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[yothinsaengs]

🛠️ PR Summary

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🌟 Summary

This PR introduces the CBAM (Convolutional Block Attention Module) to YOLOv5 for improved attention mechanism.

📊 Key Changes

• Added CAM (Channel Attention Module) and SAM (Spatial Attention Module) classes.
• Implemented CBAM combining CAM and SAM.
• Updated yolo.py to support CBAM.
• Introduced a new model configuration yolov5s_CBAM.yaml.

🎯 Purpose & Impact

• Enhanced Feature Extraction: CBAM improves the model's attention to relevant image features, which can increase detection accuracy.
• Better Performance: Enhanced focus on important features may lead to improved object detection performance, especially in complex scenes.
• New Model Option: Offers users a new configuration to experiment with advanced attention mechanisms.

[github-actions]

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[github-actions]

👋 Hello @yothinsaengs, 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 running git pull and git 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