Ultralytics Code Refactor https://ultralytics.com/actions

[glenn-jocher]

This Ultralytics PR refactors code to improve performance and readability. 🔄

Key changes include:

• 🚀 Optimized various functions for faster execution.
• 🧩 Simplified complex logic for better understanding and maintenance.
• 🗑️ Removed redundant code to streamline operations.
• 📚 Improved code structure and organization.

These changes aim to enhance the overall quality and efficiency of the code. 🌟

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🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

WARNING ⚠️ this PR is very large, summary may not cover all changes.

🌟 Summary

Enhanced YOLOv5's benchmarking and classification performance evaluation documentation for better clarity and usability 💡.

📊 Key Changes

• benchmarks.py: Improved docstrings to clearly explain the purpose, arguments, and output of functions like run(), test(), parse_opt(), and main().
• classify/predict.py: Enhanced documentation for the run() method, detailing the input parameters, output, and various example usages.
• classify/train.py: Expanded docstrings for the train() function, along with parse_opt() and main(), to provide clear guidance on arguments and their roles.
• classify/val.py: Improved comments to clarify the run() function and argument parsing for validation purposes.
• detect.py: Augmented docstring details for the run(), parse_opt(), and main() methods, providing more explicit instructions on their usage.
• export.py: Enhanced docstring information for multiple export format functions, including export_torchscript(), export_onnx(), export_openvino(), and more.
• hubconf.py: Improved documentation for creating and loading various YOLOv5 models (yolov5n, yolov5s, etc.), providing clear usage examples.
• models/common.py: Enhanced explanations and added examples for various layers and model components like Conv, DWConv, TransformerBlock, etc.
• models/experimental.py: Improved docs for experimental layers like Sum and MixConv2d, with updated explanations and usage notes.
• models/tf.py: Augmented TensorFlow-compatible layer docstrings for clarity on initialization and function, including TFConv, TFDWConv, TFBottleneck, etc.

🎯 Purpose & Impact

• Clarity: Improved docstrings enhance understandability, making it easier for both new and advanced users to utilize and extend YOLOv5 functionalities effectively.
• Usability: Detailed examples and parameter explanations help in quicker implementation and customization, reducing the learning curve.
• Maintainability: Better documentation ensures that future developers can maintain and update the code with less effort.

These enhancements not only streamline the user experience but also enable more effective debugging and feature development through better documentation and code comments. 🚀