计算机工程与应用2025，Vol.61Issue(11)：1-21,21.DOI:10.3778/j.issn.1002-8331.2411-0196
基于卷积神经网络的图像分类深度学习模型综述
Review of Deep Learning Models for Image Classification Based on Convolutional Neural Networks
摘要
Abstract
Using neural network model for classification has always been a very important research direction.With the development of deep learning technology,the requirement for neural network model is getting higher and higher.At the same time,high recognition rate,the number of parameters and training time of the model are also highly required.Convo-lutional neural networks have always been the mainstream method for image classification in deep learning.This paper mainly introduces the development history of convolutional neural networks for classification model,and analyzes the construction ideas of each model at different stages.Secondly,the paper reviews relevant examples of Transformer com-bined with convolutional neural networks as well as the application of each model in other fields.Finally,the possible development directions of convolutional neural networks are discussed.关键词
卷积神经网络/深度学习/图像分类/TransformerKey words
convolutional neural networks/deep learning/image classification/Transformer分类
计算机与自动化引用本文复制引用
刘鸿达,孙旭辉,李沂滨,韩琳,张宇..基于卷积神经网络的图像分类深度学习模型综述[J].计算机工程与应用,2025,61(11):1-21,21.基金项目
青岛市自然科学基金原创探索项目(24-4-4-zrjj-139-jch). （24-4-4-zrjj-139-jch）
