Abstract:
Abstract：This paper proposes a power vision system based on knowledge distillation technology， aiming to solve the application challenges of power vision models in resource-constrained environments. By dynamically selecting a large-scale power vision model with high performance as the teacher model and intelligently constructing a lightweight small model as the student model， the knowledge distillation technology is utilized to compare the outputs of the student model and the teacher model. By minimizing the difference between the two models， effective knowledge transfer is achieved. The research results are applied to the power vision field， which constructs a power vision system including power vision datasets， teacher model selection， student model construction， knowledge distillation， optimized training and model evaluation. Then an automated knowledge distillation process is achieved. Experimental results based on the power vision datasets show that the research can significantly reduce model complexity and computational resource consumption while maintaining high recognition accuracy， improving the applicability and efficiency of the power vision system in resource-constrained environments.

Key words: Key words: large-scale model, knowledge distillation, knowledge transfer, deep learning, power vision system