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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 · ORCID
classification cs.CV
keywords yolov6accuracydetectorsotherperformanceyearachieveachieves
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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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Forward citations

Cited by 11 Pith papers

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    On a new 11,196-image thermal dataset of people with mobility restrictions, a modified YOLOv8 detector reaches 89.1 AP, and a simulated traffic controller uses it to add up to 8 seconds of green time.

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    The VisAlgae 2023 dataset and challenge provide a new public benchmark for detecting six microalgae species in microscopy images, with baseline and top-10 leaderboard results.

  4. Efficient Vision-based Vehicle Speed Estimation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Replacing the detector in a vanishing-point-based speed estimation pipeline with YOLOv6 plus post-training quantization yields comparable or better speed accuracy at substantially higher frame rates on BrnoCompSpeed.

  5. MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection

    cs.CV 2025-02 conditional novelty 5.0 of 10

    MHAF-YOLO reaches 48.9% AP on COCO with 7.1M parameters by combining auxiliary shallow-deep fusion paths and heterogeneous reparameterized convolutions.

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    cs.CV 2026-08 reject novelty 4.0 of 10

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    YOLOv13 adds adaptive hypergraph-based high-order correlation modeling and full-pipeline feature distribution to YOLO, achieving 41.6 AP on COCO for the Nano variant.

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

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    A review-style paper that restates YOLOv6's architecture and benchmark tables from the YOLOv6 paper without adding new experiments or analysis.

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