aviola commited on
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dataset uploaded by roboflow2huggingface package

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DominoDataset.py ADDED
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+ import collections
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ _HOMEPAGE = "https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/2"
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+ _LICENSE = "CC BY 4.0"
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+ _CITATION = """\
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+ @misc{
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+ dominos-6ptm5_dataset,
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+ title = { dominos Dataset },
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+ type = { Open Source Dataset },
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+ author = { Virginia Tech },
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+ howpublished = { \\url{ https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5 } },
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+ url = { https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5 },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2024 },
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+ month = { sep },
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+ note = { visited on 2024-09-14 },
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+ }
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+ """
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+ _CATEGORIES = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15']
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+ _ANNOTATION_FILENAME = "_annotations.coco.json"
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+
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+
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+ class DOMINODATASETConfig(datasets.BuilderConfig):
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+ """Builder Config for DominoDataset"""
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+
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+ def __init__(self, data_urls, **kwargs):
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+ """
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+ BuilderConfig for DominoDataset.
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+
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+ Args:
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+ data_urls: `dict`, name to url to download the zip file from.
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(DOMINODATASETConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
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+ self.data_urls = data_urls
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+
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+
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+ class DOMINODATASET(datasets.GeneratorBasedBuilder):
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+ """DominoDataset object detection dataset"""
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+
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+ VERSION = datasets.Version("1.0.0")
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+ BUILDER_CONFIGS = [
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+ DOMINODATASETConfig(
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+ name="full",
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+ description="Full version of DominoDataset dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/data/train.zip",
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+ "validation": "https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/data/valid.zip",
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+ "test": "https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/data/test.zip",
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+ },
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+ ),
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+ DOMINODATASETConfig(
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+ name="mini",
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+ description="Mini version of DominoDataset dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/data/valid-mini.zip",
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+ "validation": "https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/data/valid-mini.zip",
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+ "test": "https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/data/valid-mini.zip",
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+ },
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+ )
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+ ]
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+
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+ def _info(self):
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+ features = datasets.Features(
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+ {
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+ "image_id": datasets.Value("int64"),
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+ "image": datasets.Image(),
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+ "width": datasets.Value("int32"),
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+ "height": datasets.Value("int32"),
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+ "objects": datasets.Sequence(
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+ {
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+ "id": datasets.Value("int64"),
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+ "area": datasets.Value("int64"),
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+ "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
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+ "category": datasets.ClassLabel(names=_CATEGORIES),
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+ }
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+ ),
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ features=features,
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+ homepage=_HOMEPAGE,
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+ citation=_CITATION,
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+ license=_LICENSE,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ data_files = dl_manager.download_and_extract(self.config.data_urls)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "folder_dir": data_files["train"],
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={
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+ "folder_dir": data_files["validation"],
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "folder_dir": data_files["test"],
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, folder_dir):
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+ def process_annot(annot, category_id_to_category):
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+ return {
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+ "id": annot["id"],
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+ "area": annot["area"],
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+ "bbox": annot["bbox"],
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+ "category": category_id_to_category[annot["category_id"]],
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+ }
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+
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+ image_id_to_image = {}
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+ idx = 0
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+
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+ annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
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+ with open(annotation_filepath, "r") as f:
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+ annotations = json.load(f)
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+ category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
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+ image_id_to_annotations = collections.defaultdict(list)
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+ for annot in annotations["annotations"]:
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+ image_id_to_annotations[annot["image_id"]].append(annot)
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+ filename_to_image = {image["file_name"]: image for image in annotations["images"]}
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+
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+ for filename in os.listdir(folder_dir):
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+ filepath = os.path.join(folder_dir, filename)
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+ if filename in filename_to_image:
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+ image = filename_to_image[filename]
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+ objects = [
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+ process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
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+ ]
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+ with open(filepath, "rb") as f:
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+ image_bytes = f.read()
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+ yield idx, {
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+ "image_id": image["id"],
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+ "image": {"path": filepath, "bytes": image_bytes},
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+ "width": image["width"],
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+ "height": image["height"],
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+ "objects": objects,
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+ }
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+ idx += 1
README.dataset.txt ADDED
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+ # dominos > 2024-09-14 6:35am
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+ https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5
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+
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+ Provided by a Roboflow user
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+ License: CC BY 4.0
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+
README.md ADDED
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+ ---
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+ task_categories:
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+ - object-detection
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+ tags:
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+ - roboflow
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+ - roboflow2huggingface
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+
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+ ---
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+
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+ <div align="center">
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+ <img width="640" alt="aviola/DominoDataset" src="https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/thumbnail.jpg">
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+ </div>
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+
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+ ### Dataset Labels
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+
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+ ```
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+ ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15']
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+ ```
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+
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+
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+ ### Number of Images
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+
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+ ```json
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+ {'valid': 11, 'test': 10, 'train': 249}
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+ ```
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+
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+
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+ ### How to Use
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+
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+ - Install [datasets](https://pypi.org/project/datasets/):
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+
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+ ```bash
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+ pip install datasets
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+ ```
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+
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+ - Load the dataset:
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("aviola/DominoDataset", name="full")
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+ example = ds['train'][0]
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+ ```
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+
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+ ### Roboflow Dataset Page
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+ [https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/2](https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/2?ref=roboflow2huggingface)
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+
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+ ### Citation
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+
50
+ ```
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+ @misc{
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+ dominos-6ptm5_dataset,
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+ title = { dominos Dataset },
54
+ type = { Open Source Dataset },
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+ author = { Virginia Tech },
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+ howpublished = { \\url{ https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5 } },
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+ url = { https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5 },
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+ journal = { Roboflow Universe },
59
+ publisher = { Roboflow },
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+ year = { 2024 },
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+ month = { sep },
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+ note = { visited on 2024-09-14 },
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+ }
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+ ```
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+
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+ ### License
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+ CC BY 4.0
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+
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+ ### Dataset Summary
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+ This dataset was exported via roboflow.com on September 14, 2024 at 6:38 AM GMT
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+
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+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
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+ * collect & organize images
75
+ * understand and search unstructured image data
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+ * annotate, and create datasets
77
+ * export, train, and deploy computer vision models
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+ * use active learning to improve your dataset over time
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+
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+ For state of the art Computer Vision training notebooks you can use with this dataset,
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+ visit https://github.com/roboflow/notebooks
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+
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+ To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
84
+
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+ The dataset includes 270 images.
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+ Dominos are annotated in COCO format.
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+
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+ The following pre-processing was applied to each image:
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+ * Auto-orientation of pixel data (with EXIF-orientation stripping)
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+ * Resize to 640x640 (Stretch)
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+
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+ The following augmentation was applied to create 3 versions of each source image:
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+ * 50% probability of horizontal flip
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+ * Randomly crop between 0 and 20 percent of the image
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+ * Random rotation of between -15 and +15 degrees
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+ * Random Gaussian blur of between 0 and 1.5 pixels
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+ * Salt and pepper noise was applied to 0.1 percent of pixels
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+
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+
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+
README.roboflow.txt ADDED
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+
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+ dominos - v2 2024-09-14 6:35am
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+ ==============================
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+
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+ This dataset was exported via roboflow.com on September 14, 2024 at 6:38 AM GMT
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+
7
+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
9
+ * collect & organize images
10
+ * understand and search unstructured image data
11
+ * annotate, and create datasets
12
+ * export, train, and deploy computer vision models
13
+ * use active learning to improve your dataset over time
14
+
15
+ For state of the art Computer Vision training notebooks you can use with this dataset,
16
+ visit https://github.com/roboflow/notebooks
17
+
18
+ To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
19
+
20
+ The dataset includes 270 images.
21
+ Dominos are annotated in COCO format.
22
+
23
+ The following pre-processing was applied to each image:
24
+ * Auto-orientation of pixel data (with EXIF-orientation stripping)
25
+ * Resize to 640x640 (Stretch)
26
+
27
+ The following augmentation was applied to create 3 versions of each source image:
28
+ * 50% probability of horizontal flip
29
+ * Randomly crop between 0 and 20 percent of the image
30
+ * Random rotation of between -15 and +15 degrees
31
+ * Random Gaussian blur of between 0 and 1.5 pixels
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+ * Salt and pepper noise was applied to 0.1 percent of pixels
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+
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+
data/test.zip ADDED
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+ size 510273
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split_name_to_num_samples.json ADDED
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thumbnail.jpg ADDED

Git LFS Details

  • SHA256: 48d040949e98db8f928f0e227d77030421fe6e38f411ae9b1a8ea394efa0c822
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  • Size of remote file: 164 kB