(flappbird) luo@luo-All-Series:~/MyFile/tf-faster-rcnn_box$
(flappbird) luo@luo-All-Series:~/MyFile/tf-faster-rcnn_box$
(flappbird) luo@luo-All-Series:~/MyFile/tf-faster-rcnn_box$
(flappbird) luo@luo-All-Series:~/MyFile/tf-faster-rcnn_box$
(flappbird) luo@luo-All-Series:~/MyFile/tf-faster-rcnn_box$
(flappbird) luo@luo-All-Series:~/MyFile/tf-faster-rcnn_box$ ./experiments/scripts/train_faster_rcnn.sh 0 pascal_voc_0712 res101
+ set -e
+ export PYTHONUNBUFFERED=True
+ PYTHONUNBUFFERED=True
+ GPU_ID=0
+ DATASET=pascal_voc_0712
+ NET=res101
+ array=($@)
+ len=3
+ EXTRA_ARGS=
+ EXTRA_ARGS_SLUG=
+ case ${DATASET} in
+ TRAIN_IMDB=voc_2007_trainval+voc_2012_trainval
+ TEST_IMDB=voc_2007_test
+ STEPSIZE='[200]'
+ ITERS=3200
+ ANCHORS='[8,16,32]'
+ RATIOS='[0.5,1,2]'
++ date +%Y-%m-%d_%H-%M-%S
+ LOG=experiments/logs/res101_voc_2007_trainval+voc_2012_trainval__res101.txt.2019-05-16_14-21-07
+ exec
++ tee -a experiments/logs/res101_voc_2007_trainval+voc_2012_trainval__res101.txt.2019-05-16_14-21-07
+ echo Logging output to experiments/logs/res101_voc_2007_trainval+voc_2012_trainval__res101.txt.2019-05-16_14-21-07
Logging output to experiments/logs/res101_voc_2007_trainval+voc_2012_trainval__res101.txt.2019-05-16_14-21-07
+ set +x
+ '[' '!' -f output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_3200.ckpt.index ']'
+ [[ ! -z '' ]]
+ CUDA_VISIBLE_DEVICES=0
+ time python ./tools/trainval_net.py --weight data/imagenet_weights/res101.ckpt --imdb voc_2007_trainval+voc_2012_trainval --imdbval voc_2007_test --iters 3200 --cfg experiments/cfgs/res101.yml --net res101 --set ANCHOR_SCALES '[8,16,32]' ANCHOR_RATIOS '[0.5,1,2]' TRAIN.STEPSIZE '[200]'
Called with args:
Namespace(cfg_file='experiments/cfgs/res101.yml', imdb_name='voc_2007_trainval+voc_2012_trainval', imdbval_name='voc_2007_test', max_iters=3200, net='res101', set_cfgs=['ANCHOR_SCALES', '[8,16,32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'TRAIN.STEPSIZE', '[200]'], tag=None, weight='data/imagenet_weights/res101.ckpt')
Using config:
{'ANCHOR_RATIOS': [0.5, 1, 2],
'ANCHOR_SCALES': [8, 16, 32],
'DATA_DIR': '/home/luo/MyFile/tf-faster-rcnn_box/data',
'EXP_DIR': 'res101',
'MATLAB': 'matlab',
'MOBILENET': {'DEPTH_MULTIPLIER': 1.0,
'FIXED_LAYERS': 5,
'REGU_DEPTH': False,
'WEIGHT_DECAY': 4e-05},
'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),
'POOLING_MODE': 'crop',
'POOLING_SIZE': 7,
'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False},
'RNG_SEED': 3,
'ROOT_DIR': '/home/luo/MyFile/tf-faster-rcnn_box',
'RPN_CHANNELS': 512,
'TEST': {'BBOX_REG': True,
'HAS_RPN': True,
'MAX_SIZE': 1000,
'MODE': 'nms',
'NMS': 0.3,
'PROPOSAL_METHOD': 'gt',
'RPN_NMS_THRESH': 0.7,
'RPN_POST_NMS_TOP_N': 300,
'RPN_PRE_NMS_TOP_N': 6000,
'RPN_TOP_N': 5000,
'SCALES': [600],
'SVM': False},
'TRAIN': {'ASPECT_GROUPING': False,
'BATCH_SIZE': 256,
'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0],
'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2],
'BBOX_NORMALIZE_TARGETS': True,
'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True,
'BBOX_REG': True,
'BBOX_THRESH': 0.5,
'BG_THRESH_HI': 0.5,
'BG_THRESH_LO': 0.0,
'BIAS_DECAY': False,
'DISPLAY': 20,
'DOUBLE_BIAS': False,
'FG_FRACTION': 0.25,
'FG_THRESH': 0.5,
'GAMMA': 0.1,
'HAS_RPN': True,
'IMS_PER_BATCH': 1,
'LEARNING_RATE': 0.001,
'MAX_SIZE': 640,
'MOMENTUM': 0.9,
'PROPOSAL_METHOD': 'gt',
'RPN_BATCHSIZE': 256,
'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
'RPN_CLOBBER_POSITIVES': False,
'RPN_FG_FRACTION': 0.5,
'RPN_NEGATIVE_OVERLAP': 0.3,
'RPN_NMS_THRESH': 0.7,
'RPN_POSITIVE_OVERLAP': 0.7,
'RPN_POSITIVE_WEIGHT': -1.0,
'RPN_POST_NMS_TOP_N': 2000,
'RPN_PRE_NMS_TOP_N': 12000,
'SCALES': [600],
'SNAPSHOT_ITERS': 500,
'SNAPSHOT_KEPT': 3,
'SNAPSHOT_PREFIX': 'res101_faster_rcnn',
'STEPSIZE': [200],
'SUMMARY_INTERVAL': 10,
'TRUNCATED': False,
'USE_ALL_GT': True,
'USE_FLIPPED': True,
'USE_GT': False,
'WEIGHT_DECAY': 0.0001},
'USE_E2E_TF': True,
'USE_GPU_NMS': False}
Loaded dataset `voc_2007_trainval` for training
Set proposal method: gt
Appending horizontally-flipped training examples...
wrote gt roidb to /home/luo/MyFile/tf-faster-rcnn_box/data/cache/voc_2007_trainval_gt_roidb.pkl
done
Preparing training data...
done
Loaded dataset `voc_2012_trainval` for training
Set proposal method: gt
Appending horizontally-flipped training examples...
wrote gt roidb to /home/luo/MyFile/tf-faster-rcnn_box/data/cache/voc_2012_trainval_gt_roidb.pkl
done
Preparing training data...
done
3100 roidb entries
Output will be saved to `/home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default`
TensorFlow summaries will be saved to `/home/luo/MyFile/tf-faster-rcnn_box/tensorboard/res101/voc_2007_trainval+voc_2012_trainval/default`
Loaded dataset `voc_2007_test` for training
Set proposal method: gt
Preparing training data...
wrote gt roidb to /home/luo/MyFile/tf-faster-rcnn_box/data/cache/voc_2007_test_gt_roidb.pkl
done
388 validation roidb entries
Filtered 0 roidb entries: 3100 -> 3100
Filtered 0 roidb entries: 388 -> 388
2019-05-16 14:21:10.101640: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Solving...
/home/luo/anaconda3/envs/flappbird/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:98: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
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Loaded.
Fix Resnet V1 layers..
Fixed.
iter: 20 / 3200, total loss: 1.135714
>>> rpn_loss_cls: 0.095185
>>> rpn_loss_box: 0.219110
>>> loss_cls: 0.245350
>>> loss_box: 0.193565
>>> lr: 0.001000
speed: 18.402s / iter
iter: 40 / 3200, total loss: 1.600461
>>> rpn_loss_cls: 0.235677
>>> rpn_loss_box: 0.519147
>>> loss_cls: 0.258725
>>> loss_box: 0.204415
>>> lr: 0.001000
speed: 18.246s / iter
iter: 60 / 3200, total loss: 1.026078
>>> rpn_loss_cls: 0.166990
>>> rpn_loss_box: 0.091634
>>> loss_cls: 0.133496
>>> loss_box: 0.251467
>>> lr: 0.001000
speed: 18.454s / iter
iter: 80 / 3200, total loss: 1.284394
>>> rpn_loss_cls: 0.224517
>>> rpn_loss_box: 0.456405
>>> loss_cls: 0.072983
>>> loss_box: 0.148006
>>> lr: 0.001000
speed: 18.529s / iter
iter: 80 / 3200, total loss: 1.284394
>>> rpn_loss_cls: 0.224517
>>> rpn_loss_box: 0.456405
>>> loss_cls: 0.072983
>>> loss_box: 0.148006
>>> lr: 0.001000
speed: 18.529s / iter
iter: 100 / 3200, total loss: 0.844565
>>> rpn_loss_cls: 0.175153
>>> rpn_loss_box: 0.030733
>>> loss_cls: 0.099979
>>> loss_box: 0.156224
>>> lr: 0.001000
speed: 18.616s / iter
iter: 120 / 3200, total loss: 1.405110
>>> rpn_loss_cls: 0.277845
>>> rpn_loss_box: 0.059538
>>> loss_cls: 0.414902
>>> loss_box: 0.270357
>>> lr: 0.001000
speed: 18.615s / iter
iter: 140 / 3200, total loss: 1.150603
>>> rpn_loss_cls: 0.331623
>>> rpn_loss_box: 0.227049
>>> loss_cls: 0.082486
>>> loss_box: 0.126985
>>> lr: 0.001000
speed: 18.609s / iter
iter: 160 / 3200, total loss: 0.838705
>>> rpn_loss_cls: 0.229634
>>> rpn_loss_box: 0.022866
>>> loss_cls: 0.052187
>>> loss_box: 0.151566
>>> lr: 0.001000
speed: 18.610s / iter
iter: 180 / 3200, total loss: 0.967498
>>> rpn_loss_cls: 0.109740
>>> rpn_loss_box: 0.070803
>>> loss_cls: 0.195030
>>> loss_box: 0.209483
>>> lr: 0.001000
speed: 18.599s / iter
iter: 200 / 3200, total loss: 0.995808
>>> rpn_loss_cls: 0.190712
>>> rpn_loss_box: 0.229901
>>> loss_cls: 0.050683
>>> loss_box: 0.142080
>>> lr: 0.001000
speed: 18.590s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_201.ckpt
iter: 220 / 3200, total loss: 0.947366
>>> rpn_loss_cls: 0.117479
>>> rpn_loss_box: 0.166095
>>> loss_cls: 0.127740
>>> loss_box: 0.153623
>>> lr: 0.000100
speed: 18.561s / iter
iter: 240 / 3200, total loss: 0.930408
>>> rpn_loss_cls: 0.091187
>>> rpn_loss_box: 0.028099
>>> loss_cls: 0.125474
>>> loss_box: 0.303220
>>> lr: 0.000100
speed: 18.544s / iter
iter: 260 / 3200, total loss: 0.783629
>>> rpn_loss_cls: 0.175871
>>> rpn_loss_box: 0.058733
>>> loss_cls: 0.047003
>>> loss_box: 0.119595
>>> lr: 0.000100
speed: 18.511s / iter
iter: 280 / 3200, total loss: 0.883182
>>> rpn_loss_cls: 0.122077
>>> rpn_loss_box: 0.177903
>>> loss_cls: 0.046702
>>> loss_box: 0.154073
>>> lr: 0.000100
speed: 18.496s / iter
iter: 300 / 3200, total loss: 0.723198
>>> rpn_loss_cls: 0.075850
>>> rpn_loss_box: 0.028023
>>> loss_cls: 0.059075
>>> loss_box: 0.177825
>>> lr: 0.000100
speed: 18.483s / iter
iter: 320 / 3200, total loss: 0.725044
>>> rpn_loss_cls: 0.070511
>>> rpn_loss_box: 0.083238
>>> loss_cls: 0.041324
>>> loss_box: 0.147548
>>> lr: 0.000100
speed: 18.473s / iter
iter: 340 / 3200, total loss: 0.664221
>>> rpn_loss_cls: 0.067252
>>> rpn_loss_box: 0.011058
>>> loss_cls: 0.053833
>>> loss_box: 0.149655
>>> lr: 0.000100
speed: 18.463s / iter
iter: 360 / 3200, total loss: 0.839485
>>> rpn_loss_cls: 0.020818
>>> rpn_loss_box: 0.048659
>>> loss_cls: 0.086075
>>> loss_box: 0.301513
>>> lr: 0.000100
speed: 18.459s / iter
iter: 380 / 3200, total loss: 0.825940
>>> rpn_loss_cls: 0.090821
>>> rpn_loss_box: 0.012293
>>> loss_cls: 0.102120
>>> loss_box: 0.238286
>>> lr: 0.000100
speed: 18.452s / iter
iter: 400 / 3200, total loss: 0.616738
>>> rpn_loss_cls: 0.038577
>>> rpn_loss_box: 0.005539
>>> loss_cls: 0.060641
>>> loss_box: 0.129562
>>> lr: 0.000100
speed: 18.448s / iter
iter: 420 / 3200, total loss: 0.788184
>>> rpn_loss_cls: 0.101999
>>> rpn_loss_box: 0.099144
>>> loss_cls: 0.070542
>>> loss_box: 0.134082
>>> lr: 0.000100
speed: 18.435s / iter
iter: 440 / 3200, total loss: 1.085997
>>> rpn_loss_cls: 0.093481
>>> rpn_loss_box: 0.019349
>>> loss_cls: 0.153576
>>> loss_box: 0.437174
>>> lr: 0.000100
speed: 18.429s / iter
iter: 460 / 3200, total loss: 1.423583
>>> rpn_loss_cls: 0.356634
>>> rpn_loss_box: 0.074707
>>> loss_cls: 0.249503
>>> loss_box: 0.360324
>>> lr: 0.000100
speed: 18.420s / iter
iter: 480 / 3200, total loss: 0.916140
>>> rpn_loss_cls: 0.162728
>>> rpn_loss_box: 0.249070
>>> loss_cls: 0.030213
>>> loss_box: 0.091716
>>> lr: 0.000100
speed: 18.414s / iter
iter: 500 / 3200, total loss: 0.761923
>>> rpn_loss_cls: 0.176307
>>> rpn_loss_box: 0.074660
>>> loss_cls: 0.031245
>>> loss_box: 0.097299
>>> lr: 0.000100
speed: 18.408s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_500.ckpt
iter: 520 / 3200, total loss: 0.885430
>>> rpn_loss_cls: 0.113050
>>> rpn_loss_box: 0.014576
>>> loss_cls: 0.103602
>>> loss_box: 0.271790
>>> lr: 0.000100
speed: 18.402s / iter
iter: 540 / 3200, total loss: 0.590627
>>> rpn_loss_cls: 0.031484
>>> rpn_loss_box: 0.032061
>>> loss_cls: 0.015204
>>> loss_box: 0.129469
>>> lr: 0.000100
speed: 18.396s / iter
iter: 560 / 3200, total loss: 0.757290
>>> rpn_loss_cls: 0.222908
>>> rpn_loss_box: 0.022937
>>> loss_cls: 0.036551
>>> loss_box: 0.092485
>>> lr: 0.000100
speed: 18.388s / iter
iter: 580 / 3200, total loss: 0.652721
>>> rpn_loss_cls: 0.040262
>>> rpn_loss_box: 0.007916
>>> loss_cls: 0.077510
>>> loss_box: 0.144626
>>> lr: 0.000100
speed: 18.386s / iter
iter: 600 / 3200, total loss: 0.812826
>>> rpn_loss_cls: 0.156050
>>> rpn_loss_box: 0.142754
>>> loss_cls: 0.028783
>>> loss_box: 0.102833
>>> lr: 0.000100
speed: 18.379s / iter
iter: 620 / 3200, total loss: 0.633658
>>> rpn_loss_cls: 0.042237
>>> rpn_loss_box: 0.018296
>>> loss_cls: 0.040488
>>> loss_box: 0.150232
>>> lr: 0.000100
speed: 18.376s / iter
iter: 640 / 3200, total loss: 0.761751
>>> rpn_loss_cls: 0.181334
>>> rpn_loss_box: 0.024330
>>> loss_cls: 0.028081
>>> loss_box: 0.145603
>>> lr: 0.000100
speed: 18.370s / iter
iter: 660 / 3200, total loss: 0.847254
>>> rpn_loss_cls: 0.173398
>>> rpn_loss_box: 0.032888
>>> loss_cls: 0.055646
>>> loss_box: 0.202919
>>> lr: 0.000100
speed: 18.363s / iter
iter: 680 / 3200, total loss: 1.182448
>>> rpn_loss_cls: 0.095425
>>> rpn_loss_box: 0.015148
>>> loss_cls: 0.255668
>>> loss_box: 0.433806
>>> lr: 0.000100
speed: 18.359s / iter
iter: 700 / 3200, total loss: 0.664434
>>> rpn_loss_cls: 0.048816
>>> rpn_loss_box: 0.061652
>>> loss_cls: 0.052419
>>> loss_box: 0.119148
>>> lr: 0.000100
speed: 18.353s / iter
iter: 720 / 3200, total loss: 0.556006
>>> rpn_loss_cls: 0.026380
>>> rpn_loss_box: 0.015842
>>> loss_cls: 0.031052
>>> loss_box: 0.100334
>>> lr: 0.000100
speed: 18.347s / iter
iter: 740 / 3200, total loss: 0.867070
>>> rpn_loss_cls: 0.144368
>>> rpn_loss_box: 0.197553
>>> loss_cls: 0.022957
>>> loss_box: 0.119795
>>> lr: 0.000100
speed: 18.340s / iter
iter: 760 / 3200, total loss: 0.866542
>>> rpn_loss_cls: 0.136555
>>> rpn_loss_box: 0.022036
>>> loss_cls: 0.139475
>>> loss_box: 0.186081
>>> lr: 0.000100
speed: 18.338s / iter
iter: 780 / 3200, total loss: 0.539158
>>> rpn_loss_cls: 0.006686
>>> rpn_loss_box: 0.008340
>>> loss_cls: 0.030934
>>> loss_box: 0.110804
>>> lr: 0.000100
speed: 18.333s / iter
iter: 800 / 3200, total loss: 0.630556
>>> rpn_loss_cls: 0.020302
>>> rpn_loss_box: 0.007729
>>> loss_cls: 0.060629
>>> loss_box: 0.159504
>>> lr: 0.000100
speed: 18.330s / iter
iter: 820 / 3200, total loss: 0.861949
>>> rpn_loss_cls: 0.243657
>>> rpn_loss_box: 0.037310
>>> loss_cls: 0.102158
>>> loss_box: 0.096434
>>> lr: 0.000100
speed: 18.326s / iter
iter: 840 / 3200, total loss: 0.775692
>>> rpn_loss_cls: 0.100457
>>> rpn_loss_box: 0.011574
>>> loss_cls: 0.121838
>>> loss_box: 0.159434
>>> lr: 0.000100
speed: 18.324s / iter
iter: 860 / 3200, total loss: 0.700040
>>> rpn_loss_cls: 0.096587
>>> rpn_loss_box: 0.133827
>>> loss_cls: 0.014659
>>> loss_box: 0.072578
>>> lr: 0.000100
speed: 18.326s / iter
iter: 880 / 3200, total loss: 0.993830
>>> rpn_loss_cls: 0.060564
>>> rpn_loss_box: 0.050651
>>> loss_cls: 0.277251
>>> loss_box: 0.222975
>>> lr: 0.000100
speed: 18.320s / iter
iter: 900 / 3200, total loss: 0.826665
>>> rpn_loss_cls: 0.131063
>>> rpn_loss_box: 0.146693
>>> loss_cls: 0.047760
>>> loss_box: 0.118763
>>> lr: 0.000100
speed: 18.313s / iter
iter: 920 / 3200, total loss: 0.627156
>>> rpn_loss_cls: 0.042170
>>> rpn_loss_box: 0.043370
>>> loss_cls: 0.026695
>>> loss_box: 0.132535
>>> lr: 0.000100
speed: 18.309s / iter
iter: 940 / 3200, total loss: 0.712300
>>> rpn_loss_cls: 0.218988
>>> rpn_loss_box: 0.018594
>>> loss_cls: 0.025722
>>> loss_box: 0.066611
>>> lr: 0.000100
speed: 18.306s / iter
iter: 960 / 3200, total loss: 0.644802
>>> rpn_loss_cls: 0.047781
>>> rpn_loss_box: 0.058776
>>> loss_cls: 0.024861
>>> loss_box: 0.131001
>>> lr: 0.000100
speed: 18.301s / iter
iter: 980 / 3200, total loss: 0.777553
>>> rpn_loss_cls: 0.174956
>>> rpn_loss_box: 0.077568
>>> loss_cls: 0.035650
>>> loss_box: 0.106997
>>> lr: 0.000100
speed: 18.300s / iter
iter: 1000 / 3200, total loss: 0.700307
>>> rpn_loss_cls: 0.185844
>>> rpn_loss_box: 0.014858
>>> loss_cls: 0.053437
>>> loss_box: 0.063788
>>> lr: 0.000100
speed: 18.296s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_1000.ckpt
iter: 1020 / 3200, total loss: 1.561479
>>> rpn_loss_cls: 0.218791
>>> rpn_loss_box: 0.087909
>>> loss_cls: 0.353962
>>> loss_box: 0.518438
>>> lr: 0.000100
speed: 18.291s / iter
iter: 1040 / 3200, total loss: 0.502503
>>> rpn_loss_cls: 0.008974
>>> rpn_loss_box: 0.041055
>>> loss_cls: 0.025225
>>> loss_box: 0.044872
>>> lr: 0.000100
speed: 18.285s / iter
iter: 1060 / 3200, total loss: 0.637269
>>> rpn_loss_cls: 0.074090
>>> rpn_loss_box: 0.005266
>>> loss_cls: 0.061586
>>> loss_box: 0.113950
>>> lr: 0.000100
speed: 18.281s / iter
iter: 1080 / 3200, total loss: 0.642691
>>> rpn_loss_cls: 0.077114
>>> rpn_loss_box: 0.061559
>>> loss_cls: 0.034487
>>> loss_box: 0.087155
>>> lr: 0.000100
speed: 18.275s / iter
iter: 1100 / 3200, total loss: 0.524348
>>> rpn_loss_cls: 0.013191
>>> rpn_loss_box: 0.000652
>>> loss_cls: 0.032331
>>> loss_box: 0.095801
>>> lr: 0.000100
speed: 18.270s / iter
iter: 1120 / 3200, total loss: 0.706850
>>> rpn_loss_cls: 0.095066
>>> rpn_loss_box: 0.120149
>>> loss_cls: 0.041416
>>> loss_box: 0.067846
>>> lr: 0.000100
speed: 18.266s / iter
iter: 1140 / 3200, total loss: 0.595206
>>> rpn_loss_cls: 0.016495
>>> rpn_loss_box: 0.018580
>>> loss_cls: 0.011464
>>> loss_box: 0.166294
>>> lr: 0.000100
speed: 18.267s / iter
iter: 1160 / 3200, total loss: 0.566315
>>> rpn_loss_cls: 0.027176
>>> rpn_loss_box: 0.006928
>>> loss_cls: 0.058577
>>> loss_box: 0.091263
>>> lr: 0.000100
speed: 18.264s / iter
iter: 1180 / 3200, total loss: 0.721197
>>> rpn_loss_cls: 0.007940
>>> rpn_loss_box: 0.019503
>>> loss_cls: 0.129445
>>> loss_box: 0.181939
>>> lr: 0.000100
speed: 18.261s / iter
iter: 1200 / 3200, total loss: 1.085414
>>> rpn_loss_cls: 0.062800
>>> rpn_loss_box: 0.031376
>>> loss_cls: 0.140148
>>> loss_box: 0.468720
>>> lr: 0.000100
speed: 18.257s / iter
iter: 1220 / 3200, total loss: 0.809050
>>> rpn_loss_cls: 0.045930
>>> rpn_loss_box: 0.008692
>>> loss_cls: 0.091820
>>> loss_box: 0.280240
>>> lr: 0.000100
speed: 18.256s / iter
iter: 1240 / 3200, total loss: 0.852630
>>> rpn_loss_cls: 0.077544
>>> rpn_loss_box: 0.008498
>>> loss_cls: 0.187316
>>> loss_box: 0.196905
>>> lr: 0.000100
speed: 18.256s / iter
iter: 1260 / 3200, total loss: 0.921142
>>> rpn_loss_cls: 0.196232
>>> rpn_loss_box: 0.177095
>>> loss_cls: 0.069370
>>> loss_box: 0.096080
>>> lr: 0.000100
speed: 18.254s / iter
iter: 1280 / 3200, total loss: 0.717685
>>> rpn_loss_cls: 0.042205
>>> rpn_loss_box: 0.008405
>>> loss_cls: 0.107432
>>> loss_box: 0.177279
>>> lr: 0.000100
speed: 18.251s / iter
iter: 1300 / 3200, total loss: 0.632722
>>> rpn_loss_cls: 0.033402
>>> rpn_loss_box: 0.022300
>>> loss_cls: 0.086850
>>> loss_box: 0.107807
>>> lr: 0.000100
speed: 18.248s / iter
iter: 1320 / 3200, total loss: 0.772178
>>> rpn_loss_cls: 0.011429
>>> rpn_loss_box: 0.025728
>>> loss_cls: 0.144161
>>> loss_box: 0.208497
>>> lr: 0.000100
speed: 18.247s / iter
iter: 1340 / 3200, total loss: 0.574342
>>> rpn_loss_cls: 0.065278
>>> rpn_loss_box: 0.014274
>>> loss_cls: 0.054535
>>> loss_box: 0.057895
>>> lr: 0.000100
speed: 18.245s / iter
iter: 1360 / 3200, total loss: 0.558155
>>> rpn_loss_cls: 0.023798
>>> rpn_loss_box: 0.014620
>>> loss_cls: 0.071267
>>> loss_box: 0.066110
>>> lr: 0.000100
speed: 18.242s / iter
iter: 1380 / 3200, total loss: 0.858874
>>> rpn_loss_cls: 0.205179
>>> rpn_loss_box: 0.135245
>>> loss_cls: 0.071671
>>> loss_box: 0.064420
>>> lr: 0.000100
speed: 18.238s / iter
iter: 1400 / 3200, total loss: 0.732612
>>> rpn_loss_cls: 0.158370
>>> rpn_loss_box: 0.011229
>>> loss_cls: 0.083095
>>> loss_box: 0.097560
>>> lr: 0.000100
speed: 18.236s / iter
iter: 1420 / 3200, total loss: 0.627655
>>> rpn_loss_cls: 0.040317
>>> rpn_loss_box: 0.020486
>>> loss_cls: 0.051815
>>> loss_box: 0.132679
>>> lr: 0.000100
speed: 18.233s / iter
iter: 1440 / 3200, total loss: 0.655073
>>> rpn_loss_cls: 0.050216
>>> rpn_loss_box: 0.010175
>>> loss_cls: 0.096886
>>> loss_box: 0.115441
>>> lr: 0.000100
speed: 18.232s / iter
iter: 1460 / 3200, total loss: 0.688864
>>> rpn_loss_cls: 0.008139
>>> rpn_loss_box: 0.005262
>>> loss_cls: 0.112913
>>> loss_box: 0.180196
>>> lr: 0.000100
speed: 18.229s / iter
iter: 1480 / 3200, total loss: 0.551693
>>> rpn_loss_cls: 0.035668
>>> rpn_loss_box: 0.057819
>>> loss_cls: 0.022829
>>> loss_box: 0.053024
>>> lr: 0.000100
speed: 18.227s / iter
iter: 1500 / 3200, total loss: 0.488739
>>> rpn_loss_cls: 0.008646
>>> rpn_loss_box: 0.022023
>>> loss_cls: 0.038600
>>> loss_box: 0.037119
>>> lr: 0.000100
speed: 18.225s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_1500.ckpt
iter: 1520 / 3200, total loss: 0.618269
>>> rpn_loss_cls: 0.034364
>>> rpn_loss_box: 0.021852
>>> loss_cls: 0.079264
>>> loss_box: 0.100438
>>> lr: 0.000100
speed: 18.222s / iter
iter: 1540 / 3200, total loss: 0.590856
>>> rpn_loss_cls: 0.044112
>>> rpn_loss_box: 0.048106
>>> loss_cls: 0.018573
>>> loss_box: 0.097715
>>> lr: 0.000100
speed: 18.221s / iter
iter: 1560 / 3200, total loss: 0.497062
>>> rpn_loss_cls: 0.005680
>>> rpn_loss_box: 0.056790
>>> loss_cls: 0.019771
>>> loss_box: 0.032473
>>> lr: 0.000100
speed: 18.219s / iter
iter: 1580 / 3200, total loss: 0.510572
>>> rpn_loss_cls: 0.018339
>>> rpn_loss_box: 0.012231
>>> loss_cls: 0.035023
>>> loss_box: 0.062633
>>> lr: 0.000100
speed: 18.218s / iter
iter: 1600 / 3200, total loss: 0.762474
>>> rpn_loss_cls: 0.011902
>>> rpn_loss_box: 0.025903
>>> loss_cls: 0.084226
>>> loss_box: 0.258098
>>> lr: 0.000100
speed: 18.217s / iter
iter: 1620 / 3200, total loss: 0.619664
>>> rpn_loss_cls: 0.070678
>>> rpn_loss_box: 0.090305
>>> loss_cls: 0.026413
>>> loss_box: 0.049923
>>> lr: 0.000100
speed: 18.216s / iter
iter: 1640 / 3200, total loss: 0.668359
>>> rpn_loss_cls: 0.115850
>>> rpn_loss_box: 0.072974
>>> loss_cls: 0.023126
>>> loss_box: 0.074065
>>> lr: 0.000100
speed: 18.215s / iter
iter: 1660 / 3200, total loss: 0.476542
>>> rpn_loss_cls: 0.013453
>>> rpn_loss_box: 0.008222
>>> loss_cls: 0.043750
>>> loss_box: 0.028776
>>> lr: 0.000100
speed: 18.212s / iter
iter: 1680 / 3200, total loss: 0.801644
>>> rpn_loss_cls: 0.034519
>>> rpn_loss_box: 0.041312
>>> loss_cls: 0.082695
>>> loss_box: 0.260777
>>> lr: 0.000100
speed: 18.210s / iter
iter: 1700 / 3200, total loss: 0.659899
>>> rpn_loss_cls: 0.079036
>>> rpn_loss_box: 0.135803
>>> loss_cls: 0.016650
>>> loss_box: 0.046071
>>> lr: 0.000100
speed: 18.210s / iter
iter: 1720 / 3200, total loss: 0.468342
>>> rpn_loss_cls: 0.012049
>>> rpn_loss_box: 0.004853
>>> loss_cls: 0.039877
>>> loss_box: 0.029225
>>> lr: 0.000100
speed: 18.208s / iter
iter: 1740 / 3200, total loss: 0.669494
>>> rpn_loss_cls: 0.133278
>>> rpn_loss_box: 0.021838
>>> loss_cls: 0.053368
>>> loss_box: 0.078672
>>> lr: 0.000100
speed: 18.207s / iter
iter: 1760 / 3200, total loss: 0.805181
>>> rpn_loss_cls: 0.054967
>>> rpn_loss_box: 0.006205
>>> loss_cls: 0.149222
>>> loss_box: 0.212452
>>> lr: 0.000100
speed: 18.207s / iter
iter: 1780 / 3200, total loss: 0.562770
>>> rpn_loss_cls: 0.010171
>>> rpn_loss_box: 0.011130
>>> loss_cls: 0.051831
>>> loss_box: 0.107303
>>> lr: 0.000100
speed: 18.206s / iter
iter: 1800 / 3200, total loss: 0.478316
>>> rpn_loss_cls: 0.008894
>>> rpn_loss_box: 0.012034
>>> loss_cls: 0.040808
>>> loss_box: 0.034247
>>> lr: 0.000100
speed: 18.205s / iter
iter: 1820 / 3200, total loss: 0.675417
>>> rpn_loss_cls: 0.034308
>>> rpn_loss_box: 0.043778
>>> loss_cls: 0.080100
>>> loss_box: 0.134900
>>> lr: 0.000100
speed: 18.205s / iter
iter: 1840 / 3200, total loss: 0.694651
>>> rpn_loss_cls: 0.145737
>>> rpn_loss_box: 0.024914
>>> loss_cls: 0.046499
>>> loss_box: 0.095171
>>> lr: 0.000100
speed: 18.204s / iter
iter: 1860 / 3200, total loss: 0.718186
>>> rpn_loss_cls: 0.127955
>>> rpn_loss_box: 0.141966
>>> loss_cls: 0.027834
>>> loss_box: 0.038102
>>> lr: 0.000100
speed: 18.204s / iter
iter: 1880 / 3200, total loss: 0.610979
>>> rpn_loss_cls: 0.056210
>>> rpn_loss_box: 0.036878
>>> loss_cls: 0.035137
>>> loss_box: 0.100427
>>> lr: 0.000100
speed: 18.202s / iter
iter: 1900 / 3200, total loss: 0.614251
>>> rpn_loss_cls: 0.038210
>>> rpn_loss_box: 0.119047
>>> loss_cls: 0.018028
>>> loss_box: 0.056639
>>> lr: 0.000100
speed: 18.203s / iter
iter: 1920 / 3200, total loss: 0.684837
>>> rpn_loss_cls: 0.219620
>>> rpn_loss_box: 0.003852
>>> loss_cls: 0.028762
>>> loss_box: 0.050277
>>> lr: 0.000100
speed: 18.202s / iter
iter: 1940 / 3200, total loss: 1.401672
>>> rpn_loss_cls: 0.214034
>>> rpn_loss_box: 0.037252
>>> loss_cls: 0.231535
>>> loss_box: 0.536528
>>> lr: 0.000100
speed: 18.204s / iter
iter: 1960 / 3200, total loss: 0.469799
>>> rpn_loss_cls: 0.010847
>>> rpn_loss_box: 0.002549
>>> loss_cls: 0.039865
>>> loss_box: 0.034215
>>> lr: 0.000100
speed: 18.205s / iter
iter: 1980 / 3200, total loss: 0.835782
>>> rpn_loss_cls: 0.106353
>>> rpn_loss_box: 0.087398
>>> loss_cls: 0.108732
>>> loss_box: 0.150977
>>> lr: 0.000100
speed: 18.206s / iter
iter: 2000 / 3200, total loss: 0.546089
>>> rpn_loss_cls: 0.031715
>>> rpn_loss_box: 0.012836
>>> loss_cls: 0.040940
>>> loss_box: 0.078277
>>> lr: 0.000100
speed: 18.206s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_2000.ckpt
iter: 2020 / 3200, total loss: 0.545260
>>> rpn_loss_cls: 0.012997
>>> rpn_loss_box: 0.019149
>>> loss_cls: 0.063566
>>> loss_box: 0.067229
>>> lr: 0.000100
speed: 18.206s / iter
iter: 2040 / 3200, total loss: 0.787888
>>> rpn_loss_cls: 0.210177
>>> rpn_loss_box: 0.132033
>>> loss_cls: 0.017017
>>> loss_box: 0.046343
>>> lr: 0.000100
speed: 18.207s / iter
iter: 2060 / 3200, total loss: 0.558850
>>> rpn_loss_cls: 0.045758
>>> rpn_loss_box: 0.025514
>>> loss_cls: 0.028708
>>> loss_box: 0.076553
>>> lr: 0.000100
speed: 18.207s / iter
iter: 2080 / 3200, total loss: 0.778635
>>> rpn_loss_cls: 0.150327
>>> rpn_loss_box: 0.013490
>>> loss_cls: 0.076305
>>> loss_box: 0.156196
>>> lr: 0.000100
speed: 18.207s / iter
iter: 2100 / 3200, total loss: 0.472197
>>> rpn_loss_cls: 0.005612
>>> rpn_loss_box: 0.001504
>>> loss_cls: 0.044761
>>> loss_box: 0.038007
>>> lr: 0.000100
speed: 18.207s / iter
iter: 2120 / 3200, total loss: 0.510658
>>> rpn_loss_cls: 0.040257
>>> rpn_loss_box: 0.027463
>>> loss_cls: 0.021357
>>> loss_box: 0.039268
>>> lr: 0.000100
speed: 18.209s / iter
iter: 2140 / 3200, total loss: 0.493665
>>> rpn_loss_cls: 0.018207
>>> rpn_loss_box: 0.011092
>>> loss_cls: 0.031297
>>> loss_box: 0.050758
>>> lr: 0.000100
speed: 18.210s / iter
iter: 2160 / 3200, total loss: 0.499123
>>> rpn_loss_cls: 0.023877
>>> rpn_loss_box: 0.030999
>>> loss_cls: 0.022961
>>> loss_box: 0.038975
>>> lr: 0.000100
speed: 18.213s / iter
iter: 2180 / 3200, total loss: 0.821315
>>> rpn_loss_cls: 0.123565
>>> rpn_loss_box: 0.022282
>>> loss_cls: 0.126760
>>> loss_box: 0.166399
>>> lr: 0.000100
speed: 18.215s / iter
iter: 2200 / 3200, total loss: 0.553932
>>> rpn_loss_cls: 0.036548
>>> rpn_loss_box: 0.024991
>>> loss_cls: 0.032991
>>> loss_box: 0.077094
>>> lr: 0.000100
speed: 18.215s / iter
iter: 2220 / 3200, total loss: 0.642815
>>> rpn_loss_cls: 0.007771
>>> rpn_loss_box: 0.011506
>>> loss_cls: 0.111587
>>> loss_box: 0.129644
>>> lr: 0.000100
speed: 18.217s / iter
iter: 2240 / 3200, total loss: 0.676707
>>> rpn_loss_cls: 0.080309
>>> rpn_loss_box: 0.091322
>>> loss_cls: 0.046900
>>> loss_box: 0.075871
>>> lr: 0.000100
speed: 18.218s / iter
iter: 2260 / 3200, total loss: 0.505770
>>> rpn_loss_cls: 0.007053
>>> rpn_loss_box: 0.005911
>>> loss_cls: 0.044429
>>> loss_box: 0.066073
>>> lr: 0.000100
speed: 18.219s / iter
iter: 2280 / 3200, total loss: 0.790898
>>> rpn_loss_cls: 0.308350
>>> rpn_loss_box: 0.017412
>>> loss_cls: 0.034671
>>> loss_box: 0.048161
>>> lr: 0.000100
speed: 18.221s / iter
iter: 2300 / 3200, total loss: 0.532100
>>> rpn_loss_cls: 0.027462
>>> rpn_loss_box: 0.053741
>>> loss_cls: 0.033639
>>> loss_box: 0.034956
>>> lr: 0.000100
speed: 18.224s / iter
iter: 2320 / 3200, total loss: 0.589589
>>> rpn_loss_cls: 0.057401
>>> rpn_loss_box: 0.070292
>>> loss_cls: 0.037399
>>> loss_box: 0.042196
>>> lr: 0.000100
speed: 18.226s / iter
iter: 2340 / 3200, total loss: 0.855214
>>> rpn_loss_cls: 0.089843
>>> rpn_loss_box: 0.269566
>>> loss_cls: 0.024967
>>> loss_box: 0.088538
>>> lr: 0.000100
speed: 18.228s / iter
iter: 2360 / 3200, total loss: 0.717431
>>> rpn_loss_cls: 0.076898
>>> rpn_loss_box: 0.158315
>>> loss_cls: 0.050144
>>> loss_box: 0.049776
>>> lr: 0.000100
speed: 18.230s / iter
iter: 2380 / 3200, total loss: 0.662857
>>> rpn_loss_cls: 0.206039
>>> rpn_loss_box: 0.003267
>>> loss_cls: 0.020686
>>> loss_box: 0.050567
>>> lr: 0.000100
speed: 18.232s / iter
iter: 2400 / 3200, total loss: 0.746430
>>> rpn_loss_cls: 0.118575
>>> rpn_loss_box: 0.016239
>>> loss_cls: 0.101525
>>> loss_box: 0.127796
>>> lr: 0.000100
speed: 18.233s / iter
iter: 2420 / 3200, total loss: 0.525143
>>> rpn_loss_cls: 0.016888
>>> rpn_loss_box: 0.017676
>>> loss_cls: 0.045701
>>> loss_box: 0.062582
>>> lr: 0.000100
speed: 18.235s / iter
iter: 2440 / 3200, total loss: 0.737239
>>> rpn_loss_cls: 0.078193
>>> rpn_loss_box: 0.022456
>>> loss_cls: 0.061732
>>> loss_box: 0.192565
>>> lr: 0.000100
speed: 18.235s / iter
iter: 2460 / 3200, total loss: 0.794321
>>> rpn_loss_cls: 0.161831
>>> rpn_loss_box: 0.136335
>>> loss_cls: 0.062428
>>> loss_box: 0.051433
>>> lr: 0.000100
speed: 18.238s / iter
iter: 2480 / 3200, total loss: 0.590657
>>> rpn_loss_cls: 0.047985
>>> rpn_loss_box: 0.059276
>>> loss_cls: 0.042091
>>> loss_box: 0.059014
>>> lr: 0.000100
speed: 18.239s / iter
iter: 2500 / 3200, total loss: 0.613719
>>> rpn_loss_cls: 0.012276
>>> rpn_loss_box: 0.012471
>>> loss_cls: 0.076417
>>> loss_box: 0.130265
>>> lr: 0.000100
speed: 18.241s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_2500.ckpt
iter: 2520 / 3200, total loss: 0.654271
>>> rpn_loss_cls: 0.095726
>>> rpn_loss_box: 0.119870
>>> loss_cls: 0.026189
>>> loss_box: 0.030197
>>> lr: 0.000100
speed: 18.241s / iter
iter: 2540 / 3200, total loss: 0.476279
>>> rpn_loss_cls: 0.009193
>>> rpn_loss_box: 0.004208
>>> loss_cls: 0.034638
>>> loss_box: 0.045952
>>> lr: 0.000100
speed: 18.239s / iter
iter: 2560 / 3200, total loss: 0.625369
>>> rpn_loss_cls: 0.019211
>>> rpn_loss_box: 0.125371
>>> loss_cls: 0.040915
>>> loss_box: 0.057586
>>> lr: 0.000100
speed: 18.240s / iter
iter: 2580 / 3200, total loss: 0.584985
>>> rpn_loss_cls: 0.025460
>>> rpn_loss_box: 0.020770
>>> loss_cls: 0.048802
>>> loss_box: 0.107668
>>> lr: 0.000100
speed: 18.240s / iter
iter: 2600 / 3200, total loss: 0.516507
>>> rpn_loss_cls: 0.015200
>>> rpn_loss_box: 0.056073
>>> loss_cls: 0.038995
>>> loss_box: 0.023955
>>> lr: 0.000100
speed: 18.240s / iter
iter: 2620 / 3200, total loss: 0.582457
>>> rpn_loss_cls: 0.028390
>>> rpn_loss_box: 0.076542
>>> loss_cls: 0.024121
>>> loss_box: 0.071122
>>> lr: 0.000100
speed: 18.241s / iter
iter: 2640 / 3200, total loss: 0.569222
>>> rpn_loss_cls: 0.073077
>>> rpn_loss_box: 0.032400
>>> loss_cls: 0.040884
>>> loss_box: 0.040580
>>> lr: 0.000100
speed: 18.242s / iter
iter: 2660 / 3200, total loss: 0.524355
>>> rpn_loss_cls: 0.058348
>>> rpn_loss_box: 0.038125
>>> loss_cls: 0.017749
>>> loss_box: 0.027854
>>> lr: 0.000100
speed: 18.242s / iter
iter: 2680 / 3200, total loss: 0.519076
>>> rpn_loss_cls: 0.043049
>>> rpn_loss_box: 0.019109
>>> loss_cls: 0.031268
>>> loss_box: 0.043372
>>> lr: 0.000100
speed: 18.242s / iter
iter: 2700 / 3200, total loss: 0.482006
>>> rpn_loss_cls: 0.022864
>>> rpn_loss_box: 0.027816
>>> loss_cls: 0.034281
>>> loss_box: 0.014768
>>> lr: 0.000100
speed: 18.242s / iter
iter: 2720 / 3200, total loss: 0.848716
>>> rpn_loss_cls: 0.095160
>>> rpn_loss_box: 0.016932
>>> loss_cls: 0.121365
>>> loss_box: 0.232982
>>> lr: 0.000100
speed: 18.243s / iter
iter: 2740 / 3200, total loss: 0.492927
>>> rpn_loss_cls: 0.029951
>>> rpn_loss_box: 0.027604
>>> loss_cls: 0.027604
>>> loss_box: 0.025493
>>> lr: 0.000100
speed: 18.244s / iter
iter: 2760 / 3200, total loss: 0.594886
>>> rpn_loss_cls: 0.106875
>>> rpn_loss_box: 0.008636
>>> loss_cls: 0.041538
>>> loss_box: 0.055565
>>> lr: 0.000100
speed: 18.244s / iter
iter: 2780 / 3200, total loss: 0.538749
>>> rpn_loss_cls: 0.037678
>>> rpn_loss_box: 0.019155
>>> loss_cls: 0.038125
>>> loss_box: 0.061520
>>> lr: 0.000100
speed: 18.245s / iter
iter: 2800 / 3200, total loss: 0.468894
>>> rpn_loss_cls: 0.010169
>>> rpn_loss_box: 0.020934
>>> loss_cls: 0.005108
>>> loss_box: 0.050412
>>> lr: 0.000100
speed: 18.247s / iter
iter: 2820 / 3200, total loss: 0.499144
>>> rpn_loss_cls: 0.024749
>>> rpn_loss_box: 0.019493
>>> loss_cls: 0.035706
>>> loss_box: 0.036926
>>> lr: 0.000100
speed: 18.248s / iter
iter: 2840 / 3200, total loss: 0.630420
>>> rpn_loss_cls: 0.070317
>>> rpn_loss_box: 0.104080
>>> loss_cls: 0.042845
>>> loss_box: 0.030909
>>> lr: 0.000100
speed: 18.249s / iter
iter: 2860 / 3200, total loss: 0.553930
>>> rpn_loss_cls: 0.027323
>>> rpn_loss_box: 0.005669
>>> loss_cls: 0.057542
>>> loss_box: 0.081128
>>> lr: 0.000100
speed: 18.250s / iter
iter: 2880 / 3200, total loss: 0.532811
>>> rpn_loss_cls: 0.041538
>>> rpn_loss_box: 0.039164
>>> loss_cls: 0.026999
>>> loss_box: 0.042844
>>> lr: 0.000100
speed: 18.251s / iter
iter: 2900 / 3200, total loss: 0.606645
>>> rpn_loss_cls: 0.093004
>>> rpn_loss_box: 0.072453
>>> loss_cls: 0.032159
>>> loss_box: 0.026763
>>> lr: 0.000100
speed: 18.252s / iter
iter: 2920 / 3200, total loss: 0.610751
>>> rpn_loss_cls: 0.035319
>>> rpn_loss_box: 0.005256
>>> loss_cls: 0.064158
>>> loss_box: 0.123755
>>> lr: 0.000100
speed: 18.252s / iter
iter: 2940 / 3200, total loss: 0.590238
>>> rpn_loss_cls: 0.023853
>>> rpn_loss_box: 0.011993
>>> loss_cls: 0.036663
>>> loss_box: 0.135465
>>> lr: 0.000100
speed: 18.253s / iter
iter: 2960 / 3200, total loss: 0.732967
>>> rpn_loss_cls: 0.042913
>>> rpn_loss_box: 0.010557
>>> loss_cls: 0.060128
>>> loss_box: 0.237108
>>> lr: 0.000100
speed: 18.254s / iter
iter: 2980 / 3200, total loss: 0.596565
>>> rpn_loss_cls: 0.071422
>>> rpn_loss_box: 0.087485
>>> loss_cls: 0.026072
>>> loss_box: 0.029326
>>> lr: 0.000100
speed: 18.255s / iter
iter: 3000 / 3200, total loss: 0.449472
>>> rpn_loss_cls: 0.007425
>>> rpn_loss_box: 0.010065
>>> loss_cls: 0.028121
>>> loss_box: 0.021603
>>> lr: 0.000100
speed: 18.256s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_3000.ckpt
iter: 3020 / 3200, total loss: 0.420432
>>> rpn_loss_cls: 0.006059
>>> rpn_loss_box: 0.003383
>>> loss_cls: 0.015263
>>> loss_box: 0.013469
>>> lr: 0.000100
speed: 18.256s / iter
iter: 3040 / 3200, total loss: 0.499304
>>> rpn_loss_cls: 0.028310
>>> rpn_loss_box: 0.016560
>>> loss_cls: 0.021902
>>> loss_box: 0.050277
>>> lr: 0.000100
speed: 18.257s / iter
iter: 3060 / 3200, total loss: 0.636695
>>> rpn_loss_cls: 0.132401
>>> rpn_loss_box: 0.043681
>>> loss_cls: 0.022125
>>> loss_box: 0.056233
>>> lr: 0.000100
speed: 18.258s / iter
iter: 3080 / 3200, total loss: 0.493683
>>> rpn_loss_cls: 0.031520
>>> rpn_loss_box: 0.012919
>>> loss_cls: 0.021214
>>> loss_box: 0.045777
>>> lr: 0.000100
speed: 18.259s / iter
iter: 3100 / 3200, total loss: 0.596595
>>> rpn_loss_cls: 0.068079
>>> rpn_loss_box: 0.014994
>>> loss_cls: 0.028614
>>> loss_box: 0.102655
>>> lr: 0.000100
speed: 18.260s / iter
iter: 3120 / 3200, total loss: 0.502758
>>> rpn_loss_cls: 0.014378
>>> rpn_loss_box: 0.032935
>>> loss_cls: 0.033033
>>> loss_box: 0.040161
>>> lr: 0.000100
speed: 18.263s / iter
iter: 3140 / 3200, total loss: 0.544400
>>> rpn_loss_cls: 0.042744
>>> rpn_loss_box: 0.029882
>>> loss_cls: 0.026032
>>> loss_box: 0.063492
>>> lr: 0.000100
speed: 18.266s / iter
iter: 3160 / 3200, total loss: 0.595721
>>> rpn_loss_cls: 0.087768
>>> rpn_loss_box: 0.085453
>>> loss_cls: 0.020308
>>> loss_box: 0.019943
>>> lr: 0.000100
speed: 18.267s / iter
iter: 3180 / 3200, total loss: 0.547231
>>> rpn_loss_cls: 0.021856
>>> rpn_loss_box: 0.027437
>>> loss_cls: 0.030406
>>> loss_box: 0.085284
>>> lr: 0.000100
speed: 18.269s / iter
iter: 3200 / 3200, total loss: 0.463937
>>> rpn_loss_cls: 0.009630
>>> rpn_loss_box: 0.001617
>>> loss_cls: 0.039288
>>> loss_box: 0.031156
>>> lr: 0.000100
speed: 18.272s / iter
Wrote snapshot to: /home/luo/MyFile/tf-faster-rcnn_box/output/res101/voc_2007_trainval+voc_2012_trainval/default/res101_faster_rcnn_iter_3200.ckpt
done solving