IIDS

Pytorch implementation of Paper "Intra-Inter Camera Similarity for Unsupervised Person Re-Identification" (TPAMI 2022)

This is the extended version of IICS on CVPR2021

Installation

1. Clone code

    git clone git@github.com:SY-Xuan/IIDS.git
    cd ./IIDS

2. Install dependency python packages

    conda create --name IIDS --file requirements.txt

3. Prepare dataset

Download Market1501, DukeMTMC-ReID, MSMT17 from website and put the zip file under the directory like

./data
├── dukemtmc
│   └── raw
|       └──DukeMTMC-reID.zip
├── market1501
|   └── raw
│       └── Market-1501-v15.09.15.zip
|── msmt17
|   └── raw
|       └── MSMT17_V2.zip

Usage

1. Download trained model

• Market1501
• DukeMTMC-ReID
• MSMT17

2. Evaluate Model

Change the checkpoint path in the ./script/test_market.sh

sh ./script/test_market.sh

3. Train Model

You need to download ResNet-50 imagenet pretrained model and change the checkpoint path in the ./script/train_market.sh

sh ./script/train_market.sh

Results

|Datasets | mAP | Rank@1| Method | | :--------: | :-----: | :----: | :----: | |Market1501 | 72.9% | 89.5% | CVPR2021 | |Market1501 | 78.0% | 91.2% | This Version | |DukeMTMC-ReID | 64.4% | 80.0% | CVPR2021 | |DukeMTMC-ReID | 68.7% | 82.1% | This Version | |MSMT17 | 26.9% | 56.4% | CVPR2021 | |MSMT17 | 35.1% | 64.4% | This Version |

Citations

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

@ARTICLE{9745321,
  author={Xuan, Shiyu and Zhang, Shiliang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Intra-Inter Domain Similarity for Unsupervised Person Re-Identification}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2022.3163451}}

@inproceedings{xuan2021intra,
  title={Intra-inter camera similarity for unsupervised person re-identification},
  author={Xuan, Shiyu and Zhang, Shiliang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11926--11935},
  year={2021}
}

Contact me

If you have any questions about this code or paper, feel free to contact me at shiyu_xuan@stu.pku.edu.cn.

Acknowledgement

Codes are built upon open-reid.