Comments (3)
michuanhaohao
commented on September 2, 2024
@jokerlc CUHK03 updated its evaluation, which is different from original paper. So 66.6% top-1 is high performance for cuhk03. The softmax baseline is 47.5% mAP for "New" CUHK03.
Use -d cuhk03 when running the training code. In default mode, we use new split (767/700). If you wanna use the original splits (1367/100) created by [13], specify --cuhk03-classic-split. As [13] computes CMC differently from Market1501, you might need to specify --use-metric-cuhk03 for fair comparison with their method. In addition, we support both labeled and detected modes. The default mode loads detected images. Specify --cuhk03-labeled if you wanna train and test on labeled images.
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jokerlc
commented on September 2, 2024
@michuanhaohao Thanks!
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michuanhaohao
commented on September 2, 2024
Done
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Related Issues (20)
- assert num_valid_q > 0, "Error: all query identities do not appear in gallery" HOT 2
- Threshold to say that it's the same person HOT 5
- what globalpooling did you use HOT 2
- how to use the test data to draw a missrate picture? HOT 1
- how to use center loss to train?
- when i train use market1501 i have face some problems HOT 1
- max identities, max images per identity required for training and min identities, min images per identity required for training
- multi-gpu training
- Training on custom dataset tutorials
- Some wrong with the code? HOT 2
- Questions about "mutual learning" in the process of reproducing the paper
- UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe3 in position 0: HOT 3
- How to calculate the aligned distance and original distance from the distance map?
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- about run demo issue HOT 4
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