| --- |
| 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. |
|
|
