--- dataset_info: - config_name: raw 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: name 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: pose dtype: class_label: names: '0': stand '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 - art - historical pretty_name: 'DEArt: Dataset of European Art' size_categories: - 10K 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 ```python {'image': , '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/) for adding this dataset.