Oxford Pets Computer Vision Project

VGG

Updated 2 years ago

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Classes (37)
cat-Abyssinian
cat-Bengal
cat-Birman
cat-Bombay
cat-British_Shorthair
cat-Egyptian_Mau
cat-Maine_Coon
cat-Persian
cat-Ragdoll
cat-Russian_Blue
cat-Siamese
cat-Sphynx
dog-american_bulldog
dog-american_pit_bull_terrier
dog-basset_hound
dog-beagle
dog-boxer
dog-chihuahua
dog-english_cocker_spaniel
dog-english_setter
dog-german_shorthaired
dog-great_pyrenees
dog-havanese
dog-japanese_chin
dog-keeshond
dog-leonberger
dog-miniature_pinscher
dog-newfoundland
dog-pomeranian
dog-pug
dog-saint_bernard
dog-samoyed
dog-scottish_terrier
dog-shiba_inu
dog-staffordshire_bull_terrier
dog-wheaten_terrier
dog-yorkshire_terrier

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Description

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About this Dataset

The Oxford Pets dataset (also known as the "dogs vs cats" dataset) is a collection of images and annotations labeling various breeds of dogs and cats. There are approximately 100 examples of each of the 37 breeds. This dataset contains the object detection portion of the original dataset with bounding boxes around the animals' heads.

Origin

This dataset was collected by the Visual Geometry Group (VGG) at the University of Oxford.

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            oxford-pets_dataset,
                            title = { Oxford Pets Dataset },
                            type = { Open Source Dataset },
                            author = { VGG },
                            howpublished = { \url{ https://universe.roboflow.com/brad-dwyer/oxford-pets } },
                            url = { https://universe.roboflow.com/brad-dwyer/oxford-pets },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { aug },
                            note = { visited on 2024-12-21 },
                            }
                        
                    

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