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基于卷积神经网络的服饰图像分类
Clothing Image Classification Based on Convolutional Neural Network
【摘要】 卷积神经网络是在多层神经网络的基础上发展起来的主要用于图像分类和识别的一种深度学习方法.本文设计了1个4层的卷积神经网络,其中包括3个卷积层和1个softmax输出层,并且在Fashion-MNIST数据集上完成了网络的训练和测试.测试结果表明本文设计的卷积神经网络模型可以有效地实现服饰图像的分类.
【Abstract】 Convolution neural network( CNN) is a kind of deep learning method based on multilayer neural network,which is mainly used for image classification and recognition. A four-layer convolutional neural network that includes three convolutional layers and a softmax output layer was designed in this paper,and the network training and testing was completed on the Fashion-MNIST data set. The test results show that the convolutional neural network model designed in this paper can classify clothing images effectively.
【关键词】 卷积神经网络;
服务图像分类;
Fashion-MNIST数据集;
【Key words】 convolutional neural network(CNN); clothing image classification; fashion-MNIST data set;
【Key words】 convolutional neural network(CNN); clothing image classification; fashion-MNIST data set;
【基金】 北京服装学院2018年研究生科研创新项目(120301990122/009),项目名称:“基于深度学习的服饰图像分类”
- 【文献出处】 北京服装学院学报(自然科学版) ,Journal of Beijing Institute of Clothing Technology(Natural Science Edition) , 编辑部邮箱 ,2018年04期
- 【分类号】TP391.41;TP183
- 【被引频次】11
- 【下载频次】599