Cluster Contrast for Unsupervised Person Re-Identification

The official repository for Cluster Contrast for Unsupervised Person Re-Identification. We achieve state-of-the-art performances on unsupervised learning tasks for object re-ID, including person re-ID and vehicle re-ID.

Our unified framework

Requirements

Installation

git clone http://gitlab.alibaba-inc.com/yixuan.wgy/ClusterContrast.git
cd ClusterContrast
python setup.py develop

Prepare Datasets

cd examples && mkdir data

Download the person datasets Market-1501,MSMT17,PersonX,DukeMTMC-reID and the vehicle datasets VeRi-776 from aliyun. Then unzip them under the directory like

ClusterContrast/examples/data
├── market1501
│   └── Market-1501-v15.09.15
├── msmt17
│   └── MSMT17_V1
├── personx
│   └── PersonX
├── dukemtmcreid
│   └── DukeMTMC-reID
└── veri
    └── VeRi

Prepare ImageNet Pre-trained Models for IBN-Net

When training with the backbone of IBN-ResNet, you need to download the ImageNet-pretrained model from this link and save it under the path of examples/pretrained/.

ImageNet-pretrained models for ResNet-50 will be automatically downloaded in the python script.

Training

We utilize 4 GTX-2080TI GPUs for training. For more parameter configuration, please check run_code.sh.

examples:

Market-1501:

CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/cluster_contrast_train_usl.py -b 256 -a resnet50 -d market1501 --iters 200 --momentum 0.1 --eps 0.4 --num-instances 16

MSMT17:

CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/cluster_contrast_train_usl.py -b 256 -a resnet50 -d msmt17 --iters 400 --momentum 0.1 --eps 0.7 --num-instances 16

DukeMTMC-reID:

CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/cluster_contrast_train_usl.py -b 256 -a resnet50 -d dukemtmcreid --iters 200 --momentum 0.1 --eps 0.7 --num-instances 16

Evaluation

We utilize 1 GTX-2080TI GPU for testing. Note that

use --width 128 --height 256 (default) for person datasets, and --height 224 --width 224 for vehicle datasets;
use -a resnet50 (default) for the backbone of ResNet-50, and -a resnet_ibn50a for the backbone of IBN-ResNet.

To evaluate the model, run:

CUDA_VISIBLE_DEVICES=0 \
python examples/test.py \
  -d $DATASET --resume $PATH

Some examples:

### Market-1501 ###
CUDA_VISIBLE_DEVICES=0 \
python examples/test.py \
  -d market1501 --resume logs/spcl_usl/market_resnet50/model_best.pth.tar

Results

You can download the above models in the paper from aliyun

Citation

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

@article{dai2021cluster,
  title={Cluster Contrast for Unsupervised Person Re-Identification},
  author={Dai, Zuozhuo and Wang, Guangyuan and Zhu, Siyu and Yuan, Weihao and Tan, Ping},
  journal={arXiv preprint arXiv:2103.11568},
  year={2021}
}

Acknowledgements

Thanks to Yixiao Ge for opening source of his excellent works SpCL.

Yimin-Liu/ClusterContrast