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Published June 28, 2024 | Version 1
Dataset Open

The DogFaceNet datasets

  • 1. ROR icon Shanghai Jiao Tong University

Description

The DogFaceNet Datasets

Datasets for dog face detection and identification.

This record contains three datasets:

  • DogFaceNet_large is the raw dog face recognition dataset. It contains 2,522 folders of images, each folder containing images of one dog. Disclaimer: This dataset is not fully curated and some mistakes are present (e.g., black and white images instead of colored ones, multiple individual dogs in the same folder, etc.).
  • DogFaceNet_alignment is the dog face detection dataset. It contains one folder of images and one CSV file. The CSV file has seven columns: filename, (x, y) coordinates of the left eye, the right eye, and the nose of each dog.
  • DogFaceNet_224resized is the aligned and resized dog face recognition dataset. It contains 1,393 folders of images. Each image is a JPG of size (224, 224, 3) with a single aligned dog face. The classes_test.txt and classes_train.txt files list which dog classes were used to train and test the deep learning models.

For any questions or issues about the datasets, please open an issue on the GitHub page: https://github.com/GuillaumeMougeot/DogFaceNet.

Files

classes_test.txt

Files (3.9 GB)

Name Size Download all
md5:0afc77355916e658a00b050979534ad7
4.0 kB Preview Download
md5:67f7bc3aecb967e43aa2585a1a4879d6
39.3 kB Preview Download
md5:010c207e202bb499039452aa7363b015
75.6 MB Preview Download
md5:025886e931991bb4e1783b86f31764b1
2.1 GB Preview Download
md5:5e626d64bd699d57a8c73c17c59ed7e6
1.8 GB Preview Download

Additional details

Software

Repository URL
https://github.com/GuillaumeMougeot/DogFaceNet
Programming language
Python
Development Status
Inactive

References

  • Mougeot, G., Li, D. & Jia, S. A Deep Learning Approach for Dog Face Verification and Recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11672 LNAI, (2019).