Unverified50% confidenceSolutionExact time
For fine-tuning strategy, it is recommended to freeze the first few layers, use a larger learning rate for the later layers and detection head, and employ gradual unfreezing for more stable convergence
1
Sources
50%
Confidence
Long-term
Relevance
8/14/2026
First Seen
Sources
小数据集训练DETR：无人机枯树检测实战解析
Related Claims
UnverifiedGradual Unfreezing progressively unfreezes and trains lower-layer parameters starting from the top layer, which can prevent Catastrophic Forgetting.72% similarUnverified在Rainbow消融实验中，移除优先经验回放和移除分布式学习对性能的伤害最大，移除dueling架构的影响相对较小65% similarUnverifiedOn convergent tasks, there is a clear performance saturation phenomenon after models reach a certain scale, where continued increases in parameter count or training data yield rapidly diminishing marginal contributions to accuracy.64% similar
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