计算机应用与软件2024，Vol.41Issue(12)：167-172,207,7.DOI:10.3969/j.issn.1000-386x.2024.12.024
基于多结构教师蒸馏的服装图像分类方法
CLOTHING IMAGE CLASSIFICATION BASED ON KNOWLEDGE DISTILLATION OF MULTI-STRUCTURE TEACHERS
摘要
Abstract
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.关键词
模型压缩/知识蒸馏/多教师知识蒸馏/服装图像分类Key words
Model compression/Knowledge distillation/Multi-teacher knowledge distillation/Clothing image classification分类
信息技术与安全科学引用本文复制引用
张晓滨,刘昊..基于多结构教师蒸馏的服装图像分类方法[J].计算机应用与软件,2024,41(12):167-172,207,7.基金项目
陕西省自然科学基金项目(2019JQ-849). （2019JQ-849）
