CONTRIBUTING.md

Contributing to YOLO Training Template

Thank you for your interest in contributing! This document provides guidelines for contributing to this project.

How to Contribute

1. Fork the Repository: Create a fork of this repo on GitHub.
2. Clone Your Fork: git clone https://github.com/your-username/yolo-training-template.git
3. Create a Branch: git checkout -b feature/your-feature-name
4. Make Changes: Implement your changes.
5. Test: Ensure your changes work as expected.
6. Commit: git commit -m "Add your message"
7. Push: git push origin feature/your-feature-name
8. Create a Pull Request: Submit a PR with a clear description.

Code Style

• Follow PEP 8 for Python code.
• Use meaningful variable and function names.
• Add docstrings to functions.
• Keep lines under 80 characters.

Reporting Issues

• Use GitHub Issues to report bugs or request features.
• Provide detailed steps to reproduce bugs.
• Include environment details (Python version, OS, etc.).

Running on Google Colab

To run the Jupyter notebook on Google Colab:

1. Open Colab: Go to Google Colab.
2. Upload Notebook: Upload yolo_template.ipynb to Colab.
3. Install Dependencies: Run the first cell in the notebook to install requirements.
4. Run Cells: Execute the cells sequentially for training and inference.
5. GPU Support: Enable GPU in Colab (Runtime > Change runtime type > Hardware accelerator > GPU).
6. Data Access: For Kaggle datasets, upload your kaggle.json or use Colab's Kaggle integration.
7. Save Outputs: Use Colab's file system to save models and results.

Note: Colab has resource limits; for long training, consider local setup.