Advancements in Neural Network Architectures for Image Recognition in Computer Vision Systems

Abstract

The field of image recognition has seen remarkable advancements over the past decade, primarily driven by innovations in neural network architectures. This paper reviews the evolution of neural network models, focusing on significant milestones and contemporary architectures that have substantially improved performance in image recognition tasks. Key developments include Convolutional Neural Networks (CNNs), Residual Networks (ResNets), Vision Transformers (ViTs), and hybrid models that integrate multiple architectural paradigms. These advancements have not only enhanced accuracy but also efficiency, enabling real-time applications across various domains.