AI-Driven Deep Learning for Flower Image Recognition

Comparative CNN Analysis and ResNet50 Transfer Learning Implementation

Abstract

Convolutional Neural Networks (CNNs) have transformed the field of computer vision, enabling computers to recognize and classify visual data with high accuracy. This paper compares the three well known CNN architectures namely VGGNet, ResNet and Inception (GoogLeNet), specifically while looking at their forward propagation, loss functions, and gradient flow mechanisms. We propose a ResNet50 based transfer learning system for flower species classification on the TensorFlow Flowers dataset with the test accuracy of 90.33%. In addition to classical methods, we examine the application of contemporary AI methods such as Vision Transformers, self-supervised learning, neural architecture search, and multimodal foundation models, which are enhancing the capabilities of image recognition and complementing the CNN based methods.