The official repository for Self-Supervised Pre-Training for Transformer-Based Person Re-Identification.

pip install -r requirements.txt

We recommend to use /torch=1.7.1 /torchvision=0.8.2 /timm=0.3.4 /cuda>10.1 /faiss-gpu=1.7.1/ 16G or 32G V100 for training and evaluation. If you find some packages are missing, please install them manually. You can refer to DINO, TransReID and cluster-contrast-reid to install the environment of pre-training, supervised ReID and unsupervised ReID, respectively.

mkdir data

Download the datasets:

• Market-1501
• MSMT17
• LUPerson. We don't have the copyright of the LUPerson dataset. Please contact authors of LUPerson to get this dataset.
• You can download the file list ordered by the CFS score for the LUPerson. [CFS_list.pkl]

Then unzip them and rename them under the directory like

data
├── market1501
│   └── bounding_box_train
│   └── bounding_box_test
│   └── ..
├── MSMT17
│   └── train
│   └── test
│   └── ..
└── LUP
    └── images 
    └── CFS_list.pkl 

Please download pre-trained models and put them into your custom file path.

We have reproduced the performance to verify the reproducibility. The reproduced results may have a gap of about 0.1~0.2% with the numbers in the paper.

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

LUPerson, DINO, TransReID, cluster-contrast-reid.

If you find this code useful for your research, please cite our paper

@article{luo2021self,
  title={Self-Supervised Pre-Training for Transformer-Based Person Re-Identification},
  author={Luo, Hao and Wang, Pichao and Xu, Yi and Ding, Feng and Zhou, Yanxin and Wang, Fan and Li, Hao and Jin, Rong},
  journal={arXiv preprint arXiv:2111.12084},
  year={2021}
}

If you have any question, please feel free to contact us. E-mail: [email protected] or [email protected]