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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="bortle/ap_obj_dataset" src="https://huggingface.co/datasets/bortle/ap_obj_dataset/resolve/main/thumbnail.jpg">
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</div>
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### Dataset Labels
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```
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['comet', 'galaxy', 'moon', 'nebula', 'saturn', 'snr', 'star cluster']
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```
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### Number of Images
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```json
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{'valid': 6, 'test': 4, 'train': 16}
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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("bortle/ap_obj_dataset", 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/bortle/astrophotographyobectdetection/dataset/3](https://universe.roboflow.com/bortle/astrophotographyobectdetection/dataset/3?ref=roboflow2huggingface)
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### Citation
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```
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@misc{
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astrophotographyobectdetection_dataset,
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title = { AstrophotographyObectDetection Dataset },
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type = { Open Source Dataset },
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author = { Bortle },
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howpublished = { \\url{ https://universe.roboflow.com/bortle/astrophotographyobectdetection } },
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url = { https://universe.roboflow.com/bortle/astrophotographyobectdetection },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2025 },
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month = { apr },
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note = { visited on 2025-04-08 },
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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.com on April 8, 2025 at 8:58 AM GMT
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Roboflow is an end-to-end computer vision platform that helps you
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* collaborate with your team on computer vision projects
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* collect & organize images
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* understand and search unstructured image data
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* annotate, and create datasets
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* export, train, and deploy computer vision models
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* use active learning to improve your dataset over time
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For state of the art Computer Vision training notebooks you can use with this dataset,
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visit https://github.com/roboflow/notebooks
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To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
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The dataset includes 26 images.
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Objects are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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* Resize to 640x640 (Stretch)
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No image augmentation techniques were applied.
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