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language:
- en
tags:
- computer-vision
- object-detection
- astronomy
- jwst
- yolo
- ultralytics
license: mit
datasets:
- norbertm/jwst-quality-analysis-dataset
metrics:
- mAP50
- precision
- recall
---
# JWST Astronomical Object Detection Model
This is a fine-tuned YOLO model specifically trained for detecting astronomical objects in JWST (James Webb Space Telescope) images.
## Model Details
- **Architecture**: YOLOv8n (nano)
- **Training Data**: 2,587 high-quality JWST images
- **Classes**: 2 (bright_object, galaxy_like)
- **Performance**: 26.7% mAP50, 52.7% precision on bright objects
- **Training Time**: 75 epochs (~25 hours)
## Usage
```python
from ultralytics import YOLO
# Load the model
model = YOLO("norbertm/jwst-astronomical-detection")
# Run inference
results = model("path/to/jwst/image.png", conf=0.15)
```
## Training Details
- **Dataset**: 2,587 JWST images with automated annotations
- **Instruments**: NIRCAM (Near-Infrared Camera)
- **Filters**: F090W, F150W, F200W, F277W, F356W, F444W
- **Targets**: Stephan's Quintet, M16, NGC 3324, NGC 3132, SMACS J0723.3-7327, WASP-39b
## Research Applications
- Automated astronomical object detection
- Multi-wavelength object correlation
- Quality assessment of JWST data
- Large-scale astronomical surveys
## Citation
If you use this model in your research, please cite:
```bibtex
@dataset{jwst_quality_analysis,
title={JWST Quality Analysis Dataset},
author={Your Name},
year={2024},
url={https://huggingface.co/datasets/norbertm/jwst-quality-analysis-dataset}
}
```
## License
MIT License - see LICENSE file for details.
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