Namespace(batch_size=64, data_shape=416, dataset='voc', epochs=200, gpus='0,1,2,3,4,5,6,7', log_interval=100, lr=0.001, lr_decay=0.1, lr_decay_epoch='160,180', momentum=0.9, network='darknet53', num_samples=16551, num_workers=16, resume='', save_interval=10, save_prefix='yolo3_darknet53_voc', seed=233, start_epoch=0, syncbn=True, val_interval=1, wd=0.0005) Start training from [Epoch 0] [Epoch 0][Batch 99], LR: 1.28E-04, Speed: 108.251 samples/sec, ObjLoss=415.712, BoxCenterLoss=7.332, BoxScaleLoss=6.285, ClassLoss=30.865 [Epoch 0][Batch 199], LR: 2.57E-04, Speed: 92.461 samples/sec, ObjLoss=215.614, BoxCenterLoss=7.009, BoxScaleLoss=4.974, ClassLoss=21.507 [Epoch 0] Training cost: 602.907, ObjLoss=170.200, BoxCenterLoss=6.865, BoxScaleLoss=4.497, ClassLoss=18.482 [Epoch 0] Validation: aeroplane=0.09142078454247118 bicycle=0.031242879927090454 bird=0.020029736618521664 boat=0.0030404378230465185 bottle=0.0010684711957973465 bus=0.16310689903853448 car=0.20333317879916382 cat=0.28327692434276597 chair=0.012479707837919919 cow=0.09090909090909091 diningtable=0.000999000999000999 dog=0.12849354751477177 horse=0.09841610139595294 motorbike=0.1793240312136389 person=0.17198761570426113 pottedplant=0.0 sheep=0.011363636363636364 sofa=0.0035304501323918797 train=0.02280328367284889 tvmonitor=0.0101010101010101 mAP=0.07634633940659577 [Epoch 1][Batch 99], LR: 4.61E-04, Speed: 144.128 samples/sec, ObjLoss=126.239, BoxCenterLoss=6.748, BoxScaleLoss=4.023, ClassLoss=15.446 [Epoch 1][Batch 199], LR: 5.90E-04, Speed: 133.964 samples/sec, ObjLoss=101.364, BoxCenterLoss=6.678, BoxScaleLoss=3.732, ClassLoss=13.520 [Epoch 1] Training cost: 578.970, ObjLoss=91.359, BoxCenterLoss=6.646, BoxScaleLoss=3.630, ClassLoss=12.704 [Epoch 1] Validation: aeroplane=0.35916979799087684 bicycle=0.30140317086069374 bird=0.26280295083169164 boat=0.034823371839946424 bottle=0.0101010101010101 bus=0.3025169840121218 car=0.3469847198737699 cat=0.5331967768698003 chair=0.19656870042348065 cow=0.15488439451146463 diningtable=0.10291687874404588 dog=0.45760247460353026 horse=0.2514574034693837 motorbike=0.23427890778583413 person=0.3978972155800739 pottedplant=0.0013468013468013469 sheep=0.19587206793759945 sofa=0.2953717726359535 train=0.36035975864726394 tvmonitor=0.13576933978221317 mAP=0.2467662248923778 [Epoch 2][Batch 99], LR: 7.95E-04, Speed: 118.246 samples/sec, ObjLoss=78.349, BoxCenterLoss=6.572, BoxScaleLoss=3.438, ClassLoss=11.527 [Epoch 2][Batch 199], LR: 9.24E-04, Speed: 96.406 samples/sec, ObjLoss=69.002, BoxCenterLoss=6.540, BoxScaleLoss=3.310, ClassLoss=10.635 [Epoch 2] Training cost: 524.740, ObjLoss=64.660, BoxCenterLoss=6.509, BoxScaleLoss=3.243, ClassLoss=10.189 [Epoch 2] Validation: aeroplane=0.27480956930589073 bicycle=0.4942914389728752 bird=0.49856901574875745 boat=0.131189811207449 bottle=0.2188016479787776 bus=0.44946240433445556 car=0.4764395431283009 cat=0.6757637833038802 chair=0.31756110517934294 cow=0.2965343395474142 diningtable=0.10796953950638964 dog=0.6006474351285063 horse=0.4533827780560707 motorbike=0.4593566389762098 person=0.467025634972983 pottedplant=0.11311988677054233 sheep=0.3705024709299435 sofa=0.30930646540208295 train=0.4463773875815084 tvmonitor=0.4052821105697293 mAP=0.37831965033005543 [Epoch 3][Batch 99], LR: 1.00E-03, Speed: 136.223 samples/sec, ObjLoss=58.488, BoxCenterLoss=6.471, BoxScaleLoss=3.157, ClassLoss=9.540 [Epoch 3][Batch 199], LR: 1.00E-03, Speed: 135.021 samples/sec, ObjLoss=53.577, BoxCenterLoss=6.435, BoxScaleLoss=3.071, ClassLoss=8.997 [Epoch 3] Training cost: 554.489, ObjLoss=51.127, BoxCenterLoss=6.421, BoxScaleLoss=3.023, ClassLoss=8.728 [Epoch 3] Validation: aeroplane=0.4991369904516855 bicycle=0.2892940941065525 bird=0.5203224324309109 boat=0.3444282307487943 bottle=0.24480948998221247 bus=0.5888376123533027 car=0.6527324676883571 cat=0.5971649890580423 chair=0.3317712932415354 cow=0.4222296958174808 diningtable=0.496069512849128 dog=0.5517219391369931 horse=0.6089511758619804 motorbike=0.5658292231300949 person=0.588986663260526 pottedplant=0.17095773032537412 sheep=0.412645398221094 sofa=0.47990285628288054 train=0.5275735390055847 tvmonitor=0.47284961935334263 mAP=0.4683107476652936 [Epoch 4][Batch 99], LR: 1.00E-03, Speed: 97.829 samples/sec, ObjLoss=47.528, BoxCenterLoss=6.396, BoxScaleLoss=2.965, ClassLoss=8.307 [Epoch 4][Batch 199], LR: 1.00E-03, Speed: 114.096 samples/sec, ObjLoss=44.481, BoxCenterLoss=6.373, BoxScaleLoss=2.902, ClassLoss=7.949 [Epoch 4] Training cost: 556.214, ObjLoss=42.918, BoxCenterLoss=6.356, BoxScaleLoss=2.869, ClassLoss=7.763 [Epoch 4] Validation: aeroplane=0.5285012849351728 bicycle=0.5068015753976354 bird=0.2507096693086613 boat=0.3824110297536758 bottle=0.3310400958694977 bus=0.5819905127691213 car=0.6877575984895309 cat=0.6512165906833217 chair=0.399182891999877 cow=0.38422602227712677 diningtable=0.4405163598070019 dog=0.6157589349901268 horse=0.4293118220823412 motorbike=0.6188320140623 person=0.6160599902339418 pottedplant=0.2697851711765433 sheep=0.4148855095437464 sofa=0.5605192664948699 train=0.570848855951418 tvmonitor=0.5764941159157355 mAP=0.4908424655870823 [Epoch 5][Batch 99], LR: 1.00E-03, Speed: 109.954 samples/sec, ObjLoss=40.557, BoxCenterLoss=6.354, BoxScaleLoss=2.824, ClassLoss=7.472 [Epoch 5][Batch 199], LR: 1.00E-03, Speed: 123.973 samples/sec, ObjLoss=38.505, BoxCenterLoss=6.336, BoxScaleLoss=2.779, ClassLoss=7.210 [Epoch 5] Training cost: 565.082, ObjLoss=37.407, BoxCenterLoss=6.322, BoxScaleLoss=2.749, ClassLoss=7.064 [Epoch 5] Validation: aeroplane=0.5690240433859783 bicycle=0.6747666851168954 bird=0.6328414714835918 boat=0.35351256261372865 bottle=0.33369260237873305 bus=0.6541946613612418 car=0.6571599654983042 cat=0.7934590523626401 chair=0.41681452865316515 cow=0.4688234832897675 diningtable=0.4367800547545073 dog=0.6526573917522217 horse=0.5736180124747788 motorbike=0.5111947272780135 person=0.5854407600049425 pottedplant=0.2784823961229806 sheep=0.4791988609671633 sofa=0.4733514567912655 train=0.6693200339322524 tvmonitor=0.465675681600269 mAP=0.534000421591122 [Epoch 6][Batch 99], LR: 1.00E-03, Speed: 107.754 samples/sec, ObjLoss=35.726, BoxCenterLoss=6.312, BoxScaleLoss=2.705, ClassLoss=6.836 [Epoch 6][Batch 199], LR: 1.00E-03, Speed: 114.526 samples/sec, ObjLoss=34.219, BoxCenterLoss=6.296, BoxScaleLoss=2.663, ClassLoss=6.631 [Epoch 6] Training cost: 558.836, ObjLoss=33.400, BoxCenterLoss=6.281, BoxScaleLoss=2.638, ClassLoss=6.515 [Epoch 6] Validation: aeroplane=0.576606555490857 bicycle=0.7121655322605855 bird=0.6016290333087627 boat=0.4464311400678724 bottle=0.4084131149200173 bus=0.7277097302323533 car=0.754073354584781 cat=0.8273474137441027 chair=0.4581357058429673 cow=0.559130266811303 diningtable=0.4972454922132747 dog=0.7956859116090733 horse=0.6231953075025777 motorbike=0.6690038711249584 person=0.6622867845913918 pottedplant=0.3689862298853049 sheep=0.47059291917057755 sofa=0.6174209629166676 train=0.700701435202073 tvmonitor=0.6214504492803942 mAP=0.6049105605379947 [Epoch 7][Batch 99], LR: 1.00E-03, Speed: 118.701 samples/sec, ObjLoss=32.139, BoxCenterLoss=6.262, BoxScaleLoss=2.599, ClassLoss=6.326 [Epoch 7][Batch 199], LR: 1.00E-03, Speed: 107.539 samples/sec, ObjLoss=30.991, BoxCenterLoss=6.244, BoxScaleLoss=2.566, ClassLoss=6.163 [Epoch 7] Training cost: 581.755, ObjLoss=30.369, BoxCenterLoss=6.235, BoxScaleLoss=2.548, ClassLoss=6.073 [Epoch 7] Validation: aeroplane=0.5729703610001701 bicycle=0.707523589889745 bird=0.5652972028476542 boat=0.3452884246837388 bottle=0.3717201367011646 bus=0.7093726789449604 car=0.694823078388202 cat=0.8106037813166155 chair=0.4694016287879033 cow=0.6081430630392195 diningtable=0.21925278460092854 dog=0.7526464554124926 horse=0.6519109897717111 motorbike=0.7031106641288902 person=0.6068782162142179 pottedplant=0.2938829947574633 sheep=0.5594966452556513 sofa=0.48329590501157943 train=0.654578470384513 tvmonitor=0.5669212499837376 mAP=0.5673559160560278 [Epoch 8][Batch 99], LR: 1.00E-03, Speed: 95.674 samples/sec, ObjLoss=29.395, BoxCenterLoss=6.218, BoxScaleLoss=2.517, ClassLoss=5.925 [Epoch 8][Batch 199], LR: 1.00E-03, Speed: 104.592 samples/sec, ObjLoss=28.487, BoxCenterLoss=6.201, BoxScaleLoss=2.488, ClassLoss=5.789 [Epoch 8] Training cost: 560.360, ObjLoss=27.988, BoxCenterLoss=6.191, BoxScaleLoss=2.470, ClassLoss=5.711 [Epoch 8] Validation: aeroplane=0.6612803409616332 bicycle=0.6301550006413077 bird=0.6553197639815945 boat=0.33814931432861955 bottle=0.4001334339784526 bus=0.6425793591801497 car=0.716546819476039 cat=0.760387358102594 chair=0.4512774124550625 cow=0.4839670960191529 diningtable=0.5691868751842086 dog=0.7325555937179087 horse=0.4045377567648552 motorbike=0.7099657274819622 person=0.6114417011684047 pottedplant=0.32014792642668266 sheep=0.6416468356705575 sofa=0.6172978612365537 train=0.7185977258583212 tvmonitor=0.6283582667715254 mAP=0.5846766084702792 [Epoch 9][Batch 99], LR: 1.00E-03, Speed: 87.049 samples/sec, ObjLoss=27.193, BoxCenterLoss=6.179, BoxScaleLoss=2.444, ClassLoss=5.590 [Epoch 9][Batch 199], LR: 1.00E-03, Speed: 123.715 samples/sec, ObjLoss=26.455, BoxCenterLoss=6.164, BoxScaleLoss=2.421, ClassLoss=5.475 [Epoch 9] Training cost: 558.740, ObjLoss=26.048, BoxCenterLoss=6.158, BoxScaleLoss=2.407, ClassLoss=5.411 [Epoch 9] Validation: aeroplane=0.7029994996827899 bicycle=0.6432152607858778 bird=0.5631762827262013 boat=0.40137688600632676 bottle=0.4750560324320986 bus=0.6688219231531772 car=0.7274773619350992 cat=0.8416007646248609 chair=0.4143579856608164 cow=0.5699061363646173 diningtable=0.6137786314428019 dog=0.7827654903146756 horse=0.4878867549264342 motorbike=0.6787756890197058 person=0.6389535415894725 pottedplant=0.29178339615767956 sheep=0.5973595162475401 sofa=0.5711478616193298 train=0.6942083210218685 tvmonitor=0.5650894782647298 mAP=0.5964868406988051 [Epoch 10][Batch 99], LR: 1.00E-03, Speed: 86.084 samples/sec, ObjLoss=25.389, BoxCenterLoss=6.146, BoxScaleLoss=2.384, ClassLoss=5.309 [Epoch 10][Batch 199], LR: 1.00E-03, Speed: 101.894 samples/sec, ObjLoss=24.792, BoxCenterLoss=6.135, BoxScaleLoss=2.367, ClassLoss=5.216 [Epoch 10] Training cost: 526.246, ObjLoss=24.456, BoxCenterLoss=6.128, BoxScaleLoss=2.355, ClassLoss=5.160 [Epoch 10] Validation: aeroplane=0.5835641176903783 bicycle=0.6842910217953151 bird=0.6963983523682589 boat=0.35902172469013777 bottle=0.465894413590005 bus=0.6992421960032494 car=0.7234237663476654 cat=0.7608148556250072 chair=0.46904076694652674 cow=0.6706468871397845 diningtable=0.5479870991714448 dog=0.7330499028384472 horse=0.7150506116162928 motorbike=0.6793039163224069 person=0.677812316839097 pottedplant=0.34404174883377053 sheep=0.5966434911212991 sofa=0.6687461166567483 train=0.5048222042035413 tvmonitor=0.6332111161390824 mAP=0.6106503312969229 [Epoch 11][Batch 99], LR: 1.00E-03, Speed: 108.951 samples/sec, ObjLoss=23.911, BoxCenterLoss=6.121, BoxScaleLoss=2.338, ClassLoss=5.072 [Epoch 11][Batch 199], LR: 1.00E-03, Speed: 123.659 samples/sec, ObjLoss=23.392, BoxCenterLoss=6.112, BoxScaleLoss=2.320, ClassLoss=4.988 [Epoch 11] Training cost: 535.410, ObjLoss=23.114, BoxCenterLoss=6.110, BoxScaleLoss=2.310, ClassLoss=4.939 [Epoch 11] Validation: aeroplane=0.7202334713779012 bicycle=0.7556017272111141 bird=0.6768790360978522 boat=0.4490183204308662 bottle=0.5092546492960758 bus=0.7775479831479865 car=0.7946197398321453 cat=0.8421619572459914 chair=0.49474244596864797 cow=0.6423977755807835 diningtable=0.630442634667147 dog=0.8165896798240019 horse=0.7656873005280398 motorbike=0.7879815724506886 person=0.7138987556238154 pottedplant=0.29772657065297814 sheep=0.6942506355469333 sofa=0.6722429255944152 train=0.7648360522228642 tvmonitor=0.6854974146440568 mAP=0.6745805323972152 [Epoch 12][Batch 99], LR: 1.00E-03, Speed: 115.337 samples/sec, ObjLoss=22.646, BoxCenterLoss=6.101, BoxScaleLoss=2.292, ClassLoss=4.862 [Epoch 12][Batch 199], LR: 1.00E-03, Speed: 117.221 samples/sec, ObjLoss=22.217, BoxCenterLoss=6.097, BoxScaleLoss=2.275, ClassLoss=4.788 [Epoch 12] Training cost: 561.995, ObjLoss=21.968, BoxCenterLoss=6.090, BoxScaleLoss=2.265, ClassLoss=4.744 [Epoch 12] Validation: aeroplane=0.6403989844399605 bicycle=0.746904196512417 bird=0.5422190963785383 boat=0.4701883545027076 bottle=0.49179419962079113 bus=0.7598936461950028 car=0.8051344988481001 cat=0.8547732274318354 chair=0.49086052587794365 cow=0.677361950836002 diningtable=0.6548050791050676 dog=0.7770995710416448 horse=0.7126125525209228 motorbike=0.7463147425155494 person=0.71532330384475 pottedplant=0.36832826996963036 sheep=0.6389193694738878 sofa=0.6609546834812847 train=0.7753218743525114 tvmonitor=0.6974165800885775 mAP=0.6613312353518562 [Epoch 13][Batch 99], LR: 1.00E-03, Speed: 125.770 samples/sec, ObjLoss=21.573, BoxCenterLoss=6.081, BoxScaleLoss=2.251, ClassLoss=4.674 [Epoch 13][Batch 199], LR: 1.00E-03, Speed: 127.363 samples/sec, ObjLoss=21.193, BoxCenterLoss=6.073, BoxScaleLoss=2.235, ClassLoss=4.605 [Epoch 13] Training cost: 559.145, ObjLoss=20.974, BoxCenterLoss=6.067, BoxScaleLoss=2.226, ClassLoss=4.569 [Epoch 13] Validation: aeroplane=0.699542450872226 bicycle=0.7203554508294414 bird=0.6864457028607972 boat=0.5283612004710085 bottle=0.4729649095419244 bus=0.7837979062994582 car=0.740256274392608 cat=0.8465619590955157 chair=0.5208852871735711 cow=0.7359836587575941 diningtable=0.6443246092244089 dog=0.7993028937761854 horse=0.7987953991432691 motorbike=0.691908433104759 person=0.7079274826646237 pottedplant=0.40442743081833377 sheep=0.6138465191829487 sofa=0.49497712489325846 train=0.7664049735273307 tvmonitor=0.6988346362110256 mAP=0.6677952151420143 [Epoch 14][Batch 99], LR: 1.00E-03, Speed: 106.147 samples/sec, ObjLoss=20.631, BoxCenterLoss=6.062, BoxScaleLoss=2.215, ClassLoss=4.510 [Epoch 14][Batch 199], LR: 1.00E-03, Speed: 110.013 samples/sec, ObjLoss=20.306, BoxCenterLoss=6.060, BoxScaleLoss=2.203, ClassLoss=4.458 [Epoch 14] Training cost: 570.116, ObjLoss=20.123, BoxCenterLoss=6.057, BoxScaleLoss=2.197, ClassLoss=4.428 [Epoch 14] Validation: aeroplane=0.7407176197427257 bicycle=0.7548558670349721 bird=0.7209377027749089 boat=0.5214806141514098 bottle=0.44835314816112926 bus=0.772727502226903 car=0.7933573262108637 cat=0.8456181308231714 chair=0.4864993670631815 cow=0.6842730653631354 diningtable=0.6307776799483753 dog=0.806029046975384 horse=0.7299304491415082 motorbike=0.7344807856700124 person=0.683481732063265 pottedplant=0.39214349995027453 sheep=0.6172740277283566 sofa=0.7143823193865236 train=0.7191830189014579 tvmonitor=0.7016491523924534 mAP=0.6749076027855005 [Epoch 15][Batch 99], LR: 1.00E-03, Speed: 100.929 samples/sec, ObjLoss=19.821, BoxCenterLoss=6.053, BoxScaleLoss=2.184, ClassLoss=4.371 [Epoch 15][Batch 199], LR: 1.00E-03, Speed: 113.882 samples/sec, ObjLoss=19.523, BoxCenterLoss=6.044, BoxScaleLoss=2.172, ClassLoss=4.320 [Epoch 15] Training cost: 571.361, ObjLoss=19.360, BoxCenterLoss=6.042, BoxScaleLoss=2.166, ClassLoss=4.291 [Epoch 15] Validation: aeroplane=0.7168723448782321 bicycle=0.8008025540444996 bird=0.7331346729774214 boat=0.5473445535684442 bottle=0.4719134798862231 bus=0.7935967015022082 car=0.8152488940979844 cat=0.8675518782357777 chair=0.48354479475746165 cow=0.7619347771983461 diningtable=0.665810616918486 dog=0.8046431627759143 horse=0.8116301601795316 motorbike=0.771946855093385 person=0.7178657682500706 pottedplant=0.43167046744097237 sheep=0.7060276158996683 sofa=0.7130144772213048 train=0.7716904542058538 tvmonitor=0.6830638465320809 mAP=0.7034654037831933 [Epoch 16][Batch 99], LR: 1.00E-03, Speed: 125.336 samples/sec, ObjLoss=19.085, BoxCenterLoss=6.036, BoxScaleLoss=2.154, ClassLoss=4.242 [Epoch 16][Batch 199], LR: 1.00E-03, Speed: 90.044 samples/sec, ObjLoss=18.825, BoxCenterLoss=6.033, BoxScaleLoss=2.143, ClassLoss=4.195 [Epoch 16] Training cost: 577.513, ObjLoss=18.683, BoxCenterLoss=6.034, BoxScaleLoss=2.137, ClassLoss=4.168 [Epoch 16] Validation: aeroplane=0.7181034130292374 bicycle=0.7904105217568965 bird=0.6860491122247255 boat=0.5517298704869578 bottle=0.5108865022101083 bus=0.7634184877972787 car=0.8074319846749726 cat=0.813943587398763 chair=0.5312831372583673 cow=0.5620535852037375 diningtable=0.6723556649400998 dog=0.8112738681663094 horse=0.7955938058480561 motorbike=0.7834277646761088 person=0.7264815928524075 pottedplant=0.3938189942471797 sheep=0.7057526058399963 sofa=0.6981255173608294 train=0.7280167774790812 tvmonitor=0.6630676504980526 mAP=0.6856612221974583 [Epoch 17][Batch 99], LR: 1.00E-03, Speed: 104.779 samples/sec, ObjLoss=18.437, BoxCenterLoss=6.029, BoxScaleLoss=2.125, ClassLoss=4.121 [Epoch 17][Batch 199], LR: 1.00E-03, Speed: 115.022 samples/sec, ObjLoss=18.201, BoxCenterLoss=6.024, BoxScaleLoss=2.115, ClassLoss=4.078 [Epoch 17] Training cost: 548.830, ObjLoss=18.067, BoxCenterLoss=6.022, BoxScaleLoss=2.109, ClassLoss=4.055 [Epoch 17] Validation: aeroplane=0.6961666482937311 bicycle=0.7859094503340458 bird=0.7159527250913842 boat=0.4659349668648504 bottle=0.5409386299116007 bus=0.7987246944255587 car=0.6991711207415081 cat=0.8650934502637929 chair=0.5452587707534619 cow=0.6877360300995252 diningtable=0.6951143682380578 dog=0.7880206867753448 horse=0.7915634402966215 motorbike=0.7694711529205659 person=0.7240182346143537 pottedplant=0.3993059525722201 sheep=0.6932685813976249 sofa=0.6560068644291641 train=0.8035173539389127 tvmonitor=0.720698566522984 mAP=0.6920935844242655 [Epoch 18][Batch 99], LR: 1.00E-03, Speed: 111.349 samples/sec, ObjLoss=17.845, BoxCenterLoss=6.019, BoxScaleLoss=2.100, ClassLoss=4.015 [Epoch 18][Batch 199], LR: 1.00E-03, Speed: 124.997 samples/sec, ObjLoss=17.637, BoxCenterLoss=6.015, BoxScaleLoss=2.092, ClassLoss=3.975 [Epoch 18] Training cost: 575.008, ObjLoss=17.518, BoxCenterLoss=6.013, BoxScaleLoss=2.087, ClassLoss=3.953 [Epoch 18] Validation: aeroplane=0.7110887477352976 bicycle=0.7425047375029065 bird=0.6506334225403535 boat=0.5308925448652252 bottle=0.510396820406577 bus=0.7803673781853496 car=0.8156447184537394 cat=0.8267296541627868 chair=0.5217647601145966 cow=0.7175605173878592 diningtable=0.6382616526655057 dog=0.8182371353350082 horse=0.808659503954215 motorbike=0.7492018619968849 person=0.7517135524187486 pottedplant=0.2744792527983849 sheep=0.6650361793737498 sofa=0.7021100127739858 train=0.7543605765029563 tvmonitor=0.6934946050165055 mAP=0.6831568817095318 [Epoch 19][Batch 99], LR: 1.00E-03, Speed: 95.312 samples/sec, ObjLoss=17.322, BoxCenterLoss=6.009, BoxScaleLoss=2.078, ClassLoss=3.911 [Epoch 19][Batch 199], LR: 1.00E-03, Speed: 109.009 samples/sec, ObjLoss=17.130, BoxCenterLoss=6.005, BoxScaleLoss=2.069, ClassLoss=3.874 [Epoch 19] Training cost: 579.114, ObjLoss=17.020, BoxCenterLoss=6.002, BoxScaleLoss=2.064, ClassLoss=3.853 [Epoch 19] Validation: aeroplane=0.7155257861033699 bicycle=0.7648171479308412 bird=0.6199496262631898 boat=0.4988347905801547 bottle=0.5524058491395178 bus=0.7546079913879195 car=0.8192565990589326 cat=0.8568873624745776 chair=0.5376624106951037 cow=0.6665014701918022 diningtable=0.6609033133372818 dog=0.7810675268728229 horse=0.6668083194928618 motorbike=0.7660586698482807 person=0.7342542106971546 pottedplant=0.44448396589921113 sheep=0.7311873007630416 sofa=0.6632199201563124 train=0.7739541991153072 tvmonitor=0.752099509427883 mAP=0.6880242984717782 [Epoch 20][Batch 99], LR: 1.00E-03, Speed: 97.505 samples/sec, ObjLoss=16.844, BoxCenterLoss=6.000, BoxScaleLoss=2.056, ClassLoss=3.818 [Epoch 20][Batch 199], LR: 1.00E-03, Speed: 128.106 samples/sec, ObjLoss=16.668, BoxCenterLoss=5.996, BoxScaleLoss=2.048, ClassLoss=3.785 [Epoch 20] Training cost: 559.700, ObjLoss=16.572, BoxCenterLoss=5.995, BoxScaleLoss=2.044, ClassLoss=3.766 [Epoch 20] Validation: aeroplane=0.722446570984831 bicycle=0.7316511179151932 bird=0.7001896635532392 boat=0.5435527397624199 bottle=0.54563055668022 bus=0.8228053056635113 car=0.8111218827766979 cat=0.8518510791977428 chair=0.5558282814377021 cow=0.693186687878407 diningtable=0.6977176737446285 dog=0.7989261866958409 horse=0.7436833207755958 motorbike=0.7971375204384137 person=0.6466013246287087 pottedplant=0.3425361321203875 sheep=0.7196774583009912 sofa=0.7084912376062965 train=0.7638631568938282 tvmonitor=0.7077554452819472 mAP=0.6952326671168301 [Epoch 21][Batch 99], LR: 1.00E-03, Speed: 103.856 samples/sec, ObjLoss=16.405, BoxCenterLoss=5.991, BoxScaleLoss=2.037, ClassLoss=3.733 [Epoch 21][Batch 199], LR: 1.00E-03, Speed: 110.527 samples/sec, ObjLoss=16.243, BoxCenterLoss=5.987, BoxScaleLoss=2.029, ClassLoss=3.699 [Epoch 21] Training cost: 584.986, ObjLoss=16.150, BoxCenterLoss=5.985, BoxScaleLoss=2.025, ClassLoss=3.680 [Epoch 21] Validation: aeroplane=0.7587050953277809 bicycle=0.7651033165194672 bird=0.6868230865256703 boat=0.5727126117463174 bottle=0.4519255137253241 bus=0.7906657264104171 car=0.8148846749824028 cat=0.8657865101299791 chair=0.549164876672386 cow=0.7530642586069036 diningtable=0.6949696112224206 dog=0.8060541714652412 horse=0.8320719144837379 motorbike=0.7609628327912663 person=0.7387884706040547 pottedplant=0.3121984931518408 sheep=0.7536771300831423 sofa=0.7333343565560227 train=0.8060601527441583 tvmonitor=0.7204502235220284 mAP=0.7083701513635281 [Epoch 22][Batch 99], LR: 1.00E-03, Speed: 115.273 samples/sec, ObjLoss=15.997, BoxCenterLoss=5.981, BoxScaleLoss=2.018, ClassLoss=3.648 [Epoch 22][Batch 199], LR: 1.00E-03, Speed: 110.386 samples/sec, ObjLoss=15.851, BoxCenterLoss=5.979, BoxScaleLoss=2.011, ClassLoss=3.618 [Epoch 22] Training cost: 567.004, ObjLoss=15.767, BoxCenterLoss=5.976, BoxScaleLoss=2.006, ClassLoss=3.601 [Epoch 22] Validation: aeroplane=0.7440601239413098 bicycle=0.7453143249843703 bird=0.7155522732703408 boat=0.5693992729190082 bottle=0.5366099297996084 bus=0.8246118365044258 car=0.8250404073598757 cat=0.8764289819683313 chair=0.525864359458305 cow=0.6042565854305116 diningtable=0.6563407207154265 dog=0.8340435517823879 horse=0.8319796216828235 motorbike=0.7823477095405275 person=0.7286306452399279 pottedplant=0.41582033821818026 sheep=0.643309623624546 sofa=0.6851631059831862 train=0.8187327067141472 tvmonitor=0.7492842375364096 mAP=0.7056395178336825 [Epoch 23][Batch 99], LR: 1.00E-03, Speed: 101.186 samples/sec, ObjLoss=15.627, BoxCenterLoss=5.974, BoxScaleLoss=2.000, ClassLoss=3.573 [Epoch 23][Batch 199], LR: 1.00E-03, Speed: 110.887 samples/sec, ObjLoss=15.487, BoxCenterLoss=5.970, BoxScaleLoss=1.992, ClassLoss=3.543 [Epoch 23] Training cost: 558.542, ObjLoss=15.412, BoxCenterLoss=5.968, BoxScaleLoss=1.988, ClassLoss=3.528 [Epoch 23] Validation: aeroplane=0.7586270765506303 bicycle=0.7705388587595585 bird=0.7256239234830147 boat=0.5575294725158317 bottle=0.5509398978477432 bus=0.7098688857995832 car=0.8400291414667355 cat=0.8406721296692246 chair=0.4802959721116197 cow=0.7206803368538173 diningtable=0.6884816122793382 dog=0.8272325333743626 horse=0.8434752342734361 motorbike=0.7698991359026464 person=0.7530088125336107 pottedplant=0.4084705131515587 sheep=0.7225670555207322 sofa=0.7232463846964463 train=0.7670272513305532 tvmonitor=0.7208091243566553 mAP=0.7089511676238549 [Epoch 24][Batch 99], LR: 1.00E-03, Speed: 115.527 samples/sec, ObjLoss=15.282, BoxCenterLoss=5.967, BoxScaleLoss=1.982, ClassLoss=3.501 [Epoch 24][Batch 199], LR: 1.00E-03, Speed: 85.554 samples/sec, ObjLoss=15.156, BoxCenterLoss=5.964, BoxScaleLoss=1.976, ClassLoss=3.476 [Epoch 24] Training cost: 583.522, ObjLoss=15.086, BoxCenterLoss=5.962, BoxScaleLoss=1.972, ClassLoss=3.462 [Epoch 24] Validation: aeroplane=0.774627868712417 bicycle=0.7718629000666604 bird=0.7613333893265283 boat=0.6103622443276521 bottle=0.5702499305360923 bus=0.817953092481532 car=0.8080938360023866 cat=0.8700789373741128 chair=0.5685223770555695 cow=0.8074202811641836 diningtable=0.656700755190432 dog=0.8405772617714367 horse=0.8651387086826636 motorbike=0.8228885467411442 person=0.7732789908699893 pottedplant=0.4626683341438267 sheep=0.7399352706866607 sofa=0.7012119028500013 train=0.7832619197216987 tvmonitor=0.6296591261281331 mAP=0.7317912836916561 [Epoch 25][Batch 99], LR: 1.00E-03, Speed: 96.905 samples/sec, ObjLoss=14.966, BoxCenterLoss=5.960, BoxScaleLoss=1.966, ClassLoss=3.436 [Epoch 25][Batch 199], LR: 1.00E-03, Speed: 91.246 samples/sec, ObjLoss=14.849, BoxCenterLoss=5.958, BoxScaleLoss=1.961, ClassLoss=3.412 [Epoch 25] Training cost: 560.145, ObjLoss=14.784, BoxCenterLoss=5.957, BoxScaleLoss=1.957, ClassLoss=3.399 [Epoch 25] Validation: aeroplane=0.7704168016931319 bicycle=0.7644611891336702 bird=0.7520298147479861 boat=0.5645612073822704 bottle=0.5582403894778851 bus=0.8175653893688967 car=0.8342247583118502 cat=0.8421914991171304 chair=0.5699567303666975 cow=0.8000954555121882 diningtable=0.6711788447501522 dog=0.8352918021985609 horse=0.8136661556253525 motorbike=0.8088492271491927 person=0.7546821295854681 pottedplant=0.5149504043521866 sheep=0.7670850092868154 sofa=0.7138697774269915 train=0.8122547073968853 tvmonitor=0.6990733402493315 mAP=0.7332322316566321 [Epoch 26][Batch 99], LR: 1.00E-03, Speed: 117.744 samples/sec, ObjLoss=14.668, BoxCenterLoss=5.953, BoxScaleLoss=1.951, ClassLoss=3.375 [Epoch 26][Batch 199], LR: 1.00E-03, Speed: 120.182 samples/sec, ObjLoss=14.557, BoxCenterLoss=5.949, BoxScaleLoss=1.946, ClassLoss=3.355 [Epoch 26] Training cost: 552.653, ObjLoss=14.492, BoxCenterLoss=5.947, BoxScaleLoss=1.942, ClassLoss=3.343 [Epoch 26] Validation: aeroplane=0.7961675175369513 bicycle=0.8226296726187522 bird=0.7271394895535331 boat=0.5635783495298364 bottle=0.6120582822593668 bus=0.8247347868336952 car=0.8292417161713745 cat=0.8821566354558821 chair=0.5836249251895248 cow=0.7843157084040622 diningtable=0.6871492621252197 dog=0.8464651797282712 horse=0.8584358191108816 motorbike=0.8211652521051029 person=0.7930507282719216 pottedplant=0.4666201540829016 sheep=0.7523893847257057 sofa=0.6960298064639429 train=0.852602509996507 tvmonitor=0.7502816595204219 mAP=0.7474918419841927 [Epoch 27][Batch 99], LR: 1.00E-03, Speed: 114.326 samples/sec, ObjLoss=14.386, BoxCenterLoss=5.944, BoxScaleLoss=1.936, ClassLoss=3.319 [Epoch 27][Batch 199], LR: 1.00E-03, Speed: 108.419 samples/sec, ObjLoss=14.282, BoxCenterLoss=5.941, BoxScaleLoss=1.930, ClassLoss=3.296 [Epoch 27] Training cost: 578.470, ObjLoss=14.223, BoxCenterLoss=5.939, BoxScaleLoss=1.927, ClassLoss=3.283 [Epoch 27] Validation: aeroplane=0.771790456178128 bicycle=0.7464620949121306 bird=0.6407897272355159 boat=0.5684862350825454 bottle=0.4747755170469883 bus=0.8155230206425872 car=0.8253100847097531 cat=0.791502353223239 chair=0.5570804322404397 cow=0.8204921710941575 diningtable=0.7060095783191697 dog=0.7359624635200137 horse=0.8343801054320029 motorbike=0.7465577542729785 person=0.7322599619397578 pottedplant=0.42099874693737244 sheep=0.7087776087524175 sofa=0.6825742201025249 train=0.7635734004300063 tvmonitor=0.7721238565452428 mAP=0.7057714894308486 [Epoch 28][Batch 99], LR: 1.00E-03, Speed: 102.925 samples/sec, ObjLoss=14.125, BoxCenterLoss=5.938, BoxScaleLoss=1.922, ClassLoss=3.260 [Epoch 28][Batch 199], LR: 1.00E-03, Speed: 125.688 samples/sec, ObjLoss=14.028, BoxCenterLoss=5.935, BoxScaleLoss=1.917, ClassLoss=3.239 [Epoch 28] Training cost: 564.892, ObjLoss=13.972, BoxCenterLoss=5.932, BoxScaleLoss=1.913, ClassLoss=3.225 [Epoch 28] Validation: aeroplane=0.7586013364219983 bicycle=0.7802392656686122 bird=0.7684625837225085 boat=0.5952976764411646 bottle=0.5850779150042535 bus=0.8402478215434219 car=0.8361738203161302 cat=0.883251936066197 chair=0.5591908328645862 cow=0.7799612367483759 diningtable=0.6317542123985896 dog=0.826678654319404 horse=0.8458446578804065 motorbike=0.7825835299259428 person=0.7751760895091846 pottedplant=0.4814500798043954 sheep=0.766392570812615 sofa=0.6676284489773036 train=0.8202238864673355 tvmonitor=0.770975246395603 mAP=0.7377605900644014 [Epoch 29][Batch 99], LR: 1.00E-03, Speed: 114.956 samples/sec, ObjLoss=13.877, BoxCenterLoss=5.930, BoxScaleLoss=1.908, ClassLoss=3.203 [Epoch 29][Batch 199], LR: 1.00E-03, Speed: 120.741 samples/sec, ObjLoss=13.786, BoxCenterLoss=5.929, BoxScaleLoss=1.903, ClassLoss=3.183 [Epoch 29] Training cost: 565.456, ObjLoss=13.733, BoxCenterLoss=5.927, BoxScaleLoss=1.900, ClassLoss=3.172 [Epoch 29] Validation: aeroplane=0.7624775886254669 bicycle=0.8229381310625556 bird=0.7643493743532828 boat=0.5731760843691563 bottle=0.560769625805267 bus=0.7480454408300962 car=0.7970967497993736 cat=0.8699398943305269 chair=0.5494795151436189 cow=0.7627961315034334 diningtable=0.7106520841887942 dog=0.8331126631902415 horse=0.8348526025085904 motorbike=0.799751434868136 person=0.7697648026923216 pottedplant=0.4031079079977509 sheep=0.7539681513344768 sofa=0.7416621317011898 train=0.7697960651921186 tvmonitor=0.7280119859338177 mAP=0.7277874182715107 [Epoch 30][Batch 99], LR: 1.00E-03, Speed: 139.063 samples/sec, ObjLoss=13.645, BoxCenterLoss=5.924, BoxScaleLoss=1.896, ClassLoss=3.153 [Epoch 30][Batch 199], LR: 1.00E-03, Speed: 133.216 samples/sec, ObjLoss=13.557, BoxCenterLoss=5.920, BoxScaleLoss=1.892, ClassLoss=3.132 [Epoch 30] Training cost: 559.233, ObjLoss=13.506, BoxCenterLoss=5.917, BoxScaleLoss=1.888, ClassLoss=3.121 [Epoch 30] Validation: aeroplane=0.8234963804526767 bicycle=0.8309634486675903 bird=0.7981317237395584 boat=0.6398701104393598 bottle=0.61717303782769 bus=0.8309416002114207 car=0.8415062776576452 cat=0.8574309077753306 chair=0.5626669411088185 cow=0.8243848476951328 diningtable=0.6934863492969323 dog=0.8414715387257556 horse=0.8357164076125033 motorbike=0.8292549258569519 person=0.7956611460507128 pottedplant=0.48005505199878895 sheep=0.7611803958510029 sofa=0.7170858754591313 train=0.8138906187627062 tvmonitor=0.7411271419083392 mAP=0.7567747363549024 [Epoch 31][Batch 99], LR: 1.00E-03, Speed: 126.726 samples/sec, ObjLoss=13.423, BoxCenterLoss=5.915, BoxScaleLoss=1.883, ClassLoss=3.101 [Epoch 31][Batch 199], LR: 1.00E-03, Speed: 141.110 samples/sec, ObjLoss=13.344, BoxCenterLoss=5.912, BoxScaleLoss=1.879, ClassLoss=3.086 [Epoch 31] Training cost: 543.926, ObjLoss=13.297, BoxCenterLoss=5.910, BoxScaleLoss=1.877, ClassLoss=3.077 [Epoch 31] Validation: aeroplane=0.7584078401158171 bicycle=0.796059777162854 bird=0.7594340681405907 boat=0.6731286181104562 bottle=0.6123100479614146 bus=0.8097903755103908 car=0.8533116874036397 cat=0.8614325649414012 chair=0.5648455524423545 cow=0.825431597557341 diningtable=0.6976332240596037 dog=0.805591989988078 horse=0.8480671711280704 motorbike=0.8371070435638931 person=0.7584610526692799 pottedplant=0.49316119801444935 sheep=0.7562764437383771 sofa=0.7144446201471762 train=0.822594553512637 tvmonitor=0.7537643678801275 mAP=0.7500626897023975 [Epoch 32][Batch 99], LR: 1.00E-03, Speed: 131.852 samples/sec, ObjLoss=13.217, BoxCenterLoss=5.906, BoxScaleLoss=1.872, ClassLoss=3.059 [Epoch 32][Batch 199], LR: 1.00E-03, Speed: 118.937 samples/sec, ObjLoss=13.140, BoxCenterLoss=5.903, BoxScaleLoss=1.867, ClassLoss=3.042 [Epoch 32] Training cost: 570.159, ObjLoss=13.099, BoxCenterLoss=5.903, BoxScaleLoss=1.865, ClassLoss=3.032 [Epoch 32] Validation: aeroplane=0.767675033582962 bicycle=0.7306822338662382 bird=0.678976849334953 boat=0.5698676036176407 bottle=0.4947934928635285 bus=0.7993633648085983 car=0.8442002140384494 cat=0.845357108453689 chair=0.5949440645965367 cow=0.7792120386829324 diningtable=0.6987068593216645 dog=0.7661923639527877 horse=0.8426626081188323 motorbike=0.7017390901382622 person=0.7529184190451993 pottedplant=0.4722378125454277 sheep=0.7378751608246987 sofa=0.7217820345377837 train=0.7805375380345332 tvmonitor=0.7504140358500114 mAP=0.7165068963107364 [Epoch 33][Batch 99], LR: 1.00E-03, Speed: 128.550 samples/sec, ObjLoss=13.023, BoxCenterLoss=5.899, BoxScaleLoss=1.861, ClassLoss=3.015 [Epoch 33][Batch 199], LR: 1.00E-03, Speed: 93.236 samples/sec, ObjLoss=12.950, BoxCenterLoss=5.897, BoxScaleLoss=1.856, ClassLoss=2.999 [Epoch 33] Training cost: 565.676, ObjLoss=12.909, BoxCenterLoss=5.897, BoxScaleLoss=1.855, ClassLoss=2.991 [Epoch 33] Validation: aeroplane=0.8071407678477256 bicycle=0.827121183394291 bird=0.7638341089836829 boat=0.6001318853145012 bottle=0.6056645929522259 bus=0.8365779188424584 car=0.8325214443882668 cat=0.8801906924757497 chair=0.5640809364054277 cow=0.7843943475842009 diningtable=0.6925070552000266 dog=0.844397492637263 horse=0.868680753109432 motorbike=0.8155348413220378 person=0.7887731179613954 pottedplant=0.45291844743260523 sheep=0.7962427988456049 sofa=0.7204167523478493 train=0.8106869649378051 tvmonitor=0.7458011388024084 mAP=0.7518808620392479 [Epoch 34][Batch 99], LR: 1.00E-03, Speed: 92.817 samples/sec, ObjLoss=12.837, BoxCenterLoss=5.893, BoxScaleLoss=1.850, ClassLoss=2.974 [Epoch 34][Batch 199], LR: 1.00E-03, Speed: 102.685 samples/sec, ObjLoss=12.767, BoxCenterLoss=5.891, BoxScaleLoss=1.846, ClassLoss=2.959 [Epoch 34] Training cost: 576.517, ObjLoss=12.728, BoxCenterLoss=5.891, BoxScaleLoss=1.844, ClassLoss=2.950 [Epoch 34] Validation: aeroplane=0.7534253254989598 bicycle=0.8197468169066913 bird=0.7452249423476908 boat=0.6306928027633104 bottle=0.5885848257076837 bus=0.8201683145307904 car=0.8369143047526851 cat=0.885353918998539 chair=0.5617658813668897 cow=0.7948096558388481 diningtable=0.7152130753424306 dog=0.8277632767987795 horse=0.8337815924399188 motorbike=0.791253474022758 person=0.7783002240339482 pottedplant=0.4210707330651891 sheep=0.7213627832621546 sofa=0.7334859988574072 train=0.8094674124592236 tvmonitor=0.7754671805448758 mAP=0.7421926269769387 [Epoch 35][Batch 99], LR: 1.00E-03, Speed: 131.312 samples/sec, ObjLoss=12.659, BoxCenterLoss=5.888, BoxScaleLoss=1.840, ClassLoss=2.934 [Epoch 35][Batch 199], LR: 1.00E-03, Speed: 105.644 samples/sec, ObjLoss=12.591, BoxCenterLoss=5.884, BoxScaleLoss=1.835, ClassLoss=2.919 [Epoch 35] Training cost: 553.067, ObjLoss=12.555, BoxCenterLoss=5.883, BoxScaleLoss=1.833, ClassLoss=2.911 [Epoch 35] Validation: aeroplane=0.6939504049307245 bicycle=0.8007295447728882 bird=0.7443761142312251 boat=0.6005605294112817 bottle=0.60098160455084 bus=0.8347448829427634 car=0.8316827929433572 cat=0.8731594767151196 chair=0.5830641968120207 cow=0.7307383793685441 diningtable=0.7199762213962958 dog=0.8470068881902048 horse=0.8208517604442496 motorbike=0.826865234971787 person=0.7855081391317092 pottedplant=0.47025780558323077 sheep=0.7204975186206094 sofa=0.6905832084296223 train=0.8036748086871555 tvmonitor=0.734343192365406 mAP=0.7356776352249518 [Epoch 36][Batch 99], LR: 1.00E-03, Speed: 117.493 samples/sec, ObjLoss=12.493, BoxCenterLoss=5.882, BoxScaleLoss=1.830, ClassLoss=2.895 [Epoch 36][Batch 199], LR: 1.00E-03, Speed: 126.729 samples/sec, ObjLoss=12.430, BoxCenterLoss=5.880, BoxScaleLoss=1.826, ClassLoss=2.880 [Epoch 36] Training cost: 551.688, ObjLoss=12.393, BoxCenterLoss=5.877, BoxScaleLoss=1.823, ClassLoss=2.871 [Epoch 36] Validation: aeroplane=0.8208858043466317 bicycle=0.7964781512269734 bird=0.7503953512821694 boat=0.5471074045925178 bottle=0.6125703782961991 bus=0.8273157892755071 car=0.8439578211721458 cat=0.8829542195292477 chair=0.5599306826080276 cow=0.7656590473447851 diningtable=0.7069291644900444 dog=0.826616122080983 horse=0.8042639209703751 motorbike=0.8214844585980409 person=0.7718160065250013 pottedplant=0.48005637799624223 sheep=0.7883754831647566 sofa=0.7072436972824393 train=0.8576440949122619 tvmonitor=0.7369880477170149 mAP=0.7454336011705682 [Epoch 37][Batch 99], LR: 1.00E-03, Speed: 132.451 samples/sec, ObjLoss=12.331, BoxCenterLoss=5.875, BoxScaleLoss=1.819, ClassLoss=2.857 [Epoch 37][Batch 199], LR: 1.00E-03, Speed: 138.205 samples/sec, ObjLoss=12.271, BoxCenterLoss=5.872, BoxScaleLoss=1.816, ClassLoss=2.845 [Epoch 37] Training cost: 558.365, ObjLoss=12.238, BoxCenterLoss=5.871, BoxScaleLoss=1.814, ClassLoss=2.837 [Epoch 37] Validation: aeroplane=0.8121481576860121 bicycle=0.7635764876348345 bird=0.7399090307363158 boat=0.6405752392347002 bottle=0.4812673880850455 bus=0.8438600452891297 car=0.8466694121631447 cat=0.8674764017557257 chair=0.6017025116941339 cow=0.7067110142954398 diningtable=0.6596452298637924 dog=0.8444099324860113 horse=0.8447415461996018 motorbike=0.8312461848118785 person=0.7688038159901533 pottedplant=0.4396143293426445 sheep=0.7147589916528181 sofa=0.7212090008509441 train=0.7816747035777276 tvmonitor=0.7758866226140729 mAP=0.7342943022982064 [Epoch 38][Batch 99], LR: 1.00E-03, Speed: 94.404 samples/sec, ObjLoss=12.179, BoxCenterLoss=5.868, BoxScaleLoss=1.810, ClassLoss=2.823 [Epoch 38][Batch 199], LR: 1.00E-03, Speed: 115.801 samples/sec, ObjLoss=12.121, BoxCenterLoss=5.866, BoxScaleLoss=1.807, ClassLoss=2.810 [Epoch 38] Training cost: 570.965, ObjLoss=12.087, BoxCenterLoss=5.864, BoxScaleLoss=1.804, ClassLoss=2.803 [Epoch 38] Validation: aeroplane=0.7737000242471971 bicycle=0.8387689106495545 bird=0.7891256895743872 boat=0.6547375524223014 bottle=0.5827113065633293 bus=0.8320690758711722 car=0.851133140911429 cat=0.8770344840585288 chair=0.5803289480209061 cow=0.8356304922058725 diningtable=0.7109100820513338 dog=0.8591507252696956 horse=0.8594488158918707 motorbike=0.831340477082249 person=0.8001986264820014 pottedplant=0.49957974270698485 sheep=0.7776261570605464 sofa=0.7393868330659018 train=0.8189337375839263 tvmonitor=0.7712621216555108 mAP=0.7641538471687349 [Epoch 39][Batch 99], LR: 1.00E-03, Speed: 119.782 samples/sec, ObjLoss=12.029, BoxCenterLoss=5.861, BoxScaleLoss=1.800, ClassLoss=2.789 [Epoch 39][Batch 199], LR: 1.00E-03, Speed: 83.272 samples/sec, ObjLoss=11.975, BoxCenterLoss=5.859, BoxScaleLoss=1.797, ClassLoss=2.775 [Epoch 39] Training cost: 564.495, ObjLoss=11.944, BoxCenterLoss=5.859, BoxScaleLoss=1.795, ClassLoss=2.768 [Epoch 39] Validation: aeroplane=0.8069736601816285 bicycle=0.8278938289651164 bird=0.8264267811571038 boat=0.6162712327097613 bottle=0.6119816964546023 bus=0.8618009061098927 car=0.8583983716397496 cat=0.8790211784717719 chair=0.5495505858158476 cow=0.8227174175507448 diningtable=0.7142002262492622 dog=0.83899890571869 horse=0.8766656886025117 motorbike=0.8470254058851096 person=0.7965188496384558 pottedplant=0.5120927523460841 sheep=0.7977903566503217 sofa=0.6931567359036012 train=0.8422934509746804 tvmonitor=0.7361275710361829 mAP=0.765795280103056 [Epoch 40][Batch 99], LR: 1.00E-03, Speed: 87.349 samples/sec, ObjLoss=11.891, BoxCenterLoss=5.858, BoxScaleLoss=1.793, ClassLoss=2.755 [Epoch 40][Batch 199], LR: 1.00E-03, Speed: 122.007 samples/sec, ObjLoss=11.839, BoxCenterLoss=5.856, BoxScaleLoss=1.789, ClassLoss=2.742 [Epoch 40] Training cost: 588.106, ObjLoss=11.809, BoxCenterLoss=5.856, BoxScaleLoss=1.787, ClassLoss=2.735 [Epoch 40] Validation: aeroplane=0.7765204488180827 bicycle=0.8531226462326253 bird=0.7627060695105266 boat=0.6478398181877228 bottle=0.5016628706110347 bus=0.830227036962252 car=0.8610222708545261 cat=0.8837730416492553 chair=0.5731667111180073 cow=0.7906503885519852 diningtable=0.7308679028705299 dog=0.8356868465454775 horse=0.845004577811955 motorbike=0.8197835889875589 person=0.7837961039097596 pottedplant=0.487181283159639 sheep=0.8188025960710513 sofa=0.7288380492061042 train=0.820673155065789 tvmonitor=0.7354207302064677 mAP=0.7543373068165174 [Epoch 41][Batch 99], LR: 1.00E-03, Speed: 125.011 samples/sec, ObjLoss=11.757, BoxCenterLoss=5.853, BoxScaleLoss=1.783, ClassLoss=2.722 [Epoch 41][Batch 199], LR: 1.00E-03, Speed: 116.653 samples/sec, ObjLoss=11.704, BoxCenterLoss=5.851, BoxScaleLoss=1.780, ClassLoss=2.709 [Epoch 41] Training cost: 567.659, ObjLoss=11.674, BoxCenterLoss=5.850, BoxScaleLoss=1.778, ClassLoss=2.703 [Epoch 41] Validation: aeroplane=0.8095425121636542 bicycle=0.8122097111895714 bird=0.7670164427289085 boat=0.651545331105102 bottle=0.6131386999104597 bus=0.8408558054518733 car=0.8550160417195836 cat=0.8678127312233924 chair=0.6036152807886264 cow=0.7854944338754669 diningtable=0.723215870799184 dog=0.8553218115844637 horse=0.8511272924788441 motorbike=0.8157375050116054 person=0.7948186398447137 pottedplant=0.5047287875182176 sheep=0.8025147303145803 sofa=0.7198925092720915 train=0.7615310903066477 tvmonitor=0.7762977019103314 mAP=0.760571646459866 [Epoch 42][Batch 99], LR: 1.00E-03, Speed: 85.924 samples/sec, ObjLoss=11.623, BoxCenterLoss=5.846, BoxScaleLoss=1.774, ClassLoss=2.691 [Epoch 42][Batch 199], LR: 1.00E-03, Speed: 104.546 samples/sec, ObjLoss=11.574, BoxCenterLoss=5.844, BoxScaleLoss=1.771, ClassLoss=2.679 [Epoch 42] Training cost: 560.195, ObjLoss=11.549, BoxCenterLoss=5.843, BoxScaleLoss=1.769, ClassLoss=2.673 [Epoch 42] Validation: aeroplane=0.7790231627351297 bicycle=0.8161524253863436 bird=0.7776997601554115 boat=0.6619935566543957 bottle=0.5923271108254813 bus=0.8347429080846571 car=0.8571246863580866 cat=0.8787341378321545 chair=0.5884815998005758 cow=0.7884227532494397 diningtable=0.7252903743144663 dog=0.8455547767850068 horse=0.84734210601858 motorbike=0.8443814851923845 person=0.7913929179487362 pottedplant=0.5004922621604145 sheep=0.7696210053104167 sofa=0.756166662573297 train=0.8332751163595873 tvmonitor=0.7529794891669234 mAP=0.7620599148455744 [Epoch 43][Batch 99], LR: 1.00E-03, Speed: 99.902 samples/sec, ObjLoss=11.499, BoxCenterLoss=5.841, BoxScaleLoss=1.766, ClassLoss=2.662 [Epoch 43][Batch 199], LR: 1.00E-03, Speed: 122.674 samples/sec, ObjLoss=11.453, BoxCenterLoss=5.840, BoxScaleLoss=1.763, ClassLoss=2.651 [Epoch 43] Training cost: 547.434, ObjLoss=11.426, BoxCenterLoss=5.839, BoxScaleLoss=1.761, ClassLoss=2.643 [Epoch 43] Validation: aeroplane=0.8132583175018482 bicycle=0.8388840697673308 bird=0.8028347096481769 boat=0.5521516214589791 bottle=0.6275023648476944 bus=0.8209834180394157 car=0.8493567389082076 cat=0.885536391732044 chair=0.5841127024209185 cow=0.7975941693135086 diningtable=0.7387258903344222 dog=0.8470211874129147 horse=0.8649241188467283 motorbike=0.8029833165202035 person=0.7783356207780774 pottedplant=0.45043725497718373 sheep=0.796096747686501 sofa=0.7285792269663303 train=0.8337162380044373 tvmonitor=0.7275623058048991 mAP=0.757029820548491 [Epoch 44][Batch 99], LR: 1.00E-03, Speed: 130.878 samples/sec, ObjLoss=11.379, BoxCenterLoss=5.837, BoxScaleLoss=1.758, ClassLoss=2.632 [Epoch 44][Batch 199], LR: 1.00E-03, Speed: 113.165 samples/sec, ObjLoss=11.335, BoxCenterLoss=5.836, BoxScaleLoss=1.756, ClassLoss=2.622 [Epoch 44] Training cost: 553.258, ObjLoss=11.308, BoxCenterLoss=5.835, BoxScaleLoss=1.754, ClassLoss=2.615 [Epoch 44] Validation: aeroplane=0.7909706022940849 bicycle=0.7683573883067749 bird=0.7977838830334532 boat=0.6465750882200434 bottle=0.6044218945462098 bus=0.8296329608957553 car=0.8492278126618024 cat=0.860686047922761 chair=0.5193790251434894 cow=0.7657901852310727 diningtable=0.7104001662002998 dog=0.819826204458191 horse=0.8587849032705801 motorbike=0.8161538956797098 person=0.7948371326606282 pottedplant=0.39623333001628724 sheep=0.7770439221798936 sofa=0.7259771483981002 train=0.8266659168470383 tvmonitor=0.7551404090306096 mAP=0.7456943958498392 [Epoch 45][Batch 99], LR: 1.00E-03, Speed: 119.754 samples/sec, ObjLoss=11.262, BoxCenterLoss=5.832, BoxScaleLoss=1.751, ClassLoss=2.604 [Epoch 45][Batch 199], LR: 1.00E-03, Speed: 119.827 samples/sec, ObjLoss=11.217, BoxCenterLoss=5.829, BoxScaleLoss=1.748, ClassLoss=2.594 [Epoch 45] Training cost: 543.830, ObjLoss=11.193, BoxCenterLoss=5.829, BoxScaleLoss=1.746, ClassLoss=2.588 [Epoch 45] Validation: aeroplane=0.762319432621422 bicycle=0.8343899898597907 bird=0.7780549269827286 boat=0.6747035376168675 bottle=0.6412306861703664 bus=0.8357890324847262 car=0.8517167340015291 cat=0.8743904969673014 chair=0.5991924138532349 cow=0.812181261463287 diningtable=0.7080697888355991 dog=0.8384942950375989 horse=0.8641778247728402 motorbike=0.8224725175570128 person=0.8030659618406545 pottedplant=0.511270002971619 sheep=0.8117578055324992 sofa=0.7232929458630569 train=0.8389347630444159 tvmonitor=0.7939801643944188 mAP=0.7689742290935484 [Epoch 46][Batch 99], LR: 1.00E-03, Speed: 104.966 samples/sec, ObjLoss=11.150, BoxCenterLoss=5.828, BoxScaleLoss=1.743, ClassLoss=2.577 [Epoch 46][Batch 199], LR: 1.00E-03, Speed: 107.789 samples/sec, ObjLoss=11.107, BoxCenterLoss=5.825, BoxScaleLoss=1.740, ClassLoss=2.567 [Epoch 46] Training cost: 558.896, ObjLoss=11.085, BoxCenterLoss=5.824, BoxScaleLoss=1.739, ClassLoss=2.563 [Epoch 46] Validation: aeroplane=0.767971905561225 bicycle=0.835894947659296 bird=0.7777248410141586 boat=0.6338351677132673 bottle=0.5917198352623702 bus=0.8240914020197874 car=0.835796414391085 cat=0.8853542299149344 chair=0.5930402763998588 cow=0.7743721577275398 diningtable=0.7028041103166888 dog=0.8366015317452732 horse=0.8694603560548527 motorbike=0.7961818636810316 person=0.8035830883751166 pottedplant=0.49040922051892505 sheep=0.749010812140533 sofa=0.7305340537810786 train=0.7800558394525566 tvmonitor=0.7900999030664959 mAP=0.7534270978398037 [Epoch 47][Batch 99], LR: 1.00E-03, Speed: 107.803 samples/sec, ObjLoss=11.042, BoxCenterLoss=5.822, BoxScaleLoss=1.736, ClassLoss=2.552 [Epoch 47][Batch 199], LR: 1.00E-03, Speed: 100.003 samples/sec, ObjLoss=11.002, BoxCenterLoss=5.820, BoxScaleLoss=1.733, ClassLoss=2.543 [Epoch 47] Training cost: 562.543, ObjLoss=10.979, BoxCenterLoss=5.819, BoxScaleLoss=1.732, ClassLoss=2.538 [Epoch 47] Validation: aeroplane=0.7498109802215052 bicycle=0.7722041067763279 bird=0.764858587145335 boat=0.5520063699995934 bottle=0.6060972995738645 bus=0.838243309736866 car=0.8376023598081171 cat=0.8755967772287148 chair=0.5819404454960387 cow=0.830841057333473 diningtable=0.7093832795473367 dog=0.8185282924676702 horse=0.8424514262467924 motorbike=0.7524374774933428 person=0.762905613806513 pottedplant=0.44310519674945537 sheep=0.8115628271510624 sofa=0.7357251298350944 train=0.83905092987938 tvmonitor=0.7449878752199144 mAP=0.7434669670858198 [Epoch 48][Batch 99], LR: 1.00E-03, Speed: 98.948 samples/sec, ObjLoss=10.940, BoxCenterLoss=5.818, BoxScaleLoss=1.729, ClassLoss=2.529 [Epoch 48][Batch 199], LR: 1.00E-03, Speed: 107.240 samples/sec, ObjLoss=10.900, BoxCenterLoss=5.816, BoxScaleLoss=1.726, ClassLoss=2.519 [Epoch 48] Training cost: 569.669, ObjLoss=10.879, BoxCenterLoss=5.815, BoxScaleLoss=1.725, ClassLoss=2.514 [Epoch 48] Validation: aeroplane=0.7393896702033204 bicycle=0.7923443214606789 bird=0.7782489586966552 boat=0.6506337500640877 bottle=0.6520463356443515 bus=0.8425557200251914 car=0.8489636213848638 cat=0.8798055980377064 chair=0.5775906987640121 cow=0.8220415502504318 diningtable=0.729030939898022 dog=0.8583759507643356 horse=0.8428602019719043 motorbike=0.8394689431553168 person=0.8065892854644452 pottedplant=0.4904486053676839 sheep=0.7633810267895238 sofa=0.7340261773272164 train=0.8280603383740727 tvmonitor=0.7512038930290031 mAP=0.7613532793336413 [Epoch 49][Batch 99], LR: 1.00E-03, Speed: 133.334 samples/sec, ObjLoss=10.840, BoxCenterLoss=5.814, BoxScaleLoss=1.722, ClassLoss=2.505 [Epoch 49][Batch 199], LR: 1.00E-03, Speed: 129.458 samples/sec, ObjLoss=10.801, BoxCenterLoss=5.811, BoxScaleLoss=1.720, ClassLoss=2.495 [Epoch 49] Training cost: 561.850, ObjLoss=10.780, BoxCenterLoss=5.811, BoxScaleLoss=1.718, ClassLoss=2.490 [Epoch 49] Validation: aeroplane=0.7997389743350835 bicycle=0.8244350129191693 bird=0.7881918359444315 boat=0.5825612282870253 bottle=0.6467983484772504 bus=0.7967636143602157 car=0.8440558159563165 cat=0.8907393456666416 chair=0.5831484963935661 cow=0.8110157052717999 diningtable=0.7138402913447845 dog=0.834193694982864 horse=0.8751576424000129 motorbike=0.8378049539589572 person=0.8076985072126255 pottedplant=0.42943221345674243 sheep=0.8355596039396181 sofa=0.7525701792323143 train=0.8427601004466411 tvmonitor=0.7822946486864554 mAP=0.7639380106636258 [Epoch 50][Batch 99], LR: 1.00E-03, Speed: 118.073 samples/sec, ObjLoss=10.743, BoxCenterLoss=5.810, BoxScaleLoss=1.716, ClassLoss=2.481 [Epoch 50][Batch 199], LR: 1.00E-03, Speed: 118.113 samples/sec, ObjLoss=10.706, BoxCenterLoss=5.808, BoxScaleLoss=1.713, ClassLoss=2.472 [Epoch 50] Training cost: 544.298, ObjLoss=10.685, BoxCenterLoss=5.807, BoxScaleLoss=1.712, ClassLoss=2.467 [Epoch 50] Validation: aeroplane=0.7787661766832965 bicycle=0.8116396019179262 bird=0.8100555348841436 boat=0.6613592975362811 bottle=0.5962698712294943 bus=0.8161090487463494 car=0.8487837436845539 cat=0.8820243498987074 chair=0.564834178197067 cow=0.8154872007575844 diningtable=0.7118359284803135 dog=0.8350648552313502 horse=0.8610547884441324 motorbike=0.8336519337373272 person=0.7987142686958791 pottedplant=0.4840026468296941 sheep=0.8074122552047795 sofa=0.6778512801533476 train=0.8334221977385 tvmonitor=0.7471816816098112 mAP=0.758776041983027 [Epoch 51][Batch 99], LR: 1.00E-03, Speed: 126.491 samples/sec, ObjLoss=10.648, BoxCenterLoss=5.805, BoxScaleLoss=1.710, ClassLoss=2.458 [Epoch 51][Batch 199], LR: 1.00E-03, Speed: 123.179 samples/sec, ObjLoss=10.611, BoxCenterLoss=5.802, BoxScaleLoss=1.707, ClassLoss=2.448 [Epoch 51] Training cost: 547.198, ObjLoss=10.591, BoxCenterLoss=5.801, BoxScaleLoss=1.705, ClassLoss=2.443 [Epoch 51] Validation: aeroplane=0.8405524686567946 bicycle=0.7835569094902712 bird=0.7766235604723416 boat=0.6510810734543313 bottle=0.6402116709032788 bus=0.7971185946903258 car=0.8422551112916384 cat=0.8828901488767225 chair=0.5600883160934447 cow=0.8255587473307205 diningtable=0.7016097429140907 dog=0.8390676588596182 horse=0.8456023313131374 motorbike=0.832745155809677 person=0.8107915488361808 pottedplant=0.5167063819481503 sheep=0.8158211803852266 sofa=0.743409595627457 train=0.8476013307931685 tvmonitor=0.7560513824460141 mAP=0.7654671455096295 [Epoch 52][Batch 99], LR: 1.00E-03, Speed: 95.949 samples/sec, ObjLoss=10.556, BoxCenterLoss=5.800, BoxScaleLoss=1.703, ClassLoss=2.434 [Epoch 52][Batch 199], LR: 1.00E-03, Speed: 144.305 samples/sec, ObjLoss=10.521, BoxCenterLoss=5.798, BoxScaleLoss=1.700, ClassLoss=2.425 [Epoch 52] Training cost: 554.894, ObjLoss=10.501, BoxCenterLoss=5.797, BoxScaleLoss=1.699, ClassLoss=2.421 [Epoch 52] Validation: aeroplane=0.7739524178654613 bicycle=0.818074816303746 bird=0.8169974899731145 boat=0.6209787100881067 bottle=0.6141162267505826 bus=0.8448683556208288 car=0.8464666454667191 cat=0.8869214448218492 chair=0.5767208647039576 cow=0.690636264145106 diningtable=0.6986921957736629 dog=0.8392300051793458 horse=0.8602579552473408 motorbike=0.8071264697700683 person=0.8009465956889701 pottedplant=0.49618035755526035 sheep=0.6483391974971625 sofa=0.7152659738668209 train=0.7939374320109276 tvmonitor=0.7353144451484176 mAP=0.7442511931738724 [Epoch 53][Batch 99], LR: 1.00E-03, Speed: 99.094 samples/sec, ObjLoss=10.466, BoxCenterLoss=5.795, BoxScaleLoss=1.696, ClassLoss=2.412 [Epoch 53][Batch 199], LR: 1.00E-03, Speed: 124.627 samples/sec, ObjLoss=10.433, BoxCenterLoss=5.794, BoxScaleLoss=1.694, ClassLoss=2.404 [Epoch 53] Training cost: 550.849, ObjLoss=10.416, BoxCenterLoss=5.793, BoxScaleLoss=1.693, ClassLoss=2.400 [Epoch 53] Validation: aeroplane=0.8368151423490295 bicycle=0.8280041274308874 bird=0.8300196023763571 boat=0.678758904301673 bottle=0.569665728588846 bus=0.8488720857686376 car=0.8533346255323938 cat=0.8862578205642191 chair=0.5854029750176528 cow=0.801133980646636 diningtable=0.7380386043331237 dog=0.8614715413231993 horse=0.8219709082669028 motorbike=0.826569424366859 person=0.8011922611797143 pottedplant=0.5241619949460886 sheep=0.7617671743108585 sofa=0.7335116158309822 train=0.8545759641622828 tvmonitor=0.7351389915728775 mAP=0.7688331736434612 [Epoch 54][Batch 99], LR: 1.00E-03, Speed: 126.508 samples/sec, ObjLoss=10.383, BoxCenterLoss=5.791, BoxScaleLoss=1.690, ClassLoss=2.392 [Epoch 54][Batch 199], LR: 1.00E-03, Speed: 128.453 samples/sec, ObjLoss=10.351, BoxCenterLoss=5.790, BoxScaleLoss=1.688, ClassLoss=2.385 [Epoch 54] Training cost: 563.908, ObjLoss=10.334, BoxCenterLoss=5.789, BoxScaleLoss=1.687, ClassLoss=2.380 [Epoch 54] Validation: aeroplane=0.8302458227383334 bicycle=0.7849240207637308 bird=0.7609166324197664 boat=0.6551760399510764 bottle=0.6067464620511036 bus=0.8401994862394725 car=0.8516611662807304 cat=0.858671137043972 chair=0.5409362856827992 cow=0.8309211655865423 diningtable=0.7692215982353813 dog=0.8367975334305362 horse=0.8767561528972059 motorbike=0.8156356723854458 person=0.7903983721005139 pottedplant=0.5034452411050101 sheep=0.8046617643546681 sofa=0.675128402700506 train=0.8345057291028883 tvmonitor=0.7553443105109258 mAP=0.7611146497790304 [Epoch 55][Batch 99], LR: 1.00E-03, Speed: 100.207 samples/sec, ObjLoss=10.301, BoxCenterLoss=5.789, BoxScaleLoss=1.685, ClassLoss=2.372 [Epoch 55][Batch 199], LR: 1.00E-03, Speed: 130.401 samples/sec, ObjLoss=10.272, BoxCenterLoss=5.788, BoxScaleLoss=1.683, ClassLoss=2.365 [Epoch 55] Training cost: 580.326, ObjLoss=10.254, BoxCenterLoss=5.787, BoxScaleLoss=1.682, ClassLoss=2.360 [Epoch 55] Validation: aeroplane=0.8026174586822498 bicycle=0.7879790076618696 bird=0.7956874732193432 boat=0.6209220737300782 bottle=0.6038785980255325 bus=0.8304115842407894 car=0.8615679696175048 cat=0.8789277233814897 chair=0.5539109598691679 cow=0.7924444810265571 diningtable=0.7275158312900265 dog=0.8494703627113916 horse=0.8735880817762092 motorbike=0.8301542080806295 person=0.7604316456276347 pottedplant=0.5081990457753334 sheep=0.8080777529909147 sofa=0.7140446944392266 train=0.8427360210840465 tvmonitor=0.7706929886819973 mAP=0.7606628980955996 [Epoch 56][Batch 99], LR: 1.00E-03, Speed: 88.206 samples/sec, ObjLoss=10.222, BoxCenterLoss=5.785, BoxScaleLoss=1.679, ClassLoss=2.352 [Epoch 56][Batch 199], LR: 1.00E-03, Speed: 131.208 samples/sec, ObjLoss=10.192, BoxCenterLoss=5.784, BoxScaleLoss=1.677, ClassLoss=2.344 [Epoch 56] Training cost: 538.896, ObjLoss=10.176, BoxCenterLoss=5.784, BoxScaleLoss=1.676, ClassLoss=2.340 [Epoch 56] Validation: aeroplane=0.8168852902658396 bicycle=0.7810672322509506 bird=0.8104818136331483 boat=0.6763578017443982 bottle=0.6403101252997339 bus=0.7835627324623948 car=0.8524604449083293 cat=0.8829379308105153 chair=0.5351409455564374 cow=0.8178312028326786 diningtable=0.7284070658525645 dog=0.8518759960979956 horse=0.8616286995193939 motorbike=0.8250045938894542 person=0.7661827150681373 pottedplant=0.4847472882614742 sheep=0.8047276935505832 sofa=0.6827785811160768 train=0.8165184904566728 tvmonitor=0.7037055387575758 mAP=0.7561306091167178 [Epoch 57][Batch 99], LR: 1.00E-03, Speed: 100.053 samples/sec, ObjLoss=10.145, BoxCenterLoss=5.782, BoxScaleLoss=1.674, ClassLoss=2.332 [Epoch 57][Batch 199], LR: 1.00E-03, Speed: 90.159 samples/sec, ObjLoss=10.115, BoxCenterLoss=5.781, BoxScaleLoss=1.671, ClassLoss=2.325 [Epoch 57] Training cost: 544.097, ObjLoss=10.098, BoxCenterLoss=5.780, BoxScaleLoss=1.670, ClassLoss=2.320 [Epoch 57] Validation: aeroplane=0.8215567204653945 bicycle=0.7793285368632047 bird=0.8059984417626878 boat=0.6254959417571352 bottle=0.6171879530076515 bus=0.8504356832494117 car=0.8163550920673415 cat=0.8883963699644764 chair=0.5728687299875982 cow=0.6968250723845777 diningtable=0.7501629473501259 dog=0.8457614451259595 horse=0.820592612656427 motorbike=0.8307266501143465 person=0.7853091480414416 pottedplant=0.4915608802644354 sheep=0.7790006869360946 sofa=0.7211628174862136 train=0.7630090954026969 tvmonitor=0.7795598647024534 mAP=0.7520647344794836 [Epoch 58][Batch 99], LR: 1.00E-03, Speed: 116.073 samples/sec, ObjLoss=10.068, BoxCenterLoss=5.778, BoxScaleLoss=1.668, ClassLoss=2.312 [Epoch 58][Batch 199], LR: 1.00E-03, Speed: 127.641 samples/sec, ObjLoss=10.038, BoxCenterLoss=5.777, BoxScaleLoss=1.665, ClassLoss=2.304 [Epoch 58] Training cost: 547.239, ObjLoss=10.022, BoxCenterLoss=5.777, BoxScaleLoss=1.664, ClassLoss=2.300 [Epoch 58] Validation: aeroplane=0.7972024275973307 bicycle=0.8430221699562751 bird=0.7747617742333576 boat=0.6607905649219008 bottle=0.6474118351337635 bus=0.8246511212158203 car=0.8714202011003102 cat=0.8751482284534895 chair=0.5980910044705914 cow=0.8164025660162668 diningtable=0.7251844995009642 dog=0.8337219176586089 horse=0.8706729131751638 motorbike=0.8381664854612861 person=0.8062154194743356 pottedplant=0.4850026908250093 sheep=0.7878244613854323 sofa=0.6869655563162678 train=0.8415345957417102 tvmonitor=0.7518101526901898 mAP=0.7668000292664037 [Epoch 59][Batch 99], LR: 1.00E-03, Speed: 86.436 samples/sec, ObjLoss=9.995, BoxCenterLoss=5.776, BoxScaleLoss=1.662, ClassLoss=2.293 [Epoch 59][Batch 199], LR: 1.00E-03, Speed: 84.913 samples/sec, ObjLoss=9.968, BoxCenterLoss=5.775, BoxScaleLoss=1.661, ClassLoss=2.286 [Epoch 59] Training cost: 569.755, ObjLoss=9.951, BoxCenterLoss=5.774, BoxScaleLoss=1.659, ClassLoss=2.282 [Epoch 59] Validation: aeroplane=0.8307096657000751 bicycle=0.7975033304035484 bird=0.8112203552252873 boat=0.669033394378177 bottle=0.5513919979464073 bus=0.8290432939905291 car=0.856712169611035 cat=0.8798523024651606 chair=0.6056625673397887 cow=0.8182376751892838 diningtable=0.7140963157404068 dog=0.8224536025625896 horse=0.8719380345780925 motorbike=0.8509924560039877 person=0.8118410637122769 pottedplant=0.5024642911387001 sheep=0.8259125783511465 sofa=0.7486001796270717 train=0.850478914385002 tvmonitor=0.7503490921050016 mAP=0.7699246640226785 [Epoch 60][Batch 99], LR: 1.00E-03, Speed: 111.041 samples/sec, ObjLoss=9.924, BoxCenterLoss=5.773, BoxScaleLoss=1.657, ClassLoss=2.275 [Epoch 60][Batch 199], LR: 1.00E-03, Speed: 94.984 samples/sec, ObjLoss=9.896, BoxCenterLoss=5.770, BoxScaleLoss=1.655, ClassLoss=2.268 [Epoch 60] Training cost: 549.586, ObjLoss=9.881, BoxCenterLoss=5.770, BoxScaleLoss=1.654, ClassLoss=2.263 [Epoch 60] Validation: aeroplane=0.8311162552661363 bicycle=0.8256381812729165 bird=0.788517568452559 boat=0.6464195937938162 bottle=0.6297765048383124 bus=0.8219090772494924 car=0.856567412003784 cat=0.8885512789814811 chair=0.6031976774657511 cow=0.8213421642880218 diningtable=0.7183496244026055 dog=0.8407950651676445 horse=0.8610569340860947 motorbike=0.8549099081156423 person=0.8065491754486638 pottedplant=0.4886860386244487 sheep=0.7937739334419602 sofa=0.7013096000239928 train=0.8522592962143919 tvmonitor=0.7645400982719686 mAP=0.7697632693704841 [Epoch 61][Batch 99], LR: 1.00E-03, Speed: 116.949 samples/sec, ObjLoss=9.854, BoxCenterLoss=5.770, BoxScaleLoss=1.652, ClassLoss=2.256 [Epoch 61][Batch 199], LR: 1.00E-03, Speed: 125.729 samples/sec, ObjLoss=9.828, BoxCenterLoss=5.769, BoxScaleLoss=1.650, ClassLoss=2.250 [Epoch 61] Training cost: 561.736, ObjLoss=9.815, BoxCenterLoss=5.769, BoxScaleLoss=1.649, ClassLoss=2.247 [Epoch 61] Validation: aeroplane=0.7934113073966367 bicycle=0.7792258775113586 bird=0.8051615512301641 boat=0.6594733604956194 bottle=0.5600343045867989 bus=0.8386698703931811 car=0.8452493327046299 cat=0.8702953693714364 chair=0.5526707487677383 cow=0.8214635907497585 diningtable=0.7060331398989657 dog=0.8394691753293723 horse=0.8519291375864902 motorbike=0.8335782330892849 person=0.7928301182648342 pottedplant=0.4664881417732142 sheep=0.7190822336600324 sofa=0.7308244003776903 train=0.8418102575578186 tvmonitor=0.7430059430642046 mAP=0.7525353046904615 [Epoch 62][Batch 99], LR: 1.00E-03, Speed: 112.453 samples/sec, ObjLoss=9.789, BoxCenterLoss=5.768, BoxScaleLoss=1.648, ClassLoss=2.240 [Epoch 62][Batch 199], LR: 1.00E-03, Speed: 117.034 samples/sec, ObjLoss=9.762, BoxCenterLoss=5.767, BoxScaleLoss=1.646, ClassLoss=2.233 [Epoch 62] Training cost: 538.222, ObjLoss=9.746, BoxCenterLoss=5.766, BoxScaleLoss=1.644, ClassLoss=2.229 [Epoch 62] Validation: aeroplane=0.8363358437820673 bicycle=0.8420943580366618 bird=0.8144913814030246 boat=0.6089296665308209 bottle=0.6478194918418787 bus=0.8521169671451285 car=0.8660262575658927 cat=0.8886541221579471 chair=0.5914355472852055 cow=0.8311382975486018 diningtable=0.7605540068052736 dog=0.8478381688672767 horse=0.8536572947055007 motorbike=0.830653072026713 person=0.8206205973975519 pottedplant=0.4871807310880966 sheep=0.8439568133284647 sofa=0.7363723776383927 train=0.8639383747093301 tvmonitor=0.7579061420325234 mAP=0.7790859755948175 [Epoch 63][Batch 99], LR: 1.00E-03, Speed: 112.264 samples/sec, ObjLoss=9.721, BoxCenterLoss=5.765, BoxScaleLoss=1.642, ClassLoss=2.223 [Epoch 63][Batch 199], LR: 1.00E-03, Speed: 122.566 samples/sec, ObjLoss=9.695, BoxCenterLoss=5.764, BoxScaleLoss=1.640, ClassLoss=2.216 [Epoch 63] Training cost: 567.395, ObjLoss=9.681, BoxCenterLoss=5.764, BoxScaleLoss=1.639, ClassLoss=2.212 [Epoch 63] Validation: aeroplane=0.8502523427307362 bicycle=0.8370600250380451 bird=0.787424848274487 boat=0.6497581142658311 bottle=0.5983314983222179 bus=0.8500784982288835 car=0.8631853236754714 cat=0.886799197089284 chair=0.5650478519992295 cow=0.8323239678230328 diningtable=0.7318235732482757 dog=0.8474282534128845 horse=0.870349139415946 motorbike=0.8318333881529485 person=0.801998428256128 pottedplant=0.540123045282151 sheep=0.7232517238195959 sofa=0.7465944806826927 train=0.8571238872501443 tvmonitor=0.7786921737188732 mAP=0.7724739880343431 [Epoch 64][Batch 99], LR: 1.00E-03, Speed: 102.994 samples/sec, ObjLoss=9.655, BoxCenterLoss=5.762, BoxScaleLoss=1.637, ClassLoss=2.205 [Epoch 64][Batch 199], LR: 1.00E-03, Speed: 129.753 samples/sec, ObjLoss=9.631, BoxCenterLoss=5.761, BoxScaleLoss=1.635, ClassLoss=2.199 [Epoch 64] Training cost: 545.190, ObjLoss=9.616, BoxCenterLoss=5.760, BoxScaleLoss=1.634, ClassLoss=2.195 [Epoch 64] Validation: aeroplane=0.8338844529590647 bicycle=0.79306694916867 bird=0.8318261238494599 boat=0.7055160692386906 bottle=0.6618084028382001 bus=0.8531096725914821 car=0.8637381338570096 cat=0.8819703888388589 chair=0.6078760348767746 cow=0.8325796397405524 diningtable=0.7121535534447141 dog=0.8446758207587056 horse=0.853946412442829 motorbike=0.8409069057851875 person=0.8057928793455502 pottedplant=0.5364567436766425 sheep=0.8017196474911378 sofa=0.7353845890056864 train=0.8410966588146356 tvmonitor=0.7970598950910143 mAP=0.7817284486907433 [Epoch 65][Batch 99], LR: 1.00E-03, Speed: 155.663 samples/sec, ObjLoss=9.592, BoxCenterLoss=5.760, BoxScaleLoss=1.633, ClassLoss=2.189 [Epoch 65][Batch 199], LR: 1.00E-03, Speed: 118.723 samples/sec, ObjLoss=9.569, BoxCenterLoss=5.759, BoxScaleLoss=1.631, ClassLoss=2.183 [Epoch 65] Training cost: 540.222, ObjLoss=9.554, BoxCenterLoss=5.757, BoxScaleLoss=1.630, ClassLoss=2.179 [Epoch 65] Validation: aeroplane=0.8562635958085019 bicycle=0.7953222031946959 bird=0.758816163847748 boat=0.6585487442774285 bottle=0.6217458322469867 bus=0.7937416588728577 car=0.8576795848452374 cat=0.8896251827268776 chair=0.5916962860893803 cow=0.8152437187115454 diningtable=0.718477145579644 dog=0.8426715543070605 horse=0.8872589265665215 motorbike=0.786255731615144 person=0.7998969131070439 pottedplant=0.5161507342104859 sheep=0.7387179413430582 sofa=0.702694704575329 train=0.8336934930424542 tvmonitor=0.7520176419430283 mAP=0.7608258878455515 [Epoch 66][Batch 99], LR: 1.00E-03, Speed: 91.630 samples/sec, ObjLoss=9.531, BoxCenterLoss=5.757, BoxScaleLoss=1.628, ClassLoss=2.173 [Epoch 66][Batch 199], LR: 1.00E-03, Speed: 123.387 samples/sec, ObjLoss=9.507, BoxCenterLoss=5.755, BoxScaleLoss=1.626, ClassLoss=2.167 [Epoch 66] Training cost: 568.874, ObjLoss=9.494, BoxCenterLoss=5.754, BoxScaleLoss=1.625, ClassLoss=2.163 [Epoch 66] Validation: aeroplane=0.7941298940699701 bicycle=0.7650249519124392 bird=0.789280197527276 boat=0.6516847488034512 bottle=0.6065735819336179 bus=0.8249769361382997 car=0.8605083960432326 cat=0.8784281221319248 chair=0.6089727878179527 cow=0.8466766414872695 diningtable=0.7355133531955687 dog=0.8563812167422972 horse=0.8790677427859587 motorbike=0.8421601765678127 person=0.8124660376506077 pottedplant=0.5141690535858434 sheep=0.7914101461372653 sofa=0.733113974181414 train=0.8331165878013294 tvmonitor=0.7253157570960352 mAP=0.7674485151804784 [Epoch 67][Batch 99], LR: 1.00E-03, Speed: 134.088 samples/sec, ObjLoss=9.469, BoxCenterLoss=5.753, BoxScaleLoss=1.623, ClassLoss=2.156 [Epoch 67][Batch 199], LR: 1.00E-03, Speed: 110.591 samples/sec, ObjLoss=9.447, BoxCenterLoss=5.752, BoxScaleLoss=1.621, ClassLoss=2.150 [Epoch 67] Training cost: 580.850, ObjLoss=9.433, BoxCenterLoss=5.751, BoxScaleLoss=1.620, ClassLoss=2.147 [Epoch 67] Validation: aeroplane=0.7928348764473165 bicycle=0.7860744359287924 bird=0.767895683245408 boat=0.709193352542673 bottle=0.5804965576456936 bus=0.8278970781180277 car=0.8547910951023927 cat=0.8748310283245875 chair=0.581974827809813 cow=0.8240894573625961 diningtable=0.7594014463752912 dog=0.8504930506982505 horse=0.8515789906491017 motorbike=0.8438894614524516 person=0.7769327066817313 pottedplant=0.48282085052442314 sheep=0.7913582180383605 sofa=0.7627793796805411 train=0.7992811317695503 tvmonitor=0.7438080474527183 mAP=0.763121083792486 [Epoch 68][Batch 99], LR: 1.00E-03, Speed: 96.204 samples/sec, ObjLoss=9.411, BoxCenterLoss=5.750, BoxScaleLoss=1.618, ClassLoss=2.141 [Epoch 68][Batch 199], LR: 1.00E-03, Speed: 106.362 samples/sec, ObjLoss=9.387, BoxCenterLoss=5.748, BoxScaleLoss=1.616, ClassLoss=2.135 [Epoch 68] Training cost: 544.506, ObjLoss=9.375, BoxCenterLoss=5.747, BoxScaleLoss=1.616, ClassLoss=2.132 [Epoch 68] Validation: aeroplane=0.8236321244000511 bicycle=0.8309253648936709 bird=0.7889088889890846 boat=0.6295272690087294 bottle=0.6207835085041423 bus=0.8567977026962111 car=0.854458696318223 cat=0.8721587710825285 chair=0.5654502640272956 cow=0.7986695303639431 diningtable=0.7618129846441621 dog=0.8543546579691186 horse=0.8599547173407696 motorbike=0.8298599745635624 person=0.7982384532065198 pottedplant=0.43070331187301186 sheep=0.8014299427831253 sofa=0.7322968193633447 train=0.8331043354460937 tvmonitor=0.7726401480045945 mAP=0.765785373273909 [Epoch 69][Batch 99], LR: 1.00E-03, Speed: 97.480 samples/sec, ObjLoss=9.352, BoxCenterLoss=5.745, BoxScaleLoss=1.613, ClassLoss=2.126 [Epoch 69][Batch 199], LR: 1.00E-03, Speed: 138.268 samples/sec, ObjLoss=9.330, BoxCenterLoss=5.743, BoxScaleLoss=1.612, ClassLoss=2.121 [Epoch 69] Training cost: 567.201, ObjLoss=9.317, BoxCenterLoss=5.742, BoxScaleLoss=1.610, ClassLoss=2.117 [Epoch 69] Validation: aeroplane=0.8201846071460952 bicycle=0.8373379472232656 bird=0.7799982159295108 boat=0.6893324424335507 bottle=0.625562958824536 bus=0.8440945231195386 car=0.8597897354624792 cat=0.8780664505470683 chair=0.5967425444270253 cow=0.8255969826018785 diningtable=0.6845232252023107 dog=0.8466762819678977 horse=0.878269560151122 motorbike=0.8454214429942586 person=0.7816141313736077 pottedplant=0.5442827916181078 sheep=0.7677112517207567 sofa=0.7432468498259993 train=0.8331892563625176 tvmonitor=0.7899270299235361 mAP=0.7735784114427531 [Epoch 70][Batch 99], LR: 1.00E-03, Speed: 121.444 samples/sec, ObjLoss=9.294, BoxCenterLoss=5.740, BoxScaleLoss=1.608, ClassLoss=2.111 [Epoch 70][Batch 199], LR: 1.00E-03, Speed: 92.241 samples/sec, ObjLoss=9.271, BoxCenterLoss=5.738, BoxScaleLoss=1.606, ClassLoss=2.105 [Epoch 70] Training cost: 544.831, ObjLoss=9.259, BoxCenterLoss=5.737, BoxScaleLoss=1.605, ClassLoss=2.102 [Epoch 70] Validation: aeroplane=0.8035075031677873 bicycle=0.8441788008022413 bird=0.817454017765454 boat=0.6463099733452429 bottle=0.6351119776808551 bus=0.8532570520387215 car=0.8719440037761482 cat=0.8868424415815034 chair=0.5975851723524581 cow=0.8149779904593188 diningtable=0.7514925584346331 dog=0.8672235354497002 horse=0.8614166666523718 motorbike=0.8334380479958345 person=0.8022177053956763 pottedplant=0.5143889410014642 sheep=0.8260972089392491 sofa=0.751124391419143 train=0.8506499997526035 tvmonitor=0.7629294346638811 mAP=0.7796073711337144 [Epoch 71][Batch 99], LR: 1.00E-03, Speed: 119.772 samples/sec, ObjLoss=9.236, BoxCenterLoss=5.736, BoxScaleLoss=1.603, ClassLoss=2.095 [Epoch 71][Batch 199], LR: 1.00E-03, Speed: 121.057 samples/sec, ObjLoss=9.214, BoxCenterLoss=5.734, BoxScaleLoss=1.601, ClassLoss=2.090 [Epoch 71] Training cost: 522.172, ObjLoss=9.202, BoxCenterLoss=5.733, BoxScaleLoss=1.600, ClassLoss=2.087 [Epoch 71] Validation: aeroplane=0.8407166943272572 bicycle=0.801684043598185 bird=0.8262567098290987 boat=0.7087335062193031 bottle=0.6298887699342505 bus=0.8631017377353135 car=0.8673908732554317 cat=0.8848622726554485 chair=0.6250340612151645 cow=0.8222857446812147 diningtable=0.7482679309192335 dog=0.8512120786004834 horse=0.878245362978501 motorbike=0.8367652877642479 person=0.8163766007236923 pottedplant=0.5135623327266863 sheep=0.7913893199607668 sofa=0.7332201647439326 train=0.8619263029919967 tvmonitor=0.7263338835060372 mAP=0.7813626839183122 [Epoch 72][Batch 99], LR: 1.00E-03, Speed: 118.816 samples/sec, ObjLoss=9.181, BoxCenterLoss=5.732, BoxScaleLoss=1.599, ClassLoss=2.081 [Epoch 72][Batch 199], LR: 1.00E-03, Speed: 82.903 samples/sec, ObjLoss=9.161, BoxCenterLoss=5.732, BoxScaleLoss=1.597, ClassLoss=2.076 [Epoch 72] Training cost: 581.425, ObjLoss=9.149, BoxCenterLoss=5.731, BoxScaleLoss=1.596, ClassLoss=2.072 [Epoch 72] Validation: aeroplane=0.8460537744802648 bicycle=0.7944713163744622 bird=0.7588216589256389 boat=0.6230831297842325 bottle=0.628864689554321 bus=0.8303529640501506 car=0.8482578432472097 cat=0.8824669924637818 chair=0.6022378804709471 cow=0.84353475005461 diningtable=0.6746444157550632 dog=0.8294291673735473 horse=0.871616345596899 motorbike=0.840580180469201 person=0.7768292490165116 pottedplant=0.5085994143213043 sheep=0.7944271998262208 sofa=0.7369893500270103 train=0.8474099330445044 tvmonitor=0.7852219932993515 mAP=0.7661946124067616 [Epoch 73][Batch 99], LR: 1.00E-03, Speed: 85.068 samples/sec, ObjLoss=9.129, BoxCenterLoss=5.731, BoxScaleLoss=1.595, ClassLoss=2.067 [Epoch 73][Batch 199], LR: 1.00E-03, Speed: 130.257 samples/sec, ObjLoss=9.110, BoxCenterLoss=5.731, BoxScaleLoss=1.594, ClassLoss=2.062 [Epoch 73] Training cost: 561.147, ObjLoss=9.099, BoxCenterLoss=5.730, BoxScaleLoss=1.593, ClassLoss=2.059 [Epoch 73] Validation: aeroplane=0.863585706207018 bicycle=0.7934119655286032 bird=0.7785505042162272 boat=0.6577678282185461 bottle=0.6533242140762245 bus=0.8663693582903483 car=0.8688319431119643 cat=0.8765598252825522 chair=0.6138432777660472 cow=0.8197619643296539 diningtable=0.7588457016184829 dog=0.8646719018847102 horse=0.8864022417858736 motorbike=0.8422568801924413 person=0.8156804253241678 pottedplant=0.5069717946854947 sheep=0.8031220129155118 sofa=0.7379165160482131 train=0.8412636008806883 tvmonitor=0.7525574647818088 mAP=0.780084756357229 [Epoch 74][Batch 99], LR: 1.00E-03, Speed: 86.106 samples/sec, ObjLoss=9.079, BoxCenterLoss=5.730, BoxScaleLoss=1.592, ClassLoss=2.053 [Epoch 74][Batch 199], LR: 1.00E-03, Speed: 108.999 samples/sec, ObjLoss=9.059, BoxCenterLoss=5.730, BoxScaleLoss=1.590, ClassLoss=2.048 [Epoch 74] Training cost: 563.710, ObjLoss=9.047, BoxCenterLoss=5.729, BoxScaleLoss=1.589, ClassLoss=2.045 [Epoch 74] Validation: aeroplane=0.7934118439078381 bicycle=0.8349493358010311 bird=0.7817295490960623 boat=0.7066336280678909 bottle=0.6542077791458094 bus=0.8540392745974258 car=0.8700287805022336 cat=0.887413371179093 chair=0.5925092628855084 cow=0.8145552513437115 diningtable=0.7547227594120616 dog=0.8619487763044785 horse=0.8752391998047846 motorbike=0.8410143249264035 person=0.816122034389463 pottedplant=0.5009408869973615 sheep=0.8072587814259167 sofa=0.7485963935913051 train=0.7877483898817648 tvmonitor=0.7389385237552966 mAP=0.7761004073507719 [Epoch 75][Batch 99], LR: 1.00E-03, Speed: 137.644 samples/sec, ObjLoss=9.027, BoxCenterLoss=5.728, BoxScaleLoss=1.587, ClassLoss=2.039 [Epoch 75][Batch 199], LR: 1.00E-03, Speed: 120.211 samples/sec, ObjLoss=9.007, BoxCenterLoss=5.727, BoxScaleLoss=1.585, ClassLoss=2.033 [Epoch 75] Training cost: 546.886, ObjLoss=8.995, BoxCenterLoss=5.726, BoxScaleLoss=1.584, ClassLoss=2.030 [Epoch 75] Validation: aeroplane=0.8617394187917968 bicycle=0.862209207922318 bird=0.7512607467958672 boat=0.6830588663565156 bottle=0.6197857753191534 bus=0.8575648327179181 car=0.857057382907577 cat=0.8925620121743016 chair=0.6146765898714815 cow=0.8405980402707367 diningtable=0.7233791465880638 dog=0.8528755211247719 horse=0.8898812729810992 motorbike=0.8611139872618989 person=0.8153876413848594 pottedplant=0.4788695106441796 sheep=0.821241689912038 sofa=0.7654595372741433 train=0.8626071824591847 tvmonitor=0.7683043274396602 mAP=0.7839816345098781 [Epoch 76][Batch 99], LR: 1.00E-03, Speed: 99.033 samples/sec, ObjLoss=8.976, BoxCenterLoss=5.725, BoxScaleLoss=1.583, ClassLoss=2.025 [Epoch 76][Batch 199], LR: 1.00E-03, Speed: 94.898 samples/sec, ObjLoss=8.956, BoxCenterLoss=5.724, BoxScaleLoss=1.581, ClassLoss=2.020 [Epoch 76] Training cost: 568.457, ObjLoss=8.945, BoxCenterLoss=5.724, BoxScaleLoss=1.580, ClassLoss=2.017 [Epoch 76] Validation: aeroplane=0.8018618651230482 bicycle=0.7935582464163176 bird=0.7700592452127317 boat=0.6318765799026209 bottle=0.6156239101991525 bus=0.8446676466025772 car=0.8634253006754465 cat=0.8743867213585804 chair=0.6016481216534825 cow=0.8147288370702492 diningtable=0.6725553767309839 dog=0.8517545895447666 horse=0.8597190639780091 motorbike=0.8347091462280741 person=0.7754095293363438 pottedplant=0.522189930196855 sheep=0.7784222016147826 sofa=0.7222286559418346 train=0.838965436318306 tvmonitor=0.7344457143312005 mAP=0.7601118059217682 [Epoch 77][Batch 99], LR: 1.00E-03, Speed: 135.717 samples/sec, ObjLoss=8.926, BoxCenterLoss=5.723, BoxScaleLoss=1.579, ClassLoss=2.011 [Epoch 77][Batch 199], LR: 1.00E-03, Speed: 92.861 samples/sec, ObjLoss=8.907, BoxCenterLoss=5.722, BoxScaleLoss=1.577, ClassLoss=2.006 [Epoch 77] Training cost: 571.255, ObjLoss=8.897, BoxCenterLoss=5.721, BoxScaleLoss=1.576, ClassLoss=2.003 [Epoch 77] Validation: aeroplane=0.8528714489849228 bicycle=0.8394683021965809 bird=0.7914607861549504 boat=0.6569652910910639 bottle=0.6638533186004727 bus=0.8434993320739137 car=0.8571977010910111 cat=0.8728134334610957 chair=0.6114088864587198 cow=0.8384456619170942 diningtable=0.7135042332711271 dog=0.8467425876599873 horse=0.8705829382832511 motorbike=0.8427687893760596 person=0.7963629051837969 pottedplant=0.5362419801715792 sheep=0.7977207072047173 sofa=0.7741625513981688 train=0.8544270690348739 tvmonitor=0.7544532637408304 mAP=0.7807475593677108 [Epoch 78][Batch 99], LR: 1.00E-03, Speed: 125.264 samples/sec, ObjLoss=8.878, BoxCenterLoss=5.720, BoxScaleLoss=1.575, ClassLoss=1.998 [Epoch 78][Batch 199], LR: 1.00E-03, Speed: 116.901 samples/sec, ObjLoss=8.860, BoxCenterLoss=5.719, BoxScaleLoss=1.573, ClassLoss=1.994 [Epoch 78] Training cost: 549.824, ObjLoss=8.849, BoxCenterLoss=5.719, BoxScaleLoss=1.572, ClassLoss=1.991 [Epoch 78] Validation: aeroplane=0.794863663802404 bicycle=0.8511831372394744 bird=0.8153840980233106 boat=0.6546341360465942 bottle=0.6537029101146856 bus=0.839357817855968 car=0.8673391843466431 cat=0.8836107033873452 chair=0.5852159748020894 cow=0.83582996163405 diningtable=0.7589154984114682 dog=0.8536315792114757 horse=0.8731491489980605 motorbike=0.833169319336235 person=0.8064877398621098 pottedplant=0.5050819433255199 sheep=0.8087322546940757 sofa=0.7143000441300342 train=0.8692973615884939 tvmonitor=0.7546002057166981 mAP=0.7779243341263369 [Epoch 79][Batch 99], LR: 1.00E-03, Speed: 87.929 samples/sec, ObjLoss=8.831, BoxCenterLoss=5.718, BoxScaleLoss=1.571, ClassLoss=1.986 [Epoch 79][Batch 199], LR: 1.00E-03, Speed: 125.582 samples/sec, ObjLoss=8.813, BoxCenterLoss=5.717, BoxScaleLoss=1.569, ClassLoss=1.981 [Epoch 79] Training cost: 545.155, ObjLoss=8.802, BoxCenterLoss=5.716, BoxScaleLoss=1.568, ClassLoss=1.978 [Epoch 79] Validation: aeroplane=0.8551425040504378 bicycle=0.8362575350702831 bird=0.8156751192368481 boat=0.6846924865488379 bottle=0.6702965217947673 bus=0.8612829134178289 car=0.8630544164823707 cat=0.8875998280679132 chair=0.611936953232257 cow=0.8595147411888974 diningtable=0.7490152351665815 dog=0.8484504039996088 horse=0.8840599910472178 motorbike=0.848650743172032 person=0.8169906567224261 pottedplant=0.5201738604763765 sheep=0.8238041707128718 sofa=0.7548226160372192 train=0.850334013253891 tvmonitor=0.7559788516140667 mAP=0.7898866780646366 [Epoch 80][Batch 99], LR: 1.00E-03, Speed: 102.317 samples/sec, ObjLoss=8.784, BoxCenterLoss=5.715, BoxScaleLoss=1.567, ClassLoss=1.973 [Epoch 80][Batch 199], LR: 1.00E-03, Speed: 119.623 samples/sec, ObjLoss=8.766, BoxCenterLoss=5.714, BoxScaleLoss=1.565, ClassLoss=1.969 [Epoch 80] Training cost: 555.394, ObjLoss=8.756, BoxCenterLoss=5.714, BoxScaleLoss=1.564, ClassLoss=1.966 [Epoch 80] Validation: aeroplane=0.8365371815127237 bicycle=0.796236175336772 bird=0.766662821011555 boat=0.6785882521007125 bottle=0.6197461978568906 bus=0.8323260072513875 car=0.8690052188348972 cat=0.8626331616606664 chair=0.5805341359917826 cow=0.8331038635394179 diningtable=0.7431729767444601 dog=0.8383675143720764 horse=0.8914203443592138 motorbike=0.8477656284867185 person=0.8136797534335938 pottedplant=0.5095716486700093 sheep=0.7745535799687637 sofa=0.7168670726619448 train=0.8302048101368795 tvmonitor=0.7561312755735685 mAP=0.7698553809752017 [Epoch 81][Batch 99], LR: 1.00E-03, Speed: 99.877 samples/sec, ObjLoss=8.739, BoxCenterLoss=5.713, BoxScaleLoss=1.563, ClassLoss=1.962 [Epoch 81][Batch 199], LR: 1.00E-03, Speed: 107.450 samples/sec, ObjLoss=8.721, BoxCenterLoss=5.712, BoxScaleLoss=1.561, ClassLoss=1.957 [Epoch 81] Training cost: 519.105, ObjLoss=8.712, BoxCenterLoss=5.712, BoxScaleLoss=1.561, ClassLoss=1.955 [Epoch 81] Validation: aeroplane=0.8315239908793741 bicycle=0.8295918048706316 bird=0.7802001203214347 boat=0.619782541211964 bottle=0.6716878822916174 bus=0.8455334230892124 car=0.8519666573372325 cat=0.8824721241838343 chair=0.6071681106147964 cow=0.7977007973071567 diningtable=0.6834098087447881 dog=0.8504925142839953 horse=0.8630935860011593 motorbike=0.8311719419831355 person=0.7943242469384149 pottedplant=0.4851950952098443 sheep=0.7320560247046202 sofa=0.7462497538721581 train=0.7825287811306196 tvmonitor=0.7929493057319565 mAP=0.7639549255353973 [Epoch 82][Batch 99], LR: 1.00E-03, Speed: 97.185 samples/sec, ObjLoss=8.695, BoxCenterLoss=5.711, BoxScaleLoss=1.559, ClassLoss=1.950 [Epoch 82][Batch 199], LR: 1.00E-03, Speed: 88.151 samples/sec, ObjLoss=8.679, BoxCenterLoss=5.710, BoxScaleLoss=1.558, ClassLoss=1.946 [Epoch 82] Training cost: 575.710, ObjLoss=8.669, BoxCenterLoss=5.710, BoxScaleLoss=1.557, ClassLoss=1.943 [Epoch 82] Validation: aeroplane=0.835301226946412 bicycle=0.8370367196641313 bird=0.7852762491609634 boat=0.7111590829163147 bottle=0.6542703829179937 bus=0.8541104836299346 car=0.8618217162737902 cat=0.8586456829086371 chair=0.6215903331993979 cow=0.8434817167210256 diningtable=0.7089004958745941 dog=0.8630304427304505 horse=0.8822439735435775 motorbike=0.8373443197453339 person=0.807837265776773 pottedplant=0.4972341332898127 sheep=0.8145668773857253 sofa=0.7622104000568412 train=0.8518290395850997 tvmonitor=0.7755063641748168 mAP=0.7831698453250813 [Epoch 83][Batch 99], LR: 1.00E-03, Speed: 89.750 samples/sec, ObjLoss=8.653, BoxCenterLoss=5.710, BoxScaleLoss=1.556, ClassLoss=1.939 [Epoch 83][Batch 199], LR: 1.00E-03, Speed: 125.126 samples/sec, ObjLoss=8.636, BoxCenterLoss=5.709, BoxScaleLoss=1.555, ClassLoss=1.934 [Epoch 83] Training cost: 578.366, ObjLoss=8.627, BoxCenterLoss=5.708, BoxScaleLoss=1.554, ClassLoss=1.932 [Epoch 83] Validation: aeroplane=0.8009707254573222 bicycle=0.8318500363080993 bird=0.7671489364295578 boat=0.682018163990264 bottle=0.64318938451663 bus=0.8515803835937774 car=0.858599031055488 cat=0.8732178364672831 chair=0.6023935894440181 cow=0.805493997364842 diningtable=0.7398309954105551 dog=0.8546518846634626 horse=0.8764462814295183 motorbike=0.8427420257098662 person=0.7971201868067099 pottedplant=0.5041853369236386 sheep=0.8099747499594598 sofa=0.7429201499033788 train=0.7954273884053825 tvmonitor=0.8154923247645907 mAP=0.7747626704301923 [Epoch 84][Batch 99], LR: 1.00E-03, Speed: 86.772 samples/sec, ObjLoss=8.609, BoxCenterLoss=5.707, BoxScaleLoss=1.552, ClassLoss=1.927 [Epoch 84][Batch 199], LR: 1.00E-03, Speed: 91.059 samples/sec, ObjLoss=8.593, BoxCenterLoss=5.707, BoxScaleLoss=1.551, ClassLoss=1.923 [Epoch 84] Training cost: 582.372, ObjLoss=8.584, BoxCenterLoss=5.706, BoxScaleLoss=1.551, ClassLoss=1.921 [Epoch 84] Validation: aeroplane=0.8467143139369978 bicycle=0.8225874764206795 bird=0.742731712455106 boat=0.6452863571181938 bottle=0.5841096226166559 bus=0.8569968239928715 car=0.8632354021317499 cat=0.8793361457650334 chair=0.6134644780396868 cow=0.8063685095451983 diningtable=0.7255909733669819 dog=0.8169246313649022 horse=0.855642473816806 motorbike=0.8435059081893761 person=0.8083385044309701 pottedplant=0.5106669154523753 sheep=0.8277486507816364 sofa=0.7216697939869571 train=0.8514811998448205 tvmonitor=0.7525670999081673 mAP=0.7687483496582583 [Epoch 85][Batch 99], LR: 1.00E-03, Speed: 98.892 samples/sec, ObjLoss=8.569, BoxCenterLoss=5.706, BoxScaleLoss=1.549, ClassLoss=1.916 [Epoch 85][Batch 199], LR: 1.00E-03, Speed: 130.727 samples/sec, ObjLoss=8.552, BoxCenterLoss=5.705, BoxScaleLoss=1.548, ClassLoss=1.912 [Epoch 85] Training cost: 558.607, ObjLoss=8.543, BoxCenterLoss=5.705, BoxScaleLoss=1.547, ClassLoss=1.909 [Epoch 85] Validation: aeroplane=0.8582406483899385 bicycle=0.7953350094955111 bird=0.7918126607043948 boat=0.6827501470683199 bottle=0.619604901547691 bus=0.8562313137464503 car=0.8705266948847186 cat=0.8874046625563363 chair=0.5945459968287593 cow=0.830145677097205 diningtable=0.7329728348425988 dog=0.8557260124197732 horse=0.8802991860215885 motorbike=0.8468123815142361 person=0.7955627777060229 pottedplant=0.5210224409688584 sheep=0.7657600404653029 sofa=0.691578308262551 train=0.8502329616896056 tvmonitor=0.7517065959160273 mAP=0.7739135626062945 [Epoch 86][Batch 99], LR: 1.00E-03, Speed: 127.208 samples/sec, ObjLoss=8.527, BoxCenterLoss=5.703, BoxScaleLoss=1.546, ClassLoss=1.905 [Epoch 86][Batch 199], LR: 1.00E-03, Speed: 113.507 samples/sec, ObjLoss=8.511, BoxCenterLoss=5.702, BoxScaleLoss=1.544, ClassLoss=1.901 [Epoch 86] Training cost: 576.089, ObjLoss=8.502, BoxCenterLoss=5.702, BoxScaleLoss=1.543, ClassLoss=1.898 [Epoch 86] Validation: aeroplane=0.8363637853170062 bicycle=0.84758978185042 bird=0.7842141972067185 boat=0.6580691345599202 bottle=0.651482672059216 bus=0.8641363175258079 car=0.8674762783500318 cat=0.88759152774832 chair=0.5969541352569598 cow=0.8260707333549954 diningtable=0.7065801714653587 dog=0.832563269160528 horse=0.8841545242832118 motorbike=0.8331889248325591 person=0.8072497821168613 pottedplant=0.5059511208654056 sheep=0.784609323719233 sofa=0.7404803088635991 train=0.7926909655992029 tvmonitor=0.755498698840632 mAP=0.7731457826487993 [Epoch 87][Batch 99], LR: 1.00E-03, Speed: 117.091 samples/sec, ObjLoss=8.486, BoxCenterLoss=5.701, BoxScaleLoss=1.542, ClassLoss=1.894 [Epoch 87][Batch 199], LR: 1.00E-03, Speed: 121.188 samples/sec, ObjLoss=8.470, BoxCenterLoss=5.699, BoxScaleLoss=1.541, ClassLoss=1.890 [Epoch 87] Training cost: 561.390, ObjLoss=8.462, BoxCenterLoss=5.699, BoxScaleLoss=1.540, ClassLoss=1.888 [Epoch 87] Validation: aeroplane=0.8572362754686376 bicycle=0.8407528310720747 bird=0.7780525136680467 boat=0.6864526115218373 bottle=0.5644730359819154 bus=0.8468071437459986 car=0.867594735377905 cat=0.8804159305331142 chair=0.5960073498792177 cow=0.7847168846089422 diningtable=0.7223310864242211 dog=0.847357929559794 horse=0.8725889854288539 motorbike=0.8421010133625484 person=0.8007774047796821 pottedplant=0.46706886695690597 sheep=0.7607941350269188 sofa=0.701878418696368 train=0.8413473889219318 tvmonitor=0.7384030312360557 mAP=0.7648578786125484 [Epoch 88][Batch 99], LR: 1.00E-03, Speed: 132.391 samples/sec, ObjLoss=8.446, BoxCenterLoss=5.698, BoxScaleLoss=1.539, ClassLoss=1.884 [Epoch 88][Batch 199], LR: 1.00E-03, Speed: 112.342 samples/sec, ObjLoss=8.432, BoxCenterLoss=5.698, BoxScaleLoss=1.537, ClassLoss=1.880 [Epoch 88] Training cost: 571.805, ObjLoss=8.423, BoxCenterLoss=5.697, BoxScaleLoss=1.537, ClassLoss=1.878 [Epoch 88] Validation: aeroplane=0.8283926716845993 bicycle=0.7773270247981007 bird=0.7749221682532279 boat=0.679300430614523 bottle=0.6585436785010549 bus=0.8463080561889507 car=0.8744177092850844 cat=0.8950473153654768 chair=0.6111275045683929 cow=0.8503134575037717 diningtable=0.7419469653563123 dog=0.8554829911723137 horse=0.8768272735385044 motorbike=0.8200632921023664 person=0.8121251119605618 pottedplant=0.543390954803027 sheep=0.8408313211582898 sofa=0.7556884201231155 train=0.8381663170108292 tvmonitor=0.7586723281187945 mAP=0.7819447496053649 [Epoch 89][Batch 99], LR: 1.00E-03, Speed: 102.280 samples/sec, ObjLoss=8.408, BoxCenterLoss=5.697, BoxScaleLoss=1.535, ClassLoss=1.874 [Epoch 89][Batch 199], LR: 1.00E-03, Speed: 102.299 samples/sec, ObjLoss=8.395, BoxCenterLoss=5.696, BoxScaleLoss=1.534, ClassLoss=1.871 [Epoch 89] Training cost: 563.350, ObjLoss=8.386, BoxCenterLoss=5.696, BoxScaleLoss=1.534, ClassLoss=1.868 [Epoch 89] Validation: aeroplane=0.7883168483232046 bicycle=0.8504439757230967 bird=0.7863091084508385 boat=0.6954328123887372 bottle=0.6472339648215404 bus=0.8573032885635757 car=0.8688608596282055 cat=0.8488370619558054 chair=0.6130773793262834 cow=0.8287337348147102 diningtable=0.7272713874515978 dog=0.8459934780024161 horse=0.8726570568701264 motorbike=0.8383955005888812 person=0.8035565364873727 pottedplant=0.5261624195328367 sheep=0.7901336966479364 sofa=0.7433622475636669 train=0.865139151816497 tvmonitor=0.7560076141308264 mAP=0.7776614061544078 [Epoch 90][Batch 99], LR: 1.00E-03, Speed: 120.226 samples/sec, ObjLoss=8.371, BoxCenterLoss=5.695, BoxScaleLoss=1.533, ClassLoss=1.864 [Epoch 90][Batch 199], LR: 1.00E-03, Speed: 120.465 samples/sec, ObjLoss=8.356, BoxCenterLoss=5.694, BoxScaleLoss=1.531, ClassLoss=1.860 [Epoch 90] Training cost: 559.208, ObjLoss=8.348, BoxCenterLoss=5.694, BoxScaleLoss=1.531, ClassLoss=1.858 [Epoch 90] Validation: aeroplane=0.796320101915812 bicycle=0.8370828869331451 bird=0.7891470004430085 boat=0.6704881884772375 bottle=0.5995352717628544 bus=0.8599079866158557 car=0.8502154113573028 cat=0.8774936698198808 chair=0.6201990688286677 cow=0.8228673203551419 diningtable=0.7422723676418788 dog=0.7929303000113034 horse=0.8545863936665485 motorbike=0.8405909313864064 person=0.8176137850682043 pottedplant=0.5338904714859269 sheep=0.8348328733712518 sofa=0.7472968386329306 train=0.7980408039799326 tvmonitor=0.7719452196202201 mAP=0.7728628445686754 [Epoch 91][Batch 99], LR: 1.00E-03, Speed: 89.624 samples/sec, ObjLoss=8.334, BoxCenterLoss=5.694, BoxScaleLoss=1.530, ClassLoss=1.854 [Epoch 91][Batch 199], LR: 1.00E-03, Speed: 104.450 samples/sec, ObjLoss=8.319, BoxCenterLoss=5.693, BoxScaleLoss=1.528, ClassLoss=1.851 [Epoch 91] Training cost: 537.642, ObjLoss=8.310, BoxCenterLoss=5.692, BoxScaleLoss=1.528, ClassLoss=1.848 [Epoch 91] Validation: aeroplane=0.8369132351051216 bicycle=0.8338115376222788 bird=0.7838820204967003 boat=0.6896732218734946 bottle=0.6054363345405099 bus=0.8633025322634843 car=0.8700996334183537 cat=0.8833469668045809 chair=0.6128364706030622 cow=0.8133566792885023 diningtable=0.7389854827224285 dog=0.8493840553122711 horse=0.8856956428167289 motorbike=0.8528792076347024 person=0.817604670229164 pottedplant=0.5160276794487433 sheep=0.7893269015483864 sofa=0.7433207380505217 train=0.8361397796573358 tvmonitor=0.7573033759983907 mAP=0.7789663082717381 [Epoch 92][Batch 99], LR: 1.00E-03, Speed: 133.466 samples/sec, ObjLoss=8.297, BoxCenterLoss=5.691, BoxScaleLoss=1.527, ClassLoss=1.845 [Epoch 92][Batch 199], LR: 1.00E-03, Speed: 125.204 samples/sec, ObjLoss=8.282, BoxCenterLoss=5.690, BoxScaleLoss=1.525, ClassLoss=1.840 [Epoch 92] Training cost: 535.811, ObjLoss=8.273, BoxCenterLoss=5.689, BoxScaleLoss=1.524, ClassLoss=1.838 [Epoch 92] Validation: aeroplane=0.7747448025534955 bicycle=0.8555083526173971 bird=0.7871053252197866 boat=0.6651105300918227 bottle=0.6373163740696276 bus=0.863936903334901 car=0.8704209522545515 cat=0.8729109360242758 chair=0.5858514811580178 cow=0.840334727576167 diningtable=0.7078135397997476 dog=0.8513069790021989 horse=0.8778608747574266 motorbike=0.8321421356087187 person=0.8202987177715407 pottedplant=0.5510849402547783 sheep=0.8363337639925942 sofa=0.7196256748833089 train=0.8642528622657896 tvmonitor=0.765124446573669 mAP=0.7789542159904908 [Epoch 93][Batch 99], LR: 1.00E-03, Speed: 132.386 samples/sec, ObjLoss=8.258, BoxCenterLoss=5.688, BoxScaleLoss=1.523, ClassLoss=1.834 [Epoch 93][Batch 199], LR: 1.00E-03, Speed: 116.633 samples/sec, ObjLoss=8.245, BoxCenterLoss=5.689, BoxScaleLoss=1.522, ClassLoss=1.830 [Epoch 93] Training cost: 570.939, ObjLoss=8.237, BoxCenterLoss=5.688, BoxScaleLoss=1.522, ClassLoss=1.828 [Epoch 93] Validation: aeroplane=0.8411505418983523 bicycle=0.8330415620962123 bird=0.779423836958203 boat=0.6646472270373172 bottle=0.6529871232983806 bus=0.8515163819940931 car=0.8651661898934341 cat=0.8741863464661098 chair=0.616612356738657 cow=0.8185611863452409 diningtable=0.7564944074899395 dog=0.859656247753467 horse=0.8638069100851005 motorbike=0.7979706948954943 person=0.8163707775204645 pottedplant=0.5417887583182811 sheep=0.7862383647321534 sofa=0.7536668541957458 train=0.8007609594424142 tvmonitor=0.7542293031552146 mAP=0.7764138015157138 [Epoch 94][Batch 99], LR: 1.00E-03, Speed: 134.488 samples/sec, ObjLoss=8.222, BoxCenterLoss=5.687, BoxScaleLoss=1.520, ClassLoss=1.825 [Epoch 94][Batch 199], LR: 1.00E-03, Speed: 109.288 samples/sec, ObjLoss=8.209, BoxCenterLoss=5.686, BoxScaleLoss=1.519, ClassLoss=1.821 [Epoch 94] Training cost: 532.551, ObjLoss=8.200, BoxCenterLoss=5.685, BoxScaleLoss=1.518, ClassLoss=1.819 [Epoch 94] Validation: aeroplane=0.7037875791677972 bicycle=0.8398400534481247 bird=0.7566449368352064 boat=0.6884493324850018 bottle=0.6239915743900651 bus=0.8692066607291464 car=0.8615797357104451 cat=0.8833159849708921 chair=0.6223810243668244 cow=0.8467753193549588 diningtable=0.6968859110721627 dog=0.8389066960420449 horse=0.8800875363338638 motorbike=0.8402218005343128 person=0.8244964586440872 pottedplant=0.5421175605228428 sheep=0.8161515735781456 sofa=0.7292080309783802 train=0.8377840132648706 tvmonitor=0.7943389906511745 mAP=0.7748085386540176 [Epoch 95][Batch 99], LR: 1.00E-03, Speed: 85.379 samples/sec, ObjLoss=8.187, BoxCenterLoss=5.685, BoxScaleLoss=1.517, ClassLoss=1.815 [Epoch 95][Batch 199], LR: 1.00E-03, Speed: 87.911 samples/sec, ObjLoss=8.173, BoxCenterLoss=5.684, BoxScaleLoss=1.516, ClassLoss=1.811 [Epoch 95] Training cost: 570.986, ObjLoss=8.165, BoxCenterLoss=5.684, BoxScaleLoss=1.515, ClassLoss=1.809 [Epoch 95] Validation: aeroplane=0.8351989956133707 bicycle=0.827817587316306 bird=0.7752109533176305 boat=0.6678700312561519 bottle=0.6574812071718344 bus=0.8554058090247348 car=0.871429677503479 cat=0.8880319833044188 chair=0.6432635126539868 cow=0.8461240829026667 diningtable=0.7301633646294925 dog=0.8486712966200161 horse=0.8793158950296918 motorbike=0.8596524037532632 person=0.8253480066147825 pottedplant=0.5023080124452495 sheep=0.8495986216528201 sofa=0.7391407060939015 train=0.8455000708268158 tvmonitor=0.7528984135901309 mAP=0.7850215315660373 [Epoch 96][Batch 99], LR: 1.00E-03, Speed: 131.257 samples/sec, ObjLoss=8.151, BoxCenterLoss=5.683, BoxScaleLoss=1.515, ClassLoss=1.806 [Epoch 96][Batch 199], LR: 1.00E-03, Speed: 86.626 samples/sec, ObjLoss=8.138, BoxCenterLoss=5.682, BoxScaleLoss=1.513, ClassLoss=1.802 [Epoch 96] Training cost: 551.535, ObjLoss=8.130, BoxCenterLoss=5.683, BoxScaleLoss=1.513, ClassLoss=1.800 [Epoch 96] Validation: aeroplane=0.8402424801763784 bicycle=0.797255805843168 bird=0.7814093261746844 boat=0.675375655696055 bottle=0.6455511663721845 bus=0.8611352952758328 car=0.865445022883787 cat=0.860691171775205 chair=0.5632724178727074 cow=0.7874696706059776 diningtable=0.7365466760024836 dog=0.8081168231111504 horse=0.8615669426770789 motorbike=0.8380825302300697 person=0.8094380528307915 pottedplant=0.475509497932258 sheep=0.8268919053600433 sofa=0.745042927523702 train=0.8424833674874544 tvmonitor=0.7875264778805976 mAP=0.7704526606855805 [Epoch 97][Batch 99], LR: 1.00E-03, Speed: 119.433 samples/sec, ObjLoss=8.117, BoxCenterLoss=5.682, BoxScaleLoss=1.512, ClassLoss=1.796 [Epoch 97][Batch 199], LR: 1.00E-03, Speed: 115.255 samples/sec, ObjLoss=8.103, BoxCenterLoss=5.681, BoxScaleLoss=1.510, ClassLoss=1.792 [Epoch 97] Training cost: 532.278, ObjLoss=8.096, BoxCenterLoss=5.681, BoxScaleLoss=1.510, ClassLoss=1.791 [Epoch 97] Validation: aeroplane=0.877716013462821 bicycle=0.7975589634396132 bird=0.7815987764266069 boat=0.6802090516844667 bottle=0.6468810264334325 bus=0.8433809541356151 car=0.868177228978727 cat=0.8694289084101547 chair=0.6171740146047304 cow=0.8369436185951321 diningtable=0.7609752737899002 dog=0.844225554432568 horse=0.8834625176187911 motorbike=0.7835678371210993 person=0.7669837157777195 pottedplant=0.5029756140812656 sheep=0.7878983577841381 sofa=0.7652864744256382 train=0.7920858861133093 tvmonitor=0.7768045398807368 mAP=0.7741667163598234 [Epoch 98][Batch 99], LR: 1.00E-03, Speed: 116.440 samples/sec, ObjLoss=8.082, BoxCenterLoss=5.680, BoxScaleLoss=1.509, ClassLoss=1.787 [Epoch 98][Batch 199], LR: 1.00E-03, Speed: 84.813 samples/sec, ObjLoss=8.070, BoxCenterLoss=5.679, BoxScaleLoss=1.508, ClassLoss=1.784 [Epoch 98] Training cost: 554.689, ObjLoss=8.063, BoxCenterLoss=5.679, BoxScaleLoss=1.507, ClassLoss=1.782 [Epoch 98] Validation: aeroplane=0.8416629587665545 bicycle=0.7969443374148363 bird=0.7833428014010926 boat=0.6876124384613147 bottle=0.6338314536574493 bus=0.8748602525858099 car=0.8693587702502126 cat=0.8849214083303168 chair=0.6359319187139243 cow=0.7908572452565652 diningtable=0.7371156498193748 dog=0.8404408759593893 horse=0.8819820466810215 motorbike=0.851764170482298 person=0.7669269894966161 pottedplant=0.5054838065842693 sheep=0.7992694812848194 sofa=0.7089678159124028 train=0.857475977684687 tvmonitor=0.8062966522600323 mAP=0.7777523525501493 [Epoch 99][Batch 99], LR: 1.00E-03, Speed: 131.022 samples/sec, ObjLoss=8.050, BoxCenterLoss=5.678, BoxScaleLoss=1.506, ClassLoss=1.778 [Epoch 99][Batch 199], LR: 1.00E-03, Speed: 90.451 samples/sec, ObjLoss=8.037, BoxCenterLoss=5.678, BoxScaleLoss=1.505, ClassLoss=1.775 [Epoch 99] Training cost: 546.872, ObjLoss=8.030, BoxCenterLoss=5.677, BoxScaleLoss=1.504, ClassLoss=1.773 [Epoch 99] Validation: aeroplane=0.858008166262379 bicycle=0.8440360816046616 bird=0.8528726794413691 boat=0.6638579046839137 bottle=0.624870093571581 bus=0.8544888348994897 car=0.8628694010139248 cat=0.8768178528022573 chair=0.621586672343013 cow=0.8285547393842414 diningtable=0.7035212968994314 dog=0.8468519236605879 horse=0.8717418269428544 motorbike=0.8319685080771828 person=0.8124242420799279 pottedplant=0.5314743470967946 sheep=0.8255123287040884 sofa=0.6890664685816984 train=0.8555731191259245 tvmonitor=0.7551839204746286 mAP=0.7805640203824975 [Epoch 100][Batch 99], LR: 1.00E-03, Speed: 91.973 samples/sec, ObjLoss=8.017, BoxCenterLoss=5.676, BoxScaleLoss=1.503, ClassLoss=1.770 [Epoch 100][Batch 199], LR: 1.00E-03, Speed: 96.238 samples/sec, ObjLoss=8.006, BoxCenterLoss=5.677, BoxScaleLoss=1.502, ClassLoss=1.766 [Epoch 100] Training cost: 554.457, ObjLoss=7.998, BoxCenterLoss=5.676, BoxScaleLoss=1.502, ClassLoss=1.764 [Epoch 100] Validation: aeroplane=0.8004655882653962 bicycle=0.8411252238342046 bird=0.7881507239521947 boat=0.6956922435511957 bottle=0.6101912932710015 bus=0.8375427686813104 car=0.8635003429292214 cat=0.8093618020185259 chair=0.5869523418781207 cow=0.8148524456195232 diningtable=0.7485042497369351 dog=0.8231868548309497 horse=0.8692715791696685 motorbike=0.8384490554473124 person=0.7814630388391003 pottedplant=0.5345558809503081 sheep=0.8180716366265351 sofa=0.7277280457514818 train=0.8591478498126189 tvmonitor=0.7647233669513014 mAP=0.7706468166058452 [Epoch 101][Batch 99], LR: 1.00E-03, Speed: 94.989 samples/sec, ObjLoss=7.985, BoxCenterLoss=5.675, BoxScaleLoss=1.500, ClassLoss=1.760 [Epoch 101][Batch 199], LR: 1.00E-03, Speed: 134.158 samples/sec, ObjLoss=7.972, BoxCenterLoss=5.675, BoxScaleLoss=1.499, ClassLoss=1.756 [Epoch 101] Training cost: 572.276, ObjLoss=7.965, BoxCenterLoss=5.674, BoxScaleLoss=1.499, ClassLoss=1.754 [Epoch 101] Validation: aeroplane=0.8446491204778935 bicycle=0.8046618422160223 bird=0.7812624564849131 boat=0.6853757802188727 bottle=0.6494062583116711 bus=0.8485125443855117 car=0.8624453081721497 cat=0.8914740029467056 chair=0.6341372931239577 cow=0.8283802268724684 diningtable=0.7476076074947293 dog=0.8573347557667486 horse=0.8622893429527575 motorbike=0.8635148144731839 person=0.8178203906293792 pottedplant=0.5419492203743395 sheep=0.8440236139081037 sofa=0.7363619017688098 train=0.8271134944721537 tvmonitor=0.7634217422048319 mAP=0.7845870858627602 [Epoch 102][Batch 99], LR: 1.00E-03, Speed: 127.909 samples/sec, ObjLoss=7.952, BoxCenterLoss=5.673, BoxScaleLoss=1.498, ClassLoss=1.751 [Epoch 102][Batch 199], LR: 1.00E-03, Speed: 120.014 samples/sec, ObjLoss=7.940, BoxCenterLoss=5.673, BoxScaleLoss=1.497, ClassLoss=1.747 [Epoch 102] Training cost: 569.333, ObjLoss=7.933, BoxCenterLoss=5.673, BoxScaleLoss=1.496, ClassLoss=1.746 [Epoch 102] Validation: aeroplane=0.8548251735708097 bicycle=0.8392226988317597 bird=0.7990626483715122 boat=0.6740731701870107 bottle=0.6162262503266805 bus=0.8596041337122823 car=0.8693550063148078 cat=0.8643443220598663 chair=0.6244661739113899 cow=0.7510189935701915 diningtable=0.7209535820810922 dog=0.8255760637679446 horse=0.8756521987364358 motorbike=0.8483247411925172 person=0.8117011111066447 pottedplant=0.5133601823524859 sheep=0.7475406968979257 sofa=0.767365768420249 train=0.8400976700509655 tvmonitor=0.7650574876909125 mAP=0.7733914036576741 [Epoch 103][Batch 99], LR: 1.00E-03, Speed: 100.146 samples/sec, ObjLoss=7.921, BoxCenterLoss=5.672, BoxScaleLoss=1.495, ClassLoss=1.743 [Epoch 103][Batch 199], LR: 1.00E-03, Speed: 133.930 samples/sec, ObjLoss=7.909, BoxCenterLoss=5.672, BoxScaleLoss=1.494, ClassLoss=1.740 [Epoch 103] Training cost: 576.202, ObjLoss=7.902, BoxCenterLoss=5.671, BoxScaleLoss=1.493, ClassLoss=1.738 [Epoch 103] Validation: aeroplane=0.7998444787248374 bicycle=0.7959056734019055 bird=0.7821896845731053 boat=0.6907379480751739 bottle=0.640275929683373 bus=0.8537797926240727 car=0.8699045705876071 cat=0.8797809685611491 chair=0.6221645354938944 cow=0.831664822381802 diningtable=0.7354258986273784 dog=0.8279656259156314 horse=0.8733710699147981 motorbike=0.8567316813039971 person=0.8170097020264053 pottedplant=0.5064852095624762 sheep=0.8084758925923725 sofa=0.7391465285280566 train=0.8551078732671267 tvmonitor=0.767746400809791 mAP=0.7776857143327477 [Epoch 104][Batch 99], LR: 1.00E-03, Speed: 92.895 samples/sec, ObjLoss=7.891, BoxCenterLoss=5.671, BoxScaleLoss=1.493, ClassLoss=1.735 [Epoch 104][Batch 199], LR: 1.00E-03, Speed: 113.080 samples/sec, ObjLoss=7.879, BoxCenterLoss=5.670, BoxScaleLoss=1.492, ClassLoss=1.731 [Epoch 104] Training cost: 566.671, ObjLoss=7.872, BoxCenterLoss=5.670, BoxScaleLoss=1.491, ClassLoss=1.730 [Epoch 104] Validation: aeroplane=0.8433303938775293 bicycle=0.8001321496839842 bird=0.7939214219030735 boat=0.6860530748235983 bottle=0.6108030860671988 bus=0.8610441696270068 car=0.8730277761233896 cat=0.8752905256139559 chair=0.5843240324452418 cow=0.8232252896936271 diningtable=0.75882093043314 dog=0.8291379537845054 horse=0.8663805908393922 motorbike=0.8380203875031568 person=0.8115894003329458 pottedplant=0.5323635234747375 sheep=0.8161455056416363 sofa=0.6926507793625177 train=0.8632291720328978 tvmonitor=0.7785123725738978 mAP=0.7769001267918717 [Epoch 105][Batch 99], LR: 1.00E-03, Speed: 106.967 samples/sec, ObjLoss=7.860, BoxCenterLoss=5.668, BoxScaleLoss=1.490, ClassLoss=1.726 [Epoch 105][Batch 199], LR: 1.00E-03, Speed: 105.009 samples/sec, ObjLoss=7.848, BoxCenterLoss=5.667, BoxScaleLoss=1.489, ClassLoss=1.723 [Epoch 105] Training cost: 552.381, ObjLoss=7.841, BoxCenterLoss=5.667, BoxScaleLoss=1.488, ClassLoss=1.721 [Epoch 105] Validation: aeroplane=0.7953447482726494 bicycle=0.8151274623332158 bird=0.7945184385581875 boat=0.6629491654698536 bottle=0.661564001562257 bus=0.8498796436558358 car=0.8712939679276102 cat=0.8865007605659649 chair=0.6270790022605897 cow=0.8427414797909218 diningtable=0.7559275574605611 dog=0.8655549580152877 horse=0.8727652868089719 motorbike=0.8527504033009949 person=0.8117680463632738 pottedplant=0.5474254786313091 sheep=0.8370426943578081 sofa=0.7360362921099797 train=0.8597515957968539 tvmonitor=0.7591480460778685 mAP=0.7852584514659998 [Epoch 106][Batch 99], LR: 1.00E-03, Speed: 110.371 samples/sec, ObjLoss=7.829, BoxCenterLoss=5.667, BoxScaleLoss=1.487, ClassLoss=1.718 [Epoch 106][Batch 199], LR: 1.00E-03, Speed: 110.605 samples/sec, ObjLoss=7.817, BoxCenterLoss=5.666, BoxScaleLoss=1.486, ClassLoss=1.715 [Epoch 106] Training cost: 551.297, ObjLoss=7.811, BoxCenterLoss=5.665, BoxScaleLoss=1.485, ClassLoss=1.713 [Epoch 106] Validation: aeroplane=0.7911898615334 bicycle=0.7887373739903973 bird=0.7745719675629636 boat=0.682755863261569 bottle=0.6313662552555488 bus=0.833592741494041 car=0.8642545379399371 cat=0.872408866230376 chair=0.5932020361393625 cow=0.8170377214124569 diningtable=0.7070706899750195 dog=0.8516011942482511 horse=0.8809361244714163 motorbike=0.7952506518680149 person=0.7782791723124546 pottedplant=0.4897084162580383 sheep=0.774891498500541 sofa=0.712872950025839 train=0.7909012105866291 tvmonitor=0.7466038866627215 mAP=0.7588616509864489 [Epoch 107][Batch 99], LR: 1.00E-03, Speed: 129.356 samples/sec, ObjLoss=7.800, BoxCenterLoss=5.665, BoxScaleLoss=1.484, ClassLoss=1.710 [Epoch 107][Batch 199], LR: 1.00E-03, Speed: 118.961 samples/sec, ObjLoss=7.787, BoxCenterLoss=5.663, BoxScaleLoss=1.483, ClassLoss=1.706 [Epoch 107] Training cost: 563.411, ObjLoss=7.780, BoxCenterLoss=5.663, BoxScaleLoss=1.482, ClassLoss=1.704 [Epoch 107] Validation: aeroplane=0.8002731081548233 bicycle=0.8442231359191169 bird=0.7842804145076313 boat=0.6932795575298494 bottle=0.6034608299522318 bus=0.8383016060133491 car=0.8609075173336482 cat=0.8728657006753124 chair=0.5717451315124399 cow=0.8378124347127769 diningtable=0.7113704591249279 dog=0.834767622362281 horse=0.88436555607402 motorbike=0.8370373393515573 person=0.8068865189784514 pottedplant=0.5190232047498843 sheep=0.8342739263039474 sofa=0.7196171357720808 train=0.8517214007461331 tvmonitor=0.7436595402780426 mAP=0.7724936070026251 [Epoch 108][Batch 99], LR: 1.00E-03, Speed: 114.708 samples/sec, ObjLoss=7.769, BoxCenterLoss=5.662, BoxScaleLoss=1.481, ClassLoss=1.701 [Epoch 108][Batch 199], LR: 1.00E-03, Speed: 86.121 samples/sec, ObjLoss=7.757, BoxCenterLoss=5.662, BoxScaleLoss=1.480, ClassLoss=1.698 [Epoch 108] Training cost: 537.673, ObjLoss=7.751, BoxCenterLoss=5.661, BoxScaleLoss=1.480, ClassLoss=1.697 [Epoch 108] Validation: aeroplane=0.7717801947980976 bicycle=0.8339576595231127 bird=0.7922479797706833 boat=0.6729201829781134 bottle=0.6086278780687285 bus=0.8484670226139648 car=0.8591223337283905 cat=0.8816166467479348 chair=0.5941854994346649 cow=0.8524696925696726 diningtable=0.7478266517790563 dog=0.8350883066157891 horse=0.875057450803179 motorbike=0.8308594991112651 person=0.822565568983172 pottedplant=0.5223779330966665 sheep=0.8147408479954933 sofa=0.7240979314384826 train=0.8563684165029569 tvmonitor=0.7519896378590701 mAP=0.7748183667209246 [Epoch 109][Batch 99], LR: 1.00E-03, Speed: 124.671 samples/sec, ObjLoss=7.741, BoxCenterLoss=5.661, BoxScaleLoss=1.479, ClassLoss=1.694 [Epoch 109][Batch 199], LR: 1.00E-03, Speed: 94.406 samples/sec, ObjLoss=7.730, BoxCenterLoss=5.660, BoxScaleLoss=1.478, ClassLoss=1.691 [Epoch 109] Training cost: 555.459, ObjLoss=7.723, BoxCenterLoss=5.660, BoxScaleLoss=1.477, ClassLoss=1.689 [Epoch 109] Validation: aeroplane=0.8614538475886252 bicycle=0.8613605880923334 bird=0.7833130123568026 boat=0.6981184655156157 bottle=0.6812250767911435 bus=0.8422582040967009 car=0.8683798169251236 cat=0.8859123408280619 chair=0.6180413926744656 cow=0.8189883135094214 diningtable=0.7317350984387835 dog=0.8462437612370494 horse=0.8625941085700399 motorbike=0.858830828738819 person=0.8160134884349627 pottedplant=0.5404217394563451 sheep=0.8145209719142406 sofa=0.7353280989631438 train=0.8356872691493199 tvmonitor=0.7746405518112576 mAP=0.7867533487546128 [Epoch 110][Batch 99], LR: 1.00E-03, Speed: 85.705 samples/sec, ObjLoss=7.713, BoxCenterLoss=5.659, BoxScaleLoss=1.476, ClassLoss=1.687 [Epoch 110][Batch 199], LR: 1.00E-03, Speed: 119.415 samples/sec, ObjLoss=7.702, BoxCenterLoss=5.659, BoxScaleLoss=1.476, ClassLoss=1.684 [Epoch 110] Training cost: 564.621, ObjLoss=7.695, BoxCenterLoss=5.658, BoxScaleLoss=1.475, ClassLoss=1.682 [Epoch 110] Validation: aeroplane=0.8421603329714197 bicycle=0.8376385782684982 bird=0.7863265785213225 boat=0.6878741342929845 bottle=0.6708501516313298 bus=0.8444178198411553 car=0.8651086269534183 cat=0.8700579851547994 chair=0.6104171666219229 cow=0.8487339403721406 diningtable=0.7327167671255035 dog=0.7936286122176268 horse=0.8701566721995628 motorbike=0.8462437150734063 person=0.8199719361408677 pottedplant=0.5453569511744141 sheep=0.7707940164923747 sofa=0.7395659214777229 train=0.7912300518856478 tvmonitor=0.7627653738050403 mAP=0.7768007666110579 [Epoch 111][Batch 99], LR: 1.00E-03, Speed: 112.105 samples/sec, ObjLoss=7.684, BoxCenterLoss=5.658, BoxScaleLoss=1.474, ClassLoss=1.679 [Epoch 111][Batch 199], LR: 1.00E-03, Speed: 116.157 samples/sec, ObjLoss=7.673, BoxCenterLoss=5.656, BoxScaleLoss=1.473, ClassLoss=1.676 [Epoch 111] Training cost: 549.444, ObjLoss=7.667, BoxCenterLoss=5.656, BoxScaleLoss=1.472, ClassLoss=1.674 [Epoch 111] Validation: aeroplane=0.8460372360394398 bicycle=0.7944829968957864 bird=0.7818166812082453 boat=0.6852314164338253 bottle=0.6519957031208274 bus=0.8608944800245464 car=0.8678316080112607 cat=0.8766368635693441 chair=0.5812768134754229 cow=0.8301355917635944 diningtable=0.7299422034142267 dog=0.8272861501103658 horse=0.86800024480084 motorbike=0.8471007680809377 person=0.8215711266173634 pottedplant=0.5299526056760175 sheep=0.8126306479965478 sofa=0.7376340783172589 train=0.8319128526255521 tvmonitor=0.7417729488715374 mAP=0.7762071508526469 [Epoch 112][Batch 99], LR: 1.00E-03, Speed: 87.896 samples/sec, ObjLoss=7.656, BoxCenterLoss=5.655, BoxScaleLoss=1.472, ClassLoss=1.671 [Epoch 112][Batch 199], LR: 1.00E-03, Speed: 122.228 samples/sec, ObjLoss=7.645, BoxCenterLoss=5.655, BoxScaleLoss=1.471, ClassLoss=1.668 [Epoch 112] Training cost: 572.824, ObjLoss=7.639, BoxCenterLoss=5.654, BoxScaleLoss=1.470, ClassLoss=1.667 [Epoch 112] Validation: aeroplane=0.8445177484107834 bicycle=0.7925631036789224 bird=0.7873602722769718 boat=0.7044699962107795 bottle=0.6267388496737678 bus=0.8010089790592183 car=0.8623264479735309 cat=0.8822293801558474 chair=0.6172841691783926 cow=0.8455739557335203 diningtable=0.7139270749076309 dog=0.8524449260673087 horse=0.879278516173246 motorbike=0.8558088536047131 person=0.8177237353327029 pottedplant=0.47231718866843647 sheep=0.843957413415149 sofa=0.7513098560378857 train=0.8479155354687917 tvmonitor=0.7488475435734118 mAP=0.7773801772800505 [Epoch 113][Batch 99], LR: 1.00E-03, Speed: 118.944 samples/sec, ObjLoss=7.628, BoxCenterLoss=5.654, BoxScaleLoss=1.469, ClassLoss=1.664 [Epoch 113][Batch 199], LR: 1.00E-03, Speed: 145.470 samples/sec, ObjLoss=7.618, BoxCenterLoss=5.653, BoxScaleLoss=1.468, ClassLoss=1.661 [Epoch 113] Training cost: 560.488, ObjLoss=7.611, BoxCenterLoss=5.652, BoxScaleLoss=1.468, ClassLoss=1.659 [Epoch 113] Validation: aeroplane=0.7977986543415385 bicycle=0.8422889632951572 bird=0.7897018218539126 boat=0.679932718324278 bottle=0.6511935909397598 bus=0.8497761049415136 car=0.8562904623464709 cat=0.8816750878080587 chair=0.6291215833844612 cow=0.845005291812755 diningtable=0.7151054713917921 dog=0.8552497216252846 horse=0.8695731849260002 motorbike=0.8371769453961714 person=0.8150965760847203 pottedplant=0.5423173799642123 sheep=0.8097379450373753 sofa=0.7136141267914822 train=0.8379094454874653 tvmonitor=0.7480737445015918 mAP=0.7783319410127001 [Epoch 114][Batch 99], LR: 1.00E-03, Speed: 121.026 samples/sec, ObjLoss=7.601, BoxCenterLoss=5.652, BoxScaleLoss=1.467, ClassLoss=1.657 [Epoch 114][Batch 199], LR: 1.00E-03, Speed: 96.824 samples/sec, ObjLoss=7.592, BoxCenterLoss=5.651, BoxScaleLoss=1.466, ClassLoss=1.654 [Epoch 114] Training cost: 535.184, ObjLoss=7.586, BoxCenterLoss=5.651, BoxScaleLoss=1.466, ClassLoss=1.653 [Epoch 114] Validation: aeroplane=0.8354883603619461 bicycle=0.857199698439567 bird=0.7942478536157017 boat=0.699148257531341 bottle=0.6693063145392201 bus=0.8557590466878495 car=0.8741879194199365 cat=0.8868617198551569 chair=0.5870832774976203 cow=0.8334363559514112 diningtable=0.7180619529397846 dog=0.8601604151516544 horse=0.8790168226978783 motorbike=0.8469856612830058 person=0.8181809984172372 pottedplant=0.5331174573169742 sheep=0.8138931928893927 sofa=0.7369709102956542 train=0.8673822926844276 tvmonitor=0.8071619174268384 mAP=0.7886825212501299 [Epoch 115][Batch 99], LR: 1.00E-03, Speed: 92.074 samples/sec, ObjLoss=7.575, BoxCenterLoss=5.650, BoxScaleLoss=1.465, ClassLoss=1.650 [Epoch 115][Batch 199], LR: 1.00E-03, Speed: 107.275 samples/sec, ObjLoss=7.565, BoxCenterLoss=5.649, BoxScaleLoss=1.464, ClassLoss=1.647 [Epoch 115] Training cost: 536.662, ObjLoss=7.558, BoxCenterLoss=5.648, BoxScaleLoss=1.463, ClassLoss=1.645 [Epoch 115] Validation: aeroplane=0.8094221361364462 bicycle=0.8382188666744759 bird=0.7959881118680172 boat=0.6658678738109751 bottle=0.642568456346088 bus=0.795922181746977 car=0.8695714635078596 cat=0.8907374185276671 chair=0.5948161575421701 cow=0.8280875911827988 diningtable=0.7503860925638511 dog=0.8611541275655937 horse=0.8771742821313042 motorbike=0.8414377066674977 person=0.8180070168630599 pottedplant=0.4922041058202095 sheep=0.7792423773555849 sofa=0.7432374986675403 train=0.77842874673017 tvmonitor=0.785691124767327 mAP=0.7729081668237806 [Epoch 116][Batch 99], LR: 1.00E-03, Speed: 104.904 samples/sec, ObjLoss=7.548, BoxCenterLoss=5.648, BoxScaleLoss=1.462, ClassLoss=1.643 [Epoch 116][Batch 199], LR: 1.00E-03, Speed: 110.543 samples/sec, ObjLoss=7.538, BoxCenterLoss=5.647, BoxScaleLoss=1.461, ClassLoss=1.641 [Epoch 116] Training cost: 557.173, ObjLoss=7.533, BoxCenterLoss=5.647, BoxScaleLoss=1.461, ClassLoss=1.639 [Epoch 116] Validation: aeroplane=0.8047741326278343 bicycle=0.794741957009904 bird=0.7932180754302225 boat=0.6986698605645068 bottle=0.6510642610952522 bus=0.8543496161788987 car=0.8715595971560899 cat=0.8922453148335503 chair=0.6042400004792459 cow=0.840229404287475 diningtable=0.7577380558566519 dog=0.8517863345905614 horse=0.8704621719609081 motorbike=0.853209503130831 person=0.7826034129083672 pottedplant=0.5368147760361108 sheep=0.8389537573387027 sofa=0.7578942587027566 train=0.8516765274044741 tvmonitor=0.7653721574669924 mAP=0.7835801587529667 [Epoch 117][Batch 99], LR: 1.00E-03, Speed: 94.323 samples/sec, ObjLoss=7.523, BoxCenterLoss=5.646, BoxScaleLoss=1.460, ClassLoss=1.636 [Epoch 117][Batch 199], LR: 1.00E-03, Speed: 106.806 samples/sec, ObjLoss=7.513, BoxCenterLoss=5.646, BoxScaleLoss=1.459, ClassLoss=1.634 [Epoch 117] Training cost: 574.612, ObjLoss=7.507, BoxCenterLoss=5.645, BoxScaleLoss=1.458, ClassLoss=1.632 [Epoch 117] Validation: aeroplane=0.8615283480186225 bicycle=0.7939600256256536 bird=0.788842597266401 boat=0.6998804248282194 bottle=0.6159882761234525 bus=0.8423274531561561 car=0.8607741121967108 cat=0.8830889665326807 chair=0.6209472779664676 cow=0.8418374350456075 diningtable=0.763016476266719 dog=0.8553040062426502 horse=0.8721549997230621 motorbike=0.8580330422147157 person=0.798897816768789 pottedplant=0.5200200468494307 sheep=0.8139133771781966 sofa=0.7601477350033266 train=0.8555684373390532 tvmonitor=0.741676637166583 mAP=0.7823953745756249 [Epoch 118][Batch 99], LR: 1.00E-03, Speed: 131.854 samples/sec, ObjLoss=7.498, BoxCenterLoss=5.644, BoxScaleLoss=1.458, ClassLoss=1.630 [Epoch 118][Batch 199], LR: 1.00E-03, Speed: 111.822 samples/sec, ObjLoss=7.488, BoxCenterLoss=5.644, BoxScaleLoss=1.457, ClassLoss=1.627 [Epoch 118] Training cost: 537.728, ObjLoss=7.481, BoxCenterLoss=5.643, BoxScaleLoss=1.456, ClassLoss=1.625 [Epoch 118] Validation: aeroplane=0.85494181909625 bicycle=0.8424585479498274 bird=0.7853227688244904 boat=0.7299768708089777 bottle=0.6359543556766103 bus=0.8422120727010141 car=0.8740636442185266 cat=0.8833806403181175 chair=0.596108877684394 cow=0.8267672433287723 diningtable=0.6901113977407284 dog=0.8413701784486358 horse=0.8552053795025948 motorbike=0.8688759279786948 person=0.8140471870653675 pottedplant=0.5320609394917464 sheep=0.7729052190119633 sofa=0.7801999657565355 train=0.845700787069993 tvmonitor=0.8050920232786707 mAP=0.7838377922975954 [Epoch 119][Batch 99], LR: 1.00E-03, Speed: 95.511 samples/sec, ObjLoss=7.471, BoxCenterLoss=5.643, BoxScaleLoss=1.455, ClassLoss=1.623 [Epoch 119][Batch 199], LR: 1.00E-03, Speed: 124.598 samples/sec, ObjLoss=7.461, BoxCenterLoss=5.642, BoxScaleLoss=1.454, ClassLoss=1.620 [Epoch 119] Training cost: 541.016, ObjLoss=7.455, BoxCenterLoss=5.641, BoxScaleLoss=1.454, ClassLoss=1.618 [Epoch 119] Validation: aeroplane=0.8399166482494969 bicycle=0.821417129399877 bird=0.7891124055243176 boat=0.6867448086415116 bottle=0.6140162693629407 bus=0.8417748649268565 car=0.8607131942338575 cat=0.874873547683941 chair=0.5954798195117792 cow=0.8181384213257783 diningtable=0.7523516294874124 dog=0.8336997296274643 horse=0.8498911428536275 motorbike=0.8578392103769606 person=0.8157361572450651 pottedplant=0.531340516929198 sheep=0.762106520812791 sofa=0.7065271643772715 train=0.8134084104305961 tvmonitor=0.7752868721798969 mAP=0.7720187231590321 [Epoch 120][Batch 99], LR: 1.00E-03, Speed: 105.763 samples/sec, ObjLoss=7.446, BoxCenterLoss=5.641, BoxScaleLoss=1.453, ClassLoss=1.616 [Epoch 120][Batch 199], LR: 1.00E-03, Speed: 97.701 samples/sec, ObjLoss=7.436, BoxCenterLoss=5.640, BoxScaleLoss=1.452, ClassLoss=1.613 [Epoch 120] Training cost: 573.342, ObjLoss=7.430, BoxCenterLoss=5.640, BoxScaleLoss=1.452, ClassLoss=1.611 [Epoch 120] Validation: aeroplane=0.7954046666420336 bicycle=0.796027062705382 bird=0.7817110854546359 boat=0.7000256492862331 bottle=0.6274770766901667 bus=0.8683322376584195 car=0.8664021450733758 cat=0.887622703383573 chair=0.6019960421542917 cow=0.8202094542074644 diningtable=0.7251047488995647 dog=0.839941878420682 horse=0.8752268038633032 motorbike=0.8396594752618871 person=0.8196166428172522 pottedplant=0.5173816414845303 sheep=0.8167059689241996 sofa=0.7504892612394589 train=0.8496702496479738 tvmonitor=0.7643812312863183 mAP=0.7771693012550374 [Epoch 121][Batch 99], LR: 1.00E-03, Speed: 88.860 samples/sec, ObjLoss=7.420, BoxCenterLoss=5.640, BoxScaleLoss=1.451, ClassLoss=1.609 [Epoch 121][Batch 199], LR: 1.00E-03, Speed: 128.806 samples/sec, ObjLoss=7.411, BoxCenterLoss=5.639, BoxScaleLoss=1.450, ClassLoss=1.606 [Epoch 121] Training cost: 579.282, ObjLoss=7.405, BoxCenterLoss=5.639, BoxScaleLoss=1.449, ClassLoss=1.604 [Epoch 121] Validation: aeroplane=0.8690894230594066 bicycle=0.8409935349466238 bird=0.7907239040088035 boat=0.704641193551685 bottle=0.6526446591655989 bus=0.8437681721588187 car=0.880306453222666 cat=0.8822785642348852 chair=0.5960981869457098 cow=0.846595585404938 diningtable=0.7244040525081031 dog=0.8466851162715148 horse=0.8703133050150887 motorbike=0.8505827714711199 person=0.8208643264081111 pottedplant=0.5303256916415472 sheep=0.8057106117635154 sofa=0.763865213398664 train=0.848141551508516 tvmonitor=0.7728318256886341 mAP=0.7870432071186975 [Epoch 122][Batch 99], LR: 1.00E-03, Speed: 120.922 samples/sec, ObjLoss=7.396, BoxCenterLoss=5.638, BoxScaleLoss=1.449, ClassLoss=1.602 [Epoch 122][Batch 199], LR: 1.00E-03, Speed: 147.270 samples/sec, ObjLoss=7.387, BoxCenterLoss=5.638, BoxScaleLoss=1.448, ClassLoss=1.600 [Epoch 122] Training cost: 547.148, ObjLoss=7.382, BoxCenterLoss=5.638, BoxScaleLoss=1.448, ClassLoss=1.599 [Epoch 122] Validation: aeroplane=0.8021850336380131 bicycle=0.8456219874375563 bird=0.780319217709505 boat=0.6844500689177404 bottle=0.6818387158678286 bus=0.862336740452348 car=0.8757748269246541 cat=0.8773914178805043 chair=0.618208621956164 cow=0.8261067306793635 diningtable=0.7185719577571946 dog=0.8526947949983682 horse=0.8645271471987062 motorbike=0.8492485543639829 person=0.8309606255191162 pottedplant=0.5383386371115566 sheep=0.8279390954548717 sofa=0.7558466774523972 train=0.8717851825303073 tvmonitor=0.7623187561821386 mAP=0.7863232395016158 [Epoch 123][Batch 99], LR: 1.00E-03, Speed: 123.637 samples/sec, ObjLoss=7.373, BoxCenterLoss=5.637, BoxScaleLoss=1.447, ClassLoss=1.596 [Epoch 123][Batch 199], LR: 1.00E-03, Speed: 109.981 samples/sec, ObjLoss=7.364, BoxCenterLoss=5.637, BoxScaleLoss=1.446, ClassLoss=1.594 [Epoch 123] Training cost: 556.656, ObjLoss=7.358, BoxCenterLoss=5.637, BoxScaleLoss=1.445, ClassLoss=1.592 [Epoch 123] Validation: aeroplane=0.8800471091244404 bicycle=0.7994079443246375 bird=0.7887664867605996 boat=0.6999884990642665 bottle=0.6608899536767294 bus=0.8751296862064828 car=0.8776443540225942 cat=0.8874004904952681 chair=0.6088173466266554 cow=0.8436805261262481 diningtable=0.7186105832291059 dog=0.850394016796979 horse=0.8870410004458482 motorbike=0.8432208021186218 person=0.818457174133369 pottedplant=0.5679595884308526 sheep=0.7715690681723508 sofa=0.7438059478241312 train=0.8633688644590921 tvmonitor=0.8090074178754487 mAP=0.7897603429956862 [Epoch 124][Batch 99], LR: 1.00E-03, Speed: 121.090 samples/sec, ObjLoss=7.349, BoxCenterLoss=5.636, BoxScaleLoss=1.444, ClassLoss=1.589 [Epoch 124][Batch 199], LR: 1.00E-03, Speed: 123.714 samples/sec, ObjLoss=7.340, BoxCenterLoss=5.635, BoxScaleLoss=1.444, ClassLoss=1.587 [Epoch 124] Training cost: 573.675, ObjLoss=7.334, BoxCenterLoss=5.635, BoxScaleLoss=1.443, ClassLoss=1.585 [Epoch 124] Validation: aeroplane=0.7990576475622597 bicycle=0.8424987165011449 bird=0.7472701413962074 boat=0.6661344545259974 bottle=0.617773825179933 bus=0.7984413423564078 car=0.8676399887622044 cat=0.8702293135647705 chair=0.6365739012289818 cow=0.840369569250484 diningtable=0.6942052597636292 dog=0.847173528255878 horse=0.8830303422239913 motorbike=0.8541904899879007 person=0.7767378421693658 pottedplant=0.550203597409002 sheep=0.7948345995414651 sofa=0.7780253312080325 train=0.820830801345854 tvmonitor=0.7667409657003004 mAP=0.7725980828966906 [Epoch 125][Batch 99], LR: 1.00E-03, Speed: 83.035 samples/sec, ObjLoss=7.326, BoxCenterLoss=5.635, BoxScaleLoss=1.442, ClassLoss=1.583 [Epoch 125][Batch 199], LR: 1.00E-03, Speed: 96.123 samples/sec, ObjLoss=7.317, BoxCenterLoss=5.635, BoxScaleLoss=1.442, ClassLoss=1.580 [Epoch 125] Training cost: 562.719, ObjLoss=7.312, BoxCenterLoss=5.634, BoxScaleLoss=1.441, ClassLoss=1.579 [Epoch 125] Validation: aeroplane=0.8032051903862238 bicycle=0.7874542269293501 bird=0.800767640503264 boat=0.6855312195821408 bottle=0.631242018901827 bus=0.8395643448243224 car=0.8689174397535767 cat=0.8646897364539738 chair=0.5663960137196643 cow=0.8478795959080431 diningtable=0.7251330166560053 dog=0.8514067876258617 horse=0.8662463062026987 motorbike=0.8547115333692802 person=0.8173498577633748 pottedplant=0.5261883065957441 sheep=0.7408814562350609 sofa=0.7319383863702059 train=0.8465170188288876 tvmonitor=0.7721799692173686 mAP=0.7714100032913437 [Epoch 126][Batch 99], LR: 1.00E-03, Speed: 130.699 samples/sec, ObjLoss=7.302, BoxCenterLoss=5.634, BoxScaleLoss=1.440, ClassLoss=1.576 [Epoch 126][Batch 199], LR: 1.00E-03, Speed: 113.439 samples/sec, ObjLoss=7.293, BoxCenterLoss=5.633, BoxScaleLoss=1.439, ClassLoss=1.574 [Epoch 126] Training cost: 565.287, ObjLoss=7.287, BoxCenterLoss=5.633, BoxScaleLoss=1.439, ClassLoss=1.572 [Epoch 126] Validation: aeroplane=0.8364606978492798 bicycle=0.794688688371031 bird=0.7880461942363856 boat=0.7014566167675336 bottle=0.689556588811473 bus=0.8637166522542018 car=0.8738412964842823 cat=0.8730788945662762 chair=0.6245308953945017 cow=0.8314475138722661 diningtable=0.7290951606309428 dog=0.8566259391581754 horse=0.8842125556342247 motorbike=0.8527549398751539 person=0.8050712438740333 pottedplant=0.5384319298234109 sheep=0.8455413546367632 sofa=0.7744454916000063 train=0.8611566555102014 tvmonitor=0.7712359434494687 mAP=0.7897697626399807 [Epoch 127][Batch 99], LR: 1.00E-03, Speed: 119.352 samples/sec, ObjLoss=7.278, BoxCenterLoss=5.632, BoxScaleLoss=1.438, ClassLoss=1.570 [Epoch 127][Batch 199], LR: 1.00E-03, Speed: 109.383 samples/sec, ObjLoss=7.269, BoxCenterLoss=5.631, BoxScaleLoss=1.437, ClassLoss=1.567 [Epoch 127] Training cost: 535.054, ObjLoss=7.264, BoxCenterLoss=5.631, BoxScaleLoss=1.437, ClassLoss=1.566 [Epoch 127] Validation: aeroplane=0.859546062003993 bicycle=0.8448879772725327 bird=0.8431226489184502 boat=0.6946026407303336 bottle=0.6448267625070585 bus=0.8623482168314385 car=0.8769431871751066 cat=0.8815863333903617 chair=0.6017901397384642 cow=0.8497597311577652 diningtable=0.7255851762176719 dog=0.8548350016168331 horse=0.8838122490561515 motorbike=0.8618263635842102 person=0.8155939844856848 pottedplant=0.5652705493113037 sheep=0.8279464086538008 sofa=0.7419520158099578 train=0.7919375026167211 tvmonitor=0.7550890491127412 mAP=0.7891631000095289 [Epoch 128][Batch 99], LR: 1.00E-03, Speed: 117.849 samples/sec, ObjLoss=7.255, BoxCenterLoss=5.630, BoxScaleLoss=1.436, ClassLoss=1.563 [Epoch 128][Batch 199], LR: 1.00E-03, Speed: 129.302 samples/sec, ObjLoss=7.247, BoxCenterLoss=5.630, BoxScaleLoss=1.435, ClassLoss=1.561 [Epoch 128] Training cost: 564.860, ObjLoss=7.243, BoxCenterLoss=5.629, BoxScaleLoss=1.435, ClassLoss=1.560 [Epoch 128] Validation: aeroplane=0.8537301848821202 bicycle=0.8011252430014546 bird=0.7940495560521273 boat=0.6815386445777168 bottle=0.6460176028013365 bus=0.8585999178209331 car=0.8662241691230289 cat=0.8805986484685026 chair=0.597078070245925 cow=0.8346880464549046 diningtable=0.7514086435652115 dog=0.8595839438872052 horse=0.8716918240530854 motorbike=0.848409094097656 person=0.8221862248938963 pottedplant=0.5345650949841911 sheep=0.8231490788048945 sofa=0.6871286430103579 train=0.8511997128539199 tvmonitor=0.7765819783340571 mAP=0.7819777160956262 [Epoch 129][Batch 99], LR: 1.00E-03, Speed: 92.505 samples/sec, ObjLoss=7.234, BoxCenterLoss=5.629, BoxScaleLoss=1.434, ClassLoss=1.558 [Epoch 129][Batch 199], LR: 1.00E-03, Speed: 96.257 samples/sec, ObjLoss=7.225, BoxCenterLoss=5.628, BoxScaleLoss=1.433, ClassLoss=1.555 [Epoch 129] Training cost: 551.965, ObjLoss=7.221, BoxCenterLoss=5.628, BoxScaleLoss=1.433, ClassLoss=1.554 [Epoch 129] Validation: aeroplane=0.7942885262367385 bicycle=0.8006194502757168 bird=0.7677615614564552 boat=0.7036457095975704 bottle=0.6482888524563523 bus=0.8595420436069491 car=0.8712356304660734 cat=0.8827027078541216 chair=0.6255117733468651 cow=0.8337120509271392 diningtable=0.6921955278544443 dog=0.822064862905401 horse=0.8571803649443813 motorbike=0.8337995459768196 person=0.7760065053106283 pottedplant=0.5627145589817452 sheep=0.764096216498255 sofa=0.7503246725727737 train=0.7969968661798381 tvmonitor=0.742308337896434 mAP=0.769249788267235 [Epoch 130][Batch 99], LR: 1.00E-03, Speed: 114.426 samples/sec, ObjLoss=7.212, BoxCenterLoss=5.627, BoxScaleLoss=1.432, ClassLoss=1.552 [Epoch 130][Batch 199], LR: 1.00E-03, Speed: 114.714 samples/sec, ObjLoss=7.203, BoxCenterLoss=5.627, BoxScaleLoss=1.431, ClassLoss=1.549 [Epoch 130] Training cost: 559.439, ObjLoss=7.198, BoxCenterLoss=5.626, BoxScaleLoss=1.430, ClassLoss=1.548 [Epoch 130] Validation: aeroplane=0.8577905411278081 bicycle=0.8679951993678314 bird=0.7952338029012302 boat=0.684275061130265 bottle=0.6304123173711007 bus=0.8722809238411848 car=0.8771697752296488 cat=0.8865114216724702 chair=0.6258517865608052 cow=0.8125372103735992 diningtable=0.7614534298291463 dog=0.8487444551540071 horse=0.8536354729107296 motorbike=0.8586651520158245 person=0.7819050958829828 pottedplant=0.48705431097594964 sheep=0.8223586999252358 sofa=0.7620976327650308 train=0.849603494306644 tvmonitor=0.7796940039938969 mAP=0.7857634893667695 [Epoch 131][Batch 99], LR: 1.00E-03, Speed: 117.783 samples/sec, ObjLoss=7.189, BoxCenterLoss=5.626, BoxScaleLoss=1.429, ClassLoss=1.546 [Epoch 131][Batch 199], LR: 1.00E-03, Speed: 117.833 samples/sec, ObjLoss=7.180, BoxCenterLoss=5.625, BoxScaleLoss=1.428, ClassLoss=1.543 [Epoch 131] Training cost: 566.221, ObjLoss=7.176, BoxCenterLoss=5.625, BoxScaleLoss=1.428, ClassLoss=1.542 [Epoch 131] Validation: aeroplane=0.801539833580154 bicycle=0.8017575478370933 bird=0.764945540907334 boat=0.6702752132344871 bottle=0.5009759720797452 bus=0.8746128019961692 car=0.8753465255249689 cat=0.8853956675900846 chair=0.587647769291358 cow=0.8259365366971317 diningtable=0.6885823178755155 dog=0.8476907336233402 horse=0.8823102509487003 motorbike=0.8232851300082048 person=0.8103287432804841 pottedplant=0.5326555133400452 sheep=0.760263544507845 sofa=0.7183837273734903 train=0.7884346475030195 tvmonitor=0.7754968478071538 mAP=0.7607932432503162 [Epoch 132][Batch 99], LR: 1.00E-03, Speed: 118.571 samples/sec, ObjLoss=7.167, BoxCenterLoss=5.624, BoxScaleLoss=1.427, ClassLoss=1.540 [Epoch 132][Batch 199], LR: 1.00E-03, Speed: 115.869 samples/sec, ObjLoss=7.158, BoxCenterLoss=5.624, BoxScaleLoss=1.426, ClassLoss=1.538 [Epoch 132] Training cost: 542.332, ObjLoss=7.154, BoxCenterLoss=5.623, BoxScaleLoss=1.426, ClassLoss=1.536 [Epoch 132] Validation: aeroplane=0.85167820720254 bicycle=0.8407463308759144 bird=0.7785014057485009 boat=0.6709529504348173 bottle=0.5996068299716873 bus=0.8624008390363531 car=0.8690080297429537 cat=0.8780871021399042 chair=0.5975428822141268 cow=0.6812569367969611 diningtable=0.7460966930601187 dog=0.8425631557902841 horse=0.8649647751418073 motorbike=0.8537419472020206 person=0.8090480282869383 pottedplant=0.49399828548161506 sheep=0.59092180546726 sofa=0.7431920171723837 train=0.8529992582223608 tvmonitor=0.762310841698217 mAP=0.7594809160843382 [Epoch 133][Batch 99], LR: 1.00E-03, Speed: 123.351 samples/sec, ObjLoss=7.146, BoxCenterLoss=5.623, BoxScaleLoss=1.425, ClassLoss=1.534 [Epoch 133][Batch 199], LR: 1.00E-03, Speed: 90.350 samples/sec, ObjLoss=7.137, BoxCenterLoss=5.623, BoxScaleLoss=1.425, ClassLoss=1.532 [Epoch 133] Training cost: 578.089, ObjLoss=7.132, BoxCenterLoss=5.623, BoxScaleLoss=1.424, ClassLoss=1.531 [Epoch 133] Validation: aeroplane=0.8525969098522796 bicycle=0.7976030951283783 bird=0.7853677232183534 boat=0.7045222852131267 bottle=0.6150313165732627 bus=0.874874694810709 car=0.8735184914624248 cat=0.8901875507381026 chair=0.630972598016791 cow=0.860629465536427 diningtable=0.7245221031221155 dog=0.8512068205491177 horse=0.874634188680692 motorbike=0.7923944561785949 person=0.7827220365081142 pottedplant=0.5161124343252161 sheep=0.789596132672591 sofa=0.7456296533744771 train=0.7823492381375146 tvmonitor=0.7801774082432326 mAP=0.776232430117076 [Epoch 134][Batch 99], LR: 1.00E-03, Speed: 89.888 samples/sec, ObjLoss=7.124, BoxCenterLoss=5.622, BoxScaleLoss=1.423, ClassLoss=1.528 [Epoch 134][Batch 199], LR: 1.00E-03, Speed: 94.098 samples/sec, ObjLoss=7.115, BoxCenterLoss=5.622, BoxScaleLoss=1.423, ClassLoss=1.526 [Epoch 134] Training cost: 586.554, ObjLoss=7.111, BoxCenterLoss=5.621, BoxScaleLoss=1.422, ClassLoss=1.525 [Epoch 134] Validation: aeroplane=0.785721415779663 bicycle=0.8396139477665968 bird=0.7862370468119895 boat=0.7206362078535883 bottle=0.660766837539306 bus=0.8741403917603546 car=0.8704740789257686 cat=0.8669113640083779 chair=0.5984899912423125 cow=0.8649629855252853 diningtable=0.7457126389756259 dog=0.8548802459968071 horse=0.877606762342954 motorbike=0.8305455426663194 person=0.8057781005882978 pottedplant=0.48824090848757346 sheep=0.8199174436116984 sofa=0.7136453110328815 train=0.8561674660738617 tvmonitor=0.7620687690330789 mAP=0.7811258728011169 [Epoch 135][Batch 99], LR: 1.00E-03, Speed: 108.387 samples/sec, ObjLoss=7.102, BoxCenterLoss=5.621, BoxScaleLoss=1.421, ClassLoss=1.522 [Epoch 135][Batch 199], LR: 1.00E-03, Speed: 119.495 samples/sec, ObjLoss=7.094, BoxCenterLoss=5.620, BoxScaleLoss=1.421, ClassLoss=1.520 [Epoch 135] Training cost: 546.641, ObjLoss=7.089, BoxCenterLoss=5.620, BoxScaleLoss=1.420, ClassLoss=1.519 [Epoch 135] Validation: aeroplane=0.8413240673842974 bicycle=0.7914336423491685 bird=0.7930174903257339 boat=0.7227488480896173 bottle=0.6457337223733294 bus=0.8636615460633168 car=0.874109040690816 cat=0.8769227754854331 chair=0.6184277117257343 cow=0.8444029391917746 diningtable=0.7333238137239935 dog=0.8575818980476819 horse=0.8735268024406042 motorbike=0.8550844420826593 person=0.7773454311279107 pottedplant=0.5462208202171273 sheep=0.834954391349925 sofa=0.7558995449363011 train=0.8604686733011883 tvmonitor=0.7247901654807991 mAP=0.7845488883193705 [Epoch 136][Batch 99], LR: 1.00E-03, Speed: 135.613 samples/sec, ObjLoss=7.081, BoxCenterLoss=5.619, BoxScaleLoss=1.419, ClassLoss=1.517 [Epoch 136][Batch 199], LR: 1.00E-03, Speed: 140.730 samples/sec, ObjLoss=7.073, BoxCenterLoss=5.619, BoxScaleLoss=1.419, ClassLoss=1.515 [Epoch 136] Training cost: 573.426, ObjLoss=7.069, BoxCenterLoss=5.618, BoxScaleLoss=1.418, ClassLoss=1.514 [Epoch 136] Validation: aeroplane=0.8609859888964368 bicycle=0.8028746160635741 bird=0.7847315667425608 boat=0.690166563847411 bottle=0.6402438983176431 bus=0.8506447842064312 car=0.8676213364376555 cat=0.8704771701015062 chair=0.6275780326245397 cow=0.8534690342412972 diningtable=0.734306193932322 dog=0.8462769508981233 horse=0.88179084307102 motorbike=0.8351698255499375 person=0.7786732933358279 pottedplant=0.5457807535797049 sheep=0.8339109132816259 sofa=0.7512159769392682 train=0.8407967162398516 tvmonitor=0.782357053997338 mAP=0.7839535756152037 [Epoch 137][Batch 99], LR: 1.00E-03, Speed: 90.485 samples/sec, ObjLoss=7.060, BoxCenterLoss=5.618, BoxScaleLoss=1.417, ClassLoss=1.511 [Epoch 137][Batch 199], LR: 1.00E-03, Speed: 109.001 samples/sec, ObjLoss=7.052, BoxCenterLoss=5.617, BoxScaleLoss=1.416, ClassLoss=1.509 [Epoch 137] Training cost: 572.378, ObjLoss=7.047, BoxCenterLoss=5.617, BoxScaleLoss=1.416, ClassLoss=1.508 [Epoch 137] Validation: aeroplane=0.8050819238166735 bicycle=0.8572631939596991 bird=0.7937264924141212 boat=0.6858866446784951 bottle=0.5348855946375713 bus=0.8569608716647468 car=0.8452412487990285 cat=0.8816239015817151 chair=0.6224892511781895 cow=0.8556506047788511 diningtable=0.68911353560182 dog=0.8584410096534726 horse=0.8862545087588669 motorbike=0.853957008762223 person=0.8256183357094393 pottedplant=0.5746865255232271 sheep=0.8337068945273737 sofa=0.7670133442905145 train=0.828601975044437 tvmonitor=0.7755897001238817 mAP=0.7815896282752174 [Epoch 138][Batch 99], LR: 1.00E-03, Speed: 103.018 samples/sec, ObjLoss=7.039, BoxCenterLoss=5.616, BoxScaleLoss=1.415, ClassLoss=1.505 [Epoch 138][Batch 199], LR: 1.00E-03, Speed: 132.732 samples/sec, ObjLoss=7.031, BoxCenterLoss=5.616, BoxScaleLoss=1.414, ClassLoss=1.503 [Epoch 138] Training cost: 574.109, ObjLoss=7.026, BoxCenterLoss=5.615, BoxScaleLoss=1.414, ClassLoss=1.502 [Epoch 138] Validation: aeroplane=0.8086680015362187 bicycle=0.8416870128162934 bird=0.7881090412812944 boat=0.6623812251025998 bottle=0.6312296911230837 bus=0.8670953895815907 car=0.8736271204941061 cat=0.8738163775005588 chair=0.6091854153246294 cow=0.8387251463079876 diningtable=0.702785199208055 dog=0.8555416397262363 horse=0.8659626458889275 motorbike=0.8322566634446913 person=0.8179085100018089 pottedplant=0.5532393564777756 sheep=0.8575181314191489 sofa=0.7780175564582209 train=0.8376402424918536 tvmonitor=0.7532922374103382 mAP=0.7824343301797709 [Epoch 139][Batch 99], LR: 1.00E-03, Speed: 109.612 samples/sec, ObjLoss=7.019, BoxCenterLoss=5.614, BoxScaleLoss=1.413, ClassLoss=1.500 [Epoch 139][Batch 199], LR: 1.00E-03, Speed: 121.564 samples/sec, ObjLoss=7.010, BoxCenterLoss=5.614, BoxScaleLoss=1.412, ClassLoss=1.498 [Epoch 139] Training cost: 516.374, ObjLoss=7.006, BoxCenterLoss=5.614, BoxScaleLoss=1.412, ClassLoss=1.497 [Epoch 139] Validation: aeroplane=0.8021034371617076 bicycle=0.7986105120163676 bird=0.7840531321162991 boat=0.6877856610147457 bottle=0.6360554177064902 bus=0.8581124695142196 car=0.8723748831608277 cat=0.8744469324964682 chair=0.5882644831770615 cow=0.8457132857889879 diningtable=0.7285309414315699 dog=0.8289834878422943 horse=0.8863937569641533 motorbike=0.8063587661673786 person=0.7826571968447958 pottedplant=0.5195995861856617 sheep=0.7710123494069843 sofa=0.712538229347412 train=0.7901313601766614 tvmonitor=0.7482839367374184 mAP=0.7661004912628753 [Epoch 140][Batch 99], LR: 1.00E-03, Speed: 90.445 samples/sec, ObjLoss=6.999, BoxCenterLoss=5.613, BoxScaleLoss=1.411, ClassLoss=1.495 [Epoch 140][Batch 199], LR: 1.00E-03, Speed: 135.499 samples/sec, ObjLoss=6.991, BoxCenterLoss=5.612, BoxScaleLoss=1.411, ClassLoss=1.493 [Epoch 140] Training cost: 574.289, ObjLoss=6.986, BoxCenterLoss=5.612, BoxScaleLoss=1.410, ClassLoss=1.491 [Epoch 140] Validation: aeroplane=0.8494172977162431 bicycle=0.7921635740844151 bird=0.7862129269295093 boat=0.7056275944018369 bottle=0.6352544948732928 bus=0.8673825457484835 car=0.8721839881130127 cat=0.8641353083996951 chair=0.5864448721548925 cow=0.8647885838659041 diningtable=0.7176063508246965 dog=0.8422724735230339 horse=0.8828071384313287 motorbike=0.846621219107717 person=0.8220772340848705 pottedplant=0.5264060026440466 sheep=0.829385083382398 sofa=0.7420584102202689 train=0.8363328333946393 tvmonitor=0.771681567747133 mAP=0.7820429749823709 [Epoch 141][Batch 99], LR: 1.00E-03, Speed: 116.582 samples/sec, ObjLoss=6.978, BoxCenterLoss=5.611, BoxScaleLoss=1.409, ClassLoss=1.489 [Epoch 141][Batch 199], LR: 1.00E-03, Speed: 123.345 samples/sec, ObjLoss=6.971, BoxCenterLoss=5.610, BoxScaleLoss=1.409, ClassLoss=1.487 [Epoch 141] Training cost: 578.642, ObjLoss=6.966, BoxCenterLoss=5.610, BoxScaleLoss=1.408, ClassLoss=1.486 [Epoch 141] Validation: aeroplane=0.8823883535703287 bicycle=0.8054745995813377 bird=0.7755233114479172 boat=0.6989590636575803 bottle=0.6336528355867306 bus=0.8597112745300283 car=0.8634009755843 cat=0.8738387091999891 chair=0.6127179114415191 cow=0.8457411909265228 diningtable=0.7439212228737233 dog=0.8543607463896529 horse=0.8832646833155038 motorbike=0.8403364402789258 person=0.7789431115786806 pottedplant=0.5282189065073016 sheep=0.8244045477500125 sofa=0.7445153642725795 train=0.7995133516445103 tvmonitor=0.7559005043887184 mAP=0.7802393552262931 [Epoch 142][Batch 99], LR: 1.00E-03, Speed: 117.516 samples/sec, ObjLoss=6.958, BoxCenterLoss=5.610, BoxScaleLoss=1.407, ClassLoss=1.484 [Epoch 142][Batch 199], LR: 1.00E-03, Speed: 131.467 samples/sec, ObjLoss=6.952, BoxCenterLoss=5.609, BoxScaleLoss=1.407, ClassLoss=1.482 [Epoch 142] Training cost: 547.916, ObjLoss=6.947, BoxCenterLoss=5.609, BoxScaleLoss=1.406, ClassLoss=1.481 [Epoch 142] Validation: aeroplane=0.8520819273562833 bicycle=0.7982986291373203 bird=0.788089746609752 boat=0.7169496947928806 bottle=0.6308073366803673 bus=0.8651471696891235 car=0.8725836631918175 cat=0.8858874968844739 chair=0.6140067481798921 cow=0.8481788794878902 diningtable=0.7454879857080637 dog=0.8710522822244944 horse=0.8899310880600352 motorbike=0.8602396626330227 person=0.8170872335840527 pottedplant=0.5508357804129278 sheep=0.823677768151305 sofa=0.7586685975812414 train=0.8707639493628992 tvmonitor=0.7737715252585848 mAP=0.7916773582493214 [Epoch 143][Batch 99], LR: 1.00E-03, Speed: 115.529 samples/sec, ObjLoss=6.939, BoxCenterLoss=5.608, BoxScaleLoss=1.406, ClassLoss=1.479 [Epoch 143][Batch 199], LR: 1.00E-03, Speed: 87.910 samples/sec, ObjLoss=6.931, BoxCenterLoss=5.608, BoxScaleLoss=1.405, ClassLoss=1.476 [Epoch 143] Training cost: 584.080, ObjLoss=6.926, BoxCenterLoss=5.607, BoxScaleLoss=1.404, ClassLoss=1.475 [Epoch 143] Validation: aeroplane=0.8059231667636422 bicycle=0.8430932931714791 bird=0.7935378245604531 boat=0.690787481668396 bottle=0.6455449866010103 bus=0.8588783911345427 car=0.8767351467263355 cat=0.8897083179977918 chair=0.6143804565145258 cow=0.8758902085771537 diningtable=0.7507838792070355 dog=0.8643127128721311 horse=0.8873930700043453 motorbike=0.8551013218221613 person=0.7844781434215448 pottedplant=0.5433452995522008 sheep=0.854727524225183 sofa=0.7675583847856952 train=0.8508157183656 tvmonitor=0.7449812539467945 mAP=0.7898988290959011 [Epoch 144][Batch 99], LR: 1.00E-03, Speed: 104.469 samples/sec, ObjLoss=6.918, BoxCenterLoss=5.607, BoxScaleLoss=1.403, ClassLoss=1.473 [Epoch 144][Batch 199], LR: 1.00E-03, Speed: 90.738 samples/sec, ObjLoss=6.910, BoxCenterLoss=5.606, BoxScaleLoss=1.403, ClassLoss=1.471 [Epoch 144] Training cost: 535.711, ObjLoss=6.905, BoxCenterLoss=5.606, BoxScaleLoss=1.402, ClassLoss=1.469 [Epoch 144] Validation: aeroplane=0.863954412923842 bicycle=0.8519414301723107 bird=0.7468345835390092 boat=0.6956927750647401 bottle=0.6261387495673839 bus=0.7922166486967707 car=0.8787283715309296 cat=0.8837989323875958 chair=0.6154547013814625 cow=0.8531327637232862 diningtable=0.7523863541659649 dog=0.8569869331454388 horse=0.873255488917308 motorbike=0.862151720508633 person=0.8239637628320684 pottedplant=0.5159633892644078 sheep=0.8366548621433052 sofa=0.7633592189111454 train=0.7875487546902105 tvmonitor=0.756708338740117 mAP=0.7818436096152964 [Epoch 145][Batch 99], LR: 1.00E-03, Speed: 94.875 samples/sec, ObjLoss=6.898, BoxCenterLoss=5.605, BoxScaleLoss=1.401, ClassLoss=1.467 [Epoch 145][Batch 199], LR: 1.00E-03, Speed: 136.642 samples/sec, ObjLoss=6.890, BoxCenterLoss=5.604, BoxScaleLoss=1.401, ClassLoss=1.466 [Epoch 145] Training cost: 527.450, ObjLoss=6.886, BoxCenterLoss=5.604, BoxScaleLoss=1.400, ClassLoss=1.464 [Epoch 145] Validation: aeroplane=0.8711579439957993 bicycle=0.7890399735068853 bird=0.7691065829166963 boat=0.6917624195159239 bottle=0.6278336064796759 bus=0.8561373295826877 car=0.8763879704544194 cat=0.8787259454485864 chair=0.6304492857664065 cow=0.8399314797486863 diningtable=0.7504128936446601 dog=0.8547507725866986 horse=0.8871992851453766 motorbike=0.8359443892340052 person=0.7794317906929451 pottedplant=0.5452479576927703 sheep=0.8122817359631789 sofa=0.7843834318959486 train=0.8541570951045567 tvmonitor=0.7875811409503571 mAP=0.7860961515163132 [Epoch 146][Batch 99], LR: 1.00E-03, Speed: 119.989 samples/sec, ObjLoss=6.879, BoxCenterLoss=5.603, BoxScaleLoss=1.400, ClassLoss=1.463 [Epoch 146][Batch 199], LR: 1.00E-03, Speed: 143.158 samples/sec, ObjLoss=6.872, BoxCenterLoss=5.603, BoxScaleLoss=1.399, ClassLoss=1.461 [Epoch 146] Training cost: 525.112, ObjLoss=6.868, BoxCenterLoss=5.603, BoxScaleLoss=1.399, ClassLoss=1.460 [Epoch 146] Validation: aeroplane=0.8703188533716051 bicycle=0.8352854115295575 bird=0.7832650512694421 boat=0.6569945404607802 bottle=0.6383836323989599 bus=0.8356340621214434 car=0.8765207156677639 cat=0.8687102815478601 chair=0.6194164253865382 cow=0.8265867765501425 diningtable=0.7164127926052131 dog=0.8555848798538 horse=0.8791567199463124 motorbike=0.8426571994714783 person=0.7827924248524093 pottedplant=0.5222643761990498 sheep=0.7868295797206244 sofa=0.7829199630699004 train=0.8474033953607556 tvmonitor=0.7703157801589928 mAP=0.7798726430771316 [Epoch 147][Batch 99], LR: 1.00E-03, Speed: 105.082 samples/sec, ObjLoss=6.861, BoxCenterLoss=5.603, BoxScaleLoss=1.398, ClassLoss=1.458 [Epoch 147][Batch 199], LR: 1.00E-03, Speed: 119.350 samples/sec, ObjLoss=6.854, BoxCenterLoss=5.602, BoxScaleLoss=1.397, ClassLoss=1.456 [Epoch 147] Training cost: 578.047, ObjLoss=6.850, BoxCenterLoss=5.602, BoxScaleLoss=1.397, ClassLoss=1.455 [Epoch 147] Validation: aeroplane=0.8441863726157687 bicycle=0.8550948854825422 bird=0.785395972439198 boat=0.6719267334114268 bottle=0.633326804730144 bus=0.8576929704079833 car=0.8711635303722017 cat=0.8752017320618751 chair=0.5881376070326864 cow=0.8508783675046103 diningtable=0.7632766676884422 dog=0.8443941669653634 horse=0.8835261908680161 motorbike=0.8477293475231367 person=0.8135841637654806 pottedplant=0.47212897116094965 sheep=0.802481235615059 sofa=0.7760947321496816 train=0.8521441148363751 tvmonitor=0.7601833963844347 mAP=0.7824273981507688 [Epoch 148][Batch 99], LR: 1.00E-03, Speed: 124.183 samples/sec, ObjLoss=6.842, BoxCenterLoss=5.601, BoxScaleLoss=1.396, ClassLoss=1.453 [Epoch 148][Batch 199], LR: 1.00E-03, Speed: 132.081 samples/sec, ObjLoss=6.835, BoxCenterLoss=5.600, BoxScaleLoss=1.395, ClassLoss=1.451 [Epoch 148] Training cost: 528.748, ObjLoss=6.831, BoxCenterLoss=5.600, BoxScaleLoss=1.395, ClassLoss=1.450 [Epoch 148] Validation: aeroplane=0.8526822383950905 bicycle=0.8503511142663135 bird=0.787361830554412 boat=0.6905544552396954 bottle=0.6411896346862405 bus=0.8589518277252333 car=0.8668767771313778 cat=0.875278017286815 chair=0.5923938645134467 cow=0.8414531618676216 diningtable=0.6966041841975327 dog=0.8442798933538183 horse=0.8810319452084969 motorbike=0.8399894596261025 person=0.811140941121318 pottedplant=0.4815766223171618 sheep=0.7973669838573797 sofa=0.7390480432846573 train=0.8413711032453304 tvmonitor=0.7433892190762321 mAP=0.7766445658477138 [Epoch 149][Batch 99], LR: 1.00E-03, Speed: 99.419 samples/sec, ObjLoss=6.824, BoxCenterLoss=5.599, BoxScaleLoss=1.394, ClassLoss=1.448 [Epoch 149][Batch 199], LR: 1.00E-03, Speed: 88.400 samples/sec, ObjLoss=6.816, BoxCenterLoss=5.599, BoxScaleLoss=1.394, ClassLoss=1.446 [Epoch 149] Training cost: 564.058, ObjLoss=6.812, BoxCenterLoss=5.598, BoxScaleLoss=1.393, ClassLoss=1.445 [Epoch 149] Validation: aeroplane=0.8444116579771614 bicycle=0.7952588218504174 bird=0.8004521486943118 boat=0.7033864463455567 bottle=0.6721809263198438 bus=0.8652762100497454 car=0.8737606179169346 cat=0.8797006102658317 chair=0.5903369883004537 cow=0.8393328798633478 diningtable=0.7313481961806632 dog=0.8424711092520912 horse=0.8063283683467608 motorbike=0.8454149405604523 person=0.8158588588122153 pottedplant=0.5209618191271616 sheep=0.841059067649755 sofa=0.7298720289896432 train=0.836281090601792 tvmonitor=0.7623041315467252 mAP=0.7797998459325433 [Epoch 150][Batch 99], LR: 1.00E-03, Speed: 87.477 samples/sec, ObjLoss=6.805, BoxCenterLoss=5.598, BoxScaleLoss=1.393, ClassLoss=1.443 [Epoch 150][Batch 199], LR: 1.00E-03, Speed: 97.110 samples/sec, ObjLoss=6.798, BoxCenterLoss=5.598, BoxScaleLoss=1.392, ClassLoss=1.441 [Epoch 150] Training cost: 567.064, ObjLoss=6.793, BoxCenterLoss=5.597, BoxScaleLoss=1.391, ClassLoss=1.440 [Epoch 150] Validation: aeroplane=0.8709859722638054 bicycle=0.8005721186369323 bird=0.7922426702859969 boat=0.7138002658150885 bottle=0.6764825493751602 bus=0.8588090226780245 car=0.8801624700673476 cat=0.8876416162437104 chair=0.601267190756291 cow=0.835410597417814 diningtable=0.6802543342196057 dog=0.8656022361676944 horse=0.8828718772448932 motorbike=0.8505617021055273 person=0.8212985383696451 pottedplant=0.534240226966368 sheep=0.828672866264683 sofa=0.7430427084519905 train=0.8570048827033042 tvmonitor=0.7720670203500124 mAP=0.7876495433191948 [Epoch 151][Batch 99], LR: 1.00E-03, Speed: 112.714 samples/sec, ObjLoss=6.786, BoxCenterLoss=5.597, BoxScaleLoss=1.391, ClassLoss=1.438 [Epoch 151][Batch 199], LR: 1.00E-03, Speed: 125.500 samples/sec, ObjLoss=6.779, BoxCenterLoss=5.596, BoxScaleLoss=1.390, ClassLoss=1.436 [Epoch 151] Training cost: 564.873, ObjLoss=6.775, BoxCenterLoss=5.596, BoxScaleLoss=1.390, ClassLoss=1.435 [Epoch 151] Validation: aeroplane=0.8593669217876448 bicycle=0.8021966819065307 bird=0.7900913346173475 boat=0.6779271749457111 bottle=0.6298794644958018 bus=0.8475358882877779 car=0.8705075771978704 cat=0.8792867847824607 chair=0.6407266349709285 cow=0.8519363098168262 diningtable=0.6950095169024284 dog=0.8617959991857147 horse=0.8892078756522094 motorbike=0.8537228009933909 person=0.824594091445705 pottedplant=0.5533849074891377 sheep=0.7640607750928516 sofa=0.7595297303008356 train=0.7980881669497042 tvmonitor=0.7683293741302338 mAP=0.7808589005475556 [Epoch 152][Batch 99], LR: 1.00E-03, Speed: 104.335 samples/sec, ObjLoss=6.768, BoxCenterLoss=5.595, BoxScaleLoss=1.389, ClassLoss=1.433 [Epoch 152][Batch 199], LR: 1.00E-03, Speed: 102.522 samples/sec, ObjLoss=6.761, BoxCenterLoss=5.595, BoxScaleLoss=1.388, ClassLoss=1.431 [Epoch 152] Training cost: 560.560, ObjLoss=6.757, BoxCenterLoss=5.595, BoxScaleLoss=1.388, ClassLoss=1.430 [Epoch 152] Validation: aeroplane=0.8468839182855149 bicycle=0.8045273522640732 bird=0.7959349869560526 boat=0.6974994591142847 bottle=0.6536054372678556 bus=0.8797216820612543 car=0.8788546935278185 cat=0.8894994299632333 chair=0.6198772668375634 cow=0.8723547848114737 diningtable=0.7345688934379139 dog=0.8636580775331848 horse=0.8718908870146915 motorbike=0.854870867694211 person=0.784662913287094 pottedplant=0.526883099126393 sheep=0.8162271401311376 sofa=0.7266727642611572 train=0.8472370695030471 tvmonitor=0.7732644433444029 mAP=0.7869347583211179 [Epoch 153][Batch 99], LR: 1.00E-03, Speed: 113.617 samples/sec, ObjLoss=6.750, BoxCenterLoss=5.594, BoxScaleLoss=1.387, ClassLoss=1.428 [Epoch 153][Batch 199], LR: 1.00E-03, Speed: 116.687 samples/sec, ObjLoss=6.743, BoxCenterLoss=5.594, BoxScaleLoss=1.387, ClassLoss=1.426 [Epoch 153] Training cost: 540.842, ObjLoss=6.739, BoxCenterLoss=5.594, BoxScaleLoss=1.386, ClassLoss=1.425 [Epoch 153] Validation: aeroplane=0.7907065604726179 bicycle=0.8014308093287756 bird=0.7934316262087499 boat=0.701366805091948 bottle=0.6791869895323558 bus=0.8576075888347873 car=0.8790056153355644 cat=0.8797021328431861 chair=0.61428905409664 cow=0.8425237693286232 diningtable=0.6993859997642236 dog=0.8589672896415756 horse=0.8778962640005643 motorbike=0.8555670189424822 person=0.821936887043174 pottedplant=0.5448757149799104 sheep=0.8224644561545869 sofa=0.7564179321879062 train=0.8625955052525591 tvmonitor=0.768065149556878 mAP=0.7853711584298553 [Epoch 154][Batch 99], LR: 1.00E-03, Speed: 96.966 samples/sec, ObjLoss=6.732, BoxCenterLoss=5.593, BoxScaleLoss=1.385, ClassLoss=1.423 [Epoch 154][Batch 199], LR: 1.00E-03, Speed: 127.715 samples/sec, ObjLoss=6.725, BoxCenterLoss=5.593, BoxScaleLoss=1.385, ClassLoss=1.422 [Epoch 154] Training cost: 569.289, ObjLoss=6.721, BoxCenterLoss=5.593, BoxScaleLoss=1.384, ClassLoss=1.420 [Epoch 154] Validation: aeroplane=0.8630947445835221 bicycle=0.8467269347699686 bird=0.7931046631972967 boat=0.6836746367280766 bottle=0.629258346968253 bus=0.8530946288140245 car=0.8749432923444108 cat=0.894991942196217 chair=0.6110892971251355 cow=0.8527421054766067 diningtable=0.7295485658344524 dog=0.8726720167582752 horse=0.8828425090114802 motorbike=0.8428006703998194 person=0.821686020189736 pottedplant=0.5324518378886892 sheep=0.8253706740275975 sofa=0.7278707213198664 train=0.8019353689048355 tvmonitor=0.7596949013624049 mAP=0.7849796938950335 [Epoch 155][Batch 99], LR: 1.00E-03, Speed: 91.233 samples/sec, ObjLoss=6.715, BoxCenterLoss=5.592, BoxScaleLoss=1.384, ClassLoss=1.419 [Epoch 155][Batch 199], LR: 1.00E-03, Speed: 105.412 samples/sec, ObjLoss=6.707, BoxCenterLoss=5.592, BoxScaleLoss=1.383, ClassLoss=1.416 [Epoch 155] Training cost: 582.744, ObjLoss=6.704, BoxCenterLoss=5.592, BoxScaleLoss=1.382, ClassLoss=1.415 [Epoch 155] Validation: aeroplane=0.8776432471375449 bicycle=0.8429665485058966 bird=0.7958719537916776 boat=0.7057402393220309 bottle=0.6529831966723537 bus=0.851395068785758 car=0.8707644676987506 cat=0.8892126169176673 chair=0.5983277770816474 cow=0.8488706376005158 diningtable=0.7124712372036283 dog=0.8649259886137495 horse=0.8685464489422448 motorbike=0.8534598237317332 person=0.8285188858605421 pottedplant=0.5716822817388459 sheep=0.827614068910906 sofa=0.7788260339102602 train=0.8539040750892309 tvmonitor=0.7641449674961496 mAP=0.7928934782505566 [Epoch 156][Batch 99], LR: 1.00E-03, Speed: 118.201 samples/sec, ObjLoss=6.696, BoxCenterLoss=5.591, BoxScaleLoss=1.382, ClassLoss=1.413 [Epoch 156][Batch 199], LR: 1.00E-03, Speed: 130.974 samples/sec, ObjLoss=6.689, BoxCenterLoss=5.590, BoxScaleLoss=1.381, ClassLoss=1.411 [Epoch 156] Training cost: 565.829, ObjLoss=6.685, BoxCenterLoss=5.590, BoxScaleLoss=1.381, ClassLoss=1.410 [Epoch 156] Validation: aeroplane=0.841389124022922 bicycle=0.8443840269580248 bird=0.7980334366502321 boat=0.7520269263533617 bottle=0.6482973809798886 bus=0.8595485143171339 car=0.8778493848443022 cat=0.882837991519442 chair=0.6053848691773192 cow=0.8458340766808674 diningtable=0.7338501566462209 dog=0.8634021410446924 horse=0.8617815978228179 motorbike=0.8670500980346714 person=0.7789608827148753 pottedplant=0.5540498106710698 sheep=0.8127509467741145 sofa=0.7746411949763323 train=0.8427758151828603 tvmonitor=0.798928101474549 mAP=0.7921888238422848 [Epoch 157][Batch 99], LR: 1.00E-03, Speed: 127.393 samples/sec, ObjLoss=6.678, BoxCenterLoss=5.590, BoxScaleLoss=1.380, ClassLoss=1.408 [Epoch 157][Batch 199], LR: 1.00E-03, Speed: 139.095 samples/sec, ObjLoss=6.672, BoxCenterLoss=5.589, BoxScaleLoss=1.379, ClassLoss=1.407 [Epoch 157] Training cost: 566.605, ObjLoss=6.668, BoxCenterLoss=5.589, BoxScaleLoss=1.379, ClassLoss=1.406 [Epoch 157] Validation: aeroplane=0.8019917620580659 bicycle=0.8518826215372824 bird=0.7812776489965279 boat=0.6656045164225092 bottle=0.6258972953136435 bus=0.8759906471887974 car=0.8821111019149236 cat=0.8791870500760991 chair=0.6130234039404305 cow=0.8566037295626197 diningtable=0.6778264008923417 dog=0.8561834447534226 horse=0.8745088515338771 motorbike=0.8452576421569209 person=0.8099783531656557 pottedplant=0.5396643468876511 sheep=0.823780183196346 sofa=0.7568417633043767 train=0.7898792507856628 tvmonitor=0.7688213739145126 mAP=0.7788155693800833 [Epoch 158][Batch 99], LR: 1.00E-03, Speed: 124.352 samples/sec, ObjLoss=6.662, BoxCenterLoss=5.588, BoxScaleLoss=1.378, ClassLoss=1.404 [Epoch 158][Batch 199], LR: 1.00E-03, Speed: 131.902 samples/sec, ObjLoss=6.655, BoxCenterLoss=5.588, BoxScaleLoss=1.377, ClassLoss=1.402 [Epoch 158] Training cost: 566.912, ObjLoss=6.651, BoxCenterLoss=5.587, BoxScaleLoss=1.377, ClassLoss=1.401 [Epoch 158] Validation: aeroplane=0.8043858959229951 bicycle=0.8047046412069669 bird=0.7921238763631426 boat=0.6909634865468656 bottle=0.636941548393421 bus=0.8584524462004582 car=0.8731169133079723 cat=0.8848980512813186 chair=0.5944143169559579 cow=0.850471853638052 diningtable=0.7244164867098684 dog=0.8630224216612848 horse=0.8820623390285993 motorbike=0.8390191564014768 person=0.8268607499257056 pottedplant=0.54666745986715 sheep=0.830832903254158 sofa=0.7485005874795815 train=0.8488790910112703 tvmonitor=0.7575327105075812 mAP=0.7829133467831914 [Epoch 159][Batch 99], LR: 1.00E-03, Speed: 133.169 samples/sec, ObjLoss=6.645, BoxCenterLoss=5.587, BoxScaleLoss=1.376, ClassLoss=1.399 [Epoch 159][Batch 199], LR: 1.00E-03, Speed: 110.739 samples/sec, ObjLoss=6.639, BoxCenterLoss=5.587, BoxScaleLoss=1.376, ClassLoss=1.398 [Epoch 159] Training cost: 540.542, ObjLoss=6.635, BoxCenterLoss=5.586, BoxScaleLoss=1.375, ClassLoss=1.397 [Epoch 159] Validation: aeroplane=0.8060907687277763 bicycle=0.8541606267663514 bird=0.7807043560240179 boat=0.6959563732465681 bottle=0.6550296575962927 bus=0.8690568347577123 car=0.8736296853875065 cat=0.8871445178374119 chair=0.6175989407314414 cow=0.8437034726213212 diningtable=0.755382616607029 dog=0.8594880332042324 horse=0.88279433227304 motorbike=0.8530722511365948 person=0.7828623862409584 pottedplant=0.5255980685627074 sheep=0.8430846842265554 sofa=0.7408451511267109 train=0.8386587658994595 tvmonitor=0.7521690681715487 mAP=0.7858515295572619 [Epoch 160][Batch 99], LR: 1.00E-04, Speed: 107.048 samples/sec, ObjLoss=6.628, BoxCenterLoss=5.586, BoxScaleLoss=1.375, ClassLoss=1.395 [Epoch 160][Batch 199], LR: 1.00E-04, Speed: 135.815 samples/sec, ObjLoss=6.621, BoxCenterLoss=5.585, BoxScaleLoss=1.374, ClassLoss=1.393 [Epoch 160] Training cost: 544.473, ObjLoss=6.616, BoxCenterLoss=5.585, BoxScaleLoss=1.373, ClassLoss=1.392 [Epoch 160] Validation: aeroplane=0.8557960781114496 bicycle=0.8614416203769786 bird=0.795196275617209 boat=0.7323914741776109 bottle=0.6607029019553746 bus=0.8823478183686664 car=0.88205475096063 cat=0.8938194854388036 chair=0.6426545468979031 cow=0.8531435747898604 diningtable=0.779877702255053 dog=0.8727165557989848 horse=0.894526051119704 motorbike=0.8672335462576897 person=0.8338053613248574 pottedplant=0.5623303811252188 sheep=0.8561052062619255 sofa=0.780521722208911 train=0.8693104798355086 tvmonitor=0.7774724667533066 mAP=0.8076723999817824 [Epoch 161][Batch 99], LR: 1.00E-04, Speed: 108.584 samples/sec, ObjLoss=6.609, BoxCenterLoss=5.584, BoxScaleLoss=1.372, ClassLoss=1.390 [Epoch 161][Batch 199], LR: 1.00E-04, Speed: 121.989 samples/sec, ObjLoss=6.601, BoxCenterLoss=5.584, BoxScaleLoss=1.371, ClassLoss=1.388 [Epoch 161] Training cost: 531.826, ObjLoss=6.596, BoxCenterLoss=5.583, BoxScaleLoss=1.370, ClassLoss=1.387 [Epoch 161] Validation: aeroplane=0.8619024730408753 bicycle=0.8652779054811024 bird=0.7988160246120621 boat=0.7367339189805131 bottle=0.6622087205528762 bus=0.8780406600343222 car=0.8784560208519846 cat=0.8896564558298174 chair=0.6405905991313093 cow=0.8491986965381387 diningtable=0.7698302185641416 dog=0.8765107645806214 horse=0.8922483173494296 motorbike=0.8610144466075359 person=0.8358911105466051 pottedplant=0.5580383197632605 sheep=0.8657260578662918 sofa=0.7713163342824404 train=0.800132154882155 tvmonitor=0.7798233638465372 mAP=0.8035706281671009 [Epoch 162][Batch 99], LR: 1.00E-04, Speed: 98.807 samples/sec, ObjLoss=6.588, BoxCenterLoss=5.582, BoxScaleLoss=1.369, ClassLoss=1.385 [Epoch 162][Batch 199], LR: 1.00E-04, Speed: 72.706 samples/sec, ObjLoss=6.580, BoxCenterLoss=5.582, BoxScaleLoss=1.368, ClassLoss=1.383 [Epoch 162] Training cost: 569.130, ObjLoss=6.575, BoxCenterLoss=5.582, BoxScaleLoss=1.367, ClassLoss=1.382 [Epoch 162] Validation: aeroplane=0.8633743077238215 bicycle=0.8633168220538463 bird=0.8027639266204144 boat=0.7458345563908108 bottle=0.6545376839330962 bus=0.8853058662267739 car=0.8823226815481476 cat=0.8969952522584103 chair=0.6180053637695339 cow=0.8610553181001699 diningtable=0.7696316939435999 dog=0.8751550022009332 horse=0.8985489366522155 motorbike=0.8639263087444715 person=0.8384091905735227 pottedplant=0.5648287975760199 sheep=0.8700771609961672 sofa=0.7748667851891653 train=0.8686407927368924 tvmonitor=0.7855056930881835 mAP=0.8091551070163098 [Epoch 163][Batch 99], LR: 1.00E-04, Speed: 120.705 samples/sec, ObjLoss=6.567, BoxCenterLoss=5.581, BoxScaleLoss=1.366, ClassLoss=1.379 [Epoch 163][Batch 199], LR: 1.00E-04, Speed: 96.806 samples/sec, ObjLoss=6.560, BoxCenterLoss=5.581, BoxScaleLoss=1.365, ClassLoss=1.377 [Epoch 163] Training cost: 576.812, ObjLoss=6.555, BoxCenterLoss=5.581, BoxScaleLoss=1.364, ClassLoss=1.376 [Epoch 163] Validation: aeroplane=0.8800883362282433 bicycle=0.8598207171146212 bird=0.8002032113725078 boat=0.7525555647448083 bottle=0.6601900775150904 bus=0.8826718585868839 car=0.8827663672504226 cat=0.8919884432709152 chair=0.6409864525463123 cow=0.8570063202305269 diningtable=0.7708010662265788 dog=0.8760793719465104 horse=0.8985541993569016 motorbike=0.8644141293066175 person=0.7918294324685852 pottedplant=0.5724499673632739 sheep=0.8689475379106723 sofa=0.7856177722185046 train=0.8683194397578196 tvmonitor=0.7821452268343576 mAP=0.8093717746125076 [Epoch 164][Batch 99], LR: 1.00E-04, Speed: 114.326 samples/sec, ObjLoss=6.547, BoxCenterLoss=5.580, BoxScaleLoss=1.363, ClassLoss=1.374 [Epoch 164][Batch 199], LR: 1.00E-04, Speed: 125.554 samples/sec, ObjLoss=6.540, BoxCenterLoss=5.580, BoxScaleLoss=1.362, ClassLoss=1.372 [Epoch 164] Training cost: 565.960, ObjLoss=6.535, BoxCenterLoss=5.579, BoxScaleLoss=1.361, ClassLoss=1.371 [Epoch 164] Validation: aeroplane=0.8770649376405735 bicycle=0.8566122977741322 bird=0.802994173003745 boat=0.741683933953252 bottle=0.6574345888211204 bus=0.8865397567085632 car=0.8839099670109111 cat=0.8941641458266512 chair=0.64920116406243 cow=0.8611134827096143 diningtable=0.7719929586038071 dog=0.8774688078844051 horse=0.8978554388503457 motorbike=0.860392175262313 person=0.8375274754440015 pottedplant=0.566066723049236 sheep=0.8595814864544636 sofa=0.7764937952046567 train=0.8694066978754683 tvmonitor=0.7847322139547216 mAP=0.8106118110047206 [Epoch 165][Batch 99], LR: 1.00E-04, Speed: 119.354 samples/sec, ObjLoss=6.527, BoxCenterLoss=5.579, BoxScaleLoss=1.360, ClassLoss=1.369 [Epoch 165][Batch 199], LR: 1.00E-04, Speed: 86.545 samples/sec, ObjLoss=6.519, BoxCenterLoss=5.578, BoxScaleLoss=1.359, ClassLoss=1.367 [Epoch 165] Training cost: 552.546, ObjLoss=6.515, BoxCenterLoss=5.578, BoxScaleLoss=1.358, ClassLoss=1.366 [Epoch 165] Validation: aeroplane=0.8597517475806634 bicycle=0.8592159638845595 bird=0.8035744514680241 boat=0.7472524750318106 bottle=0.6565202626871314 bus=0.8792899672110486 car=0.8843572686091226 cat=0.8928597361158477 chair=0.6511767675569254 cow=0.8557034871052736 diningtable=0.7589952152605051 dog=0.8763793481613698 horse=0.8919339347584061 motorbike=0.8729008940510629 person=0.8379864523273032 pottedplant=0.5575735816942532 sheep=0.8611232158247688 sofa=0.7742106143449643 train=0.8638955097206252 tvmonitor=0.7843136794208392 mAP=0.8084507286407254 [Epoch 166][Batch 99], LR: 1.00E-04, Speed: 125.415 samples/sec, ObjLoss=6.508, BoxCenterLoss=5.577, BoxScaleLoss=1.357, ClassLoss=1.364 [Epoch 166][Batch 199], LR: 1.00E-04, Speed: 127.243 samples/sec, ObjLoss=6.500, BoxCenterLoss=5.577, BoxScaleLoss=1.356, ClassLoss=1.362 [Epoch 166] Training cost: 537.912, ObjLoss=6.495, BoxCenterLoss=5.576, BoxScaleLoss=1.356, ClassLoss=1.361 [Epoch 166] Validation: aeroplane=0.8837572711839232 bicycle=0.859451935112761 bird=0.7975779288478022 boat=0.7430137617247815 bottle=0.6588291570195879 bus=0.8877299748852292 car=0.8848204888010024 cat=0.8938293803423094 chair=0.6520427369378858 cow=0.8656851188543346 diningtable=0.7713915924209045 dog=0.876857548714066 horse=0.8947789896176298 motorbike=0.8743913088459676 person=0.8419602837900856 pottedplant=0.5670360289985682 sheep=0.8712753778662252 sofa=0.7805682838324859 train=0.8713106773569497 tvmonitor=0.7846414702385531 mAP=0.8130474657695528 [Epoch 167][Batch 99], LR: 1.00E-04, Speed: 121.234 samples/sec, ObjLoss=6.488, BoxCenterLoss=5.576, BoxScaleLoss=1.355, ClassLoss=1.359 [Epoch 167][Batch 199], LR: 1.00E-04, Speed: 145.416 samples/sec, ObjLoss=6.480, BoxCenterLoss=5.575, BoxScaleLoss=1.354, ClassLoss=1.357 [Epoch 167] Training cost: 551.132, ObjLoss=6.476, BoxCenterLoss=5.575, BoxScaleLoss=1.353, ClassLoss=1.355 [Epoch 167] Validation: aeroplane=0.8783634159924001 bicycle=0.8023968308028646 bird=0.8051722155137716 boat=0.7432426097276084 bottle=0.6605971562710109 bus=0.8862333103333605 car=0.8845315336476531 cat=0.8963169377085473 chair=0.6536186291477629 cow=0.8686241638857384 diningtable=0.7838552118787909 dog=0.8765136613529428 horse=0.8966486975896167 motorbike=0.8700900058947274 person=0.8427097954626661 pottedplant=0.5713444063550509 sheep=0.8667018801530161 sofa=0.77868515500297 train=0.8717288674391931 tvmonitor=0.7794156021313942 mAP=0.8108395043145544 [Epoch 168][Batch 99], LR: 1.00E-04, Speed: 117.626 samples/sec, ObjLoss=6.468, BoxCenterLoss=5.574, BoxScaleLoss=1.352, ClassLoss=1.353 [Epoch 168][Batch 199], LR: 1.00E-04, Speed: 111.721 samples/sec, ObjLoss=6.460, BoxCenterLoss=5.573, BoxScaleLoss=1.351, ClassLoss=1.351 [Epoch 168] Training cost: 558.447, ObjLoss=6.456, BoxCenterLoss=5.573, BoxScaleLoss=1.350, ClassLoss=1.350 [Epoch 168] Validation: aeroplane=0.8747871085197441 bicycle=0.8634419495281855 bird=0.8042623993034129 boat=0.736860703042959 bottle=0.6878488021408884 bus=0.8801472912528392 car=0.8838716105628145 cat=0.8951560258988699 chair=0.6462385451283664 cow=0.8707618852946202 diningtable=0.7691801434904421 dog=0.8793878150353888 horse=0.8946680328383569 motorbike=0.8616171989430119 person=0.840403728490859 pottedplant=0.5630640897611041 sheep=0.8759733895078363 sofa=0.7793917749216047 train=0.8008557247140942 tvmonitor=0.7827925149841697 mAP=0.8095355366679783 [Epoch 169][Batch 99], LR: 1.00E-04, Speed: 121.584 samples/sec, ObjLoss=6.449, BoxCenterLoss=5.573, BoxScaleLoss=1.349, ClassLoss=1.348 [Epoch 169][Batch 199], LR: 1.00E-04, Speed: 86.529 samples/sec, ObjLoss=6.441, BoxCenterLoss=5.572, BoxScaleLoss=1.348, ClassLoss=1.346 [Epoch 169] Training cost: 589.355, ObjLoss=6.437, BoxCenterLoss=5.572, BoxScaleLoss=1.347, ClassLoss=1.345 [Epoch 169] Validation: aeroplane=0.8679522508338737 bicycle=0.8073690938405388 bird=0.8011055489046762 boat=0.7503210688006396 bottle=0.6611134352339725 bus=0.8819023471973465 car=0.8841162852527069 cat=0.8942450594071976 chair=0.6519699707730279 cow=0.8674995287954788 diningtable=0.7713036313060722 dog=0.8812138608030515 horse=0.8966228341156937 motorbike=0.8576367528650731 person=0.8402336409051753 pottedplant=0.5696980297879712 sheep=0.8660482926812938 sofa=0.77757140010798 train=0.8683829761920465 tvmonitor=0.7861834741919463 mAP=0.8091244740997882 [Epoch 170][Batch 99], LR: 1.00E-04, Speed: 129.889 samples/sec, ObjLoss=6.429, BoxCenterLoss=5.572, BoxScaleLoss=1.346, ClassLoss=1.343 [Epoch 170][Batch 199], LR: 1.00E-04, Speed: 129.855 samples/sec, ObjLoss=6.421, BoxCenterLoss=5.571, BoxScaleLoss=1.345, ClassLoss=1.341 [Epoch 170] Training cost: 549.386, ObjLoss=6.416, BoxCenterLoss=5.570, BoxScaleLoss=1.344, ClassLoss=1.340 [Epoch 170] Validation: aeroplane=0.8784589497487882 bicycle=0.8062397219485741 bird=0.8051187885403301 boat=0.7521521159348562 bottle=0.6612600671019505 bus=0.8778918904148348 car=0.8831348109417889 cat=0.8942580931434363 chair=0.6552942270660292 cow=0.8698595707583664 diningtable=0.781574820742997 dog=0.8829226713339591 horse=0.8940322468274605 motorbike=0.87565923936972 person=0.8413997651118246 pottedplant=0.5619063837210894 sheep=0.8614390604926636 sofa=0.7852165188094261 train=0.8682570801829228 tvmonitor=0.7797491158777612 mAP=0.810791256903439 [Epoch 171][Batch 99], LR: 1.00E-04, Speed: 125.796 samples/sec, ObjLoss=6.409, BoxCenterLoss=5.570, BoxScaleLoss=1.343, ClassLoss=1.338 [Epoch 171][Batch 199], LR: 1.00E-04, Speed: 102.500 samples/sec, ObjLoss=6.401, BoxCenterLoss=5.569, BoxScaleLoss=1.342, ClassLoss=1.336 [Epoch 171] Training cost: 560.171, ObjLoss=6.397, BoxCenterLoss=5.569, BoxScaleLoss=1.342, ClassLoss=1.335 [Epoch 171] Validation: aeroplane=0.8829096243864336 bicycle=0.8073987069438718 bird=0.8003143351962091 boat=0.750546985447079 bottle=0.6624787964183276 bus=0.8793271063488578 car=0.8846666865564382 cat=0.8961525405692903 chair=0.6500751653783394 cow=0.8646937534256878 diningtable=0.7780758795990012 dog=0.8804033790014553 horse=0.8998384220965192 motorbike=0.8703330086605604 person=0.8410669385905659 pottedplant=0.5696752568732765 sheep=0.8602748679948342 sofa=0.7772929490476874 train=0.8661041689873542 tvmonitor=0.7833144275381859 mAP=0.8102471499529986 [Epoch 172][Batch 99], LR: 1.00E-04, Speed: 109.046 samples/sec, ObjLoss=6.389, BoxCenterLoss=5.568, BoxScaleLoss=1.341, ClassLoss=1.333 [Epoch 172][Batch 199], LR: 1.00E-04, Speed: 84.857 samples/sec, ObjLoss=6.381, BoxCenterLoss=5.568, BoxScaleLoss=1.339, ClassLoss=1.331 [Epoch 172] Training cost: 565.918, ObjLoss=6.377, BoxCenterLoss=5.568, BoxScaleLoss=1.339, ClassLoss=1.329 [Epoch 172] Validation: aeroplane=0.8729397235058817 bicycle=0.8074536135919985 bird=0.8031695152398775 boat=0.7412381332268687 bottle=0.6632729985947432 bus=0.8825754030613568 car=0.8862250887013109 cat=0.898073999175241 chair=0.6492674435916952 cow=0.8708243354004237 diningtable=0.7429804756883865 dog=0.8791003667619027 horse=0.9007631984181792 motorbike=0.8664925131267143 person=0.7939705264226273 pottedplant=0.574736673451676 sheep=0.8751268715894885 sofa=0.7767720910565918 train=0.8658668825875505 tvmonitor=0.7819252473086358 mAP=0.8066387550250574 [Epoch 173][Batch 99], LR: 1.00E-04, Speed: 88.400 samples/sec, ObjLoss=6.369, BoxCenterLoss=5.567, BoxScaleLoss=1.338, ClassLoss=1.327 [Epoch 173][Batch 199], LR: 1.00E-04, Speed: 116.305 samples/sec, ObjLoss=6.361, BoxCenterLoss=5.566, BoxScaleLoss=1.336, ClassLoss=1.325 [Epoch 173] Training cost: 575.761, ObjLoss=6.357, BoxCenterLoss=5.566, BoxScaleLoss=1.336, ClassLoss=1.324 [Epoch 173] Validation: aeroplane=0.8797598459944469 bicycle=0.8608304849598412 bird=0.7988501186265515 boat=0.75216709653287 bottle=0.6681647077378784 bus=0.8820294927790375 car=0.886625218373755 cat=0.8984971845893847 chair=0.6232344325040097 cow=0.8599229196725645 diningtable=0.7406607242239318 dog=0.8803845612462959 horse=0.8982699023653619 motorbike=0.8612173974539968 person=0.8448645497539911 pottedplant=0.5665097049964716 sheep=0.861676565124841 sofa=0.7783003516105346 train=0.8729681569414065 tvmonitor=0.7774343168657388 mAP=0.8096183866176455 [Epoch 174][Batch 99], LR: 1.00E-04, Speed: 117.210 samples/sec, ObjLoss=6.350, BoxCenterLoss=5.565, BoxScaleLoss=1.335, ClassLoss=1.322 [Epoch 174][Batch 199], LR: 1.00E-04, Speed: 103.492 samples/sec, ObjLoss=6.342, BoxCenterLoss=5.564, BoxScaleLoss=1.334, ClassLoss=1.320 [Epoch 174] Training cost: 525.479, ObjLoss=6.338, BoxCenterLoss=5.564, BoxScaleLoss=1.333, ClassLoss=1.319 [Epoch 174] Validation: aeroplane=0.8721138255940959 bicycle=0.8034005436638771 bird=0.8034362665985333 boat=0.7511195638728833 bottle=0.66252957331394 bus=0.8818452806437737 car=0.88603427164297 cat=0.8972008868113441 chair=0.6471556251794213 cow=0.8600008185484955 diningtable=0.7277687316441092 dog=0.8795246249828729 horse=0.8974443466291102 motorbike=0.8654955316460974 person=0.8407974592945208 pottedplant=0.5755384525583876 sheep=0.8710717930331279 sofa=0.7819456321201635 train=0.8640509128130923 tvmonitor=0.7810862328791834 mAP=0.8074780186735 [Epoch 175][Batch 99], LR: 1.00E-04, Speed: 139.615 samples/sec, ObjLoss=6.331, BoxCenterLoss=5.563, BoxScaleLoss=1.332, ClassLoss=1.317 [Epoch 175][Batch 199], LR: 1.00E-04, Speed: 109.300 samples/sec, ObjLoss=6.323, BoxCenterLoss=5.562, BoxScaleLoss=1.331, ClassLoss=1.315 [Epoch 175] Training cost: 557.615, ObjLoss=6.318, BoxCenterLoss=5.562, BoxScaleLoss=1.330, ClassLoss=1.314 [Epoch 175] Validation: aeroplane=0.8710849794317744 bicycle=0.8066280615983381 bird=0.8037848132541244 boat=0.7513590218137759 bottle=0.6638713145622024 bus=0.883073330016345 car=0.8861600527900368 cat=0.8970644125866183 chair=0.6247709711535641 cow=0.8715249939577542 diningtable=0.7341349256943779 dog=0.8784664405521031 horse=0.8978779840848806 motorbike=0.860038540772372 person=0.8411315306339785 pottedplant=0.563236506473712 sheep=0.8700598524282734 sofa=0.782151741338856 train=0.8673021254007229 tvmonitor=0.7818935142672516 mAP=0.8067807556405532 [Epoch 176][Batch 99], LR: 1.00E-04, Speed: 105.906 samples/sec, ObjLoss=6.311, BoxCenterLoss=5.561, BoxScaleLoss=1.329, ClassLoss=1.312 [Epoch 176][Batch 199], LR: 1.00E-04, Speed: 89.433 samples/sec, ObjLoss=6.304, BoxCenterLoss=5.561, BoxScaleLoss=1.328, ClassLoss=1.310 [Epoch 176] Training cost: 544.454, ObjLoss=6.300, BoxCenterLoss=5.561, BoxScaleLoss=1.328, ClassLoss=1.309 [Epoch 176] Validation: aeroplane=0.8042381210418461 bicycle=0.8555474591066988 bird=0.8051588567161426 boat=0.7519564971509151 bottle=0.6971739861466317 bus=0.8827614369884902 car=0.8853843576458953 cat=0.8953041296914815 chair=0.6573029134767921 cow=0.867683574755587 diningtable=0.7397429686833341 dog=0.8811982774370253 horse=0.8982459507917024 motorbike=0.869371537554063 person=0.7927838647313509 pottedplant=0.5657213679909393 sheep=0.8536566045883889 sofa=0.7723397944784623 train=0.8664860435417826 tvmonitor=0.7897283177378936 mAP=0.8065893030127711 [Epoch 177][Batch 99], LR: 1.00E-04, Speed: 100.034 samples/sec, ObjLoss=6.293, BoxCenterLoss=5.560, BoxScaleLoss=1.327, ClassLoss=1.307 [Epoch 177][Batch 199], LR: 1.00E-04, Speed: 108.742 samples/sec, ObjLoss=6.286, BoxCenterLoss=5.560, BoxScaleLoss=1.326, ClassLoss=1.305 [Epoch 177] Training cost: 564.184, ObjLoss=6.282, BoxCenterLoss=5.559, BoxScaleLoss=1.325, ClassLoss=1.304 [Epoch 177] Validation: aeroplane=0.8666686580049507 bicycle=0.8616752024759002 bird=0.8057666857866362 boat=0.752441951012444 bottle=0.6597230836953802 bus=0.8840550582974827 car=0.8870231635063601 cat=0.896765959176742 chair=0.6563814562957573 cow=0.8652666406357229 diningtable=0.7393021146153919 dog=0.8805072099102721 horse=0.8971577189363033 motorbike=0.8678812277036426 person=0.792177854931478 pottedplant=0.5727148734369574 sheep=0.8599018075691968 sofa=0.7716855643178867 train=0.8698205083927903 tvmonitor=0.7826438820578078 mAP=0.808478031037955 [Epoch 178][Batch 99], LR: 1.00E-04, Speed: 107.681 samples/sec, ObjLoss=6.274, BoxCenterLoss=5.559, BoxScaleLoss=1.324, ClassLoss=1.302 [Epoch 178][Batch 199], LR: 1.00E-04, Speed: 97.427 samples/sec, ObjLoss=6.267, BoxCenterLoss=5.558, BoxScaleLoss=1.323, ClassLoss=1.300 [Epoch 178] Training cost: 585.138, ObjLoss=6.263, BoxCenterLoss=5.558, BoxScaleLoss=1.322, ClassLoss=1.299 [Epoch 178] Validation: aeroplane=0.8662233188104448 bicycle=0.8602221388767622 bird=0.8065544048325823 boat=0.7573541067963853 bottle=0.6637092527931374 bus=0.8844279910603026 car=0.8847946504434472 cat=0.894576067137787 chair=0.6211186201162725 cow=0.8708090217470275 diningtable=0.7397280863238576 dog=0.8814237191450273 horse=0.8923740949976575 motorbike=0.8761838055424677 person=0.8398214342359794 pottedplant=0.5759100673297453 sheep=0.8667984196331221 sofa=0.7776456351185714 train=0.8739408144212378 tvmonitor=0.7844154389677898 mAP=0.8109015544164804 [Epoch 179][Batch 99], LR: 1.00E-04, Speed: 104.455 samples/sec, ObjLoss=6.255, BoxCenterLoss=5.557, BoxScaleLoss=1.321, ClassLoss=1.297 [Epoch 179][Batch 199], LR: 1.00E-04, Speed: 82.488 samples/sec, ObjLoss=6.248, BoxCenterLoss=5.557, BoxScaleLoss=1.320, ClassLoss=1.295 [Epoch 179] Training cost: 555.735, ObjLoss=6.244, BoxCenterLoss=5.556, BoxScaleLoss=1.320, ClassLoss=1.294 [Epoch 179] Validation: aeroplane=0.8799593032285578 bicycle=0.8691776328825521 bird=0.8084507010684252 boat=0.7584197838353123 bottle=0.6649509676885352 bus=0.8857538566467348 car=0.884897289688796 cat=0.8963416564210387 chair=0.6262254507539342 cow=0.8726359245939589 diningtable=0.7389182019218169 dog=0.879219119118618 horse=0.8977718395836374 motorbike=0.8756769742042744 person=0.8418038695990684 pottedplant=0.5711949818939931 sheep=0.8618333177356515 sofa=0.777912313172663 train=0.8712801139917973 tvmonitor=0.7855655571782255 mAP=0.8123994427603798 [Epoch 180][Batch 99], LR: 1.00E-05, Speed: 120.877 samples/sec, ObjLoss=6.237, BoxCenterLoss=5.556, BoxScaleLoss=1.319, ClassLoss=1.292 [Epoch 180][Batch 199], LR: 1.00E-05, Speed: 85.888 samples/sec, ObjLoss=6.230, BoxCenterLoss=5.555, BoxScaleLoss=1.318, ClassLoss=1.290 [Epoch 180] Training cost: 586.002, ObjLoss=6.226, BoxCenterLoss=5.555, BoxScaleLoss=1.317, ClassLoss=1.289 [Epoch 180] Validation: aeroplane=0.8806091923818226 bicycle=0.8710901110131448 bird=0.8060994732821359 boat=0.7587686540002866 bottle=0.6642629865007504 bus=0.8839805303701713 car=0.8859936070573221 cat=0.8955274087701534 chair=0.6255133280429366 cow=0.8733754527514873 diningtable=0.7407228824705351 dog=0.8825955282184262 horse=0.8974213337086072 motorbike=0.8711515879940868 person=0.8432363878929133 pottedplant=0.5699513747624113 sheep=0.8642644604856421 sofa=0.7811518790424098 train=0.873101180053899 tvmonitor=0.786951362522185 mAP=0.8127884360660664 [Epoch 181][Batch 99], LR: 1.00E-05, Speed: 74.549 samples/sec, ObjLoss=6.219, BoxCenterLoss=5.554, BoxScaleLoss=1.316, ClassLoss=1.287 [Epoch 181][Batch 199], LR: 1.00E-05, Speed: 124.366 samples/sec, ObjLoss=6.211, BoxCenterLoss=5.554, BoxScaleLoss=1.315, ClassLoss=1.285 [Epoch 181] Training cost: 533.406, ObjLoss=6.207, BoxCenterLoss=5.553, BoxScaleLoss=1.314, ClassLoss=1.284 [Epoch 181] Validation: aeroplane=0.8771209998064429 bicycle=0.8693523942155842 bird=0.8076309270411693 boat=0.7527310005592125 bottle=0.66595621374817 bus=0.8866134746045287 car=0.8869279405964159 cat=0.8974235575240097 chair=0.6263210735735664 cow=0.8726904563798302 diningtable=0.7429144747664825 dog=0.8809275144406695 horse=0.8980411464744154 motorbike=0.8672229008627027 person=0.8444090082369936 pottedplant=0.5800589529157172 sheep=0.860524880934844 sofa=0.7806333496212992 train=0.8735797346439402 tvmonitor=0.7859304964645674 mAP=0.8128505248705281 [Epoch 182][Batch 99], LR: 1.00E-05, Speed: 133.860 samples/sec, ObjLoss=6.201, BoxCenterLoss=5.552, BoxScaleLoss=1.313, ClassLoss=1.283 [Epoch 182][Batch 199], LR: 1.00E-05, Speed: 113.683 samples/sec, ObjLoss=6.194, BoxCenterLoss=5.552, BoxScaleLoss=1.312, ClassLoss=1.281 [Epoch 182] Training cost: 514.992, ObjLoss=6.190, BoxCenterLoss=5.552, BoxScaleLoss=1.312, ClassLoss=1.280 [Epoch 182] Validation: aeroplane=0.8646227660468842 bicycle=0.8682240861248269 bird=0.8050395348640963 boat=0.7538279103104484 bottle=0.6629538677979486 bus=0.881794816774595 car=0.8857803686675518 cat=0.895426804532629 chair=0.6539349559641611 cow=0.8719450050707963 diningtable=0.7414391746697097 dog=0.8827770270758443 horse=0.8953613714808362 motorbike=0.8725541399377499 person=0.8434394763656994 pottedplant=0.571287850996268 sheep=0.8674461889658358 sofa=0.774715701878223 train=0.8716686825159305 tvmonitor=0.7842602511573729 mAP=0.8124249990598704 [Epoch 183][Batch 99], LR: 1.00E-05, Speed: 133.139 samples/sec, ObjLoss=6.183, BoxCenterLoss=5.551, BoxScaleLoss=1.311, ClassLoss=1.278 [Epoch 183][Batch 199], LR: 1.00E-05, Speed: 112.749 samples/sec, ObjLoss=6.176, BoxCenterLoss=5.550, BoxScaleLoss=1.310, ClassLoss=1.276 [Epoch 183] Training cost: 530.189, ObjLoss=6.172, BoxCenterLoss=5.550, BoxScaleLoss=1.309, ClassLoss=1.275 [Epoch 183] Validation: aeroplane=0.8674078007146625 bicycle=0.8688889209971277 bird=0.8045441374041781 boat=0.7518777527545736 bottle=0.6664179368376837 bus=0.8875897995458585 car=0.8866787189931824 cat=0.8992474235319389 chair=0.6262996646195049 cow=0.8735614602804211 diningtable=0.742527562240054 dog=0.8822075905669348 horse=0.8973991486615964 motorbike=0.8678721986472094 person=0.795026069727055 pottedplant=0.5785720516586643 sheep=0.8676765956133625 sofa=0.776397405745069 train=0.8711147764015182 tvmonitor=0.7838305911717066 mAP=0.809756880305615 [Epoch 184][Batch 99], LR: 1.00E-05, Speed: 95.258 samples/sec, ObjLoss=6.165, BoxCenterLoss=5.550, BoxScaleLoss=1.308, ClassLoss=1.273 [Epoch 184][Batch 199], LR: 1.00E-05, Speed: 113.807 samples/sec, ObjLoss=6.158, BoxCenterLoss=5.549, BoxScaleLoss=1.307, ClassLoss=1.271 [Epoch 184] Training cost: 558.056, ObjLoss=6.154, BoxCenterLoss=5.549, BoxScaleLoss=1.306, ClassLoss=1.270 [Epoch 184] Validation: aeroplane=0.8633314099500504 bicycle=0.8671940257965353 bird=0.8051034707104072 boat=0.7548739895986195 bottle=0.6638366170059379 bus=0.883556487446393 car=0.8860951246283562 cat=0.8954237541745012 chair=0.625603420932361 cow=0.870197612043526 diningtable=0.7368842335897643 dog=0.8827792638948865 horse=0.8953070359742569 motorbike=0.8731581153043204 person=0.8413742556892957 pottedplant=0.5685073509234375 sheep=0.8699580282682098 sofa=0.7775530215208492 train=0.8646267866575683 tvmonitor=0.7848966718800529 mAP=0.8105130337994664 [Epoch 185][Batch 99], LR: 1.00E-05, Speed: 83.766 samples/sec, ObjLoss=6.147, BoxCenterLoss=5.548, BoxScaleLoss=1.305, ClassLoss=1.268 [Epoch 185][Batch 199], LR: 1.00E-05, Speed: 106.793 samples/sec, ObjLoss=6.140, BoxCenterLoss=5.548, BoxScaleLoss=1.304, ClassLoss=1.267 [Epoch 185] Training cost: 565.536, ObjLoss=6.136, BoxCenterLoss=5.547, BoxScaleLoss=1.304, ClassLoss=1.266 [Epoch 185] Validation: aeroplane=0.8735069089424004 bicycle=0.8660261778726037 bird=0.805769489420114 boat=0.7557111340851318 bottle=0.6664627452075831 bus=0.8843023430210148 car=0.8863380682439764 cat=0.8990042076609323 chair=0.6272425743292499 cow=0.8679404180988752 diningtable=0.7409514262932843 dog=0.8831697163609363 horse=0.8978082373597557 motorbike=0.8693457753405549 person=0.8431787689348283 pottedplant=0.5777371096272098 sheep=0.8635066891025711 sofa=0.7708376823096674 train=0.8714828875696253 tvmonitor=0.7843277314065813 mAP=0.8117325045593449 [Epoch 186][Batch 99], LR: 1.00E-05, Speed: 117.977 samples/sec, ObjLoss=6.129, BoxCenterLoss=5.547, BoxScaleLoss=1.303, ClassLoss=1.264 [Epoch 186][Batch 199], LR: 1.00E-05, Speed: 110.439 samples/sec, ObjLoss=6.122, BoxCenterLoss=5.546, BoxScaleLoss=1.302, ClassLoss=1.262 [Epoch 186] Training cost: 572.145, ObjLoss=6.118, BoxCenterLoss=5.546, BoxScaleLoss=1.301, ClassLoss=1.261 [Epoch 186] Validation: aeroplane=0.8747670818490002 bicycle=0.8685382235743817 bird=0.8064591453587663 boat=0.7549967666519525 bottle=0.6674371397765826 bus=0.8885225667570557 car=0.886166839042828 cat=0.8992261602496952 chair=0.6545778767007979 cow=0.8703228811841943 diningtable=0.7406395039756895 dog=0.8821484879102708 horse=0.8970282107822312 motorbike=0.8629339108379683 person=0.8434721236247544 pottedplant=0.5741532195997796 sheep=0.8683767926988266 sofa=0.7740066111887713 train=0.8729278747545438 tvmonitor=0.7846055005734746 mAP=0.8135653458545782 [Epoch 187][Batch 99], LR: 1.00E-05, Speed: 109.361 samples/sec, ObjLoss=6.112, BoxCenterLoss=5.545, BoxScaleLoss=1.300, ClassLoss=1.259 [Epoch 187][Batch 199], LR: 1.00E-05, Speed: 115.426 samples/sec, ObjLoss=6.105, BoxCenterLoss=5.545, BoxScaleLoss=1.299, ClassLoss=1.257 [Epoch 187] Training cost: 543.670, ObjLoss=6.101, BoxCenterLoss=5.544, BoxScaleLoss=1.299, ClassLoss=1.256 [Epoch 187] Validation: aeroplane=0.8769503959445376 bicycle=0.8658599642956483 bird=0.804716471754421 boat=0.7523564739822225 bottle=0.6668340909770638 bus=0.8861178295388823 car=0.8845349269906837 cat=0.8971857574935793 chair=0.6523554206531424 cow=0.867227837647329 diningtable=0.7375202424279999 dog=0.8821667557720345 horse=0.8974695068142124 motorbike=0.8680210899102188 person=0.7945984164263481 pottedplant=0.570107494712675 sheep=0.8686289974225403 sofa=0.7805523934659258 train=0.870827004922599 tvmonitor=0.7824747430499271 mAP=0.8103252907100996 [Epoch 188][Batch 99], LR: 1.00E-05, Speed: 128.895 samples/sec, ObjLoss=6.094, BoxCenterLoss=5.544, BoxScaleLoss=1.298, ClassLoss=1.255 [Epoch 188][Batch 199], LR: 1.00E-05, Speed: 104.754 samples/sec, ObjLoss=6.088, BoxCenterLoss=5.544, BoxScaleLoss=1.297, ClassLoss=1.253 [Epoch 188] Training cost: 574.511, ObjLoss=6.084, BoxCenterLoss=5.544, BoxScaleLoss=1.296, ClassLoss=1.252 [Epoch 188] Validation: aeroplane=0.8726991232176149 bicycle=0.8679572961645475 bird=0.8034977494875437 boat=0.7520746415592925 bottle=0.6684650555323887 bus=0.8876486602927058 car=0.8855828737468656 cat=0.9009462440496924 chair=0.6238622702829211 cow=0.8713039724405417 diningtable=0.7433267273251751 dog=0.8831771904152456 horse=0.8991807542319833 motorbike=0.8687566492450709 person=0.8457538022943907 pottedplant=0.5788547499468477 sheep=0.8622492593777791 sofa=0.7722445314739231 train=0.8722039416286914 tvmonitor=0.7850357425568899 mAP=0.8122410617635056 [Epoch 189][Batch 99], LR: 1.00E-05, Speed: 94.742 samples/sec, ObjLoss=6.078, BoxCenterLoss=5.543, BoxScaleLoss=1.295, ClassLoss=1.250 [Epoch 189][Batch 199], LR: 1.00E-05, Speed: 85.691 samples/sec, ObjLoss=6.071, BoxCenterLoss=5.543, BoxScaleLoss=1.294, ClassLoss=1.249 [Epoch 189] Training cost: 580.112, ObjLoss=6.067, BoxCenterLoss=5.543, BoxScaleLoss=1.294, ClassLoss=1.248 [Epoch 189] Validation: aeroplane=0.8725423922035138 bicycle=0.8715678441706975 bird=0.8027148805841997 boat=0.7509109163881663 bottle=0.6642604947737811 bus=0.8866347145045838 car=0.8861357625457975 cat=0.9008013705051318 chair=0.624284538277972 cow=0.8718506135518813 diningtable=0.7415858962674243 dog=0.8850538044085811 horse=0.8969362941317562 motorbike=0.8720962087972047 person=0.7949941790119903 pottedplant=0.5760801403850102 sheep=0.8619892102846648 sofa=0.7752724958370601 train=0.8726683964729284 tvmonitor=0.7833028247237337 mAP=0.809584148891304 [Epoch 190][Batch 99], LR: 1.00E-05, Speed: 135.018 samples/sec, ObjLoss=6.060, BoxCenterLoss=5.542, BoxScaleLoss=1.293, ClassLoss=1.246 [Epoch 190][Batch 199], LR: 1.00E-05, Speed: 129.724 samples/sec, ObjLoss=6.054, BoxCenterLoss=5.542, BoxScaleLoss=1.292, ClassLoss=1.244 [Epoch 190] Training cost: 556.048, ObjLoss=6.050, BoxCenterLoss=5.541, BoxScaleLoss=1.291, ClassLoss=1.243 [Epoch 190] Validation: aeroplane=0.8737737886209352 bicycle=0.8689640684403837 bird=0.8069048069048069 boat=0.7517007176699771 bottle=0.6671808655434931 bus=0.8876638375319917 car=0.8867735016266183 cat=0.9011748689506092 chair=0.6246013643068651 cow=0.8736725141442667 diningtable=0.7439742186481356 dog=0.8820786242958588 horse=0.89847119614348 motorbike=0.8732696562533415 person=0.8463900185987094 pottedplant=0.5879539420288467 sheep=0.8639489962383254 sofa=0.7806721611520316 train=0.8685515288580732 tvmonitor=0.7857844459650384 mAP=0.8136752560960895 [Epoch 191][Batch 99], LR: 1.00E-05, Speed: 115.061 samples/sec, ObjLoss=6.043, BoxCenterLoss=5.541, BoxScaleLoss=1.290, ClassLoss=1.241 [Epoch 191][Batch 199], LR: 1.00E-05, Speed: 111.310 samples/sec, ObjLoss=6.037, BoxCenterLoss=5.540, BoxScaleLoss=1.289, ClassLoss=1.240 [Epoch 191] Training cost: 575.420, ObjLoss=6.034, BoxCenterLoss=5.540, BoxScaleLoss=1.289, ClassLoss=1.239 [Epoch 191] Validation: aeroplane=0.8811319442996615 bicycle=0.8708634699476134 bird=0.8048307859404938 boat=0.7543675807565441 bottle=0.6682929865535817 bus=0.8875439312801047 car=0.8860821700099478 cat=0.9009708320053148 chair=0.6535248932889237 cow=0.8689619411999839 diningtable=0.7424731365274526 dog=0.8830120552372068 horse=0.8978744810362141 motorbike=0.868962898821833 person=0.8437434102775113 pottedplant=0.5761853554517871 sheep=0.8696248976848059 sofa=0.7746286776852996 train=0.8708284691525602 tvmonitor=0.7858158481840203 mAP=0.8144859882670431 [Epoch 192][Batch 99], LR: 1.00E-05, Speed: 100.332 samples/sec, ObjLoss=6.027, BoxCenterLoss=5.539, BoxScaleLoss=1.288, ClassLoss=1.237 [Epoch 192][Batch 199], LR: 1.00E-05, Speed: 125.317 samples/sec, ObjLoss=6.021, BoxCenterLoss=5.539, BoxScaleLoss=1.287, ClassLoss=1.235 [Epoch 192] Training cost: 560.447, ObjLoss=6.017, BoxCenterLoss=5.538, BoxScaleLoss=1.286, ClassLoss=1.234 [Epoch 192] Validation: aeroplane=0.8746600772821781 bicycle=0.8697806070699854 bird=0.8071562012752571 boat=0.7552366931625847 bottle=0.667632132544369 bus=0.8856816342786239 car=0.886680115331491 cat=0.8996959078355913 chair=0.6552732742653653 cow=0.8769245394484785 diningtable=0.7401981854941668 dog=0.8824447215181708 horse=0.8988572584871966 motorbike=0.8716310886340768 person=0.8455686231309314 pottedplant=0.5769210387877718 sheep=0.8704279540670437 sofa=0.7755434976189653 train=0.8735497313616742 tvmonitor=0.7843590566252879 mAP=0.8149111169109604 [Epoch 193][Batch 99], LR: 1.00E-05, Speed: 117.611 samples/sec, ObjLoss=6.011, BoxCenterLoss=5.538, BoxScaleLoss=1.285, ClassLoss=1.233 [Epoch 193][Batch 199], LR: 1.00E-05, Speed: 122.998 samples/sec, ObjLoss=6.005, BoxCenterLoss=5.537, BoxScaleLoss=1.284, ClassLoss=1.231 [Epoch 193] Training cost: 541.828, ObjLoss=6.001, BoxCenterLoss=5.537, BoxScaleLoss=1.284, ClassLoss=1.230 [Epoch 193] Validation: aeroplane=0.8736583633809343 bicycle=0.8687363116224261 bird=0.8030554721019627 boat=0.748834892410275 bottle=0.6691938462021843 bus=0.8876324958028571 car=0.8861491871741973 cat=0.9005264564157847 chair=0.6233345115290277 cow=0.8699996183023709 diningtable=0.739200881469223 dog=0.8838460408534199 horse=0.8955577238003358 motorbike=0.8676269938473382 person=0.8453913816172306 pottedplant=0.5816869165884683 sheep=0.8636079964507125 sofa=0.7760246451687345 train=0.8714386490884715 tvmonitor=0.7844624597071443 mAP=0.811998242176655 [Epoch 194][Batch 99], LR: 1.00E-05, Speed: 136.533 samples/sec, ObjLoss=5.995, BoxCenterLoss=5.536, BoxScaleLoss=1.283, ClassLoss=1.229 [Epoch 194][Batch 199], LR: 1.00E-05, Speed: 82.072 samples/sec, ObjLoss=5.989, BoxCenterLoss=5.536, BoxScaleLoss=1.282, ClassLoss=1.227 [Epoch 194] Training cost: 556.197, ObjLoss=5.985, BoxCenterLoss=5.535, BoxScaleLoss=1.282, ClassLoss=1.226 [Epoch 194] Validation: aeroplane=0.8688195996510053 bicycle=0.8637312801898995 bird=0.8069691998225568 boat=0.7520927198864791 bottle=0.6648508739953916 bus=0.886081563789263 car=0.8865507595076796 cat=0.901172882575245 chair=0.6531178712436091 cow=0.8693300604975153 diningtable=0.7396475096054711 dog=0.880642828725183 horse=0.8961308263971702 motorbike=0.8672008394374888 person=0.8452280259661178 pottedplant=0.5771775685908412 sheep=0.868483595240363 sofa=0.7783029612768835 train=0.8713053440186838 tvmonitor=0.785538272534084 mAP=0.8131187291475465 [Epoch 195][Batch 99], LR: 1.00E-05, Speed: 128.695 samples/sec, ObjLoss=5.979, BoxCenterLoss=5.535, BoxScaleLoss=1.281, ClassLoss=1.224 [Epoch 195][Batch 199], LR: 1.00E-05, Speed: 108.131 samples/sec, ObjLoss=5.973, BoxCenterLoss=5.535, BoxScaleLoss=1.280, ClassLoss=1.223 [Epoch 195] Training cost: 569.282, ObjLoss=5.969, BoxCenterLoss=5.534, BoxScaleLoss=1.279, ClassLoss=1.222 [Epoch 195] Validation: aeroplane=0.8696482956851627 bicycle=0.8688908303866396 bird=0.8075331234422145 boat=0.7516719772304373 bottle=0.6656690526108758 bus=0.8851554559816759 car=0.8867806635091265 cat=0.9008194199168393 chair=0.6526532702563972 cow=0.8690361821724979 diningtable=0.7362412023932106 dog=0.8831453664029114 horse=0.8965151701810294 motorbike=0.8698341857734648 person=0.8464149439613925 pottedplant=0.5743195006253179 sheep=0.8691821444372874 sofa=0.7739695994207734 train=0.870283626966176 tvmonitor=0.7829869451802771 mAP=0.8130375478266852 [Epoch 196][Batch 99], LR: 1.00E-05, Speed: 95.713 samples/sec, ObjLoss=5.963, BoxCenterLoss=5.534, BoxScaleLoss=1.278, ClassLoss=1.220 [Epoch 196][Batch 199], LR: 1.00E-05, Speed: 103.381 samples/sec, ObjLoss=5.957, BoxCenterLoss=5.534, BoxScaleLoss=1.277, ClassLoss=1.219 [Epoch 196] Training cost: 558.689, ObjLoss=5.954, BoxCenterLoss=5.533, BoxScaleLoss=1.277, ClassLoss=1.218 [Epoch 196] Validation: aeroplane=0.8722011819596778 bicycle=0.8661237422565253 bird=0.8047757838023275 boat=0.7561496260411213 bottle=0.6674511782816385 bus=0.8871137627169073 car=0.8857026391810929 cat=0.9006859886972157 chair=0.6537026807626659 cow=0.8690236588889265 diningtable=0.7402250864115271 dog=0.8820840295094409 horse=0.8944120520770504 motorbike=0.867015447518605 person=0.8456438577040689 pottedplant=0.5804372736091725 sheep=0.8599436798081793 sofa=0.7755310105335061 train=0.8714398335526811 tvmonitor=0.7840922880632013 mAP=0.8131877400687767 [Epoch 197][Batch 99], LR: 1.00E-05, Speed: 135.550 samples/sec, ObjLoss=5.948, BoxCenterLoss=5.533, BoxScaleLoss=1.276, ClassLoss=1.216 [Epoch 197][Batch 199], LR: 1.00E-05, Speed: 144.280 samples/sec, ObjLoss=5.941, BoxCenterLoss=5.532, BoxScaleLoss=1.275, ClassLoss=1.214 [Epoch 197] Training cost: 566.657, ObjLoss=5.938, BoxCenterLoss=5.532, BoxScaleLoss=1.275, ClassLoss=1.213 [Epoch 197] Validation: aeroplane=0.8712140583460676 bicycle=0.8665809426298222 bird=0.8075234316414779 boat=0.759270397918883 bottle=0.6655617680325595 bus=0.8861382218411327 car=0.8868433037979153 cat=0.9002153821270161 chair=0.6550618378727493 cow=0.8710940116041752 diningtable=0.7373391201345021 dog=0.8817367942729631 horse=0.8970070945451395 motorbike=0.8676841436425337 person=0.8460316333648539 pottedplant=0.577836010604517 sheep=0.8703648001484079 sofa=0.7743983185165121 train=0.8734345932300246 tvmonitor=0.7839902453957202 mAP=0.8139663054833488 [Epoch 198][Batch 99], LR: 1.00E-05, Speed: 120.207 samples/sec, ObjLoss=5.932, BoxCenterLoss=5.532, BoxScaleLoss=1.274, ClassLoss=1.212 [Epoch 198][Batch 199], LR: 1.00E-05, Speed: 129.655 samples/sec, ObjLoss=5.926, BoxCenterLoss=5.531, BoxScaleLoss=1.273, ClassLoss=1.210 [Epoch 198] Training cost: 545.388, ObjLoss=5.922, BoxCenterLoss=5.531, BoxScaleLoss=1.272, ClassLoss=1.209 [Epoch 198] Validation: aeroplane=0.870299478864526 bicycle=0.8646702625172062 bird=0.8066393350938368 boat=0.7508989905652672 bottle=0.6602925910583225 bus=0.8843488246262422 car=0.8857405441033246 cat=0.8952842134102397 chair=0.6541801174226313 cow=0.8639125099639445 diningtable=0.7400743071883342 dog=0.8849045951021898 horse=0.8977050019677125 motorbike=0.8724199791790781 person=0.8438503841607572 pottedplant=0.5721845478253073 sheep=0.8692880855297325 sofa=0.7782255460656243 train=0.8685602847652207 tvmonitor=0.7821230695052774 mAP=0.8122801334457387 [Epoch 199][Batch 99], LR: 1.00E-05, Speed: 96.917 samples/sec, ObjLoss=5.917, BoxCenterLoss=5.530, BoxScaleLoss=1.271, ClassLoss=1.208 [Epoch 199][Batch 199], LR: 1.00E-05, Speed: 120.182 samples/sec, ObjLoss=5.911, BoxCenterLoss=5.530, BoxScaleLoss=1.271, ClassLoss=1.206 [Epoch 199] Training cost: 549.907, ObjLoss=5.907, BoxCenterLoss=5.530, BoxScaleLoss=1.270, ClassLoss=1.205 [Epoch 199] Validation: aeroplane=0.8817918433217145 bicycle=0.8668143187959991 bird=0.8051598611874158 boat=0.7544130480685846 bottle=0.6608544760159806 bus=0.8855316085296486 car=0.8853042929054735 cat=0.8958460729503355 chair=0.652942890073322 cow=0.8699489939542744 diningtable=0.7373517326291201 dog=0.8827395068290078 horse=0.897565218064805 motorbike=0.8666547373754618 person=0.8442076559222773 pottedplant=0.5769997726125092 sheep=0.868697090016946 sofa=0.775030024227119 train=0.8698573944056109 tvmonitor=0.7849756698920067 mAP=0.8131343103888804