27 hand-annotated attributes of Market-1501
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README.md

Market-1501_Attribute

The evaluation code will be added soon.

About dataset

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 27 x 751 attributes for training and 27 x 750 attributesfor test.

The 27 attributes are:

attribute representation in file label
gender gender male(1), female(2)
hair length hair short hair(1), long hair(2)
sleeve length up long sleeve(1), short sleeve(2)
length of lower-body clothing down long lower body clothing(1), short(2)
type of lower-body clothing clothes dress(1), pants(2)
wearing hat hat no(1), yes(2)
carrying backpack backpack no(1), yes(2)
carrying bag bag no(1), yes(2)
carrying handbag handbag no(1), yes(2)
age age young(1), teenager(2), adult(3), old(4)
8 color of upper-body clothing upblack, upwhite, upred, uppurple, upyellow, upgray, upblue, upgreen no(1), yes(2)
9 color of lower-body clothing downblack, downwhite, downpink, downpurple, downyellow, downgray, downblue, downgreen,downbrown no(1), yes(2)

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.

Sample

Evaluation

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.

citation

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

@article{lin2017improving,
  title={Improving Person Re-identification by Attribute and Identity Learning},
  author={Lin, Yutian and Zheng, Liang and Zheng, Zhedong and, Wu, Yu and, Yang, Yi},
  journal={arXiv preprint arXiv:1703.07220},
  year={2017}
}

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}
}

Acknowledgement

We thank Dr. Gao for annotating part of the dataset.