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[ECCV2022] PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification

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Python >=3.7 PyTorch >=1.8

PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification [pdf]

The official repository for PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification ECCV2022.

Requirements

Installation

pip install -r requirements.txt

We recommend to use /torch=1.8.0 /torchvision=0.9.0 /timm=0.3.4 /cuda>11.1 /faiss-gpu=1.7.2/ A100 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. You can also refer to TransReID-SSL to install the whole environments.

Prepare Datasets

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.

Then unzip them and rename them under the directory like

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

Pre-trained Models

Model Download
ViT-S/16 link
ViT-B/16 link

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

ReID performance

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.

Supervised ReID

Market-1501
Model Image Size mAP Rank-1 Download
ViT-S/16 256*128 92.2 96.3 model / log
ViT-S/16 384*128 92.6 96.8 model / log
ViT-B/16 256*128 93.0 96.8 model / log
ViT-B/16 384*128 93.3 96.9 model / log
MSMT17
Model Image Size mAP Rank-1 Download
ViT-S/16 256*128 69.1 86.5 model / log
ViT-S/16 384*128 71.7 87.9 model / log
ViT-B/16 256*128 71.8 88.2 model / log
ViT-B/16 384*128 74.3 89.7 model / log

UDA ReID

MSMT2Market
Model Image Size mAP Rank-1 Download
ViT-S/16 256*128 90.2 95.8 model / log
Market2MSMT
Model Image Size mAP Rank-1 Download
ViT-S/16 256*128 49.1 72.7 model / log

USL ReID

Market-1501
Model Image Size mAP Rank-1 Download
ViT-S/16 256*128 88.7 95.0 model / log
MSMT17
Model Image Size mAP Rank-1 Download
ViT-S/16 256*128 41.0 67.0 model / log

Acknowledgment

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

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

Citation

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

@article{zhu2022part,
  title={PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification},
  author={Zhu, Kuan and Guo, Haiyun and Yan, Tianyi and Zhu, Yousong and Wang, Jinqiao and Tang, Ming},
  journal={arXiv preprint arXiv:2203.03931},
  year={2022}
}

Contact

If you have any question, please feel free to contact us. E-mail: kuan.zhu@nlpr.ia.ac.cn.

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