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Is yolov5 sensitive to the size of defects and what structural improvements are needed to increase its sensitivity to defects? #13027
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@AFallDay hello! 😊 YOLOv5 can indeed be sensitive to the size of defects, especially very small or very large ones compared to the overall image size. To enhance sensitivity to defect sizes, consider the following:
For more detailed guidance on customizing your model, you might find the documentation on model configuration and training tips helpful: https://docs.ultralytics.com/yolov5/. Hope this helps! Let us know if you have any more questions. |
Hi, You may need to add some function to keep Ground Truth Boxes as you intended during Mosaic Augmentation. |
Hi @SwHaraday, Thank you for sharing your observations! 😊 You bring up a valid point about the potential impact of Mosaic Augmentation on small defects. Indeed, when ground truth boxes are cut during augmentation, it can lead to unintended training data and over-detection issues. To address this, you can modify the Mosaic Augmentation process to ensure that ground truth boxes remain intact. This can be done by adding a function to check and adjust the placement of boxes during augmentation. Additionally, you might consider experimenting with other augmentation techniques or adjusting the augmentation parameters to better suit your specific use case. Your insights are valuable to the community, and we appreciate your contribution! |
Hi Glenn, https://github.com/SwHaraday/YOLOv5-dataloaders-for-industrial-purpose |
Hi @SwHaraday, Thank you for sharing your implementation! 😊 Your contribution is valuable, and it's great to see the community working together to solve common issues. Every improvement, no matter how small, helps us all move forward. For anyone facing similar challenges, your solution could be a helpful resource. Keep up the great work! |
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Is yolov5 sensitive to the size of defects and what structural improvements are needed to increase its sensitivity to defects size?
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