Classification of Simple CNN Model and ResNet50

Abstract

Abstract: In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification tasks, achieving state-of-the-art performance in various domains. Among the plethora of CNN architectures, the Simple CNN model and ResNet50 stand out as widely used architectures with distinct characteristics. In this study, we present a comparative analysis of these two architectures in terms of their performance, computational efficiency, and robustness for classification tasks.The Simple CNN model represents a straightforward convolutional neural network architecture with a sequential arrangement of convolutional, pooling, and fully connected layers. On the other hand, ResNet50 introduces the concept of residual learning, leveraging skip connections to address the vanishing gradient problem and facilitate the training of deeper networks.We conduct experiments on benchmark datasets, evaluating the classification accuracy, training time, and model complexity of both architectures. Our findings reveal insights into the strengths and weaknesses of each model. While the Simple CNN model demonstrates simplicity and ease of implementation, ResNet50 exhibits superior performance, particularly in scenarios with a large amount of training data and complex feature representations.