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:
| 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.
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}
}