Convert to parquet (loading scripts are no longer supported)

COCO/test-00000-of-00001.parquet

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+size 59632002

COCO/train-00000-of-00001.parquet

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+size 281375209

COCO/val-00000-of-00001.parquet

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README.md

@@ -17,6 +17,100 @@ tags:
 task_categories:
 - object-detection
 task_ids: []
+dataset_info:
+- config_name: COCO
+  features:
+  - name: image_id
+    dtype: int64
+  - name: image
+    dtype: image
+  - name: width
+    dtype: int32
+  - name: height
+    dtype: int32
+  - name: objects
+    list:
+    - name: category_id
+      dtype:
+        class_label:
+          names:
+            '0': Header
+            '1': Col
+            '2': Marginal
+            '3': text
+    - name: image_id
+      dtype: int64
+    - name: id
+      dtype: int64
+    - name: area
+      dtype: int64
+    - name: bbox
+      list: float32
+      length: 4
+    - name: segmentation
+      list:
+        list: float32
+    - name: iscrowd
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 281355547
+    num_examples: 196
+  - name: val
+    num_bytes: 37193267
+    num_examples: 22
+  - name: test
+    num_bytes: 59622681
+    num_examples: 135
+  download_size: 378207654
+  dataset_size: 378171495
+- config_name: YOLO
+  features:
+  - name: image
+    dtype: image
+  - name: objects
+    struct:
+    - name: label
+      list:
+        class_label:
+          names:
+            '0': Header
+            '1': Col
+            '2': Marginal
+            '3': text
+    - name: bbox
+      list:
+        list: int32
+        length: 4
+  splits:
+  - name: train
+    num_bytes: 281327882
+    num_examples: 196
+  - name: val
+    num_bytes: 37189262
+    num_examples: 22
+  - name: test
+    num_bytes: 59596564
+    num_examples: 135
+  download_size: 378142580
+  dataset_size: 378113708
+configs:
+- config_name: COCO
+  data_files:
+  - split: train
+    path: COCO/train-*
+  - split: val
+    path: COCO/val-*
+  - split: test
+    path: COCO/test-*
+- config_name: YOLO
+  data_files:
+  - split: train
+    path: YOLO/train-*
+  - split: val
+    path: YOLO/val-*
+  - split: test
+    path: YOLO/test-*
 ---
 
 # YALTAi Tabular Dataset

YOLO/test-00000-of-00001.parquet

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+size 59604963

YOLO/train-00000-of-00001.parquet

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+size 281344230

YOLO/val-00000-of-00001.parquet

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+size 37193387

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "TODO", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "objects": {"feature": {"label": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "bbox": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60704, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 7537, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 47159, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 115400, "size_in_bytes": 376305464}, "YOLO": {"description": "Yalt AI Tabular Dataset", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "objects": {"feature": {"label": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "bbox": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "YOLO", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60704, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 7537, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 47159, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 115400, "size_in_bytes": 376305464}, "COCO": {"description": "Yalt AI Tabular Dataset", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image_id": {"dtype": "int64", "id": null, "_type": "Value"}, "image": {"decode": true, "id": null, "_type": "Image"}, "width": {"dtype": "int32", "id": null, "_type": "Value"}, "height": {"dtype": "int32", "id": null, "_type": "Value"}, "objects": [{"category_id": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "image_id": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "int64", "id": null, "_type": "Value"}, "area": {"dtype": "int64", "id": null, "_type": "Value"}, "bbox": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}, "segmentation": [[{"dtype": "float32", "id": null, "_type": "Value"}]], "iscrowd": {"dtype": "bool", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "COCO", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 87171, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 11225, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 71491, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 169887, "size_in_bytes": 376359951}}
+{"default": {"description": "TODO", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "objects": {"feature": {"label": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "bbox": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60704, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 7537, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 47159, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 115400, "size_in_bytes": 376305464}, "YOLO": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"image": {"_type": "Image"}, "objects": {"label": {"feature": {"names": ["Header", "Col", "Marginal", "text"], "_type": "ClassLabel"}, "_type": "List"}, "bbox": {"feature": {"feature": {"dtype": "int32", "_type": "Value"}, "length": 4, "_type": "List"}, "_type": "List"}}}, "config_name": "YOLO", "splits": {"train": {"name": "train", "num_bytes": 281327882, "num_examples": 196, "dataset_name": null}, "val": {"name": "val", "num_bytes": 37189262, "num_examples": 22, "dataset_name": null}, "test": {"name": "test", "num_bytes": 59596564, "num_examples": 135, "dataset_name": null}}, "download_size": 378142580, "dataset_size": 378113708, "size_in_bytes": 756256288}, "COCO": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"image_id": {"dtype": "int64", "_type": "Value"}, "image": {"_type": "Image"}, "width": {"dtype": "int32", "_type": "Value"}, "height": {"dtype": "int32", "_type": "Value"}, "objects": {"feature": {"category_id": {"names": ["Header", "Col", "Marginal", "text"], "_type": "ClassLabel"}, "image_id": {"dtype": "int64", "_type": "Value"}, "id": {"dtype": "int64", "_type": "Value"}, "area": {"dtype": "int64", "_type": "Value"}, "bbox": {"feature": {"dtype": "float32", "_type": "Value"}, "length": 4, "_type": "List"}, "segmentation": {"feature": {"feature": {"dtype": "float32", "_type": "Value"}, "_type": "List"}, "_type": "List"}, "iscrowd": {"dtype": "bool", "_type": "Value"}}, "_type": "List"}}, "config_name": "COCO", "splits": {"train": {"name": "train", "num_bytes": 281355547, "num_examples": 196, "dataset_name": null}, "val": {"name": "val", "num_bytes": 37193267, "num_examples": 22, "dataset_name": null}, "test": {"name": "test", "num_bytes": 59622681, "num_examples": 135, "dataset_name": null}}, "download_size": 378207654, "dataset_size": 378171495, "size_in_bytes": 756379149}}

yalta_ai_tabular_dataset.py

@@ -1,242 +0,0 @@
-# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Script for reading 'You Actually Look Twice At it (YALTAi)' dataset."""
-
-
-import os
-from glob import glob
-
-import datasets
-from PIL import Image
-
-_CITATION = """\
-    @dataset{clerice_thibault_2022_6827706,
-  author       = {Clérice, Thibault},
-  title        = {YALTAi: Tabular Dataset},
-  month        = jul,
-  year         = 2022,
-  publisher    = {Zenodo},
-  version      = {1.0.0},
-  doi          = {10.5281/zenodo.6827706},
-  url          = {https://doi.org/10.5281/zenodo.6827706}
-}
-"""
-
-_DESCRIPTION = """Yalt AI Tabular Dataset"""
-
-_HOMEPAGE = "https://doi.org/10.5281/zenodo.6827706"
-
-_LICENSE = "Creative Commons Attribution 4.0 International"
-
-_URL = "https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1"
-
-_CATEGORIES = ["Header", "Col", "Marginal", "text"]
-
-
-class YaltAiTabularDatasetConfig(datasets.BuilderConfig):
-    """BuilderConfig for YaltAiTabularDataset."""
-
-    def __init__(self, name, **kwargs):
-        """BuilderConfig for YaltAiTabularDataset."""
-        super(YaltAiTabularDatasetConfig, self).__init__(
-            version=datasets.Version("1.0.0"), name=name, description=None, **kwargs
-        )
-
-
-class YaltAiTabularDataset(datasets.GeneratorBasedBuilder):
-    """Object Detection for historic manuscripts"""
-
-    BUILDER_CONFIGS = [
-        YaltAiTabularDatasetConfig("YOLO"),
-        YaltAiTabularDatasetConfig("COCO"),
-    ]
-
-    def _info(self):
-        if self.config.name == "COCO":
-            features = datasets.Features(
-                {
-                    "image_id": datasets.Value("int64"),
-                    "image": datasets.Image(),
-                    "width": datasets.Value("int32"),
-                    "height": datasets.Value("int32"),
-                }
-            )
-            object_dict = {
-                "category_id": datasets.ClassLabel(names=_CATEGORIES),
-                "image_id": datasets.Value("string"),
-                "id": datasets.Value("int64"),
-                "area": datasets.Value("int64"),
-                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-                "segmentation": [[datasets.Value("float32")]],
-                "iscrowd": datasets.Value("bool"),
-            }
-            features["objects"] = [object_dict]
-        if self.config.name == "YOLO":
-            features = datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "objects": datasets.Sequence(
-                        {
-                            "label": datasets.ClassLabel(names=_CATEGORIES),
-                            "bbox": datasets.Sequence(
-                                datasets.Value("int32"), length=4
-                            ),
-                        }
-                    ),
-                }
-            )
-        return datasets.DatasetInfo(
-            features=features,
-            supervised_keys=None,
-            description=_DESCRIPTION,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_dir": os.path.join(data_dir, "yaltai-table/", "train")
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"data_dir": os.path.join(data_dir, "yaltai-table/", "val")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "data_dir": os.path.join(data_dir, "yaltai-table/", "test")
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_dir):
-        def create_annotation_from_yolo_format(
-            min_x,
-            min_y,
-            width,
-            height,
-            image_id,
-            category_id,
-            annotation_id,
-            segmentation=False,
-        ):
-            bbox = (float(min_x), float(min_y), float(width), float(height))
-            area = width * height
-            max_x = min_x + width
-            max_y = min_y + height
-            if segmentation:
-                seg = [[min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]]
-            else:
-                seg = []
-            return {
-                "id": annotation_id,
-                "image_id": image_id,
-                "bbox": bbox,
-                "area": area,
-                "iscrowd": 0,
-                "category_id": category_id,
-                "segmentation": seg,
-            }
-
-        image_dir = os.path.join(data_dir, "images")
-        label_dir = os.path.join(data_dir, "labels")
-        image_paths = sorted(glob(f"{image_dir}/*.jpg"))
-        label_paths = sorted(glob(f"{label_dir}/*.txt"))
-        if self.config.name == "COCO":
-            for idx, (image_path, label_path) in enumerate(
-                zip(image_paths, label_paths)
-            ):
-                image_id = idx
-                annotations = []
-                image = Image.open(image_path)  # Possibly conver to RGB?
-                w, h = image.size
-                with open(label_path, "r") as f:
-                    lines = f.readlines()
-                for line in lines:
-                    line = line.strip().split()
-                    category_id = line[0]
-                    x_center = float(line[1])
-                    y_center = float(line[2])
-                    width = float(line[3])
-                    height = float(line[4])
-
-                    float_x_center = w * x_center
-                    float_y_center = h * y_center
-                    float_width = w * width
-                    float_height = h * height
-
-                    min_x = int(float_x_center - float_width / 2)
-                    min_y = int(float_y_center - float_height / 2)
-                    width = int(float_width)
-                    height = int(float_height)
-
-                    annotation = create_annotation_from_yolo_format(
-                        min_x,
-                        min_y,
-                        width,
-                        height,
-                        image_id,
-                        category_id,
-                        image_id,
-                    )
-                    annotations.append(annotation)
-
-                example = {
-                    "image_id": image_id,
-                    "image": image,
-                    "width": w,
-                    "height": h,
-                    "objects": annotations,
-                }
-                yield idx, example
-        if self.config.name == "YOLO":
-            for idx, (image_path, label_path) in enumerate(
-                zip(image_paths, label_paths)
-            ):
-                im = Image.open(image_path)
-                width, height = im.size
-                image_id = idx
-                annotations = []
-                with open(label_path, "r") as f:
-                    lines = f.readlines()
-                objects = []
-                for line in lines:
-                    line = line.strip().split()
-                    bbox_class = int(line[0])
-                    bbox_xcenter = int(float(line[1]) * width)
-                    bbox_ycenter = int(float(line[2]) * height)
-                    bbox_width = int(float(line[3]) * width)
-                    bbox_height = int(float(line[4]) * height)
-                    objects.append(
-                        {
-                            "label": bbox_class,
-                            "bbox": [
-                                bbox_xcenter,
-                                bbox_ycenter,
-                                bbox_width,
-                                bbox_height,
-                            ],
-                        }
-                    )
-
-                yield idx, {
-                    "image": image_path,
-                    "objects": objects,
-                }