Automatic Coin Grading: Model Based on Siamese Neural Network with EfficientNet Encoders

Coin grading is a crucial process in numismatics that determines a coin’s value on the basis of its condition. However, the traditional grading procedure is performed by human which makes it subjective and time-consuming, with different experts potentially assigning different grades to the same coin. This paper introduces a Deep Learning (DL) model for the type-invariant automatic coin grading across the full 70-points Sheldon scale. We propose a Siamese neural network based on EfficientNet encoders for dual-sided coin evaluation, which performs the analysis of both the obverse and reverse images of coins. The model is trained and evaluated on a comprehensive dataset containing 36,205 unique professionally graded samples covering all 30 grades from Poor (P)1 to Mint State (MS)70 across the multiple coin types. The proposed DL solution achieves 0.3403 accuracy and 1.52 Mean Absolute Error (MAE) along the Sheldon scale, outperforming both the Machine Learning (ML) baseline and the state-of-the-art approaches. Unlike earlier studies that were limited to fewer coin types or reduced grading scales, our work addresses the full Sheldon scale grading across the multiple coin types, providing a more robust and generalizable solution for automatic coin grading.