The evaluation code will be added soon.

We annotate 27attributes for Market-1501. The original dataset contains 751 identities for training and 750 identities for testing. The attributes are annotated in the identity level, thus the file contains 28 x 751 attributes for training and 28 x 750 attributesfor test, where the label "image_index" denotes the identity. The annotations are contained in the file market_attribute.mat.

The 27 attributes are:

Note that the though there are 8 and 9 attributes for upper-body clothing and lower-body clothing, only one color is labeled as yes (2) for an identity.

To evaluate, you need to predict the attributes for test data(i.e., 13115 x 12 matrix) and save them in advance. "gallery_market.mat" is one prediction example. Then download the code "evaluate_market_attribute.m" in this repository, change the image path and run it to evaluate.

If you use this dataset in your research, please kindly cite our work as,

@article{lin2019improving,
  title={Improving Person Re-identification by Attribute and Identity Learning},
  author={Lin, Yutian and Zheng, Liang and Zheng, Zhedong and Wu, Yu and Hu, Zhilan and Yan, Chenggang and Yang, Yi},
  journal={Pattern Recognition},
  doi = {https://doi.org/10.1016/j.patcog.2019.06.006},
  year={2019}
}

Market-1501 Dataset:

@inproceedings{zheng2015scalable,
  title={Scalable person re-identification: A benchmark},
  author={Zheng, Liang and Shen, Liyue and Tian, Lu and Wang, Shengjin and Wang, Jingdong and Tian, Qi},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2015}
}