Evaluating CNN Architectures Using Attention Mechanisms: Convolutional Block Attention Module, Squeeze, and Excitation for Image Classification on CIFAR10 Dataset

Abstract

Abstract This paper compares the performance of various popular convolutional neural network (CNN) architectures for image classification on the CIFAR10 dataset. The comparison includes CNN architectures such as Inception V3, Inception-ResNet-v2, ResNetV1, and V2, ResNeXt, MobileNet, and DenseNet, with the addition of two attention mechanisms - Convolutional Block Attention Module (CBAM), and Squeeze and Excitation (SE). CBAM and SE are believed to improve CNNs' performance, especially for complex images with multiple objects and backgrounds. The models are evaluated using loss and accuracy. The main focus of this study is to identify the most effective CNN architecture for image classification on the CIFAR10 dataset with attention mechanisms. The study aims to compare the accuracy of various CNN architectures with and without attention mechanisms and to identify the critical differences between these architectures in terms of their ability to handle complex images. The findings of this study could have implications for developing advanced CNN architectures that can potentially improve the accuracy of computer vision systems in various applications.