Deep learning techniques for image recognition of counterfeited luxury handbags materials

P. Apipawinwongsa, Y. Limpiyakorn
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Abstract

Due to the fact that counterfeit in second-handed goods terribly affects trading in markets of second-handed luxury bags, users in this research thus present studies of methods to classify genuineness of ‘Gucci GG Canvas’ with the pretrained model from Model VGG16 and with DenseNet121 to design deep Convolutional Neural Networks (CNN) model for binary classification. The CNN together with DenseNet121 model comprises accuracy at 95%, which is more than the 2 prior models, i.e., CNN from scratch and CNN together with VGG16.
基于深度学习技术的奢侈品手袋仿冒材料图像识别
由于二手假货严重影响二手奢侈品市场的交易,因此本研究的用户利用VGG16模型的预训练模型,利用DenseNet121设计深度卷积神经网络(CNN)模型进行二元分类,研究了“Gucci GG Canvas”真伪的分类方法。CNN与DenseNet121模型的准确率达到95%,高于之前的2个模型,即CNN from scratch和CNN with VGG16。
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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