Comparison of training methods for the binarized neural object detection network

Abstract

Binarized neural networks are of great interest because they can dramatically reduce the amount of computation and memory, but they are still in the early stages of research and are still challenging. In this paper, we investigate three training methods of a binarized object detection network and measure their training speeds and detection accuracies based on Darknet platform.