The evaluation code will be added soon.
We annotate 23 attributes for Duke, which is a subset of the DukeMTMC. The original dataset contains 702 identities for training and 1110 identities for testing. The attributes are annotated in the identity level, thus the file contains 23 x 702 attributes for training and 23 x 1110 for test.
The 23 attributes are:
| attribute | representation in file | label |
|---|---|---|
| gender | gender | male(1), female(2) |
| length of upper-body clothing | top | short upper body clothing(1), long(2) |
| wearing boots | boots | no(1), yes(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) |
| color of shoes | shoes | dark(1), light(2) |
| 8 color of upper-body clothing | upblack, upwhite, upred, uppurple, upgray, upblue, upgreen, upbrown | no(1), yes(2) |
| 7 color of lower-body clothing | downblack, downwhite, downred, downgray, downblue, downgreen, downbrown | no(1), yes(2) |
Note that the though there are 7 and 8 attributes for lower-body clothing and upper-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., 17661 x 10 matrix) and save them in advance. "gallery_duke.mat" is one prediction example. Then download the code "evaluate_duke_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{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}
}
We thank Dr. Gao for annotating part of the dataset.
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