Namespace(batch_size=64, data_shape=416, dataset='voc', epochs=200, 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='160,180', 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=16551, num_workers=32, resume='', save_interval=10, save_prefix='yolo3_mobilenet1.0_voc', seed=233, start_epoch=0, syncbn=True, val_interval=1, warmup_epochs=4, warmup_lr=0.0, wd=0.0005)
Start training from [Epoch 0]
[Epoch 0][Batch 99], LR: 9.59E-05, Speed: 148.642 samples/sec, ObjLoss=429.711, BoxCenterLoss=7.307, BoxScaleLoss=5.622, ClassLoss=35.826
[Epoch 0][Batch 199], LR: 1.93E-04, Speed: 166.373 samples/sec, ObjLoss=224.125, BoxCenterLoss=6.998, BoxScaleLoss=4.730, ClassLoss=26.485
[Epoch 0] Training cost: 190.396, ObjLoss=177.344, BoxCenterLoss=6.871, BoxScaleLoss=4.365, ClassLoss=22.689
[Epoch 0] Validation:
aeroplane=0.0
bicycle=0.0
bird=0.0
boat=0.045454545454545456
bottle=0.0
bus=0.0
car=0.018181818181818184
cat=0.0030303030303030303
chair=0.0
cow=0.0
diningtable=0.0
dog=0.0106951871657754
horse=0.0
motorbike=0.0
person=0.1841615167493141
pottedplant=0.0
sheep=0.00505050505050505
sofa=0.0
train=0.0
tvmonitor=0.0
mAP=0.01332869378161306
[Epoch 1][Batch 99], LR: 3.46E-04, Speed: 155.719 samples/sec, ObjLoss=131.957, BoxCenterLoss=6.758, BoxScaleLoss=3.931, ClassLoss=18.835
[Epoch 1][Batch 199], LR: 4.43E-04, Speed: 160.943 samples/sec, ObjLoss=106.271, BoxCenterLoss=6.683, BoxScaleLoss=3.630, ClassLoss=16.515
[Epoch 1] Training cost: 159.695, ObjLoss=95.831, BoxCenterLoss=6.663, BoxScaleLoss=3.506, ClassLoss=15.560
[Epoch 1] Validation:
aeroplane=0.01133574329450618
bicycle=0.0009671179883945841
bird=0.009904492394764769
boat=0.0024906600249066002
bottle=0.0022172949002217295
bus=0.00505050505050505
car=0.19774910087291872
cat=0.17442879127713654
chair=0.04985416975629048
cow=0.10032913403699921
diningtable=0.0021645021645021645
dog=0.09126018099689692
horse=0.04795373802310023
motorbike=0.06724322874765352
person=0.3310093722332448
pottedplant=0.0
sheep=0.06363636363636363
sofa=0.0027137042062415195
train=0.0
tvmonitor=0.0006402048655569782
mAP=0.05804741522351018
[Epoch 2][Batch 99], LR: 5.96E-04, Speed: 146.562 samples/sec, ObjLoss=82.456, BoxCenterLoss=6.616, BoxScaleLoss=3.316, ClassLoss=14.261
[Epoch 2][Batch 199], LR: 6.93E-04, Speed: 161.035 samples/sec, ObjLoss=72.830, BoxCenterLoss=6.593, BoxScaleLoss=3.177, ClassLoss=13.295
[Epoch 2] Training cost: 146.669, ObjLoss=68.351, BoxCenterLoss=6.564, BoxScaleLoss=3.108, ClassLoss=12.815
[Epoch 2] Validation:
aeroplane=0.1272220618068312
bicycle=0.07087065756565185
bird=0.037633048201672334
boat=0.033404634581105166
bottle=0.03234880450070323
bus=0.11625911257780758
car=0.38329325161836164
cat=0.24689089659595534
chair=0.14584655133914495
cow=0.17068022616003517
diningtable=0.06183229850817691
dog=0.21812850852381074
horse=0.1689765761405871
motorbike=0.169925466609853
person=0.32576338147950407
pottedplant=0.001567398119122257
sheep=0.13870944999576992
sofa=0.04330813670581777
train=0.04071708345602054
tvmonitor=0.02824541334025659
mAP=0.1280811478913094
[Epoch 3][Batch 99], LR: 8.46E-04, Speed: 167.239 samples/sec, ObjLoss=62.011, BoxCenterLoss=6.545, BoxScaleLoss=3.014, ClassLoss=12.145
[Epoch 3][Batch 199], LR: 9.43E-04, Speed: 230.001 samples/sec, ObjLoss=56.938, BoxCenterLoss=6.520, BoxScaleLoss=2.935, ClassLoss=11.568
[Epoch 3] Training cost: 148.854, ObjLoss=54.412, BoxCenterLoss=6.510, BoxScaleLoss=2.893, ClassLoss=11.279
[Epoch 3] Validation:
aeroplane=0.18838259675881777
bicycle=0.29905656470009295
bird=0.037269072476790815
boat=0.027206370272063705
bottle=0.09090909090909091
bus=0.15393336533893393
car=0.4132252180372048
cat=0.22952098980192123
chair=0.20824212934815906
cow=0.15043482336237535
diningtable=0.07419603630237619
dog=0.2048422658612481
horse=0.1571814040207213
motorbike=0.204298175869145
person=0.48306799277492474
pottedplant=0.003430531732418525
sheep=0.14052832128936832
sofa=0.061372350561194326
train=0.18384227391407548
tvmonitor=0.23162917204499356
mAP=0.17712843726879582
[Epoch 4][Batch 99], LR: 1.00E-03, Speed: 176.533 samples/sec, ObjLoss=50.712, BoxCenterLoss=6.489, BoxScaleLoss=2.831, ClassLoss=10.826
[Epoch 4][Batch 199], LR: 1.00E-03, Speed: 176.467 samples/sec, ObjLoss=47.577, BoxCenterLoss=6.471, BoxScaleLoss=2.776, ClassLoss=10.420
[Epoch 4] Training cost: 137.440, ObjLoss=45.952, BoxCenterLoss=6.457, BoxScaleLoss=2.743, ClassLoss=10.202
[Epoch 4] Validation:
aeroplane=0.29187174247455244
bicycle=0.2774086667833283
bird=0.14942118395869425
boat=0.05037068194962932
bottle=0.14181092414386323
bus=0.16490635513405913
car=0.35415732003905664
cat=0.23758439852427127
chair=0.1805877017783386
cow=0.11841524782840421
diningtable=0.20265197587505374
dog=0.25713965215811385
horse=0.28555208990716563
motorbike=0.2262914419901534
person=0.4536897673340367
pottedplant=0.09090909090909091
sheep=0.1859181071245299
sofa=0.22178298011920722
train=0.23857865387341465
tvmonitor=0.2626696184162189
mAP=0.2195858800160591
[Epoch 5][Batch 99], LR: 1.00E-03, Speed: 174.960 samples/sec, ObjLoss=43.530, BoxCenterLoss=6.454, BoxScaleLoss=2.701, ClassLoss=9.874
[Epoch 5][Batch 199], LR: 1.00E-03, Speed: 197.562 samples/sec, ObjLoss=41.432, BoxCenterLoss=6.439, BoxScaleLoss=2.659, ClassLoss=9.577
[Epoch 5] Training cost: 141.122, ObjLoss=40.305, BoxCenterLoss=6.425, BoxScaleLoss=2.630, ClassLoss=9.409
[Epoch 5] Validation:
aeroplane=0.28025139225689655
bicycle=0.37502904055733666
bird=0.2394603346142379
boat=0.1385219314686937
bottle=0.2289798516839992
bus=0.30610433011986177
car=0.5110344308145581
cat=0.41548961632179815
chair=0.21883744690680623
cow=0.2952706402286803
diningtable=0.280440196548639
dog=0.3850761229499838
horse=0.3205402914109239
motorbike=0.2777383452601908
person=0.4106647111624155
pottedplant=0.11197274681776186
sheep=0.26889136918583717
sofa=0.3207284973346858
train=0.3803213629344355
tvmonitor=0.4458658043982238
mAP=0.31056092314879835
[Epoch 6][Batch 99], LR: 1.00E-03, Speed: 187.559 samples/sec, ObjLoss=38.588, BoxCenterLoss=6.420, BoxScaleLoss=2.595, ClassLoss=9.148
[Epoch 6][Batch 199], LR: 1.00E-03, Speed: 162.104 samples/sec, ObjLoss=37.047, BoxCenterLoss=6.402, BoxScaleLoss=2.559, ClassLoss=8.914
[Epoch 6] Training cost: 137.107, ObjLoss=36.183, BoxCenterLoss=6.386, BoxScaleLoss=2.534, ClassLoss=8.773
[Epoch 6] Validation:
aeroplane=0.4104310346801093
bicycle=0.4517832226375941
bird=0.42036160980851467
boat=0.2174357078164558
bottle=0.24610397232872527
bus=0.5294871393347681
car=0.6184451229100044
cat=0.6648817900392837
chair=0.2968056211611552
cow=0.3281944828756956
diningtable=0.3399325304749376
dog=0.5427294536210794
horse=0.3306633503068529
motorbike=0.44875778844919895
person=0.5768797693064991
pottedplant=0.19179537092642215
sheep=0.26618555657439663
sofa=0.44017947099317767
train=0.5028123026995215
tvmonitor=0.46263904091348346
mAP=0.4143252168928938
[Epoch 7][Batch 99], LR: 1.00E-03, Speed: 169.784 samples/sec, ObjLoss=34.899, BoxCenterLoss=6.372, BoxScaleLoss=2.501, ClassLoss=8.555
[Epoch 7][Batch 199], LR: 1.00E-03, Speed: 233.988 samples/sec, ObjLoss=33.724, BoxCenterLoss=6.365, BoxScaleLoss=2.477, ClassLoss=8.361
[Epoch 7] Training cost: 144.680, ObjLoss=33.102, BoxCenterLoss=6.360, BoxScaleLoss=2.463, ClassLoss=8.256
[Epoch 7] Validation:
aeroplane=0.3735051842236574
bicycle=0.5809566222176439
bird=0.5099323210752329
boat=0.16937370522388617
bottle=0.32007001780423355
bus=0.5073776857160277
car=0.6218479773484841
cat=0.6385743591464299
chair=0.34917995860220197
cow=0.3205938298370136
diningtable=0.290970219933346
dog=0.4865028455956962
horse=0.5271665078897347
motorbike=0.5055104763022475
person=0.5082123175956119
pottedplant=0.18726197388346583
sheep=0.35764633435948806
sofa=0.4426943322837384
train=0.28126418704525413
tvmonitor=0.46005655918346455
mAP=0.4219348707633429
[Epoch 8][Batch 99], LR: 1.00E-03, Speed: 136.391 samples/sec, ObjLoss=32.114, BoxCenterLoss=6.351, BoxScaleLoss=2.439, ClassLoss=8.082
[Epoch 8][Batch 199], LR: 1.00E-03, Speed: 168.077 samples/sec, ObjLoss=31.191, BoxCenterLoss=6.341, BoxScaleLoss=2.415, ClassLoss=7.922
[Epoch 8] Training cost: 140.733, ObjLoss=30.686, BoxCenterLoss=6.337, BoxScaleLoss=2.404, ClassLoss=7.830
[Epoch 8] Validation:
aeroplane=0.41921187487917566
bicycle=0.5695516171536887
bird=0.31403858861950246
boat=0.27489700066645834
bottle=0.15297548904386304
bus=0.5292929721380977
car=0.6412458040165393
cat=0.4632772456186221
chair=0.2681841134820318
cow=0.26019431781514196
diningtable=0.36374011506741566
dog=0.4891291347449648
horse=0.464973233613277
motorbike=0.5178141277858687
person=0.5480113560911295
pottedplant=0.19009423138720052
sheep=0.2997564432878705
sofa=0.48110631571782203
train=0.5017630330949802
tvmonitor=0.46205171148150914
mAP=0.41056543628525805
[Epoch 9][Batch 99], LR: 1.00E-03, Speed: 167.352 samples/sec, ObjLoss=29.870, BoxCenterLoss=6.331, BoxScaleLoss=2.385, ClassLoss=7.685
[Epoch 9][Batch 199], LR: 1.00E-03, Speed: 185.386 samples/sec, ObjLoss=29.117, BoxCenterLoss=6.321, BoxScaleLoss=2.365, ClassLoss=7.545
[Epoch 9] Training cost: 144.514, ObjLoss=28.695, BoxCenterLoss=6.317, BoxScaleLoss=2.355, ClassLoss=7.467
[Epoch 9] Validation:
aeroplane=0.3755403659086729
bicycle=0.37581988267700744
bird=0.4997315170558744
boat=0.2898111729170461
bottle=0.32450756166066763
bus=0.5297872605876276
car=0.6714513032120614
cat=0.6714336181329937
chair=0.34049241298540384
cow=0.4471958878878368
diningtable=0.4067225967925786
dog=0.5934804500019766
horse=0.49701407403371245
motorbike=0.49539872906145394
person=0.607623551014232
pottedplant=0.15164308686268188
sheep=0.4579993448244487
sofa=0.5110862239058978
train=0.5257674680074039
tvmonitor=0.5163154948289994
mAP=0.46444110011792883
[Epoch 10][Batch 99], LR: 1.00E-03, Speed: 156.504 samples/sec, ObjLoss=28.023, BoxCenterLoss=6.307, BoxScaleLoss=2.335, ClassLoss=7.340
[Epoch 10][Batch 199], LR: 1.00E-03, Speed: 145.312 samples/sec, ObjLoss=27.404, BoxCenterLoss=6.299, BoxScaleLoss=2.319, ClassLoss=7.223
[Epoch 10] Training cost: 130.038, ObjLoss=27.058, BoxCenterLoss=6.293, BoxScaleLoss=2.309, ClassLoss=7.155
[Epoch 10] Validation:
aeroplane=0.33923617997237665
bicycle=0.4139490012082599
bird=0.36180440318935425
boat=0.2393379778732508
bottle=0.2943008819288591
bus=0.5853766639122362
car=0.5971718579931138
cat=0.44506703515689255
chair=0.16687169182719444
cow=0.3926813271797365
diningtable=0.24519434330299542
dog=0.4694044682656317
horse=0.38828572212805096
motorbike=0.4810534633816678
person=0.58220963371702
pottedplant=0.1444393556892213
sheep=0.40169910225811983
sofa=0.36866439716536115
train=0.35190952597986025
tvmonitor=0.4656776893978173
mAP=0.386716736076351
[Epoch 11][Batch 99], LR: 1.00E-03, Speed: 165.370 samples/sec, ObjLoss=26.494, BoxCenterLoss=6.284, BoxScaleLoss=2.291, ClassLoss=7.046
[Epoch 11][Batch 199], LR: 1.00E-03, Speed: 188.367 samples/sec, ObjLoss=25.956, BoxCenterLoss=6.275, BoxScaleLoss=2.277, ClassLoss=6.941
[Epoch 11] Training cost: 137.384, ObjLoss=25.669, BoxCenterLoss=6.274, BoxScaleLoss=2.270, ClassLoss=6.881
[Epoch 11] Validation:
aeroplane=0.5770373337845839
bicycle=0.574913018876884
bird=0.573662136248353
boat=0.2831201829231497
bottle=0.2612268110953112
bus=0.6755820737855889
car=0.7175094504150734
cat=0.7757522997862264
chair=0.40431433705907116
cow=0.4439304670750946
diningtable=0.4884669159713668
dog=0.6524300181416284
horse=0.5478523780147135
motorbike=0.5614365812849136
person=0.6102626234513533
pottedplant=0.29669651539726083
sheep=0.477638483615997
sofa=0.5941544134409992
train=0.5511183915522712
tvmonitor=0.4274077975466138
mAP=0.5247256114733226
[Epoch 12][Batch 99], LR: 1.00E-03, Speed: 154.236 samples/sec, ObjLoss=25.185, BoxCenterLoss=6.266, BoxScaleLoss=2.254, ClassLoss=6.778
[Epoch 12][Batch 199], LR: 1.00E-03, Speed: 161.032 samples/sec, ObjLoss=24.746, BoxCenterLoss=6.262, BoxScaleLoss=2.242, ClassLoss=6.684
[Epoch 12] Training cost: 146.495, ObjLoss=24.491, BoxCenterLoss=6.259, BoxScaleLoss=2.234, ClassLoss=6.632
[Epoch 12] Validation:
aeroplane=0.5190914130411461
bicycle=0.418251042023143
bird=0.4748370095433071
boat=0.2512547490101253
bottle=0.2885778651887474
bus=0.5261895786566674
car=0.7362614088827287
cat=0.6502337619108056
chair=0.32651403295782894
cow=0.4827965718150511
diningtable=0.2241234849420732
dog=0.5953283990893646
horse=0.5112229402740148
motorbike=0.6155842358210668
person=0.6063538879305418
pottedplant=0.16103999593455454
sheep=0.48397846480976603
sofa=0.45251003496091086
train=0.6643336356437995
tvmonitor=0.5179187893270781
mAP=0.47532006508813607
[Epoch 13][Batch 99], LR: 1.00E-03, Speed: 219.144 samples/sec, ObjLoss=24.088, BoxCenterLoss=6.254, BoxScaleLoss=2.222, ClassLoss=6.547
[Epoch 13][Batch 199], LR: 1.00E-03, Speed: 167.280 samples/sec, ObjLoss=23.704, BoxCenterLoss=6.251, BoxScaleLoss=2.209, ClassLoss=6.464
[Epoch 13] Training cost: 142.547, ObjLoss=23.481, BoxCenterLoss=6.250, BoxScaleLoss=2.202, ClassLoss=6.419
[Epoch 13] Validation:
aeroplane=0.5749403819252388
bicycle=0.6155467560896247
bird=0.5172198977297774
boat=0.3740624223413853
bottle=0.2212450311421225
bus=0.6628191165475253
car=0.7319828697855856
cat=0.7190476599723812
chair=0.31783337700921366
cow=0.5449497163303709
diningtable=0.32946293268950894
dog=0.6385030093156612
horse=0.6030439791431511
motorbike=0.6268317537371395
person=0.6582928987706462
pottedplant=0.23739955053436573
sheep=0.4754898979156188
sofa=0.5578489480942213
train=0.6607684779938656
tvmonitor=0.5985487271538076
mAP=0.5332918702110606
[Epoch 14][Batch 99], LR: 1.00E-03, Speed: 176.969 samples/sec, ObjLoss=23.129, BoxCenterLoss=6.247, BoxScaleLoss=2.192, ClassLoss=6.344
[Epoch 14][Batch 199], LR: 1.00E-03, Speed: 165.660 samples/sec, ObjLoss=22.792, BoxCenterLoss=6.243, BoxScaleLoss=2.181, ClassLoss=6.270
[Epoch 14] Training cost: 150.787, ObjLoss=22.594, BoxCenterLoss=6.240, BoxScaleLoss=2.175, ClassLoss=6.229
[Epoch 14] Validation:
aeroplane=0.622745662173798
bicycle=0.6867551867776535
bird=0.5580791970774621
boat=0.30388840228822345
bottle=0.24516088285282664
bus=0.6995579947283994
car=0.7443794191532835
cat=0.6981408870754604
chair=0.3769046183183162
cow=0.45054068345039955
diningtable=0.3818869557515492
dog=0.6853404877301975
horse=0.5327461345549467
motorbike=0.6332541334614457
person=0.6616839885524981
pottedplant=0.21212680438188775
sheep=0.5339483533759435
sofa=0.5828755577054069
train=0.6692301821444778
tvmonitor=0.6123624682519708
mAP=0.5445803999903074
[Epoch 15][Batch 99], LR: 1.00E-03, Speed: 166.802 samples/sec, ObjLoss=22.287, BoxCenterLoss=6.238, BoxScaleLoss=2.164, ClassLoss=6.158
[Epoch 15][Batch 199], LR: 1.00E-03, Speed: 171.088 samples/sec, ObjLoss=21.976, BoxCenterLoss=6.231, BoxScaleLoss=2.153, ClassLoss=6.089
[Epoch 15] Training cost: 140.546, ObjLoss=21.807, BoxCenterLoss=6.229, BoxScaleLoss=2.148, ClassLoss=6.052
[Epoch 15] Validation:
aeroplane=0.5818057202978965
bicycle=0.6869035705788681
bird=0.49723273311430877
boat=0.37085878991968824
bottle=0.30708164180504227
bus=0.6514591681967843
car=0.7204785149348104
cat=0.7085204430407652
chair=0.40035889286797927
cow=0.5722728483464308
diningtable=0.5334494065435577
dog=0.6308130715227759
horse=0.5277916231109321
motorbike=0.6225344156155223
person=0.6167583167520443
pottedplant=0.3416578948444384
sheep=0.5841698000120621
sofa=0.5318687898794973
train=0.6002647624565348
tvmonitor=0.5824910054240392
mAP=0.5534385704631989
[Epoch 16][Batch 99], LR: 1.00E-03, Speed: 175.550 samples/sec, ObjLoss=21.522, BoxCenterLoss=6.224, BoxScaleLoss=2.137, ClassLoss=5.987
[Epoch 16][Batch 199], LR: 1.00E-03, Speed: 169.795 samples/sec, ObjLoss=21.260, BoxCenterLoss=6.220, BoxScaleLoss=2.127, ClassLoss=5.926
[Epoch 16] Training cost: 147.537, ObjLoss=21.110, BoxCenterLoss=6.220, BoxScaleLoss=2.121, ClassLoss=5.889
[Epoch 16] Validation:
aeroplane=0.6162747448511385
bicycle=0.6549424161271619
bird=0.5704095490479721
boat=0.38100334593020313
bottle=0.17680767976985481
bus=0.686336762620989
car=0.7400023950765818
cat=0.7415265849857451
chair=0.4180079845232701
cow=0.48553341699745184
diningtable=0.5748148991860247
dog=0.7222553412595801
horse=0.5191401856456159
motorbike=0.6352025400451914
person=0.6376649574183394
pottedplant=0.24237530619461978
sheep=0.528042242251292
sofa=0.6134253685741199
train=0.639602878419713
tvmonitor=0.5702930451392323
mAP=0.5576830822032048
[Epoch 17][Batch 99], LR: 1.00E-03, Speed: 163.498 samples/sec, ObjLoss=20.858, BoxCenterLoss=6.217, BoxScaleLoss=2.113, ClassLoss=5.829
[Epoch 17][Batch 199], LR: 1.00E-03, Speed: 195.806 samples/sec, ObjLoss=20.616, BoxCenterLoss=6.212, BoxScaleLoss=2.104, ClassLoss=5.775
[Epoch 17] Training cost: 130.779, ObjLoss=20.477, BoxCenterLoss=6.211, BoxScaleLoss=2.099, ClassLoss=5.744
[Epoch 17] Validation:
aeroplane=0.6672774479706297
bicycle=0.5984914117798898
bird=0.5590548487728156
boat=0.42940892027042993
bottle=0.335133963752621
bus=0.7001080382598396
car=0.7467731016247757
cat=0.6977119725175417
chair=0.40402500078689746
cow=0.5616169300024042
diningtable=0.5062960734537691
dog=0.7198499347488581
horse=0.6410366756403475
motorbike=0.6556113199476272
person=0.6690546107668993
pottedplant=0.2910188148957033
sheep=0.5998243002074006
sofa=0.574194486170999
train=0.6784171711894608
tvmonitor=0.6290538623288059
mAP=0.5831979442543858
[Epoch 18][Batch 99], LR: 1.00E-03, Speed: 184.633 samples/sec, ObjLoss=20.253, BoxCenterLoss=6.208, BoxScaleLoss=2.091, ClassLoss=5.693
[Epoch 18][Batch 199], LR: 1.00E-03, Speed: 165.387 samples/sec, ObjLoss=20.037, BoxCenterLoss=6.205, BoxScaleLoss=2.083, ClassLoss=5.642
[Epoch 18] Training cost: 138.300, ObjLoss=19.913, BoxCenterLoss=6.202, BoxScaleLoss=2.079, ClassLoss=5.614
[Epoch 18] Validation:
aeroplane=0.6958668969876534
bicycle=0.6782333856401416
bird=0.5929689404251047
boat=0.4271477749400669
bottle=0.35857649342733855
bus=0.7095959954824088
car=0.6900167632718893
cat=0.7628598739002141
chair=0.41786608704704414
cow=0.596257702410158
diningtable=0.5003296133968372
dog=0.696051974733028
horse=0.5347406877103973
motorbike=0.6142843010855336
person=0.669776201397227
pottedplant=0.30702163014150124
sheep=0.601433405834409
sofa=0.5999200492815694
train=0.6929061455736572
tvmonitor=0.5780761618872456
mAP=0.5861965042286712
[Epoch 19][Batch 99], LR: 1.00E-03, Speed: 168.570 samples/sec, ObjLoss=19.715, BoxCenterLoss=6.200, BoxScaleLoss=2.071, ClassLoss=5.562
[Epoch 19][Batch 199], LR: 1.00E-03, Speed: 191.708 samples/sec, ObjLoss=19.517, BoxCenterLoss=6.197, BoxScaleLoss=2.065, ClassLoss=5.514
[Epoch 19] Training cost: 151.402, ObjLoss=19.397, BoxCenterLoss=6.192, BoxScaleLoss=2.059, ClassLoss=5.486
[Epoch 19] Validation:
aeroplane=0.5733035696345397
bicycle=0.6375801794808819
bird=0.5355812529548547
boat=0.4467702799751137
bottle=0.37856673151221754
bus=0.6878623765657121
car=0.7390562739924549
cat=0.6558551610637858
chair=0.38610098924295805
cow=0.5503396954803019
diningtable=0.36639394518832336
dog=0.6222837574934533
horse=0.5735130657510684
motorbike=0.6403064023591685
person=0.6561288272907717
pottedplant=0.3161938085838124
sheep=0.5979749448780588
sofa=0.5392928990049257
train=0.6285437432635761
tvmonitor=0.5938091447174378
mAP=0.5562728524216708
[Epoch 20][Batch 99], LR: 1.00E-03, Speed: 161.945 samples/sec, ObjLoss=19.218, BoxCenterLoss=6.190, BoxScaleLoss=2.052, ClassLoss=5.440
[Epoch 20][Batch 199], LR: 1.00E-03, Speed: 160.172 samples/sec, ObjLoss=19.036, BoxCenterLoss=6.186, BoxScaleLoss=2.045, ClassLoss=5.397
[Epoch 20] Training cost: 142.257, ObjLoss=18.937, BoxCenterLoss=6.185, BoxScaleLoss=2.042, ClassLoss=5.372
[Epoch 20] Validation:
aeroplane=0.613177548592609
bicycle=0.6628506111232043
bird=0.5734564022604586
boat=0.4672344552851523
bottle=0.39669969845478636
bus=0.7124075290473388
car=0.7280534355314529
cat=0.7331710024025211
chair=0.3680505557100372
cow=0.6398963145118467
diningtable=0.5404045135856256
dog=0.7061191355732388
horse=0.6238837535936047
motorbike=0.6847795740961713
person=0.5872532275722647
pottedplant=0.28807406812273895
sheep=0.5406766267723326
sofa=0.5707978423033365
train=0.701517533302102
tvmonitor=0.5416982360785396
mAP=0.5840101031959681
[Epoch 21][Batch 99], LR: 1.00E-03, Speed: 162.482 samples/sec, ObjLoss=18.765, BoxCenterLoss=6.181, BoxScaleLoss=2.035, ClassLoss=5.330
[Epoch 21][Batch 199], LR: 1.00E-03, Speed: 164.934 samples/sec, ObjLoss=18.605, BoxCenterLoss=6.180, BoxScaleLoss=2.030, ClassLoss=5.289
[Epoch 21] Training cost: 140.855, ObjLoss=18.510, BoxCenterLoss=6.178, BoxScaleLoss=2.026, ClassLoss=5.266
[Epoch 21] Validation:
aeroplane=0.616410135478691
bicycle=0.7029702367751659
bird=0.6124821044596178
boat=0.44446709838425774
bottle=0.27688153456471704
bus=0.7273371688449007
car=0.7392120978322225
cat=0.7559030113271712
chair=0.3640718396099021
cow=0.6336741013605882
diningtable=0.5788471765401697
dog=0.7132656950595668
horse=0.6495442078438898
motorbike=0.6279212663939582
person=0.6879110239577778
pottedplant=0.10685070677410909
sheep=0.5747877314114176
sofa=0.5963140006166998
train=0.6883189044825386
tvmonitor=0.5590324118346117
mAP=0.5828101226775987
[Epoch 22][Batch 99], LR: 1.00E-03, Speed: 184.032 samples/sec, ObjLoss=18.352, BoxCenterLoss=6.175, BoxScaleLoss=2.020, ClassLoss=5.226
[Epoch 22][Batch 199], LR: 1.00E-03, Speed: 169.631 samples/sec, ObjLoss=18.201, BoxCenterLoss=6.174, BoxScaleLoss=2.013, ClassLoss=5.189
[Epoch 22] Training cost: 142.818, ObjLoss=18.112, BoxCenterLoss=6.170, BoxScaleLoss=2.010, ClassLoss=5.167
[Epoch 22] Validation:
aeroplane=0.6713398329751912
bicycle=0.6558776263964801
bird=0.524935952408436
boat=0.4754482142943551
bottle=0.3889941979897063
bus=0.7023010142115018
car=0.7661259426858661
cat=0.7699496256503892
chair=0.4327069805963005
cow=0.6258335082647348
diningtable=0.5080803476629229
dog=0.6736509849686313
horse=0.658527244452791
motorbike=0.6820881330168365
person=0.6574913604700307
pottedplant=0.30191823926091427
sheep=0.5252905939110243
sofa=0.5986075723846886
train=0.7045022526265007
tvmonitor=0.6514216580760313
mAP=0.5987545641151666
[Epoch 23][Batch 99], LR: 1.00E-03, Speed: 143.063 samples/sec, ObjLoss=17.969, BoxCenterLoss=6.168, BoxScaleLoss=2.005, ClassLoss=5.130
[Epoch 23][Batch 199], LR: 1.00E-03, Speed: 227.050 samples/sec, ObjLoss=17.831, BoxCenterLoss=6.166, BoxScaleLoss=1.998, ClassLoss=5.093
[Epoch 23] Training cost: 133.984, ObjLoss=17.755, BoxCenterLoss=6.165, BoxScaleLoss=1.995, ClassLoss=5.074
[Epoch 23] Validation:
aeroplane=0.6073388340821986
bicycle=0.720177659090104
bird=0.5871247290942451
boat=0.32729276515859973
bottle=0.4086514553337318
bus=0.679861256577056
car=0.782115749166013
cat=0.8047010092917062
chair=0.4009723493860617
cow=0.6236912829721204
diningtable=0.5390904230136956
dog=0.7344928562216297
horse=0.6663400901339385
motorbike=0.7343587286610901
person=0.7167220675785586
pottedplant=0.29238829818190115
sheep=0.6229393240145911
sofa=0.6377982654102732
train=0.677869801297463
tvmonitor=0.6380782107205197
mAP=0.6101002577692749
[Epoch 24][Batch 99], LR: 1.00E-03, Speed: 176.471 samples/sec, ObjLoss=17.620, BoxCenterLoss=6.163, BoxScaleLoss=1.990, ClassLoss=5.040
[Epoch 24][Batch 199], LR: 1.00E-03, Speed: 153.355 samples/sec, ObjLoss=17.495, BoxCenterLoss=6.160, BoxScaleLoss=1.985, ClassLoss=5.007
[Epoch 24] Training cost: 145.989, ObjLoss=17.421, BoxCenterLoss=6.158, BoxScaleLoss=1.982, ClassLoss=4.988
[Epoch 24] Validation:
aeroplane=0.640280385864699
bicycle=0.7245522773148725
bird=0.6523576762118648
boat=0.38406553696997253
bottle=0.4093302735945125
bus=0.7068099767078824
car=0.772232000217066
cat=0.764941602347751
chair=0.42162287426278255
cow=0.6399218149299472
diningtable=0.5840772138551318
dog=0.7480423979941665
horse=0.6917983655625236
motorbike=0.7144089226412587
person=0.6654501915016473
pottedplant=0.18216706437189767
sheep=0.5792490080087672
sofa=0.6245845355938293
train=0.7002654378793554
tvmonitor=0.6458810928458355
mAP=0.6126019324337882
[Epoch 25][Batch 99], LR: 1.00E-03, Speed: 161.569 samples/sec, ObjLoss=17.301, BoxCenterLoss=6.158, BoxScaleLoss=1.978, ClassLoss=4.956
[Epoch 25][Batch 199], LR: 1.00E-03, Speed: 168.444 samples/sec, ObjLoss=17.182, BoxCenterLoss=6.155, BoxScaleLoss=1.972, ClassLoss=4.924
[Epoch 25] Training cost: 137.044, ObjLoss=17.114, BoxCenterLoss=6.155, BoxScaleLoss=1.970, ClassLoss=4.907
[Epoch 25] Validation:
aeroplane=0.7046826531933341
bicycle=0.7160763575567095
bird=0.5891153456371251
boat=0.5038470067125826
bottle=0.41378637892918535
bus=0.741717451856235
car=0.7612640791748094
cat=0.7847177293444738
chair=0.4196510810138512
cow=0.6674749006357766
diningtable=0.619062649925423
dog=0.7540627022596949
horse=0.6991375048524638
motorbike=0.6760025161559229
person=0.6620171458516342
pottedplant=0.3533889602661538
sheep=0.544243194493071
sofa=0.6352250837403052
train=0.7313278885494189
tvmonitor=0.5520267705628729
mAP=0.6264413700355522
[Epoch 26][Batch 99], LR: 1.00E-03, Speed: 177.533 samples/sec, ObjLoss=16.996, BoxCenterLoss=6.151, BoxScaleLoss=1.965, ClassLoss=4.876
[Epoch 26][Batch 199], LR: 1.00E-03, Speed: 167.942 samples/sec, ObjLoss=16.883, BoxCenterLoss=6.149, BoxScaleLoss=1.962, ClassLoss=4.850
[Epoch 26] Training cost: 136.799, ObjLoss=16.816, BoxCenterLoss=6.147, BoxScaleLoss=1.958, ClassLoss=4.831
[Epoch 26] Validation:
aeroplane=0.6486573375763636
bicycle=0.7370633268349062
bird=0.6374940464451194
boat=0.5170140007756656
bottle=0.41079203352970967
bus=0.7508167353724837
car=0.7837370596652261
cat=0.7481441948693904
chair=0.46761310005419593
cow=0.6351166948695052
diningtable=0.5435327609250659
dog=0.7038939385018924
horse=0.7485055058489305
motorbike=0.6711970283935892
person=0.6760179407001595
pottedplant=0.3386995496572977
sheep=0.5819847138345996
sofa=0.6119556050344783
train=0.742331779529
tvmonitor=0.6403355569607362
mAP=0.6297451454689156
[Epoch 27][Batch 99], LR: 1.00E-03, Speed: 223.635 samples/sec, ObjLoss=16.710, BoxCenterLoss=6.145, BoxScaleLoss=1.953, ClassLoss=4.801
[Epoch 27][Batch 199], LR: 1.00E-03, Speed: 198.562 samples/sec, ObjLoss=16.607, BoxCenterLoss=6.143, BoxScaleLoss=1.949, ClassLoss=4.771
[Epoch 27] Training cost: 156.120, ObjLoss=16.546, BoxCenterLoss=6.142, BoxScaleLoss=1.946, ClassLoss=4.755
[Epoch 27] Validation:
aeroplane=0.604344436957885
bicycle=0.6649451470363924
bird=0.515431773242257
boat=0.4473501976125753
bottle=0.40020763951871596
bus=0.7221347295822781
car=0.7602657268779345
cat=0.754866245588253
chair=0.4040290996449214
cow=0.5997519973529473
diningtable=0.5374450047938311
dog=0.6696887301463038
horse=0.6236744140286815
motorbike=0.6416186262161707
person=0.6411080358846507
pottedplant=0.3019184226450895
sheep=0.5558172577208448
sofa=0.6205114411312243
train=0.6399540394484748
tvmonitor=0.6180678002843759
mAP=0.5861565382856903
[Epoch 28][Batch 99], LR: 1.00E-03, Speed: 148.686 samples/sec, ObjLoss=16.447, BoxCenterLoss=6.140, BoxScaleLoss=1.942, ClassLoss=4.728
[Epoch 28][Batch 199], LR: 1.00E-03, Speed: 153.622 samples/sec, ObjLoss=16.352, BoxCenterLoss=6.137, BoxScaleLoss=1.938, ClassLoss=4.704
[Epoch 28] Training cost: 145.982, ObjLoss=16.294, BoxCenterLoss=6.135, BoxScaleLoss=1.935, ClassLoss=4.687
[Epoch 28] Validation:
aeroplane=0.6515572388582387
bicycle=0.7282308856716803
bird=0.6476712809547783
boat=0.4646887018822959
bottle=0.4556108173277221
bus=0.7471332561772706
car=0.8009710378988242
cat=0.8204318292496197
chair=0.44110633263338284
cow=0.6698560209474265
diningtable=0.586002874310867
dog=0.7309117699000481
horse=0.7034098170023204
motorbike=0.7397196115178255
person=0.6617512483524881
pottedplant=0.35220069740802545
sheep=0.5646181297440082
sofa=0.6140210887922377
train=0.7248639285656061
tvmonitor=0.6489285168088961
mAP=0.6376842542001782
[Epoch 29][Batch 99], LR: 1.00E-03, Speed: 156.760 samples/sec, ObjLoss=16.200, BoxCenterLoss=6.133, BoxScaleLoss=1.931, ClassLoss=4.660
[Epoch 29][Batch 199], LR: 1.00E-03, Speed: 206.498 samples/sec, ObjLoss=16.109, BoxCenterLoss=6.132, BoxScaleLoss=1.926, ClassLoss=4.634
[Epoch 29] Training cost: 150.003, ObjLoss=16.054, BoxCenterLoss=6.129, BoxScaleLoss=1.923, ClassLoss=4.619
[Epoch 29] Validation:
aeroplane=0.6643980092615752
bicycle=0.7163617469736598
bird=0.630127506336221
boat=0.4752415082391228
bottle=0.37000766068287483
bus=0.7477349639946677
car=0.7944051679798506
cat=0.8062433032994951
chair=0.42265721692930813
cow=0.6780327803679292
diningtable=0.575624089242677
dog=0.7524767171478837
horse=0.7426214852156076
motorbike=0.7300956868976698
person=0.6854499239477063
pottedplant=0.2807768433648982
sheep=0.5821903963578607
sofa=0.641282843820393
train=0.706545695974306
tvmonitor=0.6765975890362728
mAP=0.633943556753499
[Epoch 30][Batch 99], LR: 1.00E-03, Speed: 201.899 samples/sec, ObjLoss=15.964, BoxCenterLoss=6.127, BoxScaleLoss=1.919, ClassLoss=4.593
[Epoch 30][Batch 199], LR: 1.00E-03, Speed: 195.270 samples/sec, ObjLoss=15.876, BoxCenterLoss=6.126, BoxScaleLoss=1.916, ClassLoss=4.568
[Epoch 30] Training cost: 132.264, ObjLoss=15.824, BoxCenterLoss=6.123, BoxScaleLoss=1.913, ClassLoss=4.555
[Epoch 30] Validation:
aeroplane=0.7233234153440449
bicycle=0.7304626040231224
bird=0.6404407598979136
boat=0.5101356574608499
bottle=0.401841120658017
bus=0.7376168377989298
car=0.7877007899844308
cat=0.8150160697058578
chair=0.435577469077559
cow=0.6222876603489839
diningtable=0.5875128030690994
dog=0.7590403255557563
horse=0.7515478930555844
motorbike=0.7309845370282827
person=0.7212587450395996
pottedplant=0.32016983499747204
sheep=0.6362712854578743
sofa=0.6519419957125436
train=0.7280593673683721
tvmonitor=0.6808030245280673
mAP=0.6485996098056181
[Epoch 31][Batch 99], LR: 1.00E-03, Speed: 175.308 samples/sec, ObjLoss=15.742, BoxCenterLoss=6.121, BoxScaleLoss=1.909, ClassLoss=4.530
[Epoch 31][Batch 199], LR: 1.00E-03, Speed: 205.686 samples/sec, ObjLoss=15.661, BoxCenterLoss=6.120, BoxScaleLoss=1.906, ClassLoss=4.509
[Epoch 31] Training cost: 136.353, ObjLoss=15.612, BoxCenterLoss=6.117, BoxScaleLoss=1.904, ClassLoss=4.498
[Epoch 31] Validation:
aeroplane=0.6625506072874494
bicycle=0.7168932087014
bird=0.6502729536483328
boat=0.46774054179510266
bottle=0.4024194628890439
bus=0.7283569465402767
car=0.8131976981823269
cat=0.8124340513748937
chair=0.43379667502049024
cow=0.652523074934194
diningtable=0.5891505915562196
dog=0.7262052666440335
horse=0.7293817506738447
motorbike=0.713722705635601
person=0.714111982965793
pottedplant=0.38774500065641326
sheep=0.6354058829012176
sofa=0.6280277902259499
train=0.7174007493646759
tvmonitor=0.6594162958334016
mAP=0.6420376618415331
[Epoch 32][Batch 99], LR: 1.00E-03, Speed: 199.291 samples/sec, ObjLoss=15.532, BoxCenterLoss=6.115, BoxScaleLoss=1.900, ClassLoss=4.475
[Epoch 32][Batch 199], LR: 1.00E-03, Speed: 171.530 samples/sec, ObjLoss=15.455, BoxCenterLoss=6.113, BoxScaleLoss=1.896, ClassLoss=4.452
[Epoch 32] Training cost: 140.420, ObjLoss=15.414, BoxCenterLoss=6.114, BoxScaleLoss=1.894, ClassLoss=4.440
[Epoch 32] Validation:
aeroplane=0.695770802309781
bicycle=0.7204771237102922
bird=0.5878055719908876
boat=0.47334967610091283
bottle=0.42428007912463445
bus=0.735132909228276
car=0.7975021342336337
cat=0.7682467226464188
chair=0.45190862761563605
cow=0.6412739024074606
diningtable=0.538276305173483
dog=0.7000333862731685
horse=0.7415888512151997
motorbike=0.7041979429544823
person=0.6976433784729832
pottedplant=0.33573866254226176
sheep=0.5491707356356478
sofa=0.627891834975629
train=0.731087164420674
tvmonitor=0.6558539663418917
mAP=0.6288614888686678
[Epoch 33][Batch 99], LR: 1.00E-03, Speed: 193.240 samples/sec, ObjLoss=15.337, BoxCenterLoss=6.111, BoxScaleLoss=1.891, ClassLoss=4.419
[Epoch 33][Batch 199], LR: 1.00E-03, Speed: 169.220 samples/sec, ObjLoss=15.264, BoxCenterLoss=6.110, BoxScaleLoss=1.887, ClassLoss=4.398
[Epoch 33] Training cost: 131.567, ObjLoss=15.223, BoxCenterLoss=6.110, BoxScaleLoss=1.886, ClassLoss=4.388
[Epoch 33] Validation:
aeroplane=0.6822429438764605
bicycle=0.739160737197667
bird=0.6308931746582381
boat=0.5574539096709922
bottle=0.4603890953822846
bus=0.7599348067094155
car=0.8033383319350349
cat=0.80391679469629
chair=0.4448209313359119
cow=0.6753846124743718
diningtable=0.6057222050738513
dog=0.7341882706319639
horse=0.7166239607014891
motorbike=0.7111899185071974
person=0.7273125216051447
pottedplant=0.33095279274766065
sheep=0.6259928602414104
sofa=0.6502486883442983
train=0.7279320348961084
tvmonitor=0.6761705152112397
mAP=0.6531934552948515
[Epoch 34][Batch 99], LR: 1.00E-03, Speed: 152.038 samples/sec, ObjLoss=15.151, BoxCenterLoss=6.107, BoxScaleLoss=1.883, ClassLoss=4.366
[Epoch 34][Batch 199], LR: 1.00E-03, Speed: 186.253 samples/sec, ObjLoss=15.082, BoxCenterLoss=6.106, BoxScaleLoss=1.879, ClassLoss=4.346
[Epoch 34] Training cost: 146.936, ObjLoss=15.044, BoxCenterLoss=6.106, BoxScaleLoss=1.878, ClassLoss=4.335
[Epoch 34] Validation:
aeroplane=0.6736378329149267
bicycle=0.714679470459888
bird=0.5659110457541847
boat=0.3970360129057955
bottle=0.4491331643146121
bus=0.71926763189453
car=0.7806540720974228
cat=0.79057328721718
chair=0.49210717000934895
cow=0.652226202113557
diningtable=0.5429060402998709
dog=0.7367085479815341
horse=0.710183304106375
motorbike=0.7073133573400889
person=0.6900553210164295
pottedplant=0.366946999251496
sheep=0.5832368494898348
sofa=0.6157769508243526
train=0.7138681460980812
tvmonitor=0.6588613771087758
mAP=0.6280541391599141
[Epoch 35][Batch 99], LR: 1.00E-03, Speed: 179.723 samples/sec, ObjLoss=14.977, BoxCenterLoss=6.106, BoxScaleLoss=1.875, ClassLoss=4.316
[Epoch 35][Batch 199], LR: 1.00E-03, Speed: 156.547 samples/sec, ObjLoss=14.908, BoxCenterLoss=6.103, BoxScaleLoss=1.872, ClassLoss=4.297
[Epoch 35] Training cost: 135.350, ObjLoss=14.872, BoxCenterLoss=6.102, BoxScaleLoss=1.870, ClassLoss=4.286
[Epoch 35] Validation:
aeroplane=0.7038867694973105
bicycle=0.710455497462803
bird=0.6524989741058258
boat=0.46759692567180605
bottle=0.39766712440319774
bus=0.7312610023103892
car=0.7823306756478222
cat=0.7899038131772596
chair=0.46992644706208453
cow=0.6572204526084032
diningtable=0.6226487343690246
dog=0.7315149757456633
horse=0.7372883950361598
motorbike=0.7407339555814135
person=0.6960300553928127
pottedplant=0.3362273577151686
sheep=0.6217942520221207
sofa=0.6456766908612135
train=0.7327759211674582
tvmonitor=0.5699895788749162
mAP=0.6398713799356426
[Epoch 36][Batch 99], LR: 1.00E-03, Speed: 157.131 samples/sec, ObjLoss=14.812, BoxCenterLoss=6.102, BoxScaleLoss=1.868, ClassLoss=4.266
[Epoch 36][Batch 199], LR: 1.00E-03, Speed: 171.140 samples/sec, ObjLoss=14.749, BoxCenterLoss=6.101, BoxScaleLoss=1.864, ClassLoss=4.247
[Epoch 36] Training cost: 130.664, ObjLoss=14.711, BoxCenterLoss=6.099, BoxScaleLoss=1.862, ClassLoss=4.236
[Epoch 36] Validation:
aeroplane=0.7042855842335449
bicycle=0.7355009008395703
bird=0.6095628249535486
boat=0.5277673204968423
bottle=0.4331736038993539
bus=0.7296309569392647
car=0.8087819053473388
cat=0.7445631945008787
chair=0.4441725296033555
cow=0.7091354946425201
diningtable=0.5831148178844247
dog=0.7294509894656312
horse=0.7131077046102148
motorbike=0.7094199494035778
person=0.7202491635440983
pottedplant=0.3523378428395307
sheep=0.6296434806336785
sofa=0.6795376884825473
train=0.7340429641002485
tvmonitor=0.6793933333319161
mAP=0.6488436124876042
[Epoch 37][Batch 99], LR: 1.00E-03, Speed: 167.318 samples/sec, ObjLoss=14.649, BoxCenterLoss=6.096, BoxScaleLoss=1.858, ClassLoss=4.218
[Epoch 37][Batch 199], LR: 1.00E-03, Speed: 208.188 samples/sec, ObjLoss=14.589, BoxCenterLoss=6.094, BoxScaleLoss=1.856, ClassLoss=4.201
[Epoch 37] Training cost: 140.590, ObjLoss=14.555, BoxCenterLoss=6.093, BoxScaleLoss=1.855, ClassLoss=4.191
[Epoch 37] Validation:
aeroplane=0.7108642651469688
bicycle=0.7217132876641906
bird=0.6255517163667158
boat=0.46478593459011636
bottle=0.45348463934942906
bus=0.7382606251116942
car=0.8004042385203102
cat=0.814698601719827
chair=0.451903767629667
cow=0.6873376258666716
diningtable=0.5821197786905248
dog=0.6989300776891767
horse=0.6700900505839129
motorbike=0.7090510916655671
person=0.6951421381294646
pottedplant=0.381182329313841
sheep=0.6293682314827357
sofa=0.6244053266064646
train=0.753001856217685
tvmonitor=0.6822541818919632
mAP=0.6447274882118463
[Epoch 38][Batch 99], LR: 1.00E-03, Speed: 163.270 samples/sec, ObjLoss=14.498, BoxCenterLoss=6.092, BoxScaleLoss=1.852, ClassLoss=4.173
[Epoch 38][Batch 199], LR: 1.00E-03, Speed: 155.138 samples/sec, ObjLoss=14.440, BoxCenterLoss=6.091, BoxScaleLoss=1.849, ClassLoss=4.157
[Epoch 38] Training cost: 138.818, ObjLoss=14.406, BoxCenterLoss=6.089, BoxScaleLoss=1.847, ClassLoss=4.147
[Epoch 38] Validation:
aeroplane=0.7070616234858038
bicycle=0.7357432323142272
bird=0.6567026446947702
boat=0.5751830604708119
bottle=0.41982115580294993
bus=0.7745026879118888
car=0.804917727295115
cat=0.7793412753064561
chair=0.48029811634728864
cow=0.6874860086277857
diningtable=0.6327364078228914
dog=0.722999282879899
horse=0.7739392655657394
motorbike=0.7681314142357484
person=0.7196722465882363
pottedplant=0.32903041295220314
sheep=0.5543289171791949
sofa=0.6823632247615703
train=0.7335831385475111
tvmonitor=0.677618749265977
mAP=0.6607730296028033
[Epoch 39][Batch 99], LR: 1.00E-03, Speed: 163.274 samples/sec, ObjLoss=14.349, BoxCenterLoss=6.088, BoxScaleLoss=1.845, ClassLoss=4.129
[Epoch 39][Batch 199], LR: 1.00E-03, Speed: 157.011 samples/sec, ObjLoss=14.296, BoxCenterLoss=6.087, BoxScaleLoss=1.842, ClassLoss=4.113
[Epoch 39] Training cost: 148.058, ObjLoss=14.264, BoxCenterLoss=6.086, BoxScaleLoss=1.840, ClassLoss=4.104
[Epoch 39] Validation:
aeroplane=0.7271981055949776
bicycle=0.7450538576269352
bird=0.6827767110522667
boat=0.518455823772031
bottle=0.46331038705718086
bus=0.7804959632396148
car=0.8031042481056114
cat=0.8114821803378205
chair=0.4561183224537716
cow=0.6321904958278397
diningtable=0.6184352698050141
dog=0.7222482812930796
horse=0.7647521230162488
motorbike=0.7234044625802738
person=0.7293008544502806
pottedplant=0.3757647710593912
sheep=0.5702378453123068
sofa=0.6795420781202184
train=0.7201328752838005
tvmonitor=0.6846140755890298
mAP=0.6604309365788845
[Epoch 40][Batch 99], LR: 1.00E-03, Speed: 172.058 samples/sec, ObjLoss=14.211, BoxCenterLoss=6.086, BoxScaleLoss=1.837, ClassLoss=4.087
[Epoch 40][Batch 199], LR: 1.00E-03, Speed: 179.020 samples/sec, ObjLoss=14.160, BoxCenterLoss=6.084, BoxScaleLoss=1.834, ClassLoss=4.071
[Epoch 40] Training cost: 147.938, ObjLoss=14.131, BoxCenterLoss=6.084, BoxScaleLoss=1.832, ClassLoss=4.061
[Epoch 40] Validation:
aeroplane=0.7388171013192192
bicycle=0.7564749554195764
bird=0.6618240782173798
boat=0.5157734000718783
bottle=0.48515915338554944
bus=0.743422045969087
car=0.7947140301607772
cat=0.7634897437192989
chair=0.4244963696974277
cow=0.6930389500833788
diningtable=0.6407867878828041
dog=0.7479446099829365
horse=0.7250926192058024
motorbike=0.7327780735938099
person=0.7071048807928605
pottedplant=0.37534350688611573
sheep=0.6252249556254744
sofa=0.6209163444367175
train=0.7231836165694452
tvmonitor=0.5882153988212871
mAP=0.6531900310920412
[Epoch 41][Batch 99], LR: 1.00E-03, Speed: 147.050 samples/sec, ObjLoss=14.080, BoxCenterLoss=6.083, BoxScaleLoss=1.830, ClassLoss=4.046
[Epoch 41][Batch 199], LR: 1.00E-03, Speed: 158.948 samples/sec, ObjLoss=14.029, BoxCenterLoss=6.082, BoxScaleLoss=1.827, ClassLoss=4.030
[Epoch 41] Training cost: 137.537, ObjLoss=13.999, BoxCenterLoss=6.081, BoxScaleLoss=1.826, ClassLoss=4.022
[Epoch 41] Validation:
aeroplane=0.7091228831052587
bicycle=0.7667315752880961
bird=0.6715614357677065
boat=0.5725547237870551
bottle=0.4829245371933552
bus=0.7711126982035887
car=0.8061124129378586
cat=0.7765931325982148
chair=0.4594841002762469
cow=0.6664816982569327
diningtable=0.6538366568703466
dog=0.7646907674140473
horse=0.7793910362700159
motorbike=0.7376799873051064
person=0.7328217928321974
pottedplant=0.35637190428584087
sheep=0.5851333235678806
sofa=0.6358879338953166
train=0.7326895934658958
tvmonitor=0.673368951588991
mAP=0.6667275572454976
[Epoch 42][Batch 99], LR: 1.00E-03, Speed: 153.052 samples/sec, ObjLoss=13.947, BoxCenterLoss=6.078, BoxScaleLoss=1.823, ClassLoss=4.006
[Epoch 42][Batch 199], LR: 1.00E-03, Speed: 173.178 samples/sec, ObjLoss=13.900, BoxCenterLoss=6.077, BoxScaleLoss=1.821, ClassLoss=3.992
[Epoch 42] Training cost: 145.935, ObjLoss=13.876, BoxCenterLoss=6.077, BoxScaleLoss=1.819, ClassLoss=3.982
[Epoch 42] Validation:
aeroplane=0.719162789960432
bicycle=0.731152355834326
bird=0.6667637079308844
boat=0.5034202523432026
bottle=0.4646363010386254
bus=0.7499257166683609
car=0.8091129157420194
cat=0.8084822488720964
chair=0.49729062154806314
cow=0.6566817226590229
diningtable=0.6243448578733322
dog=0.753525762873316
horse=0.7740898673017131
motorbike=0.7118990836390066
person=0.7327366644368472
pottedplant=0.4191344426809561
sheep=0.6432340469503696
sofa=0.7093785437761748
train=0.7648368515326638
tvmonitor=0.7160253036584503
mAP=0.6727917028659931
[Epoch 43][Batch 99], LR: 1.00E-03, Speed: 149.706 samples/sec, ObjLoss=13.826, BoxCenterLoss=6.075, BoxScaleLoss=1.817, ClassLoss=3.967
[Epoch 43][Batch 199], LR: 1.00E-03, Speed: 185.792 samples/sec, ObjLoss=13.779, BoxCenterLoss=6.074, BoxScaleLoss=1.814, ClassLoss=3.953
[Epoch 43] Training cost: 137.592, ObjLoss=13.752, BoxCenterLoss=6.074, BoxScaleLoss=1.813, ClassLoss=3.945
[Epoch 43] Validation:
aeroplane=0.7278484489777547
bicycle=0.7278995657632777
bird=0.6784223224813669
boat=0.5243303810447958
bottle=0.48768100674445
bus=0.7569326890585004
car=0.8247380861049636
cat=0.8265174908256416
chair=0.45149524274866604
cow=0.6802549834088836
diningtable=0.6473563384345288
dog=0.7615328974866193
horse=0.7450254350914415
motorbike=0.7285580155696005
person=0.7250314576447873
pottedplant=0.3687270192716588
sheep=0.7181006815727006
sofa=0.6559130202801305
train=0.7253716876790555
tvmonitor=0.6835034846397937
mAP=0.6722620127414308
[Epoch 44][Batch 99], LR: 1.00E-03, Speed: 190.898 samples/sec, ObjLoss=13.705, BoxCenterLoss=6.073, BoxScaleLoss=1.811, ClassLoss=3.930
[Epoch 44][Batch 199], LR: 1.00E-03, Speed: 140.237 samples/sec, ObjLoss=13.662, BoxCenterLoss=6.072, BoxScaleLoss=1.809, ClassLoss=3.916
[Epoch 44] Training cost: 134.004, ObjLoss=13.634, BoxCenterLoss=6.071, BoxScaleLoss=1.807, ClassLoss=3.909
[Epoch 44] Validation:
aeroplane=0.7022897649567239
bicycle=0.7195826094860881
bird=0.6689747037568421
boat=0.5066219651192372
bottle=0.4760975417618345
bus=0.7773855654093238
car=0.7989798241474367
cat=0.7870821686731047
chair=0.46243644274787665
cow=0.6995661831451959
diningtable=0.519024228022883
dog=0.744784393695042
horse=0.7759564707645197
motorbike=0.7096725272999316
person=0.7177916927868295
pottedplant=0.3437698955185097
sheep=0.6287758670496213
sofa=0.6753814394068112
train=0.7089532856566029
tvmonitor=0.6887016264747379
mAP=0.6555914097939576
[Epoch 45][Batch 99], LR: 1.00E-03, Speed: 149.728 samples/sec, ObjLoss=13.588, BoxCenterLoss=6.069, BoxScaleLoss=1.804, ClassLoss=3.895
[Epoch 45][Batch 199], LR: 1.00E-03, Speed: 168.011 samples/sec, ObjLoss=13.543, BoxCenterLoss=6.067, BoxScaleLoss=1.802, ClassLoss=3.881
[Epoch 45] Training cost: 135.143, ObjLoss=13.518, BoxCenterLoss=6.067, BoxScaleLoss=1.801, ClassLoss=3.874
[Epoch 45] Validation:
aeroplane=0.7117563136171494
bicycle=0.7402793144063924
bird=0.5849992434398815
boat=0.5516148356753534
bottle=0.3500191428435145
bus=0.7545356410620303
car=0.8131346117917777
cat=0.8068129864292352
chair=0.4449010921082988
cow=0.7327721181303098
diningtable=0.6060861147387916
dog=0.7036699465467858
horse=0.7621778916538672
motorbike=0.7373871322463165
person=0.7153726208718936
pottedplant=0.38101436987066745
sheep=0.6551161068518687
sofa=0.6258344423165989
train=0.7636992231490651
tvmonitor=0.6759923406182984
mAP=0.6558587744184049
[Epoch 46][Batch 99], LR: 1.00E-03, Speed: 169.935 samples/sec, ObjLoss=13.476, BoxCenterLoss=6.066, BoxScaleLoss=1.799, ClassLoss=3.860
[Epoch 46][Batch 199], LR: 1.00E-03, Speed: 179.515 samples/sec, ObjLoss=13.433, BoxCenterLoss=6.064, BoxScaleLoss=1.797, ClassLoss=3.847
[Epoch 46] Training cost: 142.090, ObjLoss=13.411, BoxCenterLoss=6.064, BoxScaleLoss=1.796, ClassLoss=3.841
[Epoch 46] Validation:
aeroplane=0.663663367744636
bicycle=0.7444278585215649
bird=0.5825447503709278
boat=0.5026330221606456
bottle=0.4379755554807202
bus=0.7326323093782192
car=0.7927525271211139
cat=0.7722255665533189
chair=0.4632475364090081
cow=0.6648944338487807
diningtable=0.5842575857105193
dog=0.7354780968656298
horse=0.7192699058623155
motorbike=0.7030158505233484
person=0.7012715874639223
pottedplant=0.3580074118697069
sheep=0.5866651073571668
sofa=0.6491644742836757
train=0.728709511837837
tvmonitor=0.6794075721092866
mAP=0.6401122015736171
[Epoch 47][Batch 99], LR: 1.00E-03, Speed: 169.854 samples/sec, ObjLoss=13.369, BoxCenterLoss=6.062, BoxScaleLoss=1.793, ClassLoss=3.827
[Epoch 47][Batch 199], LR: 1.00E-03, Speed: 156.897 samples/sec, ObjLoss=13.328, BoxCenterLoss=6.060, BoxScaleLoss=1.790, ClassLoss=3.814
[Epoch 47] Training cost: 130.162, ObjLoss=13.303, BoxCenterLoss=6.059, BoxScaleLoss=1.789, ClassLoss=3.807
[Epoch 47] Validation:
aeroplane=0.74386369788053
bicycle=0.7457941309347957
bird=0.6463290485864092
boat=0.5005006642866747
bottle=0.4301896370986521
bus=0.7633036172683472
car=0.7943457504423183
cat=0.8106350070826579
chair=0.45993446831364926
cow=0.6805924363077996
diningtable=0.6198613010584831
dog=0.7490103167785271
horse=0.700986411266106
motorbike=0.726683097387644
person=0.7271054713208144
pottedplant=0.3803880547887207
sheep=0.5906628330754593
sofa=0.6409424755426276
train=0.7346020898701389
tvmonitor=0.6883739997376361
mAP=0.6567052254513996
[Epoch 48][Batch 99], LR: 1.00E-03, Speed: 169.886 samples/sec, ObjLoss=13.266, BoxCenterLoss=6.059, BoxScaleLoss=1.787, ClassLoss=3.794
[Epoch 48][Batch 199], LR: 1.00E-03, Speed: 180.886 samples/sec, ObjLoss=13.226, BoxCenterLoss=6.057, BoxScaleLoss=1.785, ClassLoss=3.782
[Epoch 48] Training cost: 139.634, ObjLoss=13.205, BoxCenterLoss=6.057, BoxScaleLoss=1.784, ClassLoss=3.775
[Epoch 48] Validation:
aeroplane=0.6662060738933483
bicycle=0.7824731300264838
bird=0.658796805756201
boat=0.5785338577414442
bottle=0.4548911026144591
bus=0.7503644491137235
car=0.8071284052575498
cat=0.8010564719029852
chair=0.4515816972945246
cow=0.710541676673887
diningtable=0.625234146979691
dog=0.7726994425462682
horse=0.759146962149259
motorbike=0.7604375212588357
person=0.7207408611109943
pottedplant=0.3578601629567763
sheep=0.6045612966585403
sofa=0.6632618314102312
train=0.7536426177037255
tvmonitor=0.6971851412712445
mAP=0.6688171827160085
[Epoch 49][Batch 99], LR: 1.00E-03, Speed: 165.655 samples/sec, ObjLoss=13.167, BoxCenterLoss=6.056, BoxScaleLoss=1.782, ClassLoss=3.763
[Epoch 49][Batch 199], LR: 1.00E-03, Speed: 160.328 samples/sec, ObjLoss=13.129, BoxCenterLoss=6.054, BoxScaleLoss=1.780, ClassLoss=3.750
[Epoch 49] Training cost: 131.559, ObjLoss=13.108, BoxCenterLoss=6.054, BoxScaleLoss=1.779, ClassLoss=3.744
[Epoch 49] Validation:
aeroplane=0.7044824953028364
bicycle=0.7538183503515212
bird=0.6318175850768116
boat=0.5297793962046349
bottle=0.41719938222333924
bus=0.7544473008079223
car=0.831630098488795
cat=0.7892986853633527
chair=0.45676083284090596
cow=0.7009277875151397
diningtable=0.6489322520468696
dog=0.7319815490126337
horse=0.7738124312627119
motorbike=0.7299135047615946
person=0.7244133272915899
pottedplant=0.42710328323094743
sheep=0.6454558050100776
sofa=0.7123438828078531
train=0.7513265936202684
tvmonitor=0.6740670653931625
mAP=0.6694755804306484
[Epoch 50][Batch 99], LR: 1.00E-03, Speed: 168.023 samples/sec, ObjLoss=13.072, BoxCenterLoss=6.053, BoxScaleLoss=1.777, ClassLoss=3.731
[Epoch 50][Batch 199], LR: 1.00E-03, Speed: 184.964 samples/sec, ObjLoss=13.035, BoxCenterLoss=6.052, BoxScaleLoss=1.775, ClassLoss=3.719
[Epoch 50] Training cost: 144.835, ObjLoss=13.014, BoxCenterLoss=6.051, BoxScaleLoss=1.774, ClassLoss=3.712
[Epoch 50] Validation:
aeroplane=0.7232662088762974
bicycle=0.7330102565922569
bird=0.6697847223499767
boat=0.533447467887142
bottle=0.46337694860159506
bus=0.758379521341976
car=0.8129925084689352
cat=0.7880188178541624
chair=0.447969020244044
cow=0.7188260279154387
diningtable=0.6460819188905846
dog=0.7432346007080514
horse=0.7638914371438076
motorbike=0.7166640801329249
person=0.7158960980100735
pottedplant=0.3603219345758498
sheep=0.6700525301387095
sofa=0.6423725170544858
train=0.734532027243481
tvmonitor=0.6745495749852368
mAP=0.6658334109507515
[Epoch 51][Batch 99], LR: 1.00E-03, Speed: 145.031 samples/sec, ObjLoss=12.977, BoxCenterLoss=6.050, BoxScaleLoss=1.772, ClassLoss=3.700
[Epoch 51][Batch 199], LR: 1.00E-03, Speed: 173.076 samples/sec, ObjLoss=12.940, BoxCenterLoss=6.048, BoxScaleLoss=1.769, ClassLoss=3.688
[Epoch 51] Training cost: 127.234, ObjLoss=12.922, BoxCenterLoss=6.048, BoxScaleLoss=1.768, ClassLoss=3.682
[Epoch 51] Validation:
aeroplane=0.7012222939094089
bicycle=0.7545152938084768
bird=0.6858096786728987
boat=0.5970532824989861
bottle=0.432195843795728
bus=0.8016669335329691
car=0.8096140405226554
cat=0.8080542696587124
chair=0.4649289399182772
cow=0.7305571853648485
diningtable=0.6106139094641977
dog=0.7703937519019091
horse=0.7128755467494532
motorbike=0.7676357911640589
person=0.7147695862340547
pottedplant=0.4082876849347328
sheep=0.6902819553077557
sofa=0.6963343107432336
train=0.7974693955807228
tvmonitor=0.6927520088278762
mAP=0.6823515851295479
[Epoch 52][Batch 99], LR: 1.00E-03, Speed: 147.687 samples/sec, ObjLoss=12.889, BoxCenterLoss=6.047, BoxScaleLoss=1.766, ClassLoss=3.671
[Epoch 52][Batch 199], LR: 1.00E-03, Speed: 230.295 samples/sec, ObjLoss=12.853, BoxCenterLoss=6.045, BoxScaleLoss=1.764, ClassLoss=3.660
[Epoch 52] Training cost: 145.432, ObjLoss=12.833, BoxCenterLoss=6.045, BoxScaleLoss=1.763, ClassLoss=3.654
[Epoch 52] Validation:
aeroplane=0.7123915753525875
bicycle=0.7583143273119514
bird=0.6513579826220718
boat=0.5544902513134429
bottle=0.41620754095146767
bus=0.7770756665962777
car=0.8160679293364902
cat=0.8170840704756999
chair=0.4885247396139244
cow=0.6495301583363053
diningtable=0.6155823830618548
dog=0.7492349103547742
horse=0.8107635023501415
motorbike=0.7316075500133074
person=0.7119440125487718
pottedplant=0.403974909152214
sheep=0.6363885379948055
sofa=0.6970560170135363
train=0.7577394081002483
tvmonitor=0.6679345778213603
mAP=0.6711635025160616
[Epoch 53][Batch 99], LR: 1.00E-03, Speed: 158.154 samples/sec, ObjLoss=12.798, BoxCenterLoss=6.044, BoxScaleLoss=1.761, ClassLoss=3.643
[Epoch 53][Batch 199], LR: 1.00E-03, Speed: 178.771 samples/sec, ObjLoss=12.766, BoxCenterLoss=6.042, BoxScaleLoss=1.759, ClassLoss=3.631
[Epoch 53] Training cost: 135.499, ObjLoss=12.747, BoxCenterLoss=6.042, BoxScaleLoss=1.758, ClassLoss=3.625
[Epoch 53] Validation:
aeroplane=0.7375951247134395
bicycle=0.7366988666458707
bird=0.663962135982714
boat=0.5882337245052933
bottle=0.4430618938130498
bus=0.7936521855917426
car=0.809454775683532
cat=0.8160319771041528
chair=0.510877660381865
cow=0.723970701823716
diningtable=0.6244849753747039
dog=0.7561573601592931
horse=0.746924521847574
motorbike=0.7573851742118144
person=0.7059636681964286
pottedplant=0.4018238029959439
sheep=0.6193853998835229
sofa=0.6809705329118444
train=0.7970585483848276
tvmonitor=0.663570640816787
mAP=0.6788631835514056
[Epoch 54][Batch 99], LR: 1.00E-03, Speed: 140.427 samples/sec, ObjLoss=12.714, BoxCenterLoss=6.041, BoxScaleLoss=1.756, ClassLoss=3.614
[Epoch 54][Batch 199], LR: 1.00E-03, Speed: 237.626 samples/sec, ObjLoss=12.681, BoxCenterLoss=6.039, BoxScaleLoss=1.754, ClassLoss=3.603
[Epoch 54] Training cost: 147.725, ObjLoss=12.664, BoxCenterLoss=6.039, BoxScaleLoss=1.753, ClassLoss=3.598
[Epoch 54] Validation:
aeroplane=0.7710683495359768
bicycle=0.7657095032230016
bird=0.671438988957804
boat=0.6038007806641337
bottle=0.42966175650366206
bus=0.8032083105969716
car=0.7997406125854052
cat=0.8030009982302017
chair=0.4886941790197831
cow=0.7117312305226644
diningtable=0.6573299925384959
dog=0.7373339229622975
horse=0.7817060204139812
motorbike=0.7188634810140291
person=0.7136381031539485
pottedplant=0.4111299384625698
sheep=0.6892042312850982
sofa=0.7027957845030468
train=0.7877285773651785
tvmonitor=0.695882428835049
mAP=0.6871833595186649
[Epoch 55][Batch 99], LR: 1.00E-03, Speed: 151.939 samples/sec, ObjLoss=12.634, BoxCenterLoss=6.038, BoxScaleLoss=1.751, ClassLoss=3.588
[Epoch 55][Batch 199], LR: 1.00E-03, Speed: 217.279 samples/sec, ObjLoss=12.604, BoxCenterLoss=6.037, BoxScaleLoss=1.749, ClassLoss=3.577
[Epoch 55] Training cost: 152.328, ObjLoss=12.587, BoxCenterLoss=6.036, BoxScaleLoss=1.748, ClassLoss=3.571
[Epoch 55] Validation:
aeroplane=0.7186118360423626
bicycle=0.764852351255202
bird=0.6479291015683453
boat=0.5734992420844003
bottle=0.4289219393686183
bus=0.7915715784343755
car=0.8103741164515793
cat=0.8009788783604072
chair=0.48507405872719894
cow=0.6054933662216537
diningtable=0.5728990888095732
dog=0.7287198049931531
horse=0.7556564649620214
motorbike=0.7170884911583965
person=0.6908122785357339
pottedplant=0.40101153580559734
sheep=0.6446290274189921
sofa=0.6295797377680369
train=0.7650829346926185
tvmonitor=0.674894517061554
mAP=0.660384017485991
[Epoch 56][Batch 99], LR: 1.00E-03, Speed: 172.846 samples/sec, ObjLoss=12.556, BoxCenterLoss=6.035, BoxScaleLoss=1.747, ClassLoss=3.561
[Epoch 56][Batch 199], LR: 1.00E-03, Speed: 179.613 samples/sec, ObjLoss=12.526, BoxCenterLoss=6.035, BoxScaleLoss=1.745, ClassLoss=3.550
[Epoch 56] Training cost: 137.588, ObjLoss=12.510, BoxCenterLoss=6.034, BoxScaleLoss=1.744, ClassLoss=3.545
[Epoch 56] Validation:
aeroplane=0.7244774322767722
bicycle=0.7458900600825655
bird=0.44376668854989504
boat=0.4773029844798389
bottle=0.3935763495987104
bus=0.8059230295933312
car=0.8056998763242178
cat=0.7647877550887425
chair=0.46069516586503334
cow=0.7020928123250029
diningtable=0.563811435885086
dog=0.7160080470561333
horse=0.7539547937245051
motorbike=0.7721205961563956
person=0.7319637269599634
pottedplant=0.38460415641262824
sheep=0.5909846527245979
sofa=0.6574250035818882
train=0.7413750023017287
tvmonitor=0.6944909820820401
mAP=0.6465475275534539
[Epoch 57][Batch 99], LR: 1.00E-03, Speed: 180.295 samples/sec, ObjLoss=12.481, BoxCenterLoss=6.033, BoxScaleLoss=1.742, ClassLoss=3.534
[Epoch 57][Batch 199], LR: 1.00E-03, Speed: 163.181 samples/sec, ObjLoss=12.451, BoxCenterLoss=6.032, BoxScaleLoss=1.740, ClassLoss=3.524
[Epoch 57] Training cost: 137.103, ObjLoss=12.432, BoxCenterLoss=6.031, BoxScaleLoss=1.739, ClassLoss=3.518
[Epoch 57] Validation:
aeroplane=0.7122331544733149
bicycle=0.7915723595148793
bird=0.6541248954238358
boat=0.4755797806416987
bottle=0.4735465305810404
bus=0.7916044853251016
car=0.7944889461888883
cat=0.8192311460919043
chair=0.47935704501274534
cow=0.7147723572509451
diningtable=0.5678540199362385
dog=0.7649526958584796
horse=0.7575287561250017
motorbike=0.7687479817000649
person=0.7246195896207992
pottedplant=0.39900700550933244
sheep=0.6668919440043093
sofa=0.6658110186630709
train=0.7677014989771946
tvmonitor=0.6954380214043719
mAP=0.6742531616151608
[Epoch 58][Batch 99], LR: 1.00E-03, Speed: 165.595 samples/sec, ObjLoss=12.403, BoxCenterLoss=6.030, BoxScaleLoss=1.738, ClassLoss=3.508
[Epoch 58][Batch 199], LR: 1.00E-03, Speed: 184.811 samples/sec, ObjLoss=12.373, BoxCenterLoss=6.029, BoxScaleLoss=1.736, ClassLoss=3.499
[Epoch 58] Training cost: 135.343, ObjLoss=12.358, BoxCenterLoss=6.028, BoxScaleLoss=1.735, ClassLoss=3.494
[Epoch 58] Validation:
aeroplane=0.7360969405225084
bicycle=0.7419657461144711
bird=0.6513491784244569
boat=0.487957055977941
bottle=0.44975823068155507
bus=0.7439259844928816
car=0.808796430991871
cat=0.7440456024374852
chair=0.4548869000808286
cow=0.7069280372938634
diningtable=0.5905829530665434
dog=0.7380728514597122
horse=0.7435193244626948
motorbike=0.7004033129287796
person=0.7261200324162782
pottedplant=0.36246157904028065
sheep=0.6130520079232704
sofa=0.6277964240717161
train=0.6991347183762705
tvmonitor=0.6935280242906439
mAP=0.6510190667527027
[Epoch 59][Batch 99], LR: 1.00E-03, Speed: 155.688 samples/sec, ObjLoss=12.332, BoxCenterLoss=6.028, BoxScaleLoss=1.734, ClassLoss=3.484
[Epoch 59][Batch 199], LR: 1.00E-03, Speed: 154.747 samples/sec, ObjLoss=12.305, BoxCenterLoss=6.027, BoxScaleLoss=1.732, ClassLoss=3.475
[Epoch 59] Training cost: 150.596, ObjLoss=12.288, BoxCenterLoss=6.025, BoxScaleLoss=1.731, ClassLoss=3.470
[Epoch 59] Validation:
aeroplane=0.725981719671106
bicycle=0.7556749817158839
bird=0.6178515073832063
boat=0.5520474810618502
bottle=0.3804233735430366
bus=0.7724832681989919
car=0.7974868267938948
cat=0.8164425397055615
chair=0.5025995776401249
cow=0.7327010319211136
diningtable=0.6160292419245387
dog=0.7552936049460173
horse=0.7751369253127216
motorbike=0.721341582066939
person=0.7289083010712621
pottedplant=0.3870594286289234
sheep=0.6424789818065574
sofa=0.6851475123081996
train=0.7524023276873398
tvmonitor=0.6971048920093941
mAP=0.6707297552698331
[Epoch 60][Batch 99], LR: 1.00E-03, Speed: 162.350 samples/sec, ObjLoss=12.263, BoxCenterLoss=6.025, BoxScaleLoss=1.729, ClassLoss=3.461
[Epoch 60][Batch 199], LR: 1.00E-03, Speed: 153.334 samples/sec, ObjLoss=12.236, BoxCenterLoss=6.024, BoxScaleLoss=1.728, ClassLoss=3.452
[Epoch 60] Training cost: 138.519, ObjLoss=12.222, BoxCenterLoss=6.023, BoxScaleLoss=1.727, ClassLoss=3.446
[Epoch 60] Validation:
aeroplane=0.7046184672121187
bicycle=0.7457092305516566
bird=0.6851271338178936
boat=0.5477178353866947
bottle=0.47396910943055803
bus=0.8048878470582943
car=0.8292768336294369
cat=0.795872805084038
chair=0.46202719291713557
cow=0.7126285499276299
diningtable=0.6652992076248483
dog=0.7487674891246862
horse=0.7816161525113945
motorbike=0.7394414560812693
person=0.7061186636710312
pottedplant=0.3311369391817448
sheep=0.6208862521206129
sofa=0.6588197173196956
train=0.7559693060421617
tvmonitor=0.6971791408311242
mAP=0.6733534664762013
[Epoch 61][Batch 99], LR: 1.00E-03, Speed: 167.232 samples/sec, ObjLoss=12.196, BoxCenterLoss=6.022, BoxScaleLoss=1.725, ClassLoss=3.437
[Epoch 61][Batch 199], LR: 1.00E-03, Speed: 157.727 samples/sec, ObjLoss=12.169, BoxCenterLoss=6.021, BoxScaleLoss=1.723, ClassLoss=3.428
[Epoch 61] Training cost: 137.117, ObjLoss=12.156, BoxCenterLoss=6.021, BoxScaleLoss=1.723, ClassLoss=3.424
[Epoch 61] Validation:
aeroplane=0.7124334403149915
bicycle=0.7515086250362962
bird=0.6691654631886186
boat=0.5344252500358905
bottle=0.45581618175905725
bus=0.7973022746238987
car=0.825591442673576
cat=0.8416479401458832
chair=0.49571575409909474
cow=0.7566814833582715
diningtable=0.6294558014667736
dog=0.7377075338124521
horse=0.8036874852741205
motorbike=0.7777361833958271
person=0.7587436520746926
pottedplant=0.4292335674877891
sheep=0.6464629015854645
sofa=0.691036772759428
train=0.7705253531356926
tvmonitor=0.6977270642625799
mAP=0.6891302085245199
[Epoch 62][Batch 99], LR: 1.00E-03, Speed: 166.117 samples/sec, ObjLoss=12.130, BoxCenterLoss=6.020, BoxScaleLoss=1.721, ClassLoss=3.415
[Epoch 62][Batch 199], LR: 1.00E-03, Speed: 153.346 samples/sec, ObjLoss=12.104, BoxCenterLoss=6.019, BoxScaleLoss=1.720, ClassLoss=3.406
[Epoch 62] Training cost: 134.690, ObjLoss=12.090, BoxCenterLoss=6.018, BoxScaleLoss=1.718, ClassLoss=3.401
[Epoch 62] Validation:
aeroplane=0.7433429121009135
bicycle=0.7593427102253733
bird=0.7075334200530214
boat=0.55889368173337
bottle=0.48231153548757366
bus=0.8134546458820403
car=0.8172230434703786
cat=0.8289295231137099
chair=0.402452329498272
cow=0.6831490440278272
diningtable=0.6263180445963195
dog=0.776604983861686
horse=0.7774025313770737
motorbike=0.7789038341911758
person=0.7447639167204975
pottedplant=0.4031569666588055
sheep=0.6549007922110316
sofa=0.6388550725190284
train=0.7545094544743431
tvmonitor=0.6371513389039961
mAP=0.6794599890553219
[Epoch 63][Batch 99], LR: 1.00E-03, Speed: 174.730 samples/sec, ObjLoss=12.066, BoxCenterLoss=6.018, BoxScaleLoss=1.717, ClassLoss=3.392
[Epoch 63][Batch 199], LR: 1.00E-03, Speed: 144.919 samples/sec, ObjLoss=12.041, BoxCenterLoss=6.017, BoxScaleLoss=1.715, ClassLoss=3.383
[Epoch 63] Training cost: 131.891, ObjLoss=12.027, BoxCenterLoss=6.016, BoxScaleLoss=1.714, ClassLoss=3.378
[Epoch 63] Validation:
aeroplane=0.7636501356354732
bicycle=0.7592828031981891
bird=0.7393538312440473
boat=0.5966416811858338
bottle=0.5076488166231647
bus=0.7874195128157484
car=0.8330901808660371
cat=0.8443291185835484
chair=0.517043234049155
cow=0.7556527025498408
diningtable=0.6428037242754894
dog=0.7879979526576283
horse=0.8118539418039995
motorbike=0.803109033753264
person=0.7355279502137522
pottedplant=0.41147463319150895
sheep=0.6847434349241187
sofa=0.6844840611501263
train=0.7291116871687218
tvmonitor=0.7076313255876073
mAP=0.7051424880738628
[Epoch 64][Batch 99], LR: 1.00E-03, Speed: 154.144 samples/sec, ObjLoss=12.001, BoxCenterLoss=6.015, BoxScaleLoss=1.713, ClassLoss=3.370
[Epoch 64][Batch 199], LR: 1.00E-03, Speed: 150.307 samples/sec, ObjLoss=11.977, BoxCenterLoss=6.014, BoxScaleLoss=1.711, ClassLoss=3.362
[Epoch 64] Training cost: 139.042, ObjLoss=11.964, BoxCenterLoss=6.014, BoxScaleLoss=1.710, ClassLoss=3.357
[Epoch 64] Validation:
aeroplane=0.7286308754185384
bicycle=0.7195577294961741
bird=0.7015598167973327
boat=0.5936094048378624
bottle=0.5057551188409047
bus=0.8004618460773326
car=0.8208285875828364
cat=0.8120302059828419
chair=0.48215157360360905
cow=0.7460503253990468
diningtable=0.661079548302223
dog=0.7665450193813584
horse=0.7576856411886148
motorbike=0.7684206063246897
person=0.7504583752986119
pottedplant=0.4136415328152085
sheep=0.6978943496889574
sofa=0.7024755985113448
train=0.7437202391002306
tvmonitor=0.6775436548510034
mAP=0.6925050024749361
[Epoch 65][Batch 99], LR: 1.00E-03, Speed: 191.514 samples/sec, ObjLoss=11.941, BoxCenterLoss=6.013, BoxScaleLoss=1.709, ClassLoss=3.348
[Epoch 65][Batch 199], LR: 1.00E-03, Speed: 171.111 samples/sec, ObjLoss=11.918, BoxCenterLoss=6.013, BoxScaleLoss=1.708, ClassLoss=3.340
[Epoch 65] Training cost: 133.013, ObjLoss=11.902, BoxCenterLoss=6.011, BoxScaleLoss=1.706, ClassLoss=3.336
[Epoch 65] Validation:
aeroplane=0.7377332957265488
bicycle=0.7627220482701549
bird=0.7152593695318608
boat=0.5696309996614407
bottle=0.4936268984550841
bus=0.7832466937114541
car=0.8272657425971319
cat=0.8120969860241308
chair=0.5019323466056788
cow=0.7592992325946343
diningtable=0.6612116601710634
dog=0.7768597498914427
horse=0.8053458374029445
motorbike=0.7823578304967884
person=0.7367244827002819
pottedplant=0.40371084854988054
sheep=0.6950938564773446
sofa=0.6859555203228949
train=0.7636814645673985
tvmonitor=0.7112267568823273
mAP=0.6992490810320242
[Epoch 66][Batch 99], LR: 1.00E-03, Speed: 154.507 samples/sec, ObjLoss=11.880, BoxCenterLoss=6.011, BoxScaleLoss=1.705, ClassLoss=3.327
[Epoch 66][Batch 199], LR: 1.00E-03, Speed: 164.068 samples/sec, ObjLoss=11.857, BoxCenterLoss=6.010, BoxScaleLoss=1.704, ClassLoss=3.319
[Epoch 66] Training cost: 136.582, ObjLoss=11.844, BoxCenterLoss=6.010, BoxScaleLoss=1.703, ClassLoss=3.314
[Epoch 66] Validation:
aeroplane=0.7520474204950608
bicycle=0.7721080678402031
bird=0.6739979937796067
boat=0.590842955452455
bottle=0.5159282263849605
bus=0.794614859755837
car=0.8280850851666136
cat=0.8072849858993572
chair=0.49213020174295313
cow=0.702289307387992
diningtable=0.658660556396352
dog=0.7366023958020325
horse=0.8268474098623197
motorbike=0.7611046764295675
person=0.7204863846683733
pottedplant=0.4445766114865564
sheep=0.651897065375655
sofa=0.7003513419497741
train=0.7608520357506159
tvmonitor=0.7159476562141931
mAP=0.695332761892024
[Epoch 67][Batch 99], LR: 1.00E-03, Speed: 159.584 samples/sec, ObjLoss=11.822, BoxCenterLoss=6.009, BoxScaleLoss=1.701, ClassLoss=3.306
[Epoch 67][Batch 199], LR: 1.00E-03, Speed: 165.244 samples/sec, ObjLoss=11.802, BoxCenterLoss=6.009, BoxScaleLoss=1.699, ClassLoss=3.298
[Epoch 67] Training cost: 154.098, ObjLoss=11.789, BoxCenterLoss=6.008, BoxScaleLoss=1.699, ClassLoss=3.293
[Epoch 67] Validation:
aeroplane=0.7286746743457916
bicycle=0.7580717918792075
bird=0.6740563586503083
boat=0.5811977263551965
bottle=0.46532469240735247
bus=0.7663152259623122
car=0.8206679498468962
cat=0.7807174394591427
chair=0.47883768455090947
cow=0.7209915499281804
diningtable=0.6162128831836615
dog=0.6843237782231462
horse=0.7837206151081823
motorbike=0.758987366248998
person=0.7193548316734413
pottedplant=0.4137338950613033
sheep=0.6718649310195034
sofa=0.6089468477678411
train=0.6630803811664208
tvmonitor=0.6571843062336323
mAP=0.6676132464535713
[Epoch 68][Batch 99], LR: 1.00E-03, Speed: 162.497 samples/sec, ObjLoss=11.768, BoxCenterLoss=6.008, BoxScaleLoss=1.697, ClassLoss=3.285
[Epoch 68][Batch 199], LR: 1.00E-03, Speed: 165.968 samples/sec, ObjLoss=11.744, BoxCenterLoss=6.007, BoxScaleLoss=1.696, ClassLoss=3.278
[Epoch 68] Training cost: 156.656, ObjLoss=11.732, BoxCenterLoss=6.007, BoxScaleLoss=1.695, ClassLoss=3.273
[Epoch 68] Validation:
aeroplane=0.7065192839341881
bicycle=0.7335407911396512
bird=0.6787598066652754
boat=0.5541135385795897
bottle=0.4122501681388474
bus=0.8055034235190068
car=0.7404097392480418
cat=0.8030928248233518
chair=0.4929783520983059
cow=0.6840625469725778
diningtable=0.6172802405781723
dog=0.7549616614601312
horse=0.7876688507841684
motorbike=0.7356179555879347
person=0.7037570787486385
pottedplant=0.42863399959151577
sheep=0.7047153134352712
sofa=0.679627974379409
train=0.7607905004190114
tvmonitor=0.6762473680467013
mAP=0.6730265709074894
[Epoch 69][Batch 99], LR: 1.00E-03, Speed: 150.352 samples/sec, ObjLoss=11.711, BoxCenterLoss=6.006, BoxScaleLoss=1.693, ClassLoss=3.266
[Epoch 69][Batch 199], LR: 1.00E-03, Speed: 195.422 samples/sec, ObjLoss=11.690, BoxCenterLoss=6.005, BoxScaleLoss=1.692, ClassLoss=3.259
[Epoch 69] Training cost: 144.008, ObjLoss=11.677, BoxCenterLoss=6.004, BoxScaleLoss=1.692, ClassLoss=3.254
[Epoch 69] Validation:
aeroplane=0.730935975550972
bicycle=0.7576762430161482
bird=0.6904089345669772
boat=0.5372658857144247
bottle=0.4915340507773153
bus=0.7771237541280726
car=0.8336399127306098
cat=0.8072773398447732
chair=0.4962093297801992
cow=0.7436378304064541
diningtable=0.611651852411445
dog=0.7373609730496622
horse=0.7718602904198777
motorbike=0.7666737887447868
person=0.7334698749945932
pottedplant=0.4284837925212983
sheep=0.693155804712842
sofa=0.6546351264962915
train=0.7206736568889204
tvmonitor=0.6786189537965043
mAP=0.6831146685276084
[Epoch 70][Batch 99], LR: 1.00E-03, Speed: 172.614 samples/sec, ObjLoss=11.655, BoxCenterLoss=6.004, BoxScaleLoss=1.690, ClassLoss=3.247
[Epoch 70][Batch 199], LR: 1.00E-03, Speed: 166.009 samples/sec, ObjLoss=11.633, BoxCenterLoss=6.002, BoxScaleLoss=1.689, ClassLoss=3.240
[Epoch 70] Training cost: 147.263, ObjLoss=11.622, BoxCenterLoss=6.002, BoxScaleLoss=1.688, ClassLoss=3.235
[Epoch 70] Validation:
aeroplane=0.7432542484223393
bicycle=0.7500974696324448
bird=0.6845627769259217
boat=0.5595270339746072
bottle=0.4938437157238735
bus=0.7977422306376425
car=0.8273284060927376
cat=0.8251061105501393
chair=0.5045852679582177
cow=0.7476900226922887
diningtable=0.6165890370753179
dog=0.7854201027039557
horse=0.7713107169424485
motorbike=0.7822986172115188
person=0.7256757806927798
pottedplant=0.42960440199562255
sheep=0.6960862197722091
sofa=0.6704311637294472
train=0.7451364415761796
tvmonitor=0.6939641173487067
mAP=0.6925126940829198
[Epoch 71][Batch 99], LR: 1.00E-03, Speed: 158.183 samples/sec, ObjLoss=11.600, BoxCenterLoss=6.001, BoxScaleLoss=1.686, ClassLoss=3.227
[Epoch 71][Batch 199], LR: 1.00E-03, Speed: 204.128 samples/sec, ObjLoss=11.578, BoxCenterLoss=6.000, BoxScaleLoss=1.685, ClassLoss=3.220
[Epoch 71] Training cost: 128.008, ObjLoss=11.568, BoxCenterLoss=6.000, BoxScaleLoss=1.684, ClassLoss=3.216
[Epoch 71] Validation:
aeroplane=0.7606768822127714
bicycle=0.7415145829688614
bird=0.6776528759623499
boat=0.5435329487919983
bottle=0.45858259606987
bus=0.7924657176235894
car=0.8218151715674035
cat=0.840011155178245
chair=0.48206621126482035
cow=0.7272748750814193
diningtable=0.6510995831043357
dog=0.7567546854299472
horse=0.7449047876246999
motorbike=0.7598078910955558
person=0.75587489605435
pottedplant=0.4141469023861959
sheep=0.688370658177152
sofa=0.6845702479860832
train=0.7963254668485447
tvmonitor=0.6652023134542633
mAP=0.688132522444123
[Epoch 72][Batch 99], LR: 1.00E-03, Speed: 188.113 samples/sec, ObjLoss=11.547, BoxCenterLoss=5.999, BoxScaleLoss=1.683, ClassLoss=3.209
[Epoch 72][Batch 199], LR: 1.00E-03, Speed: 157.011 samples/sec, ObjLoss=11.528, BoxCenterLoss=5.999, BoxScaleLoss=1.682, ClassLoss=3.202
[Epoch 72] Training cost: 154.915, ObjLoss=11.517, BoxCenterLoss=5.998, BoxScaleLoss=1.681, ClassLoss=3.198
[Epoch 72] Validation:
aeroplane=0.7287003798679579
bicycle=0.7408089528809509
bird=0.6792762955561374
boat=0.5217891027314324
bottle=0.4531650589851405
bus=0.7786665592986334
car=0.8250829345212423
cat=0.8013393292585438
chair=0.4530943571653507
cow=0.7480793335471078
diningtable=0.6297332871814825
dog=0.7690732567212618
horse=0.7716279125730562
motorbike=0.7531421685972185
person=0.7463801019723864
pottedplant=0.39800285077862135
sheep=0.6626713311547988
sofa=0.6458511092045698
train=0.7632684966842048
tvmonitor=0.6483075486384544
mAP=0.6759030183659276
[Epoch 73][Batch 99], LR: 1.00E-03, Speed: 157.155 samples/sec, ObjLoss=11.497, BoxCenterLoss=5.998, BoxScaleLoss=1.680, ClassLoss=3.191
[Epoch 73][Batch 199], LR: 1.00E-03, Speed: 193.009 samples/sec, ObjLoss=11.478, BoxCenterLoss=5.998, BoxScaleLoss=1.679, ClassLoss=3.185
[Epoch 73] Training cost: 134.062, ObjLoss=11.467, BoxCenterLoss=5.997, BoxScaleLoss=1.678, ClassLoss=3.181
[Epoch 73] Validation:
aeroplane=0.7134429578201971
bicycle=0.740746398437136
bird=0.6896690911193767
boat=0.5530643583008386
bottle=0.4579934057548823
bus=0.7894335521206545
car=0.8194705860181248
cat=0.8462574398617825
chair=0.44223118076965945
cow=0.7155275338939098
diningtable=0.6467096714555136
dog=0.7598658061589365
horse=0.7952007767062854
motorbike=0.7915471196393312
person=0.7330785339221181
pottedplant=0.42184605708924217
sheep=0.652519463536376
sofa=0.6879048248446548
train=0.7339915904898285
tvmonitor=0.6631155564234108
mAP=0.6826807952181129
[Epoch 74][Batch 99], LR: 1.00E-03, Speed: 125.964 samples/sec, ObjLoss=11.448, BoxCenterLoss=5.996, BoxScaleLoss=1.677, ClassLoss=3.174
[Epoch 74][Batch 199], LR: 1.00E-03, Speed: 172.106 samples/sec, ObjLoss=11.430, BoxCenterLoss=5.996, BoxScaleLoss=1.675, ClassLoss=3.166
[Epoch 74] Training cost: 148.603, ObjLoss=11.419, BoxCenterLoss=5.996, BoxScaleLoss=1.675, ClassLoss=3.163
[Epoch 74] Validation:
aeroplane=0.7056120413784884
bicycle=0.7351210929617336
bird=0.6691373318930066
boat=0.5408022856414944
bottle=0.390178300525609
bus=0.7967468975973884
car=0.8260608915301353
cat=0.8189591016567209
chair=0.4666697821817124
cow=0.7354115404495657
diningtable=0.5816378948274213
dog=0.7790278675724013
horse=0.8129223659484773
motorbike=0.7583539294163274
person=0.7183584472718364
pottedplant=0.4225734401535671
sheep=0.6417369661367126
sofa=0.6885427174549433
train=0.7602838534952242
tvmonitor=0.6674769887267549
mAP=0.6757806868409759
[Epoch 75][Batch 99], LR: 1.00E-03, Speed: 194.872 samples/sec, ObjLoss=11.400, BoxCenterLoss=5.995, BoxScaleLoss=1.673, ClassLoss=3.155
[Epoch 75][Batch 199], LR: 1.00E-03, Speed: 180.889 samples/sec, ObjLoss=11.382, BoxCenterLoss=5.994, BoxScaleLoss=1.672, ClassLoss=3.148
[Epoch 75] Training cost: 146.895, ObjLoss=11.371, BoxCenterLoss=5.994, BoxScaleLoss=1.671, ClassLoss=3.144
[Epoch 75] Validation:
aeroplane=0.7233601144048301
bicycle=0.7564776790758878
bird=0.6783499529688715
boat=0.5413731342260576
bottle=0.46055349787950806
bus=0.8070663552776809
car=0.827090775773556
cat=0.8379311497717792
chair=0.5123799105686463
cow=0.791070956109889
diningtable=0.671443914302147
dog=0.7793612503633089
horse=0.8286898003204941
motorbike=0.7701921817368397
person=0.7076745214244707
pottedplant=0.39094492996547503
sheep=0.716139830614548
sofa=0.7234619505683425
train=0.7760004386231835
tvmonitor=0.7146885372563271
mAP=0.7007125440615922
[Epoch 76][Batch 99], LR: 1.00E-03, Speed: 164.499 samples/sec, ObjLoss=11.354, BoxCenterLoss=5.994, BoxScaleLoss=1.670, ClassLoss=3.137
[Epoch 76][Batch 199], LR: 1.00E-03, Speed: 173.906 samples/sec, ObjLoss=11.335, BoxCenterLoss=5.993, BoxScaleLoss=1.669, ClassLoss=3.131
[Epoch 76] Training cost: 137.826, ObjLoss=11.325, BoxCenterLoss=5.993, BoxScaleLoss=1.668, ClassLoss=3.127
[Epoch 76] Validation:
aeroplane=0.712034098883394
bicycle=0.7549394922087327
bird=0.677305546302811
boat=0.6031604799794987
bottle=0.5070364997534176
bus=0.77659935408662
car=0.8195568067282119
cat=0.8136799864924439
chair=0.477618125871843
cow=0.7156358510789262
diningtable=0.6392157194115503
dog=0.7641528270612634
horse=0.8182498210105248
motorbike=0.784337488762364
person=0.735180598446472
pottedplant=0.3693380034201879
sheep=0.674781009850728
sofa=0.6848981592666294
train=0.7149034200568979
tvmonitor=0.6752943373022221
mAP=0.6858958812987368
[Epoch 77][Batch 99], LR: 1.00E-03, Speed: 178.867 samples/sec, ObjLoss=11.308, BoxCenterLoss=5.993, BoxScaleLoss=1.667, ClassLoss=3.120
[Epoch 77][Batch 199], LR: 1.00E-03, Speed: 182.771 samples/sec, ObjLoss=11.290, BoxCenterLoss=5.992, BoxScaleLoss=1.665, ClassLoss=3.113
[Epoch 77] Training cost: 142.283, ObjLoss=11.279, BoxCenterLoss=5.992, BoxScaleLoss=1.665, ClassLoss=3.109
[Epoch 77] Validation:
aeroplane=0.7460443273712362
bicycle=0.7922739425380563
bird=0.6786953437079705
boat=0.5529024600879034
bottle=0.42316009003806276
bus=0.8268152644506033
car=0.835815269835914
cat=0.8332759057188432
chair=0.5133360400645435
cow=0.7503804571636106
diningtable=0.6364297097931159
dog=0.7812082073314145
horse=0.8148696185744109
motorbike=0.8077266485342812
person=0.7424277988252741
pottedplant=0.44383647811844196
sheep=0.7240679622425643
sofa=0.7030935799445147
train=0.811433029700374
tvmonitor=0.6541713375559058
mAP=0.7035981735798521
[Epoch 78][Batch 99], LR: 1.00E-03, Speed: 156.608 samples/sec, ObjLoss=11.262, BoxCenterLoss=5.991, BoxScaleLoss=1.663, ClassLoss=3.103
[Epoch 78][Batch 199], LR: 1.00E-03, Speed: 168.444 samples/sec, ObjLoss=11.243, BoxCenterLoss=5.990, BoxScaleLoss=1.662, ClassLoss=3.097
[Epoch 78] Training cost: 145.002, ObjLoss=11.234, BoxCenterLoss=5.990, BoxScaleLoss=1.662, ClassLoss=3.093
[Epoch 78] Validation:
aeroplane=0.7344515753606663
bicycle=0.752297298642811
bird=0.6581558905301184
boat=0.5615820519745643
bottle=0.4597786365611765
bus=0.7832598717634436
car=0.8258244649165059
cat=0.8031829725746318
chair=0.49198963204284357
cow=0.7183610335789219
diningtable=0.6548848342280654
dog=0.6993435599213883
horse=0.795861096631684
motorbike=0.7780237563995925
person=0.7432461621846366
pottedplant=0.4342187641598942
sheep=0.6606705716491762
sofa=0.6963684361193027
train=0.7541567890705524
tvmonitor=0.6759019012172371
mAP=0.6840779649763606
[Epoch 79][Batch 99], LR: 1.00E-03, Speed: 170.186 samples/sec, ObjLoss=11.217, BoxCenterLoss=5.990, BoxScaleLoss=1.661, ClassLoss=3.087
[Epoch 79][Batch 199], LR: 1.00E-03, Speed: 200.506 samples/sec, ObjLoss=11.200, BoxCenterLoss=5.989, BoxScaleLoss=1.659, ClassLoss=3.080
[Epoch 79] Training cost: 131.337, ObjLoss=11.190, BoxCenterLoss=5.988, BoxScaleLoss=1.658, ClassLoss=3.077
[Epoch 79] Validation:
aeroplane=0.7382750530451907
bicycle=0.7627882894094359
bird=0.7038044183834596
boat=0.5869747770759522
bottle=0.48980092628657573
bus=0.8246621844887695
car=0.8367754328388723
cat=0.8300220001921739
chair=0.522673084373653
cow=0.7146210678137905
diningtable=0.6660263362998264
dog=0.7839673815214537
horse=0.8128672554101606
motorbike=0.7794927315975099
person=0.7261462718631357
pottedplant=0.4192445361913623
sheep=0.6874644774687758
sofa=0.7064705773727977
train=0.766102066153754
tvmonitor=0.7406594410859383
mAP=0.7049419154436294
[Epoch 80][Batch 99], LR: 1.00E-03, Speed: 173.689 samples/sec, ObjLoss=11.173, BoxCenterLoss=5.988, BoxScaleLoss=1.657, ClassLoss=3.071
[Epoch 80][Batch 199], LR: 1.00E-03, Speed: 164.903 samples/sec, ObjLoss=11.155, BoxCenterLoss=5.987, BoxScaleLoss=1.656, ClassLoss=3.064
[Epoch 80] Training cost: 143.278, ObjLoss=11.146, BoxCenterLoss=5.986, BoxScaleLoss=1.655, ClassLoss=3.061
[Epoch 80] Validation:
aeroplane=0.7559829984714835
bicycle=0.7587968377837345
bird=0.6564834911564436
boat=0.5836141603463483
bottle=0.4475101551680919
bus=0.7807663537623623
car=0.838509058195348
cat=0.833603519909435
chair=0.5028368649741084
cow=0.7083929271965655
diningtable=0.6666323832288622
dog=0.7935632932483977
horse=0.7927214569230168
motorbike=0.7841207091611414
person=0.7255628604077982
pottedplant=0.4108439679456721
sheep=0.6424568079365658
sofa=0.6972672755041631
train=0.786712168663577
tvmonitor=0.709050331887324
mAP=0.6937713810935219
[Epoch 81][Batch 99], LR: 1.00E-03, Speed: 171.818 samples/sec, ObjLoss=11.129, BoxCenterLoss=5.986, BoxScaleLoss=1.654, ClassLoss=3.055
[Epoch 81][Batch 199], LR: 1.00E-03, Speed: 189.130 samples/sec, ObjLoss=11.111, BoxCenterLoss=5.985, BoxScaleLoss=1.653, ClassLoss=3.049
[Epoch 81] Training cost: 125.586, ObjLoss=11.101, BoxCenterLoss=5.984, BoxScaleLoss=1.652, ClassLoss=3.046
[Epoch 81] Validation:
aeroplane=0.7253613702531267
bicycle=0.7480120230415376
bird=0.6992483620747912
boat=0.595677480196845
bottle=0.46947185001598923
bus=0.8045496270465952
car=0.8302694830950634
cat=0.8090340835592448
chair=0.4894440677865987
cow=0.7289702720062738
diningtable=0.6960610440149231
dog=0.7871670552652471
horse=0.8076565202879646
motorbike=0.7862112740344938
person=0.7460344703985182
pottedplant=0.4181865345719328
sheep=0.6370563277889871
sofa=0.7388354713186346
train=0.7649433568810604
tvmonitor=0.7074748454909228
mAP=0.6994832759564374
[Epoch 82][Batch 99], LR: 1.00E-03, Speed: 166.475 samples/sec, ObjLoss=11.085, BoxCenterLoss=5.984, BoxScaleLoss=1.651, ClassLoss=3.039
[Epoch 82][Batch 199], LR: 1.00E-03, Speed: 177.370 samples/sec, ObjLoss=11.069, BoxCenterLoss=5.983, BoxScaleLoss=1.650, ClassLoss=3.033
[Epoch 82] Training cost: 146.245, ObjLoss=11.059, BoxCenterLoss=5.983, BoxScaleLoss=1.649, ClassLoss=3.030
[Epoch 82] Validation:
aeroplane=0.7540841772468186
bicycle=0.8015388998354059
bird=0.6977971514375994
boat=0.5371578998289405
bottle=0.49724320791630316
bus=0.8089891247181533
car=0.8377688757182536
cat=0.8120422744465848
chair=0.5042625098501419
cow=0.6825814973600165
diningtable=0.6861114736574955
dog=0.7864916550348053
horse=0.8204285691211626
motorbike=0.7945147788969791
person=0.7461163647941027
pottedplant=0.43348228478390133
sheep=0.6258549643478559
sofa=0.6802477357170922
train=0.7641613239489728
tvmonitor=0.7414939680066737
mAP=0.7006184368333629
[Epoch 83][Batch 99], LR: 1.00E-03, Speed: 156.894 samples/sec, ObjLoss=11.045, BoxCenterLoss=5.983, BoxScaleLoss=1.648, ClassLoss=3.024
[Epoch 83][Batch 199], LR: 1.00E-03, Speed: 175.076 samples/sec, ObjLoss=11.028, BoxCenterLoss=5.982, BoxScaleLoss=1.647, ClassLoss=3.018
[Epoch 83] Training cost: 151.539, ObjLoss=11.018, BoxCenterLoss=5.981, BoxScaleLoss=1.646, ClassLoss=3.015
[Epoch 83] Validation:
aeroplane=0.7206406743979553
bicycle=0.7641174481448226
bird=0.6688338614048966
boat=0.5438874010051069
bottle=0.4917686025231317
bus=0.7915138394001892
car=0.8227823319871957
cat=0.8181653394732638
chair=0.5354515523776342
cow=0.7000986082315883
diningtable=0.6659550197307512
dog=0.7896489606993428
horse=0.8099594857704914
motorbike=0.7922383095860182
person=0.7673022926886947
pottedplant=0.413292385856989
sheep=0.65953020446764
sofa=0.6992244772626867
train=0.7610062732378063
tvmonitor=0.7220175339643491
mAP=0.6968717301105276
[Epoch 84][Batch 99], LR: 1.00E-03, Speed: 170.283 samples/sec, ObjLoss=11.002, BoxCenterLoss=5.980, BoxScaleLoss=1.645, ClassLoss=3.009
[Epoch 84][Batch 199], LR: 1.00E-03, Speed: 177.673 samples/sec, ObjLoss=10.987, BoxCenterLoss=5.980, BoxScaleLoss=1.644, ClassLoss=3.003
[Epoch 84] Training cost: 148.298, ObjLoss=10.978, BoxCenterLoss=5.980, BoxScaleLoss=1.643, ClassLoss=3.000
[Epoch 84] Validation:
aeroplane=0.7443330659618941
bicycle=0.7514051379105237
bird=0.6892512073431276
boat=0.5419121491190294
bottle=0.48593911177133364
bus=0.7906343853271215
car=0.8235595920667337
cat=0.8235239029742634
chair=0.5163441302478207
cow=0.7230388467790042
diningtable=0.6357350842307447
dog=0.7603770138889923
horse=0.7805628141083285
motorbike=0.702843532899647
person=0.7360523537653316
pottedplant=0.4454722384373962
sheep=0.7215533822917235
sofa=0.7067592034240956
train=0.7596529099363405
tvmonitor=0.742225106217298
mAP=0.6940587584350374
[Epoch 85][Batch 99], LR: 1.00E-03, Speed: 157.703 samples/sec, ObjLoss=10.965, BoxCenterLoss=5.980, BoxScaleLoss=1.642, ClassLoss=2.994
[Epoch 85][Batch 199], LR: 1.00E-03, Speed: 195.238 samples/sec, ObjLoss=10.949, BoxCenterLoss=5.978, BoxScaleLoss=1.641, ClassLoss=2.988
[Epoch 85] Training cost: 130.806, ObjLoss=10.939, BoxCenterLoss=5.978, BoxScaleLoss=1.641, ClassLoss=2.985
[Epoch 85] Validation:
aeroplane=0.7702820386784434
bicycle=0.7430608626352581
bird=0.6678862117074572
boat=0.6193421856855982
bottle=0.4929884272565267
bus=0.7904640504126418
car=0.8336500544339411
cat=0.8242303134718767
chair=0.49258103533261594
cow=0.6957929112741077
diningtable=0.6629540362198403
dog=0.7824301821048713
horse=0.8222029842437001
motorbike=0.8100255464468029
person=0.7362914283097806
pottedplant=0.3844231624020065
sheep=0.6504530679302705
sofa=0.7020094618272738
train=0.7923692985929375
tvmonitor=0.729912023385083
mAP=0.7001674641175517
[Epoch 86][Batch 99], LR: 1.00E-03, Speed: 171.650 samples/sec, ObjLoss=10.924, BoxCenterLoss=5.977, BoxScaleLoss=1.639, ClassLoss=2.979
[Epoch 86][Batch 199], LR: 1.00E-03, Speed: 198.767 samples/sec, ObjLoss=10.910, BoxCenterLoss=5.977, BoxScaleLoss=1.638, ClassLoss=2.974
[Epoch 86] Training cost: 147.317, ObjLoss=10.901, BoxCenterLoss=5.976, BoxScaleLoss=1.638, ClassLoss=2.970
[Epoch 86] Validation:
aeroplane=0.7817586271519171
bicycle=0.7932709015546004
bird=0.7045422492134731
boat=0.5824989934786426
bottle=0.4754099692243762
bus=0.8094436547312375
car=0.8304628621420226
cat=0.8337299376721491
chair=0.5399580891906895
cow=0.7500526182744779
diningtable=0.6631361242473479
dog=0.7891130614950315
horse=0.8166768875601265
motorbike=0.8123833751405187
person=0.7699995531060534
pottedplant=0.38485566578407143
sheep=0.6930791106719127
sofa=0.7002403943424844
train=0.7566720038197516
tvmonitor=0.7072656214892076
mAP=0.7097274850145048
[Epoch 87][Batch 99], LR: 1.00E-03, Speed: 169.209 samples/sec, ObjLoss=10.886, BoxCenterLoss=5.976, BoxScaleLoss=1.637, ClassLoss=2.964
[Epoch 87][Batch 199], LR: 1.00E-03, Speed: 188.937 samples/sec, ObjLoss=10.871, BoxCenterLoss=5.975, BoxScaleLoss=1.635, ClassLoss=2.959
[Epoch 87] Training cost: 136.294, ObjLoss=10.862, BoxCenterLoss=5.974, BoxScaleLoss=1.635, ClassLoss=2.956
[Epoch 87] Validation:
aeroplane=0.7620285780535675
bicycle=0.7844104139064061
bird=0.6737774131109311
boat=0.5711363769911043
bottle=0.4719709185283311
bus=0.7652653941972365
car=0.8305791211999687
cat=0.8174184953251526
chair=0.5218166831906278
cow=0.7253568281108733
diningtable=0.6686523589770921
dog=0.7665967116978214
horse=0.7999558662368524
motorbike=0.7965946185503981
person=0.7275757091875127
pottedplant=0.4091608524670737
sheep=0.6732589954682415
sofa=0.7041645398986608
train=0.7667684745510457
tvmonitor=0.68932594292189
mAP=0.6962907146285395
[Epoch 88][Batch 99], LR: 1.00E-03, Speed: 193.125 samples/sec, ObjLoss=10.847, BoxCenterLoss=5.973, BoxScaleLoss=1.634, ClassLoss=2.950
[Epoch 88][Batch 199], LR: 1.00E-03, Speed: 161.180 samples/sec, ObjLoss=10.833, BoxCenterLoss=5.973, BoxScaleLoss=1.633, ClassLoss=2.945
[Epoch 88] Training cost: 146.834, ObjLoss=10.825, BoxCenterLoss=5.973, BoxScaleLoss=1.632, ClassLoss=2.942
[Epoch 88] Validation:
aeroplane=0.7053908276654957
bicycle=0.7906954298595108
bird=0.6630209752768094
boat=0.5488320425865606
bottle=0.4455430933032767
bus=0.7843154650586172
car=0.8242780485797975
cat=0.8254318754579958
chair=0.512272955386303
cow=0.6805755405864464
diningtable=0.6353302584533842
dog=0.7752438552141747
horse=0.7899125742018064
motorbike=0.7917490015185474
person=0.7504524297689252
pottedplant=0.43875281950672873
sheep=0.694619227437932
sofa=0.7081560764610643
train=0.7603701537079974
tvmonitor=0.7233495759472534
mAP=0.6924146112989313
[Epoch 89][Batch 99], LR: 1.00E-03, Speed: 183.085 samples/sec, ObjLoss=10.810, BoxCenterLoss=5.972, BoxScaleLoss=1.631, ClassLoss=2.936
[Epoch 89][Batch 199], LR: 1.00E-03, Speed: 177.912 samples/sec, ObjLoss=10.796, BoxCenterLoss=5.971, BoxScaleLoss=1.630, ClassLoss=2.932
[Epoch 89] Training cost: 132.931, ObjLoss=10.788, BoxCenterLoss=5.971, BoxScaleLoss=1.629, ClassLoss=2.929
[Epoch 89] Validation:
aeroplane=0.7622031031488812
bicycle=0.7493144205731136
bird=0.6722988594690558
boat=0.5673593634885915
bottle=0.46537659310682294
bus=0.8108616956627608
car=0.825973308692246
cat=0.8188109035179197
chair=0.48935845582373083
cow=0.72053180444559
diningtable=0.6190618770232543
dog=0.7569407228701119
horse=0.7929249961903755
motorbike=0.8030781578805183
person=0.7510118504560548
pottedplant=0.4043158245804995
sheep=0.7291749625098652
sofa=0.6854587176441772
train=0.7739655971544717
tvmonitor=0.702698831791032
mAP=0.6950360023014538
[Epoch 90][Batch 99], LR: 1.00E-03, Speed: 138.231 samples/sec, ObjLoss=10.773, BoxCenterLoss=5.970, BoxScaleLoss=1.628, ClassLoss=2.923
[Epoch 90][Batch 199], LR: 1.00E-03, Speed: 195.516 samples/sec, ObjLoss=10.760, BoxCenterLoss=5.970, BoxScaleLoss=1.627, ClassLoss=2.918
[Epoch 90] Training cost: 136.456, ObjLoss=10.752, BoxCenterLoss=5.970, BoxScaleLoss=1.627, ClassLoss=2.915
[Epoch 90] Validation:
aeroplane=0.7288912779607604
bicycle=0.7570371290114709
bird=0.6773751851553628
boat=0.5540859903241848
bottle=0.47201789340148276
bus=0.8153864095229352
car=0.820056867513176
cat=0.7904130348714369
chair=0.4905074614692624
cow=0.7335260845219268
diningtable=0.6643441641631473
dog=0.7528886936483299
horse=0.8001353568120676
motorbike=0.804036393535419
person=0.7414323782498693
pottedplant=0.4337737227054962
sheep=0.7063217515671549
sofa=0.7009450964142676
train=0.7311454951029789
tvmonitor=0.7259500057221833
mAP=0.6950135195836455
[Epoch 91][Batch 99], LR: 1.00E-03, Speed: 159.414 samples/sec, ObjLoss=10.738, BoxCenterLoss=5.969, BoxScaleLoss=1.626, ClassLoss=2.910
[Epoch 91][Batch 199], LR: 1.00E-03, Speed: 154.522 samples/sec, ObjLoss=10.724, BoxCenterLoss=5.968, BoxScaleLoss=1.625, ClassLoss=2.905
[Epoch 91] Training cost: 143.857, ObjLoss=10.715, BoxCenterLoss=5.967, BoxScaleLoss=1.624, ClassLoss=2.902
[Epoch 91] Validation:
aeroplane=0.741534000582238
bicycle=0.8023430025907534
bird=0.704708559339991
boat=0.6041101850001933
bottle=0.43493458445049066
bus=0.797242999460968
car=0.8374734893849582
cat=0.8298782596577975
chair=0.49337420441485447
cow=0.7706242107509549
diningtable=0.6641834314757697
dog=0.7713008928160562
horse=0.8146849599912946
motorbike=0.8151464150932838
person=0.7565810479302476
pottedplant=0.4112357059940839
sheep=0.7101303470810707
sofa=0.7020601039845179
train=0.7640533021724243
tvmonitor=0.702431898531676
mAP=0.7064015800351813
[Epoch 92][Batch 99], LR: 1.00E-03, Speed: 185.856 samples/sec, ObjLoss=10.702, BoxCenterLoss=5.967, BoxScaleLoss=1.623, ClassLoss=2.897
[Epoch 92][Batch 199], LR: 1.00E-03, Speed: 207.440 samples/sec, ObjLoss=10.688, BoxCenterLoss=5.966, BoxScaleLoss=1.622, ClassLoss=2.892
[Epoch 92] Training cost: 134.081, ObjLoss=10.679, BoxCenterLoss=5.966, BoxScaleLoss=1.622, ClassLoss=2.889
[Epoch 92] Validation:
aeroplane=0.7463048655782916
bicycle=0.7578144791091319
bird=0.7110937295288753
boat=0.564856407847447
bottle=0.4979467332083449
bus=0.8050761646181879
car=0.8218952060723659
cat=0.8400802919000387
chair=0.5208796084656403
cow=0.778094364793017
diningtable=0.6862207640014757
dog=0.7719535293804561
horse=0.8164323538747498
motorbike=0.8025920736375673
person=0.7450800748900475
pottedplant=0.4536265704485072
sheep=0.7176959304124911
sofa=0.7434965113331757
train=0.789789349716683
tvmonitor=0.7233665858426437
mAP=0.7147147797329569
[Epoch 93][Batch 99], LR: 1.00E-03, Speed: 189.094 samples/sec, ObjLoss=10.665, BoxCenterLoss=5.964, BoxScaleLoss=1.620, ClassLoss=2.884
[Epoch 93][Batch 199], LR: 1.00E-03, Speed: 199.464 samples/sec, ObjLoss=10.652, BoxCenterLoss=5.964, BoxScaleLoss=1.620, ClassLoss=2.879
[Epoch 93] Training cost: 140.090, ObjLoss=10.644, BoxCenterLoss=5.964, BoxScaleLoss=1.619, ClassLoss=2.876
[Epoch 93] Validation:
aeroplane=0.7644214957914017
bicycle=0.7888658339953107
bird=0.6993715017259398
boat=0.5876512819213024
bottle=0.43278325461727096
bus=0.8015363619247846
car=0.8307731787562498
cat=0.803656870992588
chair=0.49620919163341315
cow=0.7520329063352141
diningtable=0.6333757351485282
dog=0.7749346465070241
horse=0.8010754830526832
motorbike=0.8001842525618165
person=0.7265885123114326
pottedplant=0.3946677533875493
sheep=0.6848868920932057
sofa=0.7099595717982616
train=0.7719651761356846
tvmonitor=0.6731852041349962
mAP=0.6964062552412329
[Epoch 94][Batch 99], LR: 1.00E-03, Speed: 188.438 samples/sec, ObjLoss=10.630, BoxCenterLoss=5.963, BoxScaleLoss=1.618, ClassLoss=2.871
[Epoch 94][Batch 199], LR: 1.00E-03, Speed: 150.203 samples/sec, ObjLoss=10.617, BoxCenterLoss=5.963, BoxScaleLoss=1.617, ClassLoss=2.866
[Epoch 94] Training cost: 137.021, ObjLoss=10.609, BoxCenterLoss=5.962, BoxScaleLoss=1.617, ClassLoss=2.863
[Epoch 94] Validation:
aeroplane=0.7519729607827279
bicycle=0.7814912751038343
bird=0.6581921574565905
boat=0.5551660558709721
bottle=0.4563218539845699
bus=0.8235896726650709
car=0.8069607788929457
cat=0.8364029128615902
chair=0.5027526061684198
cow=0.7193522033347514
diningtable=0.6728395470709444
dog=0.7749346580897257
horse=0.8222752341770984
motorbike=0.7942288565671229
person=0.7398235292249802
pottedplant=0.41371196281317
sheep=0.6948336272161585
sofa=0.734377694174765
train=0.7526530176124905
tvmonitor=0.7069543512763031
mAP=0.6999417477672115
[Epoch 95][Batch 99], LR: 1.00E-03, Speed: 139.652 samples/sec, ObjLoss=10.597, BoxCenterLoss=5.962, BoxScaleLoss=1.616, ClassLoss=2.859
[Epoch 95][Batch 199], LR: 1.00E-03, Speed: 167.320 samples/sec, ObjLoss=10.584, BoxCenterLoss=5.962, BoxScaleLoss=1.615, ClassLoss=2.855
[Epoch 95] Training cost: 140.223, ObjLoss=10.577, BoxCenterLoss=5.961, BoxScaleLoss=1.615, ClassLoss=2.852
[Epoch 95] Validation:
aeroplane=0.7519935910299014
bicycle=0.7528345176966853
bird=0.6990308493307698
boat=0.6368303441256211
bottle=0.470173135016148
bus=0.8070081534350451
car=0.828443058780215
cat=0.827495841290782
chair=0.5086642174997467
cow=0.7374382085258562
diningtable=0.610634821123113
dog=0.7853979002499177
horse=0.790443128146919
motorbike=0.7951132280540116
person=0.7547240132357828
pottedplant=0.4085560402545217
sheep=0.7141380823915593
sofa=0.7175426192779792
train=0.7366475017427044
tvmonitor=0.6676893308621306
mAP=0.7000399291034703
[Epoch 96][Batch 99], LR: 1.00E-03, Speed: 167.198 samples/sec, ObjLoss=10.563, BoxCenterLoss=5.961, BoxScaleLoss=1.614, ClassLoss=2.847
[Epoch 96][Batch 199], LR: 1.00E-03, Speed: 172.016 samples/sec, ObjLoss=10.551, BoxCenterLoss=5.960, BoxScaleLoss=1.612, ClassLoss=2.842
[Epoch 96] Training cost: 139.039, ObjLoss=10.544, BoxCenterLoss=5.960, BoxScaleLoss=1.612, ClassLoss=2.839
[Epoch 96] Validation:
aeroplane=0.7682981285798319
bicycle=0.8032463632392386
bird=0.6806672614490602
boat=0.6263955616423397
bottle=0.4516318329537574
bus=0.7844744840681462
car=0.8303934056087664
cat=0.8449270353308642
chair=0.48565647218830665
cow=0.7633237197965381
diningtable=0.664465321674955
dog=0.7805804972662298
horse=0.7962887124240633
motorbike=0.787447877612291
person=0.7513750327774732
pottedplant=0.42467139941340576
sheep=0.7318407184646871
sofa=0.7425157548450664
train=0.7633671938725063
tvmonitor=0.7153406903962719
mAP=0.7098453731801899
[Epoch 97][Batch 99], LR: 1.00E-03, Speed: 156.126 samples/sec, ObjLoss=10.531, BoxCenterLoss=5.960, BoxScaleLoss=1.611, ClassLoss=2.835
[Epoch 97][Batch 199], LR: 1.00E-03, Speed: 171.305 samples/sec, ObjLoss=10.518, BoxCenterLoss=5.959, BoxScaleLoss=1.610, ClassLoss=2.830
[Epoch 97] Training cost: 133.738, ObjLoss=10.511, BoxCenterLoss=5.959, BoxScaleLoss=1.610, ClassLoss=2.828
[Epoch 97] Validation:
aeroplane=0.7396048610250243
bicycle=0.7697042396933986
bird=0.6728792183092489
boat=0.5792061377068405
bottle=0.45359279565175276
bus=0.771718666170275
car=0.8161309051145784
cat=0.8054380773728034
chair=0.5047798015800813
cow=0.7713696357535911
diningtable=0.6666096498227526
dog=0.7473657515089902
horse=0.7775161627464808
motorbike=0.7652453279833843
person=0.7404223200663321
pottedplant=0.43962918925673994
sheep=0.6777854838635292
sofa=0.6853353229632282
train=0.7317165229947712
tvmonitor=0.7225419087510612
mAP=0.6919295989167431
[Epoch 98][Batch 99], LR: 1.00E-03, Speed: 171.191 samples/sec, ObjLoss=10.498, BoxCenterLoss=5.958, BoxScaleLoss=1.609, ClassLoss=2.823
[Epoch 98][Batch 199], LR: 1.00E-03, Speed: 179.321 samples/sec, ObjLoss=10.487, BoxCenterLoss=5.958, BoxScaleLoss=1.608, ClassLoss=2.819
[Epoch 98] Training cost: 143.541, ObjLoss=10.480, BoxCenterLoss=5.957, BoxScaleLoss=1.607, ClassLoss=2.816
[Epoch 98] Validation:
aeroplane=0.7370842829461945
bicycle=0.7412473203210194
bird=0.6722847049066304
boat=0.6335113493533326
bottle=0.5010457526837817
bus=0.8048964876513626
car=0.8294425401552158
cat=0.8158255713409447
chair=0.5132948350351355
cow=0.7615905549396571
diningtable=0.6549146129492301
dog=0.7821080159694828
horse=0.8153263658811599
motorbike=0.7717658633999306
person=0.75838263958678
pottedplant=0.363346115555461
sheep=0.6863534407844101
sofa=0.6965154957355134
train=0.7760502989603131
tvmonitor=0.7188244305908831
mAP=0.701690533937322
[Epoch 99][Batch 99], LR: 1.00E-03, Speed: 193.745 samples/sec, ObjLoss=10.468, BoxCenterLoss=5.957, BoxScaleLoss=1.606, ClassLoss=2.811
[Epoch 99][Batch 199], LR: 1.00E-03, Speed: 160.176 samples/sec, ObjLoss=10.456, BoxCenterLoss=5.956, BoxScaleLoss=1.605, ClassLoss=2.807
[Epoch 99] Training cost: 139.885, ObjLoss=10.449, BoxCenterLoss=5.956, BoxScaleLoss=1.605, ClassLoss=2.804
[Epoch 99] Validation:
aeroplane=0.7604014202699957
bicycle=0.7787769983191751
bird=0.7042260330811768
boat=0.5893102414662907
bottle=0.49068735324361157
bus=0.8067525802487909
car=0.8230490419856746
cat=0.8436208286736913
chair=0.5204874706560786
cow=0.7407629455644852
diningtable=0.6770259158605904
dog=0.7859857512961276
horse=0.8318440554047559
motorbike=0.7804889725211785
person=0.7661744519870513
pottedplant=0.4235007779894812
sheep=0.7298579796960415
sofa=0.7087821183879249
train=0.8099329919909922
tvmonitor=0.7370251205809722
mAP=0.7154346524612043
[Epoch 100][Batch 99], LR: 1.00E-03, Speed: 147.542 samples/sec, ObjLoss=10.437, BoxCenterLoss=5.955, BoxScaleLoss=1.604, ClassLoss=2.799
[Epoch 100][Batch 199], LR: 1.00E-03, Speed: 132.755 samples/sec, ObjLoss=10.426, BoxCenterLoss=5.955, BoxScaleLoss=1.603, ClassLoss=2.795
[Epoch 100] Training cost: 141.553, ObjLoss=10.419, BoxCenterLoss=5.954, BoxScaleLoss=1.602, ClassLoss=2.792
[Epoch 100] Validation:
aeroplane=0.7398205467568132
bicycle=0.7747711179636377
bird=0.6842084818719737
boat=0.48932980527561093
bottle=0.4450261664360208
bus=0.8196717958922706
car=0.8347280544126789
cat=0.8486974584789568
chair=0.5171254329222341
cow=0.7805962646891355
diningtable=0.687593443669869
dog=0.7692031643001664
horse=0.8193187582240145
motorbike=0.8076141847539697
person=0.7338994086427771
pottedplant=0.4568425433298392
sheep=0.692256424861732
sofa=0.7241492799112259
train=0.7526873553843803
tvmonitor=0.7118553938732574
mAP=0.7044697540825281
[Epoch 101][Batch 99], LR: 1.00E-03, Speed: 155.048 samples/sec, ObjLoss=10.407, BoxCenterLoss=5.953, BoxScaleLoss=1.601, ClassLoss=2.788
[Epoch 101][Batch 199], LR: 1.00E-03, Speed: 167.461 samples/sec, ObjLoss=10.396, BoxCenterLoss=5.953, BoxScaleLoss=1.600, ClassLoss=2.783
[Epoch 101] Training cost: 139.916, ObjLoss=10.388, BoxCenterLoss=5.952, BoxScaleLoss=1.600, ClassLoss=2.780
[Epoch 101] Validation:
aeroplane=0.7777900754858948
bicycle=0.8063078484833169
bird=0.690887922836976
boat=0.6355942161598388
bottle=0.48849753402007556
bus=0.8080393178304457
car=0.8338295238640415
cat=0.8085296783085498
chair=0.5208035430376308
cow=0.7559234562365515
diningtable=0.6661727705685908
dog=0.7751140262534101
horse=0.805816281373924
motorbike=0.783978719338718
person=0.7424991006019857
pottedplant=0.4259611838144066
sheep=0.7250604282611626
sofa=0.7220125048313765
train=0.7715937598664273
tvmonitor=0.7274217654184012
mAP=0.7135916828295862
[Epoch 102][Batch 99], LR: 1.00E-03, Speed: 201.112 samples/sec, ObjLoss=10.377, BoxCenterLoss=5.952, BoxScaleLoss=1.599, ClassLoss=2.776
[Epoch 102][Batch 199], LR: 1.00E-03, Speed: 156.534 samples/sec, ObjLoss=10.365, BoxCenterLoss=5.951, BoxScaleLoss=1.598, ClassLoss=2.771
[Epoch 102] Training cost: 147.187, ObjLoss=10.358, BoxCenterLoss=5.951, BoxScaleLoss=1.597, ClassLoss=2.769
[Epoch 102] Validation:
aeroplane=0.7639780503300633
bicycle=0.8069819202838459
bird=0.6726126958813387
boat=0.6063642067394319
bottle=0.45201074327992113
bus=0.7864351725744407
car=0.8354326792740181
cat=0.837161723790711
chair=0.5177689308226848
cow=0.7472986503213724
diningtable=0.6384443836140846
dog=0.7698627997754013
horse=0.8213012099316852
motorbike=0.8079744397836852
person=0.7375169067267544
pottedplant=0.4365088598098343
sheep=0.6980937230417381
sofa=0.7192447901027849
train=0.7646454103278972
tvmonitor=0.7291467498454127
mAP=0.7074392023128554
[Epoch 103][Batch 99], LR: 1.00E-03, Speed: 156.255 samples/sec, ObjLoss=10.347, BoxCenterLoss=5.950, BoxScaleLoss=1.596, ClassLoss=2.765
[Epoch 103][Batch 199], LR: 1.00E-03, Speed: 203.466 samples/sec, ObjLoss=10.336, BoxCenterLoss=5.950, BoxScaleLoss=1.596, ClassLoss=2.761
[Epoch 103] Training cost: 154.385, ObjLoss=10.329, BoxCenterLoss=5.949, BoxScaleLoss=1.595, ClassLoss=2.759
[Epoch 103] Validation:
aeroplane=0.7628175094449021
bicycle=0.8148557812811406
bird=0.7133207395633382
boat=0.5803601039577677
bottle=0.5216474991892729
bus=0.8110774782454411
car=0.8368253676210979
cat=0.8392715006304973
chair=0.5292986070748125
cow=0.7288661961336955
diningtable=0.6412541691800738
dog=0.7681736743328633
horse=0.8118787565226239
motorbike=0.800625228443614
person=0.739768536264282
pottedplant=0.43533367542956986
sheep=0.7279552668209957
sofa=0.7167033525990809
train=0.7809227999172348
tvmonitor=0.7237382374029437
mAP=0.7142347240027624
[Epoch 104][Batch 99], LR: 1.00E-03, Speed: 166.247 samples/sec, ObjLoss=10.319, BoxCenterLoss=5.949, BoxScaleLoss=1.595, ClassLoss=2.755
[Epoch 104][Batch 199], LR: 1.00E-03, Speed: 213.224 samples/sec, ObjLoss=10.307, BoxCenterLoss=5.948, BoxScaleLoss=1.594, ClassLoss=2.751
[Epoch 104] Training cost: 143.223, ObjLoss=10.300, BoxCenterLoss=5.948, BoxScaleLoss=1.593, ClassLoss=2.748
[Epoch 104] Validation:
aeroplane=0.7427916678280923
bicycle=0.7123490083464844
bird=0.6851293762066114
boat=0.5869900941315674
bottle=0.42249548866523046
bus=0.804998100988988
car=0.8201312418582959
cat=0.7848712744775768
chair=0.5083070621854222
cow=0.715998156512625
diningtable=0.6682133707942137
dog=0.7693579104646433
horse=0.751174673965627
motorbike=0.7273302151654265
person=0.7244327757276987
pottedplant=0.4115911401123874
sheep=0.7159417302937637
sofa=0.7236539502157396
train=0.7527267725621786
tvmonitor=0.7158752450876941
mAP=0.6872179627795134
[Epoch 105][Batch 99], LR: 1.00E-03, Speed: 179.076 samples/sec, ObjLoss=10.288, BoxCenterLoss=5.947, BoxScaleLoss=1.592, ClassLoss=2.744
[Epoch 105][Batch 199], LR: 1.00E-03, Speed: 179.196 samples/sec, ObjLoss=10.277, BoxCenterLoss=5.947, BoxScaleLoss=1.592, ClassLoss=2.740
[Epoch 105] Training cost: 130.058, ObjLoss=10.271, BoxCenterLoss=5.947, BoxScaleLoss=1.591, ClassLoss=2.737
[Epoch 105] Validation:
aeroplane=0.7498508741326234
bicycle=0.8275297744443434
bird=0.7153339816529042
boat=0.6144384432991988
bottle=0.481470882078653
bus=0.8145680167542025
car=0.8387971284099112
cat=0.8240571451094234
chair=0.5347090992746553
cow=0.7666837090703973
diningtable=0.6527627417434024
dog=0.7600152774470051
horse=0.8064780923647534
motorbike=0.7899971329294523
person=0.7656422779267507
pottedplant=0.4289836690849789
sheep=0.7490627684312569
sofa=0.723676488374724
train=0.7813176425276661
tvmonitor=0.7349689719021646
mAP=0.7180172058479233
[Epoch 106][Batch 99], LR: 1.00E-03, Speed: 131.475 samples/sec, ObjLoss=10.260, BoxCenterLoss=5.946, BoxScaleLoss=1.590, ClassLoss=2.733
[Epoch 106][Batch 199], LR: 1.00E-03, Speed: 159.500 samples/sec, ObjLoss=10.248, BoxCenterLoss=5.945, BoxScaleLoss=1.589, ClassLoss=2.728
[Epoch 106] Training cost: 140.515, ObjLoss=10.243, BoxCenterLoss=5.945, BoxScaleLoss=1.589, ClassLoss=2.726
[Epoch 106] Validation:
aeroplane=0.7602461187460086
bicycle=0.7713688523421758
bird=0.6857558463400084
boat=0.583125427250157
bottle=0.4882354864306734
bus=0.8049172111427827
car=0.824878381457174
cat=0.7832819701300731
chair=0.493464008810508
cow=0.7061055832488045
diningtable=0.691498254127638
dog=0.7834048965442224
horse=0.7578872243505789
motorbike=0.7939220583687896
person=0.7428598178658131
pottedplant=0.42575779615783854
sheep=0.6882921035758727
sofa=0.722452803845361
train=0.7614697202298637
tvmonitor=0.6799700094383098
mAP=0.6974446785201326
[Epoch 107][Batch 99], LR: 1.00E-03, Speed: 162.444 samples/sec, ObjLoss=10.233, BoxCenterLoss=5.945, BoxScaleLoss=1.588, ClassLoss=2.722
[Epoch 107][Batch 199], LR: 1.00E-03, Speed: 180.751 samples/sec, ObjLoss=10.221, BoxCenterLoss=5.945, BoxScaleLoss=1.587, ClassLoss=2.717
[Epoch 107] Training cost: 149.211, ObjLoss=10.216, BoxCenterLoss=5.944, BoxScaleLoss=1.587, ClassLoss=2.715
[Epoch 107] Validation:
aeroplane=0.7687170787954509
bicycle=0.7997699699028791
bird=0.7268026588864124
boat=0.5681579057349004
bottle=0.4961809277680925
bus=0.7848652611416208
car=0.8444389441525203
cat=0.8345925149724565
chair=0.5407814926946336
cow=0.7558478984759074
diningtable=0.6650760365571161
dog=0.7846568273721175
horse=0.7961495868641817
motorbike=0.8027970436548196
person=0.763364759667112
pottedplant=0.4273244439266407
sheep=0.7343892319492965
sofa=0.7036179038189493
train=0.7827096928322392
tvmonitor=0.711576832471508
mAP=0.7145908505819427
[Epoch 108][Batch 99], LR: 1.00E-03, Speed: 147.472 samples/sec, ObjLoss=10.205, BoxCenterLoss=5.944, BoxScaleLoss=1.586, ClassLoss=2.710
[Epoch 108][Batch 199], LR: 1.00E-03, Speed: 164.154 samples/sec, ObjLoss=10.194, BoxCenterLoss=5.944, BoxScaleLoss=1.585, ClassLoss=2.706
[Epoch 108] Training cost: 135.661, ObjLoss=10.188, BoxCenterLoss=5.943, BoxScaleLoss=1.585, ClassLoss=2.704
[Epoch 108] Validation:
aeroplane=0.7952840779855921
bicycle=0.8036354726195496
bird=0.7106400248264706
boat=0.616149309997405
bottle=0.5015661011046135
bus=0.7623645494449197
car=0.8277380265296287
cat=0.8008800796906183
chair=0.5205049876515254
cow=0.7166160892970249
diningtable=0.6675588820036278
dog=0.7583975096365262
horse=0.782202241332233
motorbike=0.7821398871681756
person=0.7208955881798393
pottedplant=0.40116551419536184
sheep=0.691904142669391
sofa=0.7106408259338263
train=0.7431345849716813
tvmonitor=0.7076412729948363
mAP=0.7010529584116422
[Epoch 109][Batch 99], LR: 1.00E-03, Speed: 164.117 samples/sec, ObjLoss=10.177, BoxCenterLoss=5.943, BoxScaleLoss=1.584, ClassLoss=2.700
[Epoch 109][Batch 199], LR: 1.00E-03, Speed: 178.656 samples/sec, ObjLoss=10.167, BoxCenterLoss=5.943, BoxScaleLoss=1.583, ClassLoss=2.696
[Epoch 109] Training cost: 138.487, ObjLoss=10.160, BoxCenterLoss=5.942, BoxScaleLoss=1.583, ClassLoss=2.693
[Epoch 109] Validation:
aeroplane=0.7695606052447932
bicycle=0.7838765383530725
bird=0.7134014330935664
boat=0.6084726295797883
bottle=0.5021256183106568
bus=0.8077804661728275
car=0.8317273074970901
cat=0.80672665955935
chair=0.5525629320964827
cow=0.5206721169614701
diningtable=0.6285506695750513
dog=0.7767490496259467
horse=0.8006770998292239
motorbike=0.8086519091840021
person=0.7522053732750085
pottedplant=0.43599212512434216
sheep=0.680948525840054
sofa=0.698717465598692
train=0.7954520709834181
tvmonitor=0.7203282040722686
mAP=0.6997589399988553
[Epoch 110][Batch 99], LR: 1.00E-03, Speed: 160.503 samples/sec, ObjLoss=10.149, BoxCenterLoss=5.941, BoxScaleLoss=1.582, ClassLoss=2.689
[Epoch 110][Batch 199], LR: 1.00E-03, Speed: 142.583 samples/sec, ObjLoss=10.138, BoxCenterLoss=5.941, BoxScaleLoss=1.581, ClassLoss=2.685
[Epoch 110] Training cost: 138.443, ObjLoss=10.132, BoxCenterLoss=5.941, BoxScaleLoss=1.581, ClassLoss=2.683
[Epoch 110] Validation:
aeroplane=0.7830985755951482
bicycle=0.8144380192856177
bird=0.7185860401724493
boat=0.6235908921408634
bottle=0.5048799586203866
bus=0.8166147634255811
car=0.8402987845366761
cat=0.8292704098456002
chair=0.5309352610606145
cow=0.7384319844994187
diningtable=0.6976796374030807
dog=0.8065801732249229
horse=0.8061564058538181
motorbike=0.7826840409101553
person=0.7720349206762387
pottedplant=0.3578300445508057
sheep=0.7390089780290893
sofa=0.7296538563739474
train=0.8190733780231244
tvmonitor=0.7112847985607234
mAP=0.7211065461394129
[Epoch 111][Batch 99], LR: 1.00E-03, Speed: 145.636 samples/sec, ObjLoss=10.121, BoxCenterLoss=5.940, BoxScaleLoss=1.580, ClassLoss=2.679
[Epoch 111][Batch 199], LR: 1.00E-03, Speed: 154.302 samples/sec, ObjLoss=10.111, BoxCenterLoss=5.939, BoxScaleLoss=1.579, ClassLoss=2.675
[Epoch 111] Training cost: 126.567, ObjLoss=10.105, BoxCenterLoss=5.939, BoxScaleLoss=1.578, ClassLoss=2.673
[Epoch 111] Validation:
aeroplane=0.8285869869058777
bicycle=0.7844242792411407
bird=0.7088948305072325
boat=0.5894206964353265
bottle=0.5015806346835054
bus=0.8025891108018534
car=0.8441762001054156
cat=0.8396349142901537
chair=0.5388406111830712
cow=0.7429183907226864
diningtable=0.663942879264547
dog=0.7718704871071282
horse=0.7938796055083971
motorbike=0.8034379043271812
person=0.7453634997785353
pottedplant=0.425629940828321
sheep=0.666916370273525
sofa=0.7228256265334575
train=0.7815083785234994
tvmonitor=0.7013070434916752
mAP=0.7128874195256266
[Epoch 112][Batch 99], LR: 1.00E-03, Speed: 171.193 samples/sec, ObjLoss=10.094, BoxCenterLoss=5.939, BoxScaleLoss=1.578, ClassLoss=2.669
[Epoch 112][Batch 199], LR: 1.00E-03, Speed: 198.189 samples/sec, ObjLoss=10.084, BoxCenterLoss=5.939, BoxScaleLoss=1.577, ClassLoss=2.665
[Epoch 112] Training cost: 135.608, ObjLoss=10.078, BoxCenterLoss=5.938, BoxScaleLoss=1.576, ClassLoss=2.663
[Epoch 112] Validation:
aeroplane=0.7587173345807648
bicycle=0.7907765702341331
bird=0.6551028594887837
boat=0.5683326944988747
bottle=0.49798170896313654
bus=0.7718673458706717
car=0.8149490498214966
cat=0.8121577113411361
chair=0.501953847829826
cow=0.7200642134232963
diningtable=0.6283208041006301
dog=0.7335958606835308
horse=0.7787822739072713
motorbike=0.7463975802894379
person=0.736120834249611
pottedplant=0.3936516141346613
sheep=0.6673849065735988
sofa=0.6518001702567595
train=0.7488181567392797
tvmonitor=0.7173993598599507
mAP=0.6847087448423425
[Epoch 113][Batch 99], LR: 1.00E-03, Speed: 165.598 samples/sec, ObjLoss=10.067, BoxCenterLoss=5.938, BoxScaleLoss=1.576, ClassLoss=2.660
[Epoch 113][Batch 199], LR: 1.00E-03, Speed: 180.853 samples/sec, ObjLoss=10.058, BoxCenterLoss=5.937, BoxScaleLoss=1.575, ClassLoss=2.656
[Epoch 113] Training cost: 141.295, ObjLoss=10.052, BoxCenterLoss=5.937, BoxScaleLoss=1.575, ClassLoss=2.654
[Epoch 113] Validation:
aeroplane=0.7639672621899495
bicycle=0.8121387297328976
bird=0.7125177309693356
boat=0.5992567307917642
bottle=0.4850074477855914
bus=0.823147467336629
car=0.8342234119470232
cat=0.74093047946214
chair=0.5207068849064924
cow=0.7072839370329747
diningtable=0.6180024375148874
dog=0.7584392197710685
horse=0.7856666441211146
motorbike=0.7910459506528607
person=0.7550836006908777
pottedplant=0.41007407899442094
sheep=0.6984336095201924
sofa=0.6845778224016502
train=0.8116596082462051
tvmonitor=0.7237301781577755
mAP=0.7017946616112924
[Epoch 114][Batch 99], LR: 1.00E-03, Speed: 149.527 samples/sec, ObjLoss=10.041, BoxCenterLoss=5.936, BoxScaleLoss=1.574, ClassLoss=2.650
[Epoch 114][Batch 199], LR: 1.00E-03, Speed: 163.888 samples/sec, ObjLoss=10.032, BoxCenterLoss=5.936, BoxScaleLoss=1.573, ClassLoss=2.647
[Epoch 114] Training cost: 136.994, ObjLoss=10.026, BoxCenterLoss=5.936, BoxScaleLoss=1.573, ClassLoss=2.645
[Epoch 114] Validation:
aeroplane=0.8144199810166322
bicycle=0.8237656611768825
bird=0.7191859867278855
boat=0.5894458025080695
bottle=0.5307059780927377
bus=0.8090219079128049
car=0.8364815318166974
cat=0.8267919821535501
chair=0.5232400431038889
cow=0.7362532486915981
diningtable=0.6618408020529993
dog=0.7848799484160597
horse=0.8011303939083355
motorbike=0.8093197399805493
person=0.7392135012460238
pottedplant=0.42428767612083024
sheep=0.7052176455026321
sofa=0.696179463267336
train=0.7974664355152129
tvmonitor=0.7167955139192099
mAP=0.7172821621564969
[Epoch 115][Batch 99], LR: 1.00E-03, Speed: 150.188 samples/sec, ObjLoss=10.016, BoxCenterLoss=5.935, BoxScaleLoss=1.572, ClassLoss=2.640
[Epoch 115][Batch 199], LR: 1.00E-03, Speed: 182.502 samples/sec, ObjLoss=10.007, BoxCenterLoss=5.935, BoxScaleLoss=1.571, ClassLoss=2.637
[Epoch 115] Training cost: 137.451, ObjLoss=10.001, BoxCenterLoss=5.934, BoxScaleLoss=1.571, ClassLoss=2.635
[Epoch 115] Validation:
aeroplane=0.7707301089832018
bicycle=0.8318364307798456
bird=0.6953255840601579
boat=0.605050161407533
bottle=0.5157497475239523
bus=0.7945748315583521
car=0.8459908556606763
cat=0.8004809853157375
chair=0.533891314599709
cow=0.7360776882174709
diningtable=0.6483643868874761
dog=0.7845536603145581
horse=0.8108205952489077
motorbike=0.784161446109896
person=0.7415879701541683
pottedplant=0.3834770355231205
sheep=0.7011227104756843
sofa=0.6934347382686236
train=0.766620718031055
tvmonitor=0.7261109231984219
mAP=0.7084980946159273
[Epoch 116][Batch 99], LR: 1.00E-03, Speed: 162.376 samples/sec, ObjLoss=9.991, BoxCenterLoss=5.934, BoxScaleLoss=1.570, ClassLoss=2.631
[Epoch 116][Batch 199], LR: 1.00E-03, Speed: 187.873 samples/sec, ObjLoss=9.981, BoxCenterLoss=5.933, BoxScaleLoss=1.569, ClassLoss=2.627
[Epoch 116] Training cost: 124.933, ObjLoss=9.974, BoxCenterLoss=5.932, BoxScaleLoss=1.568, ClassLoss=2.625
[Epoch 116] Validation:
aeroplane=0.7849338931791124
bicycle=0.7881393983543835
bird=0.693100710052702
boat=0.5911346358885413
bottle=0.4894196710254454
bus=0.8005284873015964
car=0.8434882964629056
cat=0.8434401404881227
chair=0.5282792185005195
cow=0.763337398132941
diningtable=0.6569139582637876
dog=0.7873514358469659
horse=0.8343507753773584
motorbike=0.7775591803109512
person=0.7287894334658996
pottedplant=0.4083584474280164
sheep=0.6842964595211796
sofa=0.7207732324922579
train=0.7736547406705432
tvmonitor=0.713003566506965
mAP=0.7105426539635098
[Epoch 117][Batch 99], LR: 1.00E-03, Speed: 153.416 samples/sec, ObjLoss=9.965, BoxCenterLoss=5.932, BoxScaleLoss=1.568, ClassLoss=2.622
[Epoch 117][Batch 199], LR: 1.00E-03, Speed: 159.620 samples/sec, ObjLoss=9.956, BoxCenterLoss=5.932, BoxScaleLoss=1.567, ClassLoss=2.619
[Epoch 117] Training cost: 136.564, ObjLoss=9.951, BoxCenterLoss=5.931, BoxScaleLoss=1.567, ClassLoss=2.616
[Epoch 117] Validation:
aeroplane=0.8062840026500208
bicycle=0.8150835428009833
bird=0.7132127983372667
boat=0.6145800239124312
bottle=0.5051999524715304
bus=0.8150960260644056
car=0.8477250305089192
cat=0.8101509607734346
chair=0.5314223621403527
cow=0.7520764614365174
diningtable=0.6762762301470355
dog=0.8067317370568698
horse=0.8086701384752223
motorbike=0.8031990077061405
person=0.7458953932416302
pottedplant=0.4399344765581947
sheep=0.7231147644989516
sofa=0.7309683948138037
train=0.8045822633636525
tvmonitor=0.7312401342267666
mAP=0.7240721850592065
[Epoch 118][Batch 99], LR: 1.00E-03, Speed: 186.754 samples/sec, ObjLoss=9.941, BoxCenterLoss=5.931, BoxScaleLoss=1.566, ClassLoss=2.613
[Epoch 118][Batch 199], LR: 1.00E-03, Speed: 180.998 samples/sec, ObjLoss=9.931, BoxCenterLoss=5.930, BoxScaleLoss=1.565, ClassLoss=2.609
[Epoch 118] Training cost: 132.787, ObjLoss=9.925, BoxCenterLoss=5.930, BoxScaleLoss=1.565, ClassLoss=2.607
[Epoch 118] Validation:
aeroplane=0.7605604246951633
bicycle=0.7798699810961089
bird=0.7119481298051237
boat=0.6069295965411534
bottle=0.503196446270257
bus=0.8006412516751674
car=0.8384230402466382
cat=0.8395192739451228
chair=0.5140177997450359
cow=0.7168576566907726
diningtable=0.6928960254571416
dog=0.7809379561520383
horse=0.8123603263891559
motorbike=0.7991577386534527
person=0.7512592720186007
pottedplant=0.4480881007045851
sheep=0.6769463970065024
sofa=0.7303753769751301
train=0.805923879356672
tvmonitor=0.7358212806120893
mAP=0.7152864977017956
[Epoch 119][Batch 99], LR: 1.00E-03, Speed: 162.361 samples/sec, ObjLoss=9.916, BoxCenterLoss=5.929, BoxScaleLoss=1.564, ClassLoss=2.604
[Epoch 119][Batch 199], LR: 1.00E-03, Speed: 200.577 samples/sec, ObjLoss=9.907, BoxCenterLoss=5.929, BoxScaleLoss=1.563, ClassLoss=2.600
[Epoch 119] Training cost: 135.290, ObjLoss=9.901, BoxCenterLoss=5.928, BoxScaleLoss=1.563, ClassLoss=2.598
[Epoch 119] Validation:
aeroplane=0.7639854302236062
bicycle=0.8038643559369278
bird=0.7160107543829026
boat=0.6325516860625865
bottle=0.47567021065582077
bus=0.8126398144341457
car=0.8317023665392475
cat=0.8421122260039221
chair=0.5348116201368519
cow=0.7810394156698028
diningtable=0.6602002107559631
dog=0.762724169885276
horse=0.8259034405678183
motorbike=0.7916122216135226
person=0.766308200209104
pottedplant=0.4336286517999608
sheep=0.6635854882200867
sofa=0.6614245714659843
train=0.7776230766272842
tvmonitor=0.7129116829657698
mAP=0.7125154797078291
[Epoch 120][Batch 99], LR: 1.00E-03, Speed: 176.672 samples/sec, ObjLoss=9.892, BoxCenterLoss=5.928, BoxScaleLoss=1.562, ClassLoss=2.595
[Epoch 120][Batch 199], LR: 1.00E-03, Speed: 160.552 samples/sec, ObjLoss=9.883, BoxCenterLoss=5.927, BoxScaleLoss=1.561, ClassLoss=2.591
[Epoch 120] Training cost: 151.887, ObjLoss=9.878, BoxCenterLoss=5.927, BoxScaleLoss=1.561, ClassLoss=2.589
[Epoch 120] Validation:
aeroplane=0.7380417177897401
bicycle=0.7826247879220447
bird=0.6820138162264284
boat=0.5809833449073817
bottle=0.47191671101949745
bus=0.8249099807631958
car=0.8348810868775767
cat=0.8397453643841454
chair=0.5185510451141937
cow=0.7157194079668413
diningtable=0.667405809909268
dog=0.7717394670651957
horse=0.8304497996567467
motorbike=0.7881921800895278
person=0.7698043797946813
pottedplant=0.43604004361070015
sheep=0.7226450780394192
sofa=0.7585785444664512
train=0.7814196964658602
tvmonitor=0.7202568264853344
mAP=0.7117959544277114
[Epoch 121][Batch 99], LR: 1.00E-03, Speed: 171.572 samples/sec, ObjLoss=9.868, BoxCenterLoss=5.926, BoxScaleLoss=1.560, ClassLoss=2.585
[Epoch 121][Batch 199], LR: 1.00E-03, Speed: 204.157 samples/sec, ObjLoss=9.858, BoxCenterLoss=5.926, BoxScaleLoss=1.559, ClassLoss=2.581
[Epoch 121] Training cost: 142.402, ObjLoss=9.853, BoxCenterLoss=5.925, BoxScaleLoss=1.559, ClassLoss=2.580
[Epoch 121] Validation:
aeroplane=0.773680498081599
bicycle=0.7902865415377603
bird=0.7164792337379964
boat=0.6085839058319068
bottle=0.49561934476210273
bus=0.8156184486428038
car=0.8404272873038673
cat=0.8365418893526778
chair=0.5264571183904327
cow=0.7204610612325824
diningtable=0.6442654140123557
dog=0.7696314074739726
horse=0.8284094195487074
motorbike=0.8000450682584561
person=0.7731951840227704
pottedplant=0.4362582626066203
sheep=0.6917184703355215
sofa=0.7315192765144076
train=0.7682626844332395
tvmonitor=0.7245809877922641
mAP=0.7146020751936022
[Epoch 122][Batch 99], LR: 1.00E-03, Speed: 163.585 samples/sec, ObjLoss=9.845, BoxCenterLoss=5.924, BoxScaleLoss=1.558, ClassLoss=2.576
[Epoch 122][Batch 199], LR: 1.00E-03, Speed: 180.096 samples/sec, ObjLoss=9.836, BoxCenterLoss=5.924, BoxScaleLoss=1.558, ClassLoss=2.573
[Epoch 122] Training cost: 134.372, ObjLoss=9.831, BoxCenterLoss=5.924, BoxScaleLoss=1.557, ClassLoss=2.571
[Epoch 122] Validation:
aeroplane=0.7754915103327225
bicycle=0.8016569985595516
bird=0.7145109077399036
boat=0.6034898454350315
bottle=0.49277041296136925
bus=0.8015458347202664
car=0.8533700954825426
cat=0.8277968491815852
chair=0.510913158138569
cow=0.7522600587766901
diningtable=0.6504822952457223
dog=0.7555796768615906
horse=0.7736929921373551
motorbike=0.7891832580389858
person=0.7758547921355181
pottedplant=0.450822175866696
sheep=0.7440607610455647
sofa=0.7567311291931487
train=0.7775487379103455
tvmonitor=0.7238165850100675
mAP=0.7165789037386614
[Epoch 123][Batch 99], LR: 1.00E-03, Speed: 155.912 samples/sec, ObjLoss=9.822, BoxCenterLoss=5.923, BoxScaleLoss=1.556, ClassLoss=2.568
[Epoch 123][Batch 199], LR: 1.00E-03, Speed: 164.881 samples/sec, ObjLoss=9.813, BoxCenterLoss=5.923, BoxScaleLoss=1.556, ClassLoss=2.564
[Epoch 123] Training cost: 145.343, ObjLoss=9.808, BoxCenterLoss=5.923, BoxScaleLoss=1.555, ClassLoss=2.562
[Epoch 123] Validation:
aeroplane=0.7761030844551596
bicycle=0.7840428194608696
bird=0.7087316294480464
boat=0.5985391305196083
bottle=0.43857665332418555
bus=0.8167810810434644
car=0.8370980224147637
cat=0.8341284755237678
chair=0.5377464138187079
cow=0.766097215299388
diningtable=0.6772790143071887
dog=0.8055471812851037
horse=0.8326522090973817
motorbike=0.8057141010227838
person=0.7488977789766278
pottedplant=0.42390047934253083
sheep=0.6472162101567974
sofa=0.7422320725088499
train=0.8072277384133748
tvmonitor=0.7141853601823072
mAP=0.7151348335300454
[Epoch 124][Batch 99], LR: 1.00E-03, Speed: 164.081 samples/sec, ObjLoss=9.799, BoxCenterLoss=5.922, BoxScaleLoss=1.554, ClassLoss=2.558
[Epoch 124][Batch 199], LR: 1.00E-03, Speed: 195.856 samples/sec, ObjLoss=9.790, BoxCenterLoss=5.922, BoxScaleLoss=1.554, ClassLoss=2.555
[Epoch 124] Training cost: 149.882, ObjLoss=9.784, BoxCenterLoss=5.921, BoxScaleLoss=1.553, ClassLoss=2.553
[Epoch 124] Validation:
aeroplane=0.7689512603087958
bicycle=0.7926666886930503
bird=0.7134777458592936
boat=0.6169907411732306
bottle=0.4705471740232986
bus=0.8334245888348755
car=0.8435802490000766
cat=0.8497165775003278
chair=0.5452272501396704
cow=0.7417151148617622
diningtable=0.7107999612097038
dog=0.7914981665916594
horse=0.81518484977651
motorbike=0.814235843599871
person=0.7721622348843054
pottedplant=0.4305516453426649
sheep=0.6824048930567568
sofa=0.7253888351809888
train=0.7644196248681168
tvmonitor=0.7229875166049079
mAP=0.7202965480754933
[Epoch 125][Batch 99], LR: 1.00E-03, Speed: 145.735 samples/sec, ObjLoss=9.777, BoxCenterLoss=5.922, BoxScaleLoss=1.553, ClassLoss=2.550
[Epoch 125][Batch 199], LR: 1.00E-03, Speed: 185.129 samples/sec, ObjLoss=9.769, BoxCenterLoss=5.921, BoxScaleLoss=1.552, ClassLoss=2.546
[Epoch 125] Training cost: 134.109, ObjLoss=9.764, BoxCenterLoss=5.921, BoxScaleLoss=1.552, ClassLoss=2.545
[Epoch 125] Validation:
aeroplane=0.7221693807947921
bicycle=0.7611942941906878
bird=0.6856980887042916
boat=0.587870261230264
bottle=0.4625285854436172
bus=0.7903675043224035
car=0.8303382426132404
cat=0.8393341568343338
chair=0.49423949018716556
cow=0.7229904357872131
diningtable=0.6270305355063881
dog=0.775763251030184
horse=0.8275862686053718
motorbike=0.7924189793634467
person=0.7676744942318545
pottedplant=0.4180900195608224
sheep=0.6384091862351844
sofa=0.6868469160513597
train=0.8067031405950267
tvmonitor=0.7114825858148613
mAP=0.6974367908551254
[Epoch 126][Batch 99], LR: 1.00E-03, Speed: 171.820 samples/sec, ObjLoss=9.756, BoxCenterLoss=5.921, BoxScaleLoss=1.551, ClassLoss=2.541
[Epoch 126][Batch 199], LR: 1.00E-03, Speed: 166.726 samples/sec, ObjLoss=9.747, BoxCenterLoss=5.920, BoxScaleLoss=1.550, ClassLoss=2.538
[Epoch 126] Training cost: 136.423, ObjLoss=9.742, BoxCenterLoss=5.920, BoxScaleLoss=1.550, ClassLoss=2.536
[Epoch 126] Validation:
aeroplane=0.7422937188421317
bicycle=0.7663929484391956
bird=0.6915235821321192
boat=0.5649856782385221
bottle=0.4960454353887923
bus=0.7951931859864266
car=0.8382101764957262
cat=0.8255541058777649
chair=0.5139794983179766
cow=0.7495333329514106
diningtable=0.6203836813633766
dog=0.791158253824475
horse=0.8226469015696608
motorbike=0.804394713264952
person=0.7726094233805544
pottedplant=0.43072056345879506
sheep=0.6785669701037543
sofa=0.7194791616619136
train=0.7975491696982064
tvmonitor=0.7243226510832145
mAP=0.7072771576039484
[Epoch 127][Batch 99], LR: 1.00E-03, Speed: 177.120 samples/sec, ObjLoss=9.733, BoxCenterLoss=5.919, BoxScaleLoss=1.549, ClassLoss=2.532
[Epoch 127][Batch 199], LR: 1.00E-03, Speed: 173.016 samples/sec, ObjLoss=9.724, BoxCenterLoss=5.919, BoxScaleLoss=1.548, ClassLoss=2.529
[Epoch 127] Training cost: 135.374, ObjLoss=9.719, BoxCenterLoss=5.919, BoxScaleLoss=1.548, ClassLoss=2.527
[Epoch 127] Validation:
aeroplane=0.7504639425214795
bicycle=0.8144528887769455
bird=0.6662723381458497
boat=0.6088496871545075
bottle=0.37970050249854514
bus=0.7915126182767277
car=0.834483187097886
cat=0.8198552096866374
chair=0.4900294730180589
cow=0.7558478566815312
diningtable=0.6610724371042052
dog=0.7693369859872048
horse=0.8024988601997156
motorbike=0.7868799959785954
person=0.7586174195691915
pottedplant=0.44485754564698876
sheep=0.7201806126424655
sofa=0.6859998841927022
train=0.7905332354310386
tvmonitor=0.7436729905054551
mAP=0.7037558835557867
[Epoch 128][Batch 99], LR: 1.00E-03, Speed: 191.288 samples/sec, ObjLoss=9.711, BoxCenterLoss=5.918, BoxScaleLoss=1.547, ClassLoss=2.524
[Epoch 128][Batch 199], LR: 1.00E-03, Speed: 189.305 samples/sec, ObjLoss=9.703, BoxCenterLoss=5.918, BoxScaleLoss=1.547, ClassLoss=2.521
[Epoch 128] Training cost: 144.296, ObjLoss=9.699, BoxCenterLoss=5.918, BoxScaleLoss=1.546, ClassLoss=2.519
[Epoch 128] Validation:
aeroplane=0.7658440869178735
bicycle=0.7867179368164399
bird=0.7085333483678184
boat=0.6386445581273661
bottle=0.4465391316402038
bus=0.8159225871791665
car=0.8453322844768673
cat=0.8169762089400023
chair=0.4897414474022464
cow=0.744601575794521
diningtable=0.6737359454001736
dog=0.7774295267146987
horse=0.8292825408964166
motorbike=0.8002011003982067
person=0.7702599516223739
pottedplant=0.428495798856056
sheep=0.6515773989434079
sofa=0.7299290475502
train=0.8038137444578345
tvmonitor=0.7079759977285095
mAP=0.7115777109115191
[Epoch 129][Batch 99], LR: 1.00E-03, Speed: 151.481 samples/sec, ObjLoss=9.690, BoxCenterLoss=5.917, BoxScaleLoss=1.546, ClassLoss=2.516
[Epoch 129][Batch 199], LR: 1.00E-03, Speed: 159.231 samples/sec, ObjLoss=9.682, BoxCenterLoss=5.917, BoxScaleLoss=1.545, ClassLoss=2.512
[Epoch 129] Training cost: 150.708, ObjLoss=9.678, BoxCenterLoss=5.916, BoxScaleLoss=1.545, ClassLoss=2.511
[Epoch 129] Validation:
aeroplane=0.7803308639158999
bicycle=0.7643509216109977
bird=0.6776375931547918
boat=0.619846261565478
bottle=0.4874679559824235
bus=0.7696805285512517
car=0.8345729119915062
cat=0.8218404358761059
chair=0.5408315859776305
cow=0.769287726328292
diningtable=0.6838313461710968
dog=0.7663273314322152
horse=0.8086569048636799
motorbike=0.7671208777984371
person=0.7359546160588204
pottedplant=0.4165506635377901
sheep=0.6896255133011935
sofa=0.7071948621468314
train=0.7563484966539127
tvmonitor=0.7190697214598505
mAP=0.7058263559189102
[Epoch 130][Batch 99], LR: 1.00E-03, Speed: 154.262 samples/sec, ObjLoss=9.669, BoxCenterLoss=5.916, BoxScaleLoss=1.544, ClassLoss=2.507
[Epoch 130][Batch 199], LR: 1.00E-03, Speed: 175.681 samples/sec, ObjLoss=9.661, BoxCenterLoss=5.916, BoxScaleLoss=1.543, ClassLoss=2.504
[Epoch 130] Training cost: 142.375, ObjLoss=9.657, BoxCenterLoss=5.916, BoxScaleLoss=1.543, ClassLoss=2.502
[Epoch 130] Validation:
aeroplane=0.810970283957303
bicycle=0.80145228379423
bird=0.6878775811332607
boat=0.6253704159449546
bottle=0.49628788295594345
bus=0.8140795340698986
car=0.8379715258442516
cat=0.8475962970536236
chair=0.5378126272176073
cow=0.7054344038249304
diningtable=0.6665471018351564
dog=0.7743338371156753
horse=0.832135843836522
motorbike=0.7664263027376069
person=0.7713060176508595
pottedplant=0.4115446520102289
sheep=0.7277306989549228
sofa=0.7377542837495539
train=0.7724413621939806
tvmonitor=0.72777496733736
mAP=0.7176423951608936
[Epoch 131][Batch 99], LR: 1.00E-03, Speed: 155.512 samples/sec, ObjLoss=9.649, BoxCenterLoss=5.915, BoxScaleLoss=1.542, ClassLoss=2.499
[Epoch 131][Batch 199], LR: 1.00E-03, Speed: 158.315 samples/sec, ObjLoss=9.641, BoxCenterLoss=5.915, BoxScaleLoss=1.541, ClassLoss=2.495
[Epoch 131] Training cost: 146.265, ObjLoss=9.636, BoxCenterLoss=5.915, BoxScaleLoss=1.541, ClassLoss=2.494
[Epoch 131] Validation:
aeroplane=0.7780138037051685
bicycle=0.7798642987105162
bird=0.6687417677657433
boat=0.6046177986120744
bottle=0.47835968568382325
bus=0.7974612910628772
car=0.8427209946426687
cat=0.838659483077526
chair=0.5187441832559354
cow=0.7587139689974034
diningtable=0.6522712051418037
dog=0.7635066346620539
horse=0.8189720694843035
motorbike=0.7833953231314118
person=0.7619755148412636
pottedplant=0.41193029537844716
sheep=0.7024430307093873
sofa=0.7207764571812979
train=0.7574236835931416
tvmonitor=0.7155735353939128
mAP=0.707708251251538
[Epoch 132][Batch 99], LR: 1.00E-03, Speed: 155.204 samples/sec, ObjLoss=9.628, BoxCenterLoss=5.914, BoxScaleLoss=1.540, ClassLoss=2.491
[Epoch 132][Batch 199], LR: 1.00E-03, Speed: 209.975 samples/sec, ObjLoss=9.621, BoxCenterLoss=5.913, BoxScaleLoss=1.540, ClassLoss=2.488
[Epoch 132] Training cost: 144.759, ObjLoss=9.616, BoxCenterLoss=5.913, BoxScaleLoss=1.539, ClassLoss=2.487
[Epoch 132] Validation:
aeroplane=0.7684702184066232
bicycle=0.777811397306226
bird=0.6906499415083921
boat=0.5972799313909679
bottle=0.44818268832317176
bus=0.8172974573958441
car=0.8316182696344206
cat=0.8360274551284462
chair=0.5125098652437471
cow=0.7410735483167117
diningtable=0.6377056599870569
dog=0.7779652850809788
horse=0.8153207740668522
motorbike=0.8013509336676781
person=0.7467657847730651
pottedplant=0.4253338074340915
sheep=0.676593903177099
sofa=0.6643862316480071
train=0.7625539864753729
tvmonitor=0.714115281521013
mAP=0.7021506210242883
[Epoch 133][Batch 99], LR: 1.00E-03, Speed: 187.740 samples/sec, ObjLoss=9.608, BoxCenterLoss=5.913, BoxScaleLoss=1.539, ClassLoss=2.484
[Epoch 133][Batch 199], LR: 1.00E-03, Speed: 169.532 samples/sec, ObjLoss=9.600, BoxCenterLoss=5.912, BoxScaleLoss=1.538, ClassLoss=2.480
[Epoch 133] Training cost: 147.173, ObjLoss=9.595, BoxCenterLoss=5.912, BoxScaleLoss=1.538, ClassLoss=2.478
[Epoch 133] Validation:
aeroplane=0.7826782860126436
bicycle=0.7799194399502924
bird=0.6990480630535575
boat=0.6143139438085541
bottle=0.5024921228011281
bus=0.8180433886408054
car=0.845284768060177
cat=0.8314571514436379
chair=0.4978030703957954
cow=0.7930328217682459
diningtable=0.696384584659211
dog=0.7588052924029057
horse=0.8285538828212056
motorbike=0.7709988302614852
person=0.7356619652136651
pottedplant=0.4435844928814864
sheep=0.7112298171623758
sofa=0.690033656527262
train=0.7667583990020633
tvmonitor=0.7059790551040334
mAP=0.7136031515985265
[Epoch 134][Batch 99], LR: 1.00E-03, Speed: 157.841 samples/sec, ObjLoss=9.587, BoxCenterLoss=5.911, BoxScaleLoss=1.537, ClassLoss=2.475
[Epoch 134][Batch 199], LR: 1.00E-03, Speed: 176.232 samples/sec, ObjLoss=9.579, BoxCenterLoss=5.911, BoxScaleLoss=1.536, ClassLoss=2.472
[Epoch 134] Training cost: 145.735, ObjLoss=9.574, BoxCenterLoss=5.911, BoxScaleLoss=1.536, ClassLoss=2.470
[Epoch 134] Validation:
aeroplane=0.7739892104669313
bicycle=0.7855551853703899
bird=0.6823275057759399
boat=0.6349832648824434
bottle=0.48700780066977484
bus=0.8022292767102691
car=0.8311749626521965
cat=0.8188514768800887
chair=0.49493195953364233
cow=0.7114238040056419
diningtable=0.6645443324109319
dog=0.766555800517171
horse=0.7998644452318558
motorbike=0.7837852904911492
person=0.7519201818231168
pottedplant=0.3977699832856546
sheep=0.7305173123124274
sofa=0.6942488903426726
train=0.7760953649711633
tvmonitor=0.7032911058226331
mAP=0.7045533577078047
[Epoch 135][Batch 99], LR: 1.00E-03, Speed: 159.548 samples/sec, ObjLoss=9.566, BoxCenterLoss=5.910, BoxScaleLoss=1.535, ClassLoss=2.467
[Epoch 135][Batch 199], LR: 1.00E-03, Speed: 167.414 samples/sec, ObjLoss=9.558, BoxCenterLoss=5.910, BoxScaleLoss=1.534, ClassLoss=2.464
[Epoch 135] Training cost: 143.078, ObjLoss=9.555, BoxCenterLoss=5.910, BoxScaleLoss=1.534, ClassLoss=2.462
[Epoch 135] Validation:
aeroplane=0.7878729363159105
bicycle=0.8121774085622082
bird=0.6987128242225206
boat=0.6246383328489128
bottle=0.493885903282921
bus=0.820582978493061
car=0.8385383384403371
cat=0.8334973533462042
chair=0.5010009336784225
cow=0.7189434815268347
diningtable=0.685316449694052
dog=0.7752919538171971
horse=0.8338829820806333
motorbike=0.8121552333983597
person=0.754676501179033
pottedplant=0.45394240509747497
sheep=0.6917135076364214
sofa=0.6986141673765326
train=0.8062215677150624
tvmonitor=0.7239305701400183
mAP=0.718279791442606
[Epoch 136][Batch 99], LR: 1.00E-03, Speed: 180.122 samples/sec, ObjLoss=9.547, BoxCenterLoss=5.909, BoxScaleLoss=1.533, ClassLoss=2.459
[Epoch 136][Batch 199], LR: 1.00E-03, Speed: 192.194 samples/sec, ObjLoss=9.539, BoxCenterLoss=5.909, BoxScaleLoss=1.533, ClassLoss=2.456
[Epoch 136] Training cost: 137.940, ObjLoss=9.534, BoxCenterLoss=5.909, BoxScaleLoss=1.532, ClassLoss=2.454
[Epoch 136] Validation:
aeroplane=0.7822043953038571
bicycle=0.7965361480864689
bird=0.7068254757333668
boat=0.6004652282789329
bottle=0.47871084464611735
bus=0.8173839809702794
car=0.8275397300594244
cat=0.815296773646269
chair=0.5496867691688956
cow=0.773128392957353
diningtable=0.670232475470155
dog=0.7470176355273227
horse=0.8221718835248226
motorbike=0.828371431264823
person=0.746865269643417
pottedplant=0.43280911335763
sheep=0.6869938464355155
sofa=0.7070680674843414
train=0.7691413869376195
tvmonitor=0.7100894035396035
mAP=0.7134269126018107
[Epoch 137][Batch 99], LR: 1.00E-03, Speed: 164.053 samples/sec, ObjLoss=9.526, BoxCenterLoss=5.908, BoxScaleLoss=1.532, ClassLoss=2.451
[Epoch 137][Batch 199], LR: 1.00E-03, Speed: 163.556 samples/sec, ObjLoss=9.519, BoxCenterLoss=5.908, BoxScaleLoss=1.531, ClassLoss=2.448
[Epoch 137] Training cost: 143.696, ObjLoss=9.515, BoxCenterLoss=5.908, BoxScaleLoss=1.531, ClassLoss=2.446
[Epoch 137] Validation:
aeroplane=0.8176249356589622
bicycle=0.7737289991764207
bird=0.6902602223788421
boat=0.6340868574093413
bottle=0.49175763803160266
bus=0.8193704898835572
car=0.8311760749808769
cat=0.8352964163857908
chair=0.5051759340662411
cow=0.7473533603099896
diningtable=0.6563993721207878
dog=0.7906618436689603
horse=0.8168907886300811
motorbike=0.7763437019771233
person=0.7385108972223068
pottedplant=0.437727490214
sheep=0.7394926515330491
sofa=0.7222178829072438
train=0.7827746358063765
tvmonitor=0.7188417913300654
mAP=0.7162845991845808
[Epoch 138][Batch 99], LR: 1.00E-03, Speed: 160.492 samples/sec, ObjLoss=9.506, BoxCenterLoss=5.907, BoxScaleLoss=1.530, ClassLoss=2.444
[Epoch 138][Batch 199], LR: 1.00E-03, Speed: 184.744 samples/sec, ObjLoss=9.498, BoxCenterLoss=5.907, BoxScaleLoss=1.530, ClassLoss=2.441
[Epoch 138] Training cost: 132.209, ObjLoss=9.494, BoxCenterLoss=5.906, BoxScaleLoss=1.529, ClassLoss=2.439
[Epoch 138] Validation:
aeroplane=0.7720347371402211
bicycle=0.7697039566286629
bird=0.679420186286012
boat=0.6081652372221799
bottle=0.5068659381280642
bus=0.7968506520845274
car=0.825649926503128
cat=0.8105981990896691
chair=0.465329291790382
cow=0.7582829942154998
diningtable=0.6908005879901196
dog=0.7513665545498072
horse=0.7630335966710328
motorbike=0.7947497905856369
person=0.7407674981843893
pottedplant=0.37773581964666914
sheep=0.6770240095181876
sofa=0.7038238925207071
train=0.8059692230246157
tvmonitor=0.6974306562121368
mAP=0.6997801373995823
[Epoch 139][Batch 99], LR: 1.00E-03, Speed: 181.201 samples/sec, ObjLoss=9.486, BoxCenterLoss=5.906, BoxScaleLoss=1.529, ClassLoss=2.437
[Epoch 139][Batch 199], LR: 1.00E-03, Speed: 176.429 samples/sec, ObjLoss=9.478, BoxCenterLoss=5.905, BoxScaleLoss=1.528, ClassLoss=2.434
[Epoch 139] Training cost: 122.263, ObjLoss=9.474, BoxCenterLoss=5.905, BoxScaleLoss=1.528, ClassLoss=2.432
[Epoch 139] Validation:
aeroplane=0.7545954530701366
bicycle=0.7786904724626388
bird=0.72494640539127
boat=0.6252750566188529
bottle=0.4888175011933592
bus=0.8226778828884651
car=0.8403256024660215
cat=0.843657399113877
chair=0.5171592536017273
cow=0.7759475462767298
diningtable=0.7033411616291495
dog=0.7714956480488386
horse=0.7861280262091436
motorbike=0.8058641336722918
person=0.7535523613790248
pottedplant=0.4291176417931959
sheep=0.7109480296187173
sofa=0.7355499431022051
train=0.774085734881608
tvmonitor=0.7276744737825372
mAP=0.7184924863599894
[Epoch 140][Batch 99], LR: 1.00E-03, Speed: 146.224 samples/sec, ObjLoss=9.467, BoxCenterLoss=5.905, BoxScaleLoss=1.527, ClassLoss=2.430
[Epoch 140][Batch 199], LR: 1.00E-03, Speed: 192.278 samples/sec, ObjLoss=9.459, BoxCenterLoss=5.904, BoxScaleLoss=1.527, ClassLoss=2.427
[Epoch 140] Training cost: 144.225, ObjLoss=9.455, BoxCenterLoss=5.904, BoxScaleLoss=1.526, ClassLoss=2.425
[Epoch 140] Validation:
aeroplane=0.7815584380127234
bicycle=0.7821153630295571
bird=0.7224763536265736
boat=0.6113519559928002
bottle=0.5207338338306395
bus=0.808707623626247
car=0.8327164176485785
cat=0.8203258094699377
chair=0.52297927255578
cow=0.7294280794574405
diningtable=0.6795543435114995
dog=0.7801928216241416
horse=0.8126888337337077
motorbike=0.8118970269076379
person=0.7503830667941052
pottedplant=0.38467451124291424
sheep=0.6980538885051802
sofa=0.7339480450421314
train=0.7702163589805072
tvmonitor=0.7356531681136099
mAP=0.7144827605852856
[Epoch 141][Batch 99], LR: 1.00E-03, Speed: 158.611 samples/sec, ObjLoss=9.448, BoxCenterLoss=5.904, BoxScaleLoss=1.526, ClassLoss=2.422
[Epoch 141][Batch 199], LR: 1.00E-03, Speed: 208.071 samples/sec, ObjLoss=9.441, BoxCenterLoss=5.903, BoxScaleLoss=1.525, ClassLoss=2.419
[Epoch 141] Training cost: 146.507, ObjLoss=9.437, BoxCenterLoss=5.903, BoxScaleLoss=1.525, ClassLoss=2.418
[Epoch 141] Validation:
aeroplane=0.7435958352007827
bicycle=0.7706742736700053
bird=0.6910348462365683
boat=0.5532568077103072
bottle=0.45303731253969637
bus=0.8002695659342944
car=0.8307712705281017
cat=0.8340266252875649
chair=0.48996013499024466
cow=0.7400497306610548
diningtable=0.6693876525660428
dog=0.7398110813861333
horse=0.8288594009604652
motorbike=0.8115182774348727
person=0.7606348342457976
pottedplant=0.4266193016993782
sheep=0.7067418274570993
sofa=0.6986778051404279
train=0.8091301705134369
tvmonitor=0.6838696944865665
mAP=0.702096322432442
[Epoch 142][Batch 99], LR: 1.00E-03, Speed: 151.771 samples/sec, ObjLoss=9.429, BoxCenterLoss=5.903, BoxScaleLoss=1.524, ClassLoss=2.415
[Epoch 142][Batch 199], LR: 1.00E-03, Speed: 194.094 samples/sec, ObjLoss=9.422, BoxCenterLoss=5.902, BoxScaleLoss=1.524, ClassLoss=2.412
[Epoch 142] Training cost: 146.371, ObjLoss=9.418, BoxCenterLoss=5.902, BoxScaleLoss=1.523, ClassLoss=2.411
[Epoch 142] Validation:
aeroplane=0.7936925338646809
bicycle=0.760744295507691
bird=0.7131834114660281
boat=0.6153085782270901
bottle=0.4934132090133011
bus=0.8193363834336134
car=0.830018227548893
cat=0.8366913873178784
chair=0.5340375080863686
cow=0.7623276323882402
diningtable=0.7050039022096737
dog=0.7830326828275175
horse=0.7893623524150603
motorbike=0.8000399207726462
person=0.7453345827858407
pottedplant=0.4485216359022071
sheep=0.6881514061235832
sofa=0.7367715444726644
train=0.7641083609711506
tvmonitor=0.7400570960692913
mAP=0.717956832570171
[Epoch 143][Batch 99], LR: 1.00E-03, Speed: 164.587 samples/sec, ObjLoss=9.410, BoxCenterLoss=5.901, BoxScaleLoss=1.522, ClassLoss=2.408
[Epoch 143][Batch 199], LR: 1.00E-03, Speed: 142.622 samples/sec, ObjLoss=9.403, BoxCenterLoss=5.901, BoxScaleLoss=1.522, ClassLoss=2.405
[Epoch 143] Training cost: 146.366, ObjLoss=9.399, BoxCenterLoss=5.901, BoxScaleLoss=1.521, ClassLoss=2.403
[Epoch 143] Validation:
aeroplane=0.7798616510056224
bicycle=0.7721338284779276
bird=0.6755749505191007
boat=0.6237507159230367
bottle=0.4890928696850864
bus=0.7985879413392513
car=0.8359601486672329
cat=0.803180800170367
chair=0.49690213286545926
cow=0.6942462939862296
diningtable=0.6772003168404306
dog=0.7534854848380264
horse=0.838305402971646
motorbike=0.8075504321094495
person=0.7418940195509567
pottedplant=0.44823110764678653
sheep=0.6675054526263422
sofa=0.7128381487221066
train=0.7853648198222979
tvmonitor=0.7133988435223044
mAP=0.7057532680644829
[Epoch 144][Batch 99], LR: 1.00E-03, Speed: 155.772 samples/sec, ObjLoss=9.392, BoxCenterLoss=5.900, BoxScaleLoss=1.521, ClassLoss=2.400
[Epoch 144][Batch 199], LR: 1.00E-03, Speed: 170.942 samples/sec, ObjLoss=9.385, BoxCenterLoss=5.900, BoxScaleLoss=1.520, ClassLoss=2.397
[Epoch 144] Training cost: 136.601, ObjLoss=9.380, BoxCenterLoss=5.900, BoxScaleLoss=1.520, ClassLoss=2.396
[Epoch 144] Validation:
aeroplane=0.797662322309139
bicycle=0.7725131314851925
bird=0.6886434793108568
boat=0.6009162363002062
bottle=0.46536141432024525
bus=0.8199080751275896
car=0.8303684204159677
cat=0.8240048709464902
chair=0.5212570625318702
cow=0.7468868250740219
diningtable=0.6677386898116968
dog=0.7598236406272619
horse=0.8328512319320962
motorbike=0.8064092648425832
person=0.7711348483462149
pottedplant=0.4301305142881734
sheep=0.716811235617843
sofa=0.7021618859305558
train=0.7661684712923056
tvmonitor=0.7329117116597235
mAP=0.7126831666085016
[Epoch 145][Batch 99], LR: 1.00E-03, Speed: 159.959 samples/sec, ObjLoss=9.373, BoxCenterLoss=5.899, BoxScaleLoss=1.519, ClassLoss=2.393
[Epoch 145][Batch 199], LR: 1.00E-03, Speed: 193.174 samples/sec, ObjLoss=9.366, BoxCenterLoss=5.899, BoxScaleLoss=1.519, ClassLoss=2.390
[Epoch 145] Training cost: 124.827, ObjLoss=9.361, BoxCenterLoss=5.899, BoxScaleLoss=1.518, ClassLoss=2.389
[Epoch 145] Validation:
aeroplane=0.73398052811614
bicycle=0.7734495676185833
bird=0.7244152976508431
boat=0.5799417789701917
bottle=0.4582619351677407
bus=0.8140930815714791
car=0.8365207358403757
cat=0.8452209418640899
chair=0.5148966922621186
cow=0.7480590062839282
diningtable=0.7022750183778361
dog=0.789928342572939
horse=0.837500934850307
motorbike=0.8199747079894831
person=0.7675285977487603
pottedplant=0.4525017328725767
sheep=0.6847946786317202
sofa=0.687368634831902
train=0.7720871909984456
tvmonitor=0.7289494533347003
mAP=0.713587442877708
[Epoch 146][Batch 99], LR: 1.00E-03, Speed: 199.636 samples/sec, ObjLoss=9.354, BoxCenterLoss=5.898, BoxScaleLoss=1.518, ClassLoss=2.386
[Epoch 146][Batch 199], LR: 1.00E-03, Speed: 202.497 samples/sec, ObjLoss=9.347, BoxCenterLoss=5.898, BoxScaleLoss=1.517, ClassLoss=2.384
[Epoch 146] Training cost: 143.708, ObjLoss=9.343, BoxCenterLoss=5.898, BoxScaleLoss=1.517, ClassLoss=2.382
[Epoch 146] Validation:
aeroplane=0.776252974778598
bicycle=0.7854955924310659
bird=0.7132691691516228
boat=0.6222993721485098
bottle=0.4987353560075445
bus=0.7953920190825026
car=0.8438453967919978
cat=0.8354290552147235
chair=0.5301575450044292
cow=0.7648773097656356
diningtable=0.697086072439484
dog=0.764619013053052
horse=0.8096930449274207
motorbike=0.802001889567616
person=0.7553637069411572
pottedplant=0.4196515713044666
sheep=0.7261238073972994
sofa=0.7251937087762803
train=0.7789811848375153
tvmonitor=0.7271726451827022
mAP=0.7185820217401812
[Epoch 147][Batch 99], LR: 1.00E-03, Speed: 167.118 samples/sec, ObjLoss=9.336, BoxCenterLoss=5.898, BoxScaleLoss=1.516, ClassLoss=2.379
[Epoch 147][Batch 199], LR: 1.00E-03, Speed: 199.699 samples/sec, ObjLoss=9.329, BoxCenterLoss=5.897, BoxScaleLoss=1.516, ClassLoss=2.377
[Epoch 147] Training cost: 146.956, ObjLoss=9.325, BoxCenterLoss=5.897, BoxScaleLoss=1.515, ClassLoss=2.375
[Epoch 147] Validation:
aeroplane=0.7564293746016234
bicycle=0.7450245862725888
bird=0.7147143655335434
boat=0.6025787866293378
bottle=0.49486158666587166
bus=0.8290129976870347
car=0.8474124365289439
cat=0.835835349824121
chair=0.5213312658313953
cow=0.7045407899022925
diningtable=0.6766933188007656
dog=0.7787220748965468
horse=0.8404723764788355
motorbike=0.8014774232942098
person=0.7685500401825525
pottedplant=0.4729582220470832
sheep=0.7053620432520437
sofa=0.7003337471183471
train=0.8025977901774441
tvmonitor=0.7157778306389978
mAP=0.7157343203181789
[Epoch 148][Batch 99], LR: 1.00E-03, Speed: 155.946 samples/sec, ObjLoss=9.317, BoxCenterLoss=5.896, BoxScaleLoss=1.515, ClassLoss=2.373
[Epoch 148][Batch 199], LR: 1.00E-03, Speed: 175.252 samples/sec, ObjLoss=9.310, BoxCenterLoss=5.895, BoxScaleLoss=1.514, ClassLoss=2.370
[Epoch 148] Training cost: 126.295, ObjLoss=9.306, BoxCenterLoss=5.895, BoxScaleLoss=1.514, ClassLoss=2.368
[Epoch 148] Validation:
aeroplane=0.783548247405121
bicycle=0.7809546718940996
bird=0.6919432298915228
boat=0.5927273512382198
bottle=0.46471236780753494
bus=0.7973146849501407
car=0.8340180042559995
cat=0.8152045581506029
chair=0.5036028455066043
cow=0.72533773406652
diningtable=0.6725108255314033
dog=0.7625125717313572
horse=0.8067130890655451
motorbike=0.8179950338865252
person=0.7409363927266356
pottedplant=0.4181690360779654
sheep=0.6936831345694454
sofa=0.681813319700549
train=0.7739428041440921
tvmonitor=0.7326968754424528
mAP=0.7045168389021168
[Epoch 149][Batch 99], LR: 1.00E-03, Speed: 157.118 samples/sec, ObjLoss=9.300, BoxCenterLoss=5.895, BoxScaleLoss=1.513, ClassLoss=2.366
[Epoch 149][Batch 199], LR: 1.00E-03, Speed: 185.946 samples/sec, ObjLoss=9.293, BoxCenterLoss=5.894, BoxScaleLoss=1.513, ClassLoss=2.363
[Epoch 149] Training cost: 139.829, ObjLoss=9.289, BoxCenterLoss=5.894, BoxScaleLoss=1.512, ClassLoss=2.362
[Epoch 149] Validation:
aeroplane=0.7875672503794227
bicycle=0.8130624807082744
bird=0.7108435757695981
boat=0.6162405031742453
bottle=0.5017475581596584
bus=0.7749065144442104
car=0.8377583729710555
cat=0.8247050284255285
chair=0.5136740106510469
cow=0.7620211906042521
diningtable=0.6590287043682038
dog=0.7724521295286182
horse=0.8344187707588013
motorbike=0.8095522440053879
person=0.7776970937734485
pottedplant=0.4179902253303859
sheep=0.7277368942725007
sofa=0.6997099472680868
train=0.7717377396492466
tvmonitor=0.7495385385436487
mAP=0.718119438639281
[Epoch 150][Batch 99], LR: 1.00E-03, Speed: 172.657 samples/sec, ObjLoss=9.281, BoxCenterLoss=5.894, BoxScaleLoss=1.512, ClassLoss=2.359
[Epoch 150][Batch 199], LR: 1.00E-03, Speed: 174.807 samples/sec, ObjLoss=9.276, BoxCenterLoss=5.894, BoxScaleLoss=1.511, ClassLoss=2.357
[Epoch 150] Training cost: 143.584, ObjLoss=9.271, BoxCenterLoss=5.893, BoxScaleLoss=1.511, ClassLoss=2.355
[Epoch 150] Validation:
aeroplane=0.7930959130874166
bicycle=0.7852481527449107
bird=0.7127885147124924
boat=0.5909228974592351
bottle=0.4912355597746482
bus=0.8290726589237613
car=0.8404740069919002
cat=0.8376646219519546
chair=0.5256859437300829
cow=0.7373855620761802
diningtable=0.6673257135571351
dog=0.810962483475996
horse=0.8354293028181714
motorbike=0.826453571604826
person=0.7502884913135401
pottedplant=0.42876492276019224
sheep=0.7505204065072958
sofa=0.706534000605386
train=0.784193050589706
tvmonitor=0.730039366847479
mAP=0.7217042570766155
[Epoch 151][Batch 99], LR: 1.00E-03, Speed: 182.920 samples/sec, ObjLoss=9.264, BoxCenterLoss=5.893, BoxScaleLoss=1.510, ClassLoss=2.352
[Epoch 151][Batch 199], LR: 1.00E-03, Speed: 193.410 samples/sec, ObjLoss=9.257, BoxCenterLoss=5.892, BoxScaleLoss=1.510, ClassLoss=2.350
[Epoch 151] Training cost: 151.092, ObjLoss=9.253, BoxCenterLoss=5.892, BoxScaleLoss=1.509, ClassLoss=2.348
[Epoch 151] Validation:
aeroplane=0.7879014636946287
bicycle=0.8244852141913993
bird=0.7094230338387795
boat=0.6556224485541576
bottle=0.49047247317464665
bus=0.8243867621390016
car=0.8462732610013565
cat=0.8459254371417932
chair=0.5383954777872232
cow=0.7662432047439485
diningtable=0.6673795424691192
dog=0.8122590038433332
horse=0.8513840251159231
motorbike=0.7880087967772722
person=0.7475619693177415
pottedplant=0.4593557490238311
sheep=0.742990942001696
sofa=0.7292517908916354
train=0.7852950400771919
tvmonitor=0.7439836619980471
mAP=0.7308299648891362
[Epoch 152][Batch 99], LR: 1.00E-03, Speed: 151.332 samples/sec, ObjLoss=9.247, BoxCenterLoss=5.892, BoxScaleLoss=1.509, ClassLoss=2.346
[Epoch 152][Batch 199], LR: 1.00E-03, Speed: 196.955 samples/sec, ObjLoss=9.240, BoxCenterLoss=5.891, BoxScaleLoss=1.508, ClassLoss=2.343
[Epoch 152] Training cost: 138.893, ObjLoss=9.236, BoxCenterLoss=5.891, BoxScaleLoss=1.508, ClassLoss=2.341
[Epoch 152] Validation:
aeroplane=0.7449621831650222
bicycle=0.8128143855869397
bird=0.7073753799376864
boat=0.5882812937759324
bottle=0.5157983377510655
bus=0.7666507075093922
car=0.8140993681689195
cat=0.7821980197106417
chair=0.5000553168315327
cow=0.7649266716974462
diningtable=0.682344506790902
dog=0.799688467089163
horse=0.8210341798543311
motorbike=0.8167958341033179
person=0.7533031009813101
pottedplant=0.4225410578509232
sheep=0.7284231146915956
sofa=0.6915186320609333
train=0.7545231303094799
tvmonitor=0.7189556906278738
mAP=0.7093144689247204
[Epoch 153][Batch 99], LR: 1.00E-03, Speed: 179.126 samples/sec, ObjLoss=9.229, BoxCenterLoss=5.890, BoxScaleLoss=1.507, ClassLoss=2.339
[Epoch 153][Batch 199], LR: 1.00E-03, Speed: 167.784 samples/sec, ObjLoss=9.223, BoxCenterLoss=5.890, BoxScaleLoss=1.507, ClassLoss=2.336
[Epoch 153] Training cost: 136.270, ObjLoss=9.219, BoxCenterLoss=5.890, BoxScaleLoss=1.506, ClassLoss=2.335
[Epoch 153] Validation:
aeroplane=0.7750005411738015
bicycle=0.7740925136985007
bird=0.7248633778286843
boat=0.6120337930311511
bottle=0.5039559908743069
bus=0.812283654340639
car=0.8346376104207099
cat=0.8019201181382307
chair=0.5015117079558808
cow=0.7530293525025056
diningtable=0.640911070168608
dog=0.7824035602422994
horse=0.8254820429722413
motorbike=0.8178953857830292
person=0.7348197053645075
pottedplant=0.4349428488073206
sheep=0.7365952987514943
sofa=0.6941855200982878
train=0.8142095802685051
tvmonitor=0.7271979088071449
mAP=0.7150985790613925
[Epoch 154][Batch 99], LR: 1.00E-03, Speed: 168.999 samples/sec, ObjLoss=9.212, BoxCenterLoss=5.889, BoxScaleLoss=1.506, ClassLoss=2.332
[Epoch 154][Batch 199], LR: 1.00E-03, Speed: 156.988 samples/sec, ObjLoss=9.206, BoxCenterLoss=5.889, BoxScaleLoss=1.505, ClassLoss=2.330
[Epoch 154] Training cost: 140.575, ObjLoss=9.203, BoxCenterLoss=5.889, BoxScaleLoss=1.505, ClassLoss=2.328
[Epoch 154] Validation:
aeroplane=0.7737997660373853
bicycle=0.7751210545761325
bird=0.7067471205155381
boat=0.5984874718494793
bottle=0.4779707053120521
bus=0.8086574132126993
car=0.8355767002208475
cat=0.8338887001596365
chair=0.5007081504380662
cow=0.7068425347708663
diningtable=0.666939590874812
dog=0.7905646658939405
horse=0.8303627434601034
motorbike=0.8083933362212988
person=0.765089853286248
pottedplant=0.46116573973520547
sheep=0.6883501801392026
sofa=0.6653027472210857
train=0.7760808866946975
tvmonitor=0.7264007103101713
mAP=0.7098225035464736
[Epoch 155][Batch 99], LR: 1.00E-03, Speed: 151.518 samples/sec, ObjLoss=9.197, BoxCenterLoss=5.889, BoxScaleLoss=1.504, ClassLoss=2.326
[Epoch 155][Batch 199], LR: 1.00E-03, Speed: 169.115 samples/sec, ObjLoss=9.191, BoxCenterLoss=5.888, BoxScaleLoss=1.503, ClassLoss=2.323
[Epoch 155] Training cost: 145.639, ObjLoss=9.187, BoxCenterLoss=5.888, BoxScaleLoss=1.503, ClassLoss=2.321
[Epoch 155] Validation:
aeroplane=0.7686023020528613
bicycle=0.8175000845971614
bird=0.6965560954447816
boat=0.6037034620295532
bottle=0.3842903623305605
bus=0.813351711096769
car=0.843494297076267
cat=0.8445745603097692
chair=0.5609278275062466
cow=0.7267437668288265
diningtable=0.7207402864337717
dog=0.796479630236103
horse=0.8064047722137881
motorbike=0.813882868806614
person=0.760295221842482
pottedplant=0.41991039280921455
sheep=0.7244347990025528
sofa=0.7312094774696789
train=0.7808250882176405
tvmonitor=0.7426351534861062
mAP=0.7178281079895374
[Epoch 156][Batch 99], LR: 1.00E-03, Speed: 175.136 samples/sec, ObjLoss=9.180, BoxCenterLoss=5.887, BoxScaleLoss=1.503, ClassLoss=2.319
[Epoch 156][Batch 199], LR: 1.00E-03, Speed: 174.827 samples/sec, ObjLoss=9.174, BoxCenterLoss=5.887, BoxScaleLoss=1.502, ClassLoss=2.316
[Epoch 156] Training cost: 149.755, ObjLoss=9.170, BoxCenterLoss=5.887, BoxScaleLoss=1.502, ClassLoss=2.315
[Epoch 156] Validation:
aeroplane=0.8116175523437947
bicycle=0.7776181857977549
bird=0.7273090883573515
boat=0.6195884250779715
bottle=0.4867715750723908
bus=0.8142345090281077
car=0.8417356842081385
cat=0.83402353049727
chair=0.5339410439081413
cow=0.7607446279479533
diningtable=0.6808561116857682
dog=0.7980634357596961
horse=0.8290672609957873
motorbike=0.8066916744727616
person=0.7469620383765256
pottedplant=0.4564932497059551
sheep=0.7459061724058652
sofa=0.7283340290032044
train=0.7688162976908852
tvmonitor=0.733786940478795
mAP=0.7251280716407058
[Epoch 157][Batch 99], LR: 1.00E-03, Speed: 138.139 samples/sec, ObjLoss=9.164, BoxCenterLoss=5.887, BoxScaleLoss=1.501, ClassLoss=2.313
[Epoch 157][Batch 199], LR: 1.00E-03, Speed: 215.244 samples/sec, ObjLoss=9.158, BoxCenterLoss=5.886, BoxScaleLoss=1.501, ClassLoss=2.310
[Epoch 157] Training cost: 148.239, ObjLoss=9.154, BoxCenterLoss=5.886, BoxScaleLoss=1.501, ClassLoss=2.309
[Epoch 157] Validation:
aeroplane=0.7708474825049366
bicycle=0.8024914881675661
bird=0.6917901798593955
boat=0.6180168566791951
bottle=0.5282975699875168
bus=0.8155868842406998
car=0.8445554222919631
cat=0.8294402373058393
chair=0.5466640941387201
cow=0.7868929748035229
diningtable=0.6796867853424942
dog=0.7581646494741903
horse=0.8152448441903317
motorbike=0.8158747880627745
person=0.7729726315640096
pottedplant=0.4647398229248317
sheep=0.7396733550976455
sofa=0.7117878049642862
train=0.8170612380517025
tvmonitor=0.7157139876483974
mAP=0.7262751548650009
[Epoch 158][Batch 99], LR: 1.00E-03, Speed: 194.971 samples/sec, ObjLoss=9.148, BoxCenterLoss=5.886, BoxScaleLoss=1.500, ClassLoss=2.307
[Epoch 158][Batch 199], LR: 1.00E-03, Speed: 174.489 samples/sec, ObjLoss=9.142, BoxCenterLoss=5.886, BoxScaleLoss=1.500, ClassLoss=2.304
[Epoch 158] Training cost: 147.239, ObjLoss=9.138, BoxCenterLoss=5.885, BoxScaleLoss=1.499, ClassLoss=2.303
[Epoch 158] Validation:
aeroplane=0.7788490598745482
bicycle=0.7794706576984805
bird=0.6815765485278331
boat=0.6114928277616596
bottle=0.5110784167492224
bus=0.8075003954421632
car=0.8224826211252996
cat=0.7775323577474167
chair=0.50475107865664
cow=0.7391088618599325
diningtable=0.6762139335026547
dog=0.7809660912078779
horse=0.8356729149557032
motorbike=0.8040176401436749
person=0.7551546184070395
pottedplant=0.4641728639652354
sheep=0.6988552769320495
sofa=0.6894742514538446
train=0.7826058356573372
tvmonitor=0.709152351371331
mAP=0.7105064301519972
[Epoch 159][Batch 99], LR: 1.00E-03, Speed: 175.191 samples/sec, ObjLoss=9.132, BoxCenterLoss=5.885, BoxScaleLoss=1.499, ClassLoss=2.301
[Epoch 159][Batch 199], LR: 1.00E-03, Speed: 191.324 samples/sec, ObjLoss=9.126, BoxCenterLoss=5.885, BoxScaleLoss=1.498, ClassLoss=2.298
[Epoch 159] Training cost: 138.894, ObjLoss=9.122, BoxCenterLoss=5.884, BoxScaleLoss=1.498, ClassLoss=2.297
[Epoch 159] Validation:
aeroplane=0.7580309382460543
bicycle=0.7855654479330052
bird=0.7082947565902677
boat=0.6092828756418553
bottle=0.4725946046756729
bus=0.832630084760023
car=0.8397580206313342
cat=0.8422965802629309
chair=0.5226931664493598
cow=0.7603942812021605
diningtable=0.6846207243893178
dog=0.7442941166346451
horse=0.8169845626439771
motorbike=0.8037916386197388
person=0.7488596234174283
pottedplant=0.42947181844235444
sheep=0.7415015820861603
sofa=0.728998828417577
train=0.7990331500051048
tvmonitor=0.7165306885779903
mAP=0.7172813744813478
[Epoch 160][Batch 99], LR: 1.00E-04, Speed: 156.521 samples/sec, ObjLoss=9.116, BoxCenterLoss=5.884, BoxScaleLoss=1.498, ClassLoss=2.294
[Epoch 160][Batch 199], LR: 1.00E-04, Speed: 167.049 samples/sec, ObjLoss=9.109, BoxCenterLoss=5.884, BoxScaleLoss=1.497, ClassLoss=2.292
[Epoch 160] Training cost: 139.500, ObjLoss=9.105, BoxCenterLoss=5.884, BoxScaleLoss=1.496, ClassLoss=2.290
[Epoch 160] Validation:
aeroplane=0.8178886572786582
bicycle=0.8343387182402
bird=0.7368655401607885
boat=0.6546465467364829
bottle=0.5102060780403088
bus=0.8440602027896091
car=0.8564032094769874
cat=0.8458910832859559
chair=0.5615632093139779
cow=0.7910489912460078
diningtable=0.6998541355897903
dog=0.8150541593877814
horse=0.8423857953599929
motorbike=0.8294256470253345
person=0.7896722521652761
pottedplant=0.48249434143380665
sheep=0.7645315260235362
sofa=0.7364000510113633
train=0.8152540688776367
tvmonitor=0.7449848524538989
mAP=0.7486484532948696
[Epoch 161][Batch 99], LR: 1.00E-04, Speed: 169.503 samples/sec, ObjLoss=9.098, BoxCenterLoss=5.883, BoxScaleLoss=1.495, ClassLoss=2.287
[Epoch 161][Batch 199], LR: 1.00E-04, Speed: 186.228 samples/sec, ObjLoss=9.090, BoxCenterLoss=5.883, BoxScaleLoss=1.494, ClassLoss=2.285
[Epoch 161] Training cost: 135.505, ObjLoss=9.085, BoxCenterLoss=5.882, BoxScaleLoss=1.494, ClassLoss=2.283
[Epoch 161] Validation:
aeroplane=0.786480525408318
bicycle=0.7950153390956458
bird=0.7209923537864746
boat=0.654283863966758
bottle=0.5160913295171913
bus=0.8471117458908235
car=0.849578151870512
cat=0.8518486589602805
chair=0.5513806132905499
cow=0.7856160137511209
diningtable=0.6725634322656613
dog=0.8054049109898398
horse=0.8377192783604022
motorbike=0.826732058276767
person=0.783718164304325
pottedplant=0.472082690895487
sheep=0.7665074011682512
sofa=0.7214650367178603
train=0.8140054137915058
tvmonitor=0.7397511158189405
mAP=0.7399174049063357
[Epoch 162][Batch 99], LR: 1.00E-04, Speed: 170.616 samples/sec, ObjLoss=9.078, BoxCenterLoss=5.882, BoxScaleLoss=1.493, ClassLoss=2.280
[Epoch 162][Batch 199], LR: 1.00E-04, Speed: 147.052 samples/sec, ObjLoss=9.070, BoxCenterLoss=5.882, BoxScaleLoss=1.492, ClassLoss=2.277
[Epoch 162] Training cost: 128.703, ObjLoss=9.066, BoxCenterLoss=5.881, BoxScaleLoss=1.491, ClassLoss=2.276
[Epoch 162] Validation:
aeroplane=0.8252927565503838
bicycle=0.8409258867278574
bird=0.738411318325159
boat=0.6567364659833203
bottle=0.5277161236845619
bus=0.8463947033929211
car=0.8610888662145811
cat=0.8501750353057715
chair=0.5692836618836719
cow=0.8117571986493576
diningtable=0.6923230624868366
dog=0.8142218247312474
horse=0.8430251528102018
motorbike=0.8291581006384021
person=0.7940586690083445
pottedplant=0.48518254566793295
sheep=0.7896832772734792
sofa=0.733874096279812
train=0.82804518092984
tvmonitor=0.7466976328688509
mAP=0.7542025779706266
[Epoch 163][Batch 99], LR: 1.00E-04, Speed: 194.704 samples/sec, ObjLoss=9.058, BoxCenterLoss=5.881, BoxScaleLoss=1.490, ClassLoss=2.273
[Epoch 163][Batch 199], LR: 1.00E-04, Speed: 157.074 samples/sec, ObjLoss=9.051, BoxCenterLoss=5.880, BoxScaleLoss=1.489, ClassLoss=2.270
[Epoch 163] Training cost: 155.117, ObjLoss=9.047, BoxCenterLoss=5.880, BoxScaleLoss=1.488, ClassLoss=2.268
[Epoch 163] Validation:
aeroplane=0.7923766188702697
bicycle=0.8364915445049762
bird=0.7343512872238767
boat=0.6734235566884927
bottle=0.5338214973389394
bus=0.8502396045677898
car=0.8624087112843828
cat=0.8609183327998836
chair=0.572194208312888
cow=0.8086516218913737
diningtable=0.7010999249860489
dog=0.8064923351824744
horse=0.8540457778642866
motorbike=0.836213142509534
person=0.7936794834790638
pottedplant=0.48835597005305464
sheep=0.7939016147422071
sofa=0.7408805729643592
train=0.8207950828990855
tvmonitor=0.7502943195556958
mAP=0.7555317603859342
[Epoch 164][Batch 99], LR: 1.00E-04, Speed: 208.550 samples/sec, ObjLoss=9.040, BoxCenterLoss=5.880, BoxScaleLoss=1.487, ClassLoss=2.265
[Epoch 164][Batch 199], LR: 1.00E-04, Speed: 181.659 samples/sec, ObjLoss=9.032, BoxCenterLoss=5.880, BoxScaleLoss=1.486, ClassLoss=2.263
[Epoch 164] Training cost: 145.800, ObjLoss=9.028, BoxCenterLoss=5.879, BoxScaleLoss=1.486, ClassLoss=2.261
[Epoch 164] Validation:
aeroplane=0.8260999646577877
bicycle=0.840820110224484
bird=0.7426617118686952
boat=0.6751908160600051
bottle=0.5331326337714782
bus=0.8526661566531318
car=0.8624899821791152
cat=0.8531469731702158
chair=0.578894825420158
cow=0.8078219876671878
diningtable=0.7037895933865304
dog=0.8116091999769857
horse=0.8517615351531639
motorbike=0.8281830228834377
person=0.7968141858785409
pottedplant=0.4965502577069546
sheep=0.7889818729747305
sofa=0.7423291824327879
train=0.7867370954541157
tvmonitor=0.7519973867988766
mAP=0.7565839247159192
[Epoch 165][Batch 99], LR: 1.00E-04, Speed: 179.612 samples/sec, ObjLoss=9.020, BoxCenterLoss=5.879, BoxScaleLoss=1.485, ClassLoss=2.258
[Epoch 165][Batch 199], LR: 1.00E-04, Speed: 144.363 samples/sec, ObjLoss=9.013, BoxCenterLoss=5.878, BoxScaleLoss=1.484, ClassLoss=2.255
[Epoch 165] Training cost: 138.206, ObjLoss=9.009, BoxCenterLoss=5.878, BoxScaleLoss=1.483, ClassLoss=2.254
[Epoch 165] Validation:
aeroplane=0.7808587505017923
bicycle=0.8349767344954511
bird=0.7247230179137086
boat=0.6557262612666209
bottle=0.5232014057364981
bus=0.8346632597589283
car=0.8526246590354786
cat=0.8512781765172868
chair=0.5601310462842714
cow=0.7846202493925938
diningtable=0.6452922290037769
dog=0.809577088499418
horse=0.8411371917908955
motorbike=0.811921805424928
person=0.7860537757397273
pottedplant=0.4714566872895133
sheep=0.7720087926639749
sofa=0.7083012407901202
train=0.8094364887986132
tvmonitor=0.7452373176431099
mAP=0.7401613089273353
[Epoch 166][Batch 99], LR: 1.00E-04, Speed: 184.933 samples/sec, ObjLoss=9.001, BoxCenterLoss=5.877, BoxScaleLoss=1.482, ClassLoss=2.251
[Epoch 166][Batch 199], LR: 1.00E-04, Speed: 195.559 samples/sec, ObjLoss=8.994, BoxCenterLoss=5.877, BoxScaleLoss=1.481, ClassLoss=2.248
[Epoch 166] Training cost: 134.633, ObjLoss=8.989, BoxCenterLoss=5.877, BoxScaleLoss=1.481, ClassLoss=2.247
[Epoch 166] Validation:
aeroplane=0.7885639863978526
bicycle=0.8356831884957419
bird=0.7351319454200789
boat=0.6761915005059316
bottle=0.5342839883718988
bus=0.8446659459198644
car=0.8636754811887892
cat=0.8582461844512884
chair=0.5768308700962593
cow=0.7954790682340883
diningtable=0.6997074086252831
dog=0.8223005932118392
horse=0.8516094321988099
motorbike=0.8337013712087318
person=0.7938869067200287
pottedplant=0.49251581193639193
sheep=0.7954138447045013
sofa=0.7419786717579195
train=0.8301718932495976
tvmonitor=0.7531534488632471
mAP=0.7561595770779073
[Epoch 167][Batch 99], LR: 1.00E-04, Speed: 155.793 samples/sec, ObjLoss=8.982, BoxCenterLoss=5.876, BoxScaleLoss=1.480, ClassLoss=2.244
[Epoch 167][Batch 199], LR: 1.00E-04, Speed: 138.901 samples/sec, ObjLoss=8.975, BoxCenterLoss=5.876, BoxScaleLoss=1.479, ClassLoss=2.241
[Epoch 167] Training cost: 135.237, ObjLoss=8.970, BoxCenterLoss=5.876, BoxScaleLoss=1.478, ClassLoss=2.240
[Epoch 167] Validation:
aeroplane=0.7969591264063289
bicycle=0.8454139123128463
bird=0.7457839342442768
boat=0.6774726355343033
bottle=0.5395064449709502
bus=0.8411985035800541
car=0.8608362360692486
cat=0.8561257179989662
chair=0.5806196251889987
cow=0.8044311277396027
diningtable=0.6998326685267695
dog=0.8119439527599988
horse=0.852455870842145
motorbike=0.8305187907523544
person=0.7963807886676796
pottedplant=0.5067366548245559
sheep=0.7948488617922348
sofa=0.7425272064462131
train=0.785712885202202
tvmonitor=0.7498258539533231
mAP=0.7559565398906526
[Epoch 168][Batch 99], LR: 1.00E-04, Speed: 162.045 samples/sec, ObjLoss=8.962, BoxCenterLoss=5.875, BoxScaleLoss=1.477, ClassLoss=2.237
[Epoch 168][Batch 199], LR: 1.00E-04, Speed: 221.465 samples/sec, ObjLoss=8.955, BoxCenterLoss=5.875, BoxScaleLoss=1.476, ClassLoss=2.234
[Epoch 168] Training cost: 144.195, ObjLoss=8.951, BoxCenterLoss=5.875, BoxScaleLoss=1.476, ClassLoss=2.232
[Epoch 168] Validation:
aeroplane=0.7949659405801007
bicycle=0.8420752081704999
bird=0.7398065628878758
boat=0.677283342903455
bottle=0.543488021374607
bus=0.845449209743172
car=0.8574770348535931
cat=0.8574912183231861
chair=0.5776980289688122
cow=0.790797540774723
diningtable=0.693340657915308
dog=0.8134839098196036
horse=0.8425753325508463
motorbike=0.8388733903886851
person=0.7932213854238718
pottedplant=0.48676361227382825
sheep=0.7887583442358415
sofa=0.7340178743760075
train=0.7842014816639389
tvmonitor=0.7567909931945259
mAP=0.752927954521124
[Epoch 169][Batch 99], LR: 1.00E-04, Speed: 157.062 samples/sec, ObjLoss=8.944, BoxCenterLoss=5.874, BoxScaleLoss=1.475, ClassLoss=2.230
[Epoch 169][Batch 199], LR: 1.00E-04, Speed: 165.048 samples/sec, ObjLoss=8.937, BoxCenterLoss=5.874, BoxScaleLoss=1.474, ClassLoss=2.227
[Epoch 169] Training cost: 150.835, ObjLoss=8.933, BoxCenterLoss=5.874, BoxScaleLoss=1.473, ClassLoss=2.225
[Epoch 169] Validation:
aeroplane=0.8301343008831795
bicycle=0.838193175508323
bird=0.7433819215209139
boat=0.6775527447441284
bottle=0.5312882585643719
bus=0.8455588961308135
car=0.8571702828113448
cat=0.8620403215287229
chair=0.5717660881841906
cow=0.7995778322588137
diningtable=0.6977796264834201
dog=0.8192674767827058
horse=0.8425174300270536
motorbike=0.8357684315062287
person=0.7941495920973433
pottedplant=0.4824905021820701
sheep=0.7864826997079445
sofa=0.7390555445381461
train=0.8315923997805217
tvmonitor=0.7566223069019301
mAP=0.7571194916071085
[Epoch 170][Batch 99], LR: 1.00E-04, Speed: 171.522 samples/sec, ObjLoss=8.925, BoxCenterLoss=5.873, BoxScaleLoss=1.472, ClassLoss=2.223
[Epoch 170][Batch 199], LR: 1.00E-04, Speed: 175.480 samples/sec, ObjLoss=8.918, BoxCenterLoss=5.873, BoxScaleLoss=1.471, ClassLoss=2.220
[Epoch 170] Training cost: 146.227, ObjLoss=8.914, BoxCenterLoss=5.873, BoxScaleLoss=1.471, ClassLoss=2.218
[Epoch 170] Validation:
aeroplane=0.7877555657887054
bicycle=0.8380300260446164
bird=0.7372900433482384
boat=0.6858252185735305
bottle=0.541633548615411
bus=0.8491276122051326
car=0.8570848623940981
cat=0.8632713440997528
chair=0.5734916932525096
cow=0.8001651380978928
diningtable=0.6875890996544134
dog=0.8112563001840384
horse=0.8492429923475968
motorbike=0.8327047068024155
person=0.7995003265000035
pottedplant=0.48607758851013344
sheep=0.7903837165704268
sofa=0.7373637160975843
train=0.785083638444915
tvmonitor=0.7532264289526659
mAP=0.753305178324204
[Epoch 171][Batch 99], LR: 1.00E-04, Speed: 175.559 samples/sec, ObjLoss=8.906, BoxCenterLoss=5.872, BoxScaleLoss=1.470, ClassLoss=2.215
[Epoch 171][Batch 199], LR: 1.00E-04, Speed: 161.025 samples/sec, ObjLoss=8.899, BoxCenterLoss=5.871, BoxScaleLoss=1.469, ClassLoss=2.213
[Epoch 171] Training cost: 139.756, ObjLoss=8.895, BoxCenterLoss=5.871, BoxScaleLoss=1.468, ClassLoss=2.211
[Epoch 171] Validation:
aeroplane=0.8317358143134419
bicycle=0.8490205261545162
bird=0.742224546376399
boat=0.6786073218622493
bottle=0.5405067426179958
bus=0.8488454754797673
car=0.8598551676052291
cat=0.8606220478539706
chair=0.5756576672442799
cow=0.8068378049202163
diningtable=0.702281648356586
dog=0.8061174978627716
horse=0.8526033455192985
motorbike=0.8354649616602573
person=0.7959031165598538
pottedplant=0.4974536590975751
sheep=0.7883231141206595
sofa=0.7309490895237306
train=0.833550103331455
tvmonitor=0.7490530043134731
mAP=0.7592806327386864
[Epoch 172][Batch 99], LR: 1.00E-04, Speed: 172.785 samples/sec, ObjLoss=8.888, BoxCenterLoss=5.871, BoxScaleLoss=1.467, ClassLoss=2.208
[Epoch 172][Batch 199], LR: 1.00E-04, Speed: 145.740 samples/sec, ObjLoss=8.881, BoxCenterLoss=5.871, BoxScaleLoss=1.467, ClassLoss=2.205
[Epoch 172] Training cost: 152.874, ObjLoss=8.877, BoxCenterLoss=5.871, BoxScaleLoss=1.466, ClassLoss=2.204
[Epoch 172] Validation:
aeroplane=0.8368811563095558
bicycle=0.8443367125572814
bird=0.7388284184110155
boat=0.6808967516267764
bottle=0.5338399179996244
bus=0.8549483189050856
car=0.8592279146787193
cat=0.8506555391791927
chair=0.5813134661315724
cow=0.814052279696627
diningtable=0.7063888330834057
dog=0.8144323667918041
horse=0.8504445498389238
motorbike=0.8293697807125007
person=0.7948634668199699
pottedplant=0.5018484920863553
sheep=0.7922536865272181
sofa=0.7502187502952369
train=0.7852028901415492
tvmonitor=0.7635090198144897
mAP=0.7591756155803452
[Epoch 173][Batch 99], LR: 1.00E-04, Speed: 169.305 samples/sec, ObjLoss=8.870, BoxCenterLoss=5.870, BoxScaleLoss=1.465, ClassLoss=2.201
[Epoch 173][Batch 199], LR: 1.00E-04, Speed: 169.350 samples/sec, ObjLoss=8.862, BoxCenterLoss=5.870, BoxScaleLoss=1.464, ClassLoss=2.198
[Epoch 173] Training cost: 156.829, ObjLoss=8.858, BoxCenterLoss=5.870, BoxScaleLoss=1.464, ClassLoss=2.196
[Epoch 173] Validation:
aeroplane=0.829626396853317
bicycle=0.8556830221592682
bird=0.741362064976078
boat=0.6650732398244066
bottle=0.5522621162447765
bus=0.8504551483153857
car=0.8596952187474382
cat=0.852182510250017
chair=0.5836911228675933
cow=0.8124847240583998
diningtable=0.7084525003104588
dog=0.818787477868948
horse=0.8490544813737451
motorbike=0.8355908674833094
person=0.7650406850979466
pottedplant=0.4937166117773676
sheep=0.7936424753807543
sofa=0.7388818986117448
train=0.827037215505799
tvmonitor=0.7585352608994334
mAP=0.7595627519303093
[Epoch 174][Batch 99], LR: 1.00E-04, Speed: 165.088 samples/sec, ObjLoss=8.851, BoxCenterLoss=5.869, BoxScaleLoss=1.463, ClassLoss=2.194
[Epoch 174][Batch 199], LR: 1.00E-04, Speed: 191.268 samples/sec, ObjLoss=8.844, BoxCenterLoss=5.869, BoxScaleLoss=1.462, ClassLoss=2.191
[Epoch 174] Training cost: 137.640, ObjLoss=8.840, BoxCenterLoss=5.869, BoxScaleLoss=1.461, ClassLoss=2.190
[Epoch 174] Validation:
aeroplane=0.8297674857841929
bicycle=0.8512678136041094
bird=0.7382315628003688
boat=0.680464680247702
bottle=0.5339958739082633
bus=0.8469296486724297
car=0.8530164672701391
cat=0.8451484493804582
chair=0.5732579762263151
cow=0.8020911333268459
diningtable=0.6928556681954215
dog=0.8062948734077451
horse=0.8534735967052548
motorbike=0.8381395644846381
person=0.7951548776732578
pottedplant=0.48300674295682905
sheep=0.7824994411238557
sofa=0.7294175151555039
train=0.7840339802607623
tvmonitor=0.7554205003905504
mAP=0.7537233925787322
[Epoch 175][Batch 99], LR: 1.00E-04, Speed: 179.220 samples/sec, ObjLoss=8.834, BoxCenterLoss=5.868, BoxScaleLoss=1.460, ClassLoss=2.187
[Epoch 175][Batch 199], LR: 1.00E-04, Speed: 159.391 samples/sec, ObjLoss=8.827, BoxCenterLoss=5.868, BoxScaleLoss=1.460, ClassLoss=2.184
[Epoch 175] Training cost: 140.256, ObjLoss=8.822, BoxCenterLoss=5.867, BoxScaleLoss=1.459, ClassLoss=2.183
[Epoch 175] Validation:
aeroplane=0.795074396200181
bicycle=0.8386894431347998
bird=0.7346239768191865
boat=0.6756553471839635
bottle=0.531128083705633
bus=0.8485820903894302
car=0.8559280848412657
cat=0.855028602839988
chair=0.5701181227871046
cow=0.7984501285949174
diningtable=0.68978262551138
dog=0.8124207014092758
horse=0.8488147943754194
motorbike=0.8381773798657043
person=0.7966229186005909
pottedplant=0.4818086333002108
sheep=0.779640805521825
sofa=0.7370449161729433
train=0.7831487516418284
tvmonitor=0.7518351143138333
mAP=0.7511287458604741
[Epoch 176][Batch 99], LR: 1.00E-04, Speed: 157.442 samples/sec, ObjLoss=8.815, BoxCenterLoss=5.867, BoxScaleLoss=1.458, ClassLoss=2.180
[Epoch 176][Batch 199], LR: 1.00E-04, Speed: 157.979 samples/sec, ObjLoss=8.808, BoxCenterLoss=5.866, BoxScaleLoss=1.457, ClassLoss=2.177
[Epoch 176] Training cost: 129.187, ObjLoss=8.804, BoxCenterLoss=5.866, BoxScaleLoss=1.457, ClassLoss=2.176
[Epoch 176] Validation:
aeroplane=0.7951268802949572
bicycle=0.8347383113182536
bird=0.7424642038685171
boat=0.6696221726229542
bottle=0.5217846830448142
bus=0.8518755468933729
car=0.8560297651531203
cat=0.8427732965981581
chair=0.563963150761549
cow=0.7997323593290692
diningtable=0.676414794995855
dog=0.8093376323070735
horse=0.8552670550024677
motorbike=0.8293173131651808
person=0.79320623766256
pottedplant=0.4766302752709376
sheep=0.7804890131896671
sofa=0.7239441905232398
train=0.8283491558424019
tvmonitor=0.7781498408126387
mAP=0.7514607939328393
[Epoch 177][Batch 99], LR: 1.00E-04, Speed: 160.784 samples/sec, ObjLoss=8.798, BoxCenterLoss=5.866, BoxScaleLoss=1.456, ClassLoss=2.174
[Epoch 177][Batch 199], LR: 1.00E-04, Speed: 170.880 samples/sec, ObjLoss=8.791, BoxCenterLoss=5.866, BoxScaleLoss=1.455, ClassLoss=2.171
[Epoch 177] Training cost: 141.153, ObjLoss=8.787, BoxCenterLoss=5.865, BoxScaleLoss=1.454, ClassLoss=2.169
[Epoch 177] Validation:
aeroplane=0.80005152851399
bicycle=0.7987744172014123
bird=0.7400035768822892
boat=0.687710087673352
bottle=0.5349422617826167
bus=0.8499811009885637
car=0.8603369028142057
cat=0.8527710981806957
chair=0.5710080278154404
cow=0.7988209111288076
diningtable=0.7019921526976485
dog=0.8168881358758249
horse=0.8501700771265048
motorbike=0.8361143862785078
person=0.7649309526292676
pottedplant=0.4862460507787702
sheep=0.7895426791329271
sofa=0.7305405121263983
train=0.7821638661447352
tvmonitor=0.7535772052658877
mAP=0.7503282965518923
[Epoch 178][Batch 99], LR: 1.00E-04, Speed: 176.890 samples/sec, ObjLoss=8.780, BoxCenterLoss=5.865, BoxScaleLoss=1.454, ClassLoss=2.167
[Epoch 178][Batch 199], LR: 1.00E-04, Speed: 167.372 samples/sec, ObjLoss=8.773, BoxCenterLoss=5.865, BoxScaleLoss=1.453, ClassLoss=2.164
[Epoch 178] Training cost: 158.842, ObjLoss=8.769, BoxCenterLoss=5.865, BoxScaleLoss=1.452, ClassLoss=2.162
[Epoch 178] Validation:
aeroplane=0.7978555926053007
bicycle=0.835934565065508
bird=0.7404529805979263
boat=0.6902485363520349
bottle=0.5386158546968792
bus=0.8480273408662841
car=0.8581985970521622
cat=0.859062639399817
chair=0.5694212117489914
cow=0.8066913165448562
diningtable=0.6987665204896192
dog=0.8202800396043353
horse=0.8547568411794453
motorbike=0.8348954172718198
person=0.7654667743710697
pottedplant=0.494128022318266
sheep=0.7893001375235498
sofa=0.7260545059679174
train=0.8228783648501871
tvmonitor=0.7884525144162797
mAP=0.7569743886461124
[Epoch 179][Batch 99], LR: 1.00E-04, Speed: 147.598 samples/sec, ObjLoss=8.763, BoxCenterLoss=5.864, BoxScaleLoss=1.451, ClassLoss=2.160
[Epoch 179][Batch 199], LR: 1.00E-04, Speed: 153.556 samples/sec, ObjLoss=8.756, BoxCenterLoss=5.864, BoxScaleLoss=1.450, ClassLoss=2.157
[Epoch 179] Training cost: 143.904, ObjLoss=8.752, BoxCenterLoss=5.864, BoxScaleLoss=1.450, ClassLoss=2.156
[Epoch 179] Validation:
aeroplane=0.8360262148953919
bicycle=0.7981963856988349
bird=0.7351060928804127
boat=0.6828433757603576
bottle=0.5348787193998134
bus=0.8486164076730661
car=0.8579219701538356
cat=0.8562318807192925
chair=0.5705060484061012
cow=0.8118828815638662
diningtable=0.694067029581951
dog=0.8162101658720577
horse=0.8540545275183619
motorbike=0.8347212214550425
person=0.7946115054772499
pottedplant=0.4934556390137824
sheep=0.7944913395058826
sofa=0.7325190026740628
train=0.7852900040721787
tvmonitor=0.7505761234707895
mAP=0.7541103267896166
[Epoch 180][Batch 99], LR: 1.00E-05, Speed: 158.293 samples/sec, ObjLoss=8.745, BoxCenterLoss=5.863, BoxScaleLoss=1.449, ClassLoss=2.153
[Epoch 180][Batch 199], LR: 1.00E-05, Speed: 164.306 samples/sec, ObjLoss=8.738, BoxCenterLoss=5.863, BoxScaleLoss=1.448, ClassLoss=2.150
[Epoch 180] Training cost: 141.025, ObjLoss=8.734, BoxCenterLoss=5.863, BoxScaleLoss=1.448, ClassLoss=2.149
[Epoch 180] Validation:
aeroplane=0.8342499161273953
bicycle=0.8355593052171263
bird=0.7341867788082889
boat=0.6840119588820048
bottle=0.5419622637412229
bus=0.853544494520628
car=0.8633739997459373
cat=0.8609010351287177
chair=0.5800768371670223
cow=0.8080204825881903
diningtable=0.6948124880629233
dog=0.8166341722755198
horse=0.8541930686962438
motorbike=0.8360732303174611
person=0.7968430137197597
pottedplant=0.4762784976987884
sheep=0.7971561736376773
sofa=0.7333591141817986
train=0.7866236157238843
tvmonitor=0.7869812292545826
mAP=0.7587420837747586
[Epoch 181][Batch 99], LR: 1.00E-05, Speed: 159.749 samples/sec, ObjLoss=8.727, BoxCenterLoss=5.863, BoxScaleLoss=1.447, ClassLoss=2.147
[Epoch 181][Batch 199], LR: 1.00E-05, Speed: 158.176 samples/sec, ObjLoss=8.720, BoxCenterLoss=5.862, BoxScaleLoss=1.446, ClassLoss=2.144
[Epoch 181] Training cost: 141.956, ObjLoss=8.717, BoxCenterLoss=5.862, BoxScaleLoss=1.445, ClassLoss=2.143
[Epoch 181] Validation:
aeroplane=0.8315873163791795
bicycle=0.8326881994536016
bird=0.744651542782012
boat=0.6779307003332045
bottle=0.5440467598112787
bus=0.8478065003778942
car=0.8636142439664595
cat=0.8614988282338388
chair=0.5836218864112266
cow=0.8166890771696554
diningtable=0.6947414331644625
dog=0.8187332919638793
horse=0.8559243908966777
motorbike=0.8375741783005074
person=0.7975355037026978
pottedplant=0.5018997766738679
sheep=0.791095216624094
sofa=0.7362330384994721
train=0.8213575055155237
tvmonitor=0.7579124317557208
mAP=0.7608570911007627
[Epoch 182][Batch 99], LR: 1.00E-05, Speed: 168.896 samples/sec, ObjLoss=8.710, BoxCenterLoss=5.861, BoxScaleLoss=1.444, ClassLoss=2.140
[Epoch 182][Batch 199], LR: 1.00E-05, Speed: 179.572 samples/sec, ObjLoss=8.703, BoxCenterLoss=5.861, BoxScaleLoss=1.444, ClassLoss=2.138
[Epoch 182] Training cost: 119.097, ObjLoss=8.699, BoxCenterLoss=5.861, BoxScaleLoss=1.443, ClassLoss=2.136
[Epoch 182] Validation:
aeroplane=0.8316770813368595
bicycle=0.8388141561161773
bird=0.7414066084639763
boat=0.6893530006958358
bottle=0.5435039878620811
bus=0.8488444914990293
car=0.8573665239591353
cat=0.8587035161220589
chair=0.5743251245228742
cow=0.8066435455433629
diningtable=0.6918056744768659
dog=0.8170171876611425
horse=0.8517572836298745
motorbike=0.8386537607749467
person=0.7949962000218564
pottedplant=0.49644198951932816
sheep=0.8023953891893051
sofa=0.7344143416287514
train=0.8204846006499488
tvmonitor=0.7488117520817206
mAP=0.7593708107877566
[Epoch 183][Batch 99], LR: 1.00E-05, Speed: 182.508 samples/sec, ObjLoss=8.692, BoxCenterLoss=5.860, BoxScaleLoss=1.442, ClassLoss=2.134
[Epoch 183][Batch 199], LR: 1.00E-05, Speed: 190.468 samples/sec, ObjLoss=8.686, BoxCenterLoss=5.860, BoxScaleLoss=1.441, ClassLoss=2.131
[Epoch 183] Training cost: 144.657, ObjLoss=8.682, BoxCenterLoss=5.860, BoxScaleLoss=1.441, ClassLoss=2.130
[Epoch 183] Validation:
aeroplane=0.8390579043095776
bicycle=0.8398268500922238
bird=0.7390220559800199
boat=0.6895599762090023
bottle=0.5498279944083042
bus=0.8435040530526995
car=0.8623105372246633
cat=0.8537680401674775
chair=0.5788082304689798
cow=0.8090326118832828
diningtable=0.6965741830047273
dog=0.8233360031045066
horse=0.8552126581126884
motorbike=0.8361736820139795
person=0.7977206502055844
pottedplant=0.4993091617298082
sheep=0.7953105872567449
sofa=0.738724550087764
train=0.8313070206866512
tvmonitor=0.760473940876533
mAP=0.761943034543761
[Epoch 184][Batch 99], LR: 1.00E-05, Speed: 153.459 samples/sec, ObjLoss=8.676, BoxCenterLoss=5.859, BoxScaleLoss=1.440, ClassLoss=2.127
[Epoch 184][Batch 199], LR: 1.00E-05, Speed: 170.401 samples/sec, ObjLoss=8.669, BoxCenterLoss=5.859, BoxScaleLoss=1.439, ClassLoss=2.125
[Epoch 184] Training cost: 142.623, ObjLoss=8.665, BoxCenterLoss=5.859, BoxScaleLoss=1.439, ClassLoss=2.123
[Epoch 184] Validation:
aeroplane=0.8293966919310464
bicycle=0.8366851248885935
bird=0.7366735732066549
boat=0.688337528147566
bottle=0.5370104406106495
bus=0.8502444323203112
car=0.8607703337652142
cat=0.8606584709306435
chair=0.570727506686149
cow=0.8124445723836637
diningtable=0.6902102810433015
dog=0.8208879013434377
horse=0.8535665645083156
motorbike=0.8355313116045208
person=0.7654014340678595
pottedplant=0.4844271231248608
sheep=0.8011096693382161
sofa=0.7298314326020959
train=0.8280146692077268
tvmonitor=0.7517987852823325
mAP=0.7571863923496579
[Epoch 185][Batch 99], LR: 1.00E-05, Speed: 164.011 samples/sec, ObjLoss=8.659, BoxCenterLoss=5.858, BoxScaleLoss=1.438, ClassLoss=2.121
[Epoch 185][Batch 199], LR: 1.00E-05, Speed: 177.054 samples/sec, ObjLoss=8.653, BoxCenterLoss=5.858, BoxScaleLoss=1.437, ClassLoss=2.118
[Epoch 185] Training cost: 149.604, ObjLoss=8.649, BoxCenterLoss=5.858, BoxScaleLoss=1.436, ClassLoss=2.117
[Epoch 185] Validation:
aeroplane=0.8307152083464147
bicycle=0.8391814059000137
bird=0.7421607613986901
boat=0.6838709007386665
bottle=0.5473225911395962
bus=0.8508380320661597
car=0.8574799132421519
cat=0.8524684077089011
chair=0.579437934419402
cow=0.8088751101178538
diningtable=0.691123720724745
dog=0.8162645463609548
horse=0.8531708985819138
motorbike=0.8378986844674555
person=0.7950292910684011
pottedplant=0.49536102432238405
sheep=0.7965114312945731
sofa=0.731274227630212
train=0.7865997609638398
tvmonitor=0.7574494566435082
mAP=0.7576516653567917
[Epoch 186][Batch 99], LR: 1.00E-05, Speed: 163.814 samples/sec, ObjLoss=8.642, BoxCenterLoss=5.858, BoxScaleLoss=1.435, ClassLoss=2.115
[Epoch 186][Batch 199], LR: 1.00E-05, Speed: 213.921 samples/sec, ObjLoss=8.636, BoxCenterLoss=5.857, BoxScaleLoss=1.435, ClassLoss=2.112
[Epoch 186] Training cost: 145.623, ObjLoss=8.632, BoxCenterLoss=5.857, BoxScaleLoss=1.434, ClassLoss=2.111
[Epoch 186] Validation:
aeroplane=0.8424263928920415
bicycle=0.8431293827506033
bird=0.7346923149316864
boat=0.6830308765821531
bottle=0.5426304484097428
bus=0.8573393602714872
car=0.8598458512954814
cat=0.8625898874021716
chair=0.57771354586441
cow=0.8066909489386572
diningtable=0.6869401687894712
dog=0.8167035555876172
horse=0.8528031425855881
motorbike=0.835336414826054
person=0.7671566757487583
pottedplant=0.4949806595113406
sheep=0.7965942527340844
sofa=0.7334340992678618
train=0.7848557834447416
tvmonitor=0.7922412323107986
mAP=0.7585567497072376
[Epoch 187][Batch 99], LR: 1.00E-05, Speed: 162.430 samples/sec, ObjLoss=8.626, BoxCenterLoss=5.857, BoxScaleLoss=1.433, ClassLoss=2.108
[Epoch 187][Batch 199], LR: 1.00E-05, Speed: 179.566 samples/sec, ObjLoss=8.619, BoxCenterLoss=5.856, BoxScaleLoss=1.432, ClassLoss=2.106
[Epoch 187] Training cost: 130.177, ObjLoss=8.616, BoxCenterLoss=5.856, BoxScaleLoss=1.432, ClassLoss=2.105
[Epoch 187] Validation:
aeroplane=0.8313847454927841
bicycle=0.8430420817904529
bird=0.7388160053365092
boat=0.6930278039835422
bottle=0.5404460379991117
bus=0.8498900817923394
car=0.8564953911305021
cat=0.8630382524276355
chair=0.5844989960989826
cow=0.8045432990921377
diningtable=0.6926603148649465
dog=0.8177115174181041
horse=0.8510261835221126
motorbike=0.8378789997221786
person=0.7667851554235661
pottedplant=0.4906195684717816
sheep=0.7941345467039779
sofa=0.7403847341833016
train=0.782854373505733
tvmonitor=0.7823332944390173
mAP=0.7580785691699358
[Epoch 188][Batch 99], LR: 1.00E-05, Speed: 174.868 samples/sec, ObjLoss=8.609, BoxCenterLoss=5.856, BoxScaleLoss=1.431, ClassLoss=2.102
[Epoch 188][Batch 199], LR: 1.00E-05, Speed: 183.473 samples/sec, ObjLoss=8.603, BoxCenterLoss=5.856, BoxScaleLoss=1.430, ClassLoss=2.100
[Epoch 188] Training cost: 134.616, ObjLoss=8.599, BoxCenterLoss=5.855, BoxScaleLoss=1.430, ClassLoss=2.098
[Epoch 188] Validation:
aeroplane=0.8366458773342075
bicycle=0.8442618630255204
bird=0.7387764699919462
boat=0.6808578918413782
bottle=0.5405898242040548
bus=0.85208930508015
car=0.864133969879663
cat=0.8652829636953023
chair=0.5858787289735982
cow=0.8118465499114571
diningtable=0.6986147641464172
dog=0.8221353482569105
horse=0.859030596521009
motorbike=0.8372961735789366
person=0.7968661728357729
pottedplant=0.5015719478649979
sheep=0.7977465997766755
sofa=0.7418785548996842
train=0.7862166471356196
tvmonitor=0.7588896515356015
mAP=0.7610304950244453
[Epoch 189][Batch 99], LR: 1.00E-05, Speed: 168.080 samples/sec, ObjLoss=8.593, BoxCenterLoss=5.855, BoxScaleLoss=1.429, ClassLoss=2.096
[Epoch 189][Batch 199], LR: 1.00E-05, Speed: 166.370 samples/sec, ObjLoss=8.587, BoxCenterLoss=5.855, BoxScaleLoss=1.428, ClassLoss=2.094
[Epoch 189] Training cost: 136.990, ObjLoss=8.583, BoxCenterLoss=5.854, BoxScaleLoss=1.428, ClassLoss=2.093
[Epoch 189] Validation:
aeroplane=0.7951859829500528
bicycle=0.8429211372017306
bird=0.7370483604359354
boat=0.6873302317151023
bottle=0.5407565594874795
bus=0.844585057382805
car=0.8630454248073616
cat=0.8599817636399705
chair=0.5917772231676243
cow=0.8070060369973527
diningtable=0.69796823731846
dog=0.821666218059505
horse=0.8564213033306839
motorbike=0.839386646774631
person=0.7985896093272923
pottedplant=0.491029152703369
sheep=0.8023606829804124
sofa=0.7357592699224325
train=0.7874512055572457
tvmonitor=0.757713921598864
mAP=0.7578992012679155
[Epoch 190][Batch 99], LR: 1.00E-05, Speed: 155.877 samples/sec, ObjLoss=8.577, BoxCenterLoss=5.854, BoxScaleLoss=1.427, ClassLoss=2.090
[Epoch 190][Batch 199], LR: 1.00E-05, Speed: 175.260 samples/sec, ObjLoss=8.571, BoxCenterLoss=5.854, BoxScaleLoss=1.426, ClassLoss=2.088
[Epoch 190] Training cost: 140.045, ObjLoss=8.567, BoxCenterLoss=5.853, BoxScaleLoss=1.426, ClassLoss=2.087
[Epoch 190] Validation:
aeroplane=0.7939474673899948
bicycle=0.8423831751683354
bird=0.7381649026171795
boat=0.6855635644823208
bottle=0.5438190874796708
bus=0.8457750763667212
car=0.86261716603477
cat=0.8600597551331132
chair=0.5830691913555566
cow=0.8115542802624296
diningtable=0.7001555671349627
dog=0.8190856484287365
horse=0.853823241270837
motorbike=0.8406700001937374
person=0.7997141166126823
pottedplant=0.4941903678732401
sheep=0.7931055464999003
sofa=0.7357250332829771
train=0.7836845928235352
tvmonitor=0.7573058181753834
mAP=0.7572206799293042
[Epoch 191][Batch 99], LR: 1.00E-05, Speed: 171.418 samples/sec, ObjLoss=8.561, BoxCenterLoss=5.853, BoxScaleLoss=1.425, ClassLoss=2.084
[Epoch 191][Batch 199], LR: 1.00E-05, Speed: 160.986 samples/sec, ObjLoss=8.555, BoxCenterLoss=5.852, BoxScaleLoss=1.424, ClassLoss=2.082
[Epoch 191] Training cost: 151.178, ObjLoss=8.551, BoxCenterLoss=5.852, BoxScaleLoss=1.424, ClassLoss=2.081
[Epoch 191] Validation:
aeroplane=0.7941073896577271
bicycle=0.8511504034981787
bird=0.741529387179238
boat=0.6868076880185946
bottle=0.538897178837729
bus=0.8509518968737955
car=0.865862584203329
cat=0.8636994050004468
chair=0.5830602295573601
cow=0.814246255468044
diningtable=0.6991880036408292
dog=0.8176379289915396
horse=0.8522071451313238
motorbike=0.8394057593757328
person=0.7950968469619553
pottedplant=0.49472752644028645
sheep=0.7939757439839386
sofa=0.7381359238630855
train=0.7859593233886555
tvmonitor=0.7598398476448983
mAP=0.7583243233858343
[Epoch 192][Batch 99], LR: 1.00E-05, Speed: 153.776 samples/sec, ObjLoss=8.545, BoxCenterLoss=5.852, BoxScaleLoss=1.423, ClassLoss=2.078
[Epoch 192][Batch 199], LR: 1.00E-05, Speed: 180.642 samples/sec, ObjLoss=8.539, BoxCenterLoss=5.851, BoxScaleLoss=1.422, ClassLoss=2.076
[Epoch 192] Training cost: 138.248, ObjLoss=8.536, BoxCenterLoss=5.851, BoxScaleLoss=1.422, ClassLoss=2.075
[Epoch 192] Validation:
aeroplane=0.8405432148698214
bicycle=0.8443693031737332
bird=0.7407233691776007
boat=0.6834789543638609
bottle=0.5448094705721676
bus=0.8511321087472014
car=0.8648489165361567
cat=0.8631798663742731
chair=0.5849172593190636
cow=0.8086689438446862
diningtable=0.6983774526672217
dog=0.8254189625925097
horse=0.8556798200479201
motorbike=0.8407293457259973
person=0.7988137451764666
pottedplant=0.47690655562914247
sheep=0.7938039531171133
sofa=0.7382501056057549
train=0.8263570162145706
tvmonitor=0.7571737134777199
mAP=0.761909103861649
[Epoch 193][Batch 99], LR: 1.00E-05, Speed: 177.557 samples/sec, ObjLoss=8.529, BoxCenterLoss=5.851, BoxScaleLoss=1.421, ClassLoss=2.073
[Epoch 193][Batch 199], LR: 1.00E-05, Speed: 185.188 samples/sec, ObjLoss=8.524, BoxCenterLoss=5.850, BoxScaleLoss=1.420, ClassLoss=2.070
[Epoch 193] Training cost: 136.142, ObjLoss=8.520, BoxCenterLoss=5.850, BoxScaleLoss=1.419, ClassLoss=2.069
[Epoch 193] Validation:
aeroplane=0.8341200215058244
bicycle=0.8474017788264581
bird=0.7375340068981803
boat=0.6856452389030141
bottle=0.5484750948765822
bus=0.8482959068539593
car=0.8630732371140702
cat=0.8574584799602453
chair=0.5841519767957908
cow=0.808511708402936
diningtable=0.6945333415810282
dog=0.8191598936953755
horse=0.8524273919097145
motorbike=0.8373855646821156
person=0.7972160994529058
pottedplant=0.5003588096203524
sheep=0.7947222166265988
sofa=0.7381025524958069
train=0.7881750239058471
tvmonitor=0.7522623327028672
mAP=0.7594505338404838
[Epoch 194][Batch 99], LR: 1.00E-05, Speed: 184.329 samples/sec, ObjLoss=8.514, BoxCenterLoss=5.850, BoxScaleLoss=1.419, ClassLoss=2.067
[Epoch 194][Batch 199], LR: 1.00E-05, Speed: 164.089 samples/sec, ObjLoss=8.508, BoxCenterLoss=5.849, BoxScaleLoss=1.418, ClassLoss=2.065
[Epoch 194] Training cost: 139.446, ObjLoss=8.505, BoxCenterLoss=5.849, BoxScaleLoss=1.418, ClassLoss=2.063
[Epoch 194] Validation:
aeroplane=0.839085134207288
bicycle=0.8414860244187388
bird=0.7392232462710391
boat=0.6899218731357953
bottle=0.5397141390853596
bus=0.8469740274589989
car=0.864330502639844
cat=0.8595844636523998
chair=0.5814159024686251
cow=0.8138657630645899
diningtable=0.6967280143616037
dog=0.8212751336007964
horse=0.8599736541084726
motorbike=0.8388799497676058
person=0.7980185812793749
pottedplant=0.5008762904327358
sheep=0.7991274975946613
sofa=0.7383268356368456
train=0.7819845732071433
tvmonitor=0.766054722026054
mAP=0.7608423164208986
[Epoch 195][Batch 99], LR: 1.00E-05, Speed: 196.383 samples/sec, ObjLoss=8.498, BoxCenterLoss=5.849, BoxScaleLoss=1.417, ClassLoss=2.061
[Epoch 195][Batch 199], LR: 1.00E-05, Speed: 177.127 samples/sec, ObjLoss=8.492, BoxCenterLoss=5.848, BoxScaleLoss=1.416, ClassLoss=2.059
[Epoch 195] Training cost: 140.699, ObjLoss=8.488, BoxCenterLoss=5.848, BoxScaleLoss=1.415, ClassLoss=2.057
[Epoch 195] Validation:
aeroplane=0.7955210788876651
bicycle=0.8405403158545023
bird=0.7363019785525086
boat=0.6865137015622983
bottle=0.5409842105616365
bus=0.846895143255394
car=0.8638950318166871
cat=0.8559368937723663
chair=0.5872506456455645
cow=0.8075375839510994
diningtable=0.6975636629036792
dog=0.8184671943961844
horse=0.8559777006743191
motorbike=0.8381183542891891
person=0.7978603866777565
pottedplant=0.492273821702907
sheep=0.7982635006176988
sofa=0.7394083144408802
train=0.8221862833833623
tvmonitor=0.7568035157630868
mAP=0.7589149659354393
[Epoch 196][Batch 99], LR: 1.00E-05, Speed: 160.987 samples/sec, ObjLoss=8.483, BoxCenterLoss=5.847, BoxScaleLoss=1.415, ClassLoss=2.055
[Epoch 196][Batch 199], LR: 1.00E-05, Speed: 166.177 samples/sec, ObjLoss=8.477, BoxCenterLoss=5.847, BoxScaleLoss=1.414, ClassLoss=2.053
[Epoch 196] Training cost: 147.020, ObjLoss=8.474, BoxCenterLoss=5.847, BoxScaleLoss=1.413, ClassLoss=2.052
[Epoch 196] Validation:
aeroplane=0.8426873372261767
bicycle=0.8471173593568339
bird=0.742506021313116
boat=0.684962634476141
bottle=0.5378968101514123
bus=0.8527392292289161
car=0.8638353212168235
cat=0.8655445312105302
chair=0.5862638554399423
cow=0.8131782667865481
diningtable=0.6970154108779897
dog=0.821466294220908
horse=0.8584813350859953
motorbike=0.842003446268547
person=0.7972388147745562
pottedplant=0.4987385521111467
sheep=0.803047062854072
sofa=0.7390344074811586
train=0.78693079717839
tvmonitor=0.7557860527214528
mAP=0.7618236769990329
[Epoch 197][Batch 99], LR: 1.00E-05, Speed: 165.927 samples/sec, ObjLoss=8.468, BoxCenterLoss=5.846, BoxScaleLoss=1.413, ClassLoss=2.050
[Epoch 197][Batch 199], LR: 1.00E-05, Speed: 198.947 samples/sec, ObjLoss=8.462, BoxCenterLoss=5.846, BoxScaleLoss=1.412, ClassLoss=2.047
[Epoch 197] Training cost: 139.986, ObjLoss=8.459, BoxCenterLoss=5.846, BoxScaleLoss=1.411, ClassLoss=2.046
[Epoch 197] Validation:
aeroplane=0.831111138717387
bicycle=0.79782612883154
bird=0.7346655035076792
boat=0.6907257300951258
bottle=0.5462206923905002
bus=0.8502110709761765
car=0.8643506525020321
cat=0.8572603996255497
chair=0.5836010642027826
cow=0.8080022212366978
diningtable=0.6941968213539028
dog=0.8166778005451838
horse=0.8490285638476265
motorbike=0.8381104764274366
person=0.7978201119195418
pottedplant=0.4934613430304011
sheep=0.7983933279091802
sofa=0.7382226673683122
train=0.7828690430273552
tvmonitor=0.7855106924972728
mAP=0.7579132725005842
[Epoch 198][Batch 99], LR: 1.00E-05, Speed: 181.084 samples/sec, ObjLoss=8.454, BoxCenterLoss=5.845, BoxScaleLoss=1.411, ClassLoss=2.044
[Epoch 198][Batch 199], LR: 1.00E-05, Speed: 162.341 samples/sec, ObjLoss=8.448, BoxCenterLoss=5.845, BoxScaleLoss=1.410, ClassLoss=2.042
[Epoch 198] Training cost: 134.261, ObjLoss=8.444, BoxCenterLoss=5.845, BoxScaleLoss=1.409, ClassLoss=2.041
[Epoch 198] Validation:
aeroplane=0.7968125071999386
bicycle=0.8409437997662674
bird=0.7361481157419639
boat=0.6866043937585697
bottle=0.5405988917036973
bus=0.8504052548596062
car=0.8585559014225527
cat=0.8538019203652268
chair=0.5691852472615687
cow=0.8054564190005953
diningtable=0.689560787247892
dog=0.8172615711872309
horse=0.8479686762725147
motorbike=0.8399414826815791
person=0.7959391954212539
pottedplant=0.4624052446764976
sheep=0.7922864859104523
sofa=0.72629209806052
train=0.8239568124859645
tvmonitor=0.789327722509451
mAP=0.7561726263766672
[Epoch 199][Batch 99], LR: 1.00E-05, Speed: 149.036 samples/sec, ObjLoss=8.439, BoxCenterLoss=5.844, BoxScaleLoss=1.409, ClassLoss=2.038
[Epoch 199][Batch 199], LR: 1.00E-05, Speed: 180.581 samples/sec, ObjLoss=8.433, BoxCenterLoss=5.844, BoxScaleLoss=1.408, ClassLoss=2.036
[Epoch 199] Training cost: 132.152, ObjLoss=8.429, BoxCenterLoss=5.844, BoxScaleLoss=1.407, ClassLoss=2.035
[Epoch 199] Validation:
aeroplane=0.8384296046884944
bicycle=0.8439197548843335
bird=0.7338520808016004
boat=0.6891810189114198
bottle=0.5399773737465493
bus=0.8485777508730159
car=0.8639168368276817
cat=0.8618433891833622
chair=0.5732414128355625
cow=0.8073411333461096
diningtable=0.6951387495044566
dog=0.8195460399692324
horse=0.8491527328552199
motorbike=0.8348643080718898
person=0.7966376113652766
pottedplant=0.49417651959549475
sheep=0.7942628054140471
sofa=0.7346514491742229
train=0.785082547088116
tvmonitor=0.7549176397599353
mAP=0.757935537944801