DOCS . ULTRALYTICS . COM {}

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We are analyzing https://docs.ultralytics.com/models/yolo11/.

Title:
Ultralytics YOLO11 - Ultralytics YOLO Docs
Description:
Discover YOLO11, the latest advancement in state-of-the-art object detection, offering unmatched accuracy and efficiency for diverse computer vision tasks.
Website Age:
11 years and 4 months (reg. 2014-02-13).

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Custom-built

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🌟 Strong Traffic: 100k - 200k visitors per month

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How Does Docs.ultralytics.com Make Money? {💸}

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Keywords {🔍}

yolo, detection, model, tasks, object, ultralytics, accuracy, models, yolov, coco, fewer, parameters, devices, segmentation, architecture, training, realtime, performance, train, edge, range, computer, vision, environments, instance, classification, pose, documentation, modes, segment, key, supported, examples, greater, speed, making, feature, extraction, optimized, efficient, image, oriented, obb, docs, usage, improvements, previous, deployed, advancements, processing,

Topics {✒️}

datasets real-time object detectors ultralytics yolo11 introduces enhanced feature extraction ultralytics yolo series /ultralytics/ultralytics} ultralytics yolo11 compared refined architectural designs top previous yolov10 yolo11 models perform yolo11 detect models efficient feature extraction computer vision tasks optimized training pipelines additional supported tasks key improvements include operational modes real-time detection computer vision challenges complex segmentation tasks offering enhanced support ultralytics yolo11 ensuring maximum flexibility formal research paper rapidly evolving nature yolo11 model variants simple yolo11 training realtime detection transformer models trained yolo11 models resource-constrained devices previous yolo versions precise object detection export docs pages producing static documentation oriented object detection neural architecture search cutting-edge accuracy complex task performance pt models speed cpu onnx speed t4 tensorrt10 modes including edge devices oriented detection documentation pt yolo11s-seg pt yolo11m-seg pt yolo11l-seg pt yolo11x-seg pt yolo11s-cls

Questions {❓}

Can YOLO11 be deployed on edge devices?
How do I train a YOLO11 model for object detection?
How does YOLO11 achieve greater accuracy with fewer parameters?
What are the key improvements in Ultralytics YOLO11 compared to previous versions?
What tasks can YOLO11 models perform?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:YOLO11 🚀 NEW
      image:
         https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png
      datePublished:2024-09-30 02:59:20 +0200
      dateModified:2025-02-26 15:26:38 +0800
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Discover YOLO11, the latest advancement in state-of-the-art object detection, offering unmatched accuracy and efficiency for diverse computer vision tasks.
      mainEntity:
            type:Question
            name:What are the key improvements in Ultralytics YOLO11 compared to previous versions?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 introduces several significant advancements over its predecessors. Key improvements include:
            type:Question
            name:How do I train a YOLO11 model for object detection?
            acceptedAnswer:
               type:Answer
               text:Training a YOLO11 model for object detection can be done using Python or CLI commands. Below are examples for both methods: For more detailed instructions, refer to the Train documentation.
            type:Question
            name:What tasks can YOLO11 models perform?
            acceptedAnswer:
               type:Answer
               text:YOLO11 models are versatile and support a wide range of computer vision tasks, including: For more information on each task, see the Detection, Instance Segmentation, Classification, Pose Estimation, and Oriented Detection documentation.
            type:Question
            name:How does YOLO11 achieve greater accuracy with fewer parameters?
            acceptedAnswer:
               type:Answer
               text:YOLO11 achieves greater accuracy with fewer parameters through advancements in model design and optimization techniques. The improved architecture allows for efficient feature extraction and processing, resulting in higher mean Average Precision (mAP) on datasets like COCO while using 22% fewer parameters than YOLOv8m. This makes YOLO11 computationally efficient without compromising on accuracy, making it suitable for deployment on resource-constrained devices.
            type:Question
            name:Can YOLO11 be deployed on edge devices?
            acceptedAnswer:
               type:Answer
               text:Yes, YOLO11 is designed for adaptability across various environments, including edge devices. Its optimized architecture and efficient processing capabilities make it suitable for deployment on edge devices, cloud platforms, and systems supporting NVIDIA GPUs. This flexibility ensures that YOLO11 can be used in diverse applications, from real-time detection on mobile devices to complex segmentation tasks in cloud environments. For more details on deployment options, refer to the Export documentation.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What are the key improvements in Ultralytics YOLO11 compared to previous versions?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 introduces several significant advancements over its predecessors. Key improvements include:
      name:How do I train a YOLO11 model for object detection?
      acceptedAnswer:
         type:Answer
         text:Training a YOLO11 model for object detection can be done using Python or CLI commands. Below are examples for both methods: For more detailed instructions, refer to the Train documentation.
      name:What tasks can YOLO11 models perform?
      acceptedAnswer:
         type:Answer
         text:YOLO11 models are versatile and support a wide range of computer vision tasks, including: For more information on each task, see the Detection, Instance Segmentation, Classification, Pose Estimation, and Oriented Detection documentation.
      name:How does YOLO11 achieve greater accuracy with fewer parameters?
      acceptedAnswer:
         type:Answer
         text:YOLO11 achieves greater accuracy with fewer parameters through advancements in model design and optimization techniques. The improved architecture allows for efficient feature extraction and processing, resulting in higher mean Average Precision (mAP) on datasets like COCO while using 22% fewer parameters than YOLOv8m. This makes YOLO11 computationally efficient without compromising on accuracy, making it suitable for deployment on resource-constrained devices.
      name:Can YOLO11 be deployed on edge devices?
      acceptedAnswer:
         type:Answer
         text:Yes, YOLO11 is designed for adaptability across various environments, including edge devices. Its optimized architecture and efficient processing capabilities make it suitable for deployment on edge devices, cloud platforms, and systems supporting NVIDIA GPUs. This flexibility ensures that YOLO11 can be used in diverse applications, from real-time detection on mobile devices to complex segmentation tasks in cloud environments. For more details on deployment options, refer to the Export documentation.
Answer:
      text:Ultralytics YOLO11 introduces several significant advancements over its predecessors. Key improvements include:
      text:Training a YOLO11 model for object detection can be done using Python or CLI commands. Below are examples for both methods: For more detailed instructions, refer to the Train documentation.
      text:YOLO11 models are versatile and support a wide range of computer vision tasks, including: For more information on each task, see the Detection, Instance Segmentation, Classification, Pose Estimation, and Oriented Detection documentation.
      text:YOLO11 achieves greater accuracy with fewer parameters through advancements in model design and optimization techniques. The improved architecture allows for efficient feature extraction and processing, resulting in higher mean Average Precision (mAP) on datasets like COCO while using 22% fewer parameters than YOLOv8m. This makes YOLO11 computationally efficient without compromising on accuracy, making it suitable for deployment on resource-constrained devices.
      text:Yes, YOLO11 is designed for adaptability across various environments, including edge devices. Its optimized architecture and efficient processing capabilities make it suitable for deployment on edge devices, cloud platforms, and systems supporting NVIDIA GPUs. This flexibility ensures that YOLO11 can be used in diverse applications, from real-time detection on mobile devices to complex segmentation tasks in cloud environments. For more details on deployment options, refer to the Export documentation.

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