{
    "data_id": "44332",
    "name": "Meta_Album_MED_LF_Extended",
    "exact_name": "Meta_Album_MED_LF_Extended",
    "version": 1,
    "version_label": null,
    "description": "## **Meta-Album Medicinal Leaf Dataset (Extended)**\n***\nThe 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.  \n\n\n\n### **Dataset Details**\n![](https:\/\/meta-album.github.io\/assets\/img\/samples\/MED_LF.png)\n\n**Meta Album ID**: PLT_DIS.MED_LF  \n**Meta Album URL**: [https:\/\/meta-album.github.io\/datasets\/MED_LF.html](https:\/\/meta-album.github.io\/datasets\/MED_LF.html)  \n**Domain ID**: PLT_DIS  \n**Domain Name**: Plant Diseases  \n**Dataset ID**: MED_LF  \n**Dataset Name**: Medicinal Leaf  \n**Short Description**: Healthy Medicinal Leaf  \n**\\# Classes**: 26  \n**\\# Images**: 1596  \n**Keywords**: medicinal leaf, plants, plant diseases  \n**Data Format**: images  \n**Image size**: 128x128  \n\n**License (original data release)**: CC BY 4.0  \n**License URL(original data release)**: https:\/\/data.mendeley.com\/datasets\/nnytj2v3n5\/1\nhttps:\/\/creativecommons.org\/licenses\/by\/4.0\/\n \n**License (Meta-Album data release)**: CC BY 4.0  \n**License URL (Meta-Album data release)**: [https:\/\/creativecommons.org\/licenses\/by\/4.0\/](https:\/\/creativecommons.org\/licenses\/by\/4.0\/)  \n\n**Source**: Medicinal Leaf Dataset  \n**Source URL**: https:\/\/data.mendeley.com\/datasets\/nnytj2v3n5\/1  \n  \n**Original Author**: S Roopashree, J Anitha  \n**Original contact**:   \n\n**Meta Album author**: Phan Anh VU  \n**Created Date**: 01 March 2022  \n**Contact Name**: Ihsan Ullah  \n**Contact Email**: meta-album@chalearn.org  \n**Contact URL**: [https:\/\/meta-album.github.io\/](https:\/\/meta-album.github.io\/)  \n\n\n\n### **Cite this dataset**\n```\n @article{s_j_2020, \n     title={Medicinal Leaf Dataset}, \n     url={https:\/\/data.mendeley.com\/datasets\/nnytj2v3n5\/1}, \n     author={S, Roopashree and J, Anitha}, \n     year={2020}, \n     month={Oct},\n     doi={10.17632\/nnytj2v3n5.1},\n     version={1},\n     publisher={Mendeley Data}   \n} \n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n        title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n        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},\n        booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n        url = {https:\/\/meta-album.github.io\/},\n        year = {2022}\n    }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https:\/\/meta-album.github.io\/paper\/Meta-Album.pdf)  \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https:\/\/openreview.net\/attachment?id=70_Wx-dON3q&name=supplementary_material)  \nSupporting code can be found on our [[GitHub repo]](https:\/\/github.com\/ihsaan-ullah\/meta-album)  \nMeta-Album on Papers with Code [[Meta-Album]](https:\/\/paperswithcode.com\/dataset\/meta-album)  \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https:\/\/www.openml.org\/d\/44314)  [[Mini]](https:\/\/www.openml.org\/d\/44299)  ",
    "format": "arff",
    "uploader": "Meta Album",
    "uploader_id": 30980,
    "visibility": "public",
    "creator": "\"Ihsan Ullah\"",
    "contributor": null,
    "date": "2022-11-08 18:28:03",
    "update_comment": null,
    "last_update": "2022-11-08 18:28:03",
    "licence": "CC BY-NC 4.0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22111046\/dataset",
    "default_target_attribute": "CATEGORY",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "Meta_Album_MED_LF_Extended",
            "## **Meta-Album Medicinal Leaf Dataset (Extended)** 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 128x12 "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 1596,
        "NumberOfFeatures": 3,
        "NumberOfClasses": 26,
        "NumberOfMissingValues": 1596,
        "NumberOfInstancesWithMissingValues": 1596,
        "NumberOfNumericFeatures": 1,
        "NumberOfSymbolicFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 100,
        "AutoCorrelation": 1,
        "PercentageOfMissingValues": 33.33333333333333,
        "Dimensionality": 0.0018796992481203006,
        "PercentageOfNumericFeatures": 33.33333333333333,
        "MajorityClassPercentage": 7.644110275689223,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": 122,
        "MinorityClassPercentage": 0.8771929824561403,
        "MinorityClassSize": 14,
        "NumberOfBinaryFeatures": 0
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Statistics"
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    ],
    "features": [
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            "distinct": "26",
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            "name": "SUPER_CATEGORY",
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            "missing": "1596",
            "min": "2147483647",
            "max": "0",
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    "nr_of_issues": 0,
    "nr_of_downvotes": 0,
    "nr_of_likes": 0,
    "nr_of_downloads": 0,
    "total_downloads": 0,
    "reach": 0,
    "reuse": 1,
    "impact_of_reuse": 0,
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