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