😄 The code of journal version of this work is released IIDS. We strongly recommend you to use our journal version which has a much higher performance.
Pytorch implementation of Paper "Intra-Inter Camera Similarity for Unsupervised Person Re-Identification" (CVPR 2021)
git clone [email protected]:SY-Xuan/IICS.git
cd ./IICS
conda create --name IICS --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
We also provide a better version of our method which can adaptively determine clustering number by setting a similarity threshold
sh ./script/train_market_threshold.sh
| Datasets | mAP | Rank@1 | Method |
|---|---|---|---|
| Market1501 | 72.9% | 89.5% | original |
| Market1501 | 73.9% | 90.1% | threshold |
| DukeMTMC-ReID | 64.4% | 80.0% | original |
| DukeMTMC-ReID | 66.2% | 80.8% | threshold |
| MSMT17 | 26.9% | 56.4% | original |
| MSMT17 | 31.9% | 62.6% | threshold |
If you find this code useful for your research, please cite our paper:
@article{xuan2021intrainter,
title={Intra-Inter Camera Similarity for Unsupervised Person Re-Identification},
author={Shiyu Xuan and Shiliang Zhang},
year={2021},
journal={arXiv preprint arXiv:2103.11658},
}
If you have any questions about this code or paper, feel free to contact me at [email protected].
Codes are built upon open-reid.