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## Meta-Album Insects Dataset (Extended) * The original Insects dataset is created by the National Museum of Natural History, Paris ( It has more than 290 000 images in different sizes and orientations. The dataset has hierarchical classes which are listed from top to bottom as Order, Super-Family, Family, and Texa. Each image contains an insect in its natural environment or habitat, i.e, either on a flower or near to vegetation. The images are collected by the researchers and hundreds of volunteers from SPIPOLL Science project( The images are uploaded to a centralized server either by using the SPIPOLL website, Android application or IOS application. The preprocessed insect dataset is prepared from the original Insects dataset by carefully preprocessing the images, i.e., cropping the images from either side to make squared images. These cropped images are then resized into 128x128 using Open-CV with an anti-aliasing filter. ### Dataset Details ![]( Meta Album ID: SM_AM.INS Meta Album URL: []( Domain ID: SM_AM Domain Name: Small Aninamls Dataset ID: INS Dataset Name: Insects Short Description: Insects dataset from Science Project SPIPOLL \# Classes: 117 \# Images: 170506 Keywords: insects, ecology Data Format: images Image size: 128x128 License (original data release): CC BY-NC 2.0 License URL(original data release): License (Meta-Album data release): CC BY-NC 2.0 License URL (Meta-Album data release): []( Source: SPIPOLL; National Museum of Natural History, Paris Source URL: Original Author: Gregoire Lois, Colin Fontaine, Jean-Francois Julien Original contact: Meta Album author: Ihsan Ullah Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: Contact URL: []( ### Cite this dataset ``` @article{insects, title={Data quality and participant engagement in citizen science: comparing two approaches for monitoring pollinators in France and South Korea}, author={Serret, Hortense and Deguines, Nicolas and Jang, Yikweon and Lois, Gregoire and Julliard, Romain}, journal={Citizen Science: Theory and Practice}, volume={4}, number={1}, pages={22}, 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 = {}, 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 [[Micro]]( [[Mini]](

3 features

CATEGORY (target)string117 unique values
0 missing
FILE_NAMEstring170491 unique values
0 missing
SUPER_CATEGORYstring29 unique values
0 missing

19 properties

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

1 tasks

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