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

作     者：Ding-Nan Zou Song-Hai Zhang Tai-Jiang Mu Min Zhang Ding-Nan Zou;Song-Hai Zhang;Tai-Jiang Mu;Min Zhang

作者机构：Department of Computer Science and TechnologyBNRistTsinghua UniversityBeijing 100084China NaJiu CompanyHunan 410022China Harvard Medical SchoolBrigham and Women’s HospitalBostonMA 02115USA 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2020年第6卷第4期

页      面：477-487页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0905[农学-畜牧学] 080203[工学-机械设计及理论] 09[农学] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Science Foundation of China(Project Nos.61521002 and 61772298) a Research Grant of Beijing Higher Institution Engineering Research Center Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology。 

主　　题：fine-grained classification dog dataset benchmark 

摘      要：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://***/ThuDogs/.