An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures
The You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various...
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MDPI AG
2025-08-01
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| author | Andrzej D. Dobrzycki Ana M. Bernardos José R. Casar |
| author_facet | Andrzej D. Dobrzycki Ana M. Bernardos José R. Casar |
| author_sort | Andrzej D. Dobrzycki |
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| description | The You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various freezing configurations on contemporary YOLOv8 and YOLOv10 architectures remains unexplored, particularly with regard to the interplay between freezing depth, dataset characteristics, and training dynamics. This research addresses this gap by presenting a detailed analysis of layer-freezing strategies. We systematically investigate multiple freezing configurations across YOLOv8 and YOLOv10 variants using four challenging datasets that represent critical infrastructure monitoring. Our methodology integrates a gradient behavior analysis (L2 norm) and visual explanations (Grad-CAM) to provide deeper insights into training dynamics under different freezing strategies. Our results reveal that there is no universal optimal freezing strategy but, rather, one that depends on the properties of the data. For example, freezing the backbone is effective for preserving general-purpose features, while a shallower freeze is better suited to handling extreme class imbalance. These configurations reduce graphics processing unit (GPU) memory consumption by up to 28% compared to full fine-tuning and, in some cases, achieve mean average precision (mAP@50) scores that surpass those of full fine-tuning. Gradient analysis corroborates these findings, showing distinct convergence patterns for moderately frozen models. Ultimately, this work provides empirical findings and practical guidelines for selecting freezing strategies. It offers a practical, evidence-based approach to balanced transfer learning for object detection in scenarios with limited resources. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-08-01 |
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| spelling | doaj-art-6254331a91d94849a5cc3f578c92fb722025-08-20T03:04:43ZengMDPI AGMathematics2227-73902025-08-011315253910.3390/math13152539An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO ArchitecturesAndrzej D. Dobrzycki0Ana M. Bernardos1José R. Casar2Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense, 30, 28040 Madrid, SpainInformation Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense, 30, 28040 Madrid, SpainInformation Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense, 30, 28040 Madrid, SpainThe You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various freezing configurations on contemporary YOLOv8 and YOLOv10 architectures remains unexplored, particularly with regard to the interplay between freezing depth, dataset characteristics, and training dynamics. This research addresses this gap by presenting a detailed analysis of layer-freezing strategies. We systematically investigate multiple freezing configurations across YOLOv8 and YOLOv10 variants using four challenging datasets that represent critical infrastructure monitoring. Our methodology integrates a gradient behavior analysis (L2 norm) and visual explanations (Grad-CAM) to provide deeper insights into training dynamics under different freezing strategies. Our results reveal that there is no universal optimal freezing strategy but, rather, one that depends on the properties of the data. For example, freezing the backbone is effective for preserving general-purpose features, while a shallower freeze is better suited to handling extreme class imbalance. These configurations reduce graphics processing unit (GPU) memory consumption by up to 28% compared to full fine-tuning and, in some cases, achieve mean average precision (mAP@50) scores that surpass those of full fine-tuning. Gradient analysis corroborates these findings, showing distinct convergence patterns for moderately frozen models. Ultimately, this work provides empirical findings and practical guidelines for selecting freezing strategies. It offers a practical, evidence-based approach to balanced transfer learning for object detection in scenarios with limited resources.https://www.mdpi.com/2227-7390/13/15/2539YOLOlayer freezingtransfer learningfine-tuningobject detection |
| spellingShingle | Andrzej D. Dobrzycki Ana M. Bernardos José R. Casar An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures Mathematics YOLO layer freezing transfer learning fine-tuning object detection |
| title | An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures |
| title_full | An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures |
| title_fullStr | An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures |
| title_full_unstemmed | An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures |
| title_short | An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures |
| title_sort | analysis of layer freezing strategies for enhanced transfer learning in yolo architectures |
| topic | YOLO layer freezing transfer learning fine-tuning object detection |
| url | https://www.mdpi.com/2227-7390/13/15/2539 |
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