# MultimediaTechLab/YOLO

An MIT License of YOLOv9, YOLOv7, YOLO-RD

Repository: https://github.com/MultimediaTechLab/YOLO
Canonical: https://ross.abutalabs.com/products/yolo
Homepage: https://yolo-docs.readthedocs.io/en/latest/?badge=latest
Language: Python
License: MIT
License Family: permissive
Last push: 2026-03-16T10:30:55+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 72, release rhythm 31, longevity 64
- inputs: {"age_days": 903, "days_push": 170, "days_rel": 246, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1723, forks 265 (observed 2026-08-28T04:05:27.940118+00:00)

## What it is
Official MIT-licensed implementation of the YOLOv9, YOLOv7, and YOLO-RD real-time object detection models, including pre-trained weights, training scripts, and deployment tooling. It is a Python library installable via pip that supports training, transfer learning, and inference on images, videos, and webcam streams.

## Use cases
- train a custom object detection model on my own dataset
- run real-time object detection on a webcam feed
- detect objects in images and videos with YOLOv9
- fine-tune a pre-trained YOLO model with transfer learning
- deploy an object detector from a pip-installable package
- benchmark YOLOv7 vs YOLOv9 detection performance

## When to choose
- you need the official YOLOv9/YOLOv7/YOLO-RD implementations under a permissive MIT license
- you want to train or fine-tune state-of-the-art real-time object detectors on custom datasets
- you need quick inference via pip install with pre-trained weights

## When to avoid
- you need a production-hardened, long-term-stable detection framework (the project has noted work-in-progress status)
- you need other vision tasks like segmentation or pose estimation not covered by these models
- you prefer a more widely adopted ecosystem like Ultralytics YOLO

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, image-processing
- domain: computer-vision, deep-learning, machine-learning
- platform: python, cross-platform
- tags: object-detection, yolo, yolov9, yolov7, yolo-rd, deep-learning, inference, training, pytorch, gpu

## Member repositories
- MultimediaTechLab/YOLO (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:27.940118+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 2026-08-30T03:33:56.497059+00:00, confidence not recorded.
  - readme: https://github.com/MultimediaTechLab/YOLO (fetched 2026-08-28T04:05:27.940118+00:00, sha d0f57a48fbea)
- Data as of 2026-08-30T08:39:29.467469+00:00.