metadata
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
Dataset Labels
['comet', 'galaxy', 'moon', 'nebula', 'saturn', 'snr', 'star cluster']
Number of Images
{'valid': 6, 'test': 4, 'train': 16}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("bortle/ap_obj_dataset", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/bortle/astrophotographyobectdetection/dataset/3
Citation
@misc{
astrophotographyobectdetection_dataset,
title = { AstrophotographyObectDetection Dataset },
type = { Open Source Dataset },
author = { Bortle },
howpublished = { \\url{ https://universe.roboflow.com/bortle/astrophotographyobectdetection } },
url = { https://universe.roboflow.com/bortle/astrophotographyobectdetection },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { apr },
note = { visited on 2025-04-08 },
}
License
CC BY 4.0
Dataset Summary
This dataset was exported via roboflow.com on April 8, 2025 at 8:58 AM 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 26 images. Objects are annotated in COCO format.
The following pre-processing was applied to each image:
- Auto-orientation of pixel data (with EXIF-orientation stripping)
- Resize to 640x640 (Stretch)
No image augmentation techniques were applied.