Create README.md

README.md

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+---
+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]([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).