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

We provide a Dockerfile to help reproducing our results easily.

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

@inproceedings{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},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12046--12055},
  year={2021}
}

Credits

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