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## Meta-Album Boats Dataset (Extended) * The original version of the Meta-Album boats dataset is called MARVEL dataset ( It has more than 138 000 images of 26 different maritime vessels in their natural background. Each class can have 1 802 to 8 930 images of variable resolutions. To preprocess this dataset, we either duplicate the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image to create square images. No cropping was applied because the boats occupy most of the image, and applying this technique will lead to incomplete images. Finally, the square images were resized into 128x128 px using an anti-aliasing filter ### Dataset Details ![]( Meta Album ID: VCL.BTS Meta Album URL: []( Domain ID: VCL Domain Name: Vehicles Dataset ID: BTS Dataset Name: Boats Short Description: Dataset with images of different boats \# Classes: 26 \# Images: 138367 Keywords: vehicles, boats Data Format: images Image size: 128x128 License (original data release): Cite paper to use dataset License (Meta-Album data release): CC BY-NC 4.0 License URL (Meta-Album data release): []( Source: MARVEL: A LARGE-SCALE IMAGE DATASET FOR MARITIME VESSELS Source URL: Original Author: Gundogdu E., Solmaz B, Yucesoy V., Koc A. Original contact: Meta Album author: Dustin Carrion Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: Contact URL: []( ### Cite this dataset ``` @InProceedings{MARVEL, author="Gundogdu, Erhan and Solmaz, Berkan and Yucesoy, Veysel and Koc, Aykut", editor="Lai, Shang-Hong and Lepetit, Vincent and Nishino, Ko and Sato, Yoichi", title="MARVEL: A Large-Scale Image Dataset for Maritime Vessels", booktitle="Computer Vision -- ACCV 2016", year="2017", publisher="Springer International Publishing", address="Cham", pages="165--180", isbn="978-3-319-54193-8" } ``` ### 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)string26 unique values
0 missing
FILE_NAMEstring138367 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
138367 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.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.
Percentage of binary attributes.
Percentage of instances having missing values.
Percentage of missing values.
Average class difference between consecutive instances.
Percentage of numeric attributes.
Number of attributes divided by the number of instances.
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.

1 tasks

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