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Part-Aligned Network for Person Re-identification

Liming Zhao, Xi Li, Yueting Zhuang, and Jingdong Wang. “Deeply-Learned Part-Aligned Representations for Person Re-Identification.” Proceedings of the International Conference on Computer Vision (ICCV), 2017. (paper)

author = {Zhao, Liming and Li, Xi and Zhuang, Yueting and Wang, Jingdong},
title = {Deeply-Learned Part-Aligned Representations for Person Re-Identification},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
pages = {3219-3228},
year = {2017}

Contact: Liming Zhao (


  • Use my Caffe for using triplet loss layer.

  • Run the demo code demo/demo.ipynb to see an example usage.

  • Run in the train folder to train the model.

  • The datasets are placed in the dataset folder, you can download the archived data from here. Training list can be generated by using the code provided in the archieved data.


  • Use Caffe for implementation, please refer to the Caffe project website for installation.

  • The protocal file in proto folder is written in python.

  • The actual training scripts and protocal files will be generated in the train folder.


Code for ICCV2017 paper: Deeply-Learned Part-Aligned Representations for Person Re-Identification




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