main.py --dataset market --testset msmt --data-dir ./reid/datasets/ --exp-dir ./Exp
fpaths:./reid/datasets/market/bounding_box_train/1500_c6s3_086567_01.jpg
fpaths:./reid/datasets/market/bounding_box_test/1501_c6s4_001902_01.jpg
fpaths:./reid/datasets/market/query/1501_c6s4_001877_00.jpg
Market dataset loaded
subset | # ids | # images
train | 751 | 12935
query | 750 | 3367
gallery | 751 | 15912
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Finished epoch 1 at lr=[0.0005, 0.005, 0.005]. Loss: 14.812. Acc: 54.97%. Training time: 174 seconds.
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Finished epoch 2 at lr=[0.0005, 0.005, 0.005]. Loss: 13.333. Acc: 61.35%. Training time: 344 seconds.
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Finished epoch 3 at lr=[0.0005, 0.005, 0.005]. Loss: 11.447. Acc: 68.55%. Training time: 514 seconds.
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Finished epoch 4 at lr=[0.0005, 0.005, 0.005]. Loss: 10.338. Acc: 72.09%. Training time: 684 seconds.
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Finished epoch 5 at lr=[0.0005, 0.005, 0.005]. Loss: 9.319. Acc: 75.31%. Training time: 855 seconds.
Decay the learning rate by a factor of 0.1. Final epochs: 7.
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Finished epoch 6 at lr=[5e-05, 0.0005, 0.0005]. Loss: 8.566. Acc: 77.75%. Training time: 1025 seconds.
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Finished epoch 7 at lr=[5e-05, 0.0005, 0.0005]. Loss: 7.732. Acc: 80.22%. Training time: 1195 seconds.
The learning converges at epoch 7.
Evaluate the learned model:
test_names: ['msmt']
MSMT dataset loaded
subset | # ids | # images
train | 1041 | 32621
query | 3060 | 11659
gallery | 3060 | 82161
/home/luotao/anaconda3/envs/QAConv/lib/python3.6/site-packages/torchvision/transforms/transforms.py:288: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.
"Argument interpolation should be of type InterpolationMode instead of int. "
Time: 2690.337 seconds. / 1284. similarity 1 / 1284.
已杀死
run: python main.py --dataset market --testset msmt --data-dir ./reid/datasets/ --exp-dir ./Exp
--test_fea_batch --test_gal_batch --test_prob_batch all set to 128. Time: xx seconds, /xx similarity xx/xx. 已杀死.
Those three parameters set to 64, the errer : Time: 2690.337 seconds. / 1284. similarity 1 / 1284. 已杀死.