Comparative Evaluation of Image Classification Models for Ornamental Rock Classification

The ornamental rock industry in Brazil is distinguished by its diverse assortment of rock types, presenting a unique challenge in classification due to its inherent subjectivity and reliance on expert judgment. to address this problem, the present study introduces a publicly accessible database encompassing 12 distinct classes of ornamental rocks, including granite, marble, and quartzite. This database comprises 1,798 images sourced directly from entities within the stone sector. We employ and compare the performance of seven neural network models for ornamental rock image classification: VGG16, VGG19, ResNet50, ResNet101, Xception, InceptionV3, and the Vision Transformer (ViT). Our empirical analysis reveals that the ViT model outperforms conventional architectures, achieving an accuracy rate of 98.36%.