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README.md

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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.