Tsinghua Dogs Dataset

Ding-Nan Zou1,2, Song-Hai Zhang1, Tai-Jiang Mu1, Ming Zhang3

1Tsinghua University, 2NaJiu Company, 3Harvard Medical School

Fig. 1 Variation in Tsinghua Dogs dataset. (a) Great Danes exhibit large variations in appearance, while (b) Norwich terriers and (c) Australian terriers are quite similar to each other.


Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images, basically in proportion to their frequency of occurrence in China, so it significantly increases the diversity for each breed over existing dataset (see Fig. 1). Furthermore, Tsinghua Dogs annotated bounding boxes of the dog’s whole body and head in each image (see Fig. 2), which can be used for supervising the training of learning algorithms as well as testing them.

Fig. 2 Annoations: bounding boxes for whole dogs (blue) and their heads (red).

Following is the brief information about the dataset:


The dataset provides two versions of images to download: high resolution and low resolution. Details about Tsinghua Dogs can be found in this paper [link or PDF].

Item Download Link Size
Low resolution images low-resolution 2.5GB
Annotations for low resolution images low-annotations 36MB
High resolution images high-resolution.001 38.8GB
Annotations for high resolution images high-annotations 36MB
Train and validation splits TrainValSplit 0.2MB


We have also benchmarked several classification methods on our dataset, including both general neural networks and fine-grained models which exhibit good performance on other fine-grained datasets.

Rank Model Backbone Batchsize Epochs Accuracy(%) Year
1 WS-DAN [code] Inception v3 12 80 86.4 2019
2 TBMSL-Net [code] Resnet50 6 200 83.7 2020
3 PMG [code] Resnet50 16 200 83.5 2020
4 Inception v3 [code] N/A 64 200 77.7 2016

Citing Tsinghua Dogs

Please cite our Tsinghua Dogs in your publications if it helps your research:

          title={A new dataset of dog breed images and a benchmark for fine-grained classification},
          author={Zou, Ding-Nan and Zhang, Song-Hai and Mu, Tai-Jiang and Zhang, Min},
          journal={Computational Visual Media},