基于改进卷积神经网络的时尚分类

D. K. Mohanty, Poulami Das Gupta, Raya Dey, Sharanya Pattnaik
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引用次数: 0

摘要

时尚分类是一个应用于电子商务平台,社交媒体和犯罪鉴定等各个领域的领域。在本文中,我们使用了一种改进版本的卷积神经网络进行分类和包含服装项目的识别。在时尚分类类中,我们主要专注于不同类型服装的多类分类。将改进的卷积神经网络应用于服装分类数据,减少了过拟合。在这里,我们对CNN模型的准确率进行了比较,训练准确率和测试准确率分别达到93%和90%左右,优于其他研究人员之前的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modified Convolutional Neural Network for Fashion Classification
Fashion classification is a domain which finds its applications in various fields like e-commerce platforms, social media and criminal identification with clothing similarity or dissimilarity. In this paper, we have used a modified version of convolutional neural network for classification and encompassing the identification of clothing items. Within the fashion classification category, we mainly concentrate on the multi-class classification of different types of apparels. The modified convolution neural network is applied on fashion classification data which reduces over-fitting. Here we have compared the accuracy of the CNN models and have achieved train accuracy and test accuracy of around 93% and 90% respectively which are better than previous works done by other researchers.
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