oxford_iiit_pet

• Visualization: Explore in Know Your Data north_east

• Description:

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.

• Homepage: http://www.robots.ox.ac.uk/~vgg/data/pets/

• Source code: tfds.image_classification.OxfordIIITPet

• Versions:

  • 3.2.0 (default): No release notes.

• Download size: 773.52 MiB

• Dataset size: 774.69 MiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	3,669
'train'	3,680

• Feature structure:

FeaturesDict({
    '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
	FeaturesDict
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

• Supervised keys (See as_supervised doc): ('image', 'label')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@InProceedings{parkhi12a,
  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",
}