NN-SVG

NN-SVG: Publication-Ready Neural Network Architecture Schematics - Published in JOSS (2019)

https://github.com/alexlenail/NN-SVG

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
    Links to: joss.theoj.org
  • Committers with academic emails
    1 of 11 committers (9.1%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (10.3%) to scientific vocabulary

Keywords

d3 deep-learning diagrams drawing machine-learning neural-network svg threejs
Last synced: 6 months ago · JSON representation

Repository

Publication-ready NN-architecture schematics.

Basic Info
  • Host: GitHub
  • Owner: alexlenail
  • License: mit
  • Language: JavaScript
  • Default Branch: master
  • Homepage: http://alexlenail.me/NN-SVG/
  • Size: 394 KB
Statistics
  • Stars: 5,584
  • Watchers: 78
  • Forks: 748
  • Open Issues: 26
  • Releases: 1
Topics
d3 deep-learning diagrams drawing machine-learning neural-network svg threejs
Created about 10 years ago · Last pushed 7 months ago
Metadata Files
Readme License

README.md

NN-SVG

License: MIT status | Docs | Contributing

Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand.

NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages.

The tool provides the ability to generate figures of three kinds: classic Fully-Connected Neural Network (FCNN) figures, Convolutional Neural Network (CNN) figures of the sort introduced in the LeNet paper, and Deep Neural Network figures following the style introduced in the AlexNet paper. The former two are accomplished using the D3 javascript library and the latter with the javascript library Three.js. NN-SVG provides the ability to style the figure to the user's liking via many size, color, and layout parameters.

I hope this tool will save machine learning researchers time, and I hope this software might also serve as a pedagogical tool in some contexts.

Citation

LeNail, (2019). NN-SVG: Publication-Ready Neural Network Architecture Schematics.
Journal of Open Source Software, 4(33), 747, https://doi.org/10.21105/joss.00747

Related

Owner

  • Name: Alexander Lenail
  • Login: alexlenail
  • Kind: user
  • Location: Cambridge, MA
  • Company: MIT

GitHub Events

Total
  • Issues event: 3
  • Watch event: 917
  • Issue comment event: 7
  • Push event: 1
  • Pull request event: 3
  • Fork event: 139
Last Year
  • Issues event: 3
  • Watch event: 918
  • Issue comment event: 7
  • Push event: 1
  • Pull request event: 3
  • Fork event: 139

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 62
  • Total Committers: 11
  • Avg Commits per committer: 5.636
  • Development Distribution Score (DDS): 0.581
Past Year
  • Commits: 3
  • Committers: 3
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.667
Top Committers
Name Email Commits
Alexander Lenail z****e@g****m 26
Alexander Lenail a****x@l****g 20
Alexander Lenail a****l@t****u 8
sof s****e@g****m 1
Somanshu Rath s****e@g****m 1
Ryan Alex Martin r****n@g****m 1
Mayank Yadav 1****9 1
Johan Pauwels j****s 1
Erupturatis b****v@g****m 1
Erfan Asgari 7****1 1
Benjamin Loison 1****n 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 43
  • Total pull requests: 24
  • Average time to close issues: 4 months
  • Average time to close pull requests: 2 days
  • Total issue authors: 40
  • Total pull request authors: 15
  • Average comments per issue: 2.3
  • Average comments per pull request: 1.58
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 4
  • Average time to close issues: 2 days
  • Average time to close pull requests: 4 days
  • Issue authors: 2
  • Pull request authors: 3
  • Average comments per issue: 1.0
  • Average comments per pull request: 2.5
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • timobaumann (3)
  • eshrh (2)
  • 2ndMessiah (1)
  • harshraj22 (1)
  • dario-loi (1)
  • eleezs (1)
  • ibowennn (1)
  • massisenergy (1)
  • boyden (1)
  • solaceh (1)
  • csachs (1)
  • ceo0052 (1)
  • Pavo98 (1)
  • Ademord (1)
  • rupertbayern (1)
Pull Request Authors
  • alexlenail (8)
  • wolgwang1729 (3)
  • andrewekhalel (2)
  • tirtho109 (2)
  • somanshurath (2)
  • erfanasgari21 (2)
  • jpauwels (2)
  • ryanalexmartin (1)
  • Erlnesa (1)
  • king4arabs (1)
  • sof (1)
  • Benjamin-Loison (1)
  • erupturatis (1)
  • timobaumann (1)
  • JasonnnW3000 (1)
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