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Domain Generalization for Person Re-Identification

Installation

It’s suggested to use pytorch==1.7.1 and torchvision==0.8.2 in order to reproduce the benchmark results.

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:

Supported Methods

Supported methods include:

Usage

The shell files give the script to reproduce the benchmark with specified hyper-parameters. For example, if you want to train 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

Experiment and Results

In our experiments, we adopt modified resnet architecture from MMT. For a fair comparison, we use standard cross entropy loss and triplet loss in all methods.

Notations

  • Avg means the mAP (mean average precision) reported by TLlib.

Cross dataset mAP on ResNet-50

Methods Avg Market2Duke Duke2Market Market2MSMT MSMT2Market Duke2MSMT MSMT2Duke
Baseline 23.5 25.6 29.6 6.3 31.7 10.1 37.8
IBN 27.0 31.5 33.3 10.4 33.6 13.7 40.0
MixStyle 25.5 27.2 31.6 8.2 33.9 12.4 39.9

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}
}