Improved model lightweighting method based on GhostNet
In order to reduce the deployment cost of deep convolutional neural networks and optimize the detection performance of the models,an improved lightweight backbone network algorithm S-GhostNet is proposed.The algorithm reduces the computational effort of convolutional operations by introducing a Ghost Module structure optimized for feature map generation.It improves the accuracy of the models by combining with an improved class of residual modules.S-GhostNet has a strong plug-and-play property and can be applied to most convolutional neural network models.Experimental results show that S-GhostNet is less computationally intensive than MobileNetV2,ShuffleNetV2 and GhostNet in target classification and target detection tasks.Also the accuracy of the model is similar or even higher.
万方数据
维普
