MAMe Dataset: Museum Artworks Medium

The MAMe Dataset is an image classification dataset focused on the recognition of mediums in artworks and heritage held by museums (e.g., Oil on canvas, Bronze or Woodcut).

The classes considered in the MAMe dataset comprise a wide variety of mediums according to both interpretations of the term. These can range from simple material aspects (e.g., Bronze, Silver or Gold) to complex, high-level techniques (e.g., Faience, Woodblock or Woven fabric). The variety of relevant features in MAMe requires both attention to detail and to the overall image structure.

Paper

Journal Version: Materials in Art and Museum Environment (MAMe): A Dataset for Art Material Recognition
ArXiv Version: MAMe: A Dataset for Multi-class Classification of Materials in Artworks

Dataset Variants: TODO

MAMe_small: A toy version of the dataset, optimized for quick experimentation and lighter storage needs.
MAMe_original: The original version of the dataset, meant for detailed tasks requiring precision in material classification.

Dataset Description

The MAMe dataset contains thousands of artworks from three different museums, and proposes a classification task consisting on differentiating between 29 mediums (i.e. materials and techniques) supervised by art experts.

Curated by: HPAI
License: The MAMe dataset is available for non-commercial research purposes only.

Citation

If you use this dataset, please cite the following journal paper:

@article{pares2022mame,
  title={The MAMe dataset: on the relevance of high resolution and variable shape image properties},
  author={Par{\'e}s, Ferran and Arias-Duart, Anna and Garcia-Gasulla, Dario and others},
  journal={Applied Intelligence},
  volume={52},
  number={12},
  pages={11703--11724},
  year={2022},
  publisher={Springer},
  doi={10.1007/s10489-021-02951-w}
}

For accessibility purposes, you can also reference the ArXiv version:

@article{pares2020mame,
    title={The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties},
    author={Par{\'e}s, Ferran and Arias-Duart, Anna and Garcia-Gasulla, Dario and Campo-Franc{\'e}s, Gema and Viladrich, Nina and Labarta, Jes{\'u}s and Ayguad{\'e}, Eduard},
    journal={arXiv preprint arXiv:2007.13693},
    year={2020},
    url = {https://arxiv.org/pdf/2007.13693}
}

Dataset Card Authors

Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla

Dataset Card Contact

For more information or questions about this dataset, please contact the HPAI organization.