摘要
针对目前服装分类算法在解决多类别服装分类问题时分类精度一般的问题,提出了一种基于残差的优化卷积神经网络服装分类算法,在网络中使用了如下三种优化方法:(1)调整批量归一化层、激活函数层与卷积层在网络中的排列顺序;(2)"池化层+卷积层"的并行池化结构;(3)使用全局均值池化层替换全连接层。经过由香港中文大学多媒体实验室提供的多类别大型服装数据集(DeepFashion)和标准数据集CIFAR-10上的实验表明,所提出的网络模型在处理图片的速度和分类精度方面都优于VGGNet和AlexNet,且得到了目前为止已知的在DeepFashion数据集上最好的分类准确率。该网络也可以更好地应用于目标检测和图像分割领域。
Aiming at the problem that the current clothing classification algorithm has general accuracy in solving the multi-category clothing classification,this paper proposes an optimized clothing classification algorithm based on residual convolutional neural network,and uses the following three optimization methods in the network：1）The orders of batch normalized layer（BN）,activation function（Relu）and convolution layer in the network are adjusted;2）A parallel pooling structure of ＂pool layer＋convoluted layer＂ is adopted;3）The full connection layer is replaced by the global mean pooling layer.Experiments on the multi-category large-scale clothing data set（DeepFashion）provided by the multimedia laboratory,the Chinese university of Hong Kong and the standard data set CIFAR-10 show that the proposed network model is superior to VGGNet and AlexNet in image processing speed and classification accuracy,and obtains the best classification accuracy on DeepFashion data set so far.The network can also be better applied to target detection and image segmentation.
出处
《计算机工程与科学》
CSCD
北大核心
2018年第2期354-360,共7页
Computer Engineering & Science
关键词
深度学习
残差网络
多类别服装分类
卷积神经网络优化
deep learning
residual network
multiple categories clothing classification
convolution neural network optimization
