# hunkim/deep_architecture_genealogy

Deep Learning Architecture  Genealogy Project

Repository: https://github.com/hunkim/deep_architecture_genealogy
Canonical: https://ross.abutalabs.com/products/deep_architecture_genealogy
Homepage: https://coggle.it/diagram/Wf5mYoJbsgABUF9P 
Language: Python
License Family: other
Last push: 2021-02-14T04:24:09+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": 3224, "days_push": 2026, "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 1220, forks 191 (observed 2026-08-28T04:04:01.975010+00:00)

## What it is
A curated genealogy of deep learning architectures, organized as a mindmap (Coggle) and auto-generated text index linking to arXiv papers. It traces lineages across CNNs, object detection, segmentation, generative models, and reinforcement learning.

## Use cases
- find the lineage of neural network architectures
- discover papers on CNN evolution like AlexNet to ResNet
- explore generative model families such as VAE and PixelCNN
- get an overview of object detection model history
- find reading material for learning deep learning architectures

## When to choose
- you want a visual map of how deep learning models relate to each other
- you need a curated paper index for studying neural architecture history

## When to avoid
- you need runnable code or model implementations
- you need up-to-date coverage of recent architectures

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, documentation
- domain: deep-learning, machine-learning, tutorials
- platform: cross-platform
- tags: genealogy, mindmap, paper-index, neural-network-architectures, awesome-list

## Member repositories
- hunkim/deep_architecture_genealogy (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:01.975010+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 2026-08-30T06:16:00.837770+00:00, confidence not recorded.
  - readme: https://github.com/hunkim/deep_architecture_genealogy (fetched 2026-08-28T04:04:01.975010+00:00, sha fc03531e976c)
  - homepage: https://coggle.it/diagram/Wf5mYoJbsgABUF9P  (fetched 2026-08-29T12:24:39.048158+00:00, sha 9cade2a7c997)
- Data as of 2026-08-30T08:39:29.467469+00:00.