通过知识蒸馏提升YOLOv5s检测精度，温度影响显著
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm
- 用YOLOv5l做教师模型，YOLOv5s作学生模型，蒸馏温度调节性能
- 在特定温度下，学生模型mAP50和mAP50-95均优于原始YOLOv5s
- 训练150轮后模型收敛，适合追求高精度轻量级检测的应用
本文研究知识蒸馏在目标检测任务中的应用，重点分析不同蒸馏温度对学生模型性能的影响。以YOLOv5l为教师网络，较小的YOLOv5s为学生网络，实验发现随着蒸馏温度升高，学生模型检测精度逐步提升，最终在特定温度下实现mAP50和mAP50-95指标优于原始YOLOv5s模型。实验还记录了训练过程中的准确率曲线与损失函数下降曲线，表明模型在150个训练周期后达到稳定状态。结果表明，合理的知识蒸馏策略不仅能提升模型精度，还能增强其在实际应用中的可靠性与稳定性，为优化目标检测算法提供理论支持与技术参考。
原文摘要 · Abstract (English)
This paper explores the application of knowledge distillation technology in target detection tasks, especially the impact of different distillation temperatures on the performance of student models. By using YOLOv5l as the teacher network and a smaller YOLOv5s as the student network, we found that with the increase of distillation temperature, the student's detection accuracy gradually improved, and finally achieved mAP50 and mAP50-95 indicators that were better than the original YOLOv5s model at a specific temperature. Experimental results show that appropriate knowledge distillation strategies can not only improve the accuracy of the model but also help improve the reliability and stability of the model in practical applications. This paper also records in detail the accuracy curve and loss function descent curve during the model training process and shows that the model converges to a stable state after 150 training cycles. These findings provide a theoretical basis and technical reference for further optimizing target detection algorithms.
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