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arXiv · 2106.05624

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

Joaquin Royo-Miquel·Silvia Tolu·Frederik E. T. Schöller·Roberto Galeazzi
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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.

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Joaquin Royo-Miquel, Silvia Tolu, Frederik E. T. Schöller, Roberto Galeazzi. 2021-09-28. RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion. https://arxiv.org/abs/2106.05624

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