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