# hhaAndroid/mmdetection-mini

mmdetection最小学习版

Repository: https://github.com/hhaAndroid/mmdetection-mini
Canonical: https://ross.abutalabs.com/products/mmdetection-mini
Language: Python
License: MIT
License Family: permissive
Last push: 2021-11-21T05:11:53+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": 2186, "days_push": 1746, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1100, forks 190 (observed 2026-08-28T04:03:35.439892+00:00)

## What it is
A minimal, heavily annotated reimplementation of the mmdetection object detection framework, built from scratch for learning purposes. It mirrors mmdetection's structure and implements one-stage and two-stage detectors like RetinaNet, YOLOv3-v5, FCOS, and Faster R-CNN with detailed code explanations.

## Use cases
- learn how mmdetection implements object detection algorithms internally
- understand the code details of detectors like YOLOv5, RetinaNet, and Faster R-CNN
- study annotated implementations of one-stage object detectors
- build a simplified object detection framework from scratch to grasp every component
- quickly verify object detection algorithm ideas on VOC/COCO datasets
- add custom features or visualizations to a detection framework without merge conflicts

## When to choose
- you want to deeply understand how mmdetection and object detection frameworks work line by line
- you prefer a small, readable codebase with detailed Chinese-language explanations over the full mmdetection
- you want a minimal framework to experiment with detection algorithm modifications

## When to avoid
- you need production training with distributed multi-GPU support, which is not supported
- you want the latest features, best performance, or official model zoo of mmdetection
- you need a stable, actively maintained framework for real projects

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, deep-learning, machine-learning, education, tutorials
- platform: python
- tags: object-detection, mmdetection, pytorch, learning-project, yolo, retinanet, faster-rcnn, annotated-code, linux, gpu

## Member repositories
- hhaAndroid/mmdetection-mini (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.439892+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 2026-08-30T06:45:47.936450+00:00, confidence not recorded.
  - readme: https://github.com/hhaAndroid/mmdetection-mini (fetched 2026-08-28T04:03:35.439892+00:00, sha 4c88a7749b28)
- Data as of 2026-08-30T08:39:29.467469+00:00.