# gwding/draw_convnet

Repository: https://github.com/gwding/draw_convnet
Canonical: https://ross.abutalabs.com/products/draw_convnet
Language: Python
License Family: other
Last push: 2024-06-22T20:24:41+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3843, "days_push": 802, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2860, forks 519 (observed 2026-08-28T04:07:26.005811+00:00)

## What it is
A Python script that generates illustrations of Convolutional Neural Network (ConvNet) architectures using matplotlib. It produces publication-quality figures showing network layers and feature channels, useful for papers and teaching.

## Use cases
- draw a convolutional neural network diagram for a paper
- visualize convnet architecture layers
- generate CNN figures for teaching materials
- create neural network illustrations with matplotlib
- make a convnet figure with omitted channels

## When to choose
- you need a static ConvNet architecture figure for a paper or slides
- you want a simple, dependency-light matplotlib-based drawing script

## When to avoid
- you need interactive or web-based neural network visualization
- you want to visualize live training or real model weights
- you need diagrams of architectures beyond convolutional networks

## Facets
- artifact type: library
- maturity: maintenance
- function: data-visualization, deep-learning
- domain: deep-learning, data-visualization, education
- platform: python
- tags: convnet, diagram-generation, matplotlib, educational-figures, neural-network-visualization

## Member repositories
- gwding/draw_convnet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:26.005811+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:36:27.730346+00:00, confidence not recorded.
  - readme: https://github.com/gwding/draw_convnet (fetched 2026-08-28T04:07:26.005811+00:00, sha b1b1d66f7d6b)
- Data as of 2026-08-30T08:39:29.467469+00:00.