The image classification based on cloud computing suffers from difficult deployment as the network depth and data volume increase. Due to the depth of the model and the convolution process of each layer will produce a great amount of calculation, the GPU and storage performance of the device are extremely demanding, and the GPU and storage devices equipped on the embedded and mobile terminals cannot support large models. So it is necessary to compress the model so that the model can be deployed on these devices. Meanwhile, traditional compression based methods often miss many global features during the compression process, resulting in low classification accuracy. To solve the problem, this paper proposes a lightweight neural network model based on dilated convolution and depthwise separable convolution with twenty-nine layers for image classification. The proposed model employs the dilated convolution to expand the receptive field during the convolution process while maintaining the number of convolution parameters, which can extract more high-level global semantic features to improve the classification accuracy. Also, the depthwise separable convolution is applied to reduce the network parameters and computational complexity in convolution operations, which reduces the size of the network. The proposed model introduces three hyperparameters: width multiplier, image resolution, and dilated rate, to compress the network on the premise of ensuring accuracy. The experimental results show that compared with GoogleNet, the network proposed in this paper improves the classification accuracy by nearly 1%, and the number of parameters is reduced by 3.7 million.

中文翻译：

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使用膨胀和深度可分离卷积的轻量级图像分类器

随着网络深度和数据量的增加，基于云计算的图像分类难以部署。由于模型的深度以及每一层的卷积过程都会产生大量的计算，因此该设备的GPU和存储性能要求极高，嵌入式和移动终端上配备的GPU和存储设备无法支持大型楷模。因此，有必要压缩模型，以便可以将模型部署在这些设备上。同时，传统的基于压缩的方法在压缩过程中经常会遗漏许多全局特征，从而导致分类精度低。为了解决这个问题 本文提出了一种基于膨胀卷积和深度可分离卷积的轻量级神经网络模型，该模型具有二十九层，用于图像分类。所提出的模型利用扩张卷积在卷积过程中扩展接收场，同时保持卷积参数的数量，从而可以提取更多高级全局语义特征以提高分类精度。同样，深度可分离卷积被应用以减少卷积运算中的网络参数和计算复杂度，从而减小了网络的大小。提出的模型引入了三个超参数：宽度乘数，图像分辨率和膨胀率，以在确保精度的前提下压缩网络。实验结果表明，与GoogleNet相比，