biglam/european_art

Dataset Card for DEArt: Dataset of European Art

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

Category	Count
person	46806
tree	11356
nude	5070
halo	4944
angel	4930
horse	3368
boat	3252
bird	3022
book	2742
dog	2225
helmet	2048
lance	1761
knight	1759
sword	1691
cow	1422
jug	1396
banner	1337
sheep	1302
crown	1048
prayer	997
monk	932
devil	879
apple	829
shield	772
scroll	735
chalice	613
crucifixion	556
donkey	514
skull	490
lion	489
butterfly	485
monkey	459
lily	433
serpent	424
arrow	420
palm	398
dove	393
trumpet	389
key of heaven	384
dragon	383
mitre	374
crozier	360
tiara	351
deer	349
crown of thorns	342
hands	281
god the father	273
eagle	260
shepherd	238
head	220
camauro	208
elephant	184
centaur	177
cat	173
swan	165
rooster	150
saturno	137
unicorn	128
zucchetto	127
bear	125
fish	120
horn	119
stole	117
pegasus	116
orange	113
holy shroud	91
judith	85
zebra	82
banana	32
mouse	27

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• object-detection: This dataset can be used to train or evaluate models for object-detection on historical document images.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.