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
this https URL

本技术报告介绍了 YOLOv6 的开发和部署，该模型包括最新的网络设计、训练策略、测试技术、量化和优化方法，可适用于不同规模的应用场景，并在各种硬件平台上表现出色。