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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

@@ -1,28 +0,0 @@
-
-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/valid.zip → full/satellite-building-segmentation-test.parquet

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data/train.zip → full/satellite-building-segmentation-train.parquet

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data/test.zip → full/satellite-building-segmentation-validation.parquet

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thumbnail.jpg → mini/satellite-building-segmentation-test.parquet

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data/valid-mini.zip → mini/satellite-building-segmentation-train.parquet

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mini/satellite-building-segmentation-validation.parquet

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satellite-building-segmentation.py

@@ -1,154 +0,0 @@
-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

@@ -1 +0,0 @@
-{"train": 6764, "valid": 1934, "test": 967}