Part-Aware-Transformer
Official repo for "Part-Aware Transformer for Generalizable Person Re-identification" [ICCV 2023]
Here are some instructions to run our code. Our code is based on TransReID, thanks for their excellent work.
1. Clone this repo
git clone https://github.com/liyuke65535/Part-Aware-Transformer.git
2. Prepare your environment
conda create -n pat python==3.10
conda activate pat
bash enviroments.sh
3. Prepare pretrained model (ViT-B) and datasets
You can download it from huggingface, rwightman, or else where. For example, pretrained model is avaliable at ViT-B.
As for datasets, follow the instructions in MetaBIN.
4. Modify the config file
# modify the model path and dataset paths of the config file
vim ./config/PAT.yml
5. Train a model
bash run.sh
6. Evaluation only
# modify the trained path in config
vim ./config/PAT.yml
# evaluation
python test.py --config ./config/PAT.yml
Citation
If you find this repo useful for your research, you're welcome to cite our paper.
@inproceedings{ni2023part,
title={Part-Aware Transformer for Generalizable Person Re-identification},
author={Ni, Hao and Li, Yuke and Gao, Lianli and Shen, Heng Tao and Song, Jingkuan},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={11280--11289},
year={2023}
}