Research on Classification of Architectural Style Image Based on Convolution Neural Network
Deep learning is a new field in machine learning research.Convolution neural network is the most important factor in image recognition.This paper mainly focuses on the network design and parameter optimization of convolution neural network.This paper is first based on the traditional handwritten digital classification framework LeNet-5 to improve,and implements the test on the ten and twenty-five architectural style data set,and then based on ImageNet-k model design ideas to design a deep convolution neural network structure.The experimental results show that the deeper the network level,the more comprehensive the feature of the image,the better the training effect.In this paper,we study the network design and parameters optimization of convolution neural network,and summarize some practical rules of depth classification on image classification,which is very instructive to solve practical problems.
deep learning convolution neural networks image classification parameter optimization
Kun Guo Ning Li
School of Computer Science and Technology,Wuhan University of Technology Hubei,China,430070
国际会议
重庆
英文
1062-1066
2017-10-03（万方平台首次上网日期，不代表论文的发表时间）