VicenteAlex/Spiking_ResNet
Implementation of the paper Keys to Accurate Feature Extraction Using Residual Spiking Neural Networks
This project offers an implementation of Spiking Residual Neural Networks (S-ResNets) for image classification tasks, specifically designed for neuromorphic computing. It takes image datasets like CIFAR-10, CIFAR-100, or DVS-CIFAR10 as input and produces a trained S-ResNet model capable of accurately classifying images. This is primarily for AI/ML researchers and engineers working on energy-efficient neural networks or biologically inspired AI.
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Use this if you are an AI researcher or neuromorphic engineer looking to implement and experiment with spiking neural networks for image classification tasks, especially if energy efficiency is a key consideration.
Not ideal if you are looking for a general-purpose, off-the-shelf image classification solution using standard deep learning architectures or if you are not familiar with SNNs and neuromorphic concepts.
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Python
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Last pushed
Dec 28, 2023
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