MPSS: A Model Pruning Method for Semantic Image Segmentation Networks

This paper proposes a model pruning named MPSS for semantic image segmentation networks, so that semantic image segmentation models can be deployed into embedded devices. Most existing model pruning methods aim at image classification models. Since semantic segmentation is a fine grained task, the directly use of traditional model pruning methods greatly reduces the model accuracy. The core problems of model pruning in the semantic segmentation task are to determine the appropriate pruning kernels and the pruning structure. We propose a new composite index that defines the similarity between convolution kernels to determine the pruning kernels. Furthermore, we propose a structure mending method based on the neural architecture search to determine the pruning structure. Compared with the method of manually defining the pruning rate, the proposed structure mending method obtains a better pruning structure. We conduct experiments based on two semantic segmentation networks, the FCN and the FASSD-Net. The experimental results show that the proposed model pruning method enables the pruned network to obtain higher accuracy under the same compression rate. In addition, we deploy the compressed models on an embedded platform, and the FASSD Net inference speed is twice as fast as the unpruned model on NVIDIA Xavier NX.