Pose Estimation

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 [x, y] or 3D [x, y, visible] coordinates.

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.

Watch: Ultralytics YOLO11 Pose Estimation Tutorial | Real-Time Object Tracking and Human Pose Detection

Models

Ultralytics YOLO11 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on the COCO dataset, while Classify models are pretrained on the ImageNet dataset.

Models download automatically from the latest Ultralytics release on first use.

• mAP^val values are for single-model single-scale on COCO Keypoints val2017 dataset.
  Reproduce by yolo val pose data=coco-pose.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val pose data=coco-pose.yaml batch=1 device=0|cpu

Train

Train a YOLO11-pose model on the COCO8-pose dataset. The COCO8-pose dataset is a small sample dataset that's perfect for testing and debugging your pose estimation models.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.yaml")  # build a new model from YAML
model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.yaml").load("yolo11n-pose.pt")  # build from YAML and transfer weights

# Train the model
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)

# Build a new model from YAML and start training from scratch
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.yaml epochs=100 imgsz=640

# Start training from a pretrained *.pt model
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640

# Build a new model from YAML, transfer pretrained weights to it and start training
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.yaml pretrained=yolo11n-pose.pt epochs=100 imgsz=640

Dataset format

YOLO pose dataset format can be found in detail in the Dataset Guide. To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use the JSON2YOLO tool by Ultralytics.

For custom pose estimation tasks, you can also explore specialized datasets like Tiger-Pose for animal pose estimation, Hand Keypoints for hand tracking, or Dog-Pose for canine pose analysis.

Val

Validate trained YOLO11n-pose model accuracy on the COCO8-pose dataset. No arguments are needed as the model retains its training data and arguments as model attributes.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Validate the model
metrics = model.val()  # no arguments needed, dataset and settings remembered
metrics.box.map  # map50-95
metrics.box.map50  # map50
metrics.box.map75  # map75
metrics.box.maps  # a list contains map50-95 of each category

yolo pose val model=yolo11n-pose.pt # val official model
yolo pose val model=path/to/best.pt # val custom model

Predict

Use a trained YOLO11n-pose model to run predictions on images. The predict mode allows you to perform inference on images, videos, or real-time streams.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image

# Access the results
for result in results:
    xy = result.keypoints.xy  # x and y coordinates
    xyn = result.keypoints.xyn  # normalized
    kpts = result.keypoints.data  # x, y, visibility (if available)

yolo pose predict model=yolo11n-pose.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo pose predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model

See full predict mode details in the Predict page.

Export

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 real-time inference.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom trained model

# Export the model
model.export(format="onnx")

yolo export model=yolo11n-pose.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model

Available YOLO11-pose export formats are in the table below. You can export to any format using the format argument, i.e. format='onnx' or format='engine'. You can predict or validate directly on exported models, i.e. yolo predict model=yolo11n-pose.onnx. Usage examples are shown for your model after export completes.

See full export details in the Export page.

FAQ

What is Pose Estimation with Ultralytics YOLO11 and how does it work?

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.

How can I train a YOLO11-pose model on a custom dataset?

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.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.yaml")  # build a new model from YAML
model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)

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.

How do I validate a trained YOLO11-pose model?

Validation of a YOLO11-pose model involves assessing its accuracy using the same dataset parameters retained during training. Here's an example:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Validate the model
metrics = model.val()  # no arguments needed, dataset and settings remembered

For more information, visit the Val Section.

Can I export a YOLO11-pose model to other formats, and how?

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).

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom trained model

# Export the model
model.export(format="onnx")

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.

What are the available Ultralytics YOLO11-pose models and their performance metrics?

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^pose50-95 of 50.0 and an mAP^pose50 of 81.0. For a complete list and performance details, visit the Models Section.

📅 Created 1 year ago ✏️ Updated 1 month ago

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