|
--- |
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dataset_info: |
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- config_name: raw |
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features: |
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- name: image |
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dtype: image |
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- name: source |
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dtype: string |
|
- name: width |
|
dtype: int16 |
|
- name: height |
|
dtype: int16 |
|
- name: dept |
|
dtype: int8 |
|
- name: segmented |
|
dtype: int8 |
|
- name: objects |
|
list: |
|
- name: name |
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dtype: |
|
class_label: |
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names: |
|
'0': zebra |
|
'1': tree |
|
'2': nude |
|
'3': crucifixion |
|
'4': scroll |
|
'5': head |
|
'6': swan |
|
'7': shield |
|
'8': lily |
|
'9': mouse |
|
'10': knight |
|
'11': dragon |
|
'12': horn |
|
'13': dog |
|
'14': palm |
|
'15': tiara |
|
'16': helmet |
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'17': sheep |
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'18': deer |
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'19': person |
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'20': sword |
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'21': rooster |
|
'22': bear |
|
'23': halo |
|
'24': lion |
|
'25': monkey |
|
'26': prayer |
|
'27': crown of thorns |
|
'28': elephant |
|
'29': zucchetto |
|
'30': unicorn |
|
'31': holy shroud |
|
'32': cat |
|
'33': apple |
|
'34': banana |
|
'35': chalice |
|
'36': bird |
|
'37': eagle |
|
'38': pegasus |
|
'39': crown |
|
'40': camauro |
|
'41': saturno |
|
'42': arrow |
|
'43': dove |
|
'44': centaur |
|
'45': horse |
|
'46': hands |
|
'47': skull |
|
'48': orange |
|
'49': monk |
|
'50': trumpet |
|
'51': key of heaven |
|
'52': fish |
|
'53': cow |
|
'54': angel |
|
'55': devil |
|
'56': book |
|
'57': stole |
|
'58': butterfly |
|
'59': serpent |
|
'60': judith |
|
'61': mitre |
|
'62': banner |
|
'63': donkey |
|
'64': shepherd |
|
'65': boat |
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'66': god the father |
|
'67': crozier |
|
'68': jug |
|
'69': lance |
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- name: pose |
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dtype: |
|
class_label: |
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names: |
|
'0': stand |
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'1': sit |
|
'2': partial |
|
'3': Unspecified |
|
'4': squats |
|
'5': lie |
|
'6': bend |
|
'7': fall |
|
'8': walk |
|
'9': push |
|
'10': pray |
|
'11': undefined |
|
'12': kneel |
|
'13': unrecognize |
|
'14': unknown |
|
'15': other |
|
'16': ride |
|
- name: diffult |
|
dtype: int32 |
|
- name: xmin |
|
dtype: float64 |
|
- name: ymin |
|
dtype: float64 |
|
- name: xmax |
|
dtype: float64 |
|
- name: ymax |
|
dtype: float64 |
|
splits: |
|
- name: train |
|
num_bytes: 9046918 |
|
num_examples: 15156 |
|
download_size: 18160510195 |
|
dataset_size: 9046918 |
|
- config_name: coco |
|
features: |
|
- name: image |
|
dtype: image |
|
- name: source |
|
dtype: string |
|
- name: width |
|
dtype: int16 |
|
- name: height |
|
dtype: int16 |
|
- name: dept |
|
dtype: int8 |
|
- name: segmented |
|
dtype: int8 |
|
- name: objects |
|
list: |
|
- name: category_id |
|
dtype: |
|
class_label: |
|
names: |
|
'0': zebra |
|
'1': tree |
|
'2': nude |
|
'3': crucifixion |
|
'4': scroll |
|
'5': head |
|
'6': swan |
|
'7': shield |
|
'8': lily |
|
'9': mouse |
|
'10': knight |
|
'11': dragon |
|
'12': horn |
|
'13': dog |
|
'14': palm |
|
'15': tiara |
|
'16': helmet |
|
'17': sheep |
|
'18': deer |
|
'19': person |
|
'20': sword |
|
'21': rooster |
|
'22': bear |
|
'23': halo |
|
'24': lion |
|
'25': monkey |
|
'26': prayer |
|
'27': crown of thorns |
|
'28': elephant |
|
'29': zucchetto |
|
'30': unicorn |
|
'31': holy shroud |
|
'32': cat |
|
'33': apple |
|
'34': banana |
|
'35': chalice |
|
'36': bird |
|
'37': eagle |
|
'38': pegasus |
|
'39': crown |
|
'40': camauro |
|
'41': saturno |
|
'42': arrow |
|
'43': dove |
|
'44': centaur |
|
'45': horse |
|
'46': hands |
|
'47': skull |
|
'48': orange |
|
'49': monk |
|
'50': trumpet |
|
'51': key of heaven |
|
'52': fish |
|
'53': cow |
|
'54': angel |
|
'55': devil |
|
'56': book |
|
'57': stole |
|
'58': butterfly |
|
'59': serpent |
|
'60': judith |
|
'61': mitre |
|
'62': banner |
|
'63': donkey |
|
'64': shepherd |
|
'65': boat |
|
'66': god the father |
|
'67': crozier |
|
'68': jug |
|
'69': lance |
|
- name: image_id |
|
dtype: string |
|
- name: area |
|
dtype: int64 |
|
- name: bbox |
|
sequence: float32 |
|
length: 4 |
|
- name: segmentation |
|
list: |
|
list: float32 |
|
- name: iscrowd |
|
dtype: bool |
|
- name: image_id |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 8285204 |
|
num_examples: 15156 |
|
download_size: 18160510195 |
|
dataset_size: 8285204 |
|
license: cc-by-nc-2.0 |
|
task_categories: |
|
- object-detection |
|
- image-classification |
|
tags: |
|
- lam |
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- art |
|
- historical |
|
pretty_name: 'DEArt: Dataset of European Art' |
|
size_categories: |
|
- 10K<n<100K |
|
--- |
|
|
|
# Dataset Card for DEArt: Dataset of European Art |
|
|
|
## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
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- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** |
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- **Repository:** https://doi.org/10.5281/zenodo.6984525 |
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- **Paper:** https://arxiv.org/abs/2211.01226 |
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- **Leaderboard:** |
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- **Point of Contact:** |
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|
|
### Dataset Summary |
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|
|
> 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. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning. |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
[More Information Needed] |
|
|
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
[More Information Needed] |
|
|
|
COCO |
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|
|
```python |
|
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>, |
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'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 |
|
|
|
[More Information Needed] |
|
|
|
### Data Splits |
|
|
|
[More Information Needed] |
|
|
|
## Dataset Creation |
|
|
|
### Curation Rationale |
|
|
|
[More Information Needed] |
|
|
|
### Source Data |
|
|
|
#### 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](https://github.com/<github-username>) for adding this dataset. |
