validate_2020-03-26-17-09-14.log
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Number of model parameters: 154706
=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
* Prec@1 98.193
* Prec@1 98.193
Best accuracy: 98.1927713600986
[validate_2020-03-26-17-09-14] done
[validate_2020-03-26-17-09-14] done
Number of model parameters: 154706
=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
* Prec@1 98.193
* Prec@1 98.193
Best accuracy: 98.19277163585984
[validate_2020-03-26-17-09-34] done
[validate_2020-03-26-17-09-34] done
start test using path : ../data/Fourth_data/demo
Test start
loading checkpoint...
checkpoint already loaded!
start test
data path directory is ../data/Fourth_data/demo
finish test
set Type
start test using path : ../data/Fourth_data/demo
Test start
loading checkpoint...
checkpoint already loaded!
start test
data path directory is ../data/Fourth_data/demo
finish test
train start
load yml file
2020-03-26-17-10-28
use seed 825
use dataset : ../data/Fourth_data/All
{'task': 'All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar', 'gpu': [2], 'data': {'train': '../data/Fourth_data/All', 'val': '../data/Fourth_data/All', 'test': '../data/Fourth_data/All'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'weight': [2.0, 4.0, 1.0, 1.0, 3.0, 1.0, 1.0], 'resume': '', 'augment': True, 'size': 224, 'confidence': False}, 'predict': {'batch-size': 256, 'worker': 64, 'cam-class': 'Crack', 'cam': False, 'normalize': True}, 'optimizer': {'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 7}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-03-26-17-10-28'}
using normalize
using no dropout
using SGD
Number of model parameters: 461559
Epoch: [0][0/12] Time 3.022 (3.022) Loss 1.9330 (1.9330) Prec@1 16.016 (16.016)
Epoch: [0][10/12] Time 0.159 (0.411) Loss 1.6084 (1.7482) Prec@1 23.828 (17.898)
Test: [0/2] Time 1.651 (1.651) Loss 1.8596 (1.8596) Prec@1 10.938 (10.938)
* epoch: 0 Prec@1 12.012
* epoch: 0 Prec@1 12.012
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [1][0/12] Time 1.962 (1.962) Loss 1.5773 (1.5773) Prec@1 28.516 (28.516)
Epoch: [1][10/12] Time 0.158 (0.323) Loss 1.6759 (1.5514) Prec@1 20.312 (26.136)
Test: [0/2] Time 1.653 (1.653) Loss 1.8462 (1.8462) Prec@1 31.250 (31.250)
* epoch: 1 Prec@1 30.030
* epoch: 1 Prec@1 30.030
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [2][0/12] Time 1.952 (1.952) Loss 1.5458 (1.5458) Prec@1 26.562 (26.562)
Epoch: [2][10/12] Time 0.157 (0.331) Loss 1.2252 (1.3803) Prec@1 42.969 (31.889)
Test: [0/2] Time 1.674 (1.674) Loss 1.7407 (1.7407) Prec@1 30.859 (30.859)
* epoch: 2 Prec@1 32.733
* epoch: 2 Prec@1 32.733
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [3][0/12] Time 1.960 (1.960) Loss 1.2237 (1.2237) Prec@1 39.062 (39.062)
Epoch: [3][10/12] Time 0.155 (0.323) Loss 1.1566 (1.2237) Prec@1 41.797 (41.193)
Test: [0/2] Time 1.687 (1.687) Loss 1.7368 (1.7368) Prec@1 22.266 (22.266)
* epoch: 3 Prec@1 22.823
* epoch: 3 Prec@1 22.823
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [4][0/12] Time 1.967 (1.967) Loss 1.3065 (1.3065) Prec@1 48.828 (48.828)
Epoch: [4][10/12] Time 0.162 (0.335) Loss 1.0501 (1.1183) Prec@1 58.594 (53.906)
Test: [0/2] Time 1.665 (1.665) Loss 1.1960 (1.1960) Prec@1 53.516 (53.516)
* epoch: 4 Prec@1 52.553
* epoch: 4 Prec@1 52.553
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [5][0/12] Time 1.930 (1.930) Loss 1.0283 (1.0283) Prec@1 61.719 (61.719)
Epoch: [5][10/12] Time 0.159 (0.320) Loss 1.0155 (1.0390) Prec@1 58.594 (60.227)
Test: [0/2] Time 1.692 (1.692) Loss 1.2309 (1.2309) Prec@1 60.938 (60.938)
* epoch: 5 Prec@1 60.360
* epoch: 5 Prec@1 60.360
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [6][0/12] Time 2.214 (2.214) Loss 0.9515 (0.9515) Prec@1 64.844 (64.844)
Epoch: [6][10/12] Time 0.158 (0.346) Loss 0.7833 (0.9050) Prec@1 75.781 (68.999)
Test: [0/2] Time 1.663 (1.663) Loss 0.9625 (0.9625) Prec@1 49.609 (49.609)
* epoch: 6 Prec@1 49.850
* epoch: 6 Prec@1 49.850
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [7][0/12] Time 1.919 (1.919) Loss 0.7668 (0.7668) Prec@1 76.172 (76.172)
Epoch: [7][10/12] Time 0.158 (0.319) Loss 0.9260 (0.8527) Prec@1 63.672 (71.768)
Test: [0/2] Time 1.675 (1.675) Loss 1.1891 (1.1891) Prec@1 61.328 (61.328)
* epoch: 7 Prec@1 58.859
* epoch: 7 Prec@1 58.859
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [8][0/12] Time 2.244 (2.244) Loss 0.8546 (0.8546) Prec@1 72.266 (72.266)
Epoch: [8][10/12] Time 0.159 (0.348) Loss 0.7712 (0.8669) Prec@1 76.562 (71.094)
Test: [0/2] Time 1.707 (1.707) Loss 0.9092 (0.9092) Prec@1 63.281 (63.281)
* epoch: 8 Prec@1 62.462
* epoch: 8 Prec@1 62.462
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [9][0/12] Time 1.925 (1.925) Loss 0.7878 (0.7878) Prec@1 78.516 (78.516)
Epoch: [9][10/12] Time 0.160 (0.325) Loss 0.7174 (0.7807) Prec@1 69.922 (73.509)
Test: [0/2] Time 1.707 (1.707) Loss 0.9544 (0.9544) Prec@1 62.891 (62.891)
* epoch: 9 Prec@1 60.661
* epoch: 9 Prec@1 60.661
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [10][0/12] Time 2.220 (2.220) Loss 0.9374 (0.9374) Prec@1 67.188 (67.188)
Epoch: [10][10/12] Time 0.159 (0.347) Loss 0.7273 (0.7483) Prec@1 80.078 (75.497)
Test: [0/2] Time 1.696 (1.696) Loss 0.9895 (0.9895) Prec@1 47.656 (47.656)
* epoch: 10 Prec@1 48.649
* epoch: 10 Prec@1 48.649
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [11][0/12] Time 1.974 (1.974) Loss 0.6920 (0.6920) Prec@1 75.391 (75.391)
Epoch: [11][10/12] Time 0.154 (0.324) Loss 0.7892 (0.6962) Prec@1 71.484 (75.781)
Test: [0/2] Time 1.665 (1.665) Loss 1.1736 (1.1736) Prec@1 78.125 (78.125)
* epoch: 11 Prec@1 77.778
* epoch: 11 Prec@1 77.778
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [12][0/12] Time 1.927 (1.927) Loss 0.7375 (0.7375) Prec@1 78.906 (78.906)
Epoch: [12][10/12] Time 0.159 (0.328) Loss 0.6453 (0.6972) Prec@1 79.688 (77.308)
Test: [0/2] Time 1.703 (1.703) Loss 0.7701 (0.7701) Prec@1 79.688 (79.688)
* epoch: 12 Prec@1 79.279
* epoch: 12 Prec@1 79.279
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [13][0/12] Time 1.983 (1.983) Loss 0.6737 (0.6737) Prec@1 79.688 (79.688)
Epoch: [13][10/12] Time 0.158 (0.325) Loss 0.6125 (0.6647) Prec@1 79.688 (78.942)
Test: [0/2] Time 1.673 (1.673) Loss 1.7411 (1.7411) Prec@1 44.922 (44.922)
* epoch: 13 Prec@1 44.745
* epoch: 13 Prec@1 44.745
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [14][0/12] Time 1.969 (1.969) Loss 0.7033 (0.7033) Prec@1 76.953 (76.953)
Epoch: [14][10/12] Time 0.160 (0.328) Loss 0.7069 (0.6706) Prec@1 78.516 (78.196)
Test: [0/2] Time 1.689 (1.689) Loss 0.7330 (0.7330) Prec@1 80.078 (80.078)
* epoch: 14 Prec@1 80.781
* epoch: 14 Prec@1 80.781
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [15][0/12] Time 1.957 (1.957) Loss 0.6477 (0.6477) Prec@1 76.562 (76.562)
Epoch: [15][10/12] Time 0.160 (0.323) Loss 0.6251 (0.6128) Prec@1 81.641 (80.007)
Test: [0/2] Time 1.675 (1.675) Loss 0.7821 (0.7821) Prec@1 76.562 (76.562)
* epoch: 15 Prec@1 76.877
* epoch: 15 Prec@1 76.877
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [16][0/12] Time 2.133 (2.133) Loss 0.6314 (0.6314) Prec@1 77.734 (77.734)
Epoch: [16][10/12] Time 0.159 (0.338) Loss 0.6333 (0.6552) Prec@1 80.469 (79.190)
Test: [0/2] Time 1.686 (1.686) Loss 0.8334 (0.8334) Prec@1 81.641 (81.641)
* epoch: 16 Prec@1 81.982
* epoch: 16 Prec@1 81.982
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [17][0/12] Time 1.903 (1.903) Loss 0.5610 (0.5610) Prec@1 79.297 (79.297)
Epoch: [17][10/12] Time 0.158 (0.324) Loss 0.5548 (0.5905) Prec@1 77.734 (81.001)
Test: [0/2] Time 1.713 (1.713) Loss 0.7781 (0.7781) Prec@1 82.422 (82.422)
* epoch: 17 Prec@1 81.381
* epoch: 17 Prec@1 81.381
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [18][0/12] Time 1.914 (1.914) Loss 0.5484 (0.5484) Prec@1 77.344 (77.344)
Epoch: [18][10/12] Time 0.159 (0.320) Loss 0.6511 (0.6283) Prec@1 79.688 (79.545)
Test: [0/2] Time 1.666 (1.666) Loss 0.8708 (0.8708) Prec@1 62.500 (62.500)
* epoch: 18 Prec@1 63.664
* epoch: 18 Prec@1 63.664
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [19][0/12] Time 1.918 (1.918) Loss 0.6416 (0.6416) Prec@1 79.297 (79.297)
Epoch: [19][10/12] Time 0.158 (0.320) Loss 0.5400 (0.5515) Prec@1 83.203 (83.097)
Test: [0/2] Time 1.694 (1.694) Loss 0.6527 (0.6527) Prec@1 81.250 (81.250)
* epoch: 19 Prec@1 81.081
* epoch: 19 Prec@1 81.081
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [20][0/12] Time 2.206 (2.206) Loss 0.5358 (0.5358) Prec@1 81.641 (81.641)
Epoch: [20][10/12] Time 0.160 (0.345) Loss 0.6041 (0.5729) Prec@1 77.734 (81.428)
Test: [0/2] Time 1.720 (1.720) Loss 0.5669 (0.5669) Prec@1 83.594 (83.594)
* epoch: 20 Prec@1 81.682
* epoch: 20 Prec@1 81.682
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [21][0/12] Time 2.203 (2.203) Loss 0.4921 (0.4921) Prec@1 82.422 (82.422)
Epoch: [21][10/12] Time 0.158 (0.345) Loss 0.4483 (0.5416) Prec@1 84.375 (82.919)
Test: [0/2] Time 1.706 (1.706) Loss 0.7614 (0.7614) Prec@1 78.516 (78.516)
* epoch: 21 Prec@1 78.679
* epoch: 21 Prec@1 78.679
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [22][0/12] Time 1.921 (1.921) Loss 0.6461 (0.6461) Prec@1 81.250 (81.250)
Epoch: [22][10/12] Time 0.158 (0.319) Loss 0.5921 (0.5490) Prec@1 82.031 (82.919)
Test: [0/2] Time 1.709 (1.709) Loss 0.6422 (0.6422) Prec@1 82.812 (82.812)
* epoch: 22 Prec@1 83.784
* epoch: 22 Prec@1 83.784
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [23][0/12] Time 1.939 (1.939) Loss 0.4573 (0.4573) Prec@1 87.500 (87.500)
Epoch: [23][10/12] Time 0.159 (0.331) Loss 0.5843 (0.5141) Prec@1 80.469 (84.624)
Test: [0/2] Time 1.685 (1.685) Loss 0.9102 (0.9102) Prec@1 75.000 (75.000)
* epoch: 23 Prec@1 76.577
* epoch: 23 Prec@1 76.577
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [24][0/12] Time 1.925 (1.925) Loss 0.7865 (0.7865) Prec@1 78.125 (78.125)
Epoch: [24][10/12] Time 0.160 (0.319) Loss 0.5571 (0.6079) Prec@1 85.938 (80.859)
Test: [0/2] Time 1.712 (1.712) Loss 0.5972 (0.5972) Prec@1 82.422 (82.422)
* epoch: 24 Prec@1 82.583
* epoch: 24 Prec@1 82.583
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [25][0/12] Time 1.958 (1.958) Loss 0.5632 (0.5632) Prec@1 83.984 (83.984)
Epoch: [25][10/12] Time 0.159 (0.322) Loss 0.3754 (0.5158) Prec@1 88.281 (83.700)
Test: [0/2] Time 1.701 (1.701) Loss 0.8734 (0.8734) Prec@1 77.734 (77.734)
* epoch: 25 Prec@1 78.378
* epoch: 25 Prec@1 78.378
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [26][0/12] Time 2.162 (2.162) Loss 0.6764 (0.6764) Prec@1 81.641 (81.641)
Epoch: [26][10/12] Time 0.160 (0.342) Loss 0.5274 (0.5528) Prec@1 80.469 (82.209)
Test: [0/2] Time 1.696 (1.696) Loss 0.6240 (0.6240) Prec@1 85.938 (85.938)
* epoch: 26 Prec@1 84.084
* epoch: 26 Prec@1 84.084
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [27][0/12] Time 2.181 (2.181) Loss 0.5241 (0.5241) Prec@1 83.203 (83.203)
Epoch: [27][10/12] Time 0.158 (0.343) Loss 0.6147 (0.5227) Prec@1 79.297 (83.736)
Test: [0/2] Time 1.708 (1.708) Loss 0.7268 (0.7268) Prec@1 80.469 (80.469)
* epoch: 27 Prec@1 79.279
* epoch: 27 Prec@1 79.279
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [28][0/12] Time 1.966 (1.966) Loss 0.4439 (0.4439) Prec@1 87.500 (87.500)
Epoch: [28][10/12] Time 0.159 (0.323) Loss 0.5766 (0.5243) Prec@1 85.547 (82.955)
Test: [0/2] Time 1.684 (1.684) Loss 0.6416 (0.6416) Prec@1 82.031 (82.031)
* epoch: 28 Prec@1 82.583
* epoch: 28 Prec@1 82.583
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [29][0/12] Time 1.943 (1.943) Loss 0.5633 (0.5633) Prec@1 79.297 (79.297)
Epoch: [29][10/12] Time 0.159 (0.321) Loss 0.4740 (0.4708) Prec@1 85.547 (84.730)
Test: [0/2] Time 1.704 (1.704) Loss 0.6950 (0.6950) Prec@1 78.125 (78.125)
* epoch: 29 Prec@1 78.378
* epoch: 29 Prec@1 78.378
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [30][0/12] Time 1.953 (1.953) Loss 0.6137 (0.6137) Prec@1 82.422 (82.422)
Epoch: [30][10/12] Time 0.159 (0.322) Loss 0.3596 (0.5028) Prec@1 87.891 (84.553)
Test: [0/2] Time 1.699 (1.699) Loss 0.6546 (0.6546) Prec@1 82.031 (82.031)
* epoch: 30 Prec@1 83.483
* epoch: 30 Prec@1 83.483
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [31][0/12] Time 2.174 (2.174) Loss 0.4846 (0.4846) Prec@1 83.984 (83.984)
Epoch: [31][10/12] Time 0.160 (0.343) Loss 0.4474 (0.4548) Prec@1 84.375 (85.050)
Test: [0/2] Time 1.678 (1.678) Loss 0.6281 (0.6281) Prec@1 84.375 (84.375)
* epoch: 31 Prec@1 84.084
* epoch: 31 Prec@1 84.084
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [32][0/12] Time 1.929 (1.929) Loss 0.4780 (0.4780) Prec@1 83.594 (83.594)
Epoch: [32][10/12] Time 0.160 (0.320) Loss 0.4670 (0.5008) Prec@1 85.938 (83.842)
Test: [0/2] Time 1.698 (1.698) Loss 0.5501 (0.5501) Prec@1 82.422 (82.422)
* epoch: 32 Prec@1 82.583
* epoch: 32 Prec@1 82.583
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [33][0/12] Time 2.224 (2.224) Loss 0.4372 (0.4372) Prec@1 89.062 (89.062)
Epoch: [33][10/12] Time 0.159 (0.347) Loss 0.4002 (0.4672) Prec@1 87.500 (85.369)
Test: [0/2] Time 1.650 (1.650) Loss 0.9937 (0.9937) Prec@1 65.234 (65.234)
* epoch: 33 Prec@1 65.165
* epoch: 33 Prec@1 65.165
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [34][0/12] Time 2.233 (2.233) Loss 0.5080 (0.5080) Prec@1 82.031 (82.031)
Epoch: [34][10/12] Time 0.159 (0.347) Loss 0.4675 (0.4632) Prec@1 84.766 (84.908)
Test: [0/2] Time 1.673 (1.673) Loss 0.6285 (0.6285) Prec@1 83.984 (83.984)
* epoch: 34 Prec@1 84.084
* epoch: 34 Prec@1 84.084
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [35][0/12] Time 1.915 (1.915) Loss 0.4910 (0.4910) Prec@1 86.328 (86.328)
Epoch: [35][10/12] Time 0.159 (0.320) Loss 0.3640 (0.4289) Prec@1 88.281 (86.222)
Test: [0/2] Time 1.725 (1.725) Loss 0.9229 (0.9229) Prec@1 80.859 (80.859)
* epoch: 35 Prec@1 80.180
* epoch: 35 Prec@1 80.180
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [36][0/12] Time 1.938 (1.938) Loss 0.5173 (0.5173) Prec@1 81.641 (81.641)
Epoch: [36][10/12] Time 0.159 (0.321) Loss 0.5336 (0.4753) Prec@1 83.203 (84.553)
Test: [0/2] Time 1.725 (1.725) Loss 0.5637 (0.5637) Prec@1 87.109 (87.109)
* epoch: 36 Prec@1 86.186
* epoch: 36 Prec@1 86.186
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [37][0/12] Time 2.224 (2.224) Loss 0.3901 (0.3901) Prec@1 86.719 (86.719)
Epoch: [37][10/12] Time 0.159 (0.347) Loss 0.4682 (0.4061) Prec@1 82.422 (86.648)
Test: [0/2] Time 1.686 (1.686) Loss 0.9415 (0.9415) Prec@1 74.219 (74.219)
* epoch: 37 Prec@1 74.474
* epoch: 37 Prec@1 74.474
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [38][0/12] Time 1.960 (1.960) Loss 0.4797 (0.4797) Prec@1 84.375 (84.375)
Epoch: [38][10/12] Time 0.159 (0.324) Loss 0.4020 (0.4410) Prec@1 88.672 (85.653)
Test: [0/2] Time 1.692 (1.692) Loss 0.4875 (0.4875) Prec@1 84.375 (84.375)
* epoch: 38 Prec@1 83.483
* epoch: 38 Prec@1 83.483
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [39][0/12] Time 1.931 (1.931) Loss 0.4579 (0.4579) Prec@1 85.156 (85.156)
Epoch: [39][10/12] Time 0.159 (0.330) Loss 0.3570 (0.3953) Prec@1 87.500 (86.754)
Test: [0/2] Time 1.673 (1.673) Loss 0.6890 (0.6890) Prec@1 84.766 (84.766)
* epoch: 39 Prec@1 83.784
* epoch: 39 Prec@1 83.784
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [40][0/12] Time 1.908 (1.908) Loss 0.5327 (0.5327) Prec@1 82.031 (82.031)
Epoch: [40][10/12] Time 0.160 (0.320) Loss 0.3981 (0.4445) Prec@1 88.281 (85.724)
Test: [0/2] Time 1.706 (1.706) Loss 0.4213 (0.4213) Prec@1 88.672 (88.672)
* epoch: 40 Prec@1 87.387
* epoch: 40 Prec@1 87.387
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [41][0/12] Time 1.924 (1.924) Loss 0.4044 (0.4044) Prec@1 85.938 (85.938)
Epoch: [41][10/12] Time 0.160 (0.318) Loss 0.4958 (0.3959) Prec@1 86.719 (86.861)
Test: [0/2] Time 1.699 (1.699) Loss 0.7511 (0.7511) Prec@1 76.172 (76.172)
* epoch: 41 Prec@1 74.174
* epoch: 41 Prec@1 74.174
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [42][0/12] Time 2.204 (2.204) Loss 0.4268 (0.4268) Prec@1 88.281 (88.281)
Epoch: [42][10/12] Time 0.158 (0.345) Loss 0.4259 (0.4605) Prec@1 87.109 (84.979)
Test: [0/2] Time 1.730 (1.730) Loss 0.6196 (0.6196) Prec@1 87.109 (87.109)
* epoch: 42 Prec@1 87.387
* epoch: 42 Prec@1 87.387
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [43][0/12] Time 2.063 (2.063) Loss 0.4617 (0.4617) Prec@1 84.375 (84.375)
Epoch: [43][10/12] Time 0.159 (0.332) Loss 0.4178 (0.3862) Prec@1 87.891 (87.287)
Test: [0/2] Time 1.692 (1.692) Loss 0.7116 (0.7116) Prec@1 78.516 (78.516)
* epoch: 43 Prec@1 78.378
* epoch: 43 Prec@1 78.378
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [44][0/12] Time 1.968 (1.968) Loss 0.6055 (0.6055) Prec@1 76.953 (76.953)
Epoch: [44][10/12] Time 0.160 (0.331) Loss 0.4583 (0.4649) Prec@1 85.156 (83.487)
Test: [0/2] Time 1.690 (1.690) Loss 0.4601 (0.4601) Prec@1 87.109 (87.109)
* epoch: 44 Prec@1 86.787
* epoch: 44 Prec@1 86.787
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [45][0/12] Time 1.908 (1.908) Loss 0.4238 (0.4238) Prec@1 87.109 (87.109)
Epoch: [45][10/12] Time 0.159 (0.318) Loss 0.5030 (0.3826) Prec@1 82.812 (87.074)
Test: [0/2] Time 1.707 (1.707) Loss 0.7285 (0.7285) Prec@1 79.688 (79.688)
* epoch: 45 Prec@1 80.781
* epoch: 45 Prec@1 80.781
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [46][0/12] Time 1.955 (1.955) Loss 0.5765 (0.5765) Prec@1 81.250 (81.250)
Epoch: [46][10/12] Time 0.159 (0.321) Loss 0.3067 (0.4130) Prec@1 88.672 (86.009)
Test: [0/2] Time 1.686 (1.686) Loss 0.4962 (0.4962) Prec@1 89.062 (89.062)
* epoch: 46 Prec@1 88.288
* epoch: 46 Prec@1 88.288
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [47][0/12] Time 1.986 (1.986) Loss 0.3760 (0.3760) Prec@1 87.891 (87.891)
Epoch: [47][10/12] Time 0.159 (0.335) Loss 0.3775 (0.3699) Prec@1 84.766 (87.216)
Test: [0/2] Time 1.697 (1.697) Loss 0.5537 (0.5537) Prec@1 85.938 (85.938)
* epoch: 47 Prec@1 85.886
* epoch: 47 Prec@1 85.886
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [48][0/12] Time 1.951 (1.951) Loss 0.3737 (0.3737) Prec@1 87.500 (87.500)
Epoch: [48][10/12] Time 0.159 (0.322) Loss 0.3257 (0.3962) Prec@1 88.672 (87.145)
Test: [0/2] Time 1.693 (1.693) Loss 0.5342 (0.5342) Prec@1 87.109 (87.109)
* epoch: 48 Prec@1 85.586
* epoch: 48 Prec@1 85.586
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [49][0/12] Time 1.991 (1.991) Loss 0.3158 (0.3158) Prec@1 88.672 (88.672)
Epoch: [49][10/12] Time 0.159 (0.326) Loss 0.4068 (0.3745) Prec@1 87.109 (87.571)
Test: [0/2] Time 1.684 (1.684) Loss 0.5855 (0.5855) Prec@1 81.641 (81.641)
* epoch: 49 Prec@1 80.781
* epoch: 49 Prec@1 80.781
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [50][0/12] Time 1.904 (1.904) Loss 0.6005 (0.6005) Prec@1 79.297 (79.297)
Epoch: [50][10/12] Time 0.159 (0.319) Loss 0.3856 (0.3787) Prec@1 86.328 (87.464)
Test: [0/2] Time 1.680 (1.680) Loss 0.6797 (0.6797) Prec@1 77.344 (77.344)
* epoch: 50 Prec@1 78.979
* epoch: 50 Prec@1 78.979
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [51][0/12] Time 2.151 (2.151) Loss 0.3381 (0.3381) Prec@1 89.844 (89.844)
Epoch: [51][10/12] Time 0.157 (0.340) Loss 0.3905 (0.3525) Prec@1 83.594 (88.352)
Test: [0/2] Time 1.705 (1.705) Loss 0.5561 (0.5561) Prec@1 86.328 (86.328)
* epoch: 51 Prec@1 85.886
* epoch: 51 Prec@1 85.886
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [52][0/12] Time 1.977 (1.977) Loss 0.3419 (0.3419) Prec@1 88.281 (88.281)
Epoch: [52][10/12] Time 0.159 (0.325) Loss 0.3898 (0.3460) Prec@1 88.281 (88.565)
Test: [0/2] Time 1.704 (1.704) Loss 0.4699 (0.4699) Prec@1 86.328 (86.328)
* epoch: 52 Prec@1 85.285
* epoch: 52 Prec@1 85.285
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [53][0/12] Time 1.954 (1.954) Loss 0.2477 (0.2477) Prec@1 88.672 (88.672)
Epoch: [53][10/12] Time 0.159 (0.330) Loss 0.3368 (0.3348) Prec@1 88.672 (88.920)
Test: [0/2] Time 1.672 (1.672) Loss 0.9721 (0.9721) Prec@1 60.547 (60.547)
* epoch: 53 Prec@1 61.261
* epoch: 53 Prec@1 61.261
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [54][0/12] Time 2.249 (2.249) Loss 0.4255 (0.4255) Prec@1 82.031 (82.031)
Epoch: [54][10/12] Time 0.160 (0.349) Loss 0.3790 (0.3706) Prec@1 85.547 (86.506)
Test: [0/2] Time 1.664 (1.664) Loss 0.4914 (0.4914) Prec@1 88.281 (88.281)
* epoch: 54 Prec@1 88.889
* epoch: 54 Prec@1 88.889
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [55][0/12] Time 1.919 (1.919) Loss 0.3694 (0.3694) Prec@1 85.547 (85.547)
Epoch: [55][10/12] Time 0.159 (0.319) Loss 0.3774 (0.3204) Prec@1 88.672 (89.240)
Test: [0/2] Time 1.689 (1.689) Loss 0.7353 (0.7353) Prec@1 80.078 (80.078)
* epoch: 55 Prec@1 79.880
* epoch: 55 Prec@1 79.880
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [56][0/12] Time 1.947 (1.947) Loss 0.4603 (0.4603) Prec@1 83.594 (83.594)
Epoch: [56][10/12] Time 0.159 (0.322) Loss 0.3666 (0.3867) Prec@1 87.891 (86.932)
Test: [0/2] Time 1.722 (1.722) Loss 0.4614 (0.4614) Prec@1 86.719 (86.719)
* epoch: 56 Prec@1 86.486
* epoch: 56 Prec@1 86.486
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [57][0/12] Time 1.980 (1.980) Loss 0.3089 (0.3089) Prec@1 89.844 (89.844)
Epoch: [57][10/12] Time 0.160 (0.323) Loss 0.2758 (0.3167) Prec@1 90.234 (89.027)
Test: [0/2] Time 1.713 (1.713) Loss 0.7926 (0.7926) Prec@1 69.141 (69.141)
* epoch: 57 Prec@1 68.769
* epoch: 57 Prec@1 68.769
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [58][0/12] Time 1.897 (1.897) Loss 0.3536 (0.3536) Prec@1 86.328 (86.328)
Epoch: [58][10/12] Time 0.160 (0.318) Loss 0.3304 (0.3711) Prec@1 91.406 (87.109)
Test: [0/2] Time 1.722 (1.722) Loss 0.4612 (0.4612) Prec@1 89.062 (89.062)
* epoch: 58 Prec@1 89.790
* epoch: 58 Prec@1 89.790
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [59][0/12] Time 2.226 (2.226) Loss 0.2936 (0.2936) Prec@1 91.406 (91.406)
Epoch: [59][10/12] Time 0.159 (0.348) Loss 0.3097 (0.3106) Prec@1 87.500 (89.560)
Test: [0/2] Time 1.691 (1.691) Loss 0.5900 (0.5900) Prec@1 83.984 (83.984)
* epoch: 59 Prec@1 85.285
* epoch: 59 Prec@1 85.285
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [60][0/12] Time 2.012 (2.012) Loss 0.2896 (0.2896) Prec@1 90.234 (90.234)
Epoch: [60][10/12] Time 0.158 (0.326) Loss 0.3392 (0.3331) Prec@1 87.891 (88.246)
Test: [0/2] Time 1.725 (1.725) Loss 0.5262 (0.5262) Prec@1 89.453 (89.453)
* epoch: 60 Prec@1 88.889
* epoch: 60 Prec@1 88.889
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [61][0/12] Time 1.936 (1.936) Loss 0.3321 (0.3321) Prec@1 88.281 (88.281)
Epoch: [61][10/12] Time 0.159 (0.320) Loss 0.3931 (0.3128) Prec@1 87.891 (89.347)
Test: [0/2] Time 1.672 (1.672) Loss 0.8900 (0.8900) Prec@1 57.812 (57.812)
* epoch: 61 Prec@1 58.258
* epoch: 61 Prec@1 58.258
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [62][0/12] Time 1.955 (1.955) Loss 0.3191 (0.3191) Prec@1 89.844 (89.844)
Epoch: [62][10/12] Time 0.154 (0.322) Loss 0.3570 (0.3385) Prec@1 88.281 (88.636)
Test: [0/2] Time 1.709 (1.709) Loss 0.4341 (0.4341) Prec@1 86.719 (86.719)
* epoch: 62 Prec@1 86.486
* epoch: 62 Prec@1 86.486
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [63][0/12] Time 1.930 (1.930) Loss 0.2078 (0.2078) Prec@1 92.188 (92.188)
Epoch: [63][10/12] Time 0.159 (0.320) Loss 0.3926 (0.2808) Prec@1 88.281 (90.376)
Test: [0/2] Time 1.681 (1.681) Loss 0.5782 (0.5782) Prec@1 71.484 (71.484)
* epoch: 63 Prec@1 70.270
* epoch: 63 Prec@1 70.270
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [64][0/12] Time 2.252 (2.252) Loss 0.3744 (0.3744) Prec@1 87.891 (87.891)
Epoch: [64][10/12] Time 0.158 (0.349) Loss 0.3298 (0.3217) Prec@1 88.672 (89.276)
Test: [0/2] Time 1.717 (1.717) Loss 0.4983 (0.4983) Prec@1 89.062 (89.062)
* epoch: 64 Prec@1 89.489
* epoch: 64 Prec@1 89.489
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [65][0/12] Time 1.913 (1.913) Loss 0.3845 (0.3845) Prec@1 87.891 (87.891)
Epoch: [65][10/12] Time 0.161 (0.332) Loss 0.2949 (0.3003) Prec@1 89.062 (89.950)
Test: [0/2] Time 1.693 (1.693) Loss 0.5655 (0.5655) Prec@1 86.328 (86.328)
* epoch: 65 Prec@1 87.087
* epoch: 65 Prec@1 87.087
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [66][0/12] Time 1.949 (1.949) Loss 0.4444 (0.4444) Prec@1 87.109 (87.109)
Epoch: [66][10/12] Time 0.159 (0.322) Loss 0.3399 (0.3130) Prec@1 89.062 (89.098)
Test: [0/2] Time 1.703 (1.703) Loss 0.5085 (0.5085) Prec@1 89.844 (89.844)
* epoch: 66 Prec@1 87.688
* epoch: 66 Prec@1 87.688
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [67][0/12] Time 1.946 (1.946) Loss 0.2375 (0.2375) Prec@1 91.016 (91.016)
Epoch: [67][10/12] Time 0.159 (0.328) Loss 0.3387 (0.2769) Prec@1 85.547 (90.128)
Test: [0/2] Time 1.703 (1.703) Loss 0.5086 (0.5086) Prec@1 88.281 (88.281)
* epoch: 67 Prec@1 87.387
* epoch: 67 Prec@1 87.387
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [68][0/12] Time 1.972 (1.972) Loss 0.3379 (0.3379) Prec@1 90.234 (90.234)
Epoch: [68][10/12] Time 0.159 (0.324) Loss 0.2454 (0.3145) Prec@1 91.406 (88.672)
Test: [0/2] Time 1.719 (1.719) Loss 0.6045 (0.6045) Prec@1 87.500 (87.500)
* epoch: 68 Prec@1 88.589
* epoch: 68 Prec@1 88.589
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [69][0/12] Time 1.947 (1.947) Loss 0.2363 (0.2363) Prec@1 90.625 (90.625)
Epoch: [69][10/12] Time 0.159 (0.321) Loss 0.2486 (0.2593) Prec@1 90.234 (90.874)
Test: [0/2] Time 1.710 (1.710) Loss 0.5298 (0.5298) Prec@1 80.078 (80.078)
* epoch: 69 Prec@1 81.381
* epoch: 69 Prec@1 81.381
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [70][0/12] Time 1.949 (1.949) Loss 0.2916 (0.2916) Prec@1 89.453 (89.453)
Epoch: [70][10/12] Time 0.158 (0.328) Loss 0.2786 (0.2966) Prec@1 89.453 (89.737)
Test: [0/2] Time 1.683 (1.683) Loss 0.4976 (0.4976) Prec@1 87.500 (87.500)
* epoch: 70 Prec@1 88.288
* epoch: 70 Prec@1 88.288
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [71][0/12] Time 1.928 (1.928) Loss 0.1949 (0.1949) Prec@1 94.141 (94.141)
Epoch: [71][10/12] Time 0.159 (0.330) Loss 0.1884 (0.2445) Prec@1 93.359 (90.696)
Test: [0/2] Time 1.684 (1.684) Loss 0.5251 (0.5251) Prec@1 82.812 (82.812)
* epoch: 71 Prec@1 81.081
* epoch: 71 Prec@1 81.081
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [72][0/12] Time 1.930 (1.930) Loss 0.3282 (0.3282) Prec@1 87.500 (87.500)
Epoch: [72][10/12] Time 0.159 (0.320) Loss 0.3842 (0.3169) Prec@1 86.719 (89.666)
Test: [0/2] Time 1.709 (1.709) Loss 0.6996 (0.6996) Prec@1 89.453 (89.453)
* epoch: 72 Prec@1 90.390
* epoch: 72 Prec@1 90.390
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [73][0/12] Time 2.187 (2.187) Loss 0.3168 (0.3168) Prec@1 87.500 (87.500)
Epoch: [73][10/12] Time 0.159 (0.343) Loss 0.3492 (0.2848) Prec@1 85.938 (89.986)
Test: [0/2] Time 1.708 (1.708) Loss 0.9264 (0.9264) Prec@1 85.547 (85.547)
* epoch: 73 Prec@1 86.787
* epoch: 73 Prec@1 86.787
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [74][0/12] Time 1.913 (1.913) Loss 0.3152 (0.3152) Prec@1 88.281 (88.281)
Epoch: [74][10/12] Time 0.159 (0.319) Loss 0.2675 (0.2727) Prec@1 92.578 (90.518)
Test: [0/2] Time 1.720 (1.720) Loss 0.5490 (0.5490) Prec@1 86.328 (86.328)
* epoch: 74 Prec@1 86.186
* epoch: 74 Prec@1 86.186
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [75][0/12] Time 1.922 (1.922) Loss 0.2301 (0.2301) Prec@1 90.625 (90.625)
Epoch: [75][10/12] Time 0.159 (0.320) Loss 0.3265 (0.2525) Prec@1 87.109 (91.229)
Test: [0/2] Time 1.699 (1.699) Loss 0.6857 (0.6857) Prec@1 82.812 (82.812)
* epoch: 75 Prec@1 82.282
* epoch: 75 Prec@1 82.282
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [76][0/12] Time 2.240 (2.240) Loss 0.2848 (0.2848) Prec@1 87.500 (87.500)
Epoch: [76][10/12] Time 0.160 (0.348) Loss 0.3408 (0.3049) Prec@1 87.500 (89.560)
Test: [0/2] Time 1.707 (1.707) Loss 0.5952 (0.5952) Prec@1 81.641 (81.641)
* epoch: 76 Prec@1 81.682
* epoch: 76 Prec@1 81.682
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [77][0/12] Time 1.904 (1.904) Loss 0.2558 (0.2558) Prec@1 92.578 (92.578)
Epoch: [77][10/12] Time 0.159 (0.324) Loss 0.3256 (0.2529) Prec@1 87.500 (91.229)
Test: [0/2] Time 1.708 (1.708) Loss 0.5739 (0.5739) Prec@1 85.156 (85.156)
* epoch: 77 Prec@1 82.883
* epoch: 77 Prec@1 82.883
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [78][0/12] Time 2.032 (2.032) Loss 0.2934 (0.2934) Prec@1 89.844 (89.844)
Epoch: [78][10/12] Time 0.159 (0.330) Loss 0.2887 (0.2670) Prec@1 92.578 (91.300)
Test: [0/2] Time 1.688 (1.688) Loss 0.5590 (0.5590) Prec@1 85.156 (85.156)
* epoch: 78 Prec@1 86.186
* epoch: 78 Prec@1 86.186
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [79][0/12] Time 1.945 (1.945) Loss 0.2431 (0.2431) Prec@1 91.797 (91.797)
Epoch: [79][10/12] Time 0.160 (0.332) Loss 0.3305 (0.2164) Prec@1 87.109 (92.543)
Test: [0/2] Time 1.675 (1.675) Loss 0.7119 (0.7119) Prec@1 77.344 (77.344)
* epoch: 79 Prec@1 76.877
* epoch: 79 Prec@1 76.877
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [80][0/12] Time 2.189 (2.189) Loss 0.2332 (0.2332) Prec@1 92.969 (92.969)
Epoch: [80][10/12] Time 0.159 (0.343) Loss 0.2309 (0.2770) Prec@1 93.359 (91.335)
Test: [0/2] Time 1.680 (1.680) Loss 0.3864 (0.3864) Prec@1 90.234 (90.234)
* epoch: 80 Prec@1 87.688
* epoch: 80 Prec@1 87.688
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [81][0/12] Time 2.239 (2.239) Loss 0.2767 (0.2767) Prec@1 92.578 (92.578)
Epoch: [81][10/12] Time 0.160 (0.348) Loss 0.3311 (0.2484) Prec@1 86.328 (91.335)
Test: [0/2] Time 1.720 (1.720) Loss 0.6348 (0.6348) Prec@1 76.562 (76.562)
* epoch: 81 Prec@1 76.276
* epoch: 81 Prec@1 76.276
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [82][0/12] Time 2.256 (2.256) Loss 0.1899 (0.1899) Prec@1 92.969 (92.969)
Epoch: [82][10/12] Time 0.159 (0.349) Loss 0.2917 (0.2652) Prec@1 90.625 (91.442)
Test: [0/2] Time 1.689 (1.689) Loss 0.5168 (0.5168) Prec@1 85.156 (85.156)
* epoch: 82 Prec@1 85.886
* epoch: 82 Prec@1 85.886
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [83][0/12] Time 2.051 (2.051) Loss 0.1982 (0.1982) Prec@1 92.188 (92.188)
Epoch: [83][10/12] Time 0.159 (0.332) Loss 0.2491 (0.2078) Prec@1 89.844 (92.543)
Test: [0/2] Time 1.682 (1.682) Loss 0.6296 (0.6296) Prec@1 80.078 (80.078)
* epoch: 83 Prec@1 77.177
* epoch: 83 Prec@1 77.177
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [84][0/12] Time 1.912 (1.912) Loss 0.1927 (0.1927) Prec@1 93.359 (93.359)
Epoch: [84][10/12] Time 0.157 (0.318) Loss 0.1888 (0.2599) Prec@1 93.359 (91.406)
Test: [0/2] Time 1.707 (1.707) Loss 0.5512 (0.5512) Prec@1 88.672 (88.672)
* epoch: 84 Prec@1 89.790
* epoch: 84 Prec@1 89.790
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [85][0/12] Time 1.909 (1.909) Loss 0.1752 (0.1752) Prec@1 93.750 (93.750)
Epoch: [85][10/12] Time 0.161 (0.319) Loss 0.2747 (0.2133) Prec@1 91.797 (92.614)
Test: [0/2] Time 1.704 (1.704) Loss 0.5671 (0.5671) Prec@1 86.328 (86.328)
* epoch: 85 Prec@1 87.387
* epoch: 85 Prec@1 87.387
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [86][0/12] Time 2.240 (2.240) Loss 0.2070 (0.2070) Prec@1 89.844 (89.844)
Epoch: [86][10/12] Time 0.160 (0.348) Loss 0.2832 (0.2450) Prec@1 91.797 (91.513)
Test: [0/2] Time 1.709 (1.709) Loss 0.5368 (0.5368) Prec@1 86.719 (86.719)
* epoch: 86 Prec@1 85.586
* epoch: 86 Prec@1 85.586
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [87][0/12] Time 1.953 (1.953) Loss 0.1469 (0.1469) Prec@1 96.094 (96.094)
Epoch: [87][10/12] Time 0.161 (0.329) Loss 0.1976 (0.2014) Prec@1 92.969 (93.466)
Test: [0/2] Time 1.704 (1.704) Loss 0.6268 (0.6268) Prec@1 86.328 (86.328)
* epoch: 87 Prec@1 88.288
* epoch: 87 Prec@1 88.288
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [88][0/12] Time 1.976 (1.976) Loss 0.1892 (0.1892) Prec@1 92.969 (92.969)
Epoch: [88][10/12] Time 0.159 (0.323) Loss 0.1663 (0.2332) Prec@1 93.359 (91.442)
Test: [0/2] Time 1.725 (1.725) Loss 0.4768 (0.4768) Prec@1 89.844 (89.844)
* epoch: 88 Prec@1 89.489
* epoch: 88 Prec@1 89.489
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [89][0/12] Time 2.208 (2.208) Loss 0.1286 (0.1286) Prec@1 94.141 (94.141)
Epoch: [89][10/12] Time 0.159 (0.345) Loss 0.1622 (0.1895) Prec@1 93.750 (92.862)
Test: [0/2] Time 1.670 (1.670) Loss 0.7504 (0.7504) Prec@1 86.719 (86.719)
* epoch: 89 Prec@1 88.288
* epoch: 89 Prec@1 88.288
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [90][0/12] Time 1.919 (1.919) Loss 0.1749 (0.1749) Prec@1 94.531 (94.531)
Epoch: [90][10/12] Time 0.160 (0.319) Loss 0.3030 (0.2244) Prec@1 90.234 (92.472)
Test: [0/2] Time 1.703 (1.703) Loss 0.6520 (0.6520) Prec@1 87.500 (87.500)
* epoch: 90 Prec@1 89.489
* epoch: 90 Prec@1 89.489
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [91][0/12] Time 2.019 (2.019) Loss 0.1967 (0.1967) Prec@1 93.750 (93.750)
Epoch: [91][10/12] Time 0.160 (0.328) Loss 0.2092 (0.1974) Prec@1 91.406 (93.146)
Test: [0/2] Time 1.717 (1.717) Loss 0.5337 (0.5337) Prec@1 89.453 (89.453)
* epoch: 91 Prec@1 89.489
* epoch: 91 Prec@1 89.489
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [92][0/12] Time 1.908 (1.908) Loss 0.1796 (0.1796) Prec@1 93.750 (93.750)
Epoch: [92][10/12] Time 0.160 (0.320) Loss 0.2263 (0.2480) Prec@1 93.359 (91.868)
Test: [0/2] Time 1.715 (1.715) Loss 0.4933 (0.4933) Prec@1 88.672 (88.672)
* epoch: 92 Prec@1 89.189
* epoch: 92 Prec@1 89.189
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [93][0/12] Time 1.934 (1.934) Loss 0.1817 (0.1817) Prec@1 93.750 (93.750)
Epoch: [93][10/12] Time 0.158 (0.320) Loss 0.2488 (0.2133) Prec@1 90.625 (92.330)
Test: [0/2] Time 1.699 (1.699) Loss 0.7086 (0.7086) Prec@1 80.469 (80.469)
* epoch: 93 Prec@1 79.279
* epoch: 93 Prec@1 79.279
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [94][0/12] Time 1.955 (1.955) Loss 0.2422 (0.2422) Prec@1 89.453 (89.453)
Epoch: [94][10/12] Time 0.159 (0.322) Loss 0.1780 (0.2533) Prec@1 93.359 (90.518)
Test: [0/2] Time 1.699 (1.699) Loss 0.6035 (0.6035) Prec@1 89.062 (89.062)
* epoch: 94 Prec@1 89.189
* epoch: 94 Prec@1 89.189
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [95][0/12] Time 1.975 (1.975) Loss 0.2582 (0.2582) Prec@1 90.234 (90.234)
Epoch: [95][10/12] Time 0.159 (0.323) Loss 0.2944 (0.2084) Prec@1 92.188 (92.791)
Test: [0/2] Time 1.730 (1.730) Loss 1.0666 (1.0666) Prec@1 67.188 (67.188)
* epoch: 95 Prec@1 66.967
* epoch: 95 Prec@1 66.967
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [96][0/12] Time 1.966 (1.966) Loss 0.1643 (0.1643) Prec@1 94.141 (94.141)
Epoch: [96][10/12] Time 0.160 (0.331) Loss 0.2444 (0.2336) Prec@1 91.016 (92.294)
Test: [0/2] Time 1.694 (1.694) Loss 0.5861 (0.5861) Prec@1 86.328 (86.328)
* epoch: 96 Prec@1 86.486
* epoch: 96 Prec@1 86.486
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [97][0/12] Time 1.914 (1.914) Loss 0.1659 (0.1659) Prec@1 94.531 (94.531)
Epoch: [97][10/12] Time 0.160 (0.330) Loss 0.2504 (0.1904) Prec@1 92.578 (93.466)
Test: [0/2] Time 1.678 (1.678) Loss 0.6894 (0.6894) Prec@1 78.906 (78.906)
* epoch: 97 Prec@1 80.480
* epoch: 97 Prec@1 80.480
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [98][0/12] Time 1.902 (1.902) Loss 0.2155 (0.2155) Prec@1 91.406 (91.406)
Epoch: [98][10/12] Time 0.159 (0.317) Loss 0.2941 (0.2129) Prec@1 92.578 (92.401)
Test: [0/2] Time 1.687 (1.687) Loss 0.3753 (0.3753) Prec@1 94.922 (94.922)
* epoch: 98 Prec@1 93.093
* epoch: 98 Prec@1 93.093
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
New Best Checkpoint saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar
Epoch: [99][0/12] Time 1.923 (1.923) Loss 0.1940 (0.1940) Prec@1 95.312 (95.312)
Epoch: [99][10/12] Time 0.160 (0.321) Loss 0.1865 (0.1930) Prec@1 93.750 (93.537)
Test: [0/2] Time 1.672 (1.672) Loss 0.5767 (0.5767) Prec@1 83.594 (83.594)
* epoch: 99 Prec@1 83.483
* epoch: 99 Prec@1 83.483
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [100][0/12] Time 1.946 (1.946) Loss 0.3028 (0.3028) Prec@1 91.016 (91.016)
Epoch: [100][10/12] Time 0.159 (0.323) Loss 0.2235 (0.2103) Prec@1 91.406 (92.969)
Test: [0/2] Time 1.704 (1.704) Loss 0.5625 (0.5625) Prec@1 89.844 (89.844)
* epoch: 100 Prec@1 88.889
* epoch: 100 Prec@1 88.889
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [101][0/12] Time 1.920 (1.920) Loss 0.3380 (0.3380) Prec@1 89.844 (89.844)
Epoch: [101][10/12] Time 0.159 (0.320) Loss 0.1733 (0.1909) Prec@1 92.188 (93.679)
Test: [0/2] Time 1.678 (1.678) Loss 0.6445 (0.6445) Prec@1 89.062 (89.062)
* epoch: 101 Prec@1 88.589
* epoch: 101 Prec@1 88.589
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [102][0/12] Time 1.895 (1.895) Loss 0.2093 (0.2093) Prec@1 92.969 (92.969)
Epoch: [102][10/12] Time 0.159 (0.317) Loss 0.2647 (0.2172) Prec@1 89.844 (92.791)
Test: [0/2] Time 1.691 (1.691) Loss 0.4537 (0.4537) Prec@1 89.844 (89.844)
* epoch: 102 Prec@1 90.390
* epoch: 102 Prec@1 90.390
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [103][0/12] Time 1.975 (1.975) Loss 0.1979 (0.1979) Prec@1 92.969 (92.969)
Epoch: [103][10/12] Time 0.159 (0.324) Loss 0.2140 (0.1836) Prec@1 89.844 (93.217)
Test: [0/2] Time 1.706 (1.706) Loss 0.7860 (0.7860) Prec@1 89.062 (89.062)
* epoch: 103 Prec@1 90.090
* epoch: 103 Prec@1 90.090
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [104][0/12] Time 2.022 (2.022) Loss 0.2810 (0.2810) Prec@1 92.578 (92.578)
Epoch: [104][10/12] Time 0.159 (0.330) Loss 0.2480 (0.2219) Prec@1 91.797 (92.081)
Test: [0/2] Time 1.716 (1.716) Loss 0.6215 (0.6215) Prec@1 90.625 (90.625)
* epoch: 104 Prec@1 91.592
* epoch: 104 Prec@1 91.592
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [105][0/12] Time 1.906 (1.906) Loss 0.1642 (0.1642) Prec@1 95.312 (95.312)
Epoch: [105][10/12] Time 0.159 (0.319) Loss 0.1640 (0.1851) Prec@1 93.750 (93.928)
Test: [0/2] Time 1.708 (1.708) Loss 0.5004 (0.5004) Prec@1 90.625 (90.625)
* epoch: 105 Prec@1 90.390
* epoch: 105 Prec@1 90.390
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [106][0/12] Time 1.909 (1.909) Loss 0.1803 (0.1803) Prec@1 90.234 (90.234)
Epoch: [106][10/12] Time 0.158 (0.319) Loss 0.1994 (0.2141) Prec@1 91.797 (92.223)
Test: [0/2] Time 1.688 (1.688) Loss 0.4920 (0.4920) Prec@1 82.031 (82.031)
* epoch: 106 Prec@1 79.580
* epoch: 106 Prec@1 79.580
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [107][0/12] Time 1.971 (1.971) Loss 0.1579 (0.1579) Prec@1 92.969 (92.969)
Epoch: [107][10/12] Time 0.160 (0.330) Loss 0.1896 (0.1695) Prec@1 93.359 (93.786)
Test: [0/2] Time 1.693 (1.693) Loss 0.6627 (0.6627) Prec@1 85.156 (85.156)
* epoch: 107 Prec@1 85.285
* epoch: 107 Prec@1 85.285
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [108][0/12] Time 1.926 (1.926) Loss 0.2721 (0.2721) Prec@1 90.234 (90.234)
Epoch: [108][10/12] Time 0.159 (0.334) Loss 0.1391 (0.1956) Prec@1 94.531 (93.395)
Test: [0/2] Time 1.693 (1.693) Loss 0.6169 (0.6169) Prec@1 87.891 (87.891)
* epoch: 108 Prec@1 88.889
* epoch: 108 Prec@1 88.889
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [109][0/12] Time 1.932 (1.932) Loss 0.1255 (0.1255) Prec@1 96.094 (96.094)
Epoch: [109][10/12] Time 0.159 (0.318) Loss 0.1880 (0.1642) Prec@1 93.750 (94.567)
Test: [0/2] Time 1.702 (1.702) Loss 0.6942 (0.6942) Prec@1 86.719 (86.719)
* epoch: 109 Prec@1 88.288
* epoch: 109 Prec@1 88.288
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [110][0/12] Time 1.941 (1.941) Loss 0.1494 (0.1494) Prec@1 95.312 (95.312)
Epoch: [110][10/12] Time 0.159 (0.320) Loss 0.2451 (0.2071) Prec@1 91.016 (92.330)
Test: [0/2] Time 1.721 (1.721) Loss 0.5960 (0.5960) Prec@1 86.328 (86.328)
* epoch: 110 Prec@1 86.486
* epoch: 110 Prec@1 86.486
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [111][0/12] Time 1.969 (1.969) Loss 0.1588 (0.1588) Prec@1 94.141 (94.141)
Epoch: [111][10/12] Time 0.160 (0.323) Loss 0.1406 (0.1750) Prec@1 96.094 (93.395)
Test: [0/2] Time 1.680 (1.680) Loss 0.6977 (0.6977) Prec@1 90.625 (90.625)
* epoch: 111 Prec@1 90.090
* epoch: 111 Prec@1 90.090
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [112][0/12] Time 1.914 (1.914) Loss 0.2185 (0.2185) Prec@1 92.578 (92.578)
Epoch: [112][10/12] Time 0.161 (0.318) Loss 0.1134 (0.1589) Prec@1 94.922 (94.389)
Test: [0/2] Time 1.692 (1.692) Loss 0.6469 (0.6469) Prec@1 89.062 (89.062)
* epoch: 112 Prec@1 89.489
* epoch: 112 Prec@1 89.489
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [113][0/12] Time 1.978 (1.978) Loss 0.1283 (0.1283) Prec@1 95.703 (95.703)
Epoch: [113][10/12] Time 0.158 (0.323) Loss 0.2125 (0.1576) Prec@1 93.359 (94.815)
Test: [0/2] Time 1.699 (1.699) Loss 0.9452 (0.9452) Prec@1 67.578 (67.578)
* epoch: 113 Prec@1 65.766
* epoch: 113 Prec@1 65.766
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [114][0/12] Time 1.950 (1.950) Loss 0.2040 (0.2040) Prec@1 92.188 (92.188)
Epoch: [114][10/12] Time 0.159 (0.322) Loss 0.2110 (0.1899) Prec@1 94.531 (93.253)
Test: [0/2] Time 1.687 (1.687) Loss 0.5138 (0.5138) Prec@1 86.719 (86.719)
* epoch: 114 Prec@1 86.486
* epoch: 114 Prec@1 86.486
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [115][0/12] Time 1.998 (1.998) Loss 0.1638 (0.1638) Prec@1 95.703 (95.703)
Epoch: [115][10/12] Time 0.160 (0.331) Loss 0.2106 (0.1672) Prec@1 92.578 (94.354)
Test: [0/2] Time 1.709 (1.709) Loss 0.8591 (0.8591) Prec@1 89.062 (89.062)
* epoch: 115 Prec@1 89.790
* epoch: 115 Prec@1 89.790
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [116][0/12] Time 1.976 (1.976) Loss 0.1392 (0.1392) Prec@1 92.969 (92.969)
Epoch: [116][10/12] Time 0.160 (0.336) Loss 0.2718 (0.2105) Prec@1 92.578 (93.253)
Test: [0/2] Time 1.718 (1.718) Loss 0.4857 (0.4857) Prec@1 88.281 (88.281)
* epoch: 116 Prec@1 88.589
* epoch: 116 Prec@1 88.589
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [117][0/12] Time 2.203 (2.203) Loss 0.1616 (0.1616) Prec@1 92.969 (92.969)
Epoch: [117][10/12] Time 0.159 (0.345) Loss 0.1600 (0.1427) Prec@1 94.141 (94.638)
Test: [0/2] Time 1.732 (1.732) Loss 0.8432 (0.8432) Prec@1 89.453 (89.453)
* epoch: 117 Prec@1 90.691
* epoch: 117 Prec@1 90.691
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [118][0/12] Time 1.918 (1.918) Loss 0.1563 (0.1563) Prec@1 94.141 (94.141)
Epoch: [118][10/12] Time 0.160 (0.319) Loss 0.1545 (0.1740) Prec@1 93.750 (93.892)
Test: [0/2] Time 1.722 (1.722) Loss 0.4324 (0.4324) Prec@1 91.797 (91.797)
* epoch: 118 Prec@1 90.991
* epoch: 118 Prec@1 90.991
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [119][0/12] Time 1.912 (1.912) Loss 0.1632 (0.1632) Prec@1 92.578 (92.578)
Epoch: [119][10/12] Time 0.160 (0.317) Loss 0.1550 (0.1470) Prec@1 94.922 (94.638)
Test: [0/2] Time 1.743 (1.743) Loss 0.5448 (0.5448) Prec@1 86.719 (86.719)
* epoch: 119 Prec@1 85.886
* epoch: 119 Prec@1 85.886
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [120][0/12] Time 1.956 (1.956) Loss 0.1617 (0.1617) Prec@1 95.703 (95.703)
Epoch: [120][10/12] Time 0.159 (0.322) Loss 0.1568 (0.1884) Prec@1 94.531 (93.466)
Test: [0/2] Time 1.724 (1.724) Loss 0.4884 (0.4884) Prec@1 89.062 (89.062)
* epoch: 120 Prec@1 89.189
* epoch: 120 Prec@1 89.189
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [121][0/12] Time 2.252 (2.252) Loss 0.0956 (0.0956) Prec@1 96.484 (96.484)
Epoch: [121][10/12] Time 0.160 (0.350) Loss 0.0892 (0.1378) Prec@1 96.094 (94.425)
Test: [0/2] Time 1.702 (1.702) Loss 0.9220 (0.9220) Prec@1 71.875 (71.875)
* epoch: 121 Prec@1 72.372
* epoch: 121 Prec@1 72.372
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [122][0/12] Time 2.193 (2.193) Loss 0.1376 (0.1376) Prec@1 93.359 (93.359)
Epoch: [122][10/12] Time 0.154 (0.344) Loss 0.1217 (0.1669) Prec@1 95.312 (94.034)
Test: [0/2] Time 1.728 (1.728) Loss 0.4749 (0.4749) Prec@1 91.406 (91.406)
* epoch: 122 Prec@1 90.090
* epoch: 122 Prec@1 90.090
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [123][0/12] Time 1.969 (1.969) Loss 0.1731 (0.1731) Prec@1 93.359 (93.359)
Epoch: [123][10/12] Time 0.160 (0.332) Loss 0.1657 (0.1350) Prec@1 95.703 (95.419)
Test: [0/2] Time 1.702 (1.702) Loss 0.4422 (0.4422) Prec@1 92.188 (92.188)
* epoch: 123 Prec@1 90.991
* epoch: 123 Prec@1 90.991
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [124][0/12] Time 1.910 (1.910) Loss 0.1530 (0.1530) Prec@1 94.922 (94.922)
Epoch: [124][10/12] Time 0.161 (0.335) Loss 0.1130 (0.1614) Prec@1 96.094 (94.744)
Test: [0/2] Time 1.703 (1.703) Loss 0.6778 (0.6778) Prec@1 91.016 (91.016)
* epoch: 124 Prec@1 91.592
* epoch: 124 Prec@1 91.592
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [125][0/12] Time 1.927 (1.927) Loss 0.1401 (0.1401) Prec@1 95.312 (95.312)
Epoch: [125][10/12] Time 0.159 (0.334) Loss 0.1760 (0.1236) Prec@1 94.141 (95.774)
Test: [0/2] Time 1.697 (1.697) Loss 0.8132 (0.8132) Prec@1 70.703 (70.703)
* epoch: 125 Prec@1 72.072
* epoch: 125 Prec@1 72.072
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [126][0/12] Time 2.032 (2.032) Loss 0.1410 (0.1410) Prec@1 94.141 (94.141)
Epoch: [126][10/12] Time 0.159 (0.330) Loss 0.1733 (0.1614) Prec@1 96.094 (93.928)
Test: [0/2] Time 1.692 (1.692) Loss 0.4986 (0.4986) Prec@1 86.328 (86.328)
* epoch: 126 Prec@1 87.688
* epoch: 126 Prec@1 87.688
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [127][0/12] Time 2.220 (2.220) Loss 0.1754 (0.1754) Prec@1 93.750 (93.750)
Epoch: [127][10/12] Time 0.159 (0.346) Loss 0.1546 (0.1384) Prec@1 93.750 (94.922)
Test: [0/2] Time 1.701 (1.701) Loss 0.9395 (0.9395) Prec@1 85.156 (85.156)
* epoch: 127 Prec@1 86.787
* epoch: 127 Prec@1 86.787
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [128][0/12] Time 1.902 (1.902) Loss 0.1456 (0.1456) Prec@1 95.703 (95.703)
Epoch: [128][10/12] Time 0.160 (0.322) Loss 0.1806 (0.1682) Prec@1 93.750 (94.496)
Test: [0/2] Time 1.711 (1.711) Loss 0.5188 (0.5188) Prec@1 90.625 (90.625)
* epoch: 128 Prec@1 90.691
* epoch: 128 Prec@1 90.691
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [129][0/12] Time 1.951 (1.951) Loss 0.0664 (0.0664) Prec@1 98.047 (98.047)
Epoch: [129][10/12] Time 0.160 (0.323) Loss 0.1485 (0.1174) Prec@1 94.922 (96.058)
Test: [0/2] Time 1.704 (1.704) Loss 0.6762 (0.6762) Prec@1 89.062 (89.062)
* epoch: 129 Prec@1 90.090
* epoch: 129 Prec@1 90.090
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [130][0/12] Time 1.943 (1.943) Loss 0.2280 (0.2280) Prec@1 92.578 (92.578)
Epoch: [130][10/12] Time 0.160 (0.332) Loss 0.2291 (0.1751) Prec@1 94.531 (93.786)
Test: [0/2] Time 1.717 (1.717) Loss 0.4670 (0.4670) Prec@1 91.406 (91.406)
* epoch: 130 Prec@1 91.892
* epoch: 130 Prec@1 91.892
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [131][0/12] Time 2.158 (2.158) Loss 0.1494 (0.1494) Prec@1 94.141 (94.141)
Epoch: [131][10/12] Time 0.159 (0.341) Loss 0.1707 (0.1408) Prec@1 93.750 (94.744)
Test: [0/2] Time 1.699 (1.699) Loss 0.4758 (0.4758) Prec@1 87.891 (87.891)
* epoch: 131 Prec@1 87.387
* epoch: 131 Prec@1 87.387
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [132][0/12] Time 1.991 (1.991) Loss 0.1658 (0.1658) Prec@1 92.578 (92.578)
Epoch: [132][10/12] Time 0.159 (0.326) Loss 0.1663 (0.1657) Prec@1 94.922 (94.212)
Test: [0/2] Time 1.708 (1.708) Loss 0.5929 (0.5929) Prec@1 91.016 (91.016)
* epoch: 132 Prec@1 91.892
* epoch: 132 Prec@1 91.892
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [133][0/12] Time 1.973 (1.973) Loss 0.0819 (0.0819) Prec@1 97.656 (97.656)
Epoch: [133][10/12] Time 0.159 (0.324) Loss 0.1221 (0.1269) Prec@1 94.922 (95.135)
Test: [0/2] Time 1.696 (1.696) Loss 0.4731 (0.4731) Prec@1 92.578 (92.578)
* epoch: 133 Prec@1 92.793
* epoch: 133 Prec@1 92.793
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [134][0/12] Time 1.971 (1.971) Loss 0.0957 (0.0957) Prec@1 98.047 (98.047)
Epoch: [134][10/12] Time 0.160 (0.324) Loss 0.1335 (0.1506) Prec@1 93.359 (94.567)
Test: [0/2] Time 1.715 (1.715) Loss 0.5484 (0.5484) Prec@1 91.797 (91.797)
* epoch: 134 Prec@1 92.192
* epoch: 134 Prec@1 92.192
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [135][0/12] Time 2.178 (2.178) Loss 0.1302 (0.1302) Prec@1 93.750 (93.750)
Epoch: [135][10/12] Time 0.161 (0.342) Loss 0.1452 (0.1391) Prec@1 94.922 (94.922)
Test: [0/2] Time 1.709 (1.709) Loss 0.7818 (0.7818) Prec@1 89.453 (89.453)
* epoch: 135 Prec@1 89.489
* epoch: 135 Prec@1 89.489
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [136][0/12] Time 1.960 (1.960) Loss 0.1286 (0.1286) Prec@1 96.094 (96.094)
Epoch: [136][10/12] Time 0.160 (0.323) Loss 0.0955 (0.1380) Prec@1 97.656 (94.709)
Test: [0/2] Time 1.717 (1.717) Loss 0.5795 (0.5795) Prec@1 89.453 (89.453)
* epoch: 136 Prec@1 90.090
* epoch: 136 Prec@1 90.090
Checkpoint Saved: output/All/train_2020-03-26-17-10-28_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
Epoch: [137][0/12] Time 1.968 (1.968) Loss 0.1092 (0.1092) Prec@1 94.531 (94.531)
Epoch: [137][10/12] Time 0.160 (0.324) Loss 0.2211 (0.1232) Prec@1 93.359 (95.526)