YOLOv6 v3.0: A Full-Scale Reloading

TL;DR

YOLOv6 v3.0 introduces numerous architectural and training enhancements, achieving state-of-the-art real-time object detection accuracy and speed across various model scales, outperforming previous YOLO versions and other detectors.

Contribution

This paper presents YOLOv6 v3.0 with novel network and training improvements, delivering superior accuracy and speed in real-time object detection.

Findings

YOLOv6-N achieves 37.5% AP at 1187 FPS.

YOLOv6-S reaches 45.0% AP at 484 FPS.

YOLOv6-L6 attains state-of-the-art accuracy in real-time.

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.