Datasets:
Tasks:
Object Detection
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Commit
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Parent(s):
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dataset uploaded by roboflow2huggingface package
Browse files- README.dataset.txt +30 -0
- README.md +79 -0
- README.roboflow.txt +14 -0
- data/test.zip +3 -0
- data/train.zip +3 -0
- data/valid-mini.zip +3 -0
- data/valid.zip +3 -0
- plane-detection.py +152 -0
- split_name_to_num_samples.json +1 -0
- thumbnail.jpg +3 -0
README.dataset.txt
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# undefined > raw images
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https://public.roboflow.ai/object-detection/undefined
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Provided by undefined
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License: CC BY 4.0
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# SkyBot
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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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![Skycam Tweet](https://i.imgur.com/DhxlR8J.png)
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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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## 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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![Skybot Infrastructure](https://i.imgur.com/Lrv71Aq.png)
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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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### Use Cases
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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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## 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/
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README.md
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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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<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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### Dataset Labels
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```
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['planes']
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```
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### Number of Images
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```json
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{'test': 25, 'train': 175, 'valid': 50}
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```
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### How to Use
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- Install [datasets](https://pypi.org/project/datasets/):
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```bash
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pip install datasets
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```
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- Load the dataset:
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```python
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from datasets import load_dataset
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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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### 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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### Citation
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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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### License
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CC BY 4.0
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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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It includes 250 images.
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Planes are annotated in COCO format.
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The following pre-processing was applied to each image:
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No image augmentation techniques were applied.
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README.roboflow.txt
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Overhead Plane Detector - v4 raw images
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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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It includes 250 images.
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Planes are annotated in COCO format.
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The following pre-processing was applied to each image:
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No image augmentation techniques were applied.
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data/test.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:3aa0da3e86753e6a47b12ba3efd39ab9135bbe156bc4eca3bd42b92f395fa5ad
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size 1885664
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data/train.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:cea2a044e36e323496eae1b73941d927d8c6bea2af21213360d93bb96b6f7904
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size 14475473
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data/valid-mini.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:c29307d0eae74e556fd15fe720bf422ee694040de48241f18a61d59040580620
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size 143207
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data/valid.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:d4c59404660d6df33f083d674897e90288932989b33d38de6d71680fa172c43c
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size 3939295
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plane-detection.py
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import collections
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import json
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import os
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import datasets
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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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class PLANEDETECTIONConfig(datasets.BuilderConfig):
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"""Builder Config for plane-detection"""
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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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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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class PLANEDETECTION(datasets.GeneratorBasedBuilder):
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"""plane-detection object detection dataset"""
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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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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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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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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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image_id_to_image = {}
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idx = 0
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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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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
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split_name_to_num_samples.json
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{"test": 25, "train": 175, "valid": 50}
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thumbnail.jpg
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![]() |
Git LFS Details
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