dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# undefined > raw images
+https://public.roboflow.ai/object-detection/undefined
+
+Provided by undefined
+License: CC BY 4.0
+
+# SkyBot
+
+This is the dataset powering http://skybot.cam, an app that captures planes flying over top of my house.
+
+![Skycam Tweet](https://i.imgur.com/DhxlR8J.png)
+
+Upon the project gaining popularity on [Hacker News](https://news.ycombinator.com/item?id=30039597) from the above [tweet](https://twitter.com/LukeBerndt/status/1484916000139194375), I thought I'd share the dataset and an example model to make it easier for others to build a plane spotting app, too.
+
+## About this Project
+I built a system to take photos of all of the airplanes that fly over my house. Most of these planes are passing by at more than 30,000 feet! It uses ADS-B to track where the aircraft are relative to the camera, points the camera in the right direction and snaps a photo. I then run a few serverless functions that are running to detect where the aircraft is in the image and make a thumbnail. Much of the services are hosted on Azure. There's more details on the overall project here! http://skybot.cam/about. The project is [open source](https://github.com/IQTLabs/SkyScan/tree/main/ml-model/scripts) as a part of my work from IQT as well.
+
+
+![Skybot Infrastructure](https://i.imgur.com/Lrv71Aq.png)
+
+
+## About the Dataset
+The dataset is of airfract that was captured as they flew overhead. It includes a mix of large and small passenger jets and an assortment of business jets. There are also a images with buildings and contrails, where there is not aircraft present.
+
+### Use Cases
+
+This dataset should allow for a plane dectector model to be built like for plane spotting and plane detection. 
+
+## About Me
+I'm Luke Berndt, I work on Azure products at Microsoft. You can learn more about me here: http://lukeberndt.com/

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+- roboflow2huggingface
+
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/plane-detection" src="https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['planes']
+```
+
+
+### Number of Images
+
+```json
+{'test': 25, 'train': 175, 'valid': 50}
+```
+
+
+### 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/plane-detection", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/skybot-cam/overhead-plane-detector/dataset/4](https://universe.roboflow.com/skybot-cam/overhead-plane-detector/dataset/4?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ overhead-plane-detector_dataset,
+    title = { Overhead Plane Detector Dataset },
+    type = { Open Source Dataset },
+    author = { SkyBot Cam },
+    howpublished = { \\url{ https://universe.roboflow.com/skybot-cam/overhead-plane-detector } },
+    url = { https://universe.roboflow.com/skybot-cam/overhead-plane-detector },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { jan },
+    note = { visited on 2023-01-18 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on March 30, 2022 at 3:11 PM GMT
+
+It includes 250 images.
+Planes 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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+
+Overhead Plane Detector - v4 raw images
+==============================
+
+This dataset was exported via roboflow.ai on March 30, 2022 at 3:11 PM GMT
+
+It includes 250 images.
+Planes are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3aa0da3e86753e6a47b12ba3efd39ab9135bbe156bc4eca3bd42b92f395fa5ad
+size 1885664

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cea2a044e36e323496eae1b73941d927d8c6bea2af21213360d93bb96b6f7904
+size 14475473

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c29307d0eae74e556fd15fe720bf422ee694040de48241f18a61d59040580620
+size 143207

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d4c59404660d6df33f083d674897e90288932989b33d38de6d71680fa172c43c
+size 3939295

plane-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/skybot-cam/overhead-plane-detector/dataset/4"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ overhead-plane-detector_dataset,
+    title = { Overhead Plane Detector Dataset },
+    type = { Open Source Dataset },
+    author = { SkyBot Cam },
+    howpublished = { \\url{ https://universe.roboflow.com/skybot-cam/overhead-plane-detector } },
+    url = { https://universe.roboflow.com/skybot-cam/overhead-plane-detector },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { jan },
+    note = { visited on 2023-01-18 },
+}
+"""
+_CATEGORIES = ['planes']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class PLANEDETECTIONConfig(datasets.BuilderConfig):
+    """Builder Config for plane-detection"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for plane-detection.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PLANEDETECTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class PLANEDETECTION(datasets.GeneratorBasedBuilder):
+    """plane-detection object detection dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        PLANEDETECTIONConfig(
+            name="full",
+            description="Full version of plane-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/train.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/valid.zip",
+                "test": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/test.zip",
+            },
+        ),
+        PLANEDETECTIONConfig(
+            name="mini",
+            description="Mini version of plane-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/keremberke/plane-detection/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"],
+                "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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+{"test": 25, "train": 175, "valid": 50}