oxford_iiit_pet

Note: This dataset has been updated since the last stable release. The new versions and config marked with are only available in the tfds-nightly package.

• 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 and species. Additionally, head bounding boxes are provided for the training split, allowing using this dataset for simple object detection tasks. In the test split, the bounding boxes are empty.

• Additional Documentation: Explore on Papers With Code

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

• Source code: tfds.datasets.oxford_iiit_pet.Builder

• Versions:

  • 4.0.0 (default) : Add head bounding boxes. Fix corrupt images. Update dataset URL.

• Download size: Unknown size

• Dataset size: Unknown size

• Auto-cached (documentation): Unknown

• Splits:

Split	Examples

• Feature structure:

FeaturesDict({
    'file_name': Text(shape=(), dtype=string),
    'head_bbox': BBoxFeature(shape=(4,), dtype=float32),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=37),
    'segmentation_mask': Image(shape=(None, None, 1), dtype=uint8),
    'species': ClassLabel(shape=(), dtype=int64, num_classes=2),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
file_name	Text		string
head_bbox	BBoxFeature	(4,)	float32
image	Image	(None, None, 3)	uint8
label	ClassLabel		int64
segmentation_mask	Image	(None, None, 1)	uint8
species	ClassLabel		int64

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

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe): Missing.

• 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",
}