re_identification

Domain Generalization for Person Re-Identification

Installation

Example scripts support all models in PyTorch-Image-Models. You also need to install timm to use PyTorch-Image-Models.

pip install timm

Dataset

Following datasets can be downloaded automatically:

• Market1501
• DukeMTMC
• MSMT17

Supported Methods

Supported methods include:

• Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net (IBN-Net, 2018 ECCV)
• Domain Generalization with MixStyle (MixStyle, 2021 ICLR)

Experiment and Results

The shell files give the script to reproduce the benchmarks with specified hyper-parameters. For example, if you want to reproduce MixStyle on Market1501 -> DukeMTMC task, use the following script

# Train MixStyle on Market1501 -> DukeMTMC task using ResNet 50.
# Assume you have put the datasets under the path `data/market1501` and `data/dukemtmc`, 
# or you are glad to download the datasets automatically from the Internet to this path
CUDA_VISIBLE_DEVICES=0 python mixstyle.py data -s Market1501 -t DukeMTMC -a resnet50 \
--mix-layers layer1 layer2 --finetune --seed 0 --log logs/mixstyle/Market2Duke

For more information please refer to Get Started for help.

Citation

If you use these methods in your research, please consider citing.

@inproceedings{IBN-Net,  
    author = {Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang},  
    title = {Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net},  
    booktitle = {ECCV},  
    year = {2018}  
}

@inproceedings{mixstyle,
    title={Domain Generalization with MixStyle},
    author={Zhou, Kaiyang and Yang, Yongxin and Qiao, Yu and Xiang, Tao},
    booktitle={ICLR},
    year={2021}
}