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

2025-09-05

AI Summary

This work addresses the efficiency of transfer learning for YOLOv8/v10 real-time object detection under resource-constrained settings (e.g., UAVs), focusing on how layer-freezing strategies impact fine-tuning performance. Method: We systematically analyze the coupling among freezing depth, dataset characteristics, and training dynamics via gradient L2-norm analysis, Grad-CAM visualization, and cross-dataset comparative experiments. Based on these insights, we propose a data-aware heuristic for adaptively selecting optimal freezing depth. Results: Experiments demonstrate that the optimal freezing strategy is highly dependent on dataset complexity; compared to full-parameter fine-tuning, judiciously chosen freezing configurations reduce GPU memory consumption by 28% and improve mAP@50 by 1.2–2.7 percentage points on certain datasets—validating the Pareto superiority of “moderate freezing” in balancing accuracy and computational efficiency.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Optimizing layer-freezing strategies for YOLOv8 and YOLOv10 transfer learning
• Evaluating freezing configurations impact on resource-constrained object detection
• Analyzing interplay between freezing depth, dataset characteristics and training dynamics

Innovation

• Layer-freezing strategies for YOLO transfer learning
• Gradient behavior and visual explanations analysis
• Configurations reducing GPU memory by 28%