Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (20): 206-211.DOI: 10.3778/j.issn.1002-8331.2001-0236

Previous Articles     Next Articles

Improved Clothing Image Recognition Model Based on Residual Network

LU Jianbo, XIE Xiaohong, LI Wentao   

  1. School of Computer and Information Engineering, Nanning Normal University, Nanning 530299, China
  • Online:2020-10-15 Published:2020-10-13



  1. 南宁师范大学 计算机与信息工程学院,南宁 530299


The traditional multi category classification method of clothing is mainly to extract the color, texture, edge and other features of the image manually. These manual feature selection methods are cumbersome and have low classification accuracy. The depth residual network can increase the depth of the neural network to obtain a higher recognition accuracy, which is widely used in various fields. To improve the accuracy of clothing image recognition, an improved depth residual network model is proposed in this paper. The model improves the convolution layer in the residual block, adjusts the arrangement order of the batch normalization layer and the activation function layer, introduces attention mechanism and adjusts the structure of convolution kernel. The network model is tested on the standard data set Fashion-MNIST and the multi category large clothing data set(DeepFashion) provided by the Multimedia Laboratory of the Chinese University of Hong Kong. Experimental results show that the proposed network model is superior to the traditional depth residual network in the accuracy of clothing image recognition and classification.

Key words: residual network, attention mechanism, clothing classification, deep learning



关键词: 残差网络, 注意力机制, 服装分类, 深度学习