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<!DOCTYPE html> <html class="no-js" lang="en"> <head> <meta charset="utf-8"> <meta content="width=device-width,initial-scale=1" name="viewport"> <meta content="Discover how to use YOLO11 for pose estimation tasks. 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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."}}]}</script><script type="application/json" 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href="#how-can-i-train-a-yolo11-pose-model-on-a-custom-dataset"> <span class="md-ellipsis"> How can I train a YOLO11-pose model on a custom dataset? </span> </a> </li> <li class="md-nav__item"> <a class="md-nav__link" href="#how-do-i-validate-a-trained-yolo11-pose-model"> <span class="md-ellipsis"> How do I validate a trained YOLO11-pose model? </span> </a> </li> <li class="md-nav__item"> <a class="md-nav__link" href="#can-i-export-a-yolo11-pose-model-to-other-formats-and-how"> <span class="md-ellipsis"> Can I export a YOLO11-pose model to other formats, and how? </span> </a> </li> <li class="md-nav__item"> <a class="md-nav__link" href="#what-are-the-available-ultralytics-yolo11-pose-models-and-their-performance-metrics"> <span class="md-ellipsis"> What are the available Ultralytics YOLO11-pose models and their performance metrics? </span> </a> </li> </ul> </nav> </li> </ul> </nav> </div> </div> </div> <div class="md-content" data-md-component="content"> <article class="md-content__inner md-typeset"> <a class="md-content__button md-icon" href="https://github.com/ultralytics/ultralytics/tree/main/docs/en/tasks/pose.md" title="Edit this page"> <svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"></path></svg> </a> <h1 id="pose-estimation">Pose Estimation</h1> <p><img alt="Pose estimation examples" src="https://github.com/ultralytics/docs/releases/download/0/pose-estimation-examples.avif" width="1024"></p> <p>Pose estimation is a task that involves identifying the location of specific points in an image, usually referred to as keypoints. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive features. The locations of the keypoints are usually represented as a set of 2D <code data-wg-notranslate="">[x, y]</code> or 3D <code data-wg-notranslate="">[x, y, visible]</code> coordinates.</p> <p>The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific parts of an object in a scene, and their location in relation to each other.</p> <p align="center"> <br> <iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen="" frameborder="0" height="405" loading="lazy" src="https://www.youtube.com/embed/AAkfToU3nAc" title="YouTube video player" width="720"> </iframe> <br> <strong>Watch:</strong> Ultralytics YOLO11 Pose Estimation Tutorial | Real-Time Object Tracking and Human Pose Detection </p> <div class="admonition tip"> <p class="admonition-title">Tip</p> <p>YOLO11 <em>pose</em> models use the <code data-wg-notranslate="">-pose</code> suffix, i.e. <code data-wg-notranslate="">yolo11n-pose.pt</code>. These models are trained on the <a href="https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml">COCO keypoints</a> dataset and are suitable for a variety of pose estimation tasks.</p> <p>In the default YOLO11 pose model, there are 17 keypoints, each representing a different part of the human body. Here is the mapping of each index to its respective body joint:</p> <ol> <li>Nose</li> <li>Left Eye</li> <li>Right Eye</li> <li>Left Ear</li> <li>Right Ear</li> <li>Left Shoulder</li> <li>Right Shoulder</li> <li>Left Elbow</li> <li>Right Elbow</li> <li>Left Wrist</li> <li>Right Wrist</li> <li>Left Hip</li> <li>Right Hip</li> <li>Left Knee</li> <li>Right Knee</li> <li>Left Ankle</li> <li>Right Ankle</li> </ol> </div> <h2 id="models"><a href="https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11">Models</a></h2> <p>Ultralytics YOLO11 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on the <a href="https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml">COCO</a> dataset, while Classify models are pretrained on the <a href="https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml">ImageNet</a> dataset.</p> <p><a href="https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models">Models</a> download automatically from the latest Ultralytics <a href="https://github.com/ultralytics/assets/releases">release</a> on first use.</p> <table> <thead> <tr> <th>Model</th> <th>size<br><sup>(pixels)</sup></th> <th>mAP<sup>pose<br>50-95</sup></th> <th>mAP<sup>pose<br>50</sup></th> <th>Speed<br><sup>CPU ONNX<br>(ms)</sup></th> <th>Speed<br><sup>T4 TensorRT10<br>(ms)</sup></th> <th>params<br><sup>(M)</sup></th> <th>FLOPs<br><sup>(B)</sup></th> </tr> </thead> <tbody> <tr> <td><a href="https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt">YOLO11n-pose</a></td> <td>640</td> <td>50.0</td> <td>81.0</td> <td>52.4 ± 0.5</td> <td>1.7 ± 0.0</td> <td>2.9</td> <td>7.6</td> </tr> <tr> <td><a href="https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt">YOLO11s-pose</a></td> <td>640</td> <td>58.9</td> <td>86.3</td> <td>90.5 ± 0.6</td> <td>2.6 ± 0.0</td> <td>9.9</td> <td>23.2</td> </tr> <tr> <td><a href="https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt">YOLO11m-pose</a></td> <td>640</td> <td>64.9</td> <td>89.4</td> <td>187.3 ± 0.8</td> <td>4.9 ± 0.1</td> <td>20.9</td> <td>71.7</td> </tr> <tr> <td><a href="https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt">YOLO11l-pose</a></td> <td>640</td> <td>66.1</td> <td>89.9</td> <td>247.7 ± 1.1</td> <td>6.4 ± 0.1</td> <td>26.2</td> <td>90.7</td> </tr> <tr> <td><a href="https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt">YOLO11x-pose</a></td> <td>640</td> <td>69.5</td> <td>91.1</td> <td>488.0 ± 13.9</td> <td>12.1 ± 0.2</td> <td>58.8</td> <td>203.3</td> </tr> </tbody> </table> <ul> <li><strong>mAP<sup>val</sup></strong> values are for single-model single-scale on <a href="https://cocodataset.org/">COCO Keypoints val2017</a> dataset. <br>Reproduce by <code data-wg-notranslate="">yolo val pose data=coco-pose.yaml device=0</code></li> <li><strong>Speed</strong> averaged over COCO val images using an <a href="https://aws.amazon.com/ec2/instance-types/p4/">Amazon EC2 P4d</a> instance. <br>Reproduce by <code data-wg-notranslate="">yolo val pose data=coco-pose.yaml batch=1 device=0|cpu</code></li> </ul> <h2 id="train">Train</h2> <p>Train a YOLO11-pose model on the COCO8-pose dataset. The <a href="https://docs.ultralytics.com/datasets/pose/coco8-pose/">COCO8-pose dataset</a> is a small sample dataset that's perfect for testing and debugging your pose estimation models.</p> <div class="admonition example"> <p class="admonition-title">Example</p> <div class="tabbed-set tabbed-alternate" data-tabs="1:2"><input checked="checked" id="__tabbed_1_1" name="__tabbed_1" type="radio"><input id="__tabbed_1_2" name="__tabbed_1" type="radio"><div class="tabbed-labels"><label for="__tabbed_1_1">Python</label><label for="__tabbed_1_2">CLI</label></div> <div class="tabbed-content"> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-0-1" id="__codelineno-0-1" name="__codelineno-0-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">ultralytics</span><span class="w"> </span><span class="kn">import</span> <span class="n">YOLO</span> <a href="#__codelineno-0-2" id="__codelineno-0-2" name="__codelineno-0-2"></a> <a href="#__codelineno-0-3" id="__codelineno-0-3" name="__codelineno-0-3"></a><span class="c1"># Load a model</span> <a href="#__codelineno-0-4" id="__codelineno-0-4" name="__codelineno-0-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.yaml"</span><span class="p">)</span> <span class="c1"># build a new model from YAML</span> <a href="#__codelineno-0-5" id="__codelineno-0-5" name="__codelineno-0-5"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># load a pretrained model (recommended for training)</span> <a href="#__codelineno-0-6" id="__codelineno-0-6" name="__codelineno-0-6"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.yaml"</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># build from YAML and transfer weights</span> <a href="#__codelineno-0-7" id="__codelineno-0-7" name="__codelineno-0-7"></a> <a href="#__codelineno-0-8" id="__codelineno-0-8" name="__codelineno-0-8"></a><span class="c1"># Train the model</span> <a href="#__codelineno-0-9" id="__codelineno-0-9" name="__codelineno-0-9"></a><span class="n">results</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="s2">"coco8-pose.yaml"</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">imgsz</span><span class="o">=</span><span class="mi">640</span><span class="p">)</span> </code></pre></div> </div> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-1-1" id="__codelineno-1-1" name="__codelineno-1-1"></a><span class="c1"># Build a new model from YAML and start training from scratch</span> <a href="#__codelineno-1-2" id="__codelineno-1-2" name="__codelineno-1-2"></a>yolo<span class="w"> </span>pose<span class="w"> </span>train<span class="w"> </span><span class="nv">data</span><span class="o">=</span>coco8-pose.yaml<span class="w"> </span><span class="nv">model</span><span class="o">=</span>yolo11n-pose.yaml<span class="w"> </span><span class="nv">epochs</span><span class="o">=</span><span class="m">100</span><span class="w"> </span><span class="nv">imgsz</span><span class="o">=</span><span class="m">640</span> <a href="#__codelineno-1-3" id="__codelineno-1-3" name="__codelineno-1-3"></a> <a href="#__codelineno-1-4" id="__codelineno-1-4" name="__codelineno-1-4"></a><span class="c1"># Start training from a pretrained *.pt model</span> <a href="#__codelineno-1-5" id="__codelineno-1-5" name="__codelineno-1-5"></a>yolo<span class="w"> </span>pose<span class="w"> </span>train<span class="w"> </span><span class="nv">data</span><span class="o">=</span>coco8-pose.yaml<span class="w"> </span><span class="nv">model</span><span class="o">=</span>yolo11n-pose.pt<span class="w"> </span><span class="nv">epochs</span><span class="o">=</span><span class="m">100</span><span class="w"> </span><span class="nv">imgsz</span><span class="o">=</span><span class="m">640</span> <a href="#__codelineno-1-6" id="__codelineno-1-6" name="__codelineno-1-6"></a> <a href="#__codelineno-1-7" id="__codelineno-1-7" name="__codelineno-1-7"></a><span class="c1"># Build a new model from YAML, transfer pretrained weights to it and start training</span> <a href="#__codelineno-1-8" id="__codelineno-1-8" name="__codelineno-1-8"></a>yolo<span class="w"> </span>pose<span class="w"> </span>train<span class="w"> </span><span class="nv">data</span><span class="o">=</span>coco8-pose.yaml<span class="w"> </span><span class="nv">model</span><span class="o">=</span>yolo11n-pose.yaml<span class="w"> </span><span class="nv">pretrained</span><span class="o">=</span>yolo11n-pose.pt<span class="w"> </span><span class="nv">epochs</span><span class="o">=</span><span class="m">100</span><span class="w"> </span><span class="nv">imgsz</span><span class="o">=</span><span class="m">640</span> </code></pre></div> </div> </div> </div> </div> <h3 id="dataset-format">Dataset format</h3> <p>YOLO pose dataset format can be found in detail in the <a href="../../datasets/pose/">Dataset Guide</a>. To convert your existing dataset from other formats (like <a href="https://docs.ultralytics.com/datasets/pose/coco/">COCO</a> etc.) to YOLO format, please use the <a href="https://github.com/ultralytics/JSON2YOLO">JSON2YOLO</a> tool by Ultralytics.</p> <p>For custom pose estimation tasks, you can also explore specialized datasets like <a href="https://docs.ultralytics.com/datasets/pose/tiger-pose/">Tiger-Pose</a> for animal pose estimation, <a href="https://docs.ultralytics.com/datasets/pose/hand-keypoints/">Hand Keypoints</a> for hand tracking, or <a href="https://docs.ultralytics.com/datasets/pose/dog-pose/">Dog-Pose</a> for canine pose analysis.</p> <h2 id="val">Val</h2> <p>Validate trained YOLO11n-pose model <a href="https://www.ultralytics.com/glossary/accuracy">accuracy</a> on the COCO8-pose dataset. No arguments are needed as the <code data-wg-notranslate="">model</code> retains its training <code data-wg-notranslate="">data</code> and arguments as model attributes.</p> <div class="admonition example"> <p class="admonition-title">Example</p> <div class="tabbed-set tabbed-alternate" data-tabs="2:2"><input checked="checked" id="__tabbed_2_1" name="__tabbed_2" type="radio"><input id="__tabbed_2_2" name="__tabbed_2" type="radio"><div class="tabbed-labels"><label for="__tabbed_2_1">Python</label><label for="__tabbed_2_2">CLI</label></div> <div class="tabbed-content"> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-2-1" id="__codelineno-2-1" name="__codelineno-2-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">ultralytics</span><span class="w"> </span><span class="kn">import</span> <span class="n">YOLO</span> <a href="#__codelineno-2-2" id="__codelineno-2-2" name="__codelineno-2-2"></a> <a href="#__codelineno-2-3" id="__codelineno-2-3" name="__codelineno-2-3"></a><span class="c1"># Load a model</span> <a href="#__codelineno-2-4" id="__codelineno-2-4" name="__codelineno-2-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># load an official model</span> <a href="#__codelineno-2-5" id="__codelineno-2-5" name="__codelineno-2-5"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"path/to/best.pt"</span><span class="p">)</span> <span class="c1"># load a custom model</span> <a href="#__codelineno-2-6" id="__codelineno-2-6" name="__codelineno-2-6"></a> <a href="#__codelineno-2-7" id="__codelineno-2-7" name="__codelineno-2-7"></a><span class="c1"># Validate the model</span> <a href="#__codelineno-2-8" id="__codelineno-2-8" name="__codelineno-2-8"></a><span class="n">metrics</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">val</span><span class="p">()</span> <span class="c1"># no arguments needed, dataset and settings remembered</span> <a href="#__codelineno-2-9" id="__codelineno-2-9" name="__codelineno-2-9"></a><span class="n">metrics</span><span class="o">.</span><span class="n">box</span><span class="o">.</span><span class="n">map</span> <span class="c1"># map50-95</span> <a href="#__codelineno-2-10" id="__codelineno-2-10" name="__codelineno-2-10"></a><span class="n">metrics</span><span class="o">.</span><span class="n">box</span><span class="o">.</span><span class="n">map50</span> <span class="c1"># map50</span> <a href="#__codelineno-2-11" id="__codelineno-2-11" name="__codelineno-2-11"></a><span class="n">metrics</span><span class="o">.</span><span class="n">box</span><span class="o">.</span><span class="n">map75</span> <span class="c1"># map75</span> <a href="#__codelineno-2-12" id="__codelineno-2-12" name="__codelineno-2-12"></a><span class="n">metrics</span><span class="o">.</span><span class="n">box</span><span class="o">.</span><span class="n">maps</span> <span class="c1"># a list contains map50-95 of each category</span> </code></pre></div> </div> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-3-1" id="__codelineno-3-1" name="__codelineno-3-1"></a>yolo<span class="w"> </span>pose<span class="w"> </span>val<span class="w"> </span><span class="nv">model</span><span class="o">=</span>yolo11n-pose.pt<span class="w"> </span><span class="c1"># val official model</span> <a href="#__codelineno-3-2" id="__codelineno-3-2" name="__codelineno-3-2"></a>yolo<span class="w"> </span>pose<span class="w"> </span>val<span class="w"> </span><span class="nv">model</span><span class="o">=</span>path/to/best.pt<span class="w"> </span><span class="c1"># val custom model</span> </code></pre></div> </div> </div> </div> </div> <h2 id="predict">Predict</h2> <p>Use a trained YOLO11n-pose model to run predictions on images. The <a href="https://docs.ultralytics.com/modes/predict/">predict mode</a> allows you to perform inference on images, videos, or real-time streams.</p> <div class="admonition example"> <p class="admonition-title">Example</p> <div class="tabbed-set tabbed-alternate" data-tabs="3:2"><input checked="checked" id="__tabbed_3_1" name="__tabbed_3" type="radio"><input id="__tabbed_3_2" name="__tabbed_3" type="radio"><div class="tabbed-labels"><label for="__tabbed_3_1">Python</label><label for="__tabbed_3_2">CLI</label></div> <div class="tabbed-content"> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-4-1" id="__codelineno-4-1" name="__codelineno-4-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">ultralytics</span><span class="w"> </span><span class="kn">import</span> <span class="n">YOLO</span> <a href="#__codelineno-4-2" id="__codelineno-4-2" name="__codelineno-4-2"></a> <a href="#__codelineno-4-3" id="__codelineno-4-3" name="__codelineno-4-3"></a><span class="c1"># Load a model</span> <a href="#__codelineno-4-4" id="__codelineno-4-4" name="__codelineno-4-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># load an official model</span> <a href="#__codelineno-4-5" id="__codelineno-4-5" name="__codelineno-4-5"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"path/to/best.pt"</span><span class="p">)</span> <span class="c1"># load a custom model</span> <a href="#__codelineno-4-6" id="__codelineno-4-6" name="__codelineno-4-6"></a> <a href="#__codelineno-4-7" id="__codelineno-4-7" name="__codelineno-4-7"></a><span class="c1"># Predict with the model</span> <a href="#__codelineno-4-8" id="__codelineno-4-8" name="__codelineno-4-8"></a><span class="n">results</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">"https://ultralytics.com/images/bus.jpg"</span><span class="p">)</span> <span class="c1"># predict on an image</span> <a href="#__codelineno-4-9" id="__codelineno-4-9" name="__codelineno-4-9"></a> <a href="#__codelineno-4-10" id="__codelineno-4-10" name="__codelineno-4-10"></a><span class="c1"># Access the results</span> <a href="#__codelineno-4-11" id="__codelineno-4-11" name="__codelineno-4-11"></a><span class="k">for</span> <span class="n">result</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span> <a href="#__codelineno-4-12" id="__codelineno-4-12" name="__codelineno-4-12"></a> <span class="n">xy</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">keypoints</span><span class="o">.</span><span class="n">xy</span> <span class="c1"># x and y coordinates</span> <a href="#__codelineno-4-13" id="__codelineno-4-13" name="__codelineno-4-13"></a> <span class="n">xyn</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">keypoints</span><span class="o">.</span><span class="n">xyn</span> <span class="c1"># normalized</span> <a href="#__codelineno-4-14" id="__codelineno-4-14" name="__codelineno-4-14"></a> <span class="n">kpts</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">keypoints</span><span class="o">.</span><span class="n">data</span> <span class="c1"># x, y, visibility (if available)</span> </code></pre></div> </div> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-5-1" id="__codelineno-5-1" name="__codelineno-5-1"></a>yolo<span class="w"> </span>pose<span class="w"> </span>predict<span class="w"> </span><span class="nv">model</span><span class="o">=</span>yolo11n-pose.pt<span class="w"> </span><span class="nv">source</span><span class="o">=</span><span class="s1">'https://ultralytics.com/images/bus.jpg'</span><span class="w"> </span><span class="c1"># predict with official model</span> <a href="#__codelineno-5-2" id="__codelineno-5-2" name="__codelineno-5-2"></a>yolo<span class="w"> </span>pose<span class="w"> </span>predict<span class="w"> </span><span class="nv">model</span><span class="o">=</span>path/to/best.pt<span class="w"> </span><span class="nv">source</span><span class="o">=</span><span class="s1">'https://ultralytics.com/images/bus.jpg'</span><span class="w"> </span><span class="c1"># predict with custom model</span> </code></pre></div> </div> </div> </div> </div> <p>See full <code data-wg-notranslate="">predict</code> mode details in the <a href="../../modes/predict/">Predict</a> page.</p> <h2 id="export">Export</h2> <p>Export a YOLO11n Pose model to a different format like ONNX, CoreML, etc. This allows you to deploy your model on various platforms and devices for <a href="https://www.ultralytics.com/glossary/real-time-inference">real-time inference</a>.</p> <div class="admonition example"> <p class="admonition-title">Example</p> <div class="tabbed-set tabbed-alternate" data-tabs="4:2"><input checked="checked" id="__tabbed_4_1" name="__tabbed_4" type="radio"><input id="__tabbed_4_2" name="__tabbed_4" type="radio"><div class="tabbed-labels"><label for="__tabbed_4_1">Python</label><label for="__tabbed_4_2">CLI</label></div> <div class="tabbed-content"> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-6-1" id="__codelineno-6-1" name="__codelineno-6-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">ultralytics</span><span class="w"> </span><span class="kn">import</span> <span class="n">YOLO</span> <a href="#__codelineno-6-2" id="__codelineno-6-2" name="__codelineno-6-2"></a> <a href="#__codelineno-6-3" id="__codelineno-6-3" name="__codelineno-6-3"></a><span class="c1"># Load a model</span> <a href="#__codelineno-6-4" id="__codelineno-6-4" name="__codelineno-6-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># load an official model</span> <a href="#__codelineno-6-5" id="__codelineno-6-5" name="__codelineno-6-5"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"path/to/best.pt"</span><span class="p">)</span> <span class="c1"># load a custom trained model</span> <a href="#__codelineno-6-6" id="__codelineno-6-6" name="__codelineno-6-6"></a> <a href="#__codelineno-6-7" id="__codelineno-6-7" name="__codelineno-6-7"></a><span class="c1"># Export the model</span> <a href="#__codelineno-6-8" id="__codelineno-6-8" name="__codelineno-6-8"></a><span class="n">model</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s2">"onnx"</span><span class="p">)</span> </code></pre></div> </div> <div class="tabbed-block"> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-7-1" id="__codelineno-7-1" name="__codelineno-7-1"></a>yolo<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">model</span><span class="o">=</span>yolo11n-pose.pt<span class="w"> </span><span class="nv">format</span><span class="o">=</span>onnx<span class="w"> </span><span class="c1"># export official model</span> <a href="#__codelineno-7-2" id="__codelineno-7-2" name="__codelineno-7-2"></a>yolo<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">model</span><span class="o">=</span>path/to/best.pt<span class="w"> </span><span class="nv">format</span><span class="o">=</span>onnx<span class="w"> </span><span class="c1"># export custom trained model</span> </code></pre></div> </div> </div> </div> </div> <p>Available YOLO11-pose export formats are in the table below. You can export to any format using the <code data-wg-notranslate="">format</code> argument, i.e. <code data-wg-notranslate="">format='onnx'</code> or <code data-wg-notranslate="">format='engine'</code>. You can predict or validate directly on exported models, i.e. <code data-wg-notranslate="">yolo predict model=yolo11n-pose.onnx</code>. Usage examples are shown for your model after export completes.</p> <table> <thead> <tr> <th>Format</th> <th><code data-wg-notranslate="">format</code> Argument</th> <th>Model</th> <th>Metadata</th> <th>Arguments</th> </tr> </thead> <tbody> <tr> <td><a href="https://pytorch.org/">PyTorch</a></td> <td>-</td> <td><code data-wg-notranslate="">yolo11n-pose.pt</code></td> <td>✅</td> <td>-</td> </tr> <tr> <td><a href="../../integrations/torchscript/">TorchScript</a></td> <td><code data-wg-notranslate="">torchscript</code></td> <td><code data-wg-notranslate="">yolo11n-pose.torchscript</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">optimize</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou, agnostic_nms are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/onnx/">ONNX</a></td> <td><code data-wg-notranslate="">onnx</code></td> <td><code data-wg-notranslate="">yolo11n-pose.onnx</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">half</code>, <code data-wg-notranslate="">dynamic</code>, <code data-wg-notranslate="">simplify</code>, <code data-wg-notranslate="">opset</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou, agnostic_nms are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/openvino/">OpenVINO</a></td> <td><code data-wg-notranslate="">openvino</code></td> <td><code data-wg-notranslate="">yolo11n-pose_openvino_model/</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">half</code>, <code data-wg-notranslate="">dynamic</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou, agnostic_nms are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code>, <code data-wg-notranslate="">data</code></td> </tr> <tr> <td><a href="../../integrations/tensorrt/">TensorRT</a></td> <td><code data-wg-notranslate="">engine</code></td> <td><code data-wg-notranslate="">yolo11n-pose.engine</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">half</code>, <code data-wg-notranslate="">dynamic</code>, <code data-wg-notranslate="">simplify</code>, <code data-wg-notranslate="">workspace</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou, agnostic_nms are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code>, <code data-wg-notranslate="">data</code></td> </tr> <tr> <td><a href="../../integrations/coreml/">CoreML</a></td> <td><code data-wg-notranslate="">coreml</code></td> <td><code data-wg-notranslate="">yolo11n-pose.mlpackage</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">half</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/tf-savedmodel/">TF SavedModel</a></td> <td><code data-wg-notranslate="">saved_model</code></td> <td><code data-wg-notranslate="">yolo11n-pose_saved_model/</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">keras</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou, agnostic_nms are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/tf-graphdef/">TF GraphDef</a></td> <td><code data-wg-notranslate="">pb</code></td> <td><code data-wg-notranslate="">yolo11n-pose.pb</code></td> <td>❌</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/tflite/">TF Lite</a></td> <td><code data-wg-notranslate="">tflite</code></td> <td><code data-wg-notranslate="">yolo11n-pose.tflite</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">half</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou, agnostic_nms are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code>, <code data-wg-notranslate="">data</code></td> </tr> <tr> <td><a href="../../integrations/edge-tpu/">TF Edge TPU</a></td> <td><code data-wg-notranslate="">edgetpu</code></td> <td><code data-wg-notranslate="">yolo11n-pose_edgetpu.tflite</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code></td> </tr> <tr> <td><a href="../../integrations/tfjs/">TF.js</a></td> <td><code data-wg-notranslate="">tfjs</code></td> <td><code data-wg-notranslate="">yolo11n-pose_web_model/</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">half</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">nms</code><span class="twemoji" title="conf, iou, agnostic_nms are also available when nms=True"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/paddlepaddle/">PaddlePaddle</a></td> <td><code data-wg-notranslate="">paddle</code></td> <td><code data-wg-notranslate="">yolo11n-pose_paddle_model/</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/mnn/">MNN</a></td> <td><code data-wg-notranslate="">mnn</code></td> <td><code data-wg-notranslate="">yolo11n-pose.mnn</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">batch</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">half</code></td> </tr> <tr> <td><a href="../../integrations/ncnn/">NCNN</a></td> <td><code data-wg-notranslate="">ncnn</code></td> <td><code data-wg-notranslate="">yolo11n-pose_ncnn_model/</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">half</code>, <code data-wg-notranslate="">batch</code></td> </tr> <tr> <td><a href="../../integrations/sony-imx500/">IMX500</a><span class="twemoji" title="imx format only supported for YOLOv8n model currently"><svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8 8 3.59 8 8-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10 10 10 0 0 0 10-10A10 10 0 0 0 12 2m-1 15h2v-6h-2z"></path></svg></span></td> <td><code data-wg-notranslate="">imx</code></td> <td><code data-wg-notranslate="">yolo11n-pose_imx_model/</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">int8</code>, <code data-wg-notranslate="">data</code></td> </tr> <tr> <td><a href="../../integrations/rockchip-rknn/">RKNN</a></td> <td><code data-wg-notranslate="">rknn</code></td> <td><code data-wg-notranslate="">yolo11n-pose_rknn_model/</code></td> <td>✅</td> <td><code data-wg-notranslate="">imgsz</code>, <code data-wg-notranslate="">batch</code>, <code data-wg-notranslate="">name</code></td> </tr> </tbody> </table> <p>See full <code data-wg-notranslate="">export</code> details in the <a href="../../modes/export/">Export</a> page.</p> <h2 id="faq">FAQ</h2> <h3 id="what-is-pose-estimation-with-ultralytics-yolo11-and-how-does-it-work">What is Pose Estimation with Ultralytics YOLO11 and how does it work?</h3> <p>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 <code data-wg-notranslate="">[x, y]</code> coordinates and confidence scores for each point. YOLO11-pose models are specifically designed for this task and use the <code data-wg-notranslate="">-pose</code> suffix, such as <code data-wg-notranslate="">yolo11n-pose.pt</code>. These models are pre-trained on datasets like <a href="https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml">COCO keypoints</a> and can be used for various pose estimation tasks. For more information, visit the <a href="#pose-estimation">Pose Estimation Page</a>.</p> <h3 id="how-can-i-train-a-yolo11-pose-model-on-a-custom-dataset">How can I train a YOLO11-pose model on a custom dataset?</h3> <p>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.</p> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-8-1" id="__codelineno-8-1" name="__codelineno-8-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">ultralytics</span><span class="w"> </span><span class="kn">import</span> <span class="n">YOLO</span> <a href="#__codelineno-8-2" id="__codelineno-8-2" name="__codelineno-8-2"></a> <a href="#__codelineno-8-3" id="__codelineno-8-3" name="__codelineno-8-3"></a><span class="c1"># Load a model</span> <a href="#__codelineno-8-4" id="__codelineno-8-4" name="__codelineno-8-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.yaml"</span><span class="p">)</span> <span class="c1"># build a new model from YAML</span> <a href="#__codelineno-8-5" id="__codelineno-8-5" name="__codelineno-8-5"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># load a pretrained model (recommended for training)</span> <a href="#__codelineno-8-6" id="__codelineno-8-6" name="__codelineno-8-6"></a> <a href="#__codelineno-8-7" id="__codelineno-8-7" name="__codelineno-8-7"></a><span class="c1"># Train the model</span> <a href="#__codelineno-8-8" id="__codelineno-8-8" name="__codelineno-8-8"></a><span class="n">results</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="s2">"your-dataset.yaml"</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">imgsz</span><span class="o">=</span><span class="mi">640</span><span class="p">)</span> </code></pre></div> <p>For comprehensive details on training, refer to the <a href="#train">Train Section</a>. You can also use <a href="https://www.ultralytics.com/hub">Ultralytics HUB</a> for a no-code approach to training custom pose estimation models.</p> <h3 id="how-do-i-validate-a-trained-yolo11-pose-model">How do I validate a trained YOLO11-pose model?</h3> <p>Validation of a YOLO11-pose model involves assessing its accuracy using the same dataset parameters retained during training. Here's an example:</p> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-9-1" id="__codelineno-9-1" name="__codelineno-9-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">ultralytics</span><span class="w"> </span><span class="kn">import</span> <span class="n">YOLO</span> <a href="#__codelineno-9-2" id="__codelineno-9-2" name="__codelineno-9-2"></a> <a href="#__codelineno-9-3" id="__codelineno-9-3" name="__codelineno-9-3"></a><span class="c1"># Load a model</span> <a href="#__codelineno-9-4" id="__codelineno-9-4" name="__codelineno-9-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># load an official model</span> <a href="#__codelineno-9-5" id="__codelineno-9-5" name="__codelineno-9-5"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"path/to/best.pt"</span><span class="p">)</span> <span class="c1"># load a custom model</span> <a href="#__codelineno-9-6" id="__codelineno-9-6" name="__codelineno-9-6"></a> <a href="#__codelineno-9-7" id="__codelineno-9-7" name="__codelineno-9-7"></a><span class="c1"># Validate the model</span> <a href="#__codelineno-9-8" id="__codelineno-9-8" name="__codelineno-9-8"></a><span class="n">metrics</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">val</span><span class="p">()</span> <span class="c1"># no arguments needed, dataset and settings remembered</span> </code></pre></div> <p>For more information, visit the <a href="#val">Val Section</a>.</p> <h3 id="can-i-export-a-yolo11-pose-model-to-other-formats-and-how">Can I export a YOLO11-pose model to other formats, and how?</h3> <p>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).</p> <div class="highlight"><pre><span></span><code data-wg-notranslate=""><a href="#__codelineno-10-1" id="__codelineno-10-1" name="__codelineno-10-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">ultralytics</span><span class="w"> </span><span class="kn">import</span> <span class="n">YOLO</span> <a href="#__codelineno-10-2" id="__codelineno-10-2" name="__codelineno-10-2"></a> <a href="#__codelineno-10-3" id="__codelineno-10-3" name="__codelineno-10-3"></a><span class="c1"># Load a model</span> <a href="#__codelineno-10-4" id="__codelineno-10-4" name="__codelineno-10-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"yolo11n-pose.pt"</span><span class="p">)</span> <span class="c1"># load an official model</span> <a href="#__codelineno-10-5" id="__codelineno-10-5" name="__codelineno-10-5"></a><span class="n">model</span> <span class="o">=</span> <span class="n">YOLO</span><span class="p">(</span><span class="s2">"path/to/best.pt"</span><span class="p">)</span> <span class="c1"># load a custom trained model</span> <a href="#__codelineno-10-6" id="__codelineno-10-6" name="__codelineno-10-6"></a> <a href="#__codelineno-10-7" id="__codelineno-10-7" name="__codelineno-10-7"></a><span class="c1"># Export the model</span> <a href="#__codelineno-10-8" id="__codelineno-10-8" name="__codelineno-10-8"></a><span class="n">model</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s2">"onnx"</span><span class="p">)</span> </code></pre></div> <p>Refer to the <a href="#export">Export Section</a> for more details. Exported models can be deployed on edge devices for <a href="https://www.ultralytics.com/blog/real-time-inferences-in-vision-ai-solutions-are-making-an-impact">real-time applications</a> like fitness tracking, sports analysis, or <a href="https://www.ultralytics.com/blog/from-algorithms-to-automation-ais-role-in-robotics">robotics</a>.</p> <h3 id="what-are-the-available-ultralytics-yolo11-pose-models-and-their-performance-metrics">What are the available Ultralytics YOLO11-pose models and their performance metrics?</h3> <p>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 mAP<sup>pose</sup>50-95 of 50.0 and an mAP<sup>pose</sup>50 of 81.0. For a complete list and performance details, visit the <a href="#models">Models Section</a>.</p> <div class="git-info"> <div class="dates-container"> <span class="date-item" title="This page was first created on November 12, 2023"> <span class="hover-item">📅</span> Created 1 year ago </span> <span class="date-item" title="This page was last updated on March 20, 2025"> <span class="hover-item">✏️</span> Updated 8 days ago </span> </div> <div class="authors-container"> <a class="author-link" href="https://github.com/glenn-jocher" title="glenn-jocher (25 changes)"> <img alt="glenn-jocher" class="hover-item" loading="lazy" src="https://avatars.githubusercontent.com/u/26833433?v=4&s=96"> </a> <a class="author-link" href="https://github.com/RizwanMunawar" title="RizwanMunawar (4 changes)"> <img alt="RizwanMunawar" class="hover-item" loading="lazy" src="https://avatars.githubusercontent.com/u/62513924?v=4&s=96"> </a> <a class="author-link" href="https://github.com/Y-T-G" title="Y-T-G (1 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