# WebVision Challenge: Visual Learning and Understanding With Web Data

arXiv: https://arxiv.org/abs/1705.05640 · HTML: https://mlsanity.com/paper/1705.05640/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 7 of 7)

- [Acknowledging the Unknown for Multi-label Learning with Single Positive Labels](https://mlsanity.com/paper/2203.16219/) (2022)
- [Evaluating Multi-label Classifiers with Noisy Labels](https://mlsanity.com/paper/2102.08427/) (2021)
- [Large Scale Long-tailed Product Recognition System at Alibaba](https://mlsanity.com/paper/2102.04652/) (2021)
- [Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels](https://mlsanity.com/paper/2209.01314/) (2022)
- [May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels](https://mlsanity.com/paper/2408.14284/) (2024)
- [Label-Noise Resistant Learning via Optimal Brain Damage Masking](https://mlsanity.com/paper/2508.09697/) (2025)
- [Flatness-Aware Stochastic Gradient Langevin Dynamics](https://mlsanity.com/paper/2510.02174/) (2025)

## Abstract (arXiv)

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