UT-Zappos50K

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. We introduced this dataset in the context of a pairwise comparison task, where the goal is to predict which of two images more strongly exhibits a visual attribute. When given a novel image pair, we want to answer the question, “Does Image A contain more or less of an attribute than Image B?” Both training and evaluation are performed using pairwise labels. However, the usefulness of this dataset extends beyond the comparison task that we’ve demonstrated. The meta-data labels and the large size of the dataset makes it suitable for other tasks as well, such as:

category/brand classification fine-grained attribute learning with rationales gender-specific style matching zero-shot learning