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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.

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Article Details

Issue: Vol 26 No 1 (2023): Vol 26 (1): Under publishing
Page No.: In press
Published: Mar 13, 2023

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Nguyen, H. L., & Huynh, K. T. (2023). New model for low-end computers: ResNet and VGG-16. VNUHCM Journal of Science and Technology Development, 26(1), In press.

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