Convert dataset to Parquet

.gitattributes

@@ -1,2 +1,8 @@
 data.tar.gz filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
+full/train-00000-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+full/train-00001-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+full/train-00002-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+full/train-00003-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+full/train-00004-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+full/validation-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
+full/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text

.gitignore

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-env
-.idea
-*.json
-*.arrow

README.md

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 ---
-task_categories:
-- object-detection
-version: 2024.07.17
 license: cc-by-4.0
-pretty_name: LADaS
 size_categories:
 - 1K<n<10K
+task_categories:
+- object-detection
+pretty_name: LADaS
+dataset_info:
+  config_name: full
+  features:
+  - name: image_path
+    dtype: string
+  - name: image
+    dtype: image
+  - name: objects
+    sequence:
+    - name: bbox
+      sequence: float32
+      length: 4
+    - name: category
+      dtype:
+        class_label:
+          names:
+            '0': AdvertisementZone
+            '1': DigitizationArtefactZone
+            '2': DropCapitalZone
+            '3': FigureZone
+            '4': FigureZone-FigDesc
+            '5': FigureZone-Head
+            '6': GraphicZone
+            '7': GraphicZone-Decoration
+            '8': GraphicZone-FigDesc
+            '9': GraphicZone-Head
+            '10': GraphicZone-Maths
+            '11': GraphicZone-Part
+            '12': GraphicZone-TextualContent
+            '13': MainZone-Date
+            '14': MainZone-Entry
+            '15': MainZone-Entry-Continued
+            '16': MainZone-Form
+            '17': MainZone-Head
+            '18': MainZone-Lg
+            '19': MainZone-Lg-Continued
+            '20': MainZone-List
+            '21': MainZone-List-Continued
+            '22': MainZone-Other
+            '23': MainZone-P
+            '24': MainZone-P-Continued
+            '25': MainZone-Signature
+            '26': MainZone-Sp
+            '27': MainZone-Sp-Continued
+            '28': MarginTextZone-ManuscriptAddendum
+            '29': MarginTextZone-Notes
+            '30': MarginTextZone-Notes-Continued
+            '31': NumberingZone
+            '32': PageTitleZone
+            '33': PageTitleZone-Index
+            '34': QuireMarkZone
+            '35': RunningTitleZone
+            '36': StampZone
+            '37': StampZone-Sticker
+            '38': TableZone
+            '39': TableZone-Continued
+            '40': TableZone-Head
+  splits:
+  - name: train
+    num_bytes: 2357337025.298
+    num_examples: 4199
+  - name: validation
+    num_bytes: 237257708.0
+    num_examples: 662
+  - name: test
+    num_bytes: 92146968.0
+    num_examples: 294
+  download_size: 1535949175
+  dataset_size: 2686741701.298
+configs:
+- config_name: full
+  data_files:
+  - split: train
+    path: full/train-*
+  - split: validation
+    path: full/validation-*
+  - split: test
+    path: full/test-*
+  default: true
 ---
 
 # LADaS: Layout Analysis Dataset with Segmonto
 
-## Dataset Details
-
-LADaS, created by the [ALMANaCH team-project](https://almanach.inria.fr/index-en.html) at Inria,
-continued in partnership with other researchers, is a multidocuments diachronic layout analysis 
-dataset. This dataset includes:
-
-- Monographs from the Bibliothèque Nationale de France (17th century - today);
-- PhD Thesis, in various fields (not only STEM, 20th-21st century);
-- Selling Catalogs (for manuscripts and art pieces), in various fields (18th-20th century);
-- Noisy digitization (with fingers for example, 20th-21st century);
-- Academic papers (mostly Humanities and Social Sciences) (19th-21st century);
-- Magazines about technologies and video games, from 1920s to 2010;
-- Misc stuff found here and there.
-
-The data are in YoloV8 bbox format (center_x center_y width height).
-
-The script in document is mostly Latin script, and language is mostly French with some representation of the main
-western academic languages.
-
-### Annotations
-
-Label Annotation have been conducted using the [SegmOnto](https://segmonto.github.io/) vocabulary. 
-An annotation guide is available [here](https://github.com/DEFI-COLaF/LADaS/blob/25d4cf3f850cf79d18af572153cfbc73deff4160/AnnotationGuide.md).
-
-
-### Dataset Description
-
-
-- **Curated by:** Thibault Clérice & Juliette Janès
-- **Funded by:** Défi COLaF, Inria
-- **License:** CC-BY
-
-## Uses
-
-### Direct Use
-
-- Layout Analysis
-
-## Dataset Structure
-
-You'll find 8 fields in the parquet:
-
-- `image_path` is a string identifier for the file
-- `year` is the year of production. It can be null.
-- `dating-certainty` is used to specify when a date is automatically provided based on a date range and a century range. They will usually be centuries, such as 1600.
-- `set` represents the subset of the data, there are a few, including `theatre` for plays, `monographies` for monographs, `theses` for PhD thesis, etc.
-- `image` contains the image
-- `width` and `height` are quite clear...
-- `objects` is a dictionary with entries:
-  - `bbox` which represents a series of bbox using the COCO / YOLO format (tuple of relative center_x, center_y, width, height)
-  - `category` which contains a plain text representation of the annotation class of each bbox. There are 41 classes, further described on the annotation guide.
-  - `objects` can contain an empty `bbox` list and an empty `category` list: it means the image does not contain any objects.
-### Annotations
-
-#### Annotation process
-
-The annotation process is described in the [dataset paper](https://inria.hal.science/hal-04513725).
-
-#### Who are the annotators?
-
-- Clérice, Thibault
-- Janès, Juliette
-- Scheithauer, Hugo
-- Bénière, Sarah
-- Bougrelle, Roxane
-- [Anonymous place holder until a paper is published]
-
-## Citation
-
-**BibTeX:**
-
-```tex
-@misc{Clerice_Layout_Analysis_Dataset,
-  author = {Clérice, Thibault and Janès, Juliette and Scheithauer, Hugo and Bénière, Sarah and Bougrelle, Roxane and Romary, Laurent and Sagot, Benoit},
-  title = {{Layout Analysis Dataset with SegmOnto (LADaS)}},
-  url = {https://github.com/DEFI-COLaF/LADaS}
-}
-```
-
-## Dataset Card Contact
-
-Thibault Clérice or Juliette Janes ([email protected])

build.py

@@ -1,50 +0,0 @@
-import os
-from datasets import load_dataset
-from datasets import config
-from datasets.utils.py_utils import convert_file_size_to_int
-from datasets.table import embed_table_storage
-from tqdm import tqdm
-
-
-def build_parquet(split):
-    # Source: https://discuss.huggingface.co/t/how-to-save-audio-dataset-with-parquet-format-on-disk/66179
-    dataset = load_dataset("./src/LADaS.py", split=split, trust_remote_code=True)
-    max_shard_size = '500MB'
-
-    dataset_nbytes = dataset._estimate_nbytes()
-    max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
-    num_shards = int(dataset_nbytes / max_shard_size) + 1
-    num_shards = max(num_shards, 1)
-    shards = (dataset.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards))
-
-    def shards_with_embedded_external_files(shards):
-        for shard in shards:
-            format = shard.format
-            shard = shard.with_format("arrow")
-            shard = shard.map(
-                embed_table_storage,
-                batched=True,
-                batch_size=1000,
-                keep_in_memory=True,
-            )
-            shard = shard.with_format(**format)
-            yield shard
-
-    shards = shards_with_embedded_external_files(shards)
-
-    os.makedirs("data", exist_ok=True)
-
-    for index, shard in tqdm(
-        enumerate(shards),
-        desc="Save the dataset shards",
-        total=num_shards,
-    ):
-        shard_path = f"data/{split}-{index:05d}-of-{num_shards:05d}.parquet"
-        shard.to_parquet(shard_path)
-
-
-if __name__ == "__main__":
-    build_parquet("train")
-    build_parquet("validation")
-    build_parquet("test")
-

data/train-00003-of-00004.parquet

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-oid sha256:e07ed6a4a4a37ef5107bbb92448497f8fe1a5f634b5c7f53e6a3dea0232ab4e3
-size 152476613

{data → full}/test-00000-of-00001.parquet

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-oid sha256:8f8d4c8d639b746fef3265a8f17ade95ebbe91a71f80803343754b82401baff4
-size 109132271
+oid sha256:e2f02f4070c2af6f8026c6785c45dd91505ba5691b3444c9a25e4e85c411ba2e
+size 89745385

data/train-00001-of-00004.parquet → full/train-00000-of-00005.parquet

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+size 466051138

data/train-00002-of-00004.parquet → full/train-00001-of-00005.parquet

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data/train-00000-of-00004.parquet → full/train-00002-of-00005.parquet

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-oid sha256:50510535956adfcce298f3666a6fe04485a1b6bfc43753b8ca9254fb1086f771
-size 520039514
+oid sha256:f02c7cb4653d06c1f1a3cd1818bcd433283e4d15c24494c396bedfee4a9ba707
+size 121729125

full/train-00003-of-00005.parquet

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+size 104987339

full/train-00004-of-00005.parquet

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+size 85670348

{data → full}/validation-00000-of-00001.parquet

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-oid sha256:da1a0c88505d6de40c42452a82c9e9f1e9bdf67631b207c04e8ed7b445d3d4ad
-size 258990056
+oid sha256:dbb7122abdcfb47fa695145b3532816c5fc741e8f05b35631595f0b99034c672
+size 231834625

src/LADaS.py

@@ -1,141 +0,0 @@
-import glob
-import os
-import datasets
-from PIL import Image
-import csv
-
-
-_VERSION = "2024-07-17"
-_URL = f"https://github.com/DEFI-COLaF/LADaS/archive/refs/tags/{_VERSION}.tar.gz"
-_HOMEPAGE = "https://github.com/DEFI-COLaF/LADaS"
-_LICENSE = "CC BY 4.0"
-_CITATION = """\
-@misc{Clerice_Layout_Analysis_Dataset,
-    author = {Clérice, Thibault and Janès, Juliette and Scheithauer, Hugo and Bénière, Sarah and Romary, Laurent and Sagot, Benoit and Bougrelle, Roxane},
-    title = {{Layout Analysis Dataset with SegmOnto (LADaS)}},
-    url = {https://github.com/DEFI-COLaF/LADaS}
-}
-"""
-
-_CATEGORIES: list[str] = ["AdvertisementZone", "DigitizationArtefactZone", "DropCapitalZone", "FigureZone",
-                          "FigureZone-FigDesc", "FigureZone-Head", "GraphicZone", "GraphicZone-Decoration",
-                          "GraphicZone-FigDesc", "GraphicZone-Head", "GraphicZone-Maths", "GraphicZone-Part",
-                          "GraphicZone-TextualContent", "MainZone-Date", "MainZone-Entry", "MainZone-Entry-Continued",
-                          "MainZone-Form", "MainZone-Head", "MainZone-Lg", "MainZone-Lg-Continued", "MainZone-List",
-                          "MainZone-List-Continued", "MainZone-Other", "MainZone-P", "MainZone-P-Continued",
-                          "MainZone-Signature", "MainZone-Sp", "MainZone-Sp-Continued",
-                          "MarginTextZone-ManuscriptAddendum", "MarginTextZone-Notes", "MarginTextZone-Notes-Continued",
-                          "NumberingZone", "TitlePageZone", "TitlePageZone-Index", "QuireMarksZone", "RunningTitleZone",
-                          "StampZone", "StampZone-Sticker", "TableZone", "TableZone-Continued", "TableZone-Head"]
-
-
-class LadasConfig(datasets.BuilderConfig):
-    """Builder Config for LADaS"""
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-
-class LadasDataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version(_VERSION.replace("-", "."))
-    BUILDER_CONFIGS = [
-        LadasConfig(
-            name="full",
-            description="Full version of the dataset"
-        )
-    ]
-
-    def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features({
-            "image_path": datasets.Value("string"),
-            "year": datasets.Value("int32"),
-            "dating-certainty": datasets.Value("bool"),
-            "set": datasets.Value("string"),
-            "image": datasets.Image(),
-            "width": datasets.Value("int32"),
-            "height": datasets.Value("int32"),
-            "objects": datasets.Sequence(
-                {
-                    "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-                    "category": datasets.Value("string"),
-                }
-            )
-        })
-        return datasets.DatasetInfo(
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = _URL
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "local_dir": downloaded_files,
-                    "split": "train"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "local_dir": downloaded_files,
-                    "split": "valid"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "local_dir": downloaded_files,
-                    "split": "test"
-                },
-            ),
-        ]
-
-    def _generate_examples(self, local_dir: str, split: str):
-        idx = 0
-
-        df = {}
-        for file in glob.glob(os.path.join(local_dir, "*", "metadata.csv")):
-            with open(file) as f:
-                reader = csv.DictReader(f)
-                for line in reader:
-                   df[line["file"]] = line
-
-        for file in glob.glob(os.path.join(local_dir, "*", "data", "*", split, "labels", "*.txt")):
-            objects = []
-            with open(file) as f:
-                for line in f:
-                    cls, *bbox = line.strip().split()
-                    objects.append({"category": _CATEGORIES[int(cls)], "bbox": list(map(float, bbox))})
-
-            image_path = os.path.normpath(file).split(os.sep)
-            image_path = os.path.join(*image_path[:-2], "images", image_path[-1].replace(".txt", ".jpg"))
-            if file.startswith("/") and not image_path.startswith("/"):
-                image_path = "/" + image_path
-
-            with open(image_path, "rb") as f:
-                image_bytes = f.read()
-
-            with Image.open(image_path) as im:
-                width, height = im.size
-
-            filename = os.path.basename(image_path)
-            line = df[filename]
-
-            yield idx, {
-                "image_path": f"{line['subset']}/{filename}",
-                "image": {"path": image_path, "bytes": image_bytes},
-                "year": line["year"] or None,
-                "dating-certainty": line["dating-certainty"],
-                "set": line["subset"],
-                "width": width,
-                "height": height,
-                "objects": objects,
-            }
-            idx += 1
-
-if __name__ == "__main__":
-    LadasDataset().download_and_prepare()