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Understanding clothes from a single image has strong commercial and culturalimpacts on modern societies. However, this task remains a challenging computervision problem due to wide variations in the appearance, style, brand andlayering of clothing items. We present a new database called ModaNet, alarge-scale collection of images based on Paperdoll dataset. Our datasetprovides 55,176 street images, fully annotated with polygons on top of the 1million weakly annotated street images in Paperdoll. ModaNet aims to provide atechnical benchmark to fairly evaluate the progress of applying the latestcomputer vision techniques that rely on large data for fashion understanding.The rich annotation of the dataset allows to measure the performance ofstate-of-the-art algorithms for object detection, semantic segmentation andpolygon prediction on street fashion images in detail. The polygon-basedannotation dataset has been released https://github.com/eBay/modanet, we alsohost the leaderboard at EvalAI:https://evalai.cloudcv.org/featured-challenges/136/overview.

ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations