CLOTHING IMAGE CLASSIFICATION BASED ON KNOWLEDGE DISTILLATION OF MULTI-STRUCTURE TEACHERS

In order to solve the problems of complex structure and large number of parameters in current garment image classification models, this paper proposes a garment image classification method based on multi-teacher knowledge distillation. The key point of this method is to select multiple distillation models with different types of knowledge as a multi-teacher network, assign weight adaptively according to the model performance of each teacher, and cooperate as a supervisor to guide the target network, so as to realize the lightweight improvement of the clothing classification model. Experiments on DeepFashion show that the proposed method has an accuracy improvement of about 1.14 percentage points compared with the clothing classification model with the same network structure, and the model itself has only 0.27 M parameters.