# mil-tokyo/webdnn

The Fastest DNN Running Framework on Web Browser

Repository: https://github.com/mil-tokyo/webdnn
Canonical: https://ross.abutalabs.com/products/webdnn
Homepage: https://mil-tokyo.github.io/webdnn
Language: TypeScript
License: NOASSERTION
License Family: other
Topics: deep-neural-networks, accelerate, optimization, javascript, webgpu
Last push: 2026-07-28T09:12:57+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 8, longevity 100
- inputs: {"age_days": 3436, "days_push": 36, "days_rel": 640, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1999, forks 149 (observed 2026-08-28T04:06:03.785762+00:00)

## What it is
WebDNN is a framework for running deep neural network inference directly in the web browser, accepting ONNX models without Python preprocessing. It accelerates execution via WebGPU (WGSL), WebGL2/WebGL1, and WebAssembly backends, with optional offline model optimization.

## Use cases
- run onnx models in the browser
- accelerate neural network inference with webgpu
- deploy deep learning models client-side without a server
- run image classification models in a web page
- execute dnn inference with webgl fallback
- optimize onnx models offline for web deployment

## When to choose
- you need to run ONNX neural network inference entirely client-side in the browser
- you want GPU acceleration via WebGPU with WebGL and WebAssembly fallbacks
- you want to avoid sending user data to a server for inference
- you need offline model optimization before browser deployment

## When to avoid
- you need training or fine-tuning of models, not just inference
- you target server-side or native (non-browser) deployment
- you need broad operator coverage beyond what WebDNN's ONNX support provides
- you require long-term commercial support or a permissive license guarantee (repository license is not standard)

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-inference, deep-learning, gpu-computing
- domain: deep-learning, machine-learning, web-development, frontend
- platform: browser
- tags: onnx, webgpu, webgl, webassembly, inference, neural-networks, typescript, web-server, nodejs, javascript

## Member repositories
- mil-tokyo/webdnn (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:03.785762+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 2026-08-30T03:02:10.702056+00:00, confidence not recorded.
  - readme: https://github.com/mil-tokyo/webdnn (fetched 2026-08-28T04:06:03.785762+00:00, sha 1f6332b28fb5)
  - homepage: https://mil-tokyo.github.io/webdnn (fetched 2026-08-29T10:42:07.975942+00:00, sha 080ea59e8088)
  - registry_npm: https://registry.npmjs.org/webdnn (fetched 2026-08-29T10:42:07.984774+00:00, sha 5b85ba84d26e)
- Data as of 2026-08-30T08:39:29.467469+00:00.