RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion

Abstract

The paper proposes a method to convert a deep learning object detector into an equivalent spiking neural network. The aim is to provide a conversion framework that is not constrained to shallow network structures and classification problems as in state-of-the-art conversion libraries. The results show that models of higher complexity, such as the RetinaNet object detector, can be converted with limited loss in performance.

arXiv comment5 pages, submitted to ISCMI 2021 conference