YOLOv6 v3.0: A Full-Scale Reloading

Chuyi Li, Lulu Li, Yifei Geng, Hongliang Jiang, Meng Cheng +4 more
1/13/2023
cs.CV

Official code: meituan/YOLOv6 (5,888 stars, GPL-3.0, PyTorch, last commit 2024-08-07).

Abstract

The YOLO community has been in high spirits since our first two releases! By the advent of Chinese New Year 2023, which sees the Year of the Rabbit, we refurnish YOLOv6 with numerous novel enhancements on the network architecture and the training scheme. This release is identified as YOLOv6 v3.0. For a glimpse of performance, our YOLOv6-N hits 37.5% AP on the COCO dataset at a throughput of 1187 FPS tested with an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 45.0% AP at 484 FPS, outperforming other mainstream detectors at the same scale (YOLOv5-S, YOLOv8-S, YOLOX-S and PPYOLOE-S). Whereas, YOLOv6-M/L also achieve better accuracy performance (50.0%/52.8% respectively) than other detectors at a similar inference speed. Additionally, with an extended backbone and neck design, our YOLOv6-L6 achieves the state-of-the-art accuracy in real-time. Extensive experiments are carefully conducted to validate the effectiveness of each improving component. Our code is made available at https://github.com/meituan/YOLOv6.

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Code Implementations(1)

meituan/YOLOv6Official100%
PyTorchGPL-3.0

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

5,8881,060C++, JavaJun 8, 20222 years agoGPL-3.0
object-detectionpytorchyolo

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Cite this paper

@article{li2023yolov,
  title  = {YOLOv6 v3.0: A Full-Scale Reloading},
  author = {Chuyi Li and Lulu Li and Yifei Geng and Hongliang Jiang and Meng Cheng and Bo Zhang and Zaidan Ke and Xiaoming Xu and Xiangxiang Chu},
  year   = {2023},
  eprint = {2301.05586},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.05586v1}
}

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