dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# Crack_detection_experiment > 2023-01-14 5:06pm
+https://universe.roboflow.com/palmdetection-1cjxw/crack_detection_experiment
+
+Provided by a Roboflow user
+License: CC BY 4.0
+
+This images and some annot was taken from this:
+
+```
+@misc{ 400-img_dataset,
+    title = { 400 img Dataset },
+    type = { Open Source Dataset },
+    author = { Master dissertation },
+    howpublished = { \url{ https://universe.roboflow.com/master-dissertation/400-img } },
+    url = { https://universe.roboflow.com/master-dissertation/400-img },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { dec },
+    note = { visited on 2023-01-14 },
+}
+```
+
+Anyway, the instance segmentation format was wrong with some annot because it has bbox labeled format (class Xcenter Ycenter Width Height) instead of segmentation format (class X1 Y1 X2 Y2 .... Xn Yn).
+
+So, I corrected it

README.md

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+---
+task_categories:
+- instance-segmentation
+tags:
+- roboflow
+- roboflow2huggingface
+
+---
+
+<div align="center">
+  <img width="640" alt="fcakyon/crack-instance-segmentation" src="https://huggingface.co/datasets/fcakyon/crack-instance-segmentation/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['cracks-and-spalling', 'object']
+```
+
+
+### Number of Images
+
+```json
+{'test': 37, 'train': 323, 'valid': 73}
+```
+
+
+### How to Use
+
+- Install [datasets](https://pypi.org/project/datasets/):
+
+```bash
+pip install datasets
+```
+
+- Load the dataset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("fcakyon/crack-instance-segmentation", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/palmdetection-1cjxw/crack_detection_experiment/dataset/5](https://universe.roboflow.com/palmdetection-1cjxw/crack_detection_experiment/dataset/5?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ 400-img_dataset,
+    title = { 400 img Dataset },
+    type = { Open Source Dataset },
+    author = { Master dissertation },
+    howpublished = { \\url{ https://universe.roboflow.com/master-dissertation/400-img } },
+    url = { https://universe.roboflow.com/master-dissertation/400-img },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { dec },
+    note = { visited on 2023-01-14 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.com on January 14, 2023 at 10:08 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 433 images.
+Crack-spall are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Crack_detection_experiment - v5 2023-01-14 5:06pm
+==============================
+
+This dataset was exported via roboflow.com on January 14, 2023 at 10:08 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 433 images.
+Crack-spall are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+

crack-instance-segmentation.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/palmdetection-1cjxw/crack_detection_experiment/dataset/5"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ 400-img_dataset,
+    title = { 400 img Dataset },
+    type = { Open Source Dataset },
+    author = { Master dissertation },
+    howpublished = { \\url{ https://universe.roboflow.com/master-dissertation/400-img } },
+    url = { https://universe.roboflow.com/master-dissertation/400-img },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { dec },
+    note = { visited on 2023-01-14 },
+}
+"""
+_CATEGORIES = ['cracks-and-spalling', 'object']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class CRACKINSTANCESEGMENTATIONConfig(datasets.BuilderConfig):
+    """Builder Config for crack-instance-segmentation"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for crack-instance-segmentation.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CRACKINSTANCESEGMENTATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class CRACKINSTANCESEGMENTATION(datasets.GeneratorBasedBuilder):
+    """crack-instance-segmentation instance segmentation dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        CRACKINSTANCESEGMENTATIONConfig(
+            name="full",
+            description="Full version of crack-instance-segmentation dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/fcakyon/crack-instance-segmentation/resolve/main/data/train.zip",
+                "validation": "https://huggingface.co/datasets/fcakyon/crack-instance-segmentation/resolve/main/data/valid.zip",
+                "test": "https://huggingface.co/datasets/fcakyon/crack-instance-segmentation/resolve/main/data/test.zip",
+            },
+        ),
+        CRACKINSTANCESEGMENTATIONConfig(
+            name="mini",
+            description="Mini version of crack-instance-segmentation dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/fcakyon/crack-instance-segmentation/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/fcakyon/crack-instance-segmentation/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/fcakyon/crack-instance-segmentation/resolve/main/data/valid-mini.zip",
+            },
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                "objects": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "area": datasets.Value("int64"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                        "category": datasets.ClassLabel(names=_CATEGORIES),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(self.config.data_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "folder_dir": data_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "folder_dir": data_files["validation"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "folder_dir": data_files["test"],
+                },
+            ),
+]
+
+    def _generate_examples(self, folder_dir):
+        def process_annot(annot, category_id_to_category):
+            return {
+                "id": annot["id"],
+                "area": annot["area"],
+                "bbox": annot["bbox"],
+                "segmentation": annot["segmentation"],
+                "category": category_id_to_category[annot["category_id"]],
+            }
+
+        image_id_to_image = {}
+        idx = 0
+
+        annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
+        with open(annotation_filepath, "r") as f:
+            annotations = json.load(f)
+        category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
+        image_id_to_annotations = collections.defaultdict(list)
+        for annot in annotations["annotations"]:
+            image_id_to_annotations[annot["image_id"]].append(annot)
+        image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+
+        for filename in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, filename)
+            if filename in image_id_to_image:
+                image = image_id_to_image[filename]
+                objects = [
+                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
+                ]
+                with open(filepath, "rb") as f:
+                    image_bytes = f.read()
+                yield idx, {
+                    "image_id": image["id"],
+                    "image": {"path": filepath, "bytes": image_bytes},
+                    "width": image["width"],
+                    "height": image["height"],
+                    "objects": objects,
+                }
+                idx += 1

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b8e74948345eb41eba46e70331b16b85ec373c1c28e4fdbd9c8cd0d74b8bf2a
+size 1429165

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c797753c4743b0d10aa5a390a4a54e09de1cc51958b0d9326946ec79de1aca7
+size 10567198

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4feef110accb74c7542eecdf2099a23e24a60afe85fb9e1b04bc2338debaef8
+size 138840

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f1159a54a4b7233c5898638842fd75d23f0c08f7df740c3902569e4b5027575
+size 2306229

split_name_to_num_samples.json

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+{"test": 37, "train": 323, "valid": 73}