SOLIDER-REID

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# SOLIDER on [Person Re-identification]

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/beyond-appearance-a-semantic-controllable/person-re-identification-on-msmt17)](https://paperswithcode.com/sota/person-re-identification-on-msmt17?p=beyond-appearance-a-semantic-controllable)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/beyond-appearance-a-semantic-controllable/person-re-identification-on-market-1501)](https://paperswithcode.com/sota/person-re-identification-on-market-1501?p=beyond-appearance-a-semantic-controllable)

This repo provides details about how to use [SOLIDER](https://github.com/tinyvision/SOLIDER) pretrained representation on person re-identification task.
We modify the code from [TransReID](https://github.com/damo-cv/TransReID), and you can refer to the original repo for more details.

## Installation and Datasets

We use python version 3.7, PyTorch version 1.7.1, CUDA 10.1 and torchvision 0.8.2. More details of installation and dataset preparation can be found in [TransReID-SSL](https://github.com/damo-cv/TransReID-SSL).

## Prepare Pre-trained Models 
You can download models from [SOLIDER](https://github.com/tinyvision/SOLIDER), or use [SOLIDER](https://github.com/tinyvision/SOLIDER) to train your own models.
Before training, you should convert the models first.

```bash
python convert_model.py path/to/SOLIDER/log/lup/swin_tiny/checkpoint.pth path/to/SOLIDER/log/lup/swin_tiny/checkpoint_tea.pth
```

## Training

We utilize 1 GPU for training. Please modify the `MODEL.PRETRAIN_PATH`, `DATASETS.ROOT_DIR` and `OUTPUT_DIR` in the config file.

```bash
sh run.sh
```

## Test

```bash
sh runtest.sh
```

## Performance

| Method | Model | MSMT17 (w/o RK) | Market1501 (w/o RK) | MSMT17 (with RK) | Market1501 (with RK) |
| ------ | :---: | :---: | :---: | :---: | :---: |
| SOLIDER | Swin Tiny | 67.4/85.9 ([Link](https://drive.google.com/file/d/10YLhMbwvmxZl3gTVo2BN_828SKZHdCjr/view?usp=share_link),[Log](https://drive.google.com/file/d/1qpc7kbPn9JpV6qu2HO1-WrL75gGLtz6_/view?usp=share_link)) | 91.6/96.1 ([Link](https://drive.google.com/file/d/1YrE_r9Fk5uR0uFFQboBv203vxlOpFXU8/view?usp=share_link),[Log](https://drive.google.com/file/d/1EGdR1_tj0b8UG1FEmGbcBqhw-wXKTdg0/view?usp=share_link)) | 81.5/89.2 | 95.3/96.6 |
| SOLIDER | Swin Small | 76.9/90.8 ([Link](https://drive.google.com/file/d/1C-aIZdFyjFsZX4W4feG-Ex39RU2Qvu3b/view?usp=share_link),[Log](https://drive.google.com/file/d/1Oqzk3iZQOXTyPLJgGlnqDan1buLRx-bH/view?usp=share_link)) | 93.3/96.6 ([Link](https://drive.google.com/file/d/14uOCf5yZq0Rt5rRSJI9I7_d5kt2EOyHO/view?usp=share_link),[Log](https://drive.google.com/file/d/1lH2-ozb5ju9TTeXIyDqeEW4U450zbgrc/view?usp=share_link)) | 86.5/91.7 | 95.4/96.4 |
| SOLIDER | Swin Base | 77.1/90.7 ([Link](https://drive.google.com/file/d/1Y-RFAYdT56vnMjwxH1Ym3DVhZzZuMQZs/view?usp=share_link),[Log](https://drive.google.com/file/d/1DIxAeDJqFrZ4dIEe4VQrhCJNaS3NAk3O/view?usp=share_link)) | 93.9/96.9 ([Link](https://drive.google.com/file/d/1pZ1unW2IwSsqSN2KYHcgBhgjQztQW_fe/view?usp=share_link),[Log](https://drive.google.com/file/d/1DH0K4Ks9LXJEFVcm5PfIUUZ3A3vpFfLZ/view?usp=share_link)) | 86.5/91.7 | 95.6/96.7 |

- `mAP/Rank1` are used as evaluation metric, `RK` indicates whether re-ranking is involved.
- `RK` shares the same models with `w/o RK`.
- We use the pretrained models from [SOLIDER](https://github.com/tinyvision/SOLIDER).
- The semantic weight is set to 0.2 in these experiments.

## Citation

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

```
@inproceedings{chen2023beyond,
  title={Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks},
  author={Weihua Chen and Xianzhe Xu and Jian Jia and Hao Luo and Yaohua Wang and Fan Wang and Rong Jin and Xiuyu Sun},
  booktitle={The IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2023},
}
```

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