@article{,
title= {UT Zappos50K (Version 2.1)},
keywords= {},
author= {Aron Yu and Kristen Grauman},
abstract= {UT Zappos50K (UT-Zap50K) is a large shoe dataset consisting of 50,025 catalog images collected from Zappos.com. The images are divided into 4 major categories — shoes, sandals, slippers, and boots — followed by functional types and individual brands. The shoes are centered on a white background and pictured in the same orientation for convenient analysis.

This dataset is created in the context of an online shopping task, where users pay special attentions to fine-grained visual differences. For instance, it is more likely that a shopper is deciding between two pairs of similar men's running shoes instead of between a woman's high heel and a man's slipper. GIST and LAB color features are provided. In addition, each image has 8 associated meta-data (gender, materials, etc.) labels that are used to filter the shoes on Zappos.com.

https://i.imgur.com/RoVL6qr.jpg

# Citation

This dataset is for academic, non-commercial use only. If you use this dataset in a publication, please cite the following papers:

A. Yu and K. Grauman. "Fine-Grained Visual Comparisons with Local Learning". In CVPR, 2014.
[paper] [supp] [poster] [bibtex] [project page]

@InProceedings{finegrained,
  author = {A. Yu and K. Grauman},
  title = {Fine-Grained Visual Comparisons with Local Learning},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  month = {Jun},
  year = {2014}
}

A. Yu and K. Grauman. "Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images". In ICCV, 2017.
[paper] [supp] [poster] [bibtex] [project page]},
terms= {},
license= {},
superseded= {},
url= {http://vision.cs.utexas.edu/projects/finegrained/utzap50k/}
}