用解耦知识蒸馏让遥感图像分类模型更轻更快
Remote Sensing Image Classification with Decoupled Knowledge Distillation
- 用G-GhostNet减少冗余参数，提升推理效率
- 分离目标与非目标类别进行蒸馏，准确率接近VGG-16
- 适合在资源受限设备上部署的轻量级模型
为解决现有遥感图像分类模型参数量大、难以在资源受限设备上部署的问题，本文提出一种基于知识蒸馏的轻量级分类方法。具体采用G-GhostNet作为主干网络，通过特征复用减少冗余参数，显著提升推理效率。同时引入解耦知识蒸馏策略，将目标类与非目标类分离处理，有效提升分类准确率。在RSOD和AID数据集上的实验结果表明，相比高参数量的VGG-16模型，该方法在参数量减少6.24倍的同时，达到几乎相当的Top-1准确率。该方法在模型规模与分类性能间取得良好平衡，为资源受限设备上的部署提供了高效解决方案。
原文摘要 · Abstract (English)
To address the challenges posed by the large number of parameters in existing remote sensing image classification models, which hinder deployment on resource-constrained devices, this paper proposes a lightweight classification method based on knowledge distillation. Specifically, G-GhostNet is adopted as the backbone network, leveraging feature reuse to reduce redundant parameters and significantly improve inference efficiency. In addition, a decoupled knowledge distillation strategy is employed, which separates target and non-target classes to effectively enhance classification accuracy. Experimental results on the RSOD and AID datasets demonstrate that, compared with the high-parameter VGG-16 model, the proposed method achieves nearly equivalent Top-1 accuracy while reducing the number of parameters by 6.24 times. This approach strikes an excellent balance between model size and classification performance, offering an efficient solution for deployment on resource-limited devices.
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