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## Meta-Album PlantNet Dataset (Extended) * Meta-Album PlantNet dataset is created by sampling the Pl@ntNet-300k dataset (, itself a sampling of the Pl@ntNet Project's repository. The images and labels which enter this database are sourced by citizen botanists from around the world, then confirmed using a weighted reliability score from others users, such that each image has been reviewed by 2.03 citizen botanists on average. Of the 1 081 classes in the original Pl@ntNet-300k dataset, PLT_NET retains the 25 most populous classes, belonging to 21 genera, for a total of 120 688 images total, with min 2 914, max 9 011 image distribution per class. Each image contains a colored 128x128 image of a plant or a piece or a plant from the corresponding class (or in some cases sketches of plants or plant cells on microscope slides), scaled from the initial variable width using the INTER_AREA anti-aliasing filter from Open-CV. Almost all images were initially square; cropping by taking the largest possible square with center at the middle of the initial image was applied otherwise. ### Dataset Details ![]( Meta Album ID: PLT.PLT_NET Meta Album URL: []( Domain ID: PLT Domain Name: Plants Dataset ID: PLT_NET Dataset Name: PlantNet Short Description: Plants Dataset with different species of plants \# Classes: 25 \# Images: 120688 Keywords: ecology, plants, plant species Data Format: images Image size: 128x128 License (original data release): Creative Commons Attribution 4.0 International License URL(original data release): License (Meta-Album data release): Creative Commons Attribution 4.0 International License URL (Meta-Album data release): []( Source: PlantNet Source URL: Original Author: Garcin, Camille and Joly, Alexis and Bonnet, Pierre and Lombardo, Jean-Christophe and Affouard, Antoine and Chouet, Mathias and Servajean, Maximilien and Salmon, Joseph and Lorieul, Titouan Original contact: Meta Album author: Felix Herron Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: Contact URL: []( ### Cite this dataset ``` @inproceedings{garcin2021plntnetk, title={Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution}, author={Camille Garcin and alexis joly and Pierre Bonnet and Antoine Affouard and Jean-Christophe Lombardo and Mathias Chouet and Maximilien Servajean and Titouan Lorieul and Joseph Salmon}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={} } ``` ### 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)string25 unique values
0 missing
FILE_NAMEstring120688 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
120688 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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