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

Understanding clothes and broad fashion products from such an image would have huge commercial and cultural impacts on modern societies. Deploying such a technology would empower not only the fashion buyers to find what they want, but also those small and large sellers to have quicker sales with less hassle. This technology requires excellence in several computer vision tasks: what the product is in the image (image classification), where it is (object detection, semantic image segmentation, instance segmentation), visual similarity, how to describe the product and its image (image captioning), etc. Recent works in convolutional neural networks (CNNs) have significantly improved the state-of-the-art performance of those tasks. In the image classification task, ResNeXt-101 method has achieved 85.4% in top-1-accuracy1 in ImageNet-1K; in object detection, the best method2 has achieved 52.5% mAP in the COCO 2017 benchmark for generic object detection; in semantic image segmentation, the top-performing method3 has reached 89% mIOU in PASCAL VOC leaderboard for the generic object segmentation.