Papers › YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

7 Sep 2022arXiv:2209.02976archive 2025-07-28

Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, Yiduo Li, Bo Zhang, Yufei Liang, Linyuan Zhou, Xiaoming Xu, Xiangxiang Chu, Xiaoming Wei, Xiaolin Wei

For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical report, we strive to push its limits to the next level, stepping forward with an unwavering mindset for industry application. Considering the diverse requirements for speed and accuracy in the real environment, we extensively examine the up-to-date object detection advancements either from industry or academia. Specifically, we heavily assimilate ideas from recent network design, training strategies, testing techniques, quantization, and optimization methods. On top of this, we integrate our thoughts and practice to build a suite of deployment-ready networks at various scales to accommodate diversified use cases. With the generous permission of YOLO authors, we name it YOLOv6. We also express our warm welcome to users and contributors for further enhancement. For a glimpse of performance, our YOLOv6-N hits 35.9% AP on the COCO dataset at a throughput of 1234 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 43.5% AP at 495 FPS, outperforming other mainstream detectors at the same scale~(YOLOv5-S, YOLOX-S, and PPYOLOE-S). Our quantized version of YOLOv6-S even brings a new state-of-the-art 43.3% AP at 869 FPS. Furthermore, YOLOv6-M/L also achieves better accuracy performance (i.e., 49.5%/52.3%) than other detectors with a similar inference speed. We carefully conducted experiments to validate the effectiveness of each component. Our code is made available at https://github.com/meituan/YOLOv6.

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meituan/yolov6 officialmentioned in papermentioned on GitHubpytorchGPL-3.0 report
kadirnar/yolov6-pip pytorchGPL-3.0 report
open-mmlab/mmyolo pytorchGPL-3.0 report

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autopad yang-0201/YOLOv6_pro/models/yolov6.py community (archive-listed) ran · honoured contract GPL-3.0 (copyleft) · pointer only · 988a3c854b1b13d0 · report
conv_bn yang-0201/YOLOv6_pro/models/yolov6.py community (archive-listed) ran · our draft was wrong GPL-3.0 (copyleft) · pointer only · 1c73aef43930fcc8 · report

Tasks

Object DetectionPedestrian DetectionQuantizationReal-Time Object Detection

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Object Detection COCO-O YOLOv6-L6 Average mAP 32.5 #14 of 45 Archive leaderboard report
Object Detection COCO-O YOLOv6-L6 Effective Robustness 6.73 #14 of 45 Archive leaderboard report
Pedestrian Detection DVTOD YOLOv6 (Thermal) mAP 84.4 #1 of 8 Archive leaderboard report
Pedestrian Detection DVTOD YOLOv6 (Visible) mAP 38.1 #5 of 8 Archive leaderboard report
Real-Time Object Detection COCO (Common Objects in Context) YOLOv6-L6(1280) FPS (V100, b=1) 26 #3 of 82 Archive leaderboard report
Real-Time Object Detection COCO (Common Objects in Context) YOLOv6-L6(1280) box AP 57.2 #3 of 82 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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