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---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': banded
'1': blotchy
'2': braided
'3': bubbly
'4': bumpy
'5': chequered
'6': cobwebbed
'7': cracked
'8': crosshatched
'9': crystalline
'10': dotted
'11': fibrous
'12': flecked
'13': freckled
'14': frilly
'15': gauzy
'16': grid
'17': grooved
'18': honeycombed
'19': interlaced
'20': knitted
'21': lacelike
'22': lined
'23': marbled
'24': matted
'25': meshed
'26': paisley
'27': perforated
'28': pitted
'29': pleated
'30': polka-dotted
'31': porous
'32': potholed
'33': scaly
'34': smeared
'35': spiralled
'36': sprinkled
'37': stained
'38': stratified
'39': striped
'40': studded
'41': swirly
'42': veined
'43': waffled
'44': woven
'45': wrinkled
'46': zigzagged
splits:
- name: train
num_bytes: 463693721.28
num_examples: 3760
- name: test
num_bytes: 171623828.0
num_examples: 1880
download_size: 629499529
dataset_size: 635317549.28
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# [DTD: Describable Textures Dataset](https://www.robots.ox.ac.uk/~vgg/data/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
## Usage
```python
from datasets import load_dataset
dataset = load_dataset('tanganke/dtd')
```
- **Features:**
- **Image**: The primary data type, which is a digital image used for classification. The format and dimensions of the images are not specified in this snippet but should be included if available.
- **Label**: A categorical feature representing the texture or pattern class of each image. The dataset includes 46 classes with descriptive names ranging from 'banded' to 'zigzagged'.
- **Class Labels**:
- '0': banded
- '1': blotchy
- '2': braided
- ...
- '45': wrinkled
- '46': zigzagged
- **Splits**: The dataset is divided into training and test subsets for model evaluation.
- **Training**: containing 3760 examples with a total size of 448,550 bytes.
- **Test**: containing 1880 examples with a total size of 220,515 bytes.
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