samples/sec, ObjLoss=20.491, BoxCenterLoss=14.476, BoxScaleLoss=4.749, ClassLoss=7.460 [Epoch 234][Batch 499], LR: 1.00E-04, Speed: 70.335 samples/sec, ObjLoss=20.490, BoxCenterLoss=14.475, BoxScaleLoss=4.749, ClassLoss=7.459 [Epoch 234][Batch 599], LR: 1.00E-04, Speed: 110.452 samples/sec, ObjLoss=20.489, BoxCenterLoss=14.475, BoxScaleLoss=4.749, ClassLoss=7.458 [Epoch 234][Batch 699], LR: 1.00E-04, Speed: 163.280 samples/sec, ObjLoss=20.488, BoxCenterLoss=14.475, BoxScaleLoss=4.749, ClassLoss=7.458 [Epoch 234][Batch 799], LR: 1.00E-04, Speed: 70.692 samples/sec, ObjLoss=20.487, BoxCenterLoss=14.475, BoxScaleLoss=4.749, ClassLoss=7.457 [Epoch 234][Batch 899], LR: 1.00E-04, Speed: 68.789 samples/sec, ObjLoss=20.486, BoxCenterLoss=14.475, BoxScaleLoss=4.748, ClassLoss=7.457 [Epoch 234][Batch 999], LR: 1.00E-04, Speed: 128.968 samples/sec, ObjLoss=20.485, BoxCenterLoss=14.475, BoxScaleLoss=4.748, ClassLoss=7.456 [Epoch 234][Batch 1099], LR: 1.00E-04, Speed: 102.991 samples/sec, ObjLoss=20.485, BoxCenterLoss=14.475, BoxScaleLoss=4.748, ClassLoss=7.455 [Epoch 234][Batch 1199], LR: 1.00E-04, Speed: 76.011 samples/sec, ObjLoss=20.484, BoxCenterLoss=14.475, BoxScaleLoss=4.748, ClassLoss=7.455 [Epoch 234][Batch 1299], LR: 1.00E-04, Speed: 160.847 samples/sec, ObjLoss=20.483, BoxCenterLoss=14.475, BoxScaleLoss=4.747, ClassLoss=7.454 [Epoch 234][Batch 1399], LR: 1.00E-04, Speed: 50.535 samples/sec, ObjLoss=20.482, BoxCenterLoss=14.475, BoxScaleLoss=4.747, ClassLoss=7.453 [Epoch 234][Batch 1499], LR: 1.00E-04, Speed: 58.769 samples/sec, ObjLoss=20.482, BoxCenterLoss=14.475, BoxScaleLoss=4.747, ClassLoss=7.453 [Epoch 234][Batch 1599], LR: 1.00E-04, Speed: 86.524 samples/sec, ObjLoss=20.481, BoxCenterLoss=14.475, BoxScaleLoss=4.747, ClassLoss=7.452 [Epoch 234][Batch 1699], LR: 1.00E-04, Speed: 64.019 samples/sec, ObjLoss=20.480, BoxCenterLoss=14.475, BoxScaleLoss=4.747, ClassLoss=7.452 [Epoch 234][Batch 1799], LR: 1.00E-04, Speed: 128.303 samples/sec, ObjLoss=20.479, BoxCenterLoss=14.475, BoxScaleLoss=4.746, ClassLoss=7.451 [Epoch 234] Training cost: 1515.796, ObjLoss=20.479, BoxCenterLoss=14.475, BoxScaleLoss=4.746, ClassLoss=7.451 [Epoch 234] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.480 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.411 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.524 person=38.3 bicycle=19.4 car=26.5 motorcycle=30.6 airplane=49.9 bus=49.3 train=52.9 truck=24.7 boat=16.1 traffic light=16.7 fire hydrant=47.7 stop sign=51.5 parking meter=32.7 bench=15.5 bird=22.8 cat=51.7 dog=44.8 horse=40.5 sheep=33.9 cow=38.9 elephant=48.6 bear=54.8 zebra=50.1 giraffe=52.5 backpack=7.6 umbrella=25.8 handbag=7.3 tie=18.2 suitcase=22.3 frisbee=45.5 skis=12.6 snowboard=19.5 sports ball=30.5 kite=29.6 baseball bat=15.6 baseball glove=23.8 skateboard=34.2 surfboard=23.9 tennis racket=28.0 bottle=19.8 wine glass=20.4 cup=25.2 fork=14.6 knife=5.4 spoon=5.5 bowl=26.6 banana=14.2 apple=7.9 sandwich=23.2 orange=19.6 broccoli=13.5 carrot=12.3 hot dog=22.9 pizza=32.8 donut=26.1 cake=22.3 chair=16.8 couch=32.4 potted plant=17.1 bed=34.8 dining table=21.0 toilet=45.9 tv=44.3 laptop=43.8 mouse=44.3 remote=13.4 keyboard=37.3 cell phone=20.6 microwave=36.6 oven=25.4 toaster=3.6 sink=26.0 refrigerator=38.6 book=7.3 clock=38.3 vase=23.9 scissors=22.4 teddy bear=30.3 hair drier=0.0 toothbrush=9.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.5 [Epoch 235][Batch 99], LR: 1.00E-04, Speed: 158.063 samples/sec, ObjLoss=20.478, BoxCenterLoss=14.475, BoxScaleLoss=4.746, ClassLoss=7.450 [Epoch 235][Batch 199], LR: 1.00E-04, Speed: 155.899 samples/sec, ObjLoss=20.478, BoxCenterLoss=14.475, BoxScaleLoss=4.746, ClassLoss=7.450 [Epoch 235][Batch 299], LR: 1.00E-04, Speed: 66.098 samples/sec, ObjLoss=20.477, BoxCenterLoss=14.475, BoxScaleLoss=4.746, ClassLoss=7.449 [Epoch 235][Batch 399], LR: 1.00E-04, Speed: 102.558 samples/sec, ObjLoss=20.476, BoxCenterLoss=14.474, BoxScaleLoss=4.746, ClassLoss=7.448 [Epoch 235][Batch 499], LR: 1.00E-04, Speed: 123.372 samples/sec, ObjLoss=20.475, BoxCenterLoss=14.474, BoxScaleLoss=4.745, ClassLoss=7.448 [Epoch 235][Batch 599], LR: 1.00E-04, Speed: 67.032 samples/sec, ObjLoss=20.474, BoxCenterLoss=14.474, BoxScaleLoss=4.745, ClassLoss=7.447 [Epoch 235][Batch 699], LR: 1.00E-04, Speed: 45.802 samples/sec, ObjLoss=20.474, BoxCenterLoss=14.474, BoxScaleLoss=4.745, ClassLoss=7.447 [Epoch 235][Batch 799], LR: 1.00E-04, Speed: 89.765 samples/sec, ObjLoss=20.473, BoxCenterLoss=14.474, BoxScaleLoss=4.745, ClassLoss=7.446 [Epoch 235][Batch 899], LR: 1.00E-04, Speed: 90.732 samples/sec, ObjLoss=20.472, BoxCenterLoss=14.474, BoxScaleLoss=4.745, ClassLoss=7.445 [Epoch 235][Batch 999], LR: 1.00E-04, Speed: 52.619 samples/sec, ObjLoss=20.472, BoxCenterLoss=14.474, BoxScaleLoss=4.744, ClassLoss=7.445 [Epoch 235][Batch 1099], LR: 1.00E-04, Speed: 45.369 samples/sec, ObjLoss=20.471, BoxCenterLoss=14.474, BoxScaleLoss=4.744, ClassLoss=7.444 [Epoch 235][Batch 1199], LR: 1.00E-04, Speed: 54.855 samples/sec, ObjLoss=20.470, BoxCenterLoss=14.474, BoxScaleLoss=4.744, ClassLoss=7.443 [Epoch 235][Batch 1299], LR: 1.00E-04, Speed: 120.659 samples/sec, ObjLoss=20.469, BoxCenterLoss=14.474, BoxScaleLoss=4.744, ClassLoss=7.443 [Epoch 235][Batch 1399], LR: 1.00E-04, Speed: 114.111 samples/sec, ObjLoss=20.468, BoxCenterLoss=14.474, BoxScaleLoss=4.744, ClassLoss=7.442 [Epoch 235][Batch 1499], LR: 1.00E-04, Speed: 64.087 samples/sec, ObjLoss=20.467, BoxCenterLoss=14.474, BoxScaleLoss=4.743, ClassLoss=7.441 [Epoch 235][Batch 1599], LR: 1.00E-04, Speed: 56.965 samples/sec, ObjLoss=20.467, BoxCenterLoss=14.474, BoxScaleLoss=4.743, ClassLoss=7.441 [Epoch 235][Batch 1699], LR: 1.00E-04, Speed: 61.737 samples/sec, ObjLoss=20.466, BoxCenterLoss=14.474, BoxScaleLoss=4.743, ClassLoss=7.440 [Epoch 235][Batch 1799], LR: 1.00E-04, Speed: 85.995 samples/sec, ObjLoss=20.465, BoxCenterLoss=14.474, BoxScaleLoss=4.743, ClassLoss=7.440 [Epoch 235] Training cost: 1556.672, ObjLoss=20.465, BoxCenterLoss=14.474, BoxScaleLoss=4.743, ClassLoss=7.439 [Epoch 235] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.242 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363 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 ] = 0.379 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 person=39.0 bicycle=19.0 car=26.7 motorcycle=30.9 airplane=47.8 bus=49.2 train=53.2 truck=24.6 boat=16.2 traffic light=16.7 fire hydrant=47.2 stop sign=49.1 parking meter=32.3 bench=15.8 bird=23.2 cat=51.1 dog=45.0 horse=41.2 sheep=35.3 cow=38.5 elephant=47.6 bear=55.1 zebra=49.7 giraffe=53.0 backpack=7.8 umbrella=26.6 handbag=7.1 tie=18.9 suitcase=21.3 frisbee=45.2 skis=12.5 snowboard=18.5 sports ball=27.3 kite=29.2 baseball bat=15.8 baseball glove=24.1 skateboard=33.6 surfboard=23.5 tennis racket=28.3 bottle=21.4 wine glass=20.6 cup=26.4 fork=15.5 knife=6.2 spoon=5.7 bowl=26.4 banana=15.0 apple=9.7 sandwich=24.9 orange=20.3 broccoli=13.4 carrot=13.6 hot dog=23.6 pizza=37.9 donut=26.8 cake=21.8 chair=16.9 couch=31.6 potted plant=15.8 bed=32.5 dining table=16.8 toilet=46.4 tv=43.6 laptop=43.0 mouse=44.8 remote=12.9 keyboard=37.5 cell phone=21.1 microwave=39.6 oven=22.5 toaster=6.4 sink=26.2 refrigerator=37.2 book=7.6 clock=37.2 vase=24.0 scissors=22.6 teddy bear=31.0 hair drier=0.0 toothbrush=12.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.6 [Epoch 236][Batch 99], LR: 1.00E-04, Speed: 124.313 samples/sec, ObjLoss=20.464, BoxCenterLoss=14.473, BoxScaleLoss=4.742, ClassLoss=7.439 [Epoch 236][Batch 199], LR: 1.00E-04, Speed: 123.691 samples/sec, ObjLoss=20.463, BoxCenterLoss=14.473, BoxScaleLoss=4.742, ClassLoss=7.438 [Epoch 236][Batch 299], LR: 1.00E-04, Speed: 144.113 samples/sec, ObjLoss=20.462, BoxCenterLoss=14.473, BoxScaleLoss=4.742, ClassLoss=7.437 [Epoch 236][Batch 399], LR: 1.00E-04, Speed: 115.006 samples/sec, ObjLoss=20.461, BoxCenterLoss=14.473, BoxScaleLoss=4.742, ClassLoss=7.437 [Epoch 236][Batch 499], LR: 1.00E-04, Speed: 46.771 samples/sec, ObjLoss=20.461, BoxCenterLoss=14.473, BoxScaleLoss=4.742, ClassLoss=7.436 [Epoch 236][Batch 599], LR: 1.00E-04, Speed: 69.579 samples/sec, ObjLoss=20.460, BoxCenterLoss=14.473, BoxScaleLoss=4.741, ClassLoss=7.436 [Epoch 236][Batch 699], LR: 1.00E-04, Speed: 67.348 samples/sec, ObjLoss=20.459, BoxCenterLoss=14.473, BoxScaleLoss=4.741, ClassLoss=7.435 [Epoch 236][Batch 799], LR: 1.00E-04, Speed: 157.462 samples/sec, ObjLoss=20.458, BoxCenterLoss=14.473, BoxScaleLoss=4.741, ClassLoss=7.434 [Epoch 236][Batch 899], LR: 1.00E-04, Speed: 111.910 samples/sec, ObjLoss=20.457, BoxCenterLoss=14.473, BoxScaleLoss=4.741, ClassLoss=7.434 [Epoch 236][Batch 999], LR: 1.00E-04, Speed: 69.046 samples/sec, ObjLoss=20.457, BoxCenterLoss=14.473, BoxScaleLoss=4.741, ClassLoss=7.433 [Epoch 236][Batch 1099], LR: 1.00E-04, Speed: 54.817 samples/sec, ObjLoss=20.456, BoxCenterLoss=14.473, BoxScaleLoss=4.740, ClassLoss=7.433 [Epoch 236][Batch 1199], LR: 1.00E-04, Speed: 87.257 samples/sec, ObjLoss=20.455, BoxCenterLoss=14.473, BoxScaleLoss=4.740, ClassLoss=7.432 [Epoch 236][Batch 1299], LR: 1.00E-04, Speed: 130.008 samples/sec, ObjLoss=20.454, BoxCenterLoss=14.473, BoxScaleLoss=4.740, ClassLoss=7.431 [Epoch 236][Batch 1399], LR: 1.00E-04, Speed: 67.473 samples/sec, ObjLoss=20.453, BoxCenterLoss=14.473, BoxScaleLoss=4.740, ClassLoss=7.431 [Epoch 236][Batch 1499], LR: 1.00E-04, Speed: 87.597 samples/sec, ObjLoss=20.453, BoxCenterLoss=14.473, BoxScaleLoss=4.740, ClassLoss=7.430 [Epoch 236][Batch 1599], LR: 1.00E-04, Speed: 60.480 samples/sec, ObjLoss=20.452, BoxCenterLoss=14.473, BoxScaleLoss=4.739, ClassLoss=7.430 [Epoch 236][Batch 1699], LR: 1.00E-04, Speed: 143.753 samples/sec, ObjLoss=20.451, BoxCenterLoss=14.472, BoxScaleLoss=4.739, ClassLoss=7.429 [Epoch 236][Batch 1799], LR: 1.00E-04, Speed: 90.979 samples/sec, ObjLoss=20.450, BoxCenterLoss=14.472, BoxScaleLoss=4.739, ClassLoss=7.428 [Epoch 236] Training cost: 1604.780, ObjLoss=20.450, BoxCenterLoss=14.472, BoxScaleLoss=4.739, ClassLoss=7.428 [Epoch 236] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.274 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.401 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.377 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508 person=38.3 bicycle=19.3 car=27.0 motorcycle=30.2 airplane=48.8 bus=48.0 train=51.3 truck=23.1 boat=16.1 traffic light=16.2 fire hydrant=46.4 stop sign=51.0 parking meter=32.2 bench=16.0 bird=22.2 cat=50.7 dog=45.4 horse=40.2 sheep=34.8 cow=38.9 elephant=48.2 bear=53.7 zebra=48.9 giraffe=53.5 backpack=8.0 umbrella=26.8 handbag=6.8 tie=19.1 suitcase=21.3 frisbee=47.7 skis=12.0 snowboard=20.5 sports ball=29.5 kite=30.1 baseball bat=14.1 baseball glove=24.2 skateboard=32.2 surfboard=23.1 tennis racket=28.6 bottle=21.3 wine glass=21.1 cup=26.1 fork=14.8 knife=6.4 spoon=5.4 bowl=26.4 banana=15.3 apple=9.4 sandwich=23.0 orange=19.0 broccoli=13.5 carrot=13.0 hot dog=21.6 pizza=36.5 donut=26.5 cake=22.5 chair=16.8 couch=31.2 potted plant=15.8 bed=31.0 dining table=15.9 toilet=46.1 tv=41.7 laptop=43.4 mouse=42.9 remote=13.5 keyboard=36.9 cell phone=21.3 microwave=37.1 oven=23.2 toaster=11.3 sink=26.7 refrigerator=37.7 book=7.5 clock=37.6 vase=23.8 scissors=24.0 teddy bear=28.9 hair drier=0.0 toothbrush=10.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.4 [Epoch 237][Batch 99], LR: 1.00E-04, Speed: 121.705 samples/sec, ObjLoss=20.449, BoxCenterLoss=14.472, BoxScaleLoss=4.739, ClassLoss=7.427 [Epoch 237][Batch 199], LR: 1.00E-04, Speed: 116.117 samples/sec, ObjLoss=20.449, BoxCenterLoss=14.472, BoxScaleLoss=4.738, ClassLoss=7.427 [Epoch 237][Batch 299], LR: 1.00E-04, Speed: 68.139 samples/sec, ObjLoss=20.448, BoxCenterLoss=14.472, BoxScaleLoss=4.738, ClassLoss=7.426 [Epoch 237][Batch 399], LR: 1.00E-04, Speed: 102.337 samples/sec, ObjLoss=20.447, BoxCenterLoss=14.472, BoxScaleLoss=4.738, ClassLoss=7.426 [Epoch 237][Batch 499], LR: 1.00E-04, Speed: 105.228 samples/sec, ObjLoss=20.446, BoxCenterLoss=14.472, BoxScaleLoss=4.738, ClassLoss=7.425 [Epoch 237][Batch 599], LR: 1.00E-04, Speed: 91.288 samples/sec, ObjLoss=20.445, BoxCenterLoss=14.472, BoxScaleLoss=4.738, ClassLoss=7.424 [Epoch 237][Batch 699], LR: 1.00E-04, Speed: 102.393 samples/sec, ObjLoss=20.444, BoxCenterLoss=14.472, BoxScaleLoss=4.737, ClassLoss=7.424 [Epoch 237][Batch 799], LR: 1.00E-04, Speed: 82.953 samples/sec, ObjLoss=20.444, BoxCenterLoss=14.472, BoxScaleLoss=4.737, ClassLoss=7.423 [Epoch 237][Batch 899], LR: 1.00E-04, Speed: 106.503 samples/sec, ObjLoss=20.443, BoxCenterLoss=14.472, BoxScaleLoss=4.737, ClassLoss=7.422 [Epoch 237][Batch 999], LR: 1.00E-04, Speed: 126.736 samples/sec, ObjLoss=20.442, BoxCenterLoss=14.472, BoxScaleLoss=4.737, ClassLoss=7.422 [Epoch 237][Batch 1099], LR: 1.00E-04, Speed: 80.263 samples/sec, ObjLoss=20.441, BoxCenterLoss=14.472, BoxScaleLoss=4.737, ClassLoss=7.421 [Epoch 237][Batch 1199], LR: 1.00E-04, Speed: 96.661 samples/sec, ObjLoss=20.441, BoxCenterLoss=14.471, BoxScaleLoss=4.736, ClassLoss=7.421 [Epoch 237][Batch 1299], LR: 1.00E-04, Speed: 53.041 samples/sec, ObjLoss=20.440, BoxCenterLoss=14.471, BoxScaleLoss=4.736, ClassLoss=7.420 [Epoch 237][Batch 1399], LR: 1.00E-04, Speed: 87.651 samples/sec, ObjLoss=20.439, BoxCenterLoss=14.471, BoxScaleLoss=4.736, ClassLoss=7.420 [Epoch 237][Batch 1499], LR: 1.00E-04, Speed: 63.917 samples/sec, ObjLoss=20.438, BoxCenterLoss=14.471, BoxScaleLoss=4.736, ClassLoss=7.419 [Epoch 237][Batch 1599], LR: 1.00E-04, Speed: 48.270 samples/sec, ObjLoss=20.437, BoxCenterLoss=14.471, BoxScaleLoss=4.736, ClassLoss=7.418 [Epoch 237][Batch 1699], LR: 1.00E-04, Speed: 72.009 samples/sec, ObjLoss=20.437, BoxCenterLoss=14.471, BoxScaleLoss=4.735, ClassLoss=7.418 [Epoch 237][Batch 1799], LR: 1.00E-04, Speed: 183.021 samples/sec, ObjLoss=20.436, BoxCenterLoss=14.471, BoxScaleLoss=4.735, ClassLoss=7.417 [Epoch 237] Training cost: 1607.221, ObjLoss=20.435, BoxCenterLoss=14.471, BoxScaleLoss=4.735, ClassLoss=7.417 [Epoch 237] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.411 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518 person=38.8 bicycle=19.2 car=26.9 motorcycle=30.8 airplane=50.2 bus=49.7 train=54.5 truck=24.9 boat=15.7 traffic light=17.4 fire hydrant=46.9 stop sign=51.7 parking meter=30.7 bench=15.7 bird=22.7 cat=51.1 dog=44.3 horse=40.3 sheep=34.9 cow=37.7 elephant=48.8 bear=55.1 zebra=49.3 giraffe=53.3 backpack=7.5 umbrella=26.2 handbag=6.8 tie=19.8 suitcase=21.0 frisbee=47.0 skis=13.1 snowboard=20.4 sports ball=29.7 kite=30.0 baseball bat=15.5 baseball glove=23.1 skateboard=31.3 surfboard=23.8 tennis racket=28.7 bottle=20.6 wine glass=20.6 cup=26.9 fork=14.9 knife=6.1 spoon=5.9 bowl=26.3 banana=14.9 apple=9.2 sandwich=23.6 orange=19.6 broccoli=14.1 carrot=12.4 hot dog=23.2 pizza=34.8 donut=25.0 cake=22.2 chair=17.0 couch=33.7 potted plant=16.4 bed=34.6 dining table=20.1 toilet=45.6 tv=44.6 laptop=42.3 mouse=43.3 remote=13.2 keyboard=36.7 cell phone=21.1 microwave=38.3 oven=24.8 toaster=2.8 sink=27.2 refrigerator=39.3 book=7.4 clock=37.0 vase=23.6 scissors=23.3 teddy bear=30.9 hair drier=0.0 toothbrush=10.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.6 [Epoch 238][Batch 99], LR: 1.00E-04, Speed: 119.051 samples/sec, ObjLoss=20.435, BoxCenterLoss=14.471, BoxScaleLoss=4.735, ClassLoss=7.416 [Epoch 238][Batch 199], LR: 1.00E-04, Speed: 147.814 samples/sec, ObjLoss=20.434, BoxCenterLoss=14.471, BoxScaleLoss=4.735, ClassLoss=7.416 [Epoch 238][Batch 299], LR: 1.00E-04, Speed: 81.037 samples/sec, ObjLoss=20.433, BoxCenterLoss=14.471, BoxScaleLoss=4.735, ClassLoss=7.415 [Epoch 238][Batch 399], LR: 1.00E-04, Speed: 82.450 samples/sec, ObjLoss=20.432, BoxCenterLoss=14.471, BoxScaleLoss=4.734, ClassLoss=7.414 [Epoch 238][Batch 499], LR: 1.00E-04, Speed: 134.556 samples/sec, ObjLoss=20.432, BoxCenterLoss=14.471, BoxScaleLoss=4.734, ClassLoss=7.414 [Epoch 238][Batch 599], LR: 1.00E-04, Speed: 125.378 samples/sec, ObjLoss=20.431, BoxCenterLoss=14.471, BoxScaleLoss=4.734, ClassLoss=7.413 [Epoch 238][Batch 699], LR: 1.00E-04, Speed: 132.855 samples/sec, ObjLoss=20.430, BoxCenterLoss=14.471, BoxScaleLoss=4.734, ClassLoss=7.413 [Epoch 238][Batch 799], LR: 1.00E-04, Speed: 75.380 samples/sec, ObjLoss=20.429, BoxCenterLoss=14.471, BoxScaleLoss=4.734, ClassLoss=7.412 [Epoch 238][Batch 899], LR: 1.00E-04, Speed: 60.317 samples/sec, ObjLoss=20.428, BoxCenterLoss=14.470, BoxScaleLoss=4.733, ClassLoss=7.411 [Epoch 238][Batch 999], LR: 1.00E-04, Speed: 86.883 samples/sec, ObjLoss=20.428, BoxCenterLoss=14.470, BoxScaleLoss=4.733, ClassLoss=7.411 [Epoch 238][Batch 1099], LR: 1.00E-04, Speed: 58.726 samples/sec, ObjLoss=20.427, BoxCenterLoss=14.470, BoxScaleLoss=4.733, ClassLoss=7.410 [Epoch 238][Batch 1199], LR: 1.00E-04, Speed: 55.969 samples/sec, ObjLoss=20.426, BoxCenterLoss=14.470, BoxScaleLoss=4.733, ClassLoss=7.410 [Epoch 238][Batch 1299], LR: 1.00E-04, Speed: 63.235 samples/sec, ObjLoss=20.425, BoxCenterLoss=14.470, BoxScaleLoss=4.732, ClassLoss=7.409 [Epoch 238][Batch 1399], LR: 1.00E-04, Speed: 82.734 samples/sec, ObjLoss=20.424, BoxCenterLoss=14.470, BoxScaleLoss=4.732, ClassLoss=7.408 [Epoch 238][Batch 1499], LR: 1.00E-04, Speed: 74.062 samples/sec, ObjLoss=20.423, BoxCenterLoss=14.470, BoxScaleLoss=4.732, ClassLoss=7.408 [Epoch 238][Batch 1599], LR: 1.00E-04, Speed: 57.064 samples/sec, ObjLoss=20.423, BoxCenterLoss=14.470, BoxScaleLoss=4.732, ClassLoss=7.407 [Epoch 238][Batch 1699], LR: 1.00E-04, Speed: 87.752 samples/sec, ObjLoss=20.422, BoxCenterLoss=14.470, BoxScaleLoss=4.732, ClassLoss=7.406 [Epoch 238][Batch 1799], LR: 1.00E-04, Speed: 82.383 samples/sec, ObjLoss=20.421, BoxCenterLoss=14.470, BoxScaleLoss=4.732, ClassLoss=7.406 [Epoch 238] Training cost: 1587.957, ObjLoss=20.421, BoxCenterLoss=14.470, BoxScaleLoss=4.731, ClassLoss=7.406 [Epoch 238] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.282 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.242 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519 person=38.5 bicycle=18.7 car=26.7 motorcycle=30.3 airplane=49.9 bus=49.0 train=54.8 truck=25.0 boat=16.4 traffic light=16.6 fire hydrant=45.9 stop sign=50.8 parking meter=32.4 bench=15.9 bird=22.4 cat=51.6 dog=44.6 horse=41.0 sheep=33.3 cow=38.7 elephant=48.5 bear=54.8 zebra=49.3 giraffe=52.1 backpack=7.5 umbrella=27.0 handbag=7.0 tie=18.4 suitcase=20.9 frisbee=46.9 skis=12.5 snowboard=20.2 sports ball=30.0 kite=28.6 baseball bat=15.0 baseball glove=23.5 skateboard=32.1 surfboard=23.6 tennis racket=28.0 bottle=20.8 wine glass=20.8 cup=26.5 fork=14.5 knife=5.9 spoon=5.4 bowl=26.3 banana=15.1 apple=8.3 sandwich=24.6 orange=19.4 broccoli=13.7 carrot=12.6 hot dog=23.2 pizza=35.1 donut=27.5 cake=22.7 chair=17.2 couch=32.5 potted plant=15.9 bed=33.3 dining table=18.2 toilet=45.3 tv=42.5 laptop=42.5 mouse=43.5 remote=12.7 keyboard=36.7 cell phone=21.0 microwave=39.8 oven=23.2 toaster=2.4 sink=26.6 refrigerator=40.1 book=7.2 clock=36.9 vase=23.9 scissors=24.5 teddy bear=30.8 hair drier=0.0 toothbrush=10.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.5 [Epoch 239][Batch 99], LR: 1.00E-04, Speed: 101.361 samples/sec, ObjLoss=20.420, BoxCenterLoss=14.470, BoxScaleLoss=4.731, ClassLoss=7.405 [Epoch 239][Batch 199], LR: 1.00E-04, Speed: 127.471 samples/sec, ObjLoss=20.419, BoxCenterLoss=14.470, BoxScaleLoss=4.731, ClassLoss=7.404 [Epoch 239][Batch 299], LR: 1.00E-04, Speed: 64.415 samples/sec, ObjLoss=20.419, BoxCenterLoss=14.470, BoxScaleLoss=4.731, ClassLoss=7.404 [Epoch 239][Batch 399], LR: 1.00E-04, Speed: 71.266 samples/sec, ObjLoss=20.418, BoxCenterLoss=14.470, BoxScaleLoss=4.731, ClassLoss=7.403 [Epoch 239][Batch 499], LR: 1.00E-04, Speed: 55.568 samples/sec, ObjLoss=20.417, BoxCenterLoss=14.469, BoxScaleLoss=4.730, ClassLoss=7.402 [Epoch 239][Batch 599], LR: 1.00E-04, Speed: 135.825 samples/sec, ObjLoss=20.416, BoxCenterLoss=14.469, BoxScaleLoss=4.730, ClassLoss=7.402 [Epoch 239][Batch 699], LR: 1.00E-04, Speed: 84.176 samples/sec, ObjLoss=20.415, BoxCenterLoss=14.469, BoxScaleLoss=4.730, ClassLoss=7.401 [Epoch 239][Batch 799], LR: 1.00E-04, Speed: 129.905 samples/sec, ObjLoss=20.414, BoxCenterLoss=14.469, BoxScaleLoss=4.730, ClassLoss=7.401 [Epoch 239][Batch 899], LR: 1.00E-04, Speed: 155.600 samples/sec, ObjLoss=20.414, BoxCenterLoss=14.469, BoxScaleLoss=4.729, ClassLoss=7.400 [Epoch 239][Batch 999], LR: 1.00E-04, Speed: 78.297 samples/sec, ObjLoss=20.413, BoxCenterLoss=14.469, BoxScaleLoss=4.729, ClassLoss=7.399 [Epoch 239][Batch 1099], LR: 1.00E-04, Speed: 71.611 samples/sec, ObjLoss=20.412, BoxCenterLoss=14.469, BoxScaleLoss=4.729, ClassLoss=7.399 [Epoch 239][Batch 1199], LR: 1.00E-04, Speed: 71.862 samples/sec, ObjLoss=20.411, BoxCenterLoss=14.469, BoxScaleLoss=4.729, ClassLoss=7.398 [Epoch 239][Batch 1299], LR: 1.00E-04, Speed: 54.002 samples/sec, ObjLoss=20.411, BoxCenterLoss=14.469, BoxScaleLoss=4.729, ClassLoss=7.398 [Epoch 239][Batch 1399], LR: 1.00E-04, Speed: 126.456 samples/sec, ObjLoss=20.410, BoxCenterLoss=14.469, BoxScaleLoss=4.728, ClassLoss=7.397 [Epoch 239][Batch 1499], LR: 1.00E-04, Speed: 162.444 samples/sec, ObjLoss=20.409, BoxCenterLoss=14.469, BoxScaleLoss=4.728, ClassLoss=7.396 [Epoch 239][Batch 1599], LR: 1.00E-04, Speed: 92.431 samples/sec, ObjLoss=20.408, BoxCenterLoss=14.469, BoxScaleLoss=4.728, ClassLoss=7.396 [Epoch 239][Batch 1699], LR: 1.00E-04, Speed: 129.673 samples/sec, ObjLoss=20.408, BoxCenterLoss=14.469, BoxScaleLoss=4.728, ClassLoss=7.395 [Epoch 239][Batch 1799], LR: 1.00E-04, Speed: 80.653 samples/sec, ObjLoss=20.407, BoxCenterLoss=14.469, BoxScaleLoss=4.728, ClassLoss=7.395 [Epoch 239] Training cost: 1500.911, ObjLoss=20.406, BoxCenterLoss=14.469, BoxScaleLoss=4.728, ClassLoss=7.394 [Epoch 239] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.288 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.521 person=38.9 bicycle=19.1 car=26.3 motorcycle=30.4 airplane=47.5 bus=50.0 train=54.0 truck=25.3 boat=15.8 traffic light=16.8 fire hydrant=46.2 stop sign=50.2 parking meter=31.5 bench=15.7 bird=23.6 cat=51.4 dog=45.1 horse=41.0 sheep=33.6 cow=37.5 elephant=49.4 bear=57.2 zebra=50.1 giraffe=53.2 backpack=8.1 umbrella=26.0 handbag=7.2 tie=19.2 suitcase=21.0 frisbee=47.2 skis=12.2 snowboard=20.2 sports ball=29.5 kite=29.1 baseball bat=15.4 baseball glove=23.8 skateboard=33.0 surfboard=23.4 tennis racket=29.7 bottle=21.1 wine glass=20.6 cup=27.1 fork=15.7 knife=6.2 spoon=6.0 bowl=27.0 banana=15.3 apple=9.3 sandwich=24.8 orange=19.5 broccoli=13.5 carrot=13.3 hot dog=22.5 pizza=35.3 donut=27.2 cake=23.0 chair=16.8 couch=32.9 potted plant=16.7 bed=34.9 dining table=20.8 toilet=45.4 tv=44.3 laptop=44.0 mouse=44.8 remote=13.0 keyboard=37.8 cell phone=20.9 microwave=38.6 oven=24.5 toaster=2.8 sink=26.3 refrigerator=39.3 book=7.7 clock=37.9 vase=25.0 scissors=23.7 teddy bear=31.1 hair drier=0.0 toothbrush=10.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.8 [Epoch 240][Batch 99], LR: 1.00E-04, Speed: 133.933 samples/sec, ObjLoss=20.406, BoxCenterLoss=14.468, BoxScaleLoss=4.727, ClassLoss=7.394 [Epoch 240][Batch 199], LR: 1.00E-04, Speed: 51.233 samples/sec, ObjLoss=20.405, BoxCenterLoss=14.468, BoxScaleLoss=4.727, ClassLoss=7.393 [Epoch 240][Batch 299], LR: 1.00E-04, Speed: 72.914 samples/sec, ObjLoss=20.404, BoxCenterLoss=14.468, BoxScaleLoss=4.727, ClassLoss=7.392 [Epoch 240][Batch 399], LR: 1.00E-04, Speed: 111.793 samples/sec, ObjLoss=20.403, BoxCenterLoss=14.468, BoxScaleLoss=4.727, ClassLoss=7.392 [Epoch 240][Batch 499], LR: 1.00E-04, Speed: 63.982 samples/sec, ObjLoss=20.402, BoxCenterLoss=14.468, BoxScaleLoss=4.727, ClassLoss=7.391 [Epoch 240][Batch 599], LR: 1.00E-04, Speed: 64.175 samples/sec, ObjLoss=20.401, BoxCenterLoss=14.468, BoxScaleLoss=4.726, ClassLoss=7.391 [Epoch 240][Batch 699], LR: 1.00E-04, Speed: 79.117 samples/sec, ObjLoss=20.401, BoxCenterLoss=14.468, BoxScaleLoss=4.726, ClassLoss=7.390 [Epoch 240][Batch 799], LR: 1.00E-04, Speed: 76.165 samples/sec, ObjLoss=20.400, BoxCenterLoss=14.468, BoxScaleLoss=4.726, ClassLoss=7.389 [Epoch 240][Batch 899], LR: 1.00E-04, Speed: 67.884 samples/sec, ObjLoss=20.399, BoxCenterLoss=14.468, BoxScaleLoss=4.726, ClassLoss=7.389 [Epoch 240][Batch 999], LR: 1.00E-04, Speed: 79.073 samples/sec, ObjLoss=20.399, BoxCenterLoss=14.468, BoxScaleLoss=4.726, ClassLoss=7.388 [Epoch 240][Batch 1099], LR: 1.00E-04, Speed: 67.626 samples/sec, ObjLoss=20.398, BoxCenterLoss=14.468, BoxScaleLoss=4.725, ClassLoss=7.388 [Epoch 240][Batch 1199], LR: 1.00E-04, Speed: 92.662 samples/sec, ObjLoss=20.397, BoxCenterLoss=14.468, BoxScaleLoss=4.725, ClassLoss=7.387 [Epoch 240][Batch 1299], LR: 1.00E-04, Speed: 77.911 samples/sec, ObjLoss=20.396, BoxCenterLoss=14.468, BoxScaleLoss=4.725, ClassLoss=7.386 [Epoch 240][Batch 1399], LR: 1.00E-04, Speed: 76.507 samples/sec, ObjLoss=20.396, BoxCenterLoss=14.468, BoxScaleLoss=4.725, ClassLoss=7.386 [Epoch 240][Batch 1499], LR: 1.00E-04, Speed: 133.367 samples/sec, ObjLoss=20.395, BoxCenterLoss=14.468, BoxScaleLoss=4.725, ClassLoss=7.385 [Epoch 240][Batch 1599], LR: 1.00E-04, Speed: 81.070 samples/sec, ObjLoss=20.394, BoxCenterLoss=14.468, BoxScaleLoss=4.724, ClassLoss=7.385 [Epoch 240][Batch 1699], LR: 1.00E-04, Speed: 82.866 samples/sec, ObjLoss=20.393, BoxCenterLoss=14.468, BoxScaleLoss=4.724, ClassLoss=7.384 [Epoch 240][Batch 1799], LR: 1.00E-04, Speed: 99.950 samples/sec, ObjLoss=20.392, BoxCenterLoss=14.467, BoxScaleLoss=4.724, ClassLoss=7.384 [Epoch 240] Training cost: 1620.585, ObjLoss=20.392, BoxCenterLoss=14.467, BoxScaleLoss=4.724, ClassLoss=7.383 [Epoch 240] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.288 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.290 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.365 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.182 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.516 person=38.8 bicycle=19.4 car=26.6 motorcycle=30.8 airplane=51.2 bus=49.0 train=52.4 truck=24.5 boat=16.3 traffic light=17.6 fire hydrant=48.0 stop sign=48.5 parking meter=33.2 bench=15.2 bird=22.7 cat=51.3 dog=44.9 horse=40.4 sheep=34.6 cow=37.2 elephant=50.3 bear=58.6 zebra=50.2 giraffe=51.1 backpack=7.7 umbrella=26.9 handbag=6.8 tie=18.7 suitcase=21.2 frisbee=47.0 skis=12.2 snowboard=20.0 sports ball=30.6 kite=29.6 baseball bat=14.7 baseball glove=23.8 skateboard=32.8 surfboard=23.7 tennis racket=29.4 bottle=20.5 wine glass=20.7 cup=26.7 fork=14.5 knife=6.4 spoon=5.9 bowl=26.4 banana=15.2 apple=9.8 sandwich=23.9 orange=20.2 broccoli=13.3 carrot=12.7 hot dog=24.0 pizza=35.8 donut=28.1 cake=22.1 chair=17.4 couch=33.5 potted plant=16.8 bed=35.9 dining table=21.4 toilet=46.0 tv=44.6 laptop=44.2 mouse=42.9 remote=12.8 keyboard=37.7 cell phone=20.9 microwave=38.9 oven=25.4 toaster=2.4 sink=26.4 refrigerator=39.5 book=7.4 clock=37.8 vase=24.4 scissors=22.2 teddy bear=28.4 hair drier=0.0 toothbrush=9.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.8 [Epoch 241][Batch 99], LR: 1.00E-04, Speed: 142.605 samples/sec, ObjLoss=20.391, BoxCenterLoss=14.467, BoxScaleLoss=4.724, ClassLoss=7.383 [Epoch 241][Batch 199], LR: 1.00E-04, Speed: 73.674 samples/sec, ObjLoss=20.390, BoxCenterLoss=14.467, BoxScaleLoss=4.724, ClassLoss=7.382 [Epoch 241][Batch 299], LR: 1.00E-04, Speed: 93.997 samples/sec, ObjLoss=20.389, BoxCenterLoss=14.467, BoxScaleLoss=4.723, ClassLoss=7.382 [Epoch 241][Batch 399], LR: 1.00E-04, Speed: 106.884 samples/sec, ObjLoss=20.389, BoxCenterLoss=14.467, BoxScaleLoss=4.723, ClassLoss=7.381 [Epoch 241][Batch 499], LR: 1.00E-04, Speed: 170.247 samples/sec, ObjLoss=20.388, BoxCenterLoss=14.467, BoxScaleLoss=4.723, ClassLoss=7.380 [Epoch 241][Batch 599], LR: 1.00E-04, Speed: 57.156 samples/sec, ObjLoss=20.387, BoxCenterLoss=14.467, BoxScaleLoss=4.723, ClassLoss=7.380 [Epoch 241][Batch 699], LR: 1.00E-04, Speed: 54.039 samples/sec, ObjLoss=20.386, BoxCenterLoss=14.467, BoxScaleLoss=4.723, ClassLoss=7.379 [Epoch 241][Batch 799], LR: 1.00E-04, Speed: 76.249 samples/sec, ObjLoss=20.385, BoxCenterLoss=14.467, BoxScaleLoss=4.723, ClassLoss=7.379 [Epoch 241][Batch 899], LR: 1.00E-04, Speed: 88.811 samples/sec, ObjLoss=20.385, BoxCenterLoss=14.467, BoxScaleLoss=4.722, ClassLoss=7.378 [Epoch 241][Batch 999], LR: 1.00E-04, Speed: 57.298 samples/sec, ObjLoss=20.384, BoxCenterLoss=14.467, BoxScaleLoss=4.722, ClassLoss=7.377 [Epoch 241][Batch 1099], LR: 1.00E-04, Speed: 69.108 samples/sec, ObjLoss=20.383, BoxCenterLoss=14.467, BoxScaleLoss=4.722, ClassLoss=7.377 [Epoch 241][Batch 1199], LR: 1.00E-04, Speed: 132.302 samples/sec, ObjLoss=20.382, BoxCenterLoss=14.466, BoxScaleLoss=4.722, ClassLoss=7.376 [Epoch 241][Batch 1299], LR: 1.00E-04, Speed: 92.526 samples/sec, ObjLoss=20.381, BoxCenterLoss=14.466, BoxScaleLoss=4.722, ClassLoss=7.376 [Epoch 241][Batch 1399], LR: 1.00E-04, Speed: 84.325 samples/sec, ObjLoss=20.381, BoxCenterLoss=14.466, BoxScaleLoss=4.721, ClassLoss=7.375 [Epoch 241][Batch 1499], LR: 1.00E-04, Speed: 74.612 samples/sec, ObjLoss=20.380, BoxCenterLoss=14.466, BoxScaleLoss=4.721, ClassLoss=7.374 [Epoch 241][Batch 1599], LR: 1.00E-04, Speed: 63.526 samples/sec, ObjLoss=20.379, BoxCenterLoss=14.466, BoxScaleLoss=4.721, ClassLoss=7.374 [Epoch 241][Batch 1699], LR: 1.00E-04, Speed: 64.545 samples/sec, ObjLoss=20.378, BoxCenterLoss=14.466, BoxScaleLoss=4.721, ClassLoss=7.373 [Epoch 241][Batch 1799], LR: 1.00E-04, Speed: 108.217 samples/sec, ObjLoss=20.378, BoxCenterLoss=14.466, BoxScaleLoss=4.721, ClassLoss=7.373 [Epoch 241] Training cost: 1612.309, ObjLoss=20.377, BoxCenterLoss=14.466, BoxScaleLoss=4.721, ClassLoss=7.372 [Epoch 241] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.277 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.411 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.365 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 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.520 person=38.9 bicycle=19.3 car=26.4 motorcycle=30.2 airplane=49.5 bus=49.8 train=53.4 truck=24.8 boat=16.4 traffic light=17.1 fire hydrant=46.9 stop sign=51.6 parking meter=32.6 bench=15.8 bird=22.0 cat=51.9 dog=44.8 horse=40.9 sheep=35.0 cow=39.6 elephant=48.7 bear=56.1 zebra=49.7 giraffe=52.3 backpack=7.8 umbrella=26.6 handbag=6.7 tie=18.1 suitcase=21.0 frisbee=45.4 skis=12.5 snowboard=20.1 sports ball=29.7 kite=28.9 baseball bat=15.0 baseball glove=23.3 skateboard=31.7 surfboard=23.3 tennis racket=29.5 bottle=20.7 wine glass=20.0 cup=26.7 fork=15.4 knife=6.4 spoon=6.2 bowl=26.1 banana=15.4 apple=8.5 sandwich=22.7 orange=18.6 broccoli=13.6 carrot=12.0 hot dog=23.9 pizza=37.0 donut=28.8 cake=22.8 chair=17.0 couch=33.1 potted plant=17.0 bed=34.2 dining table=18.7 toilet=46.5 tv=43.3 laptop=43.1 mouse=44.9 remote=13.5 keyboard=38.1 cell phone=20.8 microwave=38.8 oven=24.1 toaster=4.2 sink=27.1 refrigerator=38.3 book=7.2 clock=37.7 vase=24.0 scissors=22.9 teddy bear=30.3 hair drier=0.0 toothbrush=10.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.7 [Epoch 242][Batch 99], LR: 1.00E-04, Speed: 157.793 samples/sec, ObjLoss=20.377, BoxCenterLoss=14.466, BoxScaleLoss=4.720, ClassLoss=7.372 [Epoch 242][Batch 199], LR: 1.00E-04, Speed: 62.032 samples/sec, ObjLoss=20.376, BoxCenterLoss=14.466, BoxScaleLoss=4.720, ClassLoss=7.371 [Epoch 242][Batch 299], LR: 1.00E-04, Speed: 65.606 samples/sec, ObjLoss=20.375, BoxCenterLoss=14.466, BoxScaleLoss=4.720, ClassLoss=7.371 [Epoch 242][Batch 399], LR: 1.00E-04, Speed: 61.038 samples/sec, ObjLoss=20.374, BoxCenterLoss=14.466, BoxScaleLoss=4.720, ClassLoss=7.370 [Epoch 242][Batch 499], LR: 1.00E-04, Speed: 49.387 samples/sec, ObjLoss=20.373, BoxCenterLoss=14.466, BoxScaleLoss=4.720, ClassLoss=7.369 [Epoch 242][Batch 599], LR: 1.00E-04, Speed: 68.061 samples/sec, ObjLoss=20.373, BoxCenterLoss=14.466, BoxScaleLoss=4.719, ClassLoss=7.369 [Epoch 242][Batch 699], LR: 1.00E-04, Speed: 74.576 samples/sec, ObjLoss=20.372, BoxCenterLoss=14.466, BoxScaleLoss=4.719, ClassLoss=7.368 [Epoch 242][Batch 799], LR: 1.00E-04, Speed: 66.140 samples/sec, ObjLoss=20.371, BoxCenterLoss=14.466, BoxScaleLoss=4.719, ClassLoss=7.368 [Epoch 242][Batch 899], LR: 1.00E-04, Speed: 69.944 samples/sec, ObjLoss=20.370, BoxCenterLoss=14.466, BoxScaleLoss=4.719, ClassLoss=7.367 [Epoch 242][Batch 999], LR: 1.00E-04, Speed: 51.578 samples/sec, ObjLoss=20.369, BoxCenterLoss=14.466, BoxScaleLoss=4.719, ClassLoss=7.366 [Epoch 242][Batch 1099], LR: 1.00E-04, Speed: 104.383 samples/sec, ObjLoss=20.369, BoxCenterLoss=14.466, BoxScaleLoss=4.719, ClassLoss=7.366 [Epoch 242][Batch 1199], LR: 1.00E-04, Speed: 65.222 samples/sec, ObjLoss=20.368, BoxCenterLoss=14.466, BoxScaleLoss=4.718, ClassLoss=7.365 [Epoch 242][Batch 1299], LR: 1.00E-04, Speed: 135.801 samples/sec, ObjLoss=20.367, BoxCenterLoss=14.465, BoxScaleLoss=4.718, ClassLoss=7.365 [Epoch 242][Batch 1399], LR: 1.00E-04, Speed: 74.542 samples/sec, ObjLoss=20.366, BoxCenterLoss=14.465, BoxScaleLoss=4.718, ClassLoss=7.364 [Epoch 242][Batch 1499], LR: 1.00E-04, Speed: 75.683 samples/sec, ObjLoss=20.365, BoxCenterLoss=14.465, BoxScaleLoss=4.718, ClassLoss=7.363 [Epoch 242][Batch 1599], LR: 1.00E-04, Speed: 124.608 samples/sec, ObjLoss=20.365, BoxCenterLoss=14.465, BoxScaleLoss=4.718, ClassLoss=7.363 [Epoch 242][Batch 1699], LR: 1.00E-04, Speed: 58.573 samples/sec, ObjLoss=20.364, BoxCenterLoss=14.465, BoxScaleLoss=4.717, ClassLoss=7.362 [Epoch 242][Batch 1799], LR: 1.00E-04, Speed: 121.246 samples/sec, ObjLoss=20.363, BoxCenterLoss=14.465, BoxScaleLoss=4.717, ClassLoss=7.362 [Epoch 242] Training cost: 1652.396, ObjLoss=20.363, BoxCenterLoss=14.465, BoxScaleLoss=4.717, ClassLoss=7.361 [Epoch 242] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.479 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.409 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.377 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518 person=38.8 bicycle=19.1 car=27.1 motorcycle=30.9 airplane=51.2 bus=50.4 train=54.0 truck=25.1 boat=15.6 traffic light=16.1 fire hydrant=48.1 stop sign=51.0 parking meter=31.2 bench=15.1 bird=23.0 cat=51.9 dog=45.2 horse=41.8 sheep=34.2 cow=38.5 elephant=48.2 bear=54.1 zebra=49.3 giraffe=52.6 backpack=7.5 umbrella=26.4 handbag=6.9 tie=18.5 suitcase=21.3 frisbee=46.7 skis=12.4 snowboard=18.7 sports ball=28.4 kite=28.5 baseball bat=13.8 baseball glove=22.8 skateboard=33.0 surfboard=24.4 tennis racket=30.2 bottle=19.7 wine glass=19.7 cup=25.7 fork=14.5 knife=6.2 spoon=5.7 bowl=25.2 banana=14.9 apple=9.4 sandwich=21.3 orange=18.5 broccoli=13.4 carrot=12.6 hot dog=22.8 pizza=35.6 donut=27.7 cake=23.0 chair=16.4 couch=32.8 potted plant=16.2 bed=35.0 dining table=20.5 toilet=46.3 tv=42.7 laptop=42.9 mouse=43.8 remote=13.5 keyboard=38.6 cell phone=20.6 microwave=39.0 oven=23.5 toaster=4.2 sink=26.8 refrigerator=39.5 book=7.3 clock=38.1 vase=24.0 scissors=23.1 teddy bear=31.2 hair drier=0.0 toothbrush=10.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.5 [Epoch 243][Batch 99], LR: 1.00E-04, Speed: 176.732 samples/sec, ObjLoss=20.362, BoxCenterLoss=14.465, BoxScaleLoss=4.717, ClassLoss=7.361 [Epoch 243][Batch 199], LR: 1.00E-04, Speed: 46.838 samples/sec, ObjLoss=20.361, BoxCenterLoss=14.465, BoxScaleLoss=4.717, ClassLoss=7.360 [Epoch 243][Batch 299], LR: 1.00E-04, Speed: 82.305 samples/sec, ObjLoss=20.361, BoxCenterLoss=14.465, BoxScaleLoss=4.717, ClassLoss=7.360 [Epoch 243][Batch 399], LR: 1.00E-04, Speed: 60.518 samples/sec, ObjLoss=20.360, BoxCenterLoss=14.465, BoxScaleLoss=4.716, ClassLoss=7.359 [Epoch 243][Batch 499], LR: 1.00E-04, Speed: 57.264 samples/sec, ObjLoss=20.359, BoxCenterLoss=14.465, BoxScaleLoss=4.716, ClassLoss=7.359 [Epoch 243][Batch 599], LR: 1.00E-04, Speed: 96.753 samples/sec, ObjLoss=20.358, BoxCenterLoss=14.465, BoxScaleLoss=4.716, ClassLoss=7.358 [Epoch 243][Batch 699], LR: 1.00E-04, Speed: 107.117 samples/sec, ObjLoss=20.357, BoxCenterLoss=14.464, BoxScaleLoss=4.716, ClassLoss=7.357 [Epoch 243][Batch 799], LR: 1.00E-04, Speed: 68.370 samples/sec, ObjLoss=20.357, BoxCenterLoss=14.464, BoxScaleLoss=4.716, ClassLoss=7.357 [Epoch 243][Batch 899], LR: 1.00E-04, Speed: 102.956 samples/sec, ObjLoss=20.356, BoxCenterLoss=14.464, BoxScaleLoss=4.715, ClassLoss=7.356 [Epoch 243][Batch 999], LR: 1.00E-04, Speed: 84.826 samples/sec, ObjLoss=20.355, BoxCenterLoss=14.464, BoxScaleLoss=4.715, ClassLoss=7.356 [Epoch 243][Batch 1099], LR: 1.00E-04, Speed: 45.126 samples/sec, ObjLoss=20.354, BoxCenterLoss=14.464, BoxScaleLoss=4.715, ClassLoss=7.355 [Epoch 243][Batch 1199], LR: 1.00E-04, Speed: 110.119 samples/sec, ObjLoss=20.354, BoxCenterLoss=14.464, BoxScaleLoss=4.715, ClassLoss=7.354 [Epoch 243][Batch 1299], LR: 1.00E-04, Speed: 118.226 samples/sec, ObjLoss=20.353, BoxCenterLoss=14.464, BoxScaleLoss=4.714, ClassLoss=7.354 [Epoch 243][Batch 1399], LR: 1.00E-04, Speed: 58.067 samples/sec, ObjLoss=20.352, BoxCenterLoss=14.464, BoxScaleLoss=4.714, ClassLoss=7.353 [Epoch 243][Batch 1499], LR: 1.00E-04, Speed: 57.750 samples/sec, ObjLoss=20.352, BoxCenterLoss=14.464, BoxScaleLoss=4.714, ClassLoss=7.352 [Epoch 243][Batch 1599], LR: 1.00E-04, Speed: 73.104 samples/sec, ObjLoss=20.351, BoxCenterLoss=14.464, BoxScaleLoss=4.714, ClassLoss=7.352 [Epoch 243][Batch 1699], LR: 1.00E-04, Speed: 117.976 samples/sec, ObjLoss=20.350, BoxCenterLoss=14.464, BoxScaleLoss=4.714, ClassLoss=7.351 [Epoch 243][Batch 1799], LR: 1.00E-04, Speed: 100.658 samples/sec, ObjLoss=20.349, BoxCenterLoss=14.464, BoxScaleLoss=4.713, ClassLoss=7.351 [Epoch 243] Training cost: 1656.499, ObjLoss=20.349, BoxCenterLoss=14.464, BoxScaleLoss=4.713, ClassLoss=7.350 [Epoch 243] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.480 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.182 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.520 person=39.1 bicycle=19.4 car=26.9 motorcycle=31.2 airplane=50.7 bus=50.2 train=53.6 truck=25.4 boat=15.5 traffic light=16.9 fire hydrant=48.1 stop sign=51.8 parking meter=33.2 bench=15.3 bird=23.0 cat=51.9 dog=44.6 horse=40.6 sheep=33.9 cow=38.6 elephant=49.0 bear=55.1 zebra=48.5 giraffe=52.5 backpack=7.1 umbrella=26.4 handbag=6.9 tie=19.3 suitcase=22.1 frisbee=47.1 skis=12.2 snowboard=18.7 sports ball=30.3 kite=29.8 baseball bat=15.8 baseball glove=23.6 skateboard=33.5 surfboard=24.2 tennis racket=30.2 bottle=20.7 wine glass=20.3 cup=26.2 fork=15.7 knife=6.4 spoon=5.7 bowl=26.2 banana=15.0 apple=9.8 sandwich=22.1 orange=20.3 broccoli=13.7 carrot=13.1 hot dog=24.0 pizza=33.6 donut=27.3 cake=21.7 chair=16.7 couch=32.8 potted plant=16.3 bed=35.6 dining table=20.6 toilet=46.4 tv=43.6 laptop=43.9 mouse=45.0 remote=13.1 keyboard=38.3 cell phone=21.5 microwave=38.7 oven=24.5 toaster=4.2 sink=27.4 refrigerator=39.0 book=6.9 clock=38.0 vase=24.7 scissors=23.1 teddy bear=30.6 hair drier=0.0 toothbrush=10.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.8 [Epoch 244][Batch 99], LR: 1.00E-04, Speed: 154.299 samples/sec, ObjLoss=20.348, BoxCenterLoss=14.464, BoxScaleLoss=4.713, ClassLoss=7.350 [Epoch 244][Batch 199], LR: 1.00E-04, Speed: 128.598 samples/sec, ObjLoss=20.347, BoxCenterLoss=14.464, BoxScaleLoss=4.713, ClassLoss=7.349 [Epoch 244][Batch 299], LR: 1.00E-04, Speed: 78.609 samples/sec, ObjLoss=20.347, BoxCenterLoss=14.464, BoxScaleLoss=4.713, ClassLoss=7.349 [Epoch 244][Batch 399], LR: 1.00E-04, Speed: 69.590 samples/sec, ObjLoss=20.346, BoxCenterLoss=14.464, BoxScaleLoss=4.713, ClassLoss=7.348 [Epoch 244][Batch 499], LR: 1.00E-04, Speed: 79.132 samples/sec, ObjLoss=20.345, BoxCenterLoss=14.464, BoxScaleLoss=4.712, ClassLoss=7.347 [Epoch 244][Batch 599], LR: 1.00E-04, Speed: 95.861 samples/sec, ObjLoss=20.344, BoxCenterLoss=14.463, BoxScaleLoss=4.712, ClassLoss=7.347 [Epoch 244][Batch 699], LR: 1.00E-04, Speed: 152.066 samples/sec, ObjLoss=20.344, BoxCenterLoss=14.463, BoxScaleLoss=4.712, ClassLoss=7.346 [Epoch 244][Batch 799], LR: 1.00E-04, Speed: 122.211 samples/sec, ObjLoss=20.343, BoxCenterLoss=14.463, BoxScaleLoss=4.712, ClassLoss=7.346 [Epoch 244][Batch 899], LR: 1.00E-04, Speed: 154.278 samples/sec, ObjLoss=20.342, BoxCenterLoss=14.463, BoxScaleLoss=4.712, ClassLoss=7.345 [Epoch 244][Batch 999], LR: 1.00E-04, Speed: 50.125 samples/sec, ObjLoss=20.341, BoxCenterLoss=14.463, BoxScaleLoss=4.711, ClassLoss=7.344 [Epoch 244][Batch 1099], LR: 1.00E-04, Speed: 91.441 samples/sec, ObjLoss=20.341, BoxCenterLoss=14.463, BoxScaleLoss=4.711, ClassLoss=7.344 [Epoch 244][Batch 1199], LR: 1.00E-04, Speed: 60.150 samples/sec, ObjLoss=20.340, BoxCenterLoss=14.463, BoxScaleLoss=4.711, ClassLoss=7.343 [Epoch 244][Batch 1299], LR: 1.00E-04, Speed: 112.188 samples/sec, ObjLoss=20.339, BoxCenterLoss=14.463, BoxScaleLoss=4.711, ClassLoss=7.343 [Epoch 244][Batch 1399], LR: 1.00E-04, Speed: 68.336 samples/sec, ObjLoss=20.338, BoxCenterLoss=14.463, BoxScaleLoss=4.711, ClassLoss=7.342 [Epoch 244][Batch 1499], LR: 1.00E-04, Speed: 75.383 samples/sec, ObjLoss=20.337, BoxCenterLoss=14.463, BoxScaleLoss=4.711, ClassLoss=7.342 [Epoch 244][Batch 1599], LR: 1.00E-04, Speed: 98.833 samples/sec, ObjLoss=20.336, BoxCenterLoss=14.463, BoxScaleLoss=4.710, ClassLoss=7.341 [Epoch 244][Batch 1699], LR: 1.00E-04, Speed: 84.216 samples/sec, ObjLoss=20.336, BoxCenterLoss=14.463, BoxScaleLoss=4.710, ClassLoss=7.340 [Epoch 244][Batch 1799], LR: 1.00E-04, Speed: 153.519 samples/sec, ObjLoss=20.335, BoxCenterLoss=14.463, BoxScaleLoss=4.710, ClassLoss=7.340 [Epoch 244] Training cost: 1621.857, ObjLoss=20.335, BoxCenterLoss=14.463, BoxScaleLoss=4.710, ClassLoss=7.340 [Epoch 244] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.290 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.293 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.365 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.177 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522 person=39.0 bicycle=19.4 car=27.4 motorcycle=31.5 airplane=50.3 bus=48.8 train=55.4 truck=24.7 boat=15.8 traffic light=17.6 fire hydrant=47.9 stop sign=53.2 parking meter=32.7 bench=16.1 bird=22.9 cat=52.4 dog=45.6 horse=40.6 sheep=36.0 cow=39.5 elephant=49.0 bear=56.1 zebra=48.5 giraffe=53.3 backpack=7.9 umbrella=26.6 handbag=7.2 tie=19.2 suitcase=21.5 frisbee=47.2 skis=13.0 snowboard=20.7 sports ball=29.3 kite=30.0 baseball bat=15.2 baseball glove=23.6 skateboard=32.7 surfboard=24.7 tennis racket=29.2 bottle=21.1 wine glass=20.7 cup=27.0 fork=15.3 knife=7.0 spoon=6.1 bowl=27.1 banana=14.9 apple=8.8 sandwich=23.7 orange=19.7 broccoli=13.5 carrot=12.7 hot dog=23.3 pizza=36.8 donut=26.1 cake=22.6 chair=16.9 couch=31.5 potted plant=16.4 bed=34.7 dining table=16.8 toilet=46.5 tv=43.6 laptop=43.6 mouse=44.5 remote=13.1 keyboard=38.3 cell phone=21.2 microwave=40.1 oven=23.4 toaster=4.0 sink=25.3 refrigerator=38.3 book=7.5 clock=37.9 vase=24.5 scissors=22.3 teddy bear=31.8 hair drier=0.0 toothbrush=12.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.9 [Epoch 245][Batch 99], LR: 1.00E-04, Speed: 157.934 samples/sec, ObjLoss=20.334, BoxCenterLoss=14.463, BoxScaleLoss=4.710, ClassLoss=7.339 [Epoch 245][Batch 199], LR: 1.00E-04, Speed: 85.714 samples/sec, ObjLoss=20.333, BoxCenterLoss=14.463, BoxScaleLoss=4.710, ClassLoss=7.338 [Epoch 245][Batch 299], LR: 1.00E-04, Speed: 52.290 samples/sec, ObjLoss=20.333, BoxCenterLoss=14.463, BoxScaleLoss=4.709, ClassLoss=7.338 [Epoch 245][Batch 399], LR: 1.00E-04, Speed: 71.733 samples/sec, ObjLoss=20.332, BoxCenterLoss=14.462, BoxScaleLoss=4.709, ClassLoss=7.337 [Epoch 245][Batch 499], LR: 1.00E-04, Speed: 82.646 samples/sec, ObjLoss=20.331, BoxCenterLoss=14.462, BoxScaleLoss=4.709, ClassLoss=7.337 [Epoch 245][Batch 599], LR: 1.00E-04, Speed: 131.811 samples/sec, ObjLoss=20.330, BoxCenterLoss=14.462, BoxScaleLoss=4.709, ClassLoss=7.336 [Epoch 245][Batch 699], LR: 1.00E-04, Speed: 58.971 samples/sec, ObjLoss=20.329, BoxCenterLoss=14.462, BoxScaleLoss=4.709, ClassLoss=7.335 [Epoch 245][Batch 799], LR: 1.00E-04, Speed: 83.967 samples/sec, ObjLoss=20.329, BoxCenterLoss=14.462, BoxScaleLoss=4.708, ClassLoss=7.335 [Epoch 245][Batch 899], LR: 1.00E-04, Speed: 103.664 samples/sec, ObjLoss=20.328, BoxCenterLoss=14.462, BoxScaleLoss=4.708, ClassLoss=7.334 [Epoch 245][Batch 999], LR: 1.00E-04, Speed: 72.865 samples/sec, ObjLoss=20.327, BoxCenterLoss=14.462, BoxScaleLoss=4.708, ClassLoss=7.334 [Epoch 245][Batch 1099], LR: 1.00E-04, Speed: 60.207 samples/sec, ObjLoss=20.327, BoxCenterLoss=14.462, BoxScaleLoss=4.708, ClassLoss=7.333 [Epoch 245][Batch 1199], LR: 1.00E-04, Speed: 51.383 samples/sec, ObjLoss=20.326, BoxCenterLoss=14.462, BoxScaleLoss=4.707, ClassLoss=7.332 [Epoch 245][Batch 1299], LR: 1.00E-04, Speed: 61.223 samples/sec, ObjLoss=20.325, BoxCenterLoss=14.462, BoxScaleLoss=4.707, ClassLoss=7.332 [Epoch 245][Batch 1399], LR: 1.00E-04, Speed: 88.821 samples/sec, ObjLoss=20.324, BoxCenterLoss=14.462, BoxScaleLoss=4.707, ClassLoss=7.331 [Epoch 245][Batch 1499], LR: 1.00E-04, Speed: 63.474 samples/sec, ObjLoss=20.324, BoxCenterLoss=14.462, BoxScaleLoss=4.707, ClassLoss=7.331 [Epoch 245][Batch 1599], LR: 1.00E-04, Speed: 132.994 samples/sec, ObjLoss=20.323, BoxCenterLoss=14.462, BoxScaleLoss=4.707, ClassLoss=7.330 [Epoch 245][Batch 1699], LR: 1.00E-04, Speed: 80.819 samples/sec, ObjLoss=20.322, BoxCenterLoss=14.462, BoxScaleLoss=4.707, ClassLoss=7.329 [Epoch 245][Batch 1799], LR: 1.00E-04, Speed: 153.310 samples/sec, ObjLoss=20.321, BoxCenterLoss=14.462, BoxScaleLoss=4.706, ClassLoss=7.329 [Epoch 245] Training cost: 1657.666, ObjLoss=20.321, BoxCenterLoss=14.462, BoxScaleLoss=4.706, ClassLoss=7.329 [Epoch 245] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.480 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.289 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.176 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.379 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.516 person=38.9 bicycle=19.8 car=27.1 motorcycle=31.3 airplane=48.8 bus=49.5 train=52.3 truck=24.7 boat=16.3 traffic light=17.0 fire hydrant=46.7 stop sign=50.9 parking meter=31.4 bench=16.0 bird=22.2 cat=50.6 dog=44.2 horse=40.8 sheep=34.6 cow=39.5 elephant=48.6 bear=57.7 zebra=50.0 giraffe=53.7 backpack=7.6 umbrella=27.1 handbag=7.4 tie=18.6 suitcase=22.6 frisbee=46.6 skis=12.4 snowboard=18.8 sports ball=29.8 kite=30.0 baseball bat=13.7 baseball glove=23.4 skateboard=31.6 surfboard=23.6 tennis racket=29.0 bottle=20.6 wine glass=20.5 cup=26.4 fork=15.3 knife=6.2 spoon=5.5 bowl=26.0 banana=15.1 apple=8.7 sandwich=22.5 orange=20.2 broccoli=13.0 carrot=12.6 hot dog=22.5 pizza=35.4 donut=28.6 cake=22.5 chair=17.1 couch=32.6 potted plant=16.9 bed=34.2 dining table=17.6 toilet=46.8 tv=43.3 laptop=42.6 mouse=44.1 remote=13.5 keyboard=36.2 cell phone=21.0 microwave=37.5 oven=23.1 toaster=3.6 sink=25.9 refrigerator=39.5 book=7.1 clock=37.5 vase=24.1 scissors=22.6 teddy bear=31.1 hair drier=0.0 toothbrush=10.2 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.6 [Epoch 246][Batch 99], LR: 1.00E-04, Speed: 116.468 samples/sec, ObjLoss=20.320, BoxCenterLoss=14.462, BoxScaleLoss=4.706, ClassLoss=7.328 [Epoch 246][Batch 199], LR: 1.00E-04, Speed: 113.991 samples/sec, ObjLoss=20.319, BoxCenterLoss=14.462, BoxScaleLoss=4.706, ClassLoss=7.327 [Epoch 246][Batch 299], LR: 1.00E-04, Speed: 49.721 samples/sec, ObjLoss=20.319, BoxCenterLoss=14.461, BoxScaleLoss=4.706, ClassLoss=7.327 [Epoch 246][Batch 399], LR: 1.00E-04, Speed: 125.303 samples/sec, ObjLoss=20.318, BoxCenterLoss=14.461, BoxScaleLoss=4.706, ClassLoss=7.326 [Epoch 246][Batch 499], LR: 1.00E-04, Speed: 51.233 samples/sec, ObjLoss=20.317, BoxCenterLoss=14.461, BoxScaleLoss=4.705, ClassLoss=7.326 [Epoch 246][Batch 599], LR: 1.00E-04, Speed: 71.365 samples/sec, ObjLoss=20.316, BoxCenterLoss=14.461, BoxScaleLoss=4.705, ClassLoss=7.325 [Epoch 246][Batch 699], LR: 1.00E-04, Speed: 126.698 samples/sec, ObjLoss=20.315, BoxCenterLoss=14.461, BoxScaleLoss=4.705, ClassLoss=7.324 [Epoch 246][Batch 799], LR: 1.00E-04, Speed: 71.778 samples/sec, ObjLoss=20.315, BoxCenterLoss=14.461, BoxScaleLoss=4.705, ClassLoss=7.324 [Epoch 246][Batch 899], LR: 1.00E-04, Speed: 52.790 samples/sec, ObjLoss=20.314, BoxCenterLoss=14.461, BoxScaleLoss=4.704, ClassLoss=7.323 [Epoch 246][Batch 999], LR: 1.00E-04, Speed: 80.299 samples/sec, ObjLoss=20.313, BoxCenterLoss=14.461, BoxScaleLoss=4.704, ClassLoss=7.323 [Epoch 246][Batch 1099], LR: 1.00E-04, Speed: 57.161 samples/sec, ObjLoss=20.312, BoxCenterLoss=14.461, BoxScaleLoss=4.704, ClassLoss=7.322 [Epoch 246][Batch 1199], LR: 1.00E-04, Speed: 67.431 samples/sec, ObjLoss=20.312, BoxCenterLoss=14.461, BoxScaleLoss=4.704, ClassLoss=7.321 [Epoch 246][Batch 1299], LR: 1.00E-04, Speed: 57.317 samples/sec, ObjLoss=20.311, BoxCenterLoss=14.461, BoxScaleLoss=4.704, ClassLoss=7.321 [Epoch 246][Batch 1399], LR: 1.00E-04, Speed: 64.334 samples/sec, ObjLoss=20.310, BoxCenterLoss=14.460, BoxScaleLoss=4.704, ClassLoss=7.320 [Epoch 246][Batch 1499], LR: 1.00E-04, Speed: 56.403 samples/sec, ObjLoss=20.309, BoxCenterLoss=14.460, BoxScaleLoss=4.703, ClassLoss=7.320 [Epoch 246][Batch 1599], LR: 1.00E-04, Speed: 164.463 samples/sec, ObjLoss=20.308, BoxCenterLoss=14.460, BoxScaleLoss=4.703, ClassLoss=7.319 [Epoch 246][Batch 1699], LR: 1.00E-04, Speed: 72.645 samples/sec, ObjLoss=20.308, BoxCenterLoss=14.460, BoxScaleLoss=4.703, ClassLoss=7.319 [Epoch 246][Batch 1799], LR: 1.00E-04, Speed: 126.045 samples/sec, ObjLoss=20.307, BoxCenterLoss=14.460, BoxScaleLoss=4.703, ClassLoss=7.318 [Epoch 246] Training cost: 1622.217, ObjLoss=20.307, BoxCenterLoss=14.460, BoxScaleLoss=4.703, ClassLoss=7.318 [Epoch 246] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.119 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.357 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.384 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.527 person=38.9 bicycle=19.8 car=26.9 motorcycle=31.5 airplane=51.5 bus=49.5 train=54.4 truck=24.5 boat=16.0 traffic light=16.5 fire hydrant=48.0 stop sign=51.0 parking meter=31.8 bench=16.4 bird=22.0 cat=51.7 dog=46.0 horse=40.8 sheep=34.1 cow=38.4 elephant=47.5 bear=55.3 zebra=50.2 giraffe=53.0 backpack=7.7 umbrella=26.5 handbag=7.5 tie=19.8 suitcase=23.0 frisbee=46.3 skis=12.3 snowboard=20.1 sports ball=29.4 kite=29.5 baseball bat=14.0 baseball glove=23.7 skateboard=31.8 surfboard=23.6 tennis racket=28.8 bottle=20.5 wine glass=20.3 cup=27.2 fork=14.6 knife=6.0 spoon=5.2 bowl=25.4 banana=15.6 apple=10.3 sandwich=23.2 orange=20.5 broccoli=13.2 carrot=12.8 hot dog=24.4 pizza=36.1 donut=27.3 cake=22.7 chair=17.0 couch=33.0 potted plant=16.7 bed=34.5 dining table=20.7 toilet=46.5 tv=44.0 laptop=44.3 mouse=44.6 remote=13.4 keyboard=39.3 cell phone=20.7 microwave=37.8 oven=24.5 toaster=4.2 sink=26.3 refrigerator=39.9 book=7.5 clock=37.3 vase=24.2 scissors=23.0 teddy bear=31.1 hair drier=0.0 toothbrush=11.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.8 [Epoch 247][Batch 99], LR: 1.00E-04, Speed: 121.091 samples/sec, ObjLoss=20.306, BoxCenterLoss=14.460, BoxScaleLoss=4.703, ClassLoss=7.317 [Epoch 247][Batch 199], LR: 1.00E-04, Speed: 174.314 samples/sec, ObjLoss=20.305, BoxCenterLoss=14.460, BoxScaleLoss=4.702, ClassLoss=7.317 [Epoch 247][Batch 299], LR: 1.00E-04, Speed: 137.017 samples/sec, ObjLoss=20.304, BoxCenterLoss=14.460, BoxScaleLoss=4.702, ClassLoss=7.316 [Epoch 247][Batch 399], LR: 1.00E-04, Speed: 101.585 samples/sec, ObjLoss=20.304, BoxCenterLoss=14.460, BoxScaleLoss=4.702, ClassLoss=7.315 [Epoch 247][Batch 499], LR: 1.00E-04, Speed: 144.213 samples/sec, ObjLoss=20.303, BoxCenterLoss=14.460, BoxScaleLoss=4.702, ClassLoss=7.315 [Epoch 247][Batch 599], LR: 1.00E-04, Speed: 77.355 samples/sec, ObjLoss=20.302, BoxCenterLoss=14.460, BoxScaleLoss=4.702, ClassLoss=7.314 [Epoch 247][Batch 699], LR: 1.00E-04, Speed: 106.786 samples/sec, ObjLoss=20.301, BoxCenterLoss=14.460, BoxScaleLoss=4.701, ClassLoss=7.313 [Epoch 247][Batch 799], LR: 1.00E-04, Speed: 38.463 samples/sec, ObjLoss=20.301, BoxCenterLoss=14.460, BoxScaleLoss=4.701, ClassLoss=7.313 [Epoch 247][Batch 899], LR: 1.00E-04, Speed: 70.702 samples/sec, ObjLoss=20.300, BoxCenterLoss=14.460, BoxScaleLoss=4.701, ClassLoss=7.312 [Epoch 247][Batch 999], LR: 1.00E-04, Speed: 78.335 samples/sec, ObjLoss=20.299, BoxCenterLoss=14.460, BoxScaleLoss=4.701, ClassLoss=7.312 [Epoch 247][Batch 1099], LR: 1.00E-04, Speed: 83.578 samples/sec, ObjLoss=20.298, BoxCenterLoss=14.460, BoxScaleLoss=4.701, ClassLoss=7.311 [Epoch 247][Batch 1199], LR: 1.00E-04, Speed: 61.266 samples/sec, ObjLoss=20.297, BoxCenterLoss=14.460, BoxScaleLoss=4.701, ClassLoss=7.311 [Epoch 247][Batch 1299], LR: 1.00E-04, Speed: 66.357 samples/sec, ObjLoss=20.297, BoxCenterLoss=14.459, BoxScaleLoss=4.700, ClassLoss=7.310 [Epoch 247][Batch 1399], LR: 1.00E-04, Speed: 67.059 samples/sec, ObjLoss=20.296, BoxCenterLoss=14.459, BoxScaleLoss=4.700, ClassLoss=7.309 [Epoch 247][Batch 1499], LR: 1.00E-04, Speed: 61.453 samples/sec, ObjLoss=20.295, BoxCenterLoss=14.459, BoxScaleLoss=4.700, ClassLoss=7.309 [Epoch 247][Batch 1599], LR: 1.00E-04, Speed: 120.169 samples/sec, ObjLoss=20.294, BoxCenterLoss=14.459, BoxScaleLoss=4.700, ClassLoss=7.308 [Epoch 247][Batch 1699], LR: 1.00E-04, Speed: 86.476 samples/sec, ObjLoss=20.294, BoxCenterLoss=14.459, BoxScaleLoss=4.700, ClassLoss=7.308 [Epoch 247][Batch 1799], LR: 1.00E-04, Speed: 65.300 samples/sec, ObjLoss=20.293, BoxCenterLoss=14.459, BoxScaleLoss=4.699, ClassLoss=7.307 [Epoch 247] Training cost: 1592.274, ObjLoss=20.293, BoxCenterLoss=14.459, BoxScaleLoss=4.699, ClassLoss=7.307 [Epoch 247] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.290 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.293 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 ] = 0.244 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.365 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.379 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.520 person=39.3 bicycle=19.7 car=26.4 motorcycle=30.4 airplane=48.9 bus=49.8 train=53.3 truck=24.6 boat=15.3 traffic light=17.2 fire hydrant=47.4 stop sign=52.7 parking meter=32.0 bench=15.7 bird=22.7 cat=52.1 dog=45.8 horse=40.4 sheep=34.7 cow=39.0 elephant=48.7 bear=57.9 zebra=49.5 giraffe=53.2 backpack=8.0 umbrella=27.0 handbag=7.2 tie=19.1 suitcase=22.2 frisbee=46.0 skis=11.7 snowboard=21.9 sports ball=29.3 kite=29.7 baseball bat=15.0 baseball glove=24.0 skateboard=33.2 surfboard=24.5 tennis racket=28.3 bottle=20.7 wine glass=21.0 cup=26.9 fork=15.5 knife=6.2 spoon=5.4 bowl=26.1 banana=15.1 apple=10.0 sandwich=23.3 orange=20.0 broccoli=13.5 carrot=12.7 hot dog=24.8 pizza=36.8 donut=28.9 cake=22.5 chair=17.2 couch=33.2 potted plant=16.8 bed=32.4 dining table=17.3 toilet=46.2 tv=42.9 laptop=43.5 mouse=43.8 remote=13.2 keyboard=39.3 cell phone=21.2 microwave=39.3 oven=24.0 toaster=5.7 sink=24.3 refrigerator=39.6 book=7.6 clock=37.1 vase=24.6 scissors=25.3 teddy bear=32.2 hair drier=0.0 toothbrush=11.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.9 [Epoch 248][Batch 99], LR: 1.00E-04, Speed: 136.481 samples/sec, ObjLoss=20.292, BoxCenterLoss=14.459, BoxScaleLoss=4.699, ClassLoss=7.306 [Epoch 248][Batch 199], LR: 1.00E-04, Speed: 114.071 samples/sec, ObjLoss=20.291, BoxCenterLoss=14.459, BoxScaleLoss=4.699, ClassLoss=7.306 [Epoch 248][Batch 299], LR: 1.00E-04, Speed: 88.341 samples/sec, ObjLoss=20.291, BoxCenterLoss=14.459, BoxScaleLoss=4.699, ClassLoss=7.305 [Epoch 248][Batch 399], LR: 1.00E-04, Speed: 96.343 samples/sec, ObjLoss=20.290, BoxCenterLoss=14.459, BoxScaleLoss=4.698, ClassLoss=7.304 [Epoch 248][Batch 499], LR: 1.00E-04, Speed: 87.012 samples/sec, ObjLoss=20.289, BoxCenterLoss=14.459, BoxScaleLoss=4.698, ClassLoss=7.304 [Epoch 248][Batch 599], LR: 1.00E-04, Speed: 126.445 samples/sec, ObjLoss=20.288, BoxCenterLoss=14.459, BoxScaleLoss=4.698, ClassLoss=7.303 [Epoch 248][Batch 699], LR: 1.00E-04, Speed: 97.719 samples/sec, ObjLoss=20.288, BoxCenterLoss=14.459, BoxScaleLoss=4.698, ClassLoss=7.303 [Epoch 248][Batch 799], LR: 1.00E-04, Speed: 138.429 samples/sec, ObjLoss=20.287, BoxCenterLoss=14.459, BoxScaleLoss=4.698, ClassLoss=7.302 [Epoch 248][Batch 899], LR: 1.00E-04, Speed: 147.007 samples/sec, ObjLoss=20.286, BoxCenterLoss=14.459, BoxScaleLoss=4.697, ClassLoss=7.301 [Epoch 248][Batch 999], LR: 1.00E-04, Speed: 151.010 samples/sec, ObjLoss=20.285, BoxCenterLoss=14.459, BoxScaleLoss=4.697, ClassLoss=7.301 [Epoch 248][Batch 1099], LR: 1.00E-04, Speed: 144.787 samples/sec, ObjLoss=20.285, BoxCenterLoss=14.459, BoxScaleLoss=4.697, ClassLoss=7.300 [Epoch 248][Batch 1199], LR: 1.00E-04, Speed: 80.722 samples/sec, ObjLoss=20.284, BoxCenterLoss=14.459, BoxScaleLoss=4.697, ClassLoss=7.300 [Epoch 248][Batch 1299], LR: 1.00E-04, Speed: 98.862 samples/sec, ObjLoss=20.283, BoxCenterLoss=14.458, BoxScaleLoss=4.697, ClassLoss=7.299 [Epoch 248][Batch 1399], LR: 1.00E-04, Speed: 133.526 samples/sec, ObjLoss=20.282, BoxCenterLoss=14.458, BoxScaleLoss=4.696, ClassLoss=7.298 [Epoch 248][Batch 1499], LR: 1.00E-04, Speed: 133.957 samples/sec, ObjLoss=20.282, BoxCenterLoss=14.458, BoxScaleLoss=4.696, ClassLoss=7.298 [Epoch 248][Batch 1599], LR: 1.00E-04, Speed: 70.435 samples/sec, ObjLoss=20.281, BoxCenterLoss=14.458, BoxScaleLoss=4.696, ClassLoss=7.297 [Epoch 248][Batch 1699], LR: 1.00E-04, Speed: 86.790 samples/sec, ObjLoss=20.280, BoxCenterLoss=14.458, BoxScaleLoss=4.696, ClassLoss=7.297 [Epoch 248][Batch 1799], LR: 1.00E-04, Speed: 181.026 samples/sec, ObjLoss=20.279, BoxCenterLoss=14.458, BoxScaleLoss=4.696, ClassLoss=7.296 [Epoch 248] Training cost: 1362.057, ObjLoss=20.279, BoxCenterLoss=14.458, BoxScaleLoss=4.696, ClassLoss=7.296 [Epoch 248] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.480 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.285 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 ] = 0.288 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.409 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.365 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.382 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518 person=39.1 bicycle=18.4 car=26.5 motorcycle=31.3 airplane=49.7 bus=50.8 train=54.4 truck=24.7 boat=15.2 traffic light=15.1 fire hydrant=47.8 stop sign=50.9 parking meter=33.0 bench=15.5 bird=22.5 cat=51.0 dog=46.0 horse=41.4 sheep=33.0 cow=39.0 elephant=48.8 bear=56.3 zebra=49.4 giraffe=52.0 backpack=8.1 umbrella=26.2 handbag=7.1 tie=18.3 suitcase=23.1 frisbee=44.9 skis=13.1 snowboard=21.1 sports ball=29.5 kite=28.5 baseball bat=14.9 baseball glove=23.2 skateboard=32.2 surfboard=23.7 tennis racket=28.8 bottle=20.7 wine glass=20.0 cup=26.2 fork=15.7 knife=7.1 spoon=5.8 bowl=25.8 banana=14.5 apple=9.0 sandwich=24.0 orange=19.4 broccoli=13.4 carrot=12.9 hot dog=23.0 pizza=33.8 donut=29.2 cake=22.0 chair=17.2 couch=31.4 potted plant=16.4 bed=33.1 dining table=19.7 toilet=46.3 tv=45.0 laptop=43.4 mouse=42.6 remote=13.0 keyboard=38.5 cell phone=21.3 microwave=40.3 oven=23.4 toaster=3.6 sink=26.2 refrigerator=41.4 book=7.0 clock=37.8 vase=24.5 scissors=23.2 teddy bear=29.2 hair drier=0.0 toothbrush=8.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.6 [Epoch 249][Batch 99], LR: 1.00E-04, Speed: 94.999 samples/sec, ObjLoss=20.278, BoxCenterLoss=14.458, BoxScaleLoss=4.695, ClassLoss=7.295 [Epoch 249][Batch 199], LR: 1.00E-04, Speed: 110.088 samples/sec, ObjLoss=20.277, BoxCenterLoss=14.458, BoxScaleLoss=4.695, ClassLoss=7.295 [Epoch 249][Batch 299], LR: 1.00E-04, Speed: 112.780 samples/sec, ObjLoss=20.277, BoxCenterLoss=14.458, BoxScaleLoss=4.695, ClassLoss=7.294 [Epoch 249][Batch 399], LR: 1.00E-04, Speed: 134.455 samples/sec, ObjLoss=20.276, BoxCenterLoss=14.458, BoxScaleLoss=4.695, ClassLoss=7.293 [Epoch 249][Batch 499], LR: 1.00E-04, Speed: 131.686 samples/sec, ObjLoss=20.275, BoxCenterLoss=14.458, BoxScaleLoss=4.694, ClassLoss=7.293 [Epoch 249][Batch 599], LR: 1.00E-04, Speed: 97.542 samples/sec, ObjLoss=20.274, BoxCenterLoss=14.458, BoxScaleLoss=4.694, ClassLoss=7.292 [Epoch 249][Batch 699], LR: 1.00E-04, Speed: 82.968 samples/sec, ObjLoss=20.274, BoxCenterLoss=14.458, BoxScaleLoss=4.694, ClassLoss=7.292 [Epoch 249][Batch 799], LR: 1.00E-04, Speed: 174.358 samples/sec, ObjLoss=20.273, BoxCenterLoss=14.458, BoxScaleLoss=4.694, ClassLoss=7.291 [Epoch 249][Batch 899], LR: 1.00E-04, Speed: 159.990 samples/sec, ObjLoss=20.272, BoxCenterLoss=14.457, BoxScaleLoss=4.694, ClassLoss=7.290 [Epoch 249][Batch 999], LR: 1.00E-04, Speed: 74.422 samples/sec, ObjLoss=20.271, BoxCenterLoss=14.457, BoxScaleLoss=4.694, ClassLoss=7.290 [Epoch 249][Batch 1099], LR: 1.00E-04, Speed: 123.169 samples/sec, ObjLoss=20.271, BoxCenterLoss=14.457, BoxScaleLoss=4.693, ClassLoss=7.289 [Epoch 249][Batch 1199], LR: 1.00E-04, Speed: 83.114 samples/sec, ObjLoss=20.270, BoxCenterLoss=14.457, BoxScaleLoss=4.693, ClassLoss=7.289 [Epoch 249][Batch 1299], LR: 1.00E-04, Speed: 57.147 samples/sec, ObjLoss=20.269, BoxCenterLoss=14.457, BoxScaleLoss=4.693, ClassLoss=7.288 [Epoch 249][Batch 1399], LR: 1.00E-04, Speed: 138.381 samples/sec, ObjLoss=20.268, BoxCenterLoss=14.457, BoxScaleLoss=4.693, ClassLoss=7.287 [Epoch 249][Batch 1499], LR: 1.00E-04, Speed: 65.384 samples/sec, ObjLoss=20.268, BoxCenterLoss=14.457, BoxScaleLoss=4.692, ClassLoss=7.287 [Epoch 249][Batch 1599], LR: 1.00E-04, Speed: 62.458 samples/sec, ObjLoss=20.267, BoxCenterLoss=14.457, BoxScaleLoss=4.692, ClassLoss=7.286 [Epoch 249][Batch 1699], LR: 1.00E-04, Speed: 116.016 samples/sec, ObjLoss=20.266, BoxCenterLoss=14.457, BoxScaleLoss=4.692, ClassLoss=7.286 [Epoch 249][Batch 1799], LR: 1.00E-04, Speed: 97.557 samples/sec, ObjLoss=20.265, BoxCenterLoss=14.457, BoxScaleLoss=4.692, ClassLoss=7.285 [Epoch 249] Training cost: 1440.253, ObjLoss=20.265, BoxCenterLoss=14.457, BoxScaleLoss=4.692, ClassLoss=7.285 [Epoch 249] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.287 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 ] = 0.287 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.364 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 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.527 person=39.0 bicycle=20.1 car=27.0 motorcycle=31.3 airplane=50.0 bus=51.2 train=54.3 truck=25.5 boat=15.8 traffic light=16.3 fire hydrant=47.3 stop sign=52.2 parking meter=33.4 bench=15.4 bird=23.1 cat=51.4 dog=46.2 horse=40.1 sheep=35.4 cow=39.3 elephant=48.8 bear=56.8 zebra=51.1 giraffe=53.4 backpack=8.1 umbrella=26.4 handbag=7.1 tie=18.7 suitcase=22.3 frisbee=46.4 skis=11.8 snowboard=18.5 sports ball=31.1 kite=28.9 baseball bat=15.1 baseball glove=24.3 skateboard=32.1 surfboard=24.5 tennis racket=28.9 bottle=20.9 wine glass=20.2 cup=26.5 fork=15.6 knife=6.0 spoon=5.1 bowl=26.1 banana=14.7 apple=9.5 sandwich=24.3 orange=19.8 broccoli=13.4 carrot=12.7 hot dog=22.4 pizza=35.0 donut=26.8 cake=20.5 chair=17.3 couch=32.0 potted plant=15.6 bed=33.7 dining table=18.9 toilet=46.7 tv=43.0 laptop=43.5 mouse=44.7 remote=12.8 keyboard=37.6 cell phone=21.4 microwave=38.4 oven=23.5 toaster=4.2 sink=26.0 refrigerator=39.4 book=6.9 clock=38.0 vase=24.5 scissors=23.2 teddy bear=30.8 hair drier=0.0 toothbrush=10.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =27.8 [Epoch 250][Batch 99], LR: 1.00E-05, Speed: 128.082 samples/sec, ObjLoss=20.264, BoxCenterLoss=14.457, BoxScaleLoss=4.692, ClassLoss=7.284 [Epoch 250][Batch 199], LR: 1.00E-05, Speed: 149.919 samples/sec, ObjLoss=20.263, BoxCenterLoss=14.457, BoxScaleLoss=4.691, ClassLoss=7.284 [Epoch 250][Batch 299], LR: 1.00E-05, Speed: 68.367 samples/sec, ObjLoss=20.263, BoxCenterLoss=14.457, BoxScaleLoss=4.691, ClassLoss=7.283 [Epoch 250][Batch 399], LR: 1.00E-05, Speed: 113.145 samples/sec, ObjLoss=20.262, BoxCenterLoss=14.457, BoxScaleLoss=4.691, ClassLoss=7.282 [Epoch 250][Batch 499], LR: 1.00E-05, Speed: 57.572 samples/sec, ObjLoss=20.261, BoxCenterLoss=14.457, BoxScaleLoss=4.691, ClassLoss=7.282 [Epoch 250][Batch 599], LR: 1.00E-05, Speed: 51.342 samples/sec, ObjLoss=20.261, BoxCenterLoss=14.457, BoxScaleLoss=4.690, ClassLoss=7.281 [Epoch 250][Batch 699], LR: 1.00E-05, Speed: 112.773 samples/sec, ObjLoss=20.260, BoxCenterLoss=14.456, BoxScaleLoss=4.690, ClassLoss=7.281 [Epoch 250][Batch 799], LR: 1.00E-05, Speed: 64.900 samples/sec, ObjLoss=20.259, BoxCenterLoss=14.456, BoxScaleLoss=4.690, ClassLoss=7.280 [Epoch 250][Batch 899], LR: 1.00E-05, Speed: 74.566 samples/sec, ObjLoss=20.258, BoxCenterLoss=14.456, BoxScaleLoss=4.690, ClassLoss=7.279 [Epoch 250][Batch 999], LR: 1.00E-05, Speed: 69.597 samples/sec, ObjLoss=20.258, BoxCenterLoss=14.456, BoxScaleLoss=4.690, ClassLoss=7.279 [Epoch 250][Batch 1099], LR: 1.00E-05, Speed: 66.805 samples/sec, ObjLoss=20.257, BoxCenterLoss=14.456, BoxScaleLoss=4.690, ClassLoss=7.278 [Epoch 250][Batch 1199], LR: 1.00E-05, Speed: 64.886 samples/sec, ObjLoss=20.256, BoxCenterLoss=14.456, BoxScaleLoss=4.689, ClassLoss=7.278 [Epoch 250][Batch 1299], LR: 1.00E-05, Speed: 125.860 samples/sec, ObjLoss=20.255, BoxCenterLoss=14.456, BoxScaleLoss=4.689, ClassLoss=7.277 [Epoch 250][Batch 1399], LR: 1.00E-05, Speed: 97.476 samples/sec, ObjLoss=20.255, BoxCenterLoss=14.456, BoxScaleLoss=4.689, ClassLoss=7.276 [Epoch 250][Batch 1499], LR: 1.00E-05, Speed: 82.569 samples/sec, ObjLoss=20.254, BoxCenterLoss=14.456, BoxScaleLoss=4.689, ClassLoss=7.276 [Epoch 250][Batch 1599], LR: 1.00E-05, Speed: 54.739 samples/sec, ObjLoss=20.253, BoxCenterLoss=14.456, BoxScaleLoss=4.689, ClassLoss=7.275 [Epoch 250][Batch 1699], LR: 1.00E-05, Speed: 97.111 samples/sec, ObjLoss=20.252, BoxCenterLoss=14.456, BoxScaleLoss=4.688, ClassLoss=7.275 [Epoch 250][Batch 1799], LR: 1.00E-05, Speed: 51.172 samples/sec, ObjLoss=20.251, BoxCenterLoss=14.456, BoxScaleLoss=4.688, ClassLoss=7.274 [Epoch 250] Training cost: 1618.628, ObjLoss=20.251, BoxCenterLoss=14.456, BoxScaleLoss=4.688, ClassLoss=7.274 [Epoch 250] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 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 ] = 0.420 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.247 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529 person=39.3 bicycle=20.0 car=27.2 motorcycle=32.0 airplane=51.0 bus=50.8 train=54.6 truck=25.7 boat=16.7 traffic light=17.2 fire hydrant=48.2 stop sign=53.6 parking meter=32.7 bench=15.7 bird=23.6 cat=51.4 dog=45.9 horse=41.5 sheep=35.4 cow=40.0 elephant=49.7 bear=57.0 zebra=51.4 giraffe=53.9 backpack=8.0 umbrella=27.6 handbag=7.4 tie=19.1 suitcase=22.7 frisbee=47.9 skis=12.7 snowboard=19.4 sports ball=30.8 kite=29.9 baseball bat=16.0 baseball glove=23.7 skateboard=33.8 surfboard=24.2 tennis racket=30.0 bottle=21.4 wine glass=20.6 cup=27.2 fork=15.8 knife=6.3 spoon=5.5 bowl=26.9 banana=15.4 apple=9.6 sandwich=24.8 orange=20.6 broccoli=13.4 carrot=13.2 hot dog=23.4 pizza=37.1 donut=27.8 cake=22.1 chair=17.3 couch=32.9 potted plant=17.3 bed=34.4 dining table=19.3 toilet=46.7 tv=44.9 laptop=44.3 mouse=45.4 remote=13.1 keyboard=39.0 cell phone=21.4 microwave=39.2 oven=23.3 toaster=6.8 sink=27.0 refrigerator=39.6 book=7.5 clock=38.4 vase=25.2 scissors=24.5 teddy bear=31.1 hair drier=0.0 toothbrush=11.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 251][Batch 99], LR: 1.00E-05, Speed: 137.977 samples/sec, ObjLoss=20.250, BoxCenterLoss=14.456, BoxScaleLoss=4.688, ClassLoss=7.273 [Epoch 251][Batch 199], LR: 1.00E-05, Speed: 109.337 samples/sec, ObjLoss=20.250, BoxCenterLoss=14.456, BoxScaleLoss=4.688, ClassLoss=7.273 [Epoch 251][Batch 299], LR: 1.00E-05, Speed: 53.321 samples/sec, ObjLoss=20.249, BoxCenterLoss=14.455, BoxScaleLoss=4.687, ClassLoss=7.272 [Epoch 251][Batch 399], LR: 1.00E-05, Speed: 126.054 samples/sec, ObjLoss=20.248, BoxCenterLoss=14.455, BoxScaleLoss=4.687, ClassLoss=7.271 [Epoch 251][Batch 499], LR: 1.00E-05, Speed: 93.831 samples/sec, ObjLoss=20.247, BoxCenterLoss=14.455, BoxScaleLoss=4.687, ClassLoss=7.271 [Epoch 251][Batch 599], LR: 1.00E-05, Speed: 111.739 samples/sec, ObjLoss=20.246, BoxCenterLoss=14.455, BoxScaleLoss=4.687, ClassLoss=7.270 [Epoch 251][Batch 699], LR: 1.00E-05, Speed: 68.847 samples/sec, ObjLoss=20.246, BoxCenterLoss=14.455, BoxScaleLoss=4.687, ClassLoss=7.270 [Epoch 251][Batch 799], LR: 1.00E-05, Speed: 62.598 samples/sec, ObjLoss=20.245, BoxCenterLoss=14.455, BoxScaleLoss=4.686, ClassLoss=7.269 [Epoch 251][Batch 899], LR: 1.00E-05, Speed: 62.876 samples/sec, ObjLoss=20.244, BoxCenterLoss=14.455, BoxScaleLoss=4.686, ClassLoss=7.269 [Epoch 251][Batch 999], LR: 1.00E-05, Speed: 76.610 samples/sec, ObjLoss=20.243, BoxCenterLoss=14.455, BoxScaleLoss=4.686, ClassLoss=7.268 [Epoch 251][Batch 1099], LR: 1.00E-05, Speed: 94.507 samples/sec, ObjLoss=20.243, BoxCenterLoss=14.455, BoxScaleLoss=4.686, ClassLoss=7.267 [Epoch 251][Batch 1199], LR: 1.00E-05, Speed: 81.744 samples/sec, ObjLoss=20.242, BoxCenterLoss=14.455, BoxScaleLoss=4.686, ClassLoss=7.267 [Epoch 251][Batch 1299], LR: 1.00E-05, Speed: 63.567 samples/sec, ObjLoss=20.241, BoxCenterLoss=14.455, BoxScaleLoss=4.685, ClassLoss=7.266 [Epoch 251][Batch 1399], LR: 1.00E-05, Speed: 108.150 samples/sec, ObjLoss=20.240, BoxCenterLoss=14.455, BoxScaleLoss=4.685, ClassLoss=7.266 [Epoch 251][Batch 1499], LR: 1.00E-05, Speed: 104.138 samples/sec, ObjLoss=20.239, BoxCenterLoss=14.455, BoxScaleLoss=4.685, ClassLoss=7.265 [Epoch 251][Batch 1599], LR: 1.00E-05, Speed: 71.917 samples/sec, ObjLoss=20.238, BoxCenterLoss=14.454, BoxScaleLoss=4.685, ClassLoss=7.265 [Epoch 251][Batch 1699], LR: 1.00E-05, Speed: 49.878 samples/sec, ObjLoss=20.238, BoxCenterLoss=14.454, BoxScaleLoss=4.685, ClassLoss=7.264 [Epoch 251][Batch 1799], LR: 1.00E-05, Speed: 118.281 samples/sec, ObjLoss=20.237, BoxCenterLoss=14.454, BoxScaleLoss=4.685, ClassLoss=7.263 [Epoch 251] Training cost: 1616.773, ObjLoss=20.236, BoxCenterLoss=14.454, BoxScaleLoss=4.685, ClassLoss=7.263 [Epoch 251] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134 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 ] = 0.419 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 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 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529 person=39.2 bicycle=19.9 car=27.1 motorcycle=32.2 airplane=50.5 bus=50.8 train=55.1 truck=25.7 boat=16.1 traffic light=17.5 fire hydrant=49.0 stop sign=53.6 parking meter=33.1 bench=15.9 bird=23.6 cat=51.9 dog=46.0 horse=40.8 sheep=35.4 cow=39.4 elephant=49.6 bear=57.3 zebra=51.2 giraffe=54.2 backpack=8.2 umbrella=27.1 handbag=7.3 tie=19.5 suitcase=22.2 frisbee=47.7 skis=12.4 snowboard=21.1 sports ball=30.8 kite=28.8 baseball bat=15.1 baseball glove=23.8 skateboard=33.7 surfboard=24.0 tennis racket=29.4 bottle=21.2 wine glass=21.1 cup=27.4 fork=15.8 knife=6.3 spoon=5.7 bowl=26.8 banana=15.2 apple=10.3 sandwich=24.3 orange=21.3 broccoli=12.9 carrot=13.4 hot dog=22.9 pizza=36.8 donut=28.5 cake=22.1 chair=17.4 couch=33.4 potted plant=17.3 bed=35.3 dining table=20.4 toilet=47.4 tv=45.1 laptop=44.9 mouse=45.8 remote=14.1 keyboard=39.0 cell phone=21.5 microwave=38.7 oven=24.4 toaster=6.1 sink=27.4 refrigerator=40.3 book=7.6 clock=38.7 vase=25.0 scissors=23.7 teddy bear=30.5 hair drier=0.0 toothbrush=10.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 252][Batch 99], LR: 1.00E-05, Speed: 149.723 samples/sec, ObjLoss=20.236, BoxCenterLoss=14.454, BoxScaleLoss=4.684, ClassLoss=7.263 [Epoch 252][Batch 199], LR: 1.00E-05, Speed: 118.481 samples/sec, ObjLoss=20.235, BoxCenterLoss=14.454, BoxScaleLoss=4.684, ClassLoss=7.262 [Epoch 252][Batch 299], LR: 1.00E-05, Speed: 98.675 samples/sec, ObjLoss=20.234, BoxCenterLoss=14.454, BoxScaleLoss=4.684, ClassLoss=7.262 [Epoch 252][Batch 399], LR: 1.00E-05, Speed: 52.426 samples/sec, ObjLoss=20.233, BoxCenterLoss=14.454, BoxScaleLoss=4.684, ClassLoss=7.261 [Epoch 252][Batch 499], LR: 1.00E-05, Speed: 131.951 samples/sec, ObjLoss=20.233, BoxCenterLoss=14.454, BoxScaleLoss=4.684, ClassLoss=7.260 [Epoch 252][Batch 599], LR: 1.00E-05, Speed: 65.383 samples/sec, ObjLoss=20.232, BoxCenterLoss=14.454, BoxScaleLoss=4.683, ClassLoss=7.260 [Epoch 252][Batch 699], LR: 1.00E-05, Speed: 63.576 samples/sec, ObjLoss=20.231, BoxCenterLoss=14.454, BoxScaleLoss=4.683, ClassLoss=7.259 [Epoch 252][Batch 799], LR: 1.00E-05, Speed: 84.021 samples/sec, ObjLoss=20.230, BoxCenterLoss=14.454, BoxScaleLoss=4.683, ClassLoss=7.259 [Epoch 252][Batch 899], LR: 1.00E-05, Speed: 82.837 samples/sec, ObjLoss=20.229, BoxCenterLoss=14.454, BoxScaleLoss=4.683, ClassLoss=7.258 [Epoch 252][Batch 999], LR: 1.00E-05, Speed: 44.812 samples/sec, ObjLoss=20.229, BoxCenterLoss=14.454, BoxScaleLoss=4.683, ClassLoss=7.257 [Epoch 252][Batch 1099], LR: 1.00E-05, Speed: 84.310 samples/sec, ObjLoss=20.228, BoxCenterLoss=14.454, BoxScaleLoss=4.682, ClassLoss=7.257 [Epoch 252][Batch 1199], LR: 1.00E-05, Speed: 129.752 samples/sec, ObjLoss=20.227, BoxCenterLoss=14.454, BoxScaleLoss=4.682, ClassLoss=7.256 [Epoch 252][Batch 1299], LR: 1.00E-05, Speed: 82.736 samples/sec, ObjLoss=20.226, BoxCenterLoss=14.454, BoxScaleLoss=4.682, ClassLoss=7.256 [Epoch 252][Batch 1399], LR: 1.00E-05, Speed: 63.631 samples/sec, ObjLoss=20.226, BoxCenterLoss=14.453, BoxScaleLoss=4.682, ClassLoss=7.255 [Epoch 252][Batch 1499], LR: 1.00E-05, Speed: 51.710 samples/sec, ObjLoss=20.225, BoxCenterLoss=14.453, BoxScaleLoss=4.682, ClassLoss=7.255 [Epoch 252][Batch 1599], LR: 1.00E-05, Speed: 54.934 samples/sec, ObjLoss=20.224, BoxCenterLoss=14.453, BoxScaleLoss=4.681, ClassLoss=7.254 [Epoch 252][Batch 1699], LR: 1.00E-05, Speed: 77.829 samples/sec, ObjLoss=20.223, BoxCenterLoss=14.453, BoxScaleLoss=4.681, ClassLoss=7.253 [Epoch 252][Batch 1799], LR: 1.00E-05, Speed: 165.544 samples/sec, ObjLoss=20.222, BoxCenterLoss=14.453, BoxScaleLoss=4.681, ClassLoss=7.253 [Epoch 252] Training cost: 1674.704, ObjLoss=20.222, BoxCenterLoss=14.453, BoxScaleLoss=4.681, ClassLoss=7.253 [Epoch 252] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.365 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376 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 ] = 0.391 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529 person=39.5 bicycle=19.7 car=27.1 motorcycle=31.6 airplane=51.4 bus=51.4 train=55.2 truck=25.5 boat=16.0 traffic light=17.4 fire hydrant=49.6 stop sign=53.1 parking meter=33.7 bench=16.1 bird=23.9 cat=52.4 dog=45.9 horse=41.8 sheep=35.3 cow=40.3 elephant=49.5 bear=56.0 zebra=51.8 giraffe=53.8 backpack=8.1 umbrella=27.2 handbag=7.5 tie=19.4 suitcase=22.9 frisbee=48.0 skis=12.5 snowboard=20.3 sports ball=30.6 kite=29.5 baseball bat=15.7 baseball glove=25.0 skateboard=33.3 surfboard=24.0 tennis racket=30.0 bottle=21.1 wine glass=20.9 cup=27.4 fork=16.0 knife=6.5 spoon=5.6 bowl=26.9 banana=14.9 apple=9.9 sandwich=23.9 orange=21.0 broccoli=13.3 carrot=13.3 hot dog=22.9 pizza=35.4 donut=28.3 cake=22.7 chair=17.4 couch=33.9 potted plant=17.7 bed=34.8 dining table=20.8 toilet=47.1 tv=45.1 laptop=44.8 mouse=45.6 remote=13.7 keyboard=38.1 cell phone=21.7 microwave=38.8 oven=24.0 toaster=10.5 sink=26.8 refrigerator=40.1 book=7.4 clock=38.9 vase=25.8 scissors=24.6 teddy bear=30.3 hair drier=0.0 toothbrush=11.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 253][Batch 99], LR: 1.00E-05, Speed: 120.313 samples/sec, ObjLoss=20.221, BoxCenterLoss=14.453, BoxScaleLoss=4.681, ClassLoss=7.252 [Epoch 253][Batch 199], LR: 1.00E-05, Speed: 118.684 samples/sec, ObjLoss=20.221, BoxCenterLoss=14.453, BoxScaleLoss=4.680, ClassLoss=7.251 [Epoch 253][Batch 299], LR: 1.00E-05, Speed: 68.707 samples/sec, ObjLoss=20.220, BoxCenterLoss=14.453, BoxScaleLoss=4.680, ClassLoss=7.251 [Epoch 253][Batch 399], LR: 1.00E-05, Speed: 82.677 samples/sec, ObjLoss=20.219, BoxCenterLoss=14.453, BoxScaleLoss=4.680, ClassLoss=7.250 [Epoch 253][Batch 499], LR: 1.00E-05, Speed: 112.959 samples/sec, ObjLoss=20.218, BoxCenterLoss=14.453, BoxScaleLoss=4.680, ClassLoss=7.249 [Epoch 253][Batch 599], LR: 1.00E-05, Speed: 67.629 samples/sec, ObjLoss=20.218, BoxCenterLoss=14.453, BoxScaleLoss=4.680, ClassLoss=7.249 [Epoch 253][Batch 699], LR: 1.00E-05, Speed: 139.222 samples/sec, ObjLoss=20.217, BoxCenterLoss=14.453, BoxScaleLoss=4.679, ClassLoss=7.248 [Epoch 253][Batch 799], LR: 1.00E-05, Speed: 52.872 samples/sec, ObjLoss=20.216, BoxCenterLoss=14.453, BoxScaleLoss=4.679, ClassLoss=7.248 [Epoch 253][Batch 899], LR: 1.00E-05, Speed: 50.911 samples/sec, ObjLoss=20.215, BoxCenterLoss=14.452, BoxScaleLoss=4.679, ClassLoss=7.247 [Epoch 253][Batch 999], LR: 1.00E-05, Speed: 69.259 samples/sec, ObjLoss=20.214, BoxCenterLoss=14.452, BoxScaleLoss=4.679, ClassLoss=7.247 [Epoch 253][Batch 1099], LR: 1.00E-05, Speed: 112.405 samples/sec, ObjLoss=20.214, BoxCenterLoss=14.452, BoxScaleLoss=4.679, ClassLoss=7.246 [Epoch 253][Batch 1199], LR: 1.00E-05, Speed: 60.268 samples/sec, ObjLoss=20.213, BoxCenterLoss=14.452, BoxScaleLoss=4.679, ClassLoss=7.246 [Epoch 253][Batch 1299], LR: 1.00E-05, Speed: 67.943 samples/sec, ObjLoss=20.212, BoxCenterLoss=14.452, BoxScaleLoss=4.678, ClassLoss=7.245 [Epoch 253][Batch 1399], LR: 1.00E-05, Speed: 90.651 samples/sec, ObjLoss=20.211, BoxCenterLoss=14.452, BoxScaleLoss=4.678, ClassLoss=7.244 [Epoch 253][Batch 1499], LR: 1.00E-05, Speed: 47.144 samples/sec, ObjLoss=20.210, BoxCenterLoss=14.452, BoxScaleLoss=4.678, ClassLoss=7.244 [Epoch 253][Batch 1599], LR: 1.00E-05, Speed: 146.827 samples/sec, ObjLoss=20.210, BoxCenterLoss=14.452, BoxScaleLoss=4.678, ClassLoss=7.243 [Epoch 253][Batch 1699], LR: 1.00E-05, Speed: 70.726 samples/sec, ObjLoss=20.209, BoxCenterLoss=14.452, BoxScaleLoss=4.678, ClassLoss=7.243 [Epoch 253][Batch 1799], LR: 1.00E-05, Speed: 83.898 samples/sec, ObjLoss=20.208, BoxCenterLoss=14.452, BoxScaleLoss=4.677, ClassLoss=7.242 [Epoch 253] Training cost: 1610.695, ObjLoss=20.208, BoxCenterLoss=14.452, BoxScaleLoss=4.677, ClassLoss=7.242 [Epoch 253] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.421 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530 person=39.3 bicycle=20.0 car=27.1 motorcycle=31.4 airplane=50.7 bus=50.7 train=54.1 truck=25.9 boat=16.5 traffic light=17.4 fire hydrant=50.3 stop sign=52.9 parking meter=33.1 bench=16.0 bird=23.4 cat=51.8 dog=45.8 horse=41.9 sheep=35.2 cow=39.4 elephant=49.9 bear=58.8 zebra=51.3 giraffe=53.6 backpack=8.1 umbrella=27.7 handbag=7.5 tie=19.2 suitcase=22.3 frisbee=48.3 skis=12.9 snowboard=21.1 sports ball=31.0 kite=30.2 baseball bat=15.5 baseball glove=23.9 skateboard=34.0 surfboard=24.4 tennis racket=29.6 bottle=21.1 wine glass=21.0 cup=27.2 fork=16.3 knife=6.5 spoon=5.8 bowl=27.0 banana=15.7 apple=10.1 sandwich=24.6 orange=21.1 broccoli=13.8 carrot=13.2 hot dog=23.4 pizza=37.0 donut=28.6 cake=22.6 chair=17.4 couch=32.7 potted plant=17.2 bed=33.7 dining table=18.5 toilet=46.8 tv=44.8 laptop=44.2 mouse=44.9 remote=13.9 keyboard=39.3 cell phone=21.7 microwave=39.1 oven=23.9 toaster=9.5 sink=27.6 refrigerator=40.2 book=7.3 clock=38.9 vase=25.5 scissors=24.7 teddy bear=30.7 hair drier=0.0 toothbrush=11.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 254][Batch 99], LR: 1.00E-05, Speed: 149.446 samples/sec, ObjLoss=20.207, BoxCenterLoss=14.452, BoxScaleLoss=4.677, ClassLoss=7.241 [Epoch 254][Batch 199], LR: 1.00E-05, Speed: 60.953 samples/sec, ObjLoss=20.206, BoxCenterLoss=14.452, BoxScaleLoss=4.677, ClassLoss=7.241 [Epoch 254][Batch 299], LR: 1.00E-05, Speed: 135.137 samples/sec, ObjLoss=20.206, BoxCenterLoss=14.451, BoxScaleLoss=4.677, ClassLoss=7.240 [Epoch 254][Batch 399], LR: 1.00E-05, Speed: 77.343 samples/sec, ObjLoss=20.205, BoxCenterLoss=14.451, BoxScaleLoss=4.676, ClassLoss=7.239 [Epoch 254][Batch 499], LR: 1.00E-05, Speed: 80.766 samples/sec, ObjLoss=20.204, BoxCenterLoss=14.451, BoxScaleLoss=4.676, ClassLoss=7.239 [Epoch 254][Batch 599], LR: 1.00E-05, Speed: 76.180 samples/sec, ObjLoss=20.203, BoxCenterLoss=14.451, BoxScaleLoss=4.676, ClassLoss=7.238 [Epoch 254][Batch 699], LR: 1.00E-05, Speed: 55.571 samples/sec, ObjLoss=20.203, BoxCenterLoss=14.451, BoxScaleLoss=4.676, ClassLoss=7.238 [Epoch 254][Batch 799], LR: 1.00E-05, Speed: 61.613 samples/sec, ObjLoss=20.202, BoxCenterLoss=14.451, BoxScaleLoss=4.676, ClassLoss=7.237 [Epoch 254][Batch 899], LR: 1.00E-05, Speed: 74.861 samples/sec, ObjLoss=20.201, BoxCenterLoss=14.451, BoxScaleLoss=4.675, ClassLoss=7.236 [Epoch 254][Batch 999], LR: 1.00E-05, Speed: 128.932 samples/sec, ObjLoss=20.200, BoxCenterLoss=14.451, BoxScaleLoss=4.675, ClassLoss=7.236 [Epoch 254][Batch 1099], LR: 1.00E-05, Speed: 77.726 samples/sec, ObjLoss=20.199, BoxCenterLoss=14.451, BoxScaleLoss=4.675, ClassLoss=7.235 [Epoch 254][Batch 1199], LR: 1.00E-05, Speed: 61.243 samples/sec, ObjLoss=20.199, BoxCenterLoss=14.451, BoxScaleLoss=4.675, ClassLoss=7.235 [Epoch 254][Batch 1299], LR: 1.00E-05, Speed: 88.936 samples/sec, ObjLoss=20.198, BoxCenterLoss=14.451, BoxScaleLoss=4.675, ClassLoss=7.234 [Epoch 254][Batch 1399], LR: 1.00E-05, Speed: 55.216 samples/sec, ObjLoss=20.197, BoxCenterLoss=14.451, BoxScaleLoss=4.674, ClassLoss=7.233 [Epoch 254][Batch 1499], LR: 1.00E-05, Speed: 79.456 samples/sec, ObjLoss=20.196, BoxCenterLoss=14.451, BoxScaleLoss=4.674, ClassLoss=7.233 [Epoch 254][Batch 1599], LR: 1.00E-05, Speed: 109.955 samples/sec, ObjLoss=20.196, BoxCenterLoss=14.450, BoxScaleLoss=4.674, ClassLoss=7.232 [Epoch 254][Batch 1699], LR: 1.00E-05, Speed: 80.116 samples/sec, ObjLoss=20.195, BoxCenterLoss=14.450, BoxScaleLoss=4.674, ClassLoss=7.232 [Epoch 254][Batch 1799], LR: 1.00E-05, Speed: 144.633 samples/sec, ObjLoss=20.194, BoxCenterLoss=14.450, BoxScaleLoss=4.674, ClassLoss=7.231 [Epoch 254] Training cost: 1637.019, ObjLoss=20.194, BoxCenterLoss=14.450, BoxScaleLoss=4.674, ClassLoss=7.231 [Epoch 254] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 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 ] = 0.420 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529 person=39.5 bicycle=19.9 car=27.3 motorcycle=31.1 airplane=50.9 bus=50.8 train=54.7 truck=25.9 boat=15.9 traffic light=17.2 fire hydrant=49.0 stop sign=52.8 parking meter=33.0 bench=16.4 bird=23.7 cat=52.0 dog=45.9 horse=41.7 sheep=35.6 cow=39.7 elephant=49.9 bear=57.0 zebra=50.8 giraffe=53.7 backpack=8.1 umbrella=27.3 handbag=7.3 tie=19.2 suitcase=22.4 frisbee=48.1 skis=12.5 snowboard=18.7 sports ball=30.7 kite=28.9 baseball bat=15.7 baseball glove=24.0 skateboard=34.0 surfboard=24.3 tennis racket=29.9 bottle=21.4 wine glass=21.1 cup=27.3 fork=16.4 knife=6.4 spoon=5.6 bowl=26.7 banana=15.2 apple=10.3 sandwich=24.2 orange=21.3 broccoli=13.5 carrot=13.6 hot dog=23.4 pizza=36.9 donut=28.0 cake=22.2 chair=17.3 couch=33.1 potted plant=17.8 bed=34.4 dining table=19.9 toilet=47.2 tv=44.8 laptop=43.8 mouse=45.3 remote=13.6 keyboard=38.9 cell phone=21.6 microwave=39.1 oven=24.2 toaster=6.2 sink=27.1 refrigerator=39.5 book=7.5 clock=38.9 vase=25.0 scissors=24.4 teddy bear=30.1 hair drier=0.0 toothbrush=11.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 255][Batch 99], LR: 1.00E-05, Speed: 108.091 samples/sec, ObjLoss=20.193, BoxCenterLoss=14.450, BoxScaleLoss=4.673, ClassLoss=7.230 [Epoch 255][Batch 199], LR: 1.00E-05, Speed: 62.711 samples/sec, ObjLoss=20.192, BoxCenterLoss=14.450, BoxScaleLoss=4.673, ClassLoss=7.230 [Epoch 255][Batch 299], LR: 1.00E-05, Speed: 94.393 samples/sec, ObjLoss=20.191, BoxCenterLoss=14.450, BoxScaleLoss=4.673, ClassLoss=7.229 [Epoch 255][Batch 399], LR: 1.00E-05, Speed: 64.436 samples/sec, ObjLoss=20.191, BoxCenterLoss=14.450, BoxScaleLoss=4.673, ClassLoss=7.229 [Epoch 255][Batch 499], LR: 1.00E-05, Speed: 127.705 samples/sec, ObjLoss=20.190, BoxCenterLoss=14.450, BoxScaleLoss=4.673, ClassLoss=7.228 [Epoch 255][Batch 599], LR: 1.00E-05, Speed: 54.942 samples/sec, ObjLoss=20.189, BoxCenterLoss=14.450, BoxScaleLoss=4.672, ClassLoss=7.228 [Epoch 255][Batch 699], LR: 1.00E-05, Speed: 79.776 samples/sec, ObjLoss=20.188, BoxCenterLoss=14.450, BoxScaleLoss=4.672, ClassLoss=7.227 [Epoch 255][Batch 799], LR: 1.00E-05, Speed: 79.529 samples/sec, ObjLoss=20.188, BoxCenterLoss=14.450, BoxScaleLoss=4.672, ClassLoss=7.226 [Epoch 255][Batch 899], LR: 1.00E-05, Speed: 84.797 samples/sec, ObjLoss=20.187, BoxCenterLoss=14.450, BoxScaleLoss=4.672, ClassLoss=7.226 [Epoch 255][Batch 999], LR: 1.00E-05, Speed: 73.784 samples/sec, ObjLoss=20.186, BoxCenterLoss=14.450, BoxScaleLoss=4.672, ClassLoss=7.225 [Epoch 255][Batch 1099], LR: 1.00E-05, Speed: 55.437 samples/sec, ObjLoss=20.185, BoxCenterLoss=14.449, BoxScaleLoss=4.671, ClassLoss=7.225 [Epoch 255][Batch 1199], LR: 1.00E-05, Speed: 77.154 samples/sec, ObjLoss=20.185, BoxCenterLoss=14.449, BoxScaleLoss=4.671, ClassLoss=7.224 [Epoch 255][Batch 1299], LR: 1.00E-05, Speed: 81.277 samples/sec, ObjLoss=20.184, BoxCenterLoss=14.449, BoxScaleLoss=4.671, ClassLoss=7.223 [Epoch 255][Batch 1399], LR: 1.00E-05, Speed: 86.949 samples/sec, ObjLoss=20.183, BoxCenterLoss=14.449, BoxScaleLoss=4.671, ClassLoss=7.223 [Epoch 255][Batch 1499], LR: 1.00E-05, Speed: 98.128 samples/sec, ObjLoss=20.182, BoxCenterLoss=14.449, BoxScaleLoss=4.670, ClassLoss=7.222 [Epoch 255][Batch 1599], LR: 1.00E-05, Speed: 70.725 samples/sec, ObjLoss=20.182, BoxCenterLoss=14.449, BoxScaleLoss=4.670, ClassLoss=7.221 [Epoch 255][Batch 1699], LR: 1.00E-05, Speed: 94.223 samples/sec, ObjLoss=20.181, BoxCenterLoss=14.449, BoxScaleLoss=4.670, ClassLoss=7.221 [Epoch 255][Batch 1799], LR: 1.00E-05, Speed: 93.559 samples/sec, ObjLoss=20.180, BoxCenterLoss=14.449, BoxScaleLoss=4.670, ClassLoss=7.220 [Epoch 255] Training cost: 1720.439, ObjLoss=20.180, BoxCenterLoss=14.449, BoxScaleLoss=4.670, ClassLoss=7.220 [Epoch 255] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.294 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 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 ] = 0.421 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.247 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370 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 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529 person=39.5 bicycle=19.9 car=27.2 motorcycle=31.4 airplane=51.4 bus=50.6 train=54.7 truck=25.8 boat=16.1 traffic light=17.0 fire hydrant=48.7 stop sign=52.7 parking meter=33.1 bench=15.9 bird=23.7 cat=51.6 dog=45.9 horse=41.7 sheep=35.4 cow=39.7 elephant=49.6 bear=58.2 zebra=49.9 giraffe=53.7 backpack=8.0 umbrella=27.3 handbag=7.4 tie=19.1 suitcase=23.2 frisbee=47.8 skis=12.7 snowboard=19.1 sports ball=30.7 kite=30.1 baseball bat=14.9 baseball glove=23.3 skateboard=34.1 surfboard=24.6 tennis racket=29.4 bottle=21.1 wine glass=21.2 cup=27.3 fork=16.3 knife=6.5 spoon=6.1 bowl=26.6 banana=15.4 apple=10.3 sandwich=23.9 orange=20.9 broccoli=13.4 carrot=13.6 hot dog=23.6 pizza=37.3 donut=27.6 cake=22.5 chair=17.4 couch=33.2 potted plant=17.6 bed=33.3 dining table=18.2 toilet=47.7 tv=45.3 laptop=45.0 mouse=45.5 remote=13.5 keyboard=39.2 cell phone=21.3 microwave=39.8 oven=23.5 toaster=6.2 sink=26.5 refrigerator=40.2 book=7.4 clock=38.4 vase=25.1 scissors=26.0 teddy bear=30.9 hair drier=0.0 toothbrush=10.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 256][Batch 99], LR: 1.00E-05, Speed: 125.515 samples/sec, ObjLoss=20.179, BoxCenterLoss=14.449, BoxScaleLoss=4.670, ClassLoss=7.219 [Epoch 256][Batch 199], LR: 1.00E-05, Speed: 61.764 samples/sec, ObjLoss=20.178, BoxCenterLoss=14.449, BoxScaleLoss=4.669, ClassLoss=7.219 [Epoch 256][Batch 299], LR: 1.00E-05, Speed: 87.762 samples/sec, ObjLoss=20.178, BoxCenterLoss=14.449, BoxScaleLoss=4.669, ClassLoss=7.218 [Epoch 256][Batch 399], LR: 1.00E-05, Speed: 81.125 samples/sec, ObjLoss=20.177, BoxCenterLoss=14.449, BoxScaleLoss=4.669, ClassLoss=7.218 [Epoch 256][Batch 499], LR: 1.00E-05, Speed: 81.326 samples/sec, ObjLoss=20.176, BoxCenterLoss=14.449, BoxScaleLoss=4.669, ClassLoss=7.217 [Epoch 256][Batch 599], LR: 1.00E-05, Speed: 79.402 samples/sec, ObjLoss=20.175, BoxCenterLoss=14.448, BoxScaleLoss=4.669, ClassLoss=7.217 [Epoch 256][Batch 699], LR: 1.00E-05, Speed: 73.455 samples/sec, ObjLoss=20.174, BoxCenterLoss=14.448, BoxScaleLoss=4.668, ClassLoss=7.216 [Epoch 256][Batch 799], LR: 1.00E-05, Speed: 90.804 samples/sec, ObjLoss=20.174, BoxCenterLoss=14.448, BoxScaleLoss=4.668, ClassLoss=7.215 [Epoch 256][Batch 899], LR: 1.00E-05, Speed: 69.609 samples/sec, ObjLoss=20.173, BoxCenterLoss=14.448, BoxScaleLoss=4.668, ClassLoss=7.215 [Epoch 256][Batch 999], LR: 1.00E-05, Speed: 57.604 samples/sec, ObjLoss=20.172, BoxCenterLoss=14.448, BoxScaleLoss=4.668, ClassLoss=7.214 [Epoch 256][Batch 1099], LR: 1.00E-05, Speed: 97.098 samples/sec, ObjLoss=20.171, BoxCenterLoss=14.448, BoxScaleLoss=4.668, ClassLoss=7.214 [Epoch 256][Batch 1199], LR: 1.00E-05, Speed: 137.343 samples/sec, ObjLoss=20.171, BoxCenterLoss=14.448, BoxScaleLoss=4.667, ClassLoss=7.213 [Epoch 256][Batch 1299], LR: 1.00E-05, Speed: 78.542 samples/sec, ObjLoss=20.170, BoxCenterLoss=14.448, BoxScaleLoss=4.667, ClassLoss=7.213 [Epoch 256][Batch 1399], LR: 1.00E-05, Speed: 77.002 samples/sec, ObjLoss=20.169, BoxCenterLoss=14.448, BoxScaleLoss=4.667, ClassLoss=7.212 [Epoch 256][Batch 1499], LR: 1.00E-05, Speed: 84.106 samples/sec, ObjLoss=20.168, BoxCenterLoss=14.448, BoxScaleLoss=4.667, ClassLoss=7.211 [Epoch 256][Batch 1599], LR: 1.00E-05, Speed: 117.051 samples/sec, ObjLoss=20.168, BoxCenterLoss=14.448, BoxScaleLoss=4.667, ClassLoss=7.211 [Epoch 256][Batch 1699], LR: 1.00E-05, Speed: 77.764 samples/sec, ObjLoss=20.167, BoxCenterLoss=14.448, BoxScaleLoss=4.666, ClassLoss=7.210 [Epoch 256][Batch 1799], LR: 1.00E-05, Speed: 60.384 samples/sec, ObjLoss=20.166, BoxCenterLoss=14.448, BoxScaleLoss=4.666, ClassLoss=7.210 [Epoch 256] Training cost: 1708.588, ObjLoss=20.166, BoxCenterLoss=14.447, BoxScaleLoss=4.666, ClassLoss=7.209 [Epoch 256] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130 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 ] = 0.420 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.527 person=39.4 bicycle=20.1 car=27.3 motorcycle=31.6 airplane=50.8 bus=50.2 train=54.0 truck=25.4 boat=16.5 traffic light=17.5 fire hydrant=49.0 stop sign=53.3 parking meter=33.5 bench=16.4 bird=23.4 cat=51.6 dog=46.2 horse=41.5 sheep=36.2 cow=40.0 elephant=48.9 bear=57.0 zebra=51.3 giraffe=53.4 backpack=8.1 umbrella=27.3 handbag=7.6 tie=18.7 suitcase=22.4 frisbee=48.2 skis=13.0 snowboard=20.4 sports ball=30.4 kite=30.3 baseball bat=15.6 baseball glove=23.5 skateboard=33.4 surfboard=24.8 tennis racket=29.4 bottle=21.4 wine glass=21.1 cup=27.6 fork=16.6 knife=6.4 spoon=5.7 bowl=26.6 banana=15.3 apple=10.1 sandwich=24.4 orange=20.7 broccoli=13.5 carrot=13.3 hot dog=23.1 pizza=36.9 donut=27.6 cake=22.2 chair=17.4 couch=33.6 potted plant=17.3 bed=33.1 dining table=18.1 toilet=47.8 tv=44.6 laptop=44.3 mouse=45.3 remote=13.4 keyboard=39.1 cell phone=21.4 microwave=38.8 oven=23.3 toaster=10.0 sink=26.9 refrigerator=39.6 book=7.4 clock=38.4 vase=25.2 scissors=25.5 teddy bear=30.9 hair drier=0.0 toothbrush=11.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 257][Batch 99], LR: 1.00E-05, Speed: 117.731 samples/sec, ObjLoss=20.165, BoxCenterLoss=14.447, BoxScaleLoss=4.666, ClassLoss=7.209 [Epoch 257][Batch 199], LR: 1.00E-05, Speed: 84.875 samples/sec, ObjLoss=20.164, BoxCenterLoss=14.447, BoxScaleLoss=4.666, ClassLoss=7.208 [Epoch 257][Batch 299], LR: 1.00E-05, Speed: 78.297 samples/sec, ObjLoss=20.164, BoxCenterLoss=14.447, BoxScaleLoss=4.665, ClassLoss=7.208 [Epoch 257][Batch 399], LR: 1.00E-05, Speed: 88.357 samples/sec, ObjLoss=20.163, BoxCenterLoss=14.447, BoxScaleLoss=4.665, ClassLoss=7.207 [Epoch 257][Batch 499], LR: 1.00E-05, Speed: 72.637 samples/sec, ObjLoss=20.162, BoxCenterLoss=14.447, BoxScaleLoss=4.665, ClassLoss=7.207 [Epoch 257][Batch 599], LR: 1.00E-05, Speed: 72.413 samples/sec, ObjLoss=20.161, BoxCenterLoss=14.447, BoxScaleLoss=4.665, ClassLoss=7.206 [Epoch 257][Batch 699], LR: 1.00E-05, Speed: 71.189 samples/sec, ObjLoss=20.161, BoxCenterLoss=14.447, BoxScaleLoss=4.665, ClassLoss=7.205 [Epoch 257][Batch 799], LR: 1.00E-05, Speed: 106.135 samples/sec, ObjLoss=20.160, BoxCenterLoss=14.447, BoxScaleLoss=4.664, ClassLoss=7.205 [Epoch 257][Batch 899], LR: 1.00E-05, Speed: 66.583 samples/sec, ObjLoss=20.159, BoxCenterLoss=14.447, BoxScaleLoss=4.664, ClassLoss=7.204 [Epoch 257][Batch 999], LR: 1.00E-05, Speed: 75.471 samples/sec, ObjLoss=20.158, BoxCenterLoss=14.447, BoxScaleLoss=4.664, ClassLoss=7.204 [Epoch 257][Batch 1099], LR: 1.00E-05, Speed: 86.710 samples/sec, ObjLoss=20.157, BoxCenterLoss=14.447, BoxScaleLoss=4.664, ClassLoss=7.203 [Epoch 257][Batch 1199], LR: 1.00E-05, Speed: 58.002 samples/sec, ObjLoss=20.157, BoxCenterLoss=14.447, BoxScaleLoss=4.664, ClassLoss=7.203 [Epoch 257][Batch 1299], LR: 1.00E-05, Speed: 63.116 samples/sec, ObjLoss=20.156, BoxCenterLoss=14.446, BoxScaleLoss=4.664, ClassLoss=7.202 [Epoch 257][Batch 1399], LR: 1.00E-05, Speed: 76.602 samples/sec, ObjLoss=20.155, BoxCenterLoss=14.446, BoxScaleLoss=4.663, ClassLoss=7.201 [Epoch 257][Batch 1499], LR: 1.00E-05, Speed: 108.956 samples/sec, ObjLoss=20.154, BoxCenterLoss=14.446, BoxScaleLoss=4.663, ClassLoss=7.201 [Epoch 257][Batch 1599], LR: 1.00E-05, Speed: 88.663 samples/sec, ObjLoss=20.154, BoxCenterLoss=14.446, BoxScaleLoss=4.663, ClassLoss=7.200 [Epoch 257][Batch 1699], LR: 1.00E-05, Speed: 99.959 samples/sec, ObjLoss=20.153, BoxCenterLoss=14.446, BoxScaleLoss=4.663, ClassLoss=7.200 [Epoch 257][Batch 1799], LR: 1.00E-05, Speed: 58.127 samples/sec, ObjLoss=20.152, BoxCenterLoss=14.446, BoxScaleLoss=4.663, ClassLoss=7.199 [Epoch 257] Training cost: 1742.537, ObjLoss=20.152, BoxCenterLoss=14.446, BoxScaleLoss=4.663, ClassLoss=7.199 [Epoch 257] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.283 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.484 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.293 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.126 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295 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 ] = 0.247 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.369 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.385 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530 person=39.5 bicycle=20.4 car=27.2 motorcycle=32.1 airplane=50.2 bus=50.6 train=54.0 truck=26.1 boat=15.9 traffic light=17.4 fire hydrant=48.3 stop sign=52.8 parking meter=33.0 bench=16.2 bird=23.8 cat=52.1 dog=46.2 horse=41.3 sheep=36.1 cow=39.6 elephant=50.1 bear=59.4 zebra=51.0 giraffe=53.7 backpack=8.1 umbrella=27.4 handbag=7.3 tie=19.3 suitcase=22.2 frisbee=48.2 skis=12.5 snowboard=19.3 sports ball=31.0 kite=30.1 baseball bat=15.8 baseball glove=23.3 skateboard=34.1 surfboard=24.4 tennis racket=29.8 bottle=21.3 wine glass=21.1 cup=27.5 fork=16.0 knife=6.5 spoon=5.9 bowl=27.0 banana=15.3 apple=9.6 sandwich=24.0 orange=20.8 broccoli=13.7 carrot=13.4 hot dog=23.7 pizza=37.9 donut=27.4 cake=22.2 chair=17.4 couch=32.7 potted plant=17.2 bed=32.8 dining table=17.3 toilet=47.5 tv=44.5 laptop=44.2 mouse=45.0 remote=13.5 keyboard=38.7 cell phone=21.6 microwave=38.5 oven=23.3 toaster=2.8 sink=27.6 refrigerator=39.4 book=7.3 clock=38.4 vase=24.8 scissors=25.7 teddy bear=30.7 hair drier=0.0 toothbrush=13.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.3 [Epoch 258][Batch 99], LR: 1.00E-05, Speed: 117.616 samples/sec, ObjLoss=20.151, BoxCenterLoss=14.446, BoxScaleLoss=4.662, ClassLoss=7.198 [Epoch 258][Batch 199], LR: 1.00E-05, Speed: 103.580 samples/sec, ObjLoss=20.151, BoxCenterLoss=14.446, BoxScaleLoss=4.662, ClassLoss=7.198 [Epoch 258][Batch 299], LR: 1.00E-05, Speed: 66.364 samples/sec, ObjLoss=20.150, BoxCenterLoss=14.446, BoxScaleLoss=4.662, ClassLoss=7.197 [Epoch 258][Batch 399], LR: 1.00E-05, Speed: 46.817 samples/sec, ObjLoss=20.149, BoxCenterLoss=14.446, BoxScaleLoss=4.662, ClassLoss=7.197 [Epoch 258][Batch 499], LR: 1.00E-05, Speed: 77.416 samples/sec, ObjLoss=20.148, BoxCenterLoss=14.446, BoxScaleLoss=4.662, ClassLoss=7.196 [Epoch 258][Batch 599], LR: 1.00E-05, Speed: 62.030 samples/sec, ObjLoss=20.148, BoxCenterLoss=14.446, BoxScaleLoss=4.661, ClassLoss=7.195 [Epoch 258][Batch 699], LR: 1.00E-05, Speed: 84.086 samples/sec, ObjLoss=20.147, BoxCenterLoss=14.446, BoxScaleLoss=4.661, ClassLoss=7.195 [Epoch 258][Batch 799], LR: 1.00E-05, Speed: 70.558 samples/sec, ObjLoss=20.146, BoxCenterLoss=14.446, BoxScaleLoss=4.661, ClassLoss=7.194 [Epoch 258][Batch 899], LR: 1.00E-05, Speed: 106.342 samples/sec, ObjLoss=20.145, BoxCenterLoss=14.446, BoxScaleLoss=4.661, ClassLoss=7.194 [Epoch 258][Batch 999], LR: 1.00E-05, Speed: 94.242 samples/sec, ObjLoss=20.145, BoxCenterLoss=14.445, BoxScaleLoss=4.661, ClassLoss=7.193 [Epoch 258][Batch 1099], LR: 1.00E-05, Speed: 74.035 samples/sec, ObjLoss=20.144, BoxCenterLoss=14.445, BoxScaleLoss=4.660, ClassLoss=7.193 [Epoch 258][Batch 1199], LR: 1.00E-05, Speed: 119.500 samples/sec, ObjLoss=20.143, BoxCenterLoss=14.445, BoxScaleLoss=4.660, ClassLoss=7.192 [Epoch 258][Batch 1299], LR: 1.00E-05, Speed: 78.953 samples/sec, ObjLoss=20.142, BoxCenterLoss=14.445, BoxScaleLoss=4.660, ClassLoss=7.191 [Epoch 258][Batch 1399], LR: 1.00E-05, Speed: 73.245 samples/sec, ObjLoss=20.141, BoxCenterLoss=14.445, BoxScaleLoss=4.660, ClassLoss=7.191 [Epoch 258][Batch 1499], LR: 1.00E-05, Speed: 67.727 samples/sec, ObjLoss=20.141, BoxCenterLoss=14.445, BoxScaleLoss=4.660, ClassLoss=7.190 [Epoch 258][Batch 1599], LR: 1.00E-05, Speed: 71.277 samples/sec, ObjLoss=20.140, BoxCenterLoss=14.445, BoxScaleLoss=4.659, ClassLoss=7.190 [Epoch 258][Batch 1699], LR: 1.00E-05, Speed: 76.974 samples/sec, ObjLoss=20.139, BoxCenterLoss=14.445, BoxScaleLoss=4.659, ClassLoss=7.189 [Epoch 258][Batch 1799], LR: 1.00E-05, Speed: 147.954 samples/sec, ObjLoss=20.139, BoxCenterLoss=14.445, BoxScaleLoss=4.659, ClassLoss=7.189 [Epoch 258] Training cost: 1691.207, ObjLoss=20.138, BoxCenterLoss=14.445, BoxScaleLoss=4.659, ClassLoss=7.188 [Epoch 258] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.484 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.247 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.182 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.385 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533 person=39.5 bicycle=20.0 car=27.5 motorcycle=31.9 airplane=50.8 bus=50.6 train=54.2 truck=25.9 boat=16.2 traffic light=17.1 fire hydrant=49.6 stop sign=53.4 parking meter=33.7 bench=16.1 bird=23.8 cat=51.6 dog=46.7 horse=41.9 sheep=35.0 cow=39.8 elephant=49.5 bear=59.4 zebra=51.8 giraffe=53.8 backpack=8.2 umbrella=27.4 handbag=7.3 tie=19.3 suitcase=22.6 frisbee=48.7 skis=12.9 snowboard=19.8 sports ball=30.5 kite=30.2 baseball bat=15.5 baseball glove=24.2 skateboard=34.0 surfboard=24.4 tennis racket=29.3 bottle=21.3 wine glass=20.8 cup=27.6 fork=16.0 knife=6.5 spoon=5.7 bowl=26.8 banana=15.1 apple=9.7 sandwich=24.4 orange=20.8 broccoli=13.5 carrot=13.5 hot dog=23.2 pizza=37.2 donut=28.4 cake=22.5 chair=17.3 couch=32.8 potted plant=17.4 bed=32.5 dining table=18.0 toilet=47.6 tv=44.8 laptop=44.8 mouse=45.7 remote=13.6 keyboard=38.8 cell phone=22.0 microwave=39.5 oven=23.4 toaster=4.2 sink=26.8 refrigerator=39.8 book=7.3 clock=38.5 vase=24.9 scissors=25.6 teddy bear=31.0 hair drier=0.0 toothbrush=10.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 259][Batch 99], LR: 1.00E-05, Speed: 146.820 samples/sec, ObjLoss=20.138, BoxCenterLoss=14.445, BoxScaleLoss=4.659, ClassLoss=7.188 [Epoch 259][Batch 199], LR: 1.00E-05, Speed: 148.169 samples/sec, ObjLoss=20.137, BoxCenterLoss=14.445, BoxScaleLoss=4.659, ClassLoss=7.187 [Epoch 259][Batch 299], LR: 1.00E-05, Speed: 131.623 samples/sec, ObjLoss=20.136, BoxCenterLoss=14.445, BoxScaleLoss=4.658, ClassLoss=7.187 [Epoch 259][Batch 399], LR: 1.00E-05, Speed: 90.636 samples/sec, ObjLoss=20.135, BoxCenterLoss=14.445, BoxScaleLoss=4.658, ClassLoss=7.186 [Epoch 259][Batch 499], LR: 1.00E-05, Speed: 60.270 samples/sec, ObjLoss=20.135, BoxCenterLoss=14.444, BoxScaleLoss=4.658, ClassLoss=7.186 [Epoch 259][Batch 599], LR: 1.00E-05, Speed: 61.561 samples/sec, ObjLoss=20.134, BoxCenterLoss=14.444, BoxScaleLoss=4.658, ClassLoss=7.185 [Epoch 259][Batch 699], LR: 1.00E-05, Speed: 79.342 samples/sec, ObjLoss=20.133, BoxCenterLoss=14.444, BoxScaleLoss=4.658, ClassLoss=7.184 [Epoch 259][Batch 799], LR: 1.00E-05, Speed: 71.295 samples/sec, ObjLoss=20.132, BoxCenterLoss=14.444, BoxScaleLoss=4.657, ClassLoss=7.184 [Epoch 259][Batch 899], LR: 1.00E-05, Speed: 93.214 samples/sec, ObjLoss=20.132, BoxCenterLoss=14.444, BoxScaleLoss=4.657, ClassLoss=7.183 [Epoch 259][Batch 999], LR: 1.00E-05, Speed: 55.851 samples/sec, ObjLoss=20.131, BoxCenterLoss=14.444, BoxScaleLoss=4.657, ClassLoss=7.183 [Epoch 259][Batch 1099], LR: 1.00E-05, Speed: 121.554 samples/sec, ObjLoss=20.130, BoxCenterLoss=14.444, BoxScaleLoss=4.657, ClassLoss=7.182 [Epoch 259][Batch 1199], LR: 1.00E-05, Speed: 85.293 samples/sec, ObjLoss=20.129, BoxCenterLoss=14.444, BoxScaleLoss=4.657, ClassLoss=7.182 [Epoch 259][Batch 1299], LR: 1.00E-05, Speed: 98.788 samples/sec, ObjLoss=20.129, BoxCenterLoss=14.444, BoxScaleLoss=4.656, ClassLoss=7.181 [Epoch 259][Batch 1399], LR: 1.00E-05, Speed: 47.578 samples/sec, ObjLoss=20.128, BoxCenterLoss=14.444, BoxScaleLoss=4.656, ClassLoss=7.180 [Epoch 259][Batch 1499], LR: 1.00E-05, Speed: 72.813 samples/sec, ObjLoss=20.127, BoxCenterLoss=14.444, BoxScaleLoss=4.656, ClassLoss=7.180 [Epoch 259][Batch 1599], LR: 1.00E-05, Speed: 114.409 samples/sec, ObjLoss=20.126, BoxCenterLoss=14.444, BoxScaleLoss=4.656, ClassLoss=7.179 [Epoch 259][Batch 1699], LR: 1.00E-05, Speed: 90.451 samples/sec, ObjLoss=20.126, BoxCenterLoss=14.444, BoxScaleLoss=4.656, ClassLoss=7.179 [Epoch 259][Batch 1799], LR: 1.00E-05, Speed: 83.214 samples/sec, ObjLoss=20.125, BoxCenterLoss=14.444, BoxScaleLoss=4.655, ClassLoss=7.178 [Epoch 259] Training cost: 1679.910, ObjLoss=20.125, BoxCenterLoss=14.444, BoxScaleLoss=4.655, ClassLoss=7.178 [Epoch 259] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.484 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124 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 ] = 0.425 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532 person=39.4 bicycle=19.8 car=27.4 motorcycle=31.8 airplane=51.8 bus=50.8 train=54.6 truck=26.0 boat=16.4 traffic light=17.4 fire hydrant=48.9 stop sign=54.0 parking meter=33.5 bench=16.4 bird=23.3 cat=51.0 dog=46.4 horse=41.8 sheep=34.9 cow=39.9 elephant=49.9 bear=56.2 zebra=51.2 giraffe=54.1 backpack=8.2 umbrella=27.3 handbag=7.4 tie=19.3 suitcase=22.8 frisbee=48.1 skis=12.7 snowboard=19.1 sports ball=30.7 kite=30.1 baseball bat=15.9 baseball glove=24.0 skateboard=33.3 surfboard=24.6 tennis racket=29.6 bottle=21.1 wine glass=20.8 cup=27.5 fork=15.8 knife=6.4 spoon=6.0 bowl=27.2 banana=16.0 apple=10.1 sandwich=24.0 orange=21.0 broccoli=13.6 carrot=13.0 hot dog=22.6 pizza=37.5 donut=28.3 cake=21.9 chair=17.3 couch=33.3 potted plant=17.2 bed=35.0 dining table=18.9 toilet=47.1 tv=44.9 laptop=45.1 mouse=45.8 remote=13.3 keyboard=38.7 cell phone=22.0 microwave=39.6 oven=23.9 toaster=3.6 sink=27.3 refrigerator=40.1 book=7.4 clock=38.6 vase=24.9 scissors=26.5 teddy bear=31.1 hair drier=0.0 toothbrush=11.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 260][Batch 99], LR: 1.00E-05, Speed: 110.509 samples/sec, ObjLoss=20.124, BoxCenterLoss=14.444, BoxScaleLoss=4.655, ClassLoss=7.177 [Epoch 260][Batch 199], LR: 1.00E-05, Speed: 140.525 samples/sec, ObjLoss=20.123, BoxCenterLoss=14.443, BoxScaleLoss=4.655, ClassLoss=7.177 [Epoch 260][Batch 299], LR: 1.00E-05, Speed: 111.790 samples/sec, ObjLoss=20.123, BoxCenterLoss=14.443, BoxScaleLoss=4.655, ClassLoss=7.176 [Epoch 260][Batch 399], LR: 1.00E-05, Speed: 60.257 samples/sec, ObjLoss=20.122, BoxCenterLoss=14.443, BoxScaleLoss=4.655, ClassLoss=7.176 [Epoch 260][Batch 499], LR: 1.00E-05, Speed: 65.563 samples/sec, ObjLoss=20.121, BoxCenterLoss=14.443, BoxScaleLoss=4.654, ClassLoss=7.175 [Epoch 260][Batch 599], LR: 1.00E-05, Speed: 65.550 samples/sec, ObjLoss=20.120, BoxCenterLoss=14.443, BoxScaleLoss=4.654, ClassLoss=7.174 [Epoch 260][Batch 699], LR: 1.00E-05, Speed: 67.002 samples/sec, ObjLoss=20.120, BoxCenterLoss=14.443, BoxScaleLoss=4.654, ClassLoss=7.174 [Epoch 260][Batch 799], LR: 1.00E-05, Speed: 71.502 samples/sec, ObjLoss=20.119, BoxCenterLoss=14.443, BoxScaleLoss=4.654, ClassLoss=7.173 [Epoch 260][Batch 899], LR: 1.00E-05, Speed: 91.102 samples/sec, ObjLoss=20.118, BoxCenterLoss=14.443, BoxScaleLoss=4.654, ClassLoss=7.173 [Epoch 260][Batch 999], LR: 1.00E-05, Speed: 97.617 samples/sec, ObjLoss=20.117, BoxCenterLoss=14.443, BoxScaleLoss=4.653, ClassLoss=7.172 [Epoch 260][Batch 1099], LR: 1.00E-05, Speed: 140.024 samples/sec, ObjLoss=20.117, BoxCenterLoss=14.443, BoxScaleLoss=4.653, ClassLoss=7.172 [Epoch 260][Batch 1199], LR: 1.00E-05, Speed: 62.929 samples/sec, ObjLoss=20.116, BoxCenterLoss=14.443, BoxScaleLoss=4.653, ClassLoss=7.171 [Epoch 260][Batch 1299], LR: 1.00E-05, Speed: 106.453 samples/sec, ObjLoss=20.115, BoxCenterLoss=14.443, BoxScaleLoss=4.653, ClassLoss=7.171 [Epoch 260][Batch 1399], LR: 1.00E-05, Speed: 105.760 samples/sec, ObjLoss=20.114, BoxCenterLoss=14.442, BoxScaleLoss=4.653, ClassLoss=7.170 [Epoch 260][Batch 1499], LR: 1.00E-05, Speed: 84.246 samples/sec, ObjLoss=20.114, BoxCenterLoss=14.443, BoxScaleLoss=4.652, ClassLoss=7.169 [Epoch 260][Batch 1599], LR: 1.00E-05, Speed: 114.501 samples/sec, ObjLoss=20.113, BoxCenterLoss=14.443, BoxScaleLoss=4.652, ClassLoss=7.169 [Epoch 260][Batch 1699], LR: 1.00E-05, Speed: 101.084 samples/sec, ObjLoss=20.113, BoxCenterLoss=14.443, BoxScaleLoss=4.652, ClassLoss=7.168 [Epoch 260][Batch 1799], LR: 1.00E-05, Speed: 139.912 samples/sec, ObjLoss=20.112, BoxCenterLoss=14.442, BoxScaleLoss=4.652, ClassLoss=7.168 [Epoch 260] Training cost: 1649.117, ObjLoss=20.112, BoxCenterLoss=14.442, BoxScaleLoss=4.652, ClassLoss=7.168 [Epoch 260] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.283 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.294 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.528 person=39.3 bicycle=19.4 car=27.4 motorcycle=31.6 airplane=50.6 bus=50.4 train=55.6 truck=25.3 boat=15.6 traffic light=17.1 fire hydrant=48.9 stop sign=52.7 parking meter=34.2 bench=16.2 bird=23.6 cat=51.7 dog=44.9 horse=41.8 sheep=34.5 cow=39.9 elephant=49.5 bear=56.8 zebra=50.0 giraffe=53.7 backpack=8.1 umbrella=26.7 handbag=7.4 tie=19.2 suitcase=22.8 frisbee=47.9 skis=12.3 snowboard=19.9 sports ball=30.4 kite=29.8 baseball bat=15.5 baseball glove=23.9 skateboard=33.8 surfboard=24.6 tennis racket=30.4 bottle=21.1 wine glass=20.8 cup=27.5 fork=15.5 knife=6.8 spoon=5.8 bowl=26.6 banana=14.9 apple=9.9 sandwich=24.8 orange=20.3 broccoli=12.9 carrot=13.7 hot dog=23.3 pizza=34.3 donut=27.5 cake=22.2 chair=17.2 couch=33.7 potted plant=17.4 bed=35.0 dining table=20.8 toilet=47.4 tv=44.8 laptop=44.8 mouse=46.4 remote=13.7 keyboard=38.6 cell phone=21.6 microwave=39.5 oven=24.0 toaster=3.6 sink=26.6 refrigerator=41.1 book=7.1 clock=39.0 vase=25.2 scissors=25.2 teddy bear=29.7 hair drier=0.0 toothbrush=9.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.3 [Epoch 261][Batch 99], LR: 1.00E-05, Speed: 125.436 samples/sec, ObjLoss=20.111, BoxCenterLoss=14.442, BoxScaleLoss=4.652, ClassLoss=7.167 [Epoch 261][Batch 199], LR: 1.00E-05, Speed: 142.620 samples/sec, ObjLoss=20.110, BoxCenterLoss=14.442, BoxScaleLoss=4.651, ClassLoss=7.166 [Epoch 261][Batch 299], LR: 1.00E-05, Speed: 130.838 samples/sec, ObjLoss=20.110, BoxCenterLoss=14.442, BoxScaleLoss=4.651, ClassLoss=7.166 [Epoch 261][Batch 399], LR: 1.00E-05, Speed: 68.392 samples/sec, ObjLoss=20.109, BoxCenterLoss=14.442, BoxScaleLoss=4.651, ClassLoss=7.165 [Epoch 261][Batch 499], LR: 1.00E-05, Speed: 76.488 samples/sec, ObjLoss=20.108, BoxCenterLoss=14.442, BoxScaleLoss=4.651, ClassLoss=7.165 [Epoch 261][Batch 599], LR: 1.00E-05, Speed: 126.139 samples/sec, ObjLoss=20.107, BoxCenterLoss=14.442, BoxScaleLoss=4.651, ClassLoss=7.164 [Epoch 261][Batch 699], LR: 1.00E-05, Speed: 97.550 samples/sec, ObjLoss=20.107, BoxCenterLoss=14.442, BoxScaleLoss=4.650, ClassLoss=7.164 [Epoch 261][Batch 799], LR: 1.00E-05, Speed: 71.693 samples/sec, ObjLoss=20.106, BoxCenterLoss=14.442, BoxScaleLoss=4.650, ClassLoss=7.163 [Epoch 261][Batch 899], LR: 1.00E-05, Speed: 82.689 samples/sec, ObjLoss=20.105, BoxCenterLoss=14.442, BoxScaleLoss=4.650, ClassLoss=7.163 [Epoch 261][Batch 999], LR: 1.00E-05, Speed: 72.205 samples/sec, ObjLoss=20.104, BoxCenterLoss=14.442, BoxScaleLoss=4.650, ClassLoss=7.162 [Epoch 261][Batch 1099], LR: 1.00E-05, Speed: 135.634 samples/sec, ObjLoss=20.104, BoxCenterLoss=14.442, BoxScaleLoss=4.650, ClassLoss=7.162 [Epoch 261][Batch 1199], LR: 1.00E-05, Speed: 73.438 samples/sec, ObjLoss=20.103, BoxCenterLoss=14.442, BoxScaleLoss=4.650, ClassLoss=7.161 [Epoch 261][Batch 1299], LR: 1.00E-05, Speed: 74.790 samples/sec, ObjLoss=20.102, BoxCenterLoss=14.442, BoxScaleLoss=4.649, ClassLoss=7.160 [Epoch 261][Batch 1399], LR: 1.00E-05, Speed: 94.276 samples/sec, ObjLoss=20.101, BoxCenterLoss=14.441, BoxScaleLoss=4.649, ClassLoss=7.160 [Epoch 261][Batch 1499], LR: 1.00E-05, Speed: 71.069 samples/sec, ObjLoss=20.101, BoxCenterLoss=14.441, BoxScaleLoss=4.649, ClassLoss=7.159 [Epoch 261][Batch 1599], LR: 1.00E-05, Speed: 84.353 samples/sec, ObjLoss=20.100, BoxCenterLoss=14.441, BoxScaleLoss=4.649, ClassLoss=7.159 [Epoch 261][Batch 1699], LR: 1.00E-05, Speed: 73.697 samples/sec, ObjLoss=20.099, BoxCenterLoss=14.441, BoxScaleLoss=4.649, ClassLoss=7.158 [Epoch 261][Batch 1799], LR: 1.00E-05, Speed: 88.426 samples/sec, ObjLoss=20.098, BoxCenterLoss=14.441, BoxScaleLoss=4.648, ClassLoss=7.158 [Epoch 261] Training cost: 1686.960, ObjLoss=20.098, BoxCenterLoss=14.441, BoxScaleLoss=4.648, ClassLoss=7.157 [Epoch 261] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.385 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532 person=39.6 bicycle=19.5 car=27.4 motorcycle=32.1 airplane=50.8 bus=50.4 train=54.4 truck=26.0 boat=16.7 traffic light=17.5 fire hydrant=49.7 stop sign=53.3 parking meter=33.5 bench=16.4 bird=24.3 cat=51.6 dog=47.0 horse=41.8 sheep=34.9 cow=40.1 elephant=49.4 bear=56.6 zebra=52.0 giraffe=53.9 backpack=8.6 umbrella=27.4 handbag=7.3 tie=19.5 suitcase=22.0 frisbee=47.8 skis=13.0 snowboard=20.2 sports ball=30.7 kite=30.2 baseball bat=16.2 baseball glove=24.0 skateboard=33.3 surfboard=25.0 tennis racket=29.3 bottle=21.3 wine glass=20.7 cup=27.4 fork=15.7 knife=6.6 spoon=5.6 bowl=27.1 banana=15.5 apple=10.0 sandwich=24.2 orange=20.7 broccoli=13.9 carrot=13.6 hot dog=23.3 pizza=36.8 donut=27.9 cake=22.7 chair=17.4 couch=33.1 potted plant=17.3 bed=34.6 dining table=19.3 toilet=47.2 tv=45.3 laptop=44.8 mouse=44.8 remote=13.6 keyboard=39.5 cell phone=21.9 microwave=39.4 oven=24.2 toaster=4.2 sink=26.9 refrigerator=40.4 book=7.5 clock=38.4 vase=25.2 scissors=26.8 teddy bear=30.5 hair drier=0.0 toothbrush=11.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 262][Batch 99], LR: 1.00E-05, Speed: 119.276 samples/sec, ObjLoss=20.098, BoxCenterLoss=14.441, BoxScaleLoss=4.648, ClassLoss=7.157 [Epoch 262][Batch 199], LR: 1.00E-05, Speed: 129.648 samples/sec, ObjLoss=20.097, BoxCenterLoss=14.441, BoxScaleLoss=4.648, ClassLoss=7.156 [Epoch 262][Batch 299], LR: 1.00E-05, Speed: 94.100 samples/sec, ObjLoss=20.096, BoxCenterLoss=14.441, BoxScaleLoss=4.648, ClassLoss=7.156 [Epoch 262][Batch 399], LR: 1.00E-05, Speed: 112.685 samples/sec, ObjLoss=20.095, BoxCenterLoss=14.441, BoxScaleLoss=4.647, ClassLoss=7.155 [Epoch 262][Batch 499], LR: 1.00E-05, Speed: 59.705 samples/sec, ObjLoss=20.095, BoxCenterLoss=14.441, BoxScaleLoss=4.647, ClassLoss=7.155 [Epoch 262][Batch 599], LR: 1.00E-05, Speed: 120.113 samples/sec, ObjLoss=20.094, BoxCenterLoss=14.441, BoxScaleLoss=4.647, ClassLoss=7.154 [Epoch 262][Batch 699], LR: 1.00E-05, Speed: 59.586 samples/sec, ObjLoss=20.093, BoxCenterLoss=14.441, BoxScaleLoss=4.647, ClassLoss=7.153 [Epoch 262][Batch 799], LR: 1.00E-05, Speed: 68.095 samples/sec, ObjLoss=20.092, BoxCenterLoss=14.441, BoxScaleLoss=4.647, ClassLoss=7.153 [Epoch 262][Batch 899], LR: 1.00E-05, Speed: 68.711 samples/sec, ObjLoss=20.092, BoxCenterLoss=14.441, BoxScaleLoss=4.647, ClassLoss=7.152 [Epoch 262][Batch 999], LR: 1.00E-05, Speed: 91.766 samples/sec, ObjLoss=20.091, BoxCenterLoss=14.441, BoxScaleLoss=4.646, ClassLoss=7.152 [Epoch 262][Batch 1099], LR: 1.00E-05, Speed: 72.258 samples/sec, ObjLoss=20.090, BoxCenterLoss=14.440, BoxScaleLoss=4.646, ClassLoss=7.151 [Epoch 262][Batch 1199], LR: 1.00E-05, Speed: 51.478 samples/sec, ObjLoss=20.090, BoxCenterLoss=14.440, BoxScaleLoss=4.646, ClassLoss=7.151 [Epoch 262][Batch 1299], LR: 1.00E-05, Speed: 101.326 samples/sec, ObjLoss=20.089, BoxCenterLoss=14.440, BoxScaleLoss=4.646, ClassLoss=7.150 [Epoch 262][Batch 1399], LR: 1.00E-05, Speed: 143.144 samples/sec, ObjLoss=20.088, BoxCenterLoss=14.440, BoxScaleLoss=4.646, ClassLoss=7.150 [Epoch 262][Batch 1499], LR: 1.00E-05, Speed: 89.661 samples/sec, ObjLoss=20.087, BoxCenterLoss=14.440, BoxScaleLoss=4.645, ClassLoss=7.149 [Epoch 262][Batch 1599], LR: 1.00E-05, Speed: 90.488 samples/sec, ObjLoss=20.087, BoxCenterLoss=14.440, BoxScaleLoss=4.645, ClassLoss=7.148 [Epoch 262][Batch 1699], LR: 1.00E-05, Speed: 64.420 samples/sec, ObjLoss=20.086, BoxCenterLoss=14.440, BoxScaleLoss=4.645, ClassLoss=7.148 [Epoch 262][Batch 1799], LR: 1.00E-05, Speed: 125.360 samples/sec, ObjLoss=20.085, BoxCenterLoss=14.440, BoxScaleLoss=4.645, ClassLoss=7.147 [Epoch 262] Training cost: 1680.913, ObjLoss=20.085, BoxCenterLoss=14.440, BoxScaleLoss=4.645, ClassLoss=7.147 [Epoch 262] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 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 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.372 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 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531 person=39.6 bicycle=20.0 car=27.4 motorcycle=32.3 airplane=51.2 bus=50.7 train=54.6 truck=25.7 boat=16.5 traffic light=17.3 fire hydrant=48.8 stop sign=53.3 parking meter=33.3 bench=16.4 bird=23.6 cat=51.8 dog=46.0 horse=41.5 sheep=34.7 cow=40.0 elephant=49.6 bear=58.7 zebra=51.9 giraffe=54.2 backpack=8.2 umbrella=27.2 handbag=7.4 tie=19.5 suitcase=22.2 frisbee=48.2 skis=13.0 snowboard=21.0 sports ball=30.6 kite=30.1 baseball bat=15.7 baseball glove=24.0 skateboard=33.6 surfboard=25.2 tennis racket=29.6 bottle=21.1 wine glass=20.8 cup=27.5 fork=16.2 knife=6.4 spoon=6.0 bowl=26.9 banana=15.7 apple=10.1 sandwich=24.2 orange=20.9 broccoli=14.0 carrot=13.2 hot dog=23.7 pizza=37.5 donut=28.4 cake=22.4 chair=17.5 couch=33.5 potted plant=17.1 bed=33.2 dining table=18.7 toilet=47.5 tv=44.8 laptop=44.7 mouse=45.2 remote=13.6 keyboard=39.2 cell phone=21.7 microwave=38.1 oven=23.1 toaster=6.5 sink=27.6 refrigerator=39.9 book=7.4 clock=38.5 vase=25.0 scissors=25.5 teddy bear=30.7 hair drier=0.0 toothbrush=11.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 263][Batch 99], LR: 1.00E-05, Speed: 114.127 samples/sec, ObjLoss=20.084, BoxCenterLoss=14.440, BoxScaleLoss=4.645, ClassLoss=7.147 [Epoch 263][Batch 199], LR: 1.00E-05, Speed: 74.980 samples/sec, ObjLoss=20.084, BoxCenterLoss=14.440, BoxScaleLoss=4.644, ClassLoss=7.146 [Epoch 263][Batch 299], LR: 1.00E-05, Speed: 64.775 samples/sec, ObjLoss=20.083, BoxCenterLoss=14.440, BoxScaleLoss=4.644, ClassLoss=7.146 [Epoch 263][Batch 399], LR: 1.00E-05, Speed: 131.599 samples/sec, ObjLoss=20.082, BoxCenterLoss=14.440, BoxScaleLoss=4.644, ClassLoss=7.145 [Epoch 263][Batch 499], LR: 1.00E-05, Speed: 55.083 samples/sec, ObjLoss=20.081, BoxCenterLoss=14.440, BoxScaleLoss=4.644, ClassLoss=7.144 [Epoch 263][Batch 599], LR: 1.00E-05, Speed: 90.597 samples/sec, ObjLoss=20.081, BoxCenterLoss=14.440, BoxScaleLoss=4.644, ClassLoss=7.144 [Epoch 263][Batch 699], LR: 1.00E-05, Speed: 94.667 samples/sec, ObjLoss=20.080, BoxCenterLoss=14.440, BoxScaleLoss=4.644, ClassLoss=7.143 [Epoch 263][Batch 799], LR: 1.00E-05, Speed: 94.872 samples/sec, ObjLoss=20.079, BoxCenterLoss=14.440, BoxScaleLoss=4.643, ClassLoss=7.143 [Epoch 263][Batch 899], LR: 1.00E-05, Speed: 69.800 samples/sec, ObjLoss=20.078, BoxCenterLoss=14.440, BoxScaleLoss=4.643, ClassLoss=7.142 [Epoch 263][Batch 999], LR: 1.00E-05, Speed: 166.335 samples/sec, ObjLoss=20.078, BoxCenterLoss=14.439, BoxScaleLoss=4.643, ClassLoss=7.142 [Epoch 263][Batch 1099], LR: 1.00E-05, Speed: 65.269 samples/sec, ObjLoss=20.077, BoxCenterLoss=14.439, BoxScaleLoss=4.643, ClassLoss=7.141 [Epoch 263][Batch 1199], LR: 1.00E-05, Speed: 62.883 samples/sec, ObjLoss=20.076, BoxCenterLoss=14.439, BoxScaleLoss=4.643, ClassLoss=7.141 [Epoch 263][Batch 1299], LR: 1.00E-05, Speed: 67.571 samples/sec, ObjLoss=20.075, BoxCenterLoss=14.439, BoxScaleLoss=4.643, ClassLoss=7.140 [Epoch 263][Batch 1399], LR: 1.00E-05, Speed: 76.568 samples/sec, ObjLoss=20.075, BoxCenterLoss=14.439, BoxScaleLoss=4.642, ClassLoss=7.140 [Epoch 263][Batch 1499], LR: 1.00E-05, Speed: 52.373 samples/sec, ObjLoss=20.074, BoxCenterLoss=14.439, BoxScaleLoss=4.642, ClassLoss=7.139 [Epoch 263][Batch 1599], LR: 1.00E-05, Speed: 50.542 samples/sec, ObjLoss=20.073, BoxCenterLoss=14.439, BoxScaleLoss=4.642, ClassLoss=7.138 [Epoch 263][Batch 1699], LR: 1.00E-05, Speed: 95.091 samples/sec, ObjLoss=20.073, BoxCenterLoss=14.439, BoxScaleLoss=4.642, ClassLoss=7.138 [Epoch 263][Batch 1799], LR: 1.00E-05, Speed: 105.722 samples/sec, ObjLoss=20.072, BoxCenterLoss=14.439, BoxScaleLoss=4.642, ClassLoss=7.137 [Epoch 263] Training cost: 1719.067, ObjLoss=20.071, BoxCenterLoss=14.439, BoxScaleLoss=4.642, ClassLoss=7.137 [Epoch 263] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 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 ] = 0.423 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530 person=39.5 bicycle=19.7 car=27.4 motorcycle=31.9 airplane=50.8 bus=50.4 train=54.5 truck=25.8 boat=16.1 traffic light=17.4 fire hydrant=48.8 stop sign=53.6 parking meter=34.2 bench=16.6 bird=23.8 cat=51.4 dog=46.0 horse=42.1 sheep=35.6 cow=39.9 elephant=49.4 bear=58.1 zebra=51.0 giraffe=54.3 backpack=8.3 umbrella=27.1 handbag=7.4 tie=18.9 suitcase=22.9 frisbee=49.0 skis=12.8 snowboard=20.2 sports ball=30.3 kite=29.8 baseball bat=15.5 baseball glove=24.3 skateboard=33.7 surfboard=24.9 tennis racket=29.7 bottle=21.2 wine glass=20.9 cup=27.2 fork=16.1 knife=6.4 spoon=6.0 bowl=27.0 banana=15.5 apple=10.0 sandwich=24.0 orange=20.9 broccoli=13.1 carrot=13.8 hot dog=23.9 pizza=37.5 donut=28.5 cake=22.2 chair=17.3 couch=33.1 potted plant=17.5 bed=34.9 dining table=19.9 toilet=47.5 tv=44.6 laptop=45.0 mouse=45.5 remote=13.2 keyboard=39.2 cell phone=21.8 microwave=39.0 oven=24.3 toaster=6.8 sink=26.6 refrigerator=40.1 book=7.2 clock=38.7 vase=25.0 scissors=26.4 teddy bear=30.7 hair drier=0.0 toothbrush=10.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 264][Batch 99], LR: 1.00E-05, Speed: 106.253 samples/sec, ObjLoss=20.071, BoxCenterLoss=14.439, BoxScaleLoss=4.641, ClassLoss=7.137 [Epoch 264][Batch 199], LR: 1.00E-05, Speed: 75.319 samples/sec, ObjLoss=20.070, BoxCenterLoss=14.439, BoxScaleLoss=4.641, ClassLoss=7.136 [Epoch 264][Batch 299], LR: 1.00E-05, Speed: 84.557 samples/sec, ObjLoss=20.069, BoxCenterLoss=14.439, BoxScaleLoss=4.641, ClassLoss=7.136 [Epoch 264][Batch 399], LR: 1.00E-05, Speed: 141.091 samples/sec, ObjLoss=20.069, BoxCenterLoss=14.439, BoxScaleLoss=4.641, ClassLoss=7.135 [Epoch 264][Batch 499], LR: 1.00E-05, Speed: 52.223 samples/sec, ObjLoss=20.068, BoxCenterLoss=14.438, BoxScaleLoss=4.641, ClassLoss=7.135 [Epoch 264][Batch 599], LR: 1.00E-05, Speed: 81.988 samples/sec, ObjLoss=20.067, BoxCenterLoss=14.438, BoxScaleLoss=4.641, ClassLoss=7.134 [Epoch 264][Batch 699], LR: 1.00E-05, Speed: 77.779 samples/sec, ObjLoss=20.066, BoxCenterLoss=14.438, BoxScaleLoss=4.640, ClassLoss=7.133 [Epoch 264][Batch 799], LR: 1.00E-05, Speed: 81.804 samples/sec, ObjLoss=20.066, BoxCenterLoss=14.438, BoxScaleLoss=4.640, ClassLoss=7.133 [Epoch 264][Batch 899], LR: 1.00E-05, Speed: 81.122 samples/sec, ObjLoss=20.065, BoxCenterLoss=14.438, BoxScaleLoss=4.640, ClassLoss=7.132 [Epoch 264][Batch 999], LR: 1.00E-05, Speed: 55.810 samples/sec, ObjLoss=20.064, BoxCenterLoss=14.438, BoxScaleLoss=4.640, ClassLoss=7.132 [Epoch 264][Batch 1099], LR: 1.00E-05, Speed: 70.297 samples/sec, ObjLoss=20.064, BoxCenterLoss=14.438, BoxScaleLoss=4.640, ClassLoss=7.131 [Epoch 264][Batch 1199], LR: 1.00E-05, Speed: 79.226 samples/sec, ObjLoss=20.063, BoxCenterLoss=14.438, BoxScaleLoss=4.639, ClassLoss=7.131 [Epoch 264][Batch 1299], LR: 1.00E-05, Speed: 55.561 samples/sec, ObjLoss=20.062, BoxCenterLoss=14.438, BoxScaleLoss=4.639, ClassLoss=7.130 [Epoch 264][Batch 1399], LR: 1.00E-05, Speed: 78.782 samples/sec, ObjLoss=20.061, BoxCenterLoss=14.438, BoxScaleLoss=4.639, ClassLoss=7.130 [Epoch 264][Batch 1499], LR: 1.00E-05, Speed: 58.728 samples/sec, ObjLoss=20.061, BoxCenterLoss=14.438, BoxScaleLoss=4.639, ClassLoss=7.129 [Epoch 264][Batch 1599], LR: 1.00E-05, Speed: 93.423 samples/sec, ObjLoss=20.060, BoxCenterLoss=14.438, BoxScaleLoss=4.639, ClassLoss=7.129 [Epoch 264][Batch 1699], LR: 1.00E-05, Speed: 63.393 samples/sec, ObjLoss=20.059, BoxCenterLoss=14.438, BoxScaleLoss=4.639, ClassLoss=7.128 [Epoch 264][Batch 1799], LR: 1.00E-05, Speed: 84.091 samples/sec, ObjLoss=20.059, BoxCenterLoss=14.438, BoxScaleLoss=4.638, ClassLoss=7.127 [Epoch 264] Training cost: 1696.548, ObjLoss=20.058, BoxCenterLoss=14.438, BoxScaleLoss=4.638, ClassLoss=7.127 [Epoch 264] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.283 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.294 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.527 person=39.5 bicycle=19.6 car=27.2 motorcycle=32.1 airplane=50.3 bus=50.6 train=55.5 truck=25.1 boat=15.9 traffic light=17.4 fire hydrant=49.1 stop sign=53.0 parking meter=32.6 bench=15.8 bird=23.3 cat=51.6 dog=44.9 horse=42.0 sheep=35.0 cow=39.6 elephant=49.0 bear=57.6 zebra=50.0 giraffe=53.7 backpack=8.3 umbrella=26.9 handbag=7.4 tie=18.9 suitcase=23.2 frisbee=48.5 skis=12.5 snowboard=20.0 sports ball=30.6 kite=29.5 baseball bat=15.3 baseball glove=24.0 skateboard=33.6 surfboard=24.3 tennis racket=30.5 bottle=21.1 wine glass=20.5 cup=27.4 fork=15.6 knife=6.3 spoon=5.4 bowl=26.7 banana=15.2 apple=9.4 sandwich=24.2 orange=20.4 broccoli=13.5 carrot=13.3 hot dog=24.0 pizza=34.1 donut=28.1 cake=22.4 chair=17.5 couch=33.8 potted plant=17.1 bed=34.9 dining table=20.9 toilet=47.3 tv=44.3 laptop=45.5 mouse=45.1 remote=13.3 keyboard=39.4 cell phone=22.0 microwave=39.4 oven=24.9 toaster=5.5 sink=27.2 refrigerator=40.6 book=7.3 clock=38.8 vase=25.6 scissors=25.0 teddy bear=29.8 hair drier=0.0 toothbrush=10.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.3 [Epoch 265][Batch 99], LR: 1.00E-05, Speed: 80.988 samples/sec, ObjLoss=20.057, BoxCenterLoss=14.438, BoxScaleLoss=4.638, ClassLoss=7.127 [Epoch 265][Batch 199], LR: 1.00E-05, Speed: 64.247 samples/sec, ObjLoss=20.057, BoxCenterLoss=14.437, BoxScaleLoss=4.638, ClassLoss=7.126 [Epoch 265][Batch 299], LR: 1.00E-05, Speed: 81.214 samples/sec, ObjLoss=20.056, BoxCenterLoss=14.437, BoxScaleLoss=4.638, ClassLoss=7.126 [Epoch 265][Batch 399], LR: 1.00E-05, Speed: 62.836 samples/sec, ObjLoss=20.056, BoxCenterLoss=14.437, BoxScaleLoss=4.638, ClassLoss=7.125 [Epoch 265][Batch 499], LR: 1.00E-05, Speed: 68.830 samples/sec, ObjLoss=20.055, BoxCenterLoss=14.437, BoxScaleLoss=4.637, ClassLoss=7.125 [Epoch 265][Batch 599], LR: 1.00E-05, Speed: 73.153 samples/sec, ObjLoss=20.054, BoxCenterLoss=14.437, BoxScaleLoss=4.637, ClassLoss=7.124 [Epoch 265][Batch 699], LR: 1.00E-05, Speed: 72.135 samples/sec, ObjLoss=20.053, BoxCenterLoss=14.437, BoxScaleLoss=4.637, ClassLoss=7.124 [Epoch 265][Batch 799], LR: 1.00E-05, Speed: 50.224 samples/sec, ObjLoss=20.053, BoxCenterLoss=14.437, BoxScaleLoss=4.637, ClassLoss=7.123 [Epoch 265][Batch 899], LR: 1.00E-05, Speed: 95.455 samples/sec, ObjLoss=20.052, BoxCenterLoss=14.437, BoxScaleLoss=4.637, ClassLoss=7.122 [Epoch 265][Batch 999], LR: 1.00E-05, Speed: 77.750 samples/sec, ObjLoss=20.051, BoxCenterLoss=14.437, BoxScaleLoss=4.636, ClassLoss=7.122 [Epoch 265][Batch 1099], LR: 1.00E-05, Speed: 75.673 samples/sec, ObjLoss=20.050, BoxCenterLoss=14.437, BoxScaleLoss=4.636, ClassLoss=7.121 [Epoch 265][Batch 1199], LR: 1.00E-05, Speed: 77.691 samples/sec, ObjLoss=20.050, BoxCenterLoss=14.437, BoxScaleLoss=4.636, ClassLoss=7.121 [Epoch 265][Batch 1299], LR: 1.00E-05, Speed: 88.761 samples/sec, ObjLoss=20.049, BoxCenterLoss=14.437, BoxScaleLoss=4.636, ClassLoss=7.120 [Epoch 265][Batch 1399], LR: 1.00E-05, Speed: 134.775 samples/sec, ObjLoss=20.049, BoxCenterLoss=14.437, BoxScaleLoss=4.636, ClassLoss=7.120 [Epoch 265][Batch 1499], LR: 1.00E-05, Speed: 68.192 samples/sec, ObjLoss=20.048, BoxCenterLoss=14.437, BoxScaleLoss=4.636, ClassLoss=7.119 [Epoch 265][Batch 1599], LR: 1.00E-05, Speed: 98.220 samples/sec, ObjLoss=20.047, BoxCenterLoss=14.437, BoxScaleLoss=4.635, ClassLoss=7.119 [Epoch 265][Batch 1699], LR: 1.00E-05, Speed: 47.771 samples/sec, ObjLoss=20.046, BoxCenterLoss=14.437, BoxScaleLoss=4.635, ClassLoss=7.118 [Epoch 265][Batch 1799], LR: 1.00E-05, Speed: 67.157 samples/sec, ObjLoss=20.046, BoxCenterLoss=14.437, BoxScaleLoss=4.635, ClassLoss=7.118 [Epoch 265] Training cost: 1718.625, ObjLoss=20.045, BoxCenterLoss=14.437, BoxScaleLoss=4.635, ClassLoss=7.117 [Epoch 265] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.483 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 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 ] = 0.422 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.246 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.384 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529 person=39.5 bicycle=19.7 car=27.4 motorcycle=31.7 airplane=50.6 bus=50.2 train=54.2 truck=25.7 boat=16.6 traffic light=17.3 fire hydrant=49.1 stop sign=53.1 parking meter=34.0 bench=16.4 bird=23.2 cat=52.2 dog=46.5 horse=41.3 sheep=35.9 cow=39.7 elephant=50.2 bear=57.9 zebra=51.4 giraffe=54.2 backpack=8.4 umbrella=27.1 handbag=7.6 tie=19.2 suitcase=21.7 frisbee=48.8 skis=12.9 snowboard=19.9 sports ball=30.5 kite=29.8 baseball bat=15.0 baseball glove=23.7 skateboard=33.5 surfboard=24.5 tennis racket=29.3 bottle=21.2 wine glass=20.9 cup=27.4 fork=16.1 knife=6.3 spoon=5.8 bowl=26.6 banana=15.5 apple=9.9 sandwich=23.9 orange=20.8 broccoli=13.7 carrot=13.1 hot dog=23.5 pizza=37.9 donut=27.7 cake=22.7 chair=17.4 couch=32.3 potted plant=16.7 bed=32.7 dining table=17.1 toilet=47.4 tv=43.9 laptop=45.0 mouse=46.2 remote=13.5 keyboard=39.1 cell phone=21.3 microwave=40.0 oven=22.8 toaster=2.4 sink=27.8 refrigerator=40.0 book=7.4 clock=38.2 vase=25.4 scissors=26.3 teddy bear=31.2 hair drier=0.0 toothbrush=11.9 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 266][Batch 99], LR: 1.00E-05, Speed: 144.088 samples/sec, ObjLoss=20.045, BoxCenterLoss=14.436, BoxScaleLoss=4.635, ClassLoss=7.117 [Epoch 266][Batch 199], LR: 1.00E-05, Speed: 121.923 samples/sec, ObjLoss=20.044, BoxCenterLoss=14.436, BoxScaleLoss=4.635, ClassLoss=7.116 [Epoch 266][Batch 299], LR: 1.00E-05, Speed: 116.142 samples/sec, ObjLoss=20.043, BoxCenterLoss=14.436, BoxScaleLoss=4.634, ClassLoss=7.116 [Epoch 266][Batch 399], LR: 1.00E-05, Speed: 73.833 samples/sec, ObjLoss=20.042, BoxCenterLoss=14.436, BoxScaleLoss=4.634, ClassLoss=7.115 [Epoch 266][Batch 499], LR: 1.00E-05, Speed: 119.199 samples/sec, ObjLoss=20.042, BoxCenterLoss=14.436, BoxScaleLoss=4.634, ClassLoss=7.115 [Epoch 266][Batch 599], LR: 1.00E-05, Speed: 61.492 samples/sec, ObjLoss=20.041, BoxCenterLoss=14.436, BoxScaleLoss=4.634, ClassLoss=7.114 [Epoch 266][Batch 699], LR: 1.00E-05, Speed: 150.690 samples/sec, ObjLoss=20.040, BoxCenterLoss=14.436, BoxScaleLoss=4.634, ClassLoss=7.113 [Epoch 266][Batch 799], LR: 1.00E-05, Speed: 84.234 samples/sec, ObjLoss=20.040, BoxCenterLoss=14.436, BoxScaleLoss=4.633, ClassLoss=7.113 [Epoch 266][Batch 899], LR: 1.00E-05, Speed: 140.807 samples/sec, ObjLoss=20.039, BoxCenterLoss=14.436, BoxScaleLoss=4.633, ClassLoss=7.112 [Epoch 266][Batch 999], LR: 1.00E-05, Speed: 61.392 samples/sec, ObjLoss=20.038, BoxCenterLoss=14.436, BoxScaleLoss=4.633, ClassLoss=7.112 [Epoch 266][Batch 1099], LR: 1.00E-05, Speed: 114.416 samples/sec, ObjLoss=20.037, BoxCenterLoss=14.436, BoxScaleLoss=4.633, ClassLoss=7.111 [Epoch 266][Batch 1199], LR: 1.00E-05, Speed: 69.022 samples/sec, ObjLoss=20.037, BoxCenterLoss=14.436, BoxScaleLoss=4.633, ClassLoss=7.111 [Epoch 266][Batch 1299], LR: 1.00E-05, Speed: 110.414 samples/sec, ObjLoss=20.036, BoxCenterLoss=14.436, BoxScaleLoss=4.632, ClassLoss=7.110 [Epoch 266][Batch 1399], LR: 1.00E-05, Speed: 78.954 samples/sec, ObjLoss=20.035, BoxCenterLoss=14.435, BoxScaleLoss=4.632, ClassLoss=7.110 [Epoch 266][Batch 1499], LR: 1.00E-05, Speed: 78.329 samples/sec, ObjLoss=20.035, BoxCenterLoss=14.435, BoxScaleLoss=4.632, ClassLoss=7.109 [Epoch 266][Batch 1599], LR: 1.00E-05, Speed: 117.582 samples/sec, ObjLoss=20.034, BoxCenterLoss=14.435, BoxScaleLoss=4.632, ClassLoss=7.109 [Epoch 266][Batch 1699], LR: 1.00E-05, Speed: 100.354 samples/sec, ObjLoss=20.033, BoxCenterLoss=14.435, BoxScaleLoss=4.632, ClassLoss=7.108 [Epoch 266][Batch 1799], LR: 1.00E-05, Speed: 146.443 samples/sec, ObjLoss=20.033, BoxCenterLoss=14.435, BoxScaleLoss=4.631, ClassLoss=7.107 [Epoch 266] Training cost: 1718.153, ObjLoss=20.032, BoxCenterLoss=14.435, BoxScaleLoss=4.631, ClassLoss=7.107 [Epoch 266] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.299 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 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.365 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531 person=39.6 bicycle=19.4 car=27.4 motorcycle=31.4 airplane=51.1 bus=50.9 train=55.1 truck=25.5 boat=16.6 traffic light=17.2 fire hydrant=49.0 stop sign=53.6 parking meter=34.4 bench=16.5 bird=23.6 cat=53.0 dog=45.5 horse=41.8 sheep=34.9 cow=39.9 elephant=49.9 bear=58.3 zebra=51.3 giraffe=54.4 backpack=8.3 umbrella=27.7 handbag=7.6 tie=19.0 suitcase=22.5 frisbee=48.9 skis=12.7 snowboard=20.0 sports ball=30.9 kite=29.4 baseball bat=15.9 baseball glove=24.4 skateboard=34.0 surfboard=25.1 tennis racket=29.6 bottle=21.2 wine glass=21.3 cup=27.8 fork=16.4 knife=6.3 spoon=6.0 bowl=26.6 banana=15.9 apple=9.3 sandwich=24.2 orange=20.9 broccoli=13.3 carrot=13.7 hot dog=23.3 pizza=37.2 donut=29.3 cake=22.5 chair=17.5 couch=33.5 potted plant=17.9 bed=34.2 dining table=19.0 toilet=47.0 tv=44.5 laptop=44.9 mouse=45.2 remote=13.7 keyboard=39.3 cell phone=21.6 microwave=38.8 oven=23.9 toaster=9.6 sink=27.0 refrigerator=40.9 book=7.5 clock=38.6 vase=25.1 scissors=25.3 teddy bear=30.4 hair drier=0.0 toothbrush=12.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6 [Epoch 267][Batch 99], LR: 1.00E-05, Speed: 101.761 samples/sec, ObjLoss=20.032, BoxCenterLoss=14.435, BoxScaleLoss=4.631, ClassLoss=7.107 [Epoch 267][Batch 199], LR: 1.00E-05, Speed: 128.748 samples/sec, ObjLoss=20.031, BoxCenterLoss=14.435, BoxScaleLoss=4.631, ClassLoss=7.106 [Epoch 267][Batch 299], LR: 1.00E-05, Speed: 79.906 samples/sec, ObjLoss=20.030, BoxCenterLoss=14.435, BoxScaleLoss=4.631, ClassLoss=7.106 [Epoch 267][Batch 399], LR: 1.00E-05, Speed: 89.649 samples/sec, ObjLoss=20.030, BoxCenterLoss=14.435, BoxScaleLoss=4.631, ClassLoss=7.105 [Epoch 267][Batch 499], LR: 1.00E-05, Speed: 81.786 samples/sec, ObjLoss=20.029, BoxCenterLoss=14.435, BoxScaleLoss=4.631, ClassLoss=7.105 [Epoch 267][Batch 599], LR: 1.00E-05, Speed: 86.776 samples/sec, ObjLoss=20.028, BoxCenterLoss=14.435, BoxScaleLoss=4.630, ClassLoss=7.104 [Epoch 267][Batch 699], LR: 1.00E-05, Speed: 47.297 samples/sec, ObjLoss=20.027, BoxCenterLoss=14.435, BoxScaleLoss=4.630, ClassLoss=7.104 [Epoch 267][Batch 799], LR: 1.00E-05, Speed: 111.350 samples/sec, ObjLoss=20.027, BoxCenterLoss=14.435, BoxScaleLoss=4.630, ClassLoss=7.103 [Epoch 267][Batch 899], LR: 1.00E-05, Speed: 70.699 samples/sec, ObjLoss=20.026, BoxCenterLoss=14.434, BoxScaleLoss=4.630, ClassLoss=7.103 [Epoch 267][Batch 999], LR: 1.00E-05, Speed: 76.853 samples/sec, ObjLoss=20.025, BoxCenterLoss=14.434, BoxScaleLoss=4.630, ClassLoss=7.102 [Epoch 267][Batch 1099], LR: 1.00E-05, Speed: 76.479 samples/sec, ObjLoss=20.024, BoxCenterLoss=14.434, BoxScaleLoss=4.629, ClassLoss=7.102 [Epoch 267][Batch 1199], LR: 1.00E-05, Speed: 69.714 samples/sec, ObjLoss=20.024, BoxCenterLoss=14.434, BoxScaleLoss=4.629, ClassLoss=7.101 [Epoch 267][Batch 1299], LR: 1.00E-05, Speed: 82.169 samples/sec, ObjLoss=20.023, BoxCenterLoss=14.434, BoxScaleLoss=4.629, ClassLoss=7.100 [Epoch 267][Batch 1399], LR: 1.00E-05, Speed: 69.581 samples/sec, ObjLoss=20.022, BoxCenterLoss=14.434, BoxScaleLoss=4.629, ClassLoss=7.100 [Epoch 267][Batch 1499], LR: 1.00E-05, Speed: 93.973 samples/sec, ObjLoss=20.022, BoxCenterLoss=14.434, BoxScaleLoss=4.629, ClassLoss=7.099 [Epoch 267][Batch 1599], LR: 1.00E-05, Speed: 58.590 samples/sec, ObjLoss=20.021, BoxCenterLoss=14.434, BoxScaleLoss=4.629, ClassLoss=7.099 [Epoch 267][Batch 1699], LR: 1.00E-05, Speed: 69.649 samples/sec, ObjLoss=20.020, BoxCenterLoss=14.434, BoxScaleLoss=4.628, ClassLoss=7.098 [Epoch 267][Batch 1799], LR: 1.00E-05, Speed: 109.860 samples/sec, ObjLoss=20.020, BoxCenterLoss=14.434, BoxScaleLoss=4.628, ClassLoss=7.098 [Epoch 267] Training cost: 1682.702, ObjLoss=20.019, BoxCenterLoss=14.434, BoxScaleLoss=4.628, ClassLoss=7.098 [Epoch 267] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 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 ] = 0.388 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.534 person=39.5 bicycle=19.6 car=27.2 motorcycle=31.5 airplane=51.8 bus=50.8 train=55.4 truck=25.6 boat=16.2 traffic light=17.3 fire hydrant=49.6 stop sign=53.5 parking meter=34.7 bench=16.4 bird=23.3 cat=52.2 dog=46.2 horse=42.0 sheep=35.6 cow=39.5 elephant=49.4 bear=57.9 zebra=50.4 giraffe=53.7 backpack=8.5 umbrella=27.6 handbag=7.4 tie=19.4 suitcase=22.5 frisbee=48.5 skis=12.5 snowboard=20.4 sports ball=30.6 kite=29.9 baseball bat=15.6 baseball glove=24.3 skateboard=34.1 surfboard=24.7 tennis racket=30.0 bottle=21.1 wine glass=20.8 cup=27.2 fork=16.1 knife=6.3 spoon=5.8 bowl=26.9 banana=15.3 apple=10.0 sandwich=24.3 orange=20.8 broccoli=13.6 carrot=13.1 hot dog=23.6 pizza=36.8 donut=28.5 cake=22.3 chair=17.3 couch=33.9 potted plant=17.1 bed=35.3 dining table=21.0 toilet=47.8 tv=45.2 laptop=45.0 mouse=45.6 remote=13.3 keyboard=38.8 cell phone=21.8 microwave=39.3 oven=25.0 toaster=3.6 sink=27.7 refrigerator=40.9 book=7.6 clock=38.2 vase=24.9 scissors=26.1 teddy bear=30.6 hair drier=0.0 toothbrush=10.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6 [Epoch 268][Batch 99], LR: 1.00E-05, Speed: 47.977 samples/sec, ObjLoss=20.019, BoxCenterLoss=14.434, BoxScaleLoss=4.628, ClassLoss=7.097 [Epoch 268][Batch 199], LR: 1.00E-05, Speed: 79.173 samples/sec, ObjLoss=20.018, BoxCenterLoss=14.434, BoxScaleLoss=4.628, ClassLoss=7.097 [Epoch 268][Batch 299], LR: 1.00E-05, Speed: 129.604 samples/sec, ObjLoss=20.017, BoxCenterLoss=14.434, BoxScaleLoss=4.628, ClassLoss=7.096 [Epoch 268][Batch 399], LR: 1.00E-05, Speed: 84.671 samples/sec, ObjLoss=20.017, BoxCenterLoss=14.434, BoxScaleLoss=4.628, ClassLoss=7.096 [Epoch 268][Batch 499], LR: 1.00E-05, Speed: 58.224 samples/sec, ObjLoss=20.016, BoxCenterLoss=14.434, BoxScaleLoss=4.627, ClassLoss=7.095 [Epoch 268][Batch 599], LR: 1.00E-05, Speed: 69.848 samples/sec, ObjLoss=20.015, BoxCenterLoss=14.434, BoxScaleLoss=4.627, ClassLoss=7.095 [Epoch 268][Batch 699], LR: 1.00E-05, Speed: 91.298 samples/sec, ObjLoss=20.014, BoxCenterLoss=14.434, BoxScaleLoss=4.627, ClassLoss=7.094 [Epoch 268][Batch 799], LR: 1.00E-05, Speed: 181.142 samples/sec, ObjLoss=20.014, BoxCenterLoss=14.434, BoxScaleLoss=4.627, ClassLoss=7.094 [Epoch 268][Batch 899], LR: 1.00E-05, Speed: 103.415 samples/sec, ObjLoss=20.013, BoxCenterLoss=14.433, BoxScaleLoss=4.627, ClassLoss=7.093 [Epoch 268][Batch 999], LR: 1.00E-05, Speed: 74.058 samples/sec, ObjLoss=20.012, BoxCenterLoss=14.433, BoxScaleLoss=4.627, ClassLoss=7.092 [Epoch 268][Batch 1099], LR: 1.00E-05, Speed: 61.409 samples/sec, ObjLoss=20.012, BoxCenterLoss=14.433, BoxScaleLoss=4.626, ClassLoss=7.092 [Epoch 268][Batch 1199], LR: 1.00E-05, Speed: 104.133 samples/sec, ObjLoss=20.011, BoxCenterLoss=14.433, BoxScaleLoss=4.626, ClassLoss=7.091 [Epoch 268][Batch 1299], LR: 1.00E-05, Speed: 145.309 samples/sec, ObjLoss=20.010, BoxCenterLoss=14.433, BoxScaleLoss=4.626, ClassLoss=7.091 [Epoch 268][Batch 1399], LR: 1.00E-05, Speed: 63.699 samples/sec, ObjLoss=20.010, BoxCenterLoss=14.433, BoxScaleLoss=4.626, ClassLoss=7.090 [Epoch 268][Batch 1499], LR: 1.00E-05, Speed: 38.746 samples/sec, ObjLoss=20.009, BoxCenterLoss=14.433, BoxScaleLoss=4.626, ClassLoss=7.090 [Epoch 268][Batch 1599], LR: 1.00E-05, Speed: 97.552 samples/sec, ObjLoss=20.008, BoxCenterLoss=14.433, BoxScaleLoss=4.625, ClassLoss=7.089 [Epoch 268][Batch 1699], LR: 1.00E-05, Speed: 69.116 samples/sec, ObjLoss=20.007, BoxCenterLoss=14.433, BoxScaleLoss=4.625, ClassLoss=7.089 [Epoch 268][Batch 1799], LR: 1.00E-05, Speed: 112.210 samples/sec, ObjLoss=20.007, BoxCenterLoss=14.433, BoxScaleLoss=4.625, ClassLoss=7.088 [Epoch 268] Training cost: 1732.341, ObjLoss=20.006, BoxCenterLoss=14.433, BoxScaleLoss=4.625, ClassLoss=7.088 [Epoch 268] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.296 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.126 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 ] = 0.423 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532 person=39.5 bicycle=20.0 car=27.2 motorcycle=31.9 airplane=51.6 bus=51.2 train=54.7 truck=25.9 boat=16.5 traffic light=17.4 fire hydrant=49.1 stop sign=53.0 parking meter=34.3 bench=16.6 bird=23.3 cat=51.4 dog=46.7 horse=41.8 sheep=35.1 cow=39.7 elephant=50.2 bear=57.1 zebra=50.4 giraffe=53.8 backpack=8.4 umbrella=27.6 handbag=7.3 tie=19.1 suitcase=23.3 frisbee=48.0 skis=12.7 snowboard=21.2 sports ball=30.7 kite=29.7 baseball bat=15.8 baseball glove=24.1 skateboard=33.9 surfboard=24.7 tennis racket=29.9 bottle=21.2 wine glass=20.7 cup=27.5 fork=16.2 knife=6.5 spoon=5.5 bowl=26.7 banana=15.0 apple=10.0 sandwich=24.5 orange=20.6 broccoli=13.5 carrot=13.2 hot dog=24.0 pizza=36.5 donut=28.6 cake=21.9 chair=17.3 couch=33.6 potted plant=17.1 bed=35.1 dining table=19.7 toilet=48.1 tv=45.7 laptop=45.4 mouse=45.5 remote=13.3 keyboard=38.7 cell phone=21.7 microwave=38.6 oven=24.4 toaster=5.4 sink=27.3 refrigerator=40.9 book=7.5 clock=38.9 vase=25.1 scissors=24.9 teddy bear=29.5 hair drier=0.0 toothbrush=11.1 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 269][Batch 99], LR: 1.00E-05, Speed: 107.164 samples/sec, ObjLoss=20.006, BoxCenterLoss=14.433, BoxScaleLoss=4.625, ClassLoss=7.088 [Epoch 269][Batch 199], LR: 1.00E-05, Speed: 113.306 samples/sec, ObjLoss=20.005, BoxCenterLoss=14.433, BoxScaleLoss=4.625, ClassLoss=7.087 [Epoch 269][Batch 299], LR: 1.00E-05, Speed: 118.514 samples/sec, ObjLoss=20.005, BoxCenterLoss=14.433, BoxScaleLoss=4.625, ClassLoss=7.086 [Epoch 269][Batch 399], LR: 1.00E-05, Speed: 83.198 samples/sec, ObjLoss=20.004, BoxCenterLoss=14.433, BoxScaleLoss=4.624, ClassLoss=7.086 [Epoch 269][Batch 499], LR: 1.00E-05, Speed: 39.424 samples/sec, ObjLoss=20.003, BoxCenterLoss=14.433, BoxScaleLoss=4.624, ClassLoss=7.085 [Epoch 269][Batch 599], LR: 1.00E-05, Speed: 62.779 samples/sec, ObjLoss=20.002, BoxCenterLoss=14.433, BoxScaleLoss=4.624, ClassLoss=7.085 [Epoch 269][Batch 699], LR: 1.00E-05, Speed: 98.571 samples/sec, ObjLoss=20.002, BoxCenterLoss=14.432, BoxScaleLoss=4.624, ClassLoss=7.084 [Epoch 269][Batch 799], LR: 1.00E-05, Speed: 137.052 samples/sec, ObjLoss=20.001, BoxCenterLoss=14.432, BoxScaleLoss=4.624, ClassLoss=7.084 [Epoch 269][Batch 899], LR: 1.00E-05, Speed: 78.013 samples/sec, ObjLoss=20.000, BoxCenterLoss=14.432, BoxScaleLoss=4.624, ClassLoss=7.083 [Epoch 269][Batch 999], LR: 1.00E-05, Speed: 68.455 samples/sec, ObjLoss=19.999, BoxCenterLoss=14.432, BoxScaleLoss=4.623, ClassLoss=7.083 [Epoch 269][Batch 1099], LR: 1.00E-05, Speed: 67.373 samples/sec, ObjLoss=19.999, BoxCenterLoss=14.432, BoxScaleLoss=4.623, ClassLoss=7.082 [Epoch 269][Batch 1199], LR: 1.00E-05, Speed: 48.255 samples/sec, ObjLoss=19.998, BoxCenterLoss=14.432, BoxScaleLoss=4.623, ClassLoss=7.082 [Epoch 269][Batch 1299], LR: 1.00E-05, Speed: 53.610 samples/sec, ObjLoss=19.997, BoxCenterLoss=14.432, BoxScaleLoss=4.623, ClassLoss=7.081 [Epoch 269][Batch 1399], LR: 1.00E-05, Speed: 65.913 samples/sec, ObjLoss=19.997, BoxCenterLoss=14.432, BoxScaleLoss=4.622, ClassLoss=7.081 [Epoch 269][Batch 1499], LR: 1.00E-05, Speed: 46.530 samples/sec, ObjLoss=19.996, BoxCenterLoss=14.432, BoxScaleLoss=4.622, ClassLoss=7.080 [Epoch 269][Batch 1599], LR: 1.00E-05, Speed: 74.598 samples/sec, ObjLoss=19.995, BoxCenterLoss=14.432, BoxScaleLoss=4.622, ClassLoss=7.080 [Epoch 269][Batch 1699], LR: 1.00E-05, Speed: 73.009 samples/sec, ObjLoss=19.995, BoxCenterLoss=14.432, BoxScaleLoss=4.622, ClassLoss=7.079 [Epoch 269][Batch 1799], LR: 1.00E-05, Speed: 101.762 samples/sec, ObjLoss=19.994, BoxCenterLoss=14.432, BoxScaleLoss=4.622, ClassLoss=7.079 [Epoch 269] Training cost: 1620.642, ObjLoss=19.994, BoxCenterLoss=14.432, BoxScaleLoss=4.622, ClassLoss=7.078 [Epoch 269] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 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 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531 person=39.5 bicycle=19.7 car=27.5 motorcycle=31.4 airplane=51.3 bus=50.8 train=55.1 truck=25.7 boat=16.9 traffic light=17.4 fire hydrant=49.5 stop sign=53.0 parking meter=33.5 bench=16.6 bird=23.8 cat=51.7 dog=46.1 horse=41.5 sheep=35.5 cow=40.1 elephant=50.1 bear=57.9 zebra=51.3 giraffe=53.7 backpack=8.2 umbrella=27.3 handbag=7.5 tie=19.4 suitcase=22.3 frisbee=48.2 skis=12.5 snowboard=19.9 sports ball=30.8 kite=30.3 baseball bat=15.8 baseball glove=24.1 skateboard=34.2 surfboard=25.0 tennis racket=30.1 bottle=21.3 wine glass=20.9 cup=27.5 fork=16.1 knife=6.5 spoon=5.9 bowl=27.3 banana=15.2 apple=10.0 sandwich=24.4 orange=20.5 broccoli=13.7 carrot=13.4 hot dog=23.5 pizza=36.9 donut=28.9 cake=22.8 chair=17.2 couch=33.2 potted plant=17.2 bed=33.0 dining table=18.7 toilet=46.9 tv=45.6 laptop=44.8 mouse=45.8 remote=14.0 keyboard=39.1 cell phone=21.5 microwave=37.8 oven=24.9 toaster=9.5 sink=26.9 refrigerator=40.6 book=7.4 clock=38.5 vase=25.9 scissors=25.5 teddy bear=30.6 hair drier=0.0 toothbrush=11.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6 [Epoch 270][Batch 99], LR: 1.00E-05, Speed: 131.054 samples/sec, ObjLoss=19.993, BoxCenterLoss=14.432, BoxScaleLoss=4.622, ClassLoss=7.078 [Epoch 270][Batch 199], LR: 1.00E-05, Speed: 67.916 samples/sec, ObjLoss=19.992, BoxCenterLoss=14.432, BoxScaleLoss=4.621, ClassLoss=7.077 [Epoch 270][Batch 299], LR: 1.00E-05, Speed: 67.756 samples/sec, ObjLoss=19.992, BoxCenterLoss=14.432, BoxScaleLoss=4.621, ClassLoss=7.077 [Epoch 270][Batch 399], LR: 1.00E-05, Speed: 79.461 samples/sec, ObjLoss=19.991, BoxCenterLoss=14.431, BoxScaleLoss=4.621, ClassLoss=7.076 [Epoch 270][Batch 499], LR: 1.00E-05, Speed: 63.012 samples/sec, ObjLoss=19.990, BoxCenterLoss=14.431, BoxScaleLoss=4.621, ClassLoss=7.076 [Epoch 270][Batch 599], LR: 1.00E-05, Speed: 139.100 samples/sec, ObjLoss=19.990, BoxCenterLoss=14.431, BoxScaleLoss=4.621, ClassLoss=7.075 [Epoch 270][Batch 699], LR: 1.00E-05, Speed: 53.595 samples/sec, ObjLoss=19.989, BoxCenterLoss=14.431, BoxScaleLoss=4.621, ClassLoss=7.075 [Epoch 270][Batch 799], LR: 1.00E-05, Speed: 79.503 samples/sec, ObjLoss=19.988, BoxCenterLoss=14.431, BoxScaleLoss=4.620, ClassLoss=7.074 [Epoch 270][Batch 899], LR: 1.00E-05, Speed: 94.627 samples/sec, ObjLoss=19.987, BoxCenterLoss=14.431, BoxScaleLoss=4.620, ClassLoss=7.074 [Epoch 270][Batch 999], LR: 1.00E-05, Speed: 86.238 samples/sec, ObjLoss=19.987, BoxCenterLoss=14.431, BoxScaleLoss=4.620, ClassLoss=7.073 [Epoch 270][Batch 1099], LR: 1.00E-05, Speed: 69.786 samples/sec, ObjLoss=19.986, BoxCenterLoss=14.431, BoxScaleLoss=4.620, ClassLoss=7.073 [Epoch 270][Batch 1199], LR: 1.00E-05, Speed: 56.388 samples/sec, ObjLoss=19.985, BoxCenterLoss=14.431, BoxScaleLoss=4.620, ClassLoss=7.072 [Epoch 270][Batch 1299], LR: 1.00E-05, Speed: 60.155 samples/sec, ObjLoss=19.984, BoxCenterLoss=14.431, BoxScaleLoss=4.620, ClassLoss=7.072 [Epoch 270][Batch 1399], LR: 1.00E-05, Speed: 88.239 samples/sec, ObjLoss=19.984, BoxCenterLoss=14.431, BoxScaleLoss=4.619, ClassLoss=7.071 [Epoch 270][Batch 1499], LR: 1.00E-05, Speed: 53.095 samples/sec, ObjLoss=19.983, BoxCenterLoss=14.431, BoxScaleLoss=4.619, ClassLoss=7.071 [Epoch 270][Batch 1599], LR: 1.00E-05, Speed: 112.303 samples/sec, ObjLoss=19.982, BoxCenterLoss=14.431, BoxScaleLoss=4.619, ClassLoss=7.070 [Epoch 270][Batch 1699], LR: 1.00E-05, Speed: 85.215 samples/sec, ObjLoss=19.982, BoxCenterLoss=14.431, BoxScaleLoss=4.619, ClassLoss=7.070 [Epoch 270][Batch 1799], LR: 1.00E-05, Speed: 82.397 samples/sec, ObjLoss=19.981, BoxCenterLoss=14.430, BoxScaleLoss=4.619, ClassLoss=7.069 [Epoch 270] Training cost: 1744.783, ObjLoss=19.981, BoxCenterLoss=14.430, BoxScaleLoss=4.619, ClassLoss=7.069 [Epoch 270] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.484 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.126 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532 person=39.4 bicycle=20.1 car=27.5 motorcycle=31.5 airplane=51.1 bus=50.4 train=54.4 truck=25.9 boat=16.7 traffic light=17.2 fire hydrant=49.4 stop sign=54.0 parking meter=34.1 bench=16.2 bird=23.3 cat=52.0 dog=46.1 horse=41.6 sheep=35.3 cow=40.3 elephant=49.5 bear=56.4 zebra=50.7 giraffe=54.0 backpack=8.2 umbrella=27.1 handbag=7.6 tie=18.9 suitcase=22.1 frisbee=49.4 skis=12.8 snowboard=21.5 sports ball=30.8 kite=30.2 baseball bat=15.6 baseball glove=24.4 skateboard=34.1 surfboard=24.9 tennis racket=29.7 bottle=21.4 wine glass=21.2 cup=27.7 fork=16.0 knife=6.4 spoon=5.9 bowl=26.8 banana=15.9 apple=10.3 sandwich=24.2 orange=20.6 broccoli=13.2 carrot=13.0 hot dog=23.2 pizza=38.2 donut=28.7 cake=22.0 chair=17.2 couch=32.6 potted plant=17.0 bed=33.4 dining table=17.3 toilet=47.8 tv=44.3 laptop=44.1 mouse=46.0 remote=13.6 keyboard=39.5 cell phone=21.6 microwave=39.3 oven=23.4 toaster=5.5 sink=27.5 refrigerator=41.1 book=7.6 clock=38.5 vase=25.4 scissors=25.4 teddy bear=32.1 hair drier=0.0 toothbrush=10.7 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 271][Batch 99], LR: 1.00E-05, Speed: 131.604 samples/sec, ObjLoss=19.980, BoxCenterLoss=14.430, BoxScaleLoss=4.618, ClassLoss=7.068 [Epoch 271][Batch 199], LR: 1.00E-05, Speed: 89.217 samples/sec, ObjLoss=19.980, BoxCenterLoss=14.430, BoxScaleLoss=4.618, ClassLoss=7.068 [Epoch 271][Batch 299], LR: 1.00E-05, Speed: 82.470 samples/sec, ObjLoss=19.979, BoxCenterLoss=14.430, BoxScaleLoss=4.618, ClassLoss=7.067 [Epoch 271][Batch 399], LR: 1.00E-05, Speed: 116.018 samples/sec, ObjLoss=19.978, BoxCenterLoss=14.430, BoxScaleLoss=4.618, ClassLoss=7.067 [Epoch 271][Batch 499], LR: 1.00E-05, Speed: 80.502 samples/sec, ObjLoss=19.977, BoxCenterLoss=14.430, BoxScaleLoss=4.618, ClassLoss=7.066 [Epoch 271][Batch 599], LR: 1.00E-05, Speed: 68.204 samples/sec, ObjLoss=19.977, BoxCenterLoss=14.430, BoxScaleLoss=4.617, ClassLoss=7.066 [Epoch 271][Batch 699], LR: 1.00E-05, Speed: 79.214 samples/sec, ObjLoss=19.976, BoxCenterLoss=14.430, BoxScaleLoss=4.617, ClassLoss=7.065 [Epoch 271][Batch 799], LR: 1.00E-05, Speed: 121.885 samples/sec, ObjLoss=19.975, BoxCenterLoss=14.430, BoxScaleLoss=4.617, ClassLoss=7.065 [Epoch 271][Batch 899], LR: 1.00E-05, Speed: 68.227 samples/sec, ObjLoss=19.974, BoxCenterLoss=14.430, BoxScaleLoss=4.617, ClassLoss=7.064 [Epoch 271][Batch 999], LR: 1.00E-05, Speed: 55.182 samples/sec, ObjLoss=19.974, BoxCenterLoss=14.430, BoxScaleLoss=4.617, ClassLoss=7.064 [Epoch 271][Batch 1099], LR: 1.00E-05, Speed: 71.812 samples/sec, ObjLoss=19.973, BoxCenterLoss=14.430, BoxScaleLoss=4.617, ClassLoss=7.063 [Epoch 271][Batch 1199], LR: 1.00E-05, Speed: 58.970 samples/sec, ObjLoss=19.972, BoxCenterLoss=14.430, BoxScaleLoss=4.616, ClassLoss=7.062 [Epoch 271][Batch 1299], LR: 1.00E-05, Speed: 94.386 samples/sec, ObjLoss=19.972, BoxCenterLoss=14.430, BoxScaleLoss=4.616, ClassLoss=7.062 [Epoch 271][Batch 1399], LR: 1.00E-05, Speed: 78.700 samples/sec, ObjLoss=19.971, BoxCenterLoss=14.429, BoxScaleLoss=4.616, ClassLoss=7.061 [Epoch 271][Batch 1499], LR: 1.00E-05, Speed: 112.956 samples/sec, ObjLoss=19.970, BoxCenterLoss=14.429, BoxScaleLoss=4.616, ClassLoss=7.061 [Epoch 271][Batch 1599], LR: 1.00E-05, Speed: 81.697 samples/sec, ObjLoss=19.970, BoxCenterLoss=14.429, BoxScaleLoss=4.616, ClassLoss=7.060 [Epoch 271][Batch 1699], LR: 1.00E-05, Speed: 105.234 samples/sec, ObjLoss=19.969, BoxCenterLoss=14.429, BoxScaleLoss=4.616, ClassLoss=7.060 [Epoch 271][Batch 1799], LR: 1.00E-05, Speed: 80.951 samples/sec, ObjLoss=19.968, BoxCenterLoss=14.429, BoxScaleLoss=4.615, ClassLoss=7.059 [Epoch 271] Training cost: 1711.529, ObjLoss=19.968, BoxCenterLoss=14.429, BoxScaleLoss=4.615, ClassLoss=7.059 [Epoch 271] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533 person=39.4 bicycle=19.9 car=27.3 motorcycle=31.5 airplane=51.4 bus=50.5 train=55.1 truck=25.6 boat=16.2 traffic light=17.3 fire hydrant=49.1 stop sign=52.8 parking meter=33.5 bench=16.4 bird=23.5 cat=52.3 dog=46.3 horse=41.7 sheep=35.5 cow=40.3 elephant=50.2 bear=57.6 zebra=51.9 giraffe=53.9 backpack=8.1 umbrella=27.5 handbag=7.5 tie=19.6 suitcase=22.8 frisbee=47.5 skis=12.6 snowboard=21.3 sports ball=30.5 kite=30.0 baseball bat=15.5 baseball glove=23.9 skateboard=33.7 surfboard=24.5 tennis racket=30.1 bottle=21.4 wine glass=20.7 cup=27.5 fork=16.1 knife=6.6 spoon=6.1 bowl=26.8 banana=15.3 apple=9.6 sandwich=24.3 orange=20.5 broccoli=13.8 carrot=13.7 hot dog=23.8 pizza=36.0 donut=28.1 cake=21.9 chair=17.4 couch=33.8 potted plant=17.2 bed=35.0 dining table=20.5 toilet=46.5 tv=45.2 laptop=45.5 mouse=45.9 remote=13.7 keyboard=39.1 cell phone=21.9 microwave=39.2 oven=25.1 toaster=5.6 sink=26.8 refrigerator=41.0 book=7.4 clock=39.1 vase=24.8 scissors=24.7 teddy bear=30.1 hair drier=0.0 toothbrush=11.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 272][Batch 99], LR: 1.00E-05, Speed: 65.417 samples/sec, ObjLoss=19.967, BoxCenterLoss=14.429, BoxScaleLoss=4.615, ClassLoss=7.059 [Epoch 272][Batch 199], LR: 1.00E-05, Speed: 108.661 samples/sec, ObjLoss=19.967, BoxCenterLoss=14.429, BoxScaleLoss=4.615, ClassLoss=7.058 [Epoch 272][Batch 299], LR: 1.00E-05, Speed: 81.549 samples/sec, ObjLoss=19.966, BoxCenterLoss=14.429, BoxScaleLoss=4.615, ClassLoss=7.058 [Epoch 272][Batch 399], LR: 1.00E-05, Speed: 65.595 samples/sec, ObjLoss=19.965, BoxCenterLoss=14.429, BoxScaleLoss=4.615, ClassLoss=7.057 [Epoch 272][Batch 499], LR: 1.00E-05, Speed: 88.702 samples/sec, ObjLoss=19.965, BoxCenterLoss=14.429, BoxScaleLoss=4.614, ClassLoss=7.057 [Epoch 272][Batch 599], LR: 1.00E-05, Speed: 68.749 samples/sec, ObjLoss=19.964, BoxCenterLoss=14.429, BoxScaleLoss=4.614, ClassLoss=7.056 [Epoch 272][Batch 699], LR: 1.00E-05, Speed: 57.924 samples/sec, ObjLoss=19.963, BoxCenterLoss=14.429, BoxScaleLoss=4.614, ClassLoss=7.056 [Epoch 272][Batch 799], LR: 1.00E-05, Speed: 77.475 samples/sec, ObjLoss=19.963, BoxCenterLoss=14.429, BoxScaleLoss=4.614, ClassLoss=7.055 [Epoch 272][Batch 899], LR: 1.00E-05, Speed: 60.544 samples/sec, ObjLoss=19.962, BoxCenterLoss=14.429, BoxScaleLoss=4.614, ClassLoss=7.055 [Epoch 272][Batch 999], LR: 1.00E-05, Speed: 89.579 samples/sec, ObjLoss=19.961, BoxCenterLoss=14.429, BoxScaleLoss=4.614, ClassLoss=7.054 [Epoch 272][Batch 1099], LR: 1.00E-05, Speed: 63.955 samples/sec, ObjLoss=19.961, BoxCenterLoss=14.429, BoxScaleLoss=4.613, ClassLoss=7.054 [Epoch 272][Batch 1199], LR: 1.00E-05, Speed: 55.856 samples/sec, ObjLoss=19.960, BoxCenterLoss=14.428, BoxScaleLoss=4.613, ClassLoss=7.053 [Epoch 272][Batch 1299], LR: 1.00E-05, Speed: 139.593 samples/sec, ObjLoss=19.959, BoxCenterLoss=14.428, BoxScaleLoss=4.613, ClassLoss=7.052 [Epoch 272][Batch 1399], LR: 1.00E-05, Speed: 64.701 samples/sec, ObjLoss=19.959, BoxCenterLoss=14.428, BoxScaleLoss=4.613, ClassLoss=7.052 [Epoch 272][Batch 1499], LR: 1.00E-05, Speed: 54.867 samples/sec, ObjLoss=19.958, BoxCenterLoss=14.428, BoxScaleLoss=4.613, ClassLoss=7.051 [Epoch 272][Batch 1599], LR: 1.00E-05, Speed: 39.526 samples/sec, ObjLoss=19.957, BoxCenterLoss=14.428, BoxScaleLoss=4.613, ClassLoss=7.051 [Epoch 272][Batch 1699], LR: 1.00E-05, Speed: 49.934 samples/sec, ObjLoss=19.957, BoxCenterLoss=14.428, BoxScaleLoss=4.612, ClassLoss=7.051 [Epoch 272][Batch 1799], LR: 1.00E-05, Speed: 138.658 samples/sec, ObjLoss=19.956, BoxCenterLoss=14.428, BoxScaleLoss=4.612, ClassLoss=7.050 [Epoch 272] Training cost: 1660.241, ObjLoss=19.956, BoxCenterLoss=14.428, BoxScaleLoss=4.612, ClassLoss=7.050 [Epoch 272] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.296 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.421 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 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 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529 person=39.4 bicycle=19.9 car=27.2 motorcycle=31.9 airplane=51.5 bus=50.6 train=55.4 truck=25.7 boat=16.1 traffic light=17.3 fire hydrant=48.5 stop sign=53.6 parking meter=33.2 bench=16.6 bird=23.8 cat=51.6 dog=45.8 horse=41.1 sheep=34.6 cow=40.1 elephant=49.2 bear=58.0 zebra=50.8 giraffe=53.6 backpack=8.1 umbrella=27.0 handbag=7.5 tie=19.1 suitcase=22.6 frisbee=49.4 skis=12.7 snowboard=20.1 sports ball=30.1 kite=29.2 baseball bat=15.1 baseball glove=24.1 skateboard=34.0 surfboard=24.3 tennis racket=29.6 bottle=20.9 wine glass=20.8 cup=27.2 fork=16.1 knife=6.6 spoon=5.7 bowl=26.6 banana=15.1 apple=9.7 sandwich=23.9 orange=20.9 broccoli=13.4 carrot=13.4 hot dog=23.2 pizza=36.1 donut=28.5 cake=22.8 chair=17.1 couch=33.9 potted plant=17.4 bed=35.0 dining table=21.2 toilet=47.7 tv=44.9 laptop=45.3 mouse=45.8 remote=13.7 keyboard=38.8 cell phone=21.8 microwave=38.8 oven=25.1 toaster=8.6 sink=27.1 refrigerator=40.8 book=7.4 clock=38.9 vase=25.2 scissors=25.4 teddy bear=30.4 hair drier=0.0 toothbrush=10.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 273][Batch 99], LR: 1.00E-05, Speed: 114.008 samples/sec, ObjLoss=19.955, BoxCenterLoss=14.428, BoxScaleLoss=4.612, ClassLoss=7.049 [Epoch 273][Batch 199], LR: 1.00E-05, Speed: 74.078 samples/sec, ObjLoss=19.954, BoxCenterLoss=14.428, BoxScaleLoss=4.612, ClassLoss=7.049 [Epoch 273][Batch 299], LR: 1.00E-05, Speed: 94.200 samples/sec, ObjLoss=19.954, BoxCenterLoss=14.428, BoxScaleLoss=4.612, ClassLoss=7.048 [Epoch 273][Batch 399], LR: 1.00E-05, Speed: 131.326 samples/sec, ObjLoss=19.953, BoxCenterLoss=14.428, BoxScaleLoss=4.611, ClassLoss=7.048 [Epoch 273][Batch 499], LR: 1.00E-05, Speed: 70.559 samples/sec, ObjLoss=19.953, BoxCenterLoss=14.428, BoxScaleLoss=4.611, ClassLoss=7.047 [Epoch 273][Batch 599], LR: 1.00E-05, Speed: 92.355 samples/sec, ObjLoss=19.952, BoxCenterLoss=14.428, BoxScaleLoss=4.611, ClassLoss=7.047 [Epoch 273][Batch 699], LR: 1.00E-05, Speed: 170.196 samples/sec, ObjLoss=19.951, BoxCenterLoss=14.428, BoxScaleLoss=4.611, ClassLoss=7.046 [Epoch 273][Batch 799], LR: 1.00E-05, Speed: 120.435 samples/sec, ObjLoss=19.951, BoxCenterLoss=14.428, BoxScaleLoss=4.611, ClassLoss=7.046 [Epoch 273][Batch 899], LR: 1.00E-05, Speed: 59.091 samples/sec, ObjLoss=19.950, BoxCenterLoss=14.428, BoxScaleLoss=4.611, ClassLoss=7.045 [Epoch 273][Batch 999], LR: 1.00E-05, Speed: 73.867 samples/sec, ObjLoss=19.949, BoxCenterLoss=14.428, BoxScaleLoss=4.610, ClassLoss=7.044 [Epoch 273][Batch 1099], LR: 1.00E-05, Speed: 126.236 samples/sec, ObjLoss=19.949, BoxCenterLoss=14.428, BoxScaleLoss=4.610, ClassLoss=7.044 [Epoch 273][Batch 1199], LR: 1.00E-05, Speed: 60.713 samples/sec, ObjLoss=19.948, BoxCenterLoss=14.428, BoxScaleLoss=4.610, ClassLoss=7.044 [Epoch 273][Batch 1299], LR: 1.00E-05, Speed: 129.786 samples/sec, ObjLoss=19.947, BoxCenterLoss=14.428, BoxScaleLoss=4.610, ClassLoss=7.043 [Epoch 273][Batch 1399], LR: 1.00E-05, Speed: 129.616 samples/sec, ObjLoss=19.947, BoxCenterLoss=14.428, BoxScaleLoss=4.610, ClassLoss=7.043 [Epoch 273][Batch 1499], LR: 1.00E-05, Speed: 139.819 samples/sec, ObjLoss=19.946, BoxCenterLoss=14.427, BoxScaleLoss=4.610, ClassLoss=7.042 [Epoch 273][Batch 1599], LR: 1.00E-05, Speed: 74.028 samples/sec, ObjLoss=19.945, BoxCenterLoss=14.427, BoxScaleLoss=4.609, ClassLoss=7.042 [Epoch 273][Batch 1699], LR: 1.00E-05, Speed: 73.202 samples/sec, ObjLoss=19.944, BoxCenterLoss=14.427, BoxScaleLoss=4.609, ClassLoss=7.041 [Epoch 273][Batch 1799], LR: 1.00E-05, Speed: 92.904 samples/sec, ObjLoss=19.944, BoxCenterLoss=14.427, BoxScaleLoss=4.609, ClassLoss=7.040 [Epoch 273] Training cost: 1678.781, ObjLoss=19.944, BoxCenterLoss=14.427, BoxScaleLoss=4.609, ClassLoss=7.040 [Epoch 273] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.299 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 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.251 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.366 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376 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 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.535 person=39.6 bicycle=20.1 car=27.2 motorcycle=32.1 airplane=52.1 bus=51.3 train=54.8 truck=25.8 boat=16.5 traffic light=17.2 fire hydrant=49.8 stop sign=53.1 parking meter=32.9 bench=16.5 bird=23.6 cat=52.4 dog=46.3 horse=42.2 sheep=35.2 cow=40.5 elephant=50.9 bear=58.5 zebra=50.6 giraffe=54.3 backpack=8.1 umbrella=27.6 handbag=7.5 tie=19.2 suitcase=22.9 frisbee=48.5 skis=12.6 snowboard=20.9 sports ball=30.8 kite=30.3 baseball bat=15.4 baseball glove=23.8 skateboard=33.8 surfboard=24.4 tennis racket=30.0 bottle=21.4 wine glass=21.4 cup=27.4 fork=16.2 knife=6.5 spoon=5.8 bowl=27.2 banana=14.9 apple=9.6 sandwich=23.7 orange=21.0 broccoli=13.1 carrot=13.5 hot dog=23.8 pizza=36.1 donut=28.2 cake=22.9 chair=17.3 couch=33.6 potted plant=17.4 bed=35.6 dining table=21.2 toilet=47.0 tv=45.2 laptop=44.7 mouse=45.5 remote=13.7 keyboard=38.7 cell phone=21.9 microwave=38.6 oven=24.2 toaster=10.6 sink=27.2 refrigerator=41.1 book=7.4 clock=38.8 vase=25.0 scissors=24.5 teddy bear=30.4 hair drier=0.0 toothbrush=10.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6 [Epoch 274][Batch 99], LR: 1.00E-05, Speed: 127.817 samples/sec, ObjLoss=19.943, BoxCenterLoss=14.427, BoxScaleLoss=4.609, ClassLoss=7.040 [Epoch 274][Batch 199], LR: 1.00E-05, Speed: 47.329 samples/sec, ObjLoss=19.942, BoxCenterLoss=14.427, BoxScaleLoss=4.609, ClassLoss=7.039 [Epoch 274][Batch 299], LR: 1.00E-05, Speed: 62.647 samples/sec, ObjLoss=19.942, BoxCenterLoss=14.427, BoxScaleLoss=4.609, ClassLoss=7.039 [Epoch 274][Batch 399], LR: 1.00E-05, Speed: 58.045 samples/sec, ObjLoss=19.941, BoxCenterLoss=14.427, BoxScaleLoss=4.608, ClassLoss=7.038 [Epoch 274][Batch 499], LR: 1.00E-05, Speed: 83.536 samples/sec, ObjLoss=19.940, BoxCenterLoss=14.427, BoxScaleLoss=4.608, ClassLoss=7.038 [Epoch 274][Batch 599], LR: 1.00E-05, Speed: 72.922 samples/sec, ObjLoss=19.940, BoxCenterLoss=14.427, BoxScaleLoss=4.608, ClassLoss=7.037 [Epoch 274][Batch 699], LR: 1.00E-05, Speed: 61.370 samples/sec, ObjLoss=19.939, BoxCenterLoss=14.427, BoxScaleLoss=4.608, ClassLoss=7.037 [Epoch 274][Batch 799], LR: 1.00E-05, Speed: 77.172 samples/sec, ObjLoss=19.938, BoxCenterLoss=14.427, BoxScaleLoss=4.608, ClassLoss=7.036 [Epoch 274][Batch 899], LR: 1.00E-05, Speed: 61.334 samples/sec, ObjLoss=19.938, BoxCenterLoss=14.427, BoxScaleLoss=4.608, ClassLoss=7.036 [Epoch 274][Batch 999], LR: 1.00E-05, Speed: 73.841 samples/sec, ObjLoss=19.937, BoxCenterLoss=14.427, BoxScaleLoss=4.607, ClassLoss=7.035 [Epoch 274][Batch 1099], LR: 1.00E-05, Speed: 63.513 samples/sec, ObjLoss=19.936, BoxCenterLoss=14.427, BoxScaleLoss=4.607, ClassLoss=7.035 [Epoch 274][Batch 1199], LR: 1.00E-05, Speed: 89.704 samples/sec, ObjLoss=19.936, BoxCenterLoss=14.427, BoxScaleLoss=4.607, ClassLoss=7.034 [Epoch 274][Batch 1299], LR: 1.00E-05, Speed: 62.275 samples/sec, ObjLoss=19.935, BoxCenterLoss=14.427, BoxScaleLoss=4.607, ClassLoss=7.034 [Epoch 274][Batch 1399], LR: 1.00E-05, Speed: 79.951 samples/sec, ObjLoss=19.934, BoxCenterLoss=14.426, BoxScaleLoss=4.607, ClassLoss=7.033 [Epoch 274][Batch 1499], LR: 1.00E-05, Speed: 59.986 samples/sec, ObjLoss=19.933, BoxCenterLoss=14.426, BoxScaleLoss=4.607, ClassLoss=7.033 [Epoch 274][Batch 1599], LR: 1.00E-05, Speed: 52.260 samples/sec, ObjLoss=19.933, BoxCenterLoss=14.426, BoxScaleLoss=4.606, ClassLoss=7.032 [Epoch 274][Batch 1699], LR: 1.00E-05, Speed: 71.207 samples/sec, ObjLoss=19.932, BoxCenterLoss=14.426, BoxScaleLoss=4.606, ClassLoss=7.032 [Epoch 274][Batch 1799], LR: 1.00E-05, Speed: 88.798 samples/sec, ObjLoss=19.931, BoxCenterLoss=14.426, BoxScaleLoss=4.606, ClassLoss=7.031 [Epoch 274] Training cost: 1660.624, ObjLoss=19.931, BoxCenterLoss=14.426, BoxScaleLoss=4.606, ClassLoss=7.031 [Epoch 274] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128 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 ] = 0.423 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.249 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.361 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371 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 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530 person=39.6 bicycle=20.0 car=27.6 motorcycle=31.9 airplane=50.5 bus=50.6 train=54.1 truck=25.8 boat=16.8 traffic light=17.3 fire hydrant=49.4 stop sign=53.8 parking meter=33.9 bench=16.5 bird=24.0 cat=52.1 dog=46.8 horse=41.2 sheep=34.8 cow=39.7 elephant=49.1 bear=58.1 zebra=51.0 giraffe=53.6 backpack=8.5 umbrella=27.1 handbag=7.6 tie=19.2 suitcase=22.6 frisbee=48.6 skis=12.9 snowboard=20.8 sports ball=30.7 kite=30.6 baseball bat=16.4 baseball glove=24.2 skateboard=33.7 surfboard=24.9 tennis racket=29.3 bottle=21.3 wine glass=21.2 cup=27.6 fork=15.8 knife=6.7 spoon=6.0 bowl=26.9 banana=15.4 apple=9.6 sandwich=24.0 orange=21.0 broccoli=13.4 carrot=13.8 hot dog=23.2 pizza=37.8 donut=28.2 cake=22.3 chair=17.4 couch=33.5 potted plant=16.7 bed=32.9 dining table=17.5 toilet=47.2 tv=45.1 laptop=44.9 mouse=45.8 remote=13.4 keyboard=39.2 cell phone=22.2 microwave=39.3 oven=23.6 toaster=7.2 sink=27.2 refrigerator=40.7 book=7.4 clock=38.5 vase=25.0 scissors=25.9 teddy bear=31.5 hair drier=0.0 toothbrush=12.6 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6 [Epoch 275][Batch 99], LR: 1.00E-05, Speed: 82.503 samples/sec, ObjLoss=19.931, BoxCenterLoss=14.426, BoxScaleLoss=4.606, ClassLoss=7.030 [Epoch 275][Batch 199], LR: 1.00E-05, Speed: 64.764 samples/sec, ObjLoss=19.930, BoxCenterLoss=14.426, BoxScaleLoss=4.606, ClassLoss=7.030 [Epoch 275][Batch 299], LR: 1.00E-05, Speed: 67.652 samples/sec, ObjLoss=19.929, BoxCenterLoss=14.426, BoxScaleLoss=4.605, ClassLoss=7.029 [Epoch 275][Batch 399], LR: 1.00E-05, Speed: 80.205 samples/sec, ObjLoss=19.929, BoxCenterLoss=14.426, BoxScaleLoss=4.605, ClassLoss=7.029 [Epoch 275][Batch 499], LR: 1.00E-05, Speed: 49.257 samples/sec, ObjLoss=19.928, BoxCenterLoss=14.426, BoxScaleLoss=4.605, ClassLoss=7.028 [Epoch 275][Batch 599], LR: 1.00E-05, Speed: 119.509 samples/sec, ObjLoss=19.927, BoxCenterLoss=14.426, BoxScaleLoss=4.605, ClassLoss=7.028 [Epoch 275][Batch 699], LR: 1.00E-05, Speed: 58.082 samples/sec, ObjLoss=19.926, BoxCenterLoss=14.426, BoxScaleLoss=4.605, ClassLoss=7.027 [Epoch 275][Batch 799], LR: 1.00E-05, Speed: 51.374 samples/sec, ObjLoss=19.926, BoxCenterLoss=14.425, BoxScaleLoss=4.604, ClassLoss=7.027 [Epoch 275][Batch 899], LR: 1.00E-05, Speed: 128.224 samples/sec, ObjLoss=19.925, BoxCenterLoss=14.425, BoxScaleLoss=4.604, ClassLoss=7.026 [Epoch 275][Batch 999], LR: 1.00E-05, Speed: 132.316 samples/sec, ObjLoss=19.924, BoxCenterLoss=14.425, BoxScaleLoss=4.604, ClassLoss=7.026 [Epoch 275][Batch 1099], LR: 1.00E-05, Speed: 97.622 samples/sec, ObjLoss=19.924, BoxCenterLoss=14.425, BoxScaleLoss=4.604, ClassLoss=7.025 [Epoch 275][Batch 1199], LR: 1.00E-05, Speed: 76.352 samples/sec, ObjLoss=19.923, BoxCenterLoss=14.425, BoxScaleLoss=4.604, ClassLoss=7.025 [Epoch 275][Batch 1299], LR: 1.00E-05, Speed: 79.154 samples/sec, ObjLoss=19.922, BoxCenterLoss=14.425, BoxScaleLoss=4.604, ClassLoss=7.024 [Epoch 275][Batch 1399], LR: 1.00E-05, Speed: 65.691 samples/sec, ObjLoss=19.922, BoxCenterLoss=14.425, BoxScaleLoss=4.604, ClassLoss=7.024 [Epoch 275][Batch 1499], LR: 1.00E-05, Speed: 87.958 samples/sec, ObjLoss=19.921, BoxCenterLoss=14.425, BoxScaleLoss=4.603, ClassLoss=7.023 [Epoch 275][Batch 1599], LR: 1.00E-05, Speed: 109.973 samples/sec, ObjLoss=19.921, BoxCenterLoss=14.425, BoxScaleLoss=4.603, ClassLoss=7.023 [Epoch 275][Batch 1699], LR: 1.00E-05, Speed: 134.239 samples/sec, ObjLoss=19.920, BoxCenterLoss=14.425, BoxScaleLoss=4.603, ClassLoss=7.022 [Epoch 275][Batch 1799], LR: 1.00E-05, Speed: 81.165 samples/sec, ObjLoss=19.919, BoxCenterLoss=14.425, BoxScaleLoss=4.603, ClassLoss=7.022 [Epoch 275] Training cost: 1698.507, ObjLoss=19.919, BoxCenterLoss=14.425, BoxScaleLoss=4.603, ClassLoss=7.022 [Epoch 275] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.300 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531 person=39.7 bicycle=20.0 car=27.5 motorcycle=32.0 airplane=52.2 bus=51.3 train=55.1 truck=26.0 boat=15.8 traffic light=17.3 fire hydrant=49.1 stop sign=53.0 parking meter=33.0 bench=16.8 bird=23.6 cat=52.3 dog=46.3 horse=41.7 sheep=34.9 cow=39.5 elephant=50.5 bear=57.1 zebra=51.6 giraffe=53.6 backpack=8.3 umbrella=28.0 handbag=7.5 tie=19.2 suitcase=22.8 frisbee=48.9 skis=12.9 snowboard=21.0 sports ball=30.8 kite=30.3 baseball bat=15.1 baseball glove=24.5 skateboard=34.0 surfboard=25.0 tennis racket=29.9 bottle=21.3 wine glass=20.7 cup=27.3 fork=15.8 knife=6.4 spoon=5.7 bowl=27.1 banana=15.5 apple=10.1 sandwich=23.4 orange=20.7 broccoli=13.3 carrot=13.5 hot dog=23.5 pizza=36.5 donut=27.9 cake=22.3 chair=17.5 couch=33.4 potted plant=17.5 bed=34.7 dining table=20.2 toilet=47.0 tv=44.9 laptop=44.5 mouse=45.3 remote=13.4 keyboard=38.1 cell phone=22.1 microwave=39.3 oven=25.3 toaster=6.2 sink=27.4 refrigerator=40.8 book=7.4 clock=38.9 vase=24.9 scissors=25.7 teddy bear=30.7 hair drier=0.0 toothbrush=10.8 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6 [Epoch 276][Batch 99], LR: 1.00E-05, Speed: 99.464 samples/sec, ObjLoss=19.918, BoxCenterLoss=14.425, BoxScaleLoss=4.603, ClassLoss=7.021 [Epoch 276][Batch 199], LR: 1.00E-05, Speed: 68.725 samples/sec, ObjLoss=19.917, BoxCenterLoss=14.425, BoxScaleLoss=4.602, ClassLoss=7.021 [Epoch 276][Batch 299], LR: 1.00E-05, Speed: 80.802 samples/sec, ObjLoss=19.917, BoxCenterLoss=14.425, BoxScaleLoss=4.602, ClassLoss=7.020 [Epoch 276][Batch 399], LR: 1.00E-05, Speed: 81.494 samples/sec, ObjLoss=19.916, BoxCenterLoss=14.425, BoxScaleLoss=4.602, ClassLoss=7.020 [Epoch 276][Batch 499], LR: 1.00E-05, Speed: 66.860 samples/sec, ObjLoss=19.916, BoxCenterLoss=14.425, BoxScaleLoss=4.602, ClassLoss=7.019 [Epoch 276][Batch 599], LR: 1.00E-05, Speed: 62.177 samples/sec, ObjLoss=19.915, BoxCenterLoss=14.425, BoxScaleLoss=4.602, ClassLoss=7.019 [Epoch 276][Batch 699], LR: 1.00E-05, Speed: 84.584 samples/sec, ObjLoss=19.914, BoxCenterLoss=14.425, BoxScaleLoss=4.602, ClassLoss=7.018 [Epoch 276][Batch 799], LR: 1.00E-05, Speed: 72.122 samples/sec, ObjLoss=19.914, BoxCenterLoss=14.425, BoxScaleLoss=4.601, ClassLoss=7.018 [Epoch 276][Batch 899], LR: 1.00E-05, Speed: 61.781 samples/sec, ObjLoss=19.913, BoxCenterLoss=14.424, BoxScaleLoss=4.601, ClassLoss=7.017 [Epoch 276][Batch 999], LR: 1.00E-05, Speed: 50.055 samples/sec, ObjLoss=19.912, BoxCenterLoss=14.424, BoxScaleLoss=4.601, ClassLoss=7.017 [Epoch 276][Batch 1099], LR: 1.00E-05, Speed: 118.324 samples/sec, ObjLoss=19.912, BoxCenterLoss=14.424, BoxScaleLoss=4.601, ClassLoss=7.016 [Epoch 276][Batch 1199], LR: 1.00E-05, Speed: 104.569 samples/sec, ObjLoss=19.911, BoxCenterLoss=14.424, BoxScaleLoss=4.601, ClassLoss=7.015 [Epoch 276][Batch 1299], LR: 1.00E-05, Speed: 107.876 samples/sec, ObjLoss=19.910, BoxCenterLoss=14.424, BoxScaleLoss=4.600, ClassLoss=7.015 [Epoch 276][Batch 1399], LR: 1.00E-05, Speed: 46.999 samples/sec, ObjLoss=19.910, BoxCenterLoss=14.424, BoxScaleLoss=4.600, ClassLoss=7.014 [Epoch 276][Batch 1499], LR: 1.00E-05, Speed: 70.442 samples/sec, ObjLoss=19.909, BoxCenterLoss=14.424, BoxScaleLoss=4.600, ClassLoss=7.014 [Epoch 276][Batch 1599], LR: 1.00E-05, Speed: 80.294 samples/sec, ObjLoss=19.908, BoxCenterLoss=14.424, BoxScaleLoss=4.600, ClassLoss=7.013 [Epoch 276][Batch 1699], LR: 1.00E-05, Speed: 99.334 samples/sec, ObjLoss=19.908, BoxCenterLoss=14.424, BoxScaleLoss=4.600, ClassLoss=7.013 [Epoch 276][Batch 1799], LR: 1.00E-05, Speed: 128.206 samples/sec, ObjLoss=19.907, BoxCenterLoss=14.424, BoxScaleLoss=4.600, ClassLoss=7.012 [Epoch 276] Training cost: 1729.706, ObjLoss=19.907, BoxCenterLoss=14.424, BoxScaleLoss=4.600, ClassLoss=7.012 [Epoch 276] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.483 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 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 ] = 0.425 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.248 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370 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 ] = 0.386 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530 person=39.6 bicycle=20.0 car=27.5 motorcycle=31.8 airplane=51.4 bus=50.5 train=53.7 truck=25.5 boat=16.6 traffic light=17.0 fire hydrant=49.2 stop sign=53.7 parking meter=34.3 bench=16.4 bird=23.7 cat=51.1 dog=46.3 horse=41.4 sheep=34.7 cow=40.2 elephant=48.9 bear=58.7 zebra=50.8 giraffe=53.2 backpack=8.2 umbrella=27.5 handbag=7.4 tie=19.2 suitcase=22.1 frisbee=48.8 skis=12.8 snowboard=20.6 sports ball=31.4 kite=29.6 baseball bat=15.9 baseball glove=24.5 skateboard=33.9 surfboard=24.9 tennis racket=29.6 bottle=21.5 wine glass=21.4 cup=27.5 fork=16.5 knife=6.6 spoon=5.8 bowl=26.6 banana=15.4 apple=9.9 sandwich=24.0 orange=21.0 broccoli=13.4 carrot=13.4 hot dog=23.6 pizza=37.5 donut=28.5 cake=22.4 chair=17.2 couch=32.8 potted plant=17.1 bed=32.0 dining table=17.3 toilet=47.9 tv=45.1 laptop=44.0 mouse=46.2 remote=13.7 keyboard=39.2 cell phone=22.0 microwave=38.7 oven=23.1 toaster=5.0 sink=27.3 refrigerator=40.5 book=7.4 clock=38.7 vase=24.9 scissors=26.7 teddy bear=31.0 hair drier=0.0 toothbrush=11.3 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.5 [Epoch 277][Batch 99], LR: 1.00E-05, Speed: 69.589 samples/sec, ObjLoss=19.906, BoxCenterLoss=14.424, BoxScaleLoss=4.599, ClassLoss=7.012 [Epoch 277][Batch 199], LR: 1.00E-05, Speed: 71.328 samples/sec, ObjLoss=19.906, BoxCenterLoss=14.424, BoxScaleLoss=4.599, ClassLoss=7.011 [Epoch 277][Batch 299], LR: 1.00E-05, Speed: 78.617 samples/sec, ObjLoss=19.905, BoxCenterLoss=14.424, BoxScaleLoss=4.599, ClassLoss=7.011 [Epoch 277][Batch 399], LR: 1.00E-05, Speed: 72.826 samples/sec, ObjLoss=19.904, BoxCenterLoss=14.424, BoxScaleLoss=4.599, ClassLoss=7.010 [Epoch 277][Batch 499], LR: 1.00E-05, Speed: 99.183 samples/sec, ObjLoss=19.903, BoxCenterLoss=14.424, BoxScaleLoss=4.599, ClassLoss=7.010 [Epoch 277][Batch 599], LR: 1.00E-05, Speed: 67.069 samples/sec, ObjLoss=19.903, BoxCenterLoss=14.424, BoxScaleLoss=4.599, ClassLoss=7.009 [Epoch 277][Batch 699], LR: 1.00E-05, Speed: 77.766 samples/sec, ObjLoss=19.902, BoxCenterLoss=14.423, BoxScaleLoss=4.598, ClassLoss=7.009 [Epoch 277][Batch 799], LR: 1.00E-05, Speed: 89.701 samples/sec, ObjLoss=19.901, BoxCenterLoss=14.423, BoxScaleLoss=4.598, ClassLoss=7.008 [Epoch 277][Batch 899], LR: 1.00E-05, Speed: 80.342 samples/sec, ObjLoss=19.901, BoxCenterLoss=14.423, BoxScaleLoss=4.598, ClassLoss=7.008 [Epoch 277][Batch 999], LR: 1.00E-05, Speed: 60.884 samples/sec, ObjLoss=19.900, BoxCenterLoss=14.423, BoxScaleLoss=4.598, ClassLoss=7.007 [Epoch 277][Batch 1099], LR: 1.00E-05, Speed: 69.534 samples/sec, ObjLoss=19.899, BoxCenterLoss=14.423, BoxScaleLoss=4.598, ClassLoss=7.007 [Epoch 277][Batch 1199], LR: 1.00E-05, Speed: 60.961 samples/sec, ObjLoss=19.899, BoxCenterLoss=14.423, BoxScaleLoss=4.598, ClassLoss=7.006 [Epoch 277][Batch 1299], LR: 1.00E-05, Speed: 59.164 samples/sec, ObjLoss=19.898, BoxCenterLoss=14.423, BoxScaleLoss=4.597, ClassLoss=7.006 [Epoch 277][Batch 1399], LR: 1.00E-05, Speed: 81.483 samples/sec, ObjLoss=19.897, BoxCenterLoss=14.423, BoxScaleLoss=4.597, ClassLoss=7.005 [Epoch 277][Batch 1499], LR: 1.00E-05, Speed: 48.156 samples/sec, ObjLoss=19.897, BoxCenterLoss=14.423, BoxScaleLoss=4.597, ClassLoss=7.005 [Epoch 277][Batch 1599], LR: 1.00E-05, Speed: 103.211 samples/sec, ObjLoss=19.896, BoxCenterLoss=14.423, BoxScaleLoss=4.597, ClassLoss=7.004 [Epoch 277][Batch 1699], LR: 1.00E-05, Speed: 68.340 samples/sec, ObjLoss=19.896, BoxCenterLoss=14.423, BoxScaleLoss=4.597, ClassLoss=7.004 [Epoch 277][Batch 1799], LR: 1.00E-05, Speed: 134.956 samples/sec, ObjLoss=19.895, BoxCenterLoss=14.423, BoxScaleLoss=4.597, ClassLoss=7.003 [Epoch 277] Training cost: 1700.209, ObjLoss=19.895, BoxCenterLoss=14.423, BoxScaleLoss=4.597, ClassLoss=7.003 [Epoch 277] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.296 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.364 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.375 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 ] = 0.392 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.528 person=39.5 bicycle=20.1 car=27.3 motorcycle=31.5 airplane=50.6 bus=49.8 train=55.0 truck=25.5 boat=16.1 traffic light=17.4 fire hydrant=48.9 stop sign=53.1 parking meter=31.6 bench=16.2 bird=23.5 cat=52.0 dog=45.0 horse=41.3 sheep=35.1 cow=39.9 elephant=50.3 bear=58.5 zebra=51.1 giraffe=53.5 backpack=8.3 umbrella=27.4 handbag=7.7 tie=19.6 suitcase=22.7 frisbee=48.8 skis=12.6 snowboard=20.3 sports ball=30.2 kite=29.6 baseball bat=15.2 baseball glove=24.6 skateboard=34.0 surfboard=24.5 tennis racket=30.5 bottle=21.4 wine glass=20.6 cup=27.0 fork=15.7 knife=6.5 spoon=5.8 bowl=26.7 banana=14.5 apple=9.8 sandwich=24.0 orange=20.9 broccoli=13.0 carrot=13.5 hot dog=23.3 pizza=34.5 donut=28.3 cake=22.1 chair=17.4 couch=33.0 potted plant=17.4 bed=34.6 dining table=20.9 toilet=47.2 tv=45.0 laptop=45.0 mouse=45.9 remote=13.0 keyboard=39.0 cell phone=21.7 microwave=39.0 oven=25.3 toaster=6.4 sink=27.8 refrigerator=39.8 book=7.4 clock=38.3 vase=25.5 scissors=23.4 teddy bear=30.1 hair drier=0.0 toothbrush=10.5 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.4 [Epoch 278][Batch 99], LR: 1.00E-05, Speed: 119.396 samples/sec, ObjLoss=19.894, BoxCenterLoss=14.423, BoxScaleLoss=4.596, ClassLoss=7.003 [Epoch 278][Batch 199], LR: 1.00E-05, Speed: 60.268 samples/sec, ObjLoss=19.894, BoxCenterLoss=14.423, BoxScaleLoss=4.596, ClassLoss=7.002 [Epoch 278][Batch 299], LR: 1.00E-05, Speed: 67.889 samples/sec, ObjLoss=19.893, BoxCenterLoss=14.423, BoxScaleLoss=4.596, ClassLoss=7.002 [Epoch 278][Batch 399], LR: 1.00E-05, Speed: 86.161 samples/sec, ObjLoss=19.892, BoxCenterLoss=14.423, BoxScaleLoss=4.596, ClassLoss=7.001 [Epoch 278][Batch 499], LR: 1.00E-05, Speed: 57.592 samples/sec, ObjLoss=19.892, BoxCenterLoss=14.423, BoxScaleLoss=4.596, ClassLoss=7.001 [Epoch 278][Batch 599], LR: 1.00E-05, Speed: 49.629 samples/sec, ObjLoss=19.891, BoxCenterLoss=14.423, BoxScaleLoss=4.596, ClassLoss=7.000 [Epoch 278][Batch 699], LR: 1.00E-05, Speed: 81.777 samples/sec, ObjLoss=19.891, BoxCenterLoss=14.423, BoxScaleLoss=4.595, ClassLoss=7.000 [Epoch 278][Batch 799], LR: 1.00E-05, Speed: 55.953 samples/sec, ObjLoss=19.890, BoxCenterLoss=14.423, BoxScaleLoss=4.595, ClassLoss=6.999 [Epoch 278][Batch 899], LR: 1.00E-05, Speed: 56.968 samples/sec, ObjLoss=19.889, BoxCenterLoss=14.423, BoxScaleLoss=4.595, ClassLoss=6.999 [Epoch 278][Batch 999], LR: 1.00E-05, Speed: 105.157 samples/sec, ObjLoss=19.889, BoxCenterLoss=14.423, BoxScaleLoss=4.595, ClassLoss=6.998 [Epoch 278][Batch 1099], LR: 1.00E-05, Speed: 96.771 samples/sec, ObjLoss=19.888, BoxCenterLoss=14.423, BoxScaleLoss=4.595, ClassLoss=6.998 [Epoch 278][Batch 1199], LR: 1.00E-05, Speed: 62.002 samples/sec, ObjLoss=19.887, BoxCenterLoss=14.423, BoxScaleLoss=4.595, ClassLoss=6.997 [Epoch 278][Batch 1299], LR: 1.00E-05, Speed: 85.042 samples/sec, ObjLoss=19.887, BoxCenterLoss=14.423, BoxScaleLoss=4.594, ClassLoss=6.997 [Epoch 278][Batch 1399], LR: 1.00E-05, Speed: 79.295 samples/sec, ObjLoss=19.886, BoxCenterLoss=14.423, BoxScaleLoss=4.594, ClassLoss=6.996 [Epoch 278][Batch 1499], LR: 1.00E-05, Speed: 46.380 samples/sec, ObjLoss=19.885, BoxCenterLoss=14.423, BoxScaleLoss=4.594, ClassLoss=6.996 [Epoch 278][Batch 1599], LR: 1.00E-05, Speed: 69.123 samples/sec, ObjLoss=19.885, BoxCenterLoss=14.423, BoxScaleLoss=4.594, ClassLoss=6.995 [Epoch 278][Batch 1699], LR: 1.00E-05, Speed: 65.677 samples/sec, ObjLoss=19.884, BoxCenterLoss=14.423, BoxScaleLoss=4.594, ClassLoss=6.995 [Epoch 278][Batch 1799], LR: 1.00E-05, Speed: 99.745 samples/sec, ObjLoss=19.883, BoxCenterLoss=14.422, BoxScaleLoss=4.594, ClassLoss=6.994 [Epoch 278] Training cost: 1689.920, ObjLoss=19.883, BoxCenterLoss=14.422, BoxScaleLoss=4.594, ClassLoss=6.994 [Epoch 278] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.129 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.298 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532 person=39.7 bicycle=19.9 car=27.6 motorcycle=31.5 airplane=51.0 bus=50.3 train=54.7 truck=25.8 boat=16.1 traffic light=17.5 fire hydrant=49.2 stop sign=53.6 parking meter=34.0 bench=16.4 bird=23.8 cat=51.5 dog=46.9 horse=41.5 sheep=35.0 cow=39.3 elephant=49.6 bear=58.8 zebra=51.2 giraffe=54.1 backpack=8.0 umbrella=27.3 handbag=7.4 tie=19.4 suitcase=22.2 frisbee=48.8 skis=12.6 snowboard=20.1 sports ball=30.6 kite=30.0 baseball bat=15.8 baseball glove=24.4 skateboard=34.9 surfboard=24.8 tennis racket=29.7 bottle=21.3 wine glass=20.9 cup=27.5 fork=15.6 knife=6.4 spoon=5.6 bowl=26.9 banana=15.5 apple=9.7 sandwich=24.4 orange=21.0 broccoli=13.4 carrot=13.7 hot dog=23.6 pizza=37.0 donut=28.3 cake=22.6 chair=17.4 couch=33.0 potted plant=17.4 bed=34.5 dining table=19.5 toilet=47.7 tv=45.1 laptop=44.8 mouse=46.0 remote=13.9 keyboard=38.8 cell phone=21.8 microwave=38.7 oven=24.5 toaster=8.0 sink=27.3 refrigerator=40.4 book=7.6 clock=38.7 vase=25.5 scissors=25.6 teddy bear=30.9 hair drier=0.0 toothbrush=11.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6 [Epoch 279][Batch 99], LR: 1.00E-05, Speed: 166.015 samples/sec, ObjLoss=19.882, BoxCenterLoss=14.422, BoxScaleLoss=4.593, ClassLoss=6.993 [Epoch 279][Batch 199], LR: 1.00E-05, Speed: 104.281 samples/sec, ObjLoss=19.882, BoxCenterLoss=14.422, BoxScaleLoss=4.593, ClassLoss=6.993 [Epoch 279][Batch 299], LR: 1.00E-05, Speed: 66.371 samples/sec, ObjLoss=19.881, BoxCenterLoss=14.422, BoxScaleLoss=4.593, ClassLoss=6.992 [Epoch 279][Batch 399], LR: 1.00E-05, Speed: 93.555 samples/sec, ObjLoss=19.880, BoxCenterLoss=14.422, BoxScaleLoss=4.593, ClassLoss=6.992 [Epoch 279][Batch 499], LR: 1.00E-05, Speed: 55.678 samples/sec, ObjLoss=19.880, BoxCenterLoss=14.422, BoxScaleLoss=4.593, ClassLoss=6.991 [Epoch 279][Batch 599], LR: 1.00E-05, Speed: 77.472 samples/sec, ObjLoss=19.879, BoxCenterLoss=14.422, BoxScaleLoss=4.593, ClassLoss=6.991 [Epoch 279][Batch 699], LR: 1.00E-05, Speed: 72.422 samples/sec, ObjLoss=19.878, BoxCenterLoss=14.422, BoxScaleLoss=4.592, ClassLoss=6.990 [Epoch 279][Batch 799], LR: 1.00E-05, Speed: 71.443 samples/sec, ObjLoss=19.878, BoxCenterLoss=14.422, BoxScaleLoss=4.592, ClassLoss=6.990 [Epoch 279][Batch 899], LR: 1.00E-05, Speed: 134.213 samples/sec, ObjLoss=19.877, BoxCenterLoss=14.422, BoxScaleLoss=4.592, ClassLoss=6.989 [Epoch 279][Batch 999], LR: 1.00E-05, Speed: 132.199 samples/sec, ObjLoss=19.877, BoxCenterLoss=14.422, BoxScaleLoss=4.592, ClassLoss=6.989 [Epoch 279][Batch 1099], LR: 1.00E-05, Speed: 62.672 samples/sec, ObjLoss=19.876, BoxCenterLoss=14.422, BoxScaleLoss=4.592, ClassLoss=6.988 [Epoch 279][Batch 1199], LR: 1.00E-05, Speed: 65.871 samples/sec, ObjLoss=19.875, BoxCenterLoss=14.422, BoxScaleLoss=4.592, ClassLoss=6.988 [Epoch 279][Batch 1299], LR: 1.00E-05, Speed: 78.500 samples/sec, ObjLoss=19.875, BoxCenterLoss=14.422, BoxScaleLoss=4.591, ClassLoss=6.987 [Epoch 279][Batch 1399], LR: 1.00E-05, Speed: 46.358 samples/sec, ObjLoss=19.874, BoxCenterLoss=14.422, BoxScaleLoss=4.591, ClassLoss=6.987 [Epoch 279][Batch 1499], LR: 1.00E-05, Speed: 65.495 samples/sec, ObjLoss=19.873, BoxCenterLoss=14.422, BoxScaleLoss=4.591, ClassLoss=6.986 [Epoch 279][Batch 1599], LR: 1.00E-05, Speed: 55.383 samples/sec, ObjLoss=19.873, BoxCenterLoss=14.422, BoxScaleLoss=4.591, ClassLoss=6.986 [Epoch 279][Batch 1699], LR: 1.00E-05, Speed: 64.179 samples/sec, ObjLoss=19.872, BoxCenterLoss=14.422, BoxScaleLoss=4.591, ClassLoss=6.985 [Epoch 279][Batch 1799], LR: 1.00E-05, Speed: 75.742 samples/sec, ObjLoss=19.872, BoxCenterLoss=14.422, BoxScaleLoss=4.591, ClassLoss=6.985 [Epoch 279] Training cost: 1738.403, ObjLoss=19.872, BoxCenterLoss=14.422, BoxScaleLoss=4.591, ClassLoss=6.985 [Epoch 279] Validation: ~~~~ Summary metrics ~~~~ =Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.286 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.299 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 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.362 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.183 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531 person=39.5 bicycle=20.0 car=27.6 motorcycle=31.6 airplane=51.1 bus=50.9 train=54.1 truck=25.6 boat=16.4 traffic light=17.2 fire hydrant=48.4 stop sign=53.0 parking meter=34.2 bench=16.7 bird=23.8 cat=52.7 dog=46.5 horse=41.9 sheep=35.2 cow=39.9 elephant=49.4 bear=58.4 zebra=50.8 giraffe=54.2 backpack=8.2 umbrella=27.0 handbag=7.6 tie=18.9 suitcase=22.4 frisbee=48.9 skis=12.7 snowboard=21.4 sports ball=31.0 kite=29.9 baseball bat=16.3 baseball glove=23.9 skateboard=34.3 surfboard=24.7 tennis racket=30.0 bottle=21.4 wine glass=21.3 cup=27.7 fork=16.3 knife=6.7 spoon=5.9 bowl=26.8 banana=15.5 apple=10.1 sandwich=23.8 orange=21.0 broccoli=13.6 carrot=13.4 hot dog=24.2 pizza=36.4 donut=28.4 cake=22.2 chair=17.4 couch=32.6 potted plant=17.0 bed=33.1 dining table=18.2 toilet=47.5 tv=44.6 laptop=44.9 mouse=45.8 remote=13.7 keyboard=39.2 cell phone=21.8 microwave=39.4 oven=24.4 toaster=8.0 sink=27.0 refrigerator=40.8 book=7.6 clock=37.8 vase=24.8 scissors=25.0 teddy bear=31.2 hair drier=0.0 toothbrush=12.0 ~~~~ MeanAP @ IoU=[0.50,0.95] ~~~~ =28.6