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Meta_Album_TEX_DTD_Mini

Meta_Album_TEX_DTD_Mini

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## Meta-Album Textures-DTD Dataset (Mini) * The Textures DTD dataset(https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which consists of 5 640 images. The data is collected from Google and Flicker by the original authors of the paper 'Describing Textures in the Wild'. The data was annotated using Amazon Mechanical Turk. The data collection process is mentioned on the dataset overview page. For Meta-Album meta-dataset, this dataset is preprocessed by cropping the images to square images and then resizing them to 128x128 using Open-CV with an anti-aliasing filter. This dataset has 47 class labels. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/TEX_DTD.png) Meta Album ID: MNF.TEX_DTD Meta Album URL: [https://meta-album.github.io/datasets/TEX_DTD.html](https://meta-album.github.io/datasets/TEX_DTD.html) Domain ID: MNF Domain Name: Manufacturing Dataset ID: TEX_DTD Dataset Name: Textures-DTD Short Description: Textures dataset from Describable Textures Dataset \# Classes: 47 \# Images: 1880 Keywords: textures, manufacturing Data Format: images Image size: 128x128 License (original data release): Open for research purposes License (Meta-Album data release): CC BY-NC 4.0 License URL (Meta-Album data release): [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) Source: Describable Textures Dataset (DTD), University of Oxford, England Source URL: https://www.robots.ox.ac.uk/~vgg/data/dtd/ Original Author: Mircea Cimpoi, Subhransu354Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi Original contact: {mircea, vedaldi}@robots.ox.ac.uk Meta Album author: Ihsan Ullah Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: meta-album@chalearn.org Contact URL: [https://meta-album.github.io/](https://meta-album.github.io/) ### Cite this dataset ``` @InProceedings{cimpoi14describing, Author = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi}, Title = {Describing Textures in the Wild}, Booktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})}, Year = {2014} } ``` ### Cite Meta-Album ``` @inproceedings{meta-album-2022, title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification}, author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, url = {https://meta-album.github.io/}, year = {2022} } ``` ### More For more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) For details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) Supporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) Meta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) ### Other versions of this dataset [[Micro]](https://www.openml.org/d/44250) [[Extended]](https://www.openml.org/d/44328)

3 features

CATEGORY (target)string47 unique values
0 missing
FILE_NAMEstring1880 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
1880 missing

19 properties

1880
Number of instances (rows) of the dataset.
3
Number of attributes (columns) of the dataset.
47
Number of distinct values of the target attribute (if it is nominal).
1880
Number of missing values in the dataset.
1880
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of nominal attributes.
1
Average class difference between consecutive instances.
33.33
Percentage of numeric attributes.
33.33
Percentage of missing values.
100
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
40
Number of instances belonging to the least frequent class.
2.13
Percentage of instances belonging to the least frequent class.
40
Number of instances belonging to the most frequent class.
2.13
Percentage of instances belonging to the most frequent class.
0
Number of attributes divided by the number of instances.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CATEGORY
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