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Meta_Album_MED_LF_Micro

Meta_Album_MED_LF_Micro

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## Meta-Album Medicinal Leaf Dataset (Micro) * The Medicinal Leaf Database(https://data.mendeley.com/datasets/nnytj2v3n5/1) gathers 30 species of healthy and mature medicinal herbs. The leaves are plucked from different plants of the same species, then placed on a white uniform background. There are around 1 800 images in total, captured with a mobile phone camera. The original resolution is 1 600x1 200 px. We create Medleaf for Meta-Album by cropping them at the center and resize to 128x128 px. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/MED_LF.png) Meta Album ID: PLT_DIS.MED_LF Meta Album URL: [https://meta-album.github.io/datasets/MED_LF.html](https://meta-album.github.io/datasets/MED_LF.html) Domain ID: PLT_DIS Domain Name: Plant Diseases Dataset ID: MED_LF Dataset Name: Medicinal Leaf Short Description: Healthy Medicinal Leaf \# Classes: 20 \# Images: 800 Keywords: medicinal leaf, plants, plant diseases Data Format: images Image size: 128x128 License (original data release): CC BY 4.0 License URL(original data release): https://data.mendeley.com/datasets/nnytj2v3n5/1 https://creativecommons.org/licenses/by/4.0/ License (Meta-Album data release): CC BY 4.0 License URL (Meta-Album data release): [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) Source: Medicinal Leaf Dataset Source URL: https://data.mendeley.com/datasets/nnytj2v3n5/1 Original Author: S Roopashree, J Anitha Original contact: 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{s_j_2020, title={Medicinal Leaf Dataset}, url={https://data.mendeley.com/datasets/nnytj2v3n5/1}, author={S, Roopashree and J, Anitha}, year={2020}, month={Oct}, doi={10.17632/nnytj2v3n5.1}, version={1}, publisher={Mendeley Data} } ``` ### 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/44299) [[Extended]](https://www.openml.org/d/44332)

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 nominal attributes.
1
Average class difference between consecutive instances.
33.33
Percentage of numeric attributes.
33.33
Percentage of missing values.
100
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
40
Number of instances belonging to the least frequent class.
5
Percentage of instances belonging to the least frequent class.
40
Number of instances belonging to the most frequent class.
5
Percentage of instances belonging to the most frequent class.
0
Number of attributes divided by the number of instances.

1 tasks

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