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)

@InProceedings{Zhao_2017_ICCV,
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 ([email protected])

• Use my Caffe for using triplet loss layer.

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

• Run train.sh 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.