Data
Meta_Album_RESISC_Micro

Meta_Album_RESISC_Micro

active ARFF CC BY-NC 4.0 Visibility: public Uploaded 11-10-2022 by Meta Album
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## Meta-Album RESISC Dataset (Micro) * RESISC45 dataset(https://gcheng-nwpu.github.io/) gathers 700 RGB images of size 256x256 px for each of 45 scene categories. The data authors strive to provide a challenging dataset by increasing both within-class diversity and between-class similarity, as well as integrating many image variations. Even though RESISC45 does not propose a label hierarchy, it can be created from other common aerial image label organization scheme. We have preprocessed RESISC for Meta-Album by resizing the dataset to 128x128 px. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/RESISC.png) Meta Album ID: REM_SEN.RESISC Meta Album URL: [https://meta-album.github.io/datasets/RESISC.html](https://meta-album.github.io/datasets/RESISC.html) Domain ID: REM_SEN Domain Name: Remote Sensing Dataset ID: RESISC Dataset Name: RESISC Short Description: Remote sensing dataset \# Classes: 20 \# Images: 800 Keywords: remote sensing, satellite image, aerial image, land cover Data Format: images Image size: 128x128 License (original data release): CC-BY-NC 4.0 License URL(original data release): https://creativecommons.org/licenses/by-nc/4.0/ 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: NWPU-RESISC45 Dataset Source URL: https://gcheng-nwpu.github.io/ Original Author: Gong Cheng, Junwei Han, and Xiaoqiang Lu Original contact: chenggong1119@gmail.com Meta Album author: Phan Anh VU 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 ``` @article{DBLP:journals/corr/ChengHL17, author = {Gong Cheng and Junwei Han and Xiaoqiang Lu}, title = {Remote Sensing Image Scene Classification: Benchmark and State of the Art}, journal = {CoRR}, volume = {abs/1703.00121}, year = {2017}, url = {http://arxiv.org/abs/1703.00121}, eprinttype = {arXiv}, eprint = {1703.00121}, timestamp = {Mon, 02 Dec 2019 09:32:19 +0100}, biburl = {https://dblp.org/rec/journals/corr/ChengHL17.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### 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 [[Mini]](https://www.openml.org/d/44290) [[Extended]](https://www.openml.org/d/44324)

3 features

CATEGORY (target)string20 unique values
0 missing
FILE_NAMEstring800 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
800 missing

19 properties

800
Number of instances (rows) of the dataset.
3
Number of attributes (columns) of the dataset.
20
Number of distinct values of the target attribute (if it is nominal).
800
Number of missing values in the dataset.
800
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
100
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
33.33
Percentage of missing values.
0
Number of attributes divided by the number of instances.
33.33
Percentage of numeric attributes.
5
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
40
Number of instances belonging to the most frequent class.
5
Percentage of instances belonging to the least frequent class.
40
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

1 tasks

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