Datasets:

biglam
/

european_art

@@ -1,258 +1,259 @@
 ---
 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<n<100K
 ---
 # Dataset Card for DEArt: Dataset of European Art
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
@@ -294,17 +295,17 @@ size_categories:
 - `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`](https://huggingface.co/docs/transformers/main_classes/image_processor) (formerly known as`feature_extractor`s) 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:
 ```python
 {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
  'source': 'Europeana Collection',
@@ -406,7 +407,21 @@ An instance from the `coco` config:
 ### Data Fields
-[More Information Needed]
 ### Data Splits
@@ -414,12 +429,11 @@ The dataset doesn't define set splits, so only a train split is provided. The pa
 ## 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
@@ -506,4 +520,4 @@ The source data comes from several cultural heritage institutions that have shar
 ### Contributions
-Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

 ---
 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<n<100K
 ---
 # Dataset Card for DEArt: Dataset of European Art
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
 - `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`](https://huggingface.co/docs/transformers/main_classes/image_processor) (formerly known as`feature_extractor`s) 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:
 ```python
 {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
  'source': 'Europeana Collection',
 ### 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
 ## 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
 ### Contributions
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.