re_identification

Domain Adaptation 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)
• Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification (MMT, 2020 ICLR)
• Similarity Preserving Generative Adversarial Network (SPGAN, 2018 CVPR)

Experiment and Results

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

# Train MMT 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

# MMT involves two training steps:
# step1: pretrain
CUDA_VISIBLE_DEVICES=0 python baseline.py data -s Market1501 -t DukeMTMC -a reid_resnet50 \
--iters-per-epoch 800 --print-freq 80 --finetune --seed 0 --log logs/baseline/Market2DukeSeed0
CUDA_VISIBLE_DEVICES=0 python baseline.py data -s Market1501 -t DukeMTMC -a reid_resnet50 \
--iters-per-epoch 800 --print-freq 80 --finetune --seed 1 --log logs/baseline/Market2DukeSeed1

# step2: train mmt
CUDA_VISIBLE_DEVICES=0,1,2,3 python mmt.py data -t DukeMTMC -a reid_resnet50 \
--pretrained-model-1-path logs/baseline/Market2DukeSeed0/checkpoints/best.pth \
--pretrained-model-2-path logs/baseline/Market2DukeSeed1/checkpoints/best.pth \
--finetune --seed 0 --log logs/mmt/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{SPGAN,
    title={Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification},
    author={Deng, Weijian and Zheng, Liang and Ye, Qixiang and Kang, Guoliang and Yang, Yi and Jiao, Jianbin},
    booktitle={CVPR},
    year={2018}
}

@inproceedings{
    MMT,
    title={Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification},
    author={Yixiao Ge and Dapeng Chen and Hongsheng Li},
    booktitle={ICLR},
    year={2020},
}