# open-mmlab/mmengine

OpenMMLab Foundational Library for Training Deep Learning Models

Repository: https://github.com/open-mmlab/mmengine
Canonical: https://ross.abutalabs.com/products/mmengine
Homepage: https://mmengine.readthedocs.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: computer-vision, deep-learning, pytorch, ai, machine-learning, python
Last push: 2026-07-13T07:30:05+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 92, release rhythm 28, longevity 100
- inputs: {"age_days": 1667, "days_push": 51, "days_rel": 547, "gap_med": 82.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1492, forks 459 (observed 2026-08-28T04:04:52.659048+00:00)

## What it is
MMEngine is the foundational training engine library for OpenMMLab projects, providing a unified training loop, config system, registry, hooks, and logging on top of PyTorch. It powers frameworks like MMDetection, MMSegmentation, and MMPose.

## Use cases
- train deep learning models with a unified PyTorch engine
- build custom training loops with hooks and callbacks
- manage experiment configs for computer vision model training
- run distributed training for detection or segmentation models
- standardize training infrastructure across OpenMMLab projects

## When to choose
- you are using or extending OpenMMLab frameworks like MMDetection or MMSegmentation
- you want a battle-tested, configurable training engine on PyTorch
- you need hook-based customization of training, validation, and logging

## When to avoid
- you want a lightweight plain-PyTorch training script without framework overhead
- your project is not PyTorch-based
- you need a general-purpose ML framework outside the OpenMMLab ecosystem

## Facets
- artifact type: library
- maturity: active
- function: deep-learning, machine-learning, llm-training
- domain: deep-learning, computer-vision, machine-learning
- platform: python, cross-platform
- tags: pytorch, training-loop, openmmlab, config-system, hooks, gpu

## Member repositories
- open-mmlab/mmengine (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:52.659048+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 2026-08-30T04:33:30.980697+00:00, confidence not recorded.
  - readme: https://github.com/open-mmlab/mmengine (fetched 2026-08-28T04:04:52.659048+00:00, sha 2ec6a3318230)
  - registry_pypi: https://pypi.org/pypi/mmengine/json (fetched 2026-08-29T11:39:39.807325+00:00, sha dd22eaf8fe47)
- Data as of 2026-08-30T08:39:29.467469+00:00.