dtd

• Description:

The Describable Textures Dataset (DTD) is an evolving collection of textural images in the wild, annotated with a series of human-centric attributes, inspired by the perceptual properties of textures. This data is made available to the computer vision community for research purposes.

The "label" of each example is its "key attribute" (see the official website). The official release of the dataset defines a 10-fold cross-validation partition. Our TRAIN/TEST/VALIDATION splits are those of the first fold.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html

• Source code: tfds.image_classification.Dtd

• Versions:

  • 3.0.1 (default): No release notes.

• Download size: 596.28 MiB

• Dataset size: 603.00 MiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	1,880
'train'	1,880
'validation'	1,880

• Feature structure:

FeaturesDict({
    'file_name': Text(shape=(), dtype=string),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=47),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
file_name	Text		string
image	Image	(None, None, 3)	uint8
label	ClassLabel		int64

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@InProceedings{cimpoi14describing,
Author    = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and A. Vedaldi},
Title     = {Describing Textures in the Wild},
Booktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},
Year      = {2014} }