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

arXiv: https://arxiv.org/abs/2106.05624 · HTML: https://mlsanity.com/paper/2106.05624/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2021.

## Built on by (most cited first, 3 of 3)

- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation](https://mlsanity.com/paper/2207.02702/) (2022)
- [Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2310.06578/) (2023)

## Abstract (arXiv)

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.