metadata
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
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:
@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.