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

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arxiv 2209.02976 v1 pith:DKNVOQHO submitted 2022-09-07 cs.CV

Chuyi Li , Lulu Li , Hongliang Jiang , Kaiheng Weng , Yifei Geng , Liang Li , Zaidan Ke , Qingyuan Li
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Meng Cheng Weiqiang Nie Yiduo Li Bo Zhang Yufei Liang Linyuan Zhou Xiaoming Xu Xiangxiang Chu Xiaoming Wei Xiaolin Wei
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classification cs.CV
keywords detectionobjectyoloyolov6accuracydetectorsindustryother
verification ladder T0 review T1 audit T2 compute T3 formal
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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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