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Research Article | Open Access

A new dataset of dog breed images and a benchmark for fine-grained classification

Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing 100084, China
NaJiu Company, Hunan 410022, China
Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA
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Abstract

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 fine-grained 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/.

Keywords

fine-grained classificationdogdatasetbenchmark

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Computational Visual Media
Volume 6 Issue 4,
December 2020
Pages 477-487
DOI: 10.1007/s41095-020-0184-6

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Zou D-N, Zhang S-H, Mu T-J, et al. A new dataset of dog breed images and a benchmark for fine-grained classification. Computational Visual Media, 2020, 6(4): 477-487. https://doi.org/10.1007/s41095-020-0184-6

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Received: 18 May 2020
Accepted: 14 June 2020
Published: 01 October 2020
© The Author(s) 2020

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