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.
翻译:多年来, YOLO 系列是事实上的高效天体探测的行业级标准。 YOLO 群落已经取得了巨大的繁荣,在众多硬件平台和大量假设情景中丰富了它的应用。在本技术报告中,我们努力将它的极限推向下一个层次,以坚定的工业应用心态向前推进。考虑到在实际环境中对速度和准确性的不同要求,我们从行业或学术界广泛研究最新的天体探测进步。具体地说,我们大量吸收了最近网络设计、培训战略、测试技术、量化和优化方法中的想法。此外,我们集思广益,在各种规模的硬件平台上建立一套适合部署的网络,以适应多样化的使用案例。在YOLO 的作者们的慷慨许可下,我们称之为 YOLOv 6。我们还向用户和贡献者表示热烈欢迎进一步增强。为了表现的一瞥,我们的YOLO6-NPO 将新的天文节节节节节节节节点中的1,1,334 FPSO-talentex, 在VA Tela T4GPU. YOLO6-S 级上实现了4:YOL-VO5-S-S-SVLV-S 45.5xxxxxxxxxxxxxxxxx