VPRSNN | repository contains code for our RA-L paper

by QVPR Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | VPRSNN Summary

VPRSNN is a Python library. VPRSNN has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This repository contains code for our RA-L paper "Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments" which will also be presented at ICRA 2022. DOI: 10.1109/LRA.2022.3149030. The pre-print version of the paper is available on arxiv.

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License

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Support

VPRSNN has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

VPRSNN has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of VPRSNN is current.

Quality

VPRSNN has no bugs reported.

Security

VPRSNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

VPRSNN is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

VPRSNN releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

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VPRSNN Key Features

No Key Features are available at this moment for VPRSNN.

VPRSNN Examples and Code Snippets

No Code Snippets are available at this moment for VPRSNN.

Community Discussions

No Community Discussions are available at this moment for VPRSNN.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install VPRSNN

Our recommended tool to install all dependencies is conda (or better: mamba). Please download and install conda or mamba, if you have not installed it yet. You can create the conda environment using one of the following options.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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