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Source Code is released for the paper entitled Unsupervised Person Re-identification via Multi-domain Joint Leatning.

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Prerequisites

• Python 3.6
• GPU Memory >= 20G
• Numpy
• Pytorch 0.4+

Preparation 1: create folder for dataset.

first, download Market-1501 and DukeMTMC-reID dataset from the links below:

google drive: https://drive.google.com/file/d/0B8-rUzbwVRk0c054eEozWG9COHM/view?usp=sharing https://drive.google.com/open?id=1jjE85dRCMOgRtvJ5RQV9-Afs-2_5dY3O baidu disk: https://pan.baidu.com/s/1ntIi2Op https://pan.baidu.com/s/1jS0XM7Var5nQGcbf9xUztw

second,

mkdir data
unzip Market-1501-v15.09.15.zip
ln -s Market-1501-v15.09.15 market
unzip DukeMTMC-reID.zip
ln -s DukeMTMC-reID duke

then, get the directory structure

├── MDJL
    ├── data
            ├── market
            ├── Market-1501-v15.09.15
            ├── duke
            ├── DukeMTMC-reID

Preparation 2: Put the images with the same id in one folder. You may use

python prepare.py

Finally, train, test and evaluate the re-ID model with the below command:

python train.py

If you refer to this code, please cite our paper as follows: @article{CHEN2023109369, title = {Unsupervised person re-identification via multi-domain joint learning}, journal = {Pattern Recognition}, volume = {138}, pages = {109369}, year = {2023}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2023.109369}, url = {https://www.sciencedirect.com/science/article/pii/S0031320323000705}, author = {Feng Chen and Nian Wang and Jun Tang and Pu Yan and Jun Yu}, }