# GroupSampling

**Repository Path**: t919089607/GroupSampling

## Basic Information

- **Project Name**: GroupSampling
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-28
- **Last Updated**: 2024-08-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

![Python >=3.5](https://img.shields.io/badge/Python->=3.5-blue.svg)
![PyTorch >=1.0](https://img.shields.io/badge/PyTorch->=1.0-yellow.svg)

# Group Sampling

> **[Rethinking Sampling Strategies for Unsupervised Person Re-identification](https://doi.org/10.1109/TIP.2022.3224325)**<br>
> Xumeng Han, Xuehui Yu, Guorong Li, Jian Zhao, Gang Pan, Qixiang Ye, Jianbin Jiao and Zhenjun Han<br>
> *IEEE Transactions on Image Processing (**TIP**) 2023 ([arXiv:2107.03024](https://arxiv.org/abs/2107.03024))*<br>

## Requirements

### Installation

```shell
git clone https://github.com/wavinflaghxm/GroupSampling.git
cd GroupSampling
python setup.py develop
```

### Prepare Datasets

```shell
cd examples && mkdir data
```
Download the person datasets [Market-1501](https://drive.google.com/file/d/0B8-rUzbwVRk0c054eEozWG9COHM/view), [DukeMTMC-reID](https://arxiv.org/abs/1609.01775), [MSMT17](https://arxiv.org/abs/1711.08565).
Then unzip them under the directory like:
```
GroupSampling/examples/data
├── market1501
│   └── Market-1501-v15.09.15
├── dukemtmc
│   └── DukeMTMC-reID
└── msmt17
    └── MSMT17_V2
```

## Training

We utilize 1 GTX-2080TI GPU for training.

+ Use `--group-n 256` for Market-1501, `--group-n 128` for DukeMTMC-reID, and `--group-n 1024` for MSMT17.

*Market-1501:*
```
CUDA_VISIBLE_DEVICES=0 python examples/train.py -d market1501 --logs-dir logs/market_resnet50 --group-n 256
```

*DukeMTMC-reID:*
```
CUDA_VISIBLE_DEVICES=0 python examples/train.py -d dukemtmc --logs-dir logs/duke_resnet50 --group-n 128
```

*MSMT17:*
```
CUDA_VISIBLE_DEVICES=0 python examples/train.py -d msmt17 --logs-dir logs/msmt_resnet50 --group-n 1024 --iters 800
```

We recommend using 4 GPUs to train MSMT17 for better performance.
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/train.py -d msmt17 --logs-dir logs/msmt_resnet50-gpu4 --group-n 1024 -b 256 --momentum 0.1 --lr 0.00005
```

## Evaluation
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/market_resnet50/model_best.pth.tar
```


## Results
![results](figs/results.png)

## Citation

If you find this work useful for your research, please cite:
```
@article{han2022rethinking,
  title={Rethinking Sampling Strategies for Unsupervised Person Re-Identification}, 
  author={Han, Xumeng and Yu, Xuehui and Li, Guorong and Zhao, Jian and Pan, Gang and Ye, Qixiang and Jiao, Jianbin and Han, Zhenjun},
  journal={IEEE Transactions on Image Processing}, 
  year={2023},
  volume={32},
  pages={29-42},
  doi={10.1109/TIP.2022.3224325}}
```

## Acknowledgements

Codes are built upon [SpCL](https://github.com/yxgeee/SpCL). Thanks to Yixiao Ge for opening source.