# open-mmlab/OpenMMLabCourse

OpenMMLab course index and stuff

Repository: https://github.com/open-mmlab/OpenMMLabCourse
Canonical: https://ross.abutalabs.com/products/openmmlabcourse
Homepage: https://open-mmlab.github.io/OpenMMLabCourse/
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: computer-vision, tutorials
Last push: 2024-06-28T06:52:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1568, "days_push": 796, "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 1280, forks 143 (observed 2026-08-28T04:04:13.630891+00:00)

## What it is
A collection of articles, video lectures, and Jupyter notebook tutorials from the OpenMMLab team covering computer vision algorithms and their toolbox ecosystem (MMDetection, MMPose, MMOCR, etc.). It serves as the official course index for learning OpenMMLab frameworks.

## Use cases
- learn computer vision with OpenMMLab toolboxes
- find tutorials for MMDetection object detection
- watch lectures on pose estimation and 3D detection
- get Jupyter notebook examples for image classification
- learn semantic segmentation with MMSegmentation
- find course materials for teaching OpenMMLab at university

## When to choose
- you want structured video lectures and notebooks for OpenMMLab toolboxes
- you are learning computer vision algorithms like detection, segmentation, or pose estimation
- you need official course materials to teach OpenMMLab in a classroom

## When to avoid
- you need production-ready code or a maintained library rather than tutorials
- you use vision frameworks outside the OpenMMLab ecosystem
- you need up-to-date content - the latest materials date from 2023-2024

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: computer-vision, machine-learning, deep-learning
- domain: computer-vision, deep-learning, tutorials
- platform: python, cross-platform
- tags: openmmlab, jupyter-notebooks, video-lectures, object-detection, image-classification, semantic-segmentation, pose-estimation, ocr, mmdetection

## Member repositories
- open-mmlab/OpenMMLabCourse (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:13.630891+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 2026-08-30T05:02:38.531954+00:00, confidence not recorded.
  - readme: https://github.com/open-mmlab/OpenMMLabCourse (fetched 2026-08-28T04:04:13.630891+00:00, sha 6253495895ce)
  - homepage: https://open-mmlab.github.io/OpenMMLabCourse/ (fetched 2026-08-29T12:13:17.998460+00:00, sha cbf82139eb39)
- Data as of 2026-08-30T08:39:29.467469+00:00.