Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distinguishing subordinate categories, it will significantly reduce the labeling costs by leveraging free web data. Despite its significant practical and research value, the webly supervised fine-grained recognition problem is not extensively studied in the computer vision community, largely due to the lack of high-quality datasets. To fill this gap, in this paper we construct two new benchmark webly supervised fine-grained datasets, termed WebFG-496 and WebiNat-5089, respectively. In concretely, WebFG-496 consists of three sub-datasets containing a total of 53,339 web training images with 200 species of birds (Web-bird), 100 types of aircrafts (Web-aircraft), and 196 models of cars (Web-car). For WebiNat-5089, it contains 5089 sub-categories and more than 1.1 million web training images, which is the largest webly supervised fine-grained dataset ever. As a minor contribution, we also propose a novel webly supervised method (termed ``{Peer-learning}'') for benchmarking these datasets.~Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art. Our benchmark datasets and the source codes of Peer-learning have been made available at {\url{https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset}}.

翻译：从网上学习,可以减轻在大规模手工标签标签的数据集上深深学习的极度依赖。特别是精确的识别,这是以区别子类为目标的子类,通过利用免费网络数据,将大幅降低标签成本。尽管网络监督的微微的识别问题具有重要的实用和研究价值,但计算机视觉界并未广泛研究网络监督的微微的识别问题,这主要是因为缺少高质量的数据集。为了填补这一差距,在本文中,我们建造了两个新的基准,即大规模人工的、人工标签标签的数据集(分别为WebFG-496和WebiNatt-5089)。具体地说,WeF-496通过利用免费网络数据,将大幅降低标签成本成本成本。尽管它具有重要的实用和研究价值,但尽管它具有重要的实际价值和研究价值,但网络监督的精精精细精细精细精细的高级数据系统(Web-ab-co)和196个汽车模型(WebNat-5089,它包含5089个子目录和超过110万个网络培训源,这是最大的网络监督精细的精细的、精细的、精细的高级的高级数据-高级数据库-高级数据-高级数据-高级数据设置数据设置)。