# open-mmlab/mmflow

OpenMMLab optical flow toolbox and benchmark

Repository: https://github.com/open-mmlab/mmflow
Canonical: https://ross.abutalabs.com/products/mmflow
Homepage: https://mmflow.readthedocs.io/en/latest/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: optical-flow, openmmlab, pytorch
Last push: 2024-06-07T04:25:20+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1751, "days_push": 817, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1062, forks 116 (observed 2026-08-28T04:03:26.051051+00:00)

## What it is
MMFlow is an open-source optical flow toolbox and benchmark built on PyTorch as part of the OpenMMLab project. It provides a unified, modular framework for implementing, training, and evaluating optical flow algorithms such as FlowNet, PWC-Net, and RAFT.

## Use cases
- estimate optical flow between video frames
- train RAFT or PWC-Net on FlyingChairs
- benchmark optical flow models on Sintel and KITTI
- implement a custom optical flow architecture with modular components
- run pretrained optical flow inference in a computer vision pipeline

## When to choose
- you need a PyTorch framework for optical flow estimation or evaluation
- you want pretrained optical flow models and standard benchmark datasets out of the box
- you prefer OpenMMLab-style modular configuration-driven training

## When to avoid
- you need general video processing rather than flow estimation
- you require a framework actively adding new models, as development has slowed
- you work outside PyTorch or need lightweight production inference without GPU tooling

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, computer-vision, image-processing, benchmarking
- domain: computer-vision, deep-learning, machine-learning
- platform: python, windows
- tags: optical-flow, pytorch, openmmlab, model-zoo, deep-learning-toolbox, linux, macos, gpu

## Member repositories
- open-mmlab/mmflow (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:26.051051+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 2026-08-30T06:56:45.757186+00:00, confidence not recorded.
  - readme: https://github.com/open-mmlab/mmflow (fetched 2026-08-28T04:03:26.051051+00:00, sha c486acc65121)
  - registry_pypi: https://pypi.org/pypi/mmflow/json (fetched 2026-08-29T12:59:06.675899+00:00, sha caff1b7fca04)
- Data as of 2026-08-30T08:39:29.467469+00:00.