In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater, with between 200 and 7000+ images for each breed. Annotation of the whole body and head makes the dataset not only suitable for the improvement of finegrained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://cg.cs.tsinghua.edu.cn/ThuDogs/.

在本文中，我们引入了用于狗品种的细分类的图像数据集：清华狗数据集。目前，它是用于狗的细分类的最大数据集，包括130个犬种和70,428个真实世界的图像。每个图像中只有一只狗，并为整个身体和头部提供带注释的边界框。与以前的类似数据集相比，它包含更多的品种和每个品种的更精心选择的图像。每个品种内的多样性更大，每个品种有200至7000+张图像。全身和头部的注释使该数据集不仅适合基于整体特征的细粒度图像分类模型的改进，而且还适合定位局部信息部分的那些。我们证明，通过对几个最新的深度神经模型进行基准测试，数据集提出了严峻的挑战。该数据集可用于学术目的，网址为https://cg.cs.tsinghua.edu.cn/ThuDogs/。