How many Convolutional Layers are required for a Granite Classification Neural Network?

• Eduardo Henrique Próspero Souza IFES
• Karin Satie Komati IFES

DOI: https://doi.org/10.5753/eniac.2022.227624

Abstract

The purpose of this article is to analyze the result of the classification accuracy of types of granite, according to the variation in the number of convolutional layers of a CNN (Convolutional Neural Network). From a standard CNN architecture, only the number of convolutional layers is varied, starting with 2 layers up to 10 layers, increasing one layer for each experiment. A public database, Rock Image Datasets, was used. In the end, the architecture with 5 convolutional layers was the one that achieved the best results, reaching 99% accuracy.

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