New model for low-end computers: ResNet and VGG-16

Abstract

ResNet-50 is a powerful architecture of convolutional neural networks, which gives truly high accuracy and very small error rate. However, this architecture seems not to be very effective when executing in low-end computers because of the small batch size for satisfying limited resources, which is not good for batch normalization. There is also an attempt to use VGG-16 as an alternative method, but vanishing gradients occur often. The proposed model is an improvement of VGG-16 using ResNet for shortcuts to prevent vanishing gradients, and the new architecture does not require batch normalization. As a result, the proposed model achieves a high test accuracy of 85.4%, while ResNet-50 achieves a test accuracy of 75.9% after 40 epochs of training 14,034 images from the Natural Scenes from Image Classification Challenge by Intel. This model is effective for applications related to image processing.