An official PyTorch implementation of the paper "Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences", ICCV 2021.

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Computer VisionLbA
Overview

PyTorch implementation of Learning by Aligning (ICCV 2021)

This is an official PyTorch implementation of the paper "Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences", ICCV 2021.

For more details, visit our project site or see our paper.

Requirements

  • Python 3.8
  • PyTorch 1.7.1
  • GPU memory >= 11GB

Getting started

First, clone our git repository.

git clone https://github.com/cvlab-yonsei/LbA.git
cd LbA

Docker

You can use docker pull sanghslee/ps:1.7.1-cuda11.0-cudnn8-runtime

Prepare datasets

  • SYSU-MM01: download from this link.
    • For SYSU-MM01, you need to preprocess the .jpg files into .npy files by running:
      • python utils/pre_preprocess_sysu.py --data_dir /path/to/SYSU-MM01
    • Modify the dataset directory below accordingly.
      • L63 of train.py
      • L54 of test.py

Train

  • run python train.py --method full

  • Important:

    • Performances reported during training does not reflect exact performances of your model. This is due to 1) evaluation protocols of the datasets and 2) random seed configurations.
    • Make sure you seperately run test.py to obtain correct results to be reported in your paper.

Test

  • run python test.py --method full
  • The results should be around:
dataset method mAP rank-1
SYSU-MM01 baseline 49.54 50.43
SYSU-MM01 full 54.14 55.41

Pretrained weights

  • Download [SYSU-MM01]
  • The results should be:
dataset method mAP rank-1
SYSU-MM01 full 55.22 56.31

Bibtex

@article{park2021learning,
  title={Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences},
  author={Park, Hyunjong and Lee, Sanghoon and Lee, Junghyup and Ham, Bumsub},
  journal={arXiv preprint arXiv:2108.07422},
  year={2021}
}

Credits

Our implementation is based on Mang Ye's code here.

Comments
  • something about run this code

    something about run this code

    thanks for your code, there is something wrong when i run you code,in this line: loss = torch.mean(comask_pos * self.criterion(feat, feat_recon_pos, feat_recon_neg)) the wrong is:RuntimeError: The size of tensor a (9) must match the size of tensor b (18) at non-singleton dimension 3 could you give me some help?

    opened by zhuchuanleiqq 12
  • When running

    When running "train. Py", there is a problem on line 132 of the "model. Py" file:

    When running "train. Py", there is a problem on line（loss = torch.mean(comask_pos * self.criterion(feat, feat_recon_pos, feat_recon_neg))） 132 of the "model. Py" file: Traceback：RuntimeError: The size of tensor a (9) must match the size of tensor b (18) at non-singleton dimension 3

    opened by redsoup 1
  • Question about the training speed

    Question about the training speed

    Thanks for your work.

    When I tried to reproduce your results with an Nvidia 2080Ti (as recommended by the paper), however, the training speed seemed very slow. It nearly took 20 minutes for each epoch on SYSU-MM01, which mismatched with the reported 8 hours training time.

    I have already used cuda for acceleration. Thus, I wonder how did this happen. Thank you.

    opened by hansonchen1996 1
  • Problems about the performance

    Problems about the performance

    I have run your source code on both SYSU and RegDB datasets, but I didn't get the performance of your paper. So I want to know how to set the hyper-parameter to get the performance of your paper?

    opened by Mrkkew 1
  • Visualization problem

    Visualization problem

    Hello， Thanks for your great work, I am wondering about the visualization part, use mask and comask matrix in SYSU-MM01 dataset. Can I get some details about the steps of your visualization method? Thank you very much.

    opened by sunset233 0
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CV Lab @ Yonsei University
CV Lab @ Yonsei University
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