Transfer-learned ResNet18 and ViT models classify thin-section images of Levantine ceramics into ten petrographic fabrics with up to 92% accuracy, and saliency maps show the models focus on mineral inclusions.
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Interpretable Classification of Levantine Ceramic Thin Sections via Neural Networks
Transfer-learned ResNet18 and ViT models classify thin-section images of Levantine ceramics into ten petrographic fabrics with up to 92% accuracy, and saliency maps show the models focus on mineral inclusions.