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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.
Issue: Vol 26 No 1 (2023): Vol 26 (1): Under publishing
Page No.: In press
Published: Mar 13, 2023
Section: ENGINEERING AND TECHNOLOGY
DOI: https://doi.org/10.32508/stdj.v26i1.4030
Uncorrection proof = 22 times
Total = 22 times