mlsanityData to 7 Oct 2026

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

arXiv 2412.13006 ↗First posted 17 Dec 2024cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 62 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

28 of 62 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  5. 2018YOLOv3: An Incremental Improvementcited by 2,652
  6. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  7. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  8. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  9. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  10. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  11. 2018Path Aggregation Network for Instance Segmentationcited by 796
  12. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
16 more
  1. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  2. 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 408
  3. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  4. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 273
  5. 2020VarifocalNet: An IoU-aware Dense Object Detectorcited by 230
  6. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 177
  7. 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 149
  8. 2020Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detectioncited by 104
  9. 2021You Only Learn One Representation: Unified Network for Multiple Taskscited by 86
  10. 2022PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functionscited by 62
  11. 2023YOLOv6 v3.0: A Full-Scale Reloadingcited by 58
  12. 2022SIoU Loss: More Powerful Learning for Bounding Box Regressioncited by 49
  13. 2022Re-parameterizing Your Optimizers rather than Architecturescited by 35
  14. 2024YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domaincited by 14
  15. 2024A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safetycited by 4
  16. 2024Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysiscited by 2

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Traffic Scene Small Target Detection Method Based on YOLOv8n-SPTS Model for Autonomous Drivingcited by 0