https://github.com/aa-samad/conv_snn

Code for "Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction" paper
https://github.com/aa-samad/conv_snn

cifar10 cnn convolutional-neural-networks deep-learning neuromorphic pytorch snn spatio-temporal-analysis

Last synced: about 2 months ago
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Code for "Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction" paper

• Host: GitHub
• URL: https://github.com/aa-samad/conv_snn
• Owner: aa-samad
• License: gpl-3.0
• Created: 2020-02-06T10:12:59.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-10-22T07:25:52.000Z (almost 4 years ago)
• Last Synced: 2024-02-17T11:32:13.978Z (7 months ago)
• Topics: cifar10, cnn, convolutional-neural-networks, deep-learning, neuromorphic, pytorch, snn, spatio-temporal-analysis
• Language: Python
• Homepage:
• Size: 75.2 KB
• Stars: 111
• Watchers: 2
• Forks: 27
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Conv-SNN

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/convolutional-spiking-neural-networks-for/event-data-classification-on-cifar10-dvs)](https://paperswithcode.com/sota/event-data-classification-on-cifar10-dvs?p=convolutional-spiking-neural-networks-for)

### Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction

This paper highlights potentials of Convolutional spiking neural networks and introduces a new architecture to tackle training deep convolutional SNN problems.

## Prerequisites

The Following Setup is tested and it is working:

- Python>=3.5

- Pytorch>=0.4.1

- Cuda>=9.0

- opencv>=3.4.2

## Docker

- Set up the environment where all the programs can run

    + Run ```./run.sh```

## Data preparation

- Download CIFAR10-DVS dataset

    + Extract the dataset under DVS-CIFAR10/dvs-cifar10 folder

    + Use test_dvs.m in matlab to convert events into matrix of ```t, x, y, p``` (make sure to adjust the test_dvs.m folder addresses inside the code)

    + Run ```python3 dvscifar_dataloader.py``` to prepare the dataset (make sure to have files like dvs-cifar10/airplane/0.mat inside main.py directory)

## Training & Testing

- CIFAR10-DVS model

    + Run ```python3 main.py```

- Spatio-temporal feature extraction tests

    + For each architecture simply run main file with python3

- Note: There are problems with training SNNs, such as extreme importance of initialization; Therefore, you may not reach the highest accuracy as mentioned in the paper.

The solution is to try other torch versions and parameters or contact me / make an issue if you truly need the highest accuracy.

## Citing

Please adequately refer to the papers any time this Work is being used. If you do publish a paper where this Work helped your research, Please cite the following papers in your publications.

	@misc{samadzadeh2020convolutional,

            title={Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction},

            author={Ali Samadzadeh and Fatemeh Sadat Tabatabaei Far and Ali Javadi and Ahmad Nickabadi and Morteza Haghir Chehreghani},

            year={2020},

            eprint={2003.12346},

            archivePrefix={arXiv},

            primaryClass={cs.CV}

        }