Large Shoe Dataset (UT Zappos50k)

Large Shoe Dataset (UT Zappos50k)

Datasets

Large Shoe Dataset (UT Zappos50k)

File

Large Shoe Dataset (UT Zappos50k)

Use Case

Computer Vision

Description

This dataset is created in the context of an online shopping task, where users pay special attentions to fine-grained visual differences.

Large Shoe Dataset (UT Zappos50k)

About Dataset

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.

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]

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