UT Zappos50K (UT-Zap50K) is a large shoe dataset consisting of 50,025 catalog images collected from 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 running shoes instead of between a women high heel and a men 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 Reference: Fine-Grained Visual Comparisons with Local Learning Aron Yu and Kristen Grauman Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, Jun 2014.

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