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The Oxford-IIIT pet dataset is a 37 category pet image dataset with roughly 200 images for each class. The images have large variations in scale, pose and lighting. All images have an associated ground truth annotation of breed.

Split Examples
'test' 3,669
'train' 3,680
  • Feature structure:
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=37),
    'segmentation_mask': Image(shape=(None, None, 1), dtype=tf.uint8),
    'species': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
  • Feature documentation:
Feature Class Shape Dtype Description
file_name Text tf.string
image Image (None, None, 3) tf.uint8
label ClassLabel tf.int64
segmentation_mask Image (None, None, 1) tf.uint8
species ClassLabel tf.int64
  • Citation:
  author       = "Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.",
  title        = "Cats and Dogs",
  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition",
  year         = "2012",