biglam/european_art

Dataset Card for DEArt: Dataset of European Art

Table of Contents

• —Table of Contents
• —Dataset Description
• —Dataset Summary
• —Supported Tasks and Leaderboards
• —Languages
• —Dataset Structure
• —Data Instances
• —Data Fields
• —Data Splits
• —Dataset Creation
• —Curation Rationale
• —Source Data
• —Annotations
• —Personal and Sensitive Information
• —Considerations for Using the Data
• —Social Impact of Dataset
• —Discussion of Biases
• —Other Known Limitations
• —Additional Information
• —Dataset Curators
• —Licensing Information
• —Citation Information
• —Contributions

Dataset Description

• —Homepage:
• —Repository: https://doi.org/10.5281/zenodo.6984525
• —Paper: https://arxiv.org/abs/2211.01226
• —Leaderboard:
• —Point of Contact:

Dataset Summary

DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural heritage specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art.

Label Counts

Supported Tasks and Leaderboards

• —object-detection: This dataset can be used to train or evaluate models for object-detection on historical document images.
• —image-classification: This dataset can be used for image classification tasks by using only the labels and not the bounding box information

Dataset Structure

This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.

• —The first configuration, `raw, uses the data's original format.
• —The second configuration converts the annotations into a format that is closer to the COCO annotation format. This is done to make it easier to work with the `image_processors` (formerly known asfeature_extractors) from the Transformers models for object detection, which expects data to be in a COCO-style format.

Data Instances

An instance from the raw config:

{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
 'source': 'Europeana Collection',
 'width': 1019,
 'height': 1680,
 'dept': 3,
 'segmented': None,
 'objects': [{'name': 40,
   'pose': 3,
   'diffult': 0,
   'xmin': 259.0,
   'ymin': 166.0,
   'xmax': 679.0,
   'ymax': 479.0},
  {'name': 19,
   'pose': 2,
   'diffult': 0,
   'xmin': 115.0,
   'ymin': 354.0,
   'xmax': 882.0,
   'ymax': 1168.0},
  {'name': 15,
   'pose': 3,
   'diffult': 0,
   'xmin': 445.0,
   'ymin': 1170.0,
   'xmax': 579.0,
   'ymax': 1302.0},
  {'name': 51,
   'pose': 3,
   'diffult': 0,
   'xmin': 354.0,
   'ymin': 1196.0,
   'xmax': 445.0,
   'ymax': 1330.0},
  {'name': 51,
   'pose': 3,
   'diffult': 0,
   'xmin': 580.0,
   'ymin': 1203.0,
   'xmax': 701.0,
   'ymax': 1326.0},
  {'name': 57,
   'pose': 3,
   'diffult': 0,
   'xmin': 203.0,
   'ymin': 642.0,
   'xmax': 882.0,
   'ymax': 1172.0}]}

An instance from the coco config:

{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
 'source': 'Europeana Collection',
 'width': 1019,
 'height': 1680,
 'dept': 3,
 'segmented': None,
 'image_id': '0',
 'annotations': [{'category_id': 40,
   'image_id': '0',
   'area': 131460,
   'bbox': [259.0, 166.0, 420.0, 313.0],
   'segmentation': [],
   'iscrowd': False},
  {'category_id': 19,
   'image_id': '0',
   'area': 624338,
   'bbox': [115.0, 354.0, 767.0, 814.0],
   'segmentation': [],
   'iscrowd': False},
  {'category_id': 15,
   'image_id': '0',
   'area': 17688,
   'bbox': [445.0, 1170.0, 134.0, 132.0],
   'segmentation': [],
   'iscrowd': False},
  {'category_id': 51,
   'image_id': '0',
   'area': 12194,
   'bbox': [354.0, 1196.0, 91.0, 134.0],
   'segmentation': [],
   'iscrowd': False},
  {'category_id': 51,
   'image_id': '0',
   'area': 14883,
   'bbox': [580.0, 1203.0, 121.0, 123.0],
   'segmentation': [],
   'iscrowd': False},
  {'category_id': 57,
   'image_id': '0',
   'area': 359870,
   'bbox': [203.0, 642.0, 679.0, 530.0],
   'segmentation': [],
   'iscrowd': False}]}

Data Fields

The fields for the COCO config:

• —image: The Image being annotated
• —source: source of the image i.e.'Europeana Collection'
• —width: width of the image
• —height: height of the image
• —dept: number of channels in the image
• —segmented: Whether the image has been segmented
• —image_id: ID for the image
• —annotations: annotations in coco format, consisting of a list containing dictionaries with the following keys:
• —bbox: bounding boxes for the images
• —category_id: a label for the image
• —image_id: id for the image
• —iscrowd: COCO iscrowd flag
• —segmentation: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)

Data Splits

The dataset doesn't define set splits, so only a train split is provided. The paper associated with the dataset does discuss a train and validation split, but it doesn't appear this split was shared with the dataset or associated paper.

Dataset Creation

Curation Rationale

The creators of the dataset authors outline some of their motivations for creating the dataset in the abstract for their paper:

Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations.

Source Data

The source data comes from several cultural heritage institutions that have shared openly licenced images. The dictionary below shows the institutions and the frequency with which they are the provider of images in the dataset.

{'National Museum in Warsaw': 2030,
 'Europeana Collection': 1991,
 'The Art Institute of Chicago': 1237,
 'The Metropolitan Museum of Art': 1218,
 'Rijksmuseum': 1066,
 'National Gallery of Art': 871,
 'Philadelphia Museum of Art': 774,
 'WikiArt': 687,
 'National museum in Krakow': 661,
 'National Gallery of Denmark': 660,
 'British Museum': 618,
 'Victoria and Albert Museum': 561,
 'Paul Mellon Centre': 391,
 'National Gallery of Scotland': 384,
 'Yale University Art Gallery': 376,
 'Museo Nacional Thyssen-Bornemisza': 332,
 'Harvard Art Museum': 279,
 'The National Museum of Norvay': 270,
 'LACMA': 262,
 'The Cleveland Museum of Art': 242,
 'The Leiden Collection': 159,
 'The Clark Museum': 77,
 'Pharos': 6,
 'Wikimedia Commons': 2,
 'Wikipedia': 1,
 'Unknown': 1}

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @github-username for adding this dataset.