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  • Ultralytics YOLO11 - Ultralytics YOLO Docs
    This table provides an overview of the YOLO11 model variants, showcasing their applicability in specific tasks and compatibility with operational modes such as Inference, Validation, Training, and Export
  • Models Supported by Ultralytics - Ultralytics YOLO Docs
    Ultralytics supports a comprehensive range of YOLO (You Only Look Once) versions from YOLOv3 to YOLO11, along with models like YOLO-NAS, SAM, and RT-DETR Each version is optimized for various tasks such as detection, segmentation, and classification
  • Model Comparisons: Choose the Best Object Detection Model for Your Project
    Explore comprehensive comparisons of Ultralytics YOLO models and other popular object detection models Dive into detailed analyses to help you select the optimal model for your computer vision tasks
  • YOLO11 vs YOLOv8: Detailed Comparison - Ultralytics YOLO Docs
    Compare YOLO11 and YOLOv8 architectures, performance, use cases, and benchmarks Discover which YOLO model fits your object detection needs
  • YOLO12: 以注意力为中心的物体检测 -Ultralytics YOLO 文档
    YOLO12 引入了一种以注意力为中心的架构，它不同于以往YOLO 模型中使用的基于 CNN 的传统方法，但仍保持了许多应用所必需的实时推理速度。 该模型通过对注意力机制和整体网络架构进行新颖的方法创新，实现了最先进的物体检测精度，同时保持了实时性能。
  • YOLOv9: A Leap Forward in Object Detection Technology - Ultralytics
    Medium to Large Models: YOLOv9m and YOLOv9e show notable advancements in balancing the trade-off between model complexity and detection performance, offering significant reductions in parameters and computations against the backdrop of improved accuracy
  • Ultralytics YOLOv8 調べる -Ultralytics YOLO Docs
    私たちは、静的なドキュメントを作成するよりも、技術を進歩させ、使いやすくすることに重点を置いています。YOLO のアーキテクチャ、機能、使用方法に関する最新情報については、GitHub リポジトリと ドキュメントを参照してください。
  • YOLOE: Real-Time Seeing Anything - Ultralytics YOLO Docs
    YOLOE is a real-time open-vocabulary detection and segmentation model that extends YOLO with text, image, or internal vocabulary prompts, enabling detection of any object class with state-of-the-art zero-shot performance
  • YOLOv10: Real-Time End-to-End Object Detection - Ultralytics YOLO Docs
    YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in both accuracy and efficiency For example, YOLOv10s is 1 8x faster than RT-DETR-R18 with a similar AP on the COCO dataset
  • COCO8-Grayscale Dataset - Ultralytics YOLO Docs
    This dataset is specifically designed for rapid testing, debugging, and experimentation with YOLO grayscale models and training pipelines Its small size makes it highly manageable, while its diversity ensures it serves as an effective sanity check before scaling up to larger datasets





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