Initialize (#1)

* add files

* update files

* update

* update COCOA.py

* update

* update

* update

* add `push_to_hub.yaml`

* update

* update

* add README.md

* update

* update

.github/workflows/ci.yaml

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+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+    paths-ignore:
+      - 'README.md'
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.9', '3.10']
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          pip install -U pip setuptools wheel poetry
+          poetry install
+
+      - name: Format
+        run: |
+          poetry run black --check .
+
+      - name: Lint
+        run: |
+          poetry run ruff .
+          
+      - name: Type check
+        run: |
+          poetry run mypy . \
+            --ignore-missing-imports \
+            --no-strict-optional \
+            --no-site-packages \
+            --cache-dir=/dev/null
+            
+      # - name: Run tests
+      #   run: |
+      #     poetry run pytest --color=yes -rf

.github/workflows/push_to_hub.yaml

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+name: Sync to Hugging Face Hub
+
+on:
+  workflow_run:
+    workflows:
+      - CI
+    branches:
+      - main
+    types: 
+      - completed
+
+jobs:
+  push_to_hub:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+        
+      - name: Push to Huggingface hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_USERNAME: ${{ secrets.HF_USERNAME }}
+        run: |
+          git fetch --unshallow
+          git push --force https://${HF_USERNAME}:${HF_TOKEN}@huggingface.co/datasets/${HF_USERNAME}/COCOA main

.gitignore

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+# Created by https://www.toptal.com/developers/gitignore/api/python
+# Edit at https://www.toptal.com/developers/gitignore?templates=python
+
+*.tar.gz
+*.jpg
+*.png
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+# End of https://www.toptal.com/developers/gitignore/api/python

COCOA.py

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+import json
+import logging
+import os
+from collections import defaultdict
+from dataclasses import asdict, dataclass
+from typing import Any, Dict, List, Literal, Optional, Tuple, Type, Union
+
+import datasets as ds
+import numpy as np
+from PIL import Image
+from PIL.Image import Image as PilImage
+from pycocotools import mask as cocomask
+from tqdm.auto import tqdm
+
+logger = logging.getLogger(__name__)
+JsonDict = Dict[str, Any]
+
+ImageId = int
+AnnotationId = int
+LicenseId = int
+
+
+_CITATION = """\
+@inproceedings{zhu2017semantic,
+  title={Semantic amodal segmentation},
+  author={Zhu, Yan and Tian, Yuandong and Metaxas, Dimitris and Doll{\'a}r, Piotr},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={1464--1472},
+  year={2017}
+}
+@inproceedings{lin2014microsoft,
+  title={Microsoft coco: Common objects in context},
+  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
+  booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13},
+  pages={740--755},
+  year={2014},
+  organization={Springer}
+}
+@article{arbelaez2010contour,
+  title={Contour detection and hierarchical image segmentation},
+  author={Arbelaez, Pablo and Maire, Michael and Fowlkes, Charless and Malik, Jitendra},
+  journal={IEEE transactions on pattern analysis and machine intelligence},
+  volume={33},
+  number={5},
+  pages={898--916},
+  year={2010},
+  publisher={IEEE}
+}
+"""
+
+_DESCRIPTION = """\
+COCOA dataset targets amodal segmentation, which aims to recognize and segment objects beyond their visible parts. \
+This dataset includes labels not only for the visible parts of objects, but also for their occluded parts hidden \
+by other objects. This enables learning to understand the full shape and position of objects. 
+"""
+
+_HOMEPAGE = "https://github.com/Wakeupbuddy/amodalAPI"
+
+_LICENSE = """\
+The annotations in the COCO dataset along with this website belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License.
+"""
+
+_URLS = {
+    "COCO": {
+        "images": {
+            "train": "http://images.cocodataset.org/zips/train2014.zip",
+            "validation": "http://images.cocodataset.org/zips/val2014.zip",
+            "test": "http://images.cocodataset.org/zips/test2014.zip",
+        },
+    },
+    "BSDS": {
+        "images": "http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz",
+    },
+}
+
+
+def _load_image(image_path: str) -> PilImage:
+    return Image.open(image_path)
+
+
+@dataclass
+class ImageData(object):
+    image_id: ImageId
+    license_id: LicenseId
+    file_name: str
+    height: int
+    width: int
+    date_captured: str
+    flickr_url: str
+
+    @classmethod
+    def get_date_captured(cls, json_dict: JsonDict) -> str:
+        date_captured = json_dict.get("date_captured")
+        if date_captured is None:
+            date_captured = json_dict["data_captured"]  # typo?
+        return date_captured
+
+    @classmethod
+    def get_license_id(cls, json_dict: JsonDict) -> int:
+        license_id = json_dict["license"]
+        if license_id == "?":
+            # Since the test data in BSDS has a license id of `?`,
+            # convert it to -100 instead.
+            return -100
+        else:
+            return int(license_id)
+
+    @classmethod
+    def to_base_dict(cls, json_dict: JsonDict) -> JsonDict:
+        return {
+            "image_id": json_dict["id"],
+            "file_name": json_dict["file_name"],
+            "height": json_dict["height"],
+            "width": json_dict["width"],
+            "flickr_url": json_dict["flickr_url"],
+            "license_id": cls.get_license_id(json_dict),
+            "date_captured": cls.get_date_captured(json_dict),
+        }
+
+    @property
+    def shape(self) -> Tuple[int, int]:
+        return (self.height, self.width)
+
+
+@dataclass
+class CocoImageData(ImageData):
+    coco_url: str
+
+    @classmethod
+    def from_dict(cls, json_dict: JsonDict) -> "CocoImageData":
+        return cls(
+            **cls.to_base_dict(json_dict),
+            coco_url=json_dict["coco_url"],
+        )
+
+
+@dataclass
+class BsDsImageData(ImageData):
+    bsds_url: str
+
+    @classmethod
+    def from_dict(cls, json_dict: JsonDict) -> "BsDsImageData":
+        return cls(
+            **cls.to_base_dict(json_dict),
+            bsds_url=json_dict["bsds_url"],
+        )
+
+
+@dataclass
+class RegionAnnotationData(object):
+    segmentation: np.ndarray
+    name: str
+    area: float
+    is_stuff: bool
+    occlude_rate: float
+    order: int
+    visible_mask: Optional[np.ndarray] = None
+    invisible_mask: Optional[np.ndarray] = None
+
+    @classmethod
+    def rle_segmentation_to_binary_mask(
+        cls, segmentation, height: int, width: int
+    ) -> np.ndarray:
+        if isinstance(segmentation, list):
+            rles = cocomask.frPyObjects([segmentation], h=height, w=width)
+            rle = cocomask.merge(rles)
+        else:
+            raise NotImplementedError
+
+        return cocomask.decode(rle)
+
+    @classmethod
+    def rle_segmentation_to_mask(
+        cls, segmentation, height: int, width: int
+    ) -> np.ndarray:
+        binary_mask = cls.rle_segmentation_to_binary_mask(
+            segmentation=segmentation, height=height, width=width
+        )
+        return binary_mask * 255
+
+    @classmethod
+    def get_visible_binary_mask(cls, rle_visible_mask=None) -> Optional[np.ndarray]:
+        if rle_visible_mask is None:
+            return None
+        return cocomask.decode(rle_visible_mask)
+
+    @classmethod
+    def get_invisible_binary_mask(cls, rle_invisible_mask=None) -> Optional[np.ndarray]:
+        return cls.get_visible_binary_mask(rle_invisible_mask)
+
+    @classmethod
+    def get_visible_mask(cls, rle_visible_mask=None) -> Optional[np.ndarray]:
+        visible_mask = cls.get_visible_binary_mask(rle_visible_mask=rle_visible_mask)
+        return visible_mask * 255 if visible_mask is not None else None
+
+    @classmethod
+    def get_invisible_mask(cls, rle_invisible_mask=None) -> Optional[np.ndarray]:
+        return cls.get_visible_mask(rle_invisible_mask)
+
+    @classmethod
+    def from_dict(
+        cls, json_dict: JsonDict, image_data: ImageData
+    ) -> "RegionAnnotationData":
+        segmentation = json_dict["segmentation"]
+
+        segmentation_mask = cls.rle_segmentation_to_mask(
+            segmentation=segmentation,
+            height=image_data.height,
+            width=image_data.width,
+        )
+        visible_mask = cls.get_visible_mask(
+            rle_visible_mask=json_dict.get("visible_mask")
+        )
+        invisible_mask = cls.get_invisible_mask(
+            rle_invisible_mask=json_dict.get("invisible_mask")
+        )
+        return cls(
+            segmentation=segmentation_mask,
+            visible_mask=visible_mask,
+            invisible_mask=invisible_mask,
+            name=json_dict["name"],
+            area=json_dict["area"],
+            is_stuff=json_dict["isStuff"],
+            occlude_rate=json_dict["occlude_rate"],
+            order=json_dict["order"],
+        )
+
+
+@dataclass
+class CocoaAnnotationData(object):
+    author: str
+    url: str
+    regions: List[RegionAnnotationData]
+    image_id: ImageId
+    depth_constraint: str
+    size: int
+
+    @classmethod
+    def from_dict(
+        cls, json_dict: JsonDict, images: Dict[ImageId, ImageData]
+    ) -> "CocoaAnnotationData":
+        image_id = json_dict["image_id"]
+
+        regions = [
+            RegionAnnotationData.from_dict(
+                json_dict=region_dict, image_data=images[image_id]
+            )
+            for region_dict in json_dict["regions"]
+        ]
+
+        return cls(
+            author=json_dict["author"],
+            url=json_dict["url"],
+            regions=regions,
+            image_id=image_id,
+            depth_constraint=json_dict["depth_constraint"],
+            size=json_dict["size"],
+        )
+
+
+def _load_images_data(
+    image_dicts: List[JsonDict],
+    dataset_name: Literal["COCO", "BSDS"],
+    tqdm_desc: str = "Load images",
+) -> Dict[ImageId, ImageData]:
+    ImageDataClass: Union[Type[CocoImageData], Type[BsDsImageData]]
+
+    if dataset_name == "COCO":
+        ImageDataClass = CocoImageData
+    elif dataset_name == "BSDS":
+        ImageDataClass = BsDsImageData
+    else:
+        raise ValueError(f"Invalid dataset name: {dataset_name}")
+
+    images: Dict[ImageId, Union[CocoImageData, BsDsImageData]] = {}
+    for image_dict in tqdm(image_dicts, desc=tqdm_desc):
+        image_data = ImageDataClass.from_dict(image_dict)
+        images[image_data.image_id] = image_data
+    return images  # type: ignore
+
+
+def _load_cocoa_data(
+    ann_dicts: List[JsonDict],
+    images: Dict[ImageId, ImageData],
+    tqdm_desc: str = "Load COCOA annotations",
+):
+    annotations = defaultdict(list)
+    ann_dicts = sorted(ann_dicts, key=lambda d: d["image_id"])
+
+    for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
+        cocoa_data = CocoaAnnotationData.from_dict(ann_dict, images=images)
+        annotations[cocoa_data.image_id].append(cocoa_data)
+
+    return annotations
+
+
+class CocoaDataset(ds.GeneratorBasedBuilder):
+    VERSION = ds.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        ds.BuilderConfig(name="COCO", version=VERSION),
+        ds.BuilderConfig(name="BSDS", version=VERSION),
+    ]
+
+    def load_amodal_annotation(self, ann_json_path: str) -> JsonDict:
+        logger.info(f"Load from {ann_json_path}")
+        with open(ann_json_path, "r") as rf:
+            ann_json = json.load(rf)
+        return ann_json
+
+    @property
+    def manual_download_instructions(self) -> str:
+        return (
+            "To use COCOA, you need to download the annotations "
+            "from the google drive in the official repositories "
+            "(https://github.com/Wakeupbuddy/amodalAPI#setup)."
+            "Downloading of annotations currently appears to be restricted, "
+            "but the author will allow us to download them if we request access privileges."
+        )
+
+    def _info(self) -> ds.DatasetInfo:
+        features_dict = {
+            "image_id": ds.Value("int64"),
+            "license_id": ds.Value("int32"),
+            "file_name": ds.Value("string"),
+            "height": ds.Value("int32"),
+            "width": ds.Value("int32"),
+            "date_captured": ds.Value("string"),
+            "flickr_url": ds.Value("string"),
+            "image": ds.Image(),
+        }
+
+        if self.config.name == "COCO":
+            features_dict["coco_url"] = ds.Value("string")
+        elif self.config.name == "BSDS":
+            features_dict["bsds_url"] = ds.Value("string")
+        else:
+            raise ValueError(f"Invalid dataset name: {self.config.name}")
+
+        features_dict["annotations"] = ds.Sequence(
+            {
+                "author": ds.Value("string"),
+                "url": ds.Value("string"),
+                "regions": ds.Sequence(
+                    {
+                        "segmentation": ds.Image(),
+                        "name": ds.Value("string"),
+                        "area": ds.Value("float32"),
+                        "is_stuff": ds.Value("bool"),
+                        "occlude_rate": ds.Value("float32"),
+                        "order": ds.Value("int32"),
+                        "visible_mask": ds.Image(),
+                        "invisible_mask": ds.Image(),
+                    }
+                ),
+                "image_id": ds.Value("int64"),
+                "depth_constraint": ds.Value("string"),
+                "size": ds.Value("int32"),
+            }
+        )
+        features = ds.Features(features_dict)
+
+        return ds.DatasetInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            features=features,
+        )
+
+    def _split_generators_coco(self, ann_dir: str, image_dirs: Dict[str, str]):
+        tng_ann_path = os.path.join(
+            ann_dir,
+            f"{self.config.name}_amodal_train2014.json",
+        )
+        val_ann_path = os.path.join(
+            ann_dir,
+            f"{self.config.name}_amodal_val2014.json",
+        )
+        tst_ann_path = os.path.join(
+            ann_dir,
+            f"{self.config.name}_amodal_test2014.json",
+        )
+        return [
+            ds.SplitGenerator(
+                name=ds.Split.TRAIN,  # type: ignore
+                gen_kwargs={
+                    "base_image_dir": image_dirs["train"],
+                    "amodal_annotation_path": tng_ann_path,
+                    "split": "train",
+                },
+            ),
+            ds.SplitGenerator(
+                name=ds.Split.VALIDATION,  # type: ignore
+                gen_kwargs={
+                    "base_image_dir": image_dirs["validation"],
+                    "amodal_annotation_path": val_ann_path,
+                    "split": "val",
+                },
+            ),
+            ds.SplitGenerator(
+                name=ds.Split.TEST,  # type: ignore
+                gen_kwargs={
+                    "base_image_dir": image_dirs["test"],
+                    "amodal_annotation_path": tst_ann_path,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _split_generators_bsds(self, ann_dir: str, image_dir: str):
+        tng_ann_path = os.path.join(
+            ann_dir,
+            f"{self.config.name}_amodal_train.json",
+        )
+        val_ann_path = os.path.join(
+            ann_dir,
+            f"{self.config.name}_amodal_val.json",
+        )
+        tst_ann_path = os.path.join(
+            ann_dir,
+            f"{self.config.name}_amodal_test.json",
+        )
+        image_dir = os.path.join(image_dir, "BSR", "BSDS500", "data", "images")
+        return [
+            ds.SplitGenerator(
+                name=ds.Split.TRAIN,  # type: ignore
+                gen_kwargs={
+                    "base_image_dir": os.path.join(image_dir, "train"),
+                    "amodal_annotation_path": tng_ann_path,
+                    "split": "train",
+                },
+            ),
+            ds.SplitGenerator(
+                name=ds.Split.VALIDATION,  # type: ignore
+                gen_kwargs={
+                    "base_image_dir": os.path.join(image_dir, "val"),
+                    "amodal_annotation_path": val_ann_path,
+                    "split": "validation",
+                },
+            ),
+            ds.SplitGenerator(
+                name=ds.Split.TEST,  # type: ignore
+                gen_kwargs={
+                    "base_image_dir": os.path.join(image_dir, "test"),
+                    "amodal_annotation_path": tst_ann_path,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _split_generators(self, dl_manager: ds.DownloadManager):
+        file_paths = dl_manager.download_and_extract(_URLS[self.config.name])
+        image_dirs = file_paths["images"]  # type: ignore
+
+        assert dl_manager.manual_dir is not None, dl_manager.manual_dir
+        data_path = os.path.expanduser(dl_manager.manual_dir)
+
+        if not os.path.exists(data_path):
+            raise FileNotFoundError(
+                f"{data_path} does not exists. Make sure you insert a manual dir "
+                'via `datasets.load_dataset("shunk031/COCOA", data_dir=...)` '
+                "that includes tar/untar files from the COCOA annotation tar.gz. "
+                f"Manual download instructions: {self.manual_download_instructions}"
+            )
+        else:
+            data_path = (
+                dl_manager.extract(data_path)
+                if not os.path.isdir(data_path)
+                else data_path
+            )
+
+        assert isinstance(data_path, str)
+        ann_dir = os.path.join(data_path, "annotations")
+
+        if self.config.name == "COCO":
+            return self._split_generators_coco(ann_dir=ann_dir, image_dirs=image_dirs)
+
+        elif self.config.name == "BSDS":
+            return self._split_generators_bsds(ann_dir=ann_dir, image_dir=image_dirs)
+
+        else:
+            raise ValueError(f"Invalid name: {self.config.name}")
+
+    def _generate_examples(
+        self,
+        split: str,
+        base_image_dir: str,
+        amodal_annotation_path: str,
+    ):
+        if self.config.name == "COCO":
+            image_dir = os.path.join(base_image_dir, f"{split}2014")
+        elif self.config.name == "BSDS":
+            image_dir = base_image_dir
+        else:
+            raise ValueError(f"Invalid task: {self.config.name}")
+
+        ann_json = self.load_amodal_annotation(amodal_annotation_path)
+
+        images = _load_images_data(
+            image_dicts=ann_json["images"],
+            dataset_name=self.config.name,
+        )
+        annotations = _load_cocoa_data(ann_dicts=ann_json["annotations"], images=images)
+
+        for idx, image_id in enumerate(images.keys()):
+            image_data = images[image_id]
+            image_anns = annotations[image_id]
+
+            if len(image_anns) < 1:
+                continue
+
+            image = _load_image(
+                image_path=os.path.join(image_dir, image_data.file_name)
+            )
+            example = asdict(image_data)
+            example["image"] = image
+            example["annotations"] = []
+            for ann in image_anns:
+                ann_dict = asdict(ann)
+                example["annotations"].append(ann_dict)
+
+            yield idx, example

README.md

@@ -0,0 +1,496 @@
+---
+language:
+- en
+license: cc-by-4.0
+
+tags:
+- computer-vision
+- object-detection
+- ms-coco
+
+datasets:
+- stuff-thing
+- stuff-only
+
+metrics:
+- accuracy
+- iou
+---
+
+# Dataset Card for COCOA
+
+[![CI](https://github.com/shunk031/huggingface-datasets_COCOA/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunk031/huggingface-datasets_COCOA/actions/workflows/ci.yaml)
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [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: https://github.com/Wakeupbuddy/amodalAPI
+- Repository: https://github.com/shunk031/huggingface-datasets_COCOA
+- Paper (preprint): https://arxiv.org/abs/1509.01329
+- Paper (CVPR2017): https://openaccess.thecvf.com/content_cvpr_2017/html/Zhu_Semantic_Amodal_Segmentation_CVPR_2017_paper.html
+
+### Dataset Summary
+
+COCOA dataset targets amodal segmentation, which aims to recognize and segment objects beyond their visible parts. This dataset includes labels not only for the visible parts of objects, but also for their occluded parts hidden by other objects. This enables learning to understand the full shape and position of objects. 
+
+From the paper:
+
+> We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering.
+
+### Dataset Preprocessing
+
+### Supported Tasks and Leaderboards
+
+### Languages
+
+All of annotations use English as primary language.
+
+## Dataset Structure
+
+### Data Instances
+
+To use COCOA, you need to download the annotations from [the google drive](https://drive.google.com/open?id=0B8e3LNo7STslZURoTzhhMFpCelE) in the official repositories (https://github.com/Wakeupbuddy/amodalAPI#setup). Downloading of annotations currently appears to be restricted, but the author will allow us to download them if we request access privileges.
+
+When loading a specific configuration, users has to append a version dependent suffix:
+
+```python
+import datasets as ds
+
+dataset = ds.load_dataset("shunk031/COCOA", name="COCO", data_dir="/path/to/cocoa_annotation.tar.gz")
+```
+
+#### COCO
+
+An example of looks as follows.
+
+```json
+{
+    "image_id": 321, 
+    "license_id": 1, 
+    "file_name": "COCO_train2014_000000000321.jpg", 
+    "height": 480, 
+    "width": 640, 
+    "date_captured": "2013-11-20 12: 36: 25", 
+    "flickr_url": "http: //farm5.staticflickr.com/4096/4750559893_49fb0baf7f_z.jpg", 
+    "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FD21970F5E0>, 
+    "coco_url": "http://mscoco.org/images/321", 
+    "annotations": {
+        "author": ["ash2"], 
+        "url": ["https://s3-us-west-1.amazonaws.com/coco-ann/coco-train/COCO_train2014_000000000321.jpg"], 
+        "regions": [
+            {
+                "segmentation": [
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FBE0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970F8E0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970F400>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970F790>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FCA0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FF40>
+                ], 
+                "name": ["sandwich", "container", "hot dog", "hot dog", "container", "table"], 
+                "area": [63328.0, 141246.0, 31232.0, 28735.0, 265844.0, 307200.0], 
+                "is_stuff": [False, False, False, False, False, True], 
+                "occlude_rate": [0.0, 0.44835251569747925, 0.0, 0.022307291626930237, 0.7122523188591003, 0.9019140601158142], 
+                "order": [1, 2, 3, 4, 5, 6], 
+                "visible_mask": [
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FD90>, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FB50>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FE80>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479460>
+                ], 
+                "invisible_mask": [
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479160>, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD2194793A0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479490>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479130>
+                ]
+            }
+        ], 
+        "image_id": [321], 
+        "depth_constraint": ["1-2,1-5,1-6,2-5,2-6,3-4,3-5,3-6,4-5,4-6,5-6"], 
+        "size": [6]
+    }
+}
+```
+
+#### BSDS
+
+An example of looks as follows.
+
+```json
+{
+    "image_id": 100075, 
+    "license_id": -100, 
+    "file_name": "100075.jpg", 
+    "height": 321, 
+    "width": 481, 
+    "date_captured": "?", 
+    "flickr_url": "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg", 
+    "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=481x321 at 0x7FD22A328CA0>, 
+    "bsds_url": "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg", 
+    "annotations": {
+        "author": ["acherian", "amorgan", "dromero", "jdayal", "kjyou", "ttouneh"], 
+        "url": [
+            "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg", 
+            "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg", 
+            "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg", 
+            "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg", 
+            "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg", 
+            "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg"
+        ], 
+        "regions": [
+            {
+                "segmentation": [
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3288E0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328430>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328070>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328610>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3280D0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328BE0>
+                ], 
+                "name": ["rocks", "bear", "bear", "bear", "sand", "water"], 
+                "area": [31872.0, 5603.0, 38819.0, 12869.0, 27883.0, 124695.0], 
+                "is_stuff": [False, False, False, False, False, False], 
+                "occlude_rate": [0.0, 0.0, 0.0, 0.3645193874835968, 0.13043789565563202, 0.6487349271774292], 
+                "order": [1, 2, 3, 4, 5, 6], 
+                "visible_mask": [
+                    None, 
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328AF0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328A30>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328220>
+                ], 
+                "invisible_mask": [
+                    None, 
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3282E0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328400>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328310>
+                ]
+            }, 
+            {
+                "segmentation": [
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328340>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328B80>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328670>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328520>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328460>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328D00>
+                ], 
+                "name": ["bear", "bear", "bear", "shore line", "water", "shore line"], 
+                "area": [38772.0, 5178.0, 13575.0, 31977.0, 84224.0, 37418.0], 
+                "is_stuff": [False, False, False, False, False, False], 
+                "occlude_rate": [0.0, 0.0, 0.35889503359794617, 0.1458861082792282, 0.5715591907501221, 0.0], 
+                "order": [1, 2, 3, 4, 5, 6], 
+                "visible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328A00>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328D60>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3285E0>, 
+                    None
+                ], 
+                "invisible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3286A0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328490>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328100>, 
+                    None
+                ]
+            }, 
+            {
+                "segmentation": [
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3282B0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328EE0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3284C0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3285B0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328C40>
+                ], 
+                "name": ["bear", "bear", "bear", "beach", "ocean"], 
+                "area": [38522.0, 5496.0, 12581.0, 27216.0, 126090.0], 
+                "is_stuff": [False, False, False, False, False], 
+                "occlude_rate": [0.0, 0.0, 0.3449646234512329, 0.11258083581924438, 0.39141881465911865], 
+                "order": [1, 2, 3, 4, 5], 
+                "visible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328940>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328880>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830A00>
+                ], 
+                "invisible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830CD0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830BB0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830940>
+                ]
+            }, 
+            {
+                "segmentation": [
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830910>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2198308E0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830C70>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830970>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830CA0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2198309A0>
+                ], 
+                "name": ["Bear", "Bear", "Bear", "Water", "ground", "Ground"], 
+                "area": [39133.0, 7120.0, 13053.0, 97052.0, 33441.0, 26313.0], 
+                "is_stuff": [False, False, False, False, False, False], 
+                "occlude_rate": [0.0, 0.0, 0.4422737956047058, 0.5332708358764648, 0.007117012050002813, 0.1584388017654419], 
+                "order": [1, 2, 3, 4, 5, 6], 
+                "visible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830A30>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830C40>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830B80>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6820>
+                ], 
+                "invisible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A68B0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6610>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A69D0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6730>
+                ]
+            }, 
+            {
+                "segmentation": [
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6790>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6550>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6850>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6940>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A66D0>
+                ], 
+                "name": ["bear", "bear", "bear", "water", "rock beach"], 
+                "area": [38378.0, 6130.0, 12649.0, 98377.0, 153118.0], 
+                "is_stuff": [False, False, False, False, False], 
+                "occlude_rate": [0.0, 0.0, 0.41094157099723816, 0.5013265013694763, 0.65973299741745], 
+                "order": [1, 2, 3, 4, 5], 
+                "visible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD268700F10>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2687004F0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2687002B0>
+                ], 
+                "invisible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A64C0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD28805FB50>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD28805F580>
+                ]
+            }, 
+            {
+                "segmentation": [
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6880>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB190>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB8E0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB070>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB610>
+                ], 
+                "name": ["bear", "bear", "bear", "sand", "water"], 
+                "area": [38802.0, 5926.0, 12248.0, 27857.0, 126748.0], 
+                "is_stuff": [False, False, False, False, False], 
+                "occlude_rate": [0.0, 0.0, 0.37026453018188477, 0.13170836865901947, 0.3872092664241791], 
+                "order": [1, 2, 3, 4, 5], 
+                "visible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479DC0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479C70>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479A90>
+                ], 
+                "invisible_mask": [
+                    None, 
+                    None, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479AF0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2194795B0>, 
+                    <PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479670>
+                ]
+            }
+        ], 
+        "image_id": [100075, 100075, 100075, 100075, 100075, 100075], 
+        "depth_constraint": [
+            "1-6,2-4,2-5,2-6,3-4,3-5,3-6,4-5,4-6,5-6", 
+            "1-3,1-4,1-5,2-3,2-4,2-5,3-4,3-5,4-5", 
+            "1-3,1-4,1-5,2-3,2-4,2-5,3-4,3-5,4-5", 
+            "1-3,1-4,1-6,2-3,2-4,2-6,3-4,3-6,4-5,4-6", 
+            "1-4,1-5,2-3,2-4,2-5,3-4,3-5,4-5", 
+            "1-3,1-4,1-5,2-3,2-4,2-5,3-4,3-5,4-5"
+        ], 
+        "size": [6, 6, 5, 6, 5, 5]
+    }
+}
+```
+
+### Data Fields
+
+#### COCO
+
+- `image_id`: Unique numeric ID of the image.
+- `license_id`: Unique numeric ID of the image license.
+- `file_name`: File name of the image.
+- `width`: Image width.
+- `height`: Image height.
+- `date_captured`: Date of capturing data
+- `flickr_url`: Original flickr url of the image.
+- `image`: A `PIL.Image.Image` object containing the image.
+- `coco_url`: COCO url of the image.
+- `annotations`: Holds a list of `Annotation` data classes:
+    - `author`: TBD
+    - `url`: TBD
+    - `image_id`: TBD
+    - `depth_constraint`: TBD
+    - `size`: TBD
+    - `regions`: TBD
+      - `segmentation`: TBD
+        - `name`: TBD
+        - `area`: TBD
+        - `is_stuff`: TBD
+        - `occlude_rate`: TBD
+        - `order`: TBD
+        - `visible_mask`: TBD
+        - `invisible_mask`: TBD
+
+#### BSDS
+
+- `image_id`: Unique numeric ID of the image.
+- `license_id`: Unique numeric ID of the image license.
+- `file_name`: File name of the image.
+- `width`: Image width.
+- `height`: Image height.
+- `date_captured`: Date of capturing data
+- `flickr_url`: Original flickr url of the image.
+- `image`: A `PIL.Image.Image` object containing the image.
+- `bsds_url`: BSDS url of the image.
+- `annotations`: Holds a list of `Annotation` data classes:
+    - `author`: TBD
+    - `url`: TBD
+    - `image_id`: TBD
+    - `depth_constraint`: TBD
+    - `size`: TBD
+    - `regions`: TBD
+      - `segmentation`: TBD
+        - `name`: TBD
+        - `area`: TBD
+        - `is_stuff`: TBD
+        - `occlude_rate`: TBD
+        - `order`: TBD
+        - `visible_mask`: TBD
+        - `invisible_mask`: TBD
+
+### Data Splits
+
+| name | train | validation | test  |
+|------|------:|-----------:|------:|
+| COCO | 2,500 | 1,323      | 1,250 |
+| BSDS | 200   | 100        | 200   |
+
+## Dataset Creation
+
+### Curation Rationale
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+#### Who are the source language producers?
+
+### Annotations
+
+#### Annotation process
+
+#### Who are the annotators?
+
+### Personal and Sensitive Information
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+### Discussion of Biases
+
+### Other Known Limitations
+
+## Additional Information
+
+### Dataset Curators
+
+### Licensing Information
+
+COCOA is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply:
+- COCO images: [Flickr Terms of use](http://cocodataset.org/#termsofuse) 
+- COCO annotations: [Creative Commons Attribution 4.0 License](http://cocodataset.org/#termsofuse)
+
+### Citation Information
+
+```bibtex
+@inproceedings{zhu2017semantic,
+  title={Semantic amodal segmentation},
+  author={Zhu, Yan and Tian, Yuandong and Metaxas, Dimitris and Doll{\'a}r, Piotr},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={1464--1472},
+  year={2017}
+}
+
+@inproceedings{lin2014microsoft,
+  title={Microsoft coco: Common objects in context},
+  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
+  booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13},
+  pages={740--755},
+  year={2014},
+  organization={Springer}
+}
+
+@article{arbelaez2010contour,
+  title={Contour detection and hierarchical image segmentation},
+  author={Arbelaez, Pablo and Maire, Michael and Fowlkes, Charless and Malik, Jitendra},
+  journal={IEEE transactions on pattern analysis and machine intelligence},
+  volume={33},
+  number={5},
+  pages={898--916},
+  year={2010},
+  publisher={IEEE}
+}
+```
+
+### Contributions
+
+Thanks to [@Wakeupbuddy](https://github.com/Wakeupbuddy) for publishing the COCOA dataset.

pyproject.toml

@@ -0,0 +1,24 @@
+[tool.poetry]
+name = "huggingface-datasets-cocoa"
+version = "0.1.0"
+description = ""
+authors = ["Shunsuke KITADA <shunsuke.kitada.0831@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+datasets = { extras = ["vision"], version = "^2.14.4" }
+pycocotools = "^2.0.7"
+
+[tool.poetry.group.dev.dependencies]
+ruff = "^0.0.287"
+black = "^23.7.0"
+mypy = "^1.5.1"
+pytest = "^7.4.1"
+
+[tool.ruff]
+line-length = 170
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

tests/COCOA_test.py

@@ -0,0 +1,45 @@
+import os
+
+import datasets as ds
+import pytest
+
+
+@pytest.fixture
+def dataset_path() -> str:
+    return "COCOA.py"
+
+
+@pytest.fixture
+def data_dir() -> str:
+    is_ci = bool(os.environ.get("CI", False))
+    if is_ci:
+        raise NotImplementedError
+    else:
+        return "annotations.tar.gz"
+
+
+@pytest.mark.parametrize(
+    argnames=(
+        "dataset_name",
+        "expected_num_train",
+        "expected_num_validation",
+        "expected_num_test",
+    ),
+    argvalues=(
+        ("COCO", 2500, 1323, 1250),
+        ("BSDS", 200, 100, 200),
+    ),
+)
+def test_load_dataset(
+    dataset_path: str,
+    dataset_name: str,
+    data_dir: str,
+    expected_num_train: int,
+    expected_num_validation: int,
+    expected_num_test: int,
+):
+    dataset = ds.load_dataset(path=dataset_path, name=dataset_name, data_dir=data_dir)
+
+    assert dataset["train"].num_rows == expected_num_train  # type: ignore
+    assert dataset["validation"].num_rows == expected_num_validation  # type: ignore
+    assert dataset["test"].num_rows == expected_num_test  # type: ignore