面向移动端图像分类的轻量级CNN优化

Lightweight CNN optimization for mobile image classification

为解决图像分类算法由于计算量大和参数冗余难以应用在存储空间与计算能力受限的移动设备上的问题,提出一种轻量的卷积计算模块Extremely Lightweight Block(ELBlock),采用逐点卷积叠加深度可分离卷积的方法,对逐点卷积进行分组,增加相邻层过滤器之间的对角相关性,进一步降低卷积操作的计算复杂度;利用通道混洗关联输入和输出通道,提高特征的信息表达能力;基于ELBlock设计一个极其轻量的小型神经网络架构ELNet,结构更加简洁、高效。在Android手机上的实验结果表明,所提ELNet在保证分类精度的同时,具有计算量小、参数少和推理时间短的优点。

To solve the problem that the image classification algorithm is difficult to be applied to mobile devices with limited sto-rage space and computing power due to the large amount of calculation and the parameter redundancy,a lightweight convolution calculation module,namely Extremely Lightweight Block(ELBlock)was proposed.The method of point-by-point convolution superposition depthwise separable convolution was adopted.The point-by-point convolution was grouped to increase the diagonal correlation between filters of adjacent layers and the computational complexity of convolution operation was further reduced.The channel shuffle was used to correlate the input and output channels to improve the information expression ability of features.Based on ELBlock,an extremely lightweight small neural network architecture ELNet was designed,which was more concise and efficient.Experimental results on Android mobile phones show that the proposed ELNet has the advantages of less computation,fewer parameters and shorter inference time while ensuring the prediction accuracy.