AI-Augmented Convolutional Neural Networks for Image Recognition

Architectural Analysis and ResNet50 Implementation with Modern Deep Learning Perspectives

Abstract

The Convolutional Neural Network (CNN) is now the backbone of image recognition technology, powering computers to make accurate classifications and understand visual content. In this paper, we do a detailed analysis of the architecture of three popular CNN models namely ResNet, GoogLeNet (Inception v1) and VGGNet, and discuss the forward propagation, loss functions and back propagation mechanisms. We also explain in detail a practical implementation of ResNet50 on the CIFAR-10 dataset, which involves data preparation, building of the model, optimization of training using the Adam optimizer, and evaluation. In addition to this classic analysis, we discuss the latest developments in artificial intelligence paradigms such as Vision Transformers (ViTs), self-supervised learning, neural architecture search (NAS), and multimodal foundation models, which are transforming the image recognition field and supplementing what was already achieved by CNN models.