Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
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○DOI references
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○Academic publication links
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○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Repository
snncutoff-vit-imgnet
Basic Info
- Host: GitHub
- Owner: szh1456
- License: mit
- Language: Python
- Default Branch: main
- Size: 37.7 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
.github/readme.md
SNNCutoff is a Python package developed with a PyTorch backend, designed primarily for evaluating Spiking Neural Networks (SNNs). It offers:
SNN Evaluation:
- Utilizing detailed performance metrics, e.g., accuracy, latency and operations.
- Capabilities for conducting adaptive inference or cutoff of SNNs.
SNN Training:
- While the emphasis is on evaluation, the toolkit also supports a diverse array of training algorithms.
Overview
SNN Training Algorithms:
A New Metric:
- Optimal Cutoff Timestep (OCT): A optimal timestep that determines the minimal input processing duration for maintaining predictive reliability in SNNs. OCT is grounded in theoretical analysis and serves as a robust benchmark for assessing SNN models under different optimization algorithms.
Cutoff Approximation:
- Timestep (Baseline): Cutoff triggered using fixed timestep.
- Top-K: Cutoff triggered using the gap between the top-1 and top-2 output predictions at each timestep.
- Others: Coming soon.
More details in Documentation.
Getting Started
To begin using SNNCutoff, clone this repository and follow the setup instructions below.
Installation
Clone the repo
sh git clone https://github.com/Dengyu-Wu/snncutoff.gitInstall Pytorch
sh pip install -r requirements. txt
Training and Evaluation
We provide training and evaluation examples in scripts.
Contributing
Check the contributing guidelines if you want to get involved with developing SNNCutoff.
Acknowledgments
We extend our appreciation to everyone who has contributed to the development of this project, both directly and indirectly.
Owner
- Login: szh1456
- Kind: user
- Repositories: 1
- Profile: https://github.com/szh1456
Citation (CITATION.cff)
cff-version: 1.2.0
type: software
title: SNNCutoff
authors:
- family-names: Wu
given-names: Dengyu
- family-names: Xu
given-names: Minghong
# TODO: Issue a DOI for the repo, e.g. 10.5281/zenodo.1234. See:
# https://docs.github.com/en/repositories/archiving-a-github-repository/referencing-and-citing-content
license: MIT
repository-code: https://github.com/Dengyu-Wu/snncutoff
preferred-citation:
type: article
title: Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
authors:
- family-names: Wu
given-names: Dengyu
- family-names: Jin
given-names: Gaojie
- family-names: Yu
given-names: Han
- family-names: Yi
given-names: Xinping
- family-names: Huang
given-names: Xiaowei
year: 2023
identifiers:
- type: other
value: arXiv.2301.09522 [cs.CV]
description: The ArXiv preprint of the paper
- type: doi
value: 10.48550/arXiv.2301.09522
description: The ArXiv preprint of the paper
doi: 10.48550/arXiv.2301.09522
url: https://arxiv.org/abs/2301.09522
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Dependencies
- actions/checkout v4 composite
- actions/deploy-pages v4 composite
- actions/setup-python v5 composite
- actions/upload-pages-artifact v3 composite
- numpy *
- torch *
- tqdm *