DOCS . ULTRALYTICS . COM {}

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We are analyzing https://docs.ultralytics.com/tasks/pose/.

Title:
Pose Estimation - Ultralytics YOLO Docs
Description:
Discover how to use YOLO11 for pose estimation tasks. Learn about model training, validation, prediction, and exporting in various formats.
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

Based on our best estimate, this website will receive around 100,019 visitors per month in the current month.
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How Does Docs.ultralytics.com Make Money? {💸}

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Keywords {🔍}

model, pose, yolo, ultralytics, models, dataset, load, imgsz, device, export, estimation, yolopose, batch, keypoints, predict, custom, train, format, half, val, left, pretrained, training, nms, int, trained, import, yoloyolonposept, validate, coco, tasks, formats, object, onnx, yolonpose, python, cli, results, arguments, data, official, yolopathtobestpt, map, details, metrics, involves, image, realtime, mappose, speed,

Topics {✒️}

yolo predict model=yolo11n-pose ultralytics yolo11-pose models single-model single-scale trained yolo11-pose model pose estimation tasks ultralytics hub ultralytics yolo11 offers yolo11n-pose model achieves trained yolo11n-pose model top previous classify yolo11-pose export formats latest ultralytics release explore specialized datasets yolo11-pose models knee left ankle real-time object tracking pose estimation models coco val images classify models yolo11-pose model pretrained pose models models download automatically ultralytics yolo11 pose estimation model animal pose estimation real-time streams real-time applications pre-trained model coco8-pose dataset full export details nose left eye eye left ear ear left shoulder shoulder left elbow elbow left wrist wrist left hip hip left knee small sample dataset real-time inference command line interface pose estimation page canine pose analysis yolo11n pose model speed t4 tensorrt10 identify specific parts respective body joint device tf savedmodel device tf graphdef device tf lite involves identifying

Questions {❓}

Can I export a YOLO11-pose model to other formats, and how?
How can I train a YOLO11-pose model on a custom dataset?
How do I validate a trained YOLO11-pose model?
What are the available Ultralytics YOLO11-pose models and their performance metrics?
What is Pose Estimation with Ultralytics YOLO11 and how does it work?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:Pose
      image:
         https://github.com/ultralytics/docs/releases/download/0/pose-estimation-examples.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-05-15 16:13:10 +0500
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Discover how to use YOLO11 for pose estimation tasks. Learn about model training, validation, prediction, and exporting in various formats.
      mainEntity:
            type:Question
            name:What is Pose Estimation with Ultralytics YOLO11 and how does it work?
            acceptedAnswer:
               type:Answer
               text:Pose estimation with Ultralytics YOLO11 involves identifying specific points, known as keypoints, in an image. These keypoints typically represent joints or other important features of the object. The output includes the [x, y] coordinates and confidence scores for each point. YOLO11-pose models are specifically designed for this task and use the -pose suffix, such as yolo11n-pose.pt. These models are pre-trained on datasets like COCO keypoints and can be used for various pose estimation tasks. For more information, visit the Pose Estimation Page.
            type:Question
            name:How can I train a YOLO11-pose model on a custom dataset?
            acceptedAnswer:
               type:Answer
               text:Training a YOLO11-pose model on a custom dataset involves loading a model, either a new model defined by a YAML file or a pre-trained model. You can then start the training process using your specified dataset and parameters. For comprehensive details on training, refer to the Train Section. You can also use Ultralytics HUB for a no-code approach to training custom pose estimation models.
            type:Question
            name:How do I validate a trained YOLO11-pose model?
            acceptedAnswer:
               type:Answer
               text:Validation of a YOLO11-pose model involves assessing its accuracy using the same dataset parameters retained during training. Here's an example: For more information, visit the Val Section.
            type:Question
            name:Can I export a YOLO11-pose model to other formats, and how?
            acceptedAnswer:
               type:Answer
               text:Yes, you can export a YOLO11-pose model to various formats like ONNX, CoreML, TensorRT, and more. This can be done using either Python or the Command Line Interface (CLI). Refer to the Export Section for more details. Exported models can be deployed on edge devices for real-time applications like fitness tracking, sports analysis, or robotics.
            type:Question
            name:What are the available Ultralytics YOLO11-pose models and their performance metrics?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 offers various pretrained pose models such as YOLO11n-pose, YOLO11s-pose, YOLO11m-pose, among others. These models differ in size, accuracy (mAP), and speed. For instance, the YOLO11n-pose model achieves a mAPpose50-95 of 50.0 and an mAPpose50 of 81.0. For a complete list and performance details, visit the Models Section.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What is Pose Estimation with Ultralytics YOLO11 and how does it work?
      acceptedAnswer:
         type:Answer
         text:Pose estimation with Ultralytics YOLO11 involves identifying specific points, known as keypoints, in an image. These keypoints typically represent joints or other important features of the object. The output includes the [x, y] coordinates and confidence scores for each point. YOLO11-pose models are specifically designed for this task and use the -pose suffix, such as yolo11n-pose.pt. These models are pre-trained on datasets like COCO keypoints and can be used for various pose estimation tasks. For more information, visit the Pose Estimation Page.
      name:How can I train a YOLO11-pose model on a custom dataset?
      acceptedAnswer:
         type:Answer
         text:Training a YOLO11-pose model on a custom dataset involves loading a model, either a new model defined by a YAML file or a pre-trained model. You can then start the training process using your specified dataset and parameters. For comprehensive details on training, refer to the Train Section. You can also use Ultralytics HUB for a no-code approach to training custom pose estimation models.
      name:How do I validate a trained YOLO11-pose model?
      acceptedAnswer:
         type:Answer
         text:Validation of a YOLO11-pose model involves assessing its accuracy using the same dataset parameters retained during training. Here's an example: For more information, visit the Val Section.
      name:Can I export a YOLO11-pose model to other formats, and how?
      acceptedAnswer:
         type:Answer
         text:Yes, you can export a YOLO11-pose model to various formats like ONNX, CoreML, TensorRT, and more. This can be done using either Python or the Command Line Interface (CLI). Refer to the Export Section for more details. Exported models can be deployed on edge devices for real-time applications like fitness tracking, sports analysis, or robotics.
      name:What are the available Ultralytics YOLO11-pose models and their performance metrics?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 offers various pretrained pose models such as YOLO11n-pose, YOLO11s-pose, YOLO11m-pose, among others. These models differ in size, accuracy (mAP), and speed. For instance, the YOLO11n-pose model achieves a mAPpose50-95 of 50.0 and an mAPpose50 of 81.0. For a complete list and performance details, visit the Models Section.
Answer:
      text:Pose estimation with Ultralytics YOLO11 involves identifying specific points, known as keypoints, in an image. These keypoints typically represent joints or other important features of the object. The output includes the [x, y] coordinates and confidence scores for each point. YOLO11-pose models are specifically designed for this task and use the -pose suffix, such as yolo11n-pose.pt. These models are pre-trained on datasets like COCO keypoints and can be used for various pose estimation tasks. For more information, visit the Pose Estimation Page.
      text:Training a YOLO11-pose model on a custom dataset involves loading a model, either a new model defined by a YAML file or a pre-trained model. You can then start the training process using your specified dataset and parameters. For comprehensive details on training, refer to the Train Section. You can also use Ultralytics HUB for a no-code approach to training custom pose estimation models.
      text:Validation of a YOLO11-pose model involves assessing its accuracy using the same dataset parameters retained during training. Here's an example: For more information, visit the Val Section.
      text:Yes, you can export a YOLO11-pose model to various formats like ONNX, CoreML, TensorRT, and more. This can be done using either Python or the Command Line Interface (CLI). Refer to the Export Section for more details. Exported models can be deployed on edge devices for real-time applications like fitness tracking, sports analysis, or robotics.
      text:Ultralytics YOLO11 offers various pretrained pose models such as YOLO11n-pose, YOLO11s-pose, YOLO11m-pose, among others. These models differ in size, accuracy (mAP), and speed. For instance, the YOLO11n-pose model achieves a mAPpose50-95 of 50.0 and an mAPpose50 of 81.0. For a complete list and performance details, visit the Models Section.

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