BI-DIRECTIONAL CLASSIFICATION OF ROMAN PERIOD COINS BY DEEP LEARNING METHODS

Year 2023, , 161 - 169, 30.06.2023

Kıyas Kayaalp , Fehmi Özkaner

https://doi.org/10.47933/ijeir.1269680

Abstract

In this study, the problem of classification of coins, which have historical importance and can only be distinguished by experts, is discussed with pre-learning deep learning algorithms. In the solution of the problem, the RRC-60 dataset, which consists of the images of the coins used in the Roman Republic period, was used. In this study, pre-learning Xception, MobileNetV3-L, EfficientNetB0 and DenseNet201 models were trained using the images on both sides of the coins in the data set. As a result of the training, the best values, Precision, Recall and F1-Score metrics in the MobileNetV3-L model were 98.2%, 96.8%, 97.5%, respectively, and the test accuracy was 95.2%

Keywords

classification, CNN, coin, deep learning

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