biglam/v4design_europeana_style_dataset

Dataset Card for V4Design Europeana style dataset

Dataset Summary

This dataset contains:

1614 paintings belonging to the categories Baroque, Rococo, and Other. The images were obtained using the Europeana Search API, selecting open objects from the art thematic collection. 24k images were obtained, from which the current dataset was derived. The labels were added by the V4Design team, using a custom annotation tool. As described in the project documentation, other categories were used besides Baroque and Rococo. But for the sake of training a machine learning model we have retained only the categories with a significant number of annotations source

This version of the dataset is generated using the CSV file hosted on Zenodo. This CSV file contains the labels with URLs for the relevant images. Some of these URLs no longer resolve to an image. For consitency with the original dataset and if these URLs become valid again, these rows of the data are preserved here. If you want only successfully loaded images in your dataset, you can filter out the missing images as follows.

ds = ds.filter(lambda x: x['image'] is not None)

Supported Tasks and Leaderboards

This dataset is primarily intended for image-classification. 

Languages

[More Information Needed]

Dataset Structure

Data Instances

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Data Fields

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Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@dataset{europeana_2021_4896487,
  author       = {Europeana and
                  V4Design},
  title        = {V4Design/Europeana style dataset},
  month        = jun,
  year         = 2021,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4896487},
  url          = {https://doi.org/10.5281/zenodo.4896487}
}

Contributions

Thanks to @davanstrien for adding this dataset.