dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# Buildings Instance Segmentation > raw-images
+https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation
+
+Provided by a Roboflow user
+License: CC BY 4.0
+

README.md

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+---
+task_categories:
+- image-segmentation
+tags:
+- roboflow
+- roboflow2huggingface
+- Aerial
+- Logistics
+- Construction
+- Damage Risk
+- Other
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/satellite-building-segmentation" src="https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['building']
+```
+
+
+### Number of Images
+
+```json
+{'train': 6764, 'valid': 1934, 'test': 967}
+```
+
+
+### 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("keremberke/satellite-building-segmentation", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1](https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ buildings-instance-segmentation_dataset,
+    title = { Buildings Instance Segmentation Dataset },
+    type = { Open Source Dataset },
+    author = { Roboflow Universe Projects },
+    howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } },
+    url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2023 },
+    month = { jan },
+    note = { visited on 2023-01-16 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.com on January 16, 2023 at 9:06 PM 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 9665 images.
+Buildings are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Buildings Instance Segmentation - v1 raw-images
+==============================
+
+This dataset was exported via roboflow.com on January 16, 2023 at 9:06 PM 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 9665 images.
+Buildings are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e6f6cca4c6fa01db541330f196bbe32c8ca1e8fe25f9acf4b3d55430f2866d5
+size 49805610

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c285d673a81534507d6261230ac004784ba6dbc0ab5904f8d957cc385c4f4db4
+size 345338954

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4997e150e79a7a7ed4bfe1863f2b6ea8c8bc8a633a4bce1b618ec07138fe2688
+size 130275

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dbbc6828ef700233c4013a73a3d8aa949ce1cfd3a7734fd0834d351a8a46d398
+size 98753011

satellite-building-segmentation.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ buildings-instance-segmentation_dataset,
+    title = { Buildings Instance Segmentation Dataset },
+    type = { Open Source Dataset },
+    author = { Roboflow Universe Projects },
+    howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } },
+    url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2023 },
+    month = { jan },
+    note = { visited on 2023-01-16 },
+}
+"""
+_CATEGORIES = ['building']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class SATELLITEBUILDINGSEGMENTATIONConfig(datasets.BuilderConfig):
+    """Builder Config for satellite-building-segmentation"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for satellite-building-segmentation.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SATELLITEBUILDINGSEGMENTATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class SATELLITEBUILDINGSEGMENTATION(datasets.GeneratorBasedBuilder):
+    """satellite-building-segmentation instance segmentation dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        SATELLITEBUILDINGSEGMENTATIONConfig(
+            name="full",
+            description="Full version of satellite-building-segmentation dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/data/train.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/data/valid.zip",
+                "test": "https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/data/test.zip",
+            },
+        ),
+        SATELLITEBUILDINGSEGMENTATIONConfig(
+            name="mini",
+            description="Mini version of satellite-building-segmentation dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/keremberke/satellite-building-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),
+                        "segmentation": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                        "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)
+        filename_to_image = {image["file_name"]: image for image in annotations["images"]}
+
+        for filename in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, filename)
+            if filename in filename_to_image:
+                image = filename_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

split_name_to_num_samples.json

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+{"train": 6764, "valid": 1934, "test": 967}