Performance Comparison of Convolutional Neural Network Models on GPU

Abstract

Deep learning methods are used in many popular areas such: image processing, computer vision, autonomous vehicles, character recognition, audio and video processing. These methods require high processing power, such as graphics cards (GPUs), to obtain successful results in the solution of NP hard problems which have big data. In this study, performance comparison of convolutional neural network (CNN) architectures were performed on GPU. ResNet, VGGNet19 and DenseNet CNN models, and GPDS signature dataset were used for comparison. According to the obtained results, ResNet50 took up the least amount of GPU memory space. The best classification results were obtained with DenseNet121 and the second one was from ResNet50.