zhouchenlin2096/Spikingformer-CML
Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation
This project offers advanced Spiking Neural Network (SNN) models designed for image recognition tasks. It takes standard image datasets, like ImageNet or CIFAR, as input and produces highly accurate classifications. It's built for AI researchers and machine learning engineers who are developing or evaluating state-of-the-art SNNs for computer vision.
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Use this if you are working with Spiking Neural Networks and need to achieve leading performance on image classification benchmarks using efficient models.
Not ideal if you are looking for a general-purpose deep learning framework or a solution for non-image data types.
Stars
46
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2
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 02, 2024
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