DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification

DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification

News

• [2024.02.16] Source code will be coming soon 🚀. I update the decouple design with condition control to adapt re-identification and human parsing.

Set up

Installation

Make sure conda is installed.

# Clone this repository
git clone https://github.com/shuguang-52/DROP

# create conda environment
cd DROP # enter project folder
conda create --name drop python=3.10
conda activate drop

# install dependencies
# make sure `which python` and `which pip` point to the correct path
pip install -r requirements.txt

# install torch and torchvision (select the proper cuda version to suit your machine)
pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116

# install torchreid (don't need to re-build it if you modify the source code)
python setup.py develop

Download human parsing labels

You can download the human parsing labels of five datasets: Market-1501, DukeMTMC-reID, Occluded-Duke, Occluded-ReID, and P-DukeMTMC from BPBreID.

Training

Training configs for five datasets (Market-1501, DukeMTMC-reID, Occluded-Duke, Occluded-ReID, and P-DukeMTMC) are provided in the configs/drop/ folder.

CUDA_VISIBLE_DEVICES=6,7 python scripts/main.py --config-file configs/drop/drop_occ_duke_train.yaml

Comparsion with SOTA Occluded ReID methods

The effect of different sizes

Results on large-scale dataset MSMT17v1

Citation

If you use this repository for your research or wish to refer to our method DROP, please use the following BibTeX entry:

@article{dou2024drop,
  title={DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification},
  author={Dou, Shuguang and Jiang, Xiangyang and Tu, Yuanpeng and Gao, Junyao and Qu, Zefan and Zhao, Qingsong and Zhao, Cairong},
  journal={arXiv preprint arXiv:2401.18032},
  year={2024}
}

Acknowledgment

This codebase is based on BPBreID (A strong ReID baseline). Thanks for their work.