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