Transfer Learning Utilization for Banknote Recognition: a Comparative Study Based on Bosnian Currency

A. Almisreb, M. A. Saleh
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引用次数: 6

Abstract

Transfer learning introduces the ability to perform deep learning models over a small set of data. This paper investigates the utilization of three fine-tuned Convolutional Neural Networks (CNNs), namely, Alexnet, Googlenet, and Vgg16. Alexnet and Googlenet consider as the state-of-the-art models in deep learning, while Vgg16 preference due to its depth. Each model was fine-tuned, trained, and tested over a dataset contains Bosnian Banknotes (BAM). The dataset covers 11 classes where 10 images were collected through mobile phone camera for each class. Alexnet showed a better performance in terms of completing the training while Vgg16 showed better performance in terms of accuracy as it achieved 100% compared to 95.24% for Alexnet. Googlenet showed less efficient performance by achieving 88.65%.
迁移学习在纸币识别中的应用:基于波斯尼亚货币的比较研究
迁移学习引入了在小数据集上执行深度学习模型的能力。本文研究了三种微调卷积神经网络(cnn)的使用,即Alexnet, Googlenet和Vgg16。Alexnet和Googlenet被认为是深度学习领域最先进的模型,而Vgg16则因其深度而受到青睐。每个模型都在包含波斯尼亚钞票(BAM)的数据集上进行了微调、训练和测试。数据集涵盖11个类别,每个类别通过手机摄像头采集10张图像。Alexnet在完成训练方面表现更好,而Vgg16在准确率方面表现更好,达到100%,而Alexnet的准确率为95.24%。Googlenet的效率较低,为88.65%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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