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

Abstract

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.

arXiv commentAccepted as a full paper for an oral presentation at ACM Multimedia 2018, Seoul, South Korea. ModaNet is only for non-commercial research

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Cited by in corpus (8)

• Fashion Meets Computer Vision: A Survey
• Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
• DeepMark: One-Shot Clothing Detection
• Describe me if you can! Characterized Instance-level Human Parsing
• OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation
• Complete the Look: Scene-based Complementary Product Recommendation
• Image-based Virtual Fitting Room
• Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems