Abstract
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.
Highlights
In recent years, deep networks have made significant progress in many fields, such as image processing, object detection, and semantic segmentation
This paper proposes a lightweight neural network model combining dilated convolution and depthwise separable convolution
This joint module reduces the computational burden with depthwise separable convolution, making it possible to apply the network model to resources or computationally constrained devices
Summary
Deep networks have made significant progress in many fields, such as image processing, object detection, and semantic segmentation. Krizhevsky, et al [1] first adopted deep learning algorithm and the AlexNet and won the champion of ImageNet Large Scale Visual Recognition Challenge in 2012, which improved the recognition accuracy by 10% compared to the traditional machine learning algorithm. Cloud computing technology, which combines the characteristics of distributed computing, parallel computing and grid computing, provides users with scalable computing resources and storage space by using massive computing clusters built by ordinary servers and storage clusters built by a large number of low-cost devices. Renting the cloud computing servers need extra cost for individuals and small companies. For example,The model training in this article can be run on an NVIDIA P4 cloud server with 8g memory. This server is the most basic server and costs $335 per month. There is the need to design a lightweight network to reduce the model’s dependence on high-performance devices [13, 14]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have