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MPQ-YOLACT: Mixed-Precision Quantization for Lightweight YOLACT

Segmentation network, which are widely used in various multimedia applications in recent years, are capable of precise image processing in units of pixels, but have higher computational complexity compared to other computer vision algorithms such as classification and object detection. In this paper, we propose a technique that applies mixed-precision quantization to the existing YOLACT network, which performs accurate instance segmentation. By adaptively applying quantization according to the parameter size and the effect on the accuracy of modules in YOLACT, it is possible to significantly reduce the network size while maintaining the accuracy of YOLACT as much as possible. The experimental results show the parameter size of the entire network is reduced by 75.4% with only a negligible drop in accuracy of less than 0.1%.