CNN Architecture Assessment: Exploring Depth, Width, and Kernel Size for Image Classification - Buss, Eduardo T.; Radmann, Erick; Conceicao, Ruhan; Zatt, Bruno; Agostini, Luciano (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16782, 2025)
This paper investigates the impact of different convolutional neural network (CNN) architectures on image classification performance using the CIFAR-10 dataset. We evaluate variations in the number of convolutional layers, kernel size, and number of filters. Experiments indicate that increasing the number of filters and slightly enlarging kernel size generally improves accuracy, while deeper models do not always yield better results. The CIFAR-10 dataset contains 60,000 color images across 10 classes, with 45,000 for training, 5,000 for validation, and 10,000 for final testing.