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We present the 2017 WebVision Challenge, a public image recognition challengedesigned for deep learning based on web images without instance-level humanannotation. Following the spirit of previous vision challenges, such as ILSVRC,Places2 and PASCAL VOC, which have played critical roles in the development ofcomputer vision by contributing to the community with large scale annotateddata for model designing and standardized benchmarking, we contribute with thischallenge a large scale web images dataset, and a public competition with aworkshop co-located with CVPR 2017. The WebVision dataset contains more than$2.4$ million web images crawled from the Internet by using queries generatedfrom the $1,000$ semantic concepts of the benchmark ILSVRC 2012 dataset. Metainformation is also included. A validation set and test set containing humanannotated images are also provided to facilitate algorithmic development. The2017 WebVision challenge consists of two tracks, the image classification taskon WebVision test set, and the transfer learning task on PASCAL VOC 2012dataset. In this paper, we describe the details of data collection andannotation, highlight the characteristics of the dataset, and introduce theevaluation metrics.

WebVision Challenge: Visual Learning and Understanding With Web Data