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YOLOv6 v3.0: A Full-Scale Reloading

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arxiv 2301.05586 v1 pith:E6M4GXJL submitted 2023-01-13 cs.CV

Chuyi Li , Lulu Li , Yifei Geng , Hongliang Jiang , Meng Cheng , Bo Zhang , Zaidan Ke , Xiaoming Xu
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Xiangxiang Chu
This is my paper
classification cs.CV
keywords yolov6accuracydetectorsotherperformanceyearachieveachieves
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 5 Pith papers

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