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## Meta-Album Plant Doc Dataset (Extended) * The PlantDoc dataset( is made up of images of leaves of healthy and unhealthy plants. The images were downloaded from Google Images and Ecosia, and later cropped by the authors, so generally, one complete leaf fits in one image. The original, uncropped images are generally different in scale, light conditions, and pose. However, within one category, images of leaves that came from the same original image can be found. The images correspond to 27 classes, including plant disease names and plant species names, e.g.: Corn Leaf Blight and Cherry Leaf respectively. The dataset was created for a benchmarking classification model work, published in 2020 by Singh et al. The PlantDoc dataset in the Meta-Album benchmark is extracted from a preprocessed version of the original PlantDoc dataset. First, to get i.i.d. samples, only one leaf image per each original image is randomly picked. Then, leaves images are cropped and made into squared images which are then resized into 128x128 with anti-aliasing filter. ### Dataset Details ![]( Meta Album ID: PLT_DIS.PLT_DOC Meta Album URL: []( Domain ID: PLT_DIS Domain Name: Plant Diseases Dataset ID: PLT_DOC Dataset Name: Plant Doc Short Description: Plant disease dataset \# Classes: 27 \# Images: 2549 Keywords: plants, plant diseases, 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: PlantDoc: A Dataset for Visual Plant Disease Detection Source URL: Original Author: Sharada Mohanty, David Hughes, and Marcel Salathe Original contact: Meta Album author: Maria Belen Guaranda Cabezas Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: Contact URL: []( ### Cite this dataset ``` @inproceedings{10.1145/3371158.3371196, author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun}, title = {PlantDoc: A Dataset for Visual Plant Disease Detection}, year = {2020}, isbn = {9781450377386}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {}, doi = {10.1145/3371158.3371196}, booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD}, pages = {249-253}, numpages = {5}, keywords = {Object Detection, Image Classification, Deep Learning}, location = {Hyderabad, India}, series = {CoDS COMAD 2020} } ``` ### 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)string27 unique values
0 missing
FILE_NAMEstring2549 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
2549 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.
Number of binary 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.

1 tasks

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