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September 10, 2025MathematicsOpen Access

An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures

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Strategically freezing layers improves transfer learning in YOLO architectures, adapting to data characteristics.
Up to 28% reduction in GPU memory consumption compared to full fine-tuning helps in resource-limited environments.
Gradient behavior analysis and Grad-CAM provide insights into training dynamics across different freezing strategies.
Optimal freezing strategy varies depending on data properties, impacting mean average precision scores significantly.

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Analysis reveals tailored freezing configurations improve transfer learning in YOLOv8 and YOLOv10, suggesting efficiency in constrained settings.

Dobrzycki et al. (2025) studied this question.

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Analysis reveals tailored freezing configurations improve transfer learning in YOLOv8 and YOLOv10, suggesting efficiency in constrained settings.