  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.
