---
task_categories:
- image-classification
language:
- en
tags:
- image
pretty_name: MAMe Dataset
size_categories:
- 10K<n<100K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: medium
      dtype:
        class_label:
          names:
            '0': Albumen photograph
            '1': Bronze
            '2': Ceramic
            '3': Clay
            '4': Engraving
            '5': Etching
            '6': Faience
            '7': Glass
            '8': Gold
            '9': Graphite
            '10': Hand-colored engraving
            '11': Hand-colored etching
            '12': Iron
            '13': Ivory
            '14': Limestone
            '15': Lithograph
            '16': Marble
            '17': Oil on canvas
            '18': Pen and brown ink
            '19': Polychromed wood
            '20': Porcelain
            '21': Silk and metal thread
            '22': Silver
            '23': Steel
            '24': Wood
            '25': Wood engraving
            '26': Woodblock
            '27': Woodcut
            '28': Woven fabric
    - name: museum
      dtype: string
    - name: museum_id
      dtype: string
    - name: subset
      dtype: string
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: product_size
      dtype: int32
    - name: aspect_ratio
      dtype: float32
---

## 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](https://link.springer.com/article/10.1007/s10489-021-02951-w)
- ArXiv Version: [MAMe: A Dataset for Multi-class Classification of Materials in Artworks](https://arxiv.org/pdf/2007.13693)

---

### 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:

```bibtex
@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:

```bibtex
@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](ferran.pares@bsc.es), [Anna Arias-Duart](anna.ariasduart@bsc.es), [Dario Garcia-Gasulla](dario.garcia@bsc.es)


### Dataset Card Contact

For more information or questions about this dataset, please contact the [HPAI organization](https://hpai.bsc.es).