# THU-MIG/yoloe

YOLOE: Real-Time Seeing Anything [ICCV 2025]

Repository: https://github.com/THU-MIG/yoloe
Canonical: https://ross.abutalabs.com/products/yoloe
Homepage: https://arxiv.org/abs/2503.07465
Language: Python
License: AGPL-3.0
License Family: copyleft
Last push: 2025-06-26T03:45:33+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 28, release rhythm 35, longevity 38
- inputs: {"age_days": 541, "days_push": 433, "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 2256, forks 208 (observed 2026-08-28T04:06:31.390555+00:00)

## What it is
YOLOE is the official PyTorch implementation of an open-vocabulary object detection and segmentation model presented at ICCV 2025. It unifies text-prompted, visual-prompted, and prompt-free detection in a single efficient real-time model with zero inference overhead compared to closed-set YOLOs.

## Use cases
- detect arbitrary objects in images with text prompts
- zero-shot object detection without retraining
- real-time open-vocabulary instance segmentation
- detect objects using a visual reference prompt
- detect everything in an image without specifying classes
- transfer a detector to new categories with no inference cost

## When to choose
- you need real-time open-vocabulary detection and segmentation in one model
- you want YOLO-level speed but with flexible text, visual, or no prompts
- you need zero-shot transfer to unseen object categories
- you want an efficient alternative to heavier open-set detectors like YOLO-World

## When to avoid
- you only need fixed closed-set classes and a standard YOLO suffices
- you require a permissive license - it is AGPL-3.0
- you need a non-PyTorch framework or non-GPU deployment
- your use case is classification or captioning rather than detection/segmentation

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: object-detection, instance-segmentation, open-vocabulary, zero-shot, yolo, pytorch, iccv-2025, prompt-based-detection, gpu

## Member repositories
- THU-MIG/yoloe (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:31.390555+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 2026-08-30T02:43:42.017804+00:00, confidence not recorded.
  - readme: https://github.com/THU-MIG/yoloe (fetched 2026-08-28T04:06:31.390555+00:00, sha ff32ef60d5f6)
  - homepage: https://arxiv.org/abs/2503.07465 (fetched 2026-08-29T10:23:47.590743+00:00, sha 72b54f1316d3)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T10:23:47.599919+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T10:23:47.603535+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T10:23:47.605415+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T10:23:47.601831+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.