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## Meta-Album Flowers Dataset (Extended) * The Flowers dataset( consists of a variety of flowers gathered from different websites and some are photographed by the original creators. These flowers are commonly found in the UK. The images generally have large scale, pose and light variations. Some categories of flowers in the dataset has large variations of flowers while other have similar flowers in a category. The dataset was created back in 2008 at Oxford University by Nilsback, M-E. and Zisserman, A.[21]. The Flowers dataset in the Meta-Album meta-dataset is a preprocessed version of the original flowers dataset. The images are first cropped and made into squared images which are then resized into 128x128 with anti-aliasing filter. ### Dataset Details ![]( Meta Album ID: PLT.FLW Meta Album URL: []( Domain ID: PLT Domain Name: Plants Dataset ID: FLW Dataset Name: Flowers Short Description: Flowers dataset from Visual Geometry Group, University of Oxford \# Classes: 102 \# Images: 8189 Keywords: flowers, ecology, plants Data Format: images Image size: 128x128 License (original data release): GNU General Public License Version 2 License URL(original data release): License (Meta-Album data release): GNU General Public License Version 2 License URL (Meta-Album data release): []( Source: Visual Geometry Group, University of Oxford, England Source URL: Original Author: Maria-Elena Nilsback, Andrew Zisserman Original contact: {men,az} Meta Album author: Felix Mohr Created Date: 01 March 2022 Contact Name: Felix Mohr Contact Email: Contact URL: []( ### Cite this dataset ``` @InProceedings{Nilsback08, author = "Maria-Elena Nilsback and Andrew Zisserman", title = "Automated Flower Classification over a Large Number of Classes", booktitle = "Indian Conference on Computer Vision, Graphics and Image Processing", month = "Dec", year = "2008", } ``` ### 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)string102 unique values
0 missing
FILE_NAMEstring8189 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
8189 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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