Pytorch implementation of Paper "Intra-Inter Camera Similarity for Unsupervised Person Re-Identification" (TPAMI 2022)
This is the extended version of IICS on CVPR2021
git clone [email protected]:SY-Xuan/IIDS.git
cd ./IIDS
conda create --name IIDS --file requirements.txt
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
Change the checkpoint path in the ./script/test_market.sh
sh ./script/test_market.sh
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
| 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 |
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}
}
If you have any questions about this code or paper, feel free to contact me at [email protected].
Codes are built upon open-reid.