Meta_Album_TEX_DTD_Micro

OpenML dataset with id 44250

Ihsan Ullah

Full work available at URL: https://api.openml.org/data/v1/download/22109931/Meta_Album_TEX_DTD_Micro.arff

Upload date: 12 October 2022
Copyright license: Creative Commons Attribution-NonCommercial 4.0 International

Dataset Characteristics

Number of classes: 20
Number of features: 3 (numeric: 1, symbolic: 0 and in total binary: 0 )
Number of instances: 800
Number of instances with missing values: 800
Number of missing values: 800

Description

Meta-Album Textures-DTD Dataset (Micro)

* 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

![1]

Meta Album ID: MNF.TEX_DTD Meta Album URL: 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: 20 \# Images: 800 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/

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/

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] For details on the dataset preprocessing, please see the [supplementary materials] Supporting code can be found on our [GitHub repo] Meta-Album on Papers with Code [Meta-Album]

Other versions of this dataset**

[Mini] [Extended]

This page was built for dataset: Meta_Album_TEX_DTD_Micro