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Meta_Album_INS_2_Extended

Meta_Album_INS_2_Extended

active ARFF CC BY-NC 4.0 Visibility: public Uploaded 08-11-2022 by Meta Album
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## Meta-Album Insects2 Dataset (Extended) * The pest insects dataset was originally created as a large scale benchmark dataset for Insect Pest Recognition (https://github.com/xpwu95/IP102). It contains more than 75 000 images belongs to 102 categories. It also has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upper-level category. The preprocessed version is made from the original dataset by cropping the images in perfect squares and then resizing them into the required images size of 128x128. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/INS_2.png) Meta Album ID: SM_AM.INS2 Meta Album URL: [https://meta-album.github.io/datasets/INS_2.html](https://meta-album.github.io/datasets/INS_2.html) Domain ID: SM_AM Domain Name: Small Aninamls Dataset ID: INS_2 Dataset Name: Insects2 Short Description: Insects dataset for Insect Pest Recognition \# Classes: 102 \# Images: 75222 Keywords: insects, ecology Data Format: images Image size: 128x128 License (original data release): Free for academic usage, cite to use dataset License URL(original data release): https://github.com/xpwu95/IP102 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: IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition Source URL: https://github.com/xpwu95/IP102 Original Author: Xiaoping Wu, Chi Zhan, Yukun Lai, Ming-Ming Cheng, Jufeng Yang Original contact: xpwu95@163.com 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{Wu2019Insect, title={IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition}, author={Xiaoping Wu and Chi Zhan and Yukun Lai and Ming-Ming Cheng and Jufeng Yang}, booktitle={IEEE CVPR}, pages={8787--8796}, year={2019}, } ``` ### 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/44248) [[Mini]](https://www.openml.org/d/44292)

3 features

CATEGORY (target)string102 unique values
0 missing
FILE_NAMEstring75222 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
75222 missing

19 properties

75222
Number of instances (rows) of the dataset.
3
Number of attributes (columns) of the dataset.
102
Number of distinct values of the target attribute (if it is nominal).
75222
Number of missing values in the dataset.
75222
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.
33.33
Percentage of missing values.
1
Average class difference between consecutive instances.
33.33
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
7.63
Percentage of instances belonging to the most frequent class.
5740
Number of instances belonging to the most frequent class.
0.09
Percentage of instances belonging to the least frequent class.
71
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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