We are proud to announce that Spiking-FullSubNet has emerged as the winner of Intel N-DNS Challenge Track 1 (Algorithmic). Please refer to our brief write-up here for more details. This repository serves as the official home of the Spiking-FullSubNet implementation. Here, you will find:
- A PyTorch-based implementation of the Spiking-FullSubNet model.
- Scripts for training the model and evaluating its performance.
- The pre-trained models in the
model_zoodirectory, ready to be further fine-tuned on the other datasets.
We are actively working on improving the documentation, fixing bugs and removing redundancies. Please feel free to raise an issue or submit a pull request if you have suggestions for enhancements.
Our team is diligently working on a comprehensive paper that will delve into the intricate details of Spiking-FullSuNet's architecture, its operational excellence, and the broad spectrum of its potential applications. Please stay tuned!
See the Documentation for installation and usage. Our team is actively working to improve the documentation. Please feel free to raise an issue or submit a pull request if you have suggestions for enhancements.
All the code in this repository is released under the MIT License, for more details see the LICENSE file.