The Oxford-IIIT Pet Dataset is a fine-grained image dataset of pets created by the Visual Geometry Group (VGG) at the University of Oxford. It contains images of 37 pet breeds (cats and dogs) with large variations in scale, pose and lighting. The dataset provides per-image annotations including the breed label and species (cat/dog), a tight head ROI (bounding box), and pixel-level trimap segmentation (foreground / background / ignore). Common splits used in ML libraries (e.g., TensorFlow Datasets) have 3,680 training images and 3,669 test images (7,349 images total). Typical uses: breed classification (37-way), binary species classification (cat vs dog), and segmentation/foreground extraction. Licensing: CC BY-SA 4.0 (as listed on several mirrors). Source / references: original VGG dataset page (robots.ox.ac.uk), TensorFlow Datasets entry (oxford_iiit_pet), and Hugging Face dataset mirrors (e.g., timm/oxford-iiit-pet).
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