Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

ABSTRACT: Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model based on an openly available PyTorch implementation of Faster R-CNN ResNet 50. Results indicate 100% agreement between the model and original human derived classifications, a better performance than th

SUBMITTER: Emmitt J 

PROVIDER: S-EPMC9365149 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature