Gemtelligence: Accelerating Gemstone classification with Deep Learning
Abstract
The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dollars. Traditionally, human experts have determined the origin and detected treatments on gemstones through visual inspections and a range of analytical methods. However, the interpretation of the data can be subjective and time-consuming, resulting in inconsistencies. In this study, we propose Gemtelligence, a novel approach based on deep learning that enables accurate and consistent origin determination and treatment detection. Gemtelligence comprises convolutional and attention-based neural networks that process heterogeneous data types collected by multiple instruments. Notably, the algorithm demonstrated comparable predictive performance to expensive laser-ablation inductively-coupled-plasma mass-spectrometry (ICP-MS) analysis and visual examination by human experts, despite using input data from relatively inexpensive analytical methods. Our innovative methodology represents a major breakthrough in the field of gemstone analysis by significantly improving the automation and robustness of the entire analytical process pipeline.
Cite
@article{arxiv.2306.06069,
title = {Gemtelligence: Accelerating Gemstone classification with Deep Learning},
author = {Tommaso Bendinelli and Luca Biggio and Daniel Nyfeler and Abhigyan Ghosh and Peter Tollan and Moritz Alexander Kirschmann and Olga Fink},
journal= {arXiv preprint arXiv:2306.06069},
year = {2023}
}