What is YOLOv6? A Deep Insight into the Object Detection Model

TL;DR

This paper provides an in-depth analysis of YOLOv6, highlighting its innovative backbone and neck design, and demonstrates its superior detection performance and speed on the COCO dataset compared to previous models.

Contribution

It introduces the EfficientRep Backbone and Rep-PAN Neck, enhancing feature extraction and aggregation for improved real-time object detection.

Findings

YOLOv6-S achieves 45.0% AP at 484 FPS.

YOLOv6-N reaches 37.5% AP at 1187 FPS.

YOLOv6-L6 offers state-of-the-art accuracy with real-time speed.

Abstract

This work explores the YOLOv6 object detection model in depth, concentrating on its design framework, optimization techniques, and detection capabilities. YOLOv6's core elements consist of the EfficientRep Backbone for robust feature extraction and the Rep-PAN Neck for seamless feature aggregation, ensuring high-performance object detection. Evaluated on the COCO dataset, YOLOv6-N achieves 37.5\% AP at 1187 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S reaches 45.0\% AP at 484 FPS, outperforming models like PPYOLOE-S, YOLOv5-S, YOLOX-S, and YOLOv8-S in the same class. Moreover, YOLOv6-M and YOLOv6-L also show better accuracy (50.0\% and 52.8\%) while maintaining comparable inference speeds to other detectors. With an upgraded backbone and neck structure, YOLOv6-L6 delivers cutting-edge accuracy in real-time.