Comparison of CNN architectures for INTERCO

The problem of selecting the best convolutional neural network (CNN) architecture appears when dealing with a large number of classes and images. The selection problem resulted in the consideration of varying layer configurations and strategies, and the reliability of the individual deep learning models was unknown. On the other hand, as CNN architectures evolved, each new iteration proposed, from a theoretical point of view, was a more effective method for achieving better results. Thus, this was the motivation to validate the selected CNNs on image recognition and classification. This paper focuses on classifying the International Code of Signals (INTERCO) flags using different CNN architectures, including AlexNet, VGG-16, VGG-19, InceptionV3, ResNet-18, ResNet-34, ResNet-50, MobileNetV2, EfficientNet-B0, EfficientNet-B1, CSPNet, and ConvNeXt-Tiny. The performance validation of these architectures through the analysis of metrics such as the accuracy, precision, recall, F1-score, training time, number of epochs, and single-image processing time has been carried out. The results of the computational experiments are presented and discussed.