keremberke commited on
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dataset uploaded by roboflow2huggingface package

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README.dataset.txt ADDED
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+ # undefined > raw images
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+ https://public.roboflow.ai/object-detection/undefined
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+
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+ Provided by undefined
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+ License: CC BY 4.0
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+
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+ # SkyBot
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+
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+ This is the dataset powering http://skybot.cam, an app that captures planes flying over top of my house.
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+
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+ ![Skycam Tweet](https://i.imgur.com/DhxlR8J.png)
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+
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+ 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.
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+
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+ ## About this Project
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+ 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.
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+
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+
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+ ![Skybot Infrastructure](https://i.imgur.com/Lrv71Aq.png)
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+
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+
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+ ## About the Dataset
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+ 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.
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+
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+ ### Use Cases
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+
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+ This dataset should allow for a plane dectector model to be built like for plane spotting and plane detection.
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+
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+ ## About Me
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+ I'm Luke Berndt, I work on Azure products at Microsoft. You can learn more about me here: http://lukeberndt.com/
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="keremberke/plane-detection" src="https://huggingface.co/datasets/keremberke/plane-detection/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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+ ['planes']
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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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+ {'test': 25, 'train': 175, 'valid': 50}
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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("keremberke/plane-detection", 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/skybot-cam/overhead-plane-detector/dataset/4](https://universe.roboflow.com/skybot-cam/overhead-plane-detector/dataset/4?ref=roboflow2huggingface)
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+
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+ ### Citation
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+
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+ ```
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+ @misc{ overhead-plane-detector_dataset,
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+ title = { Overhead Plane Detector Dataset },
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+ type = { Open Source Dataset },
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+ author = { SkyBot Cam },
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+ howpublished = { \\url{ https://universe.roboflow.com/skybot-cam/overhead-plane-detector } },
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+ url = { https://universe.roboflow.com/skybot-cam/overhead-plane-detector },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2022 },
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+ month = { jan },
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+ note = { visited on 2023-01-18 },
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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.ai on March 30, 2022 at 3:11 PM GMT
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+
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+ It includes 250 images.
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+ Planes 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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+
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+ No image augmentation techniques were applied.
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+
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+
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+
README.roboflow.txt ADDED
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+
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+ Overhead Plane Detector - v4 raw images
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+ ==============================
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+
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+ This dataset was exported via roboflow.ai on March 30, 2022 at 3:11 PM GMT
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+
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+ It includes 250 images.
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+ Planes 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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+
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+ No image augmentation techniques were applied.
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+
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+
data/test.zip ADDED
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+ size 1885664
data/train.zip ADDED
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+ size 14475473
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plane-detection.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/skybot-cam/overhead-plane-detector/dataset/4"
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+ _LICENSE = "CC BY 4.0"
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+ _CITATION = """\
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+ @misc{ overhead-plane-detector_dataset,
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+ title = { Overhead Plane Detector Dataset },
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+ type = { Open Source Dataset },
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+ author = { SkyBot Cam },
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+ howpublished = { \\url{ https://universe.roboflow.com/skybot-cam/overhead-plane-detector } },
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+ url = { https://universe.roboflow.com/skybot-cam/overhead-plane-detector },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2022 },
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+ month = { jan },
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+ note = { visited on 2023-01-18 },
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+ }
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+ """
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+ _CATEGORIES = ['planes']
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+ _ANNOTATION_FILENAME = "_annotations.coco.json"
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+
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+
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+ class PLANEDETECTIONConfig(datasets.BuilderConfig):
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+ """Builder Config for plane-detection"""
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+
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+ def __init__(self, data_urls, **kwargs):
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+ """
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+ BuilderConfig for plane-detection.
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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(PLANEDETECTIONConfig, 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 PLANEDETECTION(datasets.GeneratorBasedBuilder):
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+ """plane-detection 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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+ PLANEDETECTIONConfig(
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+ name="full",
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+ description="Full version of plane-detection dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/train.zip",
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+ "validation": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/valid.zip",
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+ "test": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/test.zip",
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+ },
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+ ),
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+ PLANEDETECTIONConfig(
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+ name="mini",
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+ description="Mini version of plane-detection dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/valid-mini.zip",
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+ "validation": "https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/data/valid-mini.zip",
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+ "test": "https://huggingface.co/datasets/keremberke/plane-detection/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
split_name_to_num_samples.json ADDED
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+ {"test": 25, "train": 175, "valid": 50}
thumbnail.jpg ADDED

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