---
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.