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dtd

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.

Features

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=47),
})

Statistics

Split Examples
ALL 5,640
VALIDATION 1,880
TRAIN 1,880
TEST 1,880

Urls

Supervised keys (for as_supervised=True)

None

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}}