# philipperemy/yolo-9000

YOLO9000: Better, Faster, Stronger - Real-Time Object Detection. 9000 classes!

Repository: https://github.com/philipperemy/yolo-9000
Canonical: https://ross.abutalabs.com/products/yolo-9000
License: Apache-2.0
License Family: permissive
Topics: darknet, yolo, yolo2, deep-learning, machine-learning, yolo9000, yolo-9000
Last push: 2021-03-24T11:59:33+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": 3427, "days_push": 1988, "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 1194, forks 300 (observed 2026-08-28T04:03:56.699099+00:00)

## What it is
A packaging of YOLO9000 (YOLOv2), a real-time object detection model that can detect over 9,000 object classes, built around the Darknet framework with pre-trained weights. It provides build instructions and CLI commands to run detection on images and videos on CPU or GPU.

## Use cases
- detect objects in images in real time
- run yolo9000 pretrained model on 9000 classes
- build darknet with gpu support for object detection
- detect objects in a video with yolo
- reproduce cvpr17 yolo9000 results

## When to choose
- you need real-time detection across a very large set of classes with pre-trained weights
- you want the original Darknet-based YOLOv2 implementation

## When to avoid
- you need a modern, actively maintained detector like YOLOv5/v8 or PyTorch-based tooling
- you need training pipelines rather than inference with provided weights

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, deep-learning, machine-learning
- domain: computer-vision, deep-learning, machine-learning, image-processing
- platform: windows, cpp
- tags: yolo, object-detection, darknet, real-time-detection, yolo9000, pretrained-weights, linux, macos, gpu

## Member repositories
- philipperemy/yolo-9000 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:56.699099+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 2026-08-30T06:22:10.525028+00:00, confidence not recorded.
  - readme: https://github.com/philipperemy/yolo-9000 (fetched 2026-08-28T04:03:56.699099+00:00, sha 5bebc8c8c80e)
- Data as of 2026-08-30T08:39:29.467469+00:00.