# AILab-CVC/YOLO-World

[CVPR 2024] Real-Time Open-Vocabulary Object Detection

Repository: https://github.com/AILab-CVC/YOLO-World
Canonical: https://ross.abutalabs.com/products/yolo-world
Homepage: https://www.yoloworld.cc
Language: Python
License: GPL-3.0
License Family: copyleft
Last push: 2025-02-26T18:29:03+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 8, release rhythm 35, longevity 67
- inputs: {"age_days": 948, "days_push": 553, "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 6529, forks 612 (observed 2026-08-28T04:09:44.957744+00:00)

## What it is
YOLO-World is a real-time open-vocabulary object detection model and Python toolkit from Tencent AI Lab and HUST, published at CVPR 2024. It detects arbitrary objects described in text without custom training, using a fast CNN-based YOLO architecture that is significantly faster and smaller than Transformer-based open-vocabulary detectors.

## Use cases
- detect objects in images by text prompt without training
- run real-time zero-shot object detection on edge devices
- auto-label image datasets for training custom detectors
- detect custom object classes in video streams in real time
- build open-vocabulary detection pipelines in Python
- segment detected objects with YOLO-World plus EfficientSAM

## When to choose
- you need fast, real-time detection of classes not in a fixed label set
- you want zero-shot detection without annotating or training a custom model
- you need a lighter, faster alternative to Grounding DINO or GLIP
- you want to auto-label data or deploy detection to edge devices

## When to avoid
- you need a permissively licensed model for commercial products (GPL-3.0)
- you need closed-set detection with maximum accuracy and can train a specialized model
- you need text generation or multimodal reasoning beyond detection
- you require a maintained stable API, as the project is still under active development

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, image-processing, deep-learning
- domain: computer-vision, machine-learning, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: object-detection, open-vocabulary-detection, zero-shot, yolo, real-time-inference, cvpr-2024, vision-language, gpu

## Member repositories
- AILab-CVC/YOLO-World (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:44.957744+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 2026-08-29T17:44:08.742496+00:00, confidence not recorded.
  - readme: https://github.com/AILab-CVC/YOLO-World (fetched 2026-08-28T04:09:44.957744+00:00, sha 22965f4e5492)
  - homepage: https://www.yoloworld.cc (fetched 2026-08-29T08:40:44.102101+00:00, sha cae67e310a56)
  - site_page: https://docs.roboflow.com/ (fetched 2026-08-29T08:40:44.113440+00:00, sha 100579892504)
  - site_page: https://docs.roboflow.com (fetched 2026-08-29T08:40:44.115172+00:00, sha 100579892504)
  - site_page: https://docs.roboflow.com/changelog (fetched 2026-08-29T08:40:44.117039+00:00, sha c1df4b7ee119)
  - site_page: https://roboflow.com/about (fetched 2026-08-29T08:40:44.118781+00:00, sha ee0cc2e5c8ca)
  - site_page: https://roboflow.com/pricing (fetched 2026-08-29T08:40:44.111273+00:00, sha 75cb73f55872)
- Data as of 2026-08-30T08:39:29.467469+00:00.