Comments (7)
@pengleigithub The default settings are the hyper-parameters of the paper. If you directly run the train_alignedreid.py, you will get similar results shown in README.
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In the paper, it says 'mini-batch size is set to160,in which each identity has 4 images. Each epoch includes 2000 mini-batches'. I am wondering if there is a typo. Each mini-batch has 40 identities and there are 2000 mini-batches. So there are 40 * 2000 = 80000 identities in each epoch. But none of the datasets has 80000 identities.
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@pengleigithub Please wait our new paper Alignedreid++. The experiments of AlignedReID exist some mistakes.
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Can you share the details since it might be a long time before the paper is posted? Thx.
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AlignedReID++ trains and tests on each datasets, and add some theory explanation.
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Thx. Can you share the training details on market1501?
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All details are in the train_alignedreid.py. There are no any tricks.
- Adam
- 300 epoch
- lr:0.0002 (0-150); 0.00002 (150-300)
- loss: softmax + triplet loss
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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?
- poor metrics when training on market1501 HOT 2
- Is there any performance (speed) results that I can refer to?
- Reranking Consume way too much memory space especially for MSMT17 dataset
- about run demo issue HOT 4
- The actual performance is far from the report HOT 3
- If the person in the detection bbox is incomplete, can the features be aligned?
- Why is local feature (lf) different in training phase and test phase?
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