Image Discrimination and Parameter Analysis Based on Convolutional Neural Networks (CNN)

Jiahan Hu

2024

Abstract

In the realm of deep learning, various fields benefit from its wide-ranging applications. Image classification stands out as a classic task in computer vision, demanding meticulous selection of model parameters. The objective of this study is to investigate how model structure, regularization techniques, and optimizers influence model performance and identify the optimal configuration from available options. The research compares the accuracy fluctuations of two model architectures, three regularization methods, and four optimizers in classifying images sourced from the Cifar-10 dataset. Through this analysis, the optimal convolutional neural networks (CNN) model configuration is determined, exhibiting superior performance in the task. Additionally, the findings underscore the importance of judiciously selecting model parameters based on specific needs and computational costs when deploying deep learning techniques. This study offers valuable insights into parameter selection and further refinement of deep learning models, aiding their optimization for practical applications. Notably, the approach sheds light on the intricate interplay between model architecture, regularization techniques, and optimizer selection, enriching the understanding of deep learning model design and optimization strategies.

Paper Citation

in Harvard Style

Hu J. (2024). Image Discrimination and Parameter Analysis Based on Convolutional Neural Networks (CNN). In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 295-300. DOI: 10.5220/0012937000004508

in Bibtex Style

@conference{emiti24,
author={Jiahan Hu},
title={Image Discrimination and Parameter Analysis Based on Convolutional Neural Networks (CNN)},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={295-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012937000004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Image Discrimination and Parameter Analysis Based on Convolutional Neural Networks (CNN)
SN - 978-989-758-713-9
AU - Hu J.
PY - 2024
SP - 295
EP - 300
DO - 10.5220/0012937000004508
PB - SciTePress