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Refactor YOLOv5 code for readability, maintainability, and efficiency #13117
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👋 Hello @sanowl, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
- ✅ Verify your PR is up-to-date with
ultralytics/yolov5
master
branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by runninggit pull
andgit merge master
locally. - ✅ Verify all YOLOv5 Continuous Integration (CI) checks are passing.
- ✅ Reduce changes to the absolute minimum required for your bug fix or feature addition. "It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is." — Bruce Lee
…tecture (Conv, Bottleneck, C3, SPPF, Detect) - Implement custom loss function (YOLOLoss) - Add utility functions (bbox_iou, non_max_suppression, xywh2xyxy) - Create basic training loop and data loading structure - Set up main function with argument parsing for easy execution
…owl/yolov5 into refactor/yolov5-improvements
@sanowl hello, Thank you for your pull request and for contributing to the YOLOv5 project! We appreciate your efforts to enhance the readability, maintainability, and efficiency of the codebase. Your detailed changes, including the addition of comprehensive comments, type hints, and adherence to PEP 8 guidelines, are highly valuable. To ensure a smooth integration, could you please confirm that your changes are compatible with the latest versions of Additionally, if you have any specific examples or tests that demonstrate the improvements and verify the functionality of your refactored code, please include them. This will help us thoroughly review and validate your contributions. For further guidance on creating reproducible examples, you can refer to our minimum reproducible example documentation. Thank you again for your valuable contributions. We look forward to reviewing your changes! |
This pull request refactors the YOLOv5 codebase to enhance readability, maintainability, and efficiency. Key improvements include:
These changes aim to make the code more maintainable, readable, and easier to understand for current and future developers.
🛠️ PR Summary
Made with ❤️ by Ultralytics Actions
🌟 Summary
Minor typo correction in the YOLOv5 classification model code.
📊 Key Changes
🎯 Purpose & Impact