yolov5-sunspot-hunter

YOLOv5 Sunspot Hunter

This repository contains a YOLOv5 model trained to detect and identify sunspots in solar images. The model is designed to facilitate the monitoring and study of solar activity.

Model Information

• Model: YOLOv5
• Task: Object Detection
• Classes: Sunspots
• File: best.pt

How to Use

To use the YOLOv5 Sunspot Hunter model, follow these steps:

1. Loading the Model

You can load the model using the torch library and the ultralytics/yolov5 repository. Here's an example in Python:

import torch

# Load the model
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')

# Load an image
img = 'path/to/your/image.jpg'

# Perform inference
results = model(img)

# Display results
results.show()

2. Running Inference

After loading the model, you can perform inference on your images to detect and identify sunspots. The model will output the image with bounding boxes around detected sunspots and other relevant details.

3. Example Usage

Here's an example of how to use the model to detect sunspots in an image:

import torch
from PIL import Image

# Load the model
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')

# Load an image
img = Image.open('path/to/your/solar_image.jpg')

# Perform inference
results = model(img)

# Print results
results.print()  # Print results to console
results.show()   # Display results

# Save results
results.save('path/to/save/results/')  # Save results to a directory

Model Training

The model was trained using a custom dataset of solar images. The dataset was annotated with bounding boxes around sunspots using MakeSense, and the YOLOv5 model was trained using these annotations. The training process involved:

1. Collecting and annotating the data.
2. Training the YOLOv5 model with the annotated data.
3. Fine-tuning the model to improve accuracy.

Training Configuration

Soon

Acknowledgements

Special thanks to the contributors and the open-source community for providing resources and support.

License

This project is licensed under the Eclipse Public License 2.0.

Contact

For more information or questions about the model, please contact Ramon Mayor Martins:

• Email: [email protected]
• Homepage: https://rmayormartins.github.io/
• Twitter: @rmayormartins
• GitHub: https://github.com/rmayormartins
• My Radio Callsign (PU4MAY) Brazil