Update README.md

README.md

@@ -1,17 +1,28 @@
----
+pretty_name: MAMe Dataset
 task_categories:
-- image-classification
+  - image-classification
+size_categories:
+  - 10K<n<100K
 language:
-- en
+  - en
 tags:
-- image
-pretty_name: MAMe Dataset
-size_categories:
-- 10K<n<100K
+  - image
+  - artwork
+  - museum
+configs:
+  - config_name: mame_dataset
+    data_files:
+      - path: data/dataset.csv
+        description: "Main dataset file containing all splits (train, val, test)"
+      - path: data/images/small.zip
+        description: "ZIP file containing all image files"
+      - path: data/labels.csv
+        description: "File containing label mappings"
 dataset_info:
   features:
     - name: image
-      dtype: image
+      dtype: string
+      description: "Filename of the image (e.g., '1234.jpg'). The actual image file is located in data/images/small.zip"
     - name: medium
       dtype:
         class_label:
@@ -51,6 +62,7 @@ dataset_info:
       dtype: string
     - name: subset
       dtype: string
+      description: "Indicates whether the sample belongs to 'train', 'val', or 'test' split"
     - name: width
       dtype: int32
     - name: height
@@ -58,75 +70,4 @@ dataset_info:
     - 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).
+      dtype: float32