## Meta-Album Plant Doc Dataset (Extended)
*
The PlantDoc dataset(https://github.com/pratikkayal/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: [https://meta-album.github.io/datasets/PLT_DOC.html](https://meta-album.github.io/datasets/PLT_DOC.html)
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): https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/blob/master/LICENSE.txt
License (Meta-Album data release): Creative Commons Attribution 4.0 International
License URL (Meta-Album data release): [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)
Source: PlantDoc: A Dataset for Visual Plant Disease Detection
Source URL: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset
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: meta-album@chalearn.org
Contact URL: [https://meta-album.github.io/](https://meta-album.github.io/)
### 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 = {https://doi.org/10.1145/3371158.3371196},
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 = {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
[[Micro]](https://www.openml.org/d/44273) [[Mini]](https://www.openml.org/d/44303)