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.

2017 年 WebVision 挑战赛，旨在提供一组无需人工标记的互联网图像数据集和公开竞赛。数据集中包含超过 240 万张使用 ILSVRC 2012 语义概念生成的查询的互联网图像，用于在 WebVision 测试集上进行图像分类和在 PASCAL VOC 2012 数据集上进行迁移学习的任务。本文描述了数据收集与注释的详细信息、数据集的特点以及评估指标。