The Relationship between training data volume and performance of rock identification AI using CNN

Abstract

To address the recent shortage of engineers, research on rock identification using AI has been progressing. In this study, we investigated the relationship between the number of training images and AI performance by creating and testing eight CNN models with different amounts of training data. The results showed that using approximately 500 images per rock type yielded the highest performance. Additionally, it was found that image generation through data augmentation could cause overfitting. Future prospects include testing models with 500 training images without data augmentation and verifying models with increased rock classification categories.