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Understanding fashion images has been advanced by benchmarks with richannotations such as DeepFashion, whose labels include clothing categories,landmarks, and consumer-commercial image pairs. However, DeepFashion hasnonnegligible issues such as single clothing-item per image, sparse landmarks(4~8 only), and no per-pixel masks, making it had significant gap fromreal-world scenarios. We fill in the gap by presenting DeepFashion2 to addressthese issues. It is a versatile benchmark of four tasks including clothesdetection, pose estimation, segmentation, and retrieval. It has 801K clothingitems where each item has rich annotations such as style, scale, viewpoint,occlusion, bounding box, dense landmarks and masks. There are also 873KCommercial-Consumer clothes pairs. A strong baseline is proposed, called MatchR-CNN, which builds upon Mask R-CNN to solve the above four tasks in anend-to-end manner. Extensive evaluations are conducted with differentcriterions in DeepFashion2.

DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images