# THU-MIG/yolov10

YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]

Repository: https://github.com/THU-MIG/yolov10
Canonical: https://ross.abutalabs.com/products/yolov10
Homepage: https://arxiv.org/abs/2405.14458
Language: Python
License: AGPL-3.0
License Family: copyleft
Last push: 2025-03-14T02:53:58+00:00

## Health v2 (maintenance only)
Score: 20/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 11, release rhythm 8, longevity 59
- inputs: {"age_days": 832, "days_push": 537, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11336, forks 1180 (observed 2026-08-28T04:10:46.799872+00:00)

## What it is
YOLOv10 is a real-time end-to-end object detection model family that removes NMS post-processing via consistent dual assignments and optimizes architecture for efficiency-accuracy tradeoffs. The repository also introduces YOLOE, an extension supporting open-set detection and segmentation with text, visual, and prompt-free mechanisms.

## Use cases
- detect objects in images in real time
- train a custom YOLO object detector on my own dataset
- run NMS-free end-to-end object detection for low-latency deployment
- detect objects from open-vocabulary text prompts
- perform real-time instance segmentation
- deploy an efficient detector on edge devices

## When to choose
- you need state-of-the-art real-time object detection with low inference latency
- you want NMS-free end-to-end deployment
- you need open-vocabulary or prompt-free detection and segmentation
- you want pretrained YOLO variants spanning nano to extra-large scales

## When to avoid
- your project requires a permissive license (AGPL-3.0 is copyleft)
- you need classification-only or generative vision tasks
- you prefer a mature ecosystem like Ultralytics with broader tooling and integrations

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning, image-processing
- domain: computer-vision, machine-learning, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: object-detection, yolo, nms-free, real-time-inference, instance-segmentation, open-set-detection, neurips-2024, pytorch, gpu

## Member repositories
- THU-MIG/yolov10 (main) score 20

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:46.799872+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 2026-08-29T17:15:56.639018+00:00, confidence not recorded.
  - readme: https://github.com/THU-MIG/yolov10 (fetched 2026-08-28T04:10:46.799872+00:00, sha 2667593de869)
  - homepage: https://arxiv.org/abs/2405.14458 (fetched 2026-08-29T08:14:33.117076+00:00, sha d2ce007a6240)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T08:14:33.126486+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T08:14:33.130319+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T08:14:33.132308+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T08:14:33.128546+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.