README.md

---
dataset_info:
  features:
    - name: image
      dtype: string
    - 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
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/dataset.csv
    download_mode: reuse_dataset_if_exists
    download_size: ???
    features:
      image: string
      medium: int64
      museum: string
      museum_id: string
      subset: string
      width: int32
      height: int32
      product_size: int32
      aspect_ratio: float32
    dataset_size: ???
pretty_name: MAMe Dataset
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification
tags:
  - image
  - artwork
  - museum
---

## 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]([email protected]), [Anna Arias-Duart]([email protected]), [Dario Garcia-Gasulla]([email protected])


### Dataset Card Contact

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