Algorithm-Repo/SourceCode/MachineLearningUtils/VINNOFramework/YOLO/docs/en/tasks/pose.md

---

comments: true description: Discover how to use YOLOv8 for pose estimation tasks. Learn about model training, validation, prediction, and exporting in various formats.

keywords: pose estimation, YOLOv8, Ultralytics, keypoints, model training, image recognition, deep learning

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: Pose Estimation with Ultralytics YOLOv8.

Watch: Pose Estimation with Ultralytics HUB.

!!! Tip "Tip"

YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt`. These models are trained on the [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) dataset and are suitable for a variety of pose estimation tasks.

In the default YOLOv8 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:

0: Nose
1: Left Eye
2: Right Eye
3: Left Ear
4: Right Ear
5: Left Shoulder
6: Right Shoulder
7: Left Elbow
8: Right Elbow
9: Left Wrist
10: Right Wrist
11: Left Hip
12: Right Hip
13: Left Knee
14: Right Knee
15: Left Ankle
16: Right Ankle

Models

YOLOv8 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=coco8-pose.yaml batch=1 device=0|cpu

Train

Train a YOLOv8-pose model on the COCO128-pose dataset.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n-pose.yaml")  # build a new model from YAML
    model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
    model = YOLO("yolov8n-pose.yaml").load("yolov8n-pose.pt")  # build from YAML and transfer weights

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

=== "CLI"

    ```bash
    # Build a new model from YAML and start training from scratch
    yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml epochs=100 imgsz=640

    # Start training from a pretrained *.pt model
    yolo pose train data=coco8-pose.yaml model=yolov8n-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=yolov8n-pose.yaml pretrained=yolov8n-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 JSON2YOLO tool by Ultralytics.

Val

Validate trained YOLOv8n-pose model accuracy on the COCO128-pose dataset. No argument need to passed as the model retains its training data and arguments as model attributes.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n-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
    ```

=== "CLI"

    ```bash
    yolo pose val model=yolov8n-pose.pt  # val official model
    yolo pose val model=path/to/best.pt  # val custom model
    ```

Predict

Use a trained YOLOv8n-pose model to run predictions on images.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n-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
    ```

=== "CLI"

    ```bash
    yolo pose predict model=yolov8n-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 YOLOv8n Pose model to a different format like ONNX, CoreML, etc.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n-pose.pt")  # load an official model
    model = YOLO("path/to/best.pt")  # load a custom trained model

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

=== "CLI"

    ```bash
    yolo export model=yolov8n-pose.pt format=onnx  # export official model
    yolo export model=path/to/best.pt format=onnx  # export custom trained model
    ```

Available YOLOv8-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=yolov8n-pose.onnx. Usage examples are shown for your model after export completes.

Model	size (pixels)	mAPpose 50-95	mAPpose 50	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-pose	640	50.4	80.1	131.8	1.18	3.3	9.2
YOLOv8s-pose	640	60.0	86.2	233.2	1.42	11.6	30.2
YOLOv8m-pose	640	65.0	88.8	456.3	2.00	26.4	81.0
YOLOv8l-pose	640	67.6	90.0	784.5	2.59	44.4	168.6
YOLOv8x-pose	640	69.2	90.2	1607.1	3.73	69.4	263.2
YOLOv8x-pose-p6	1280	71.6	91.2	4088.7	10.04	99.1	1066.4

Format	format Argument	Model	Metadata	Arguments
PyTorch	-	yolov8n-pose.pt	✅	-
TorchScript	torchscript	yolov8n-pose.torchscript	✅	imgsz, optimize, batch
ONNX	onnx	yolov8n-pose.onnx	✅	imgsz, half, dynamic, simplify, opset, batch
OpenVINO	openvino	yolov8n-pose_openvino_model/	✅	imgsz, half, int8, batch
TensorRT	engine	yolov8n-pose.engine	✅	imgsz, half, dynamic, simplify, workspace, int8, batch
CoreML	coreml	yolov8n-pose.mlpackage	✅	imgsz, half, int8, nms, batch
TF SavedModel	saved_model	yolov8n-pose_saved_model/	✅	imgsz, keras, int8, batch
TF GraphDef	pb	yolov8n-pose.pb	❌	imgsz, batch
TF Lite	tflite	yolov8n-pose.tflite	✅	imgsz, half, int8, batch
TF Edge TPU	edgetpu	yolov8n-pose_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n-pose_web_model/	✅	imgsz, half, int8, batch
PaddlePaddle	paddle	yolov8n-pose_paddle_model/	✅	imgsz, batch
NCNN	ncnn	yolov8n-pose_ncnn_model/	✅	imgsz, half, batch

See full export details in the Export page.

FAQ

What is Pose Estimation with Ultralytics YOLOv8 and how does it work?

Pose estimation with Ultralytics YOLOv8 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. YOLOv8-pose models are specifically designed for this task and use the -pose suffix, such as yolov8n-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 YOLOv8-pose model on a custom dataset?

Training a YOLOv8-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("yolov8n-pose.yaml")  # build a new model from YAML
model = YOLO("yolov8n-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.

How do I validate a trained YOLOv8-pose model?

Validation of a YOLOv8-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("yolov8n-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 YOLOv8-pose model to other formats, and how?

Yes, you can export a YOLOv8-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("yolov8n-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.

What are the available Ultralytics YOLOv8-pose models and their performance metrics?

Ultralytics YOLOv8 offers various pretrained pose models such as YOLOv8n-pose, YOLOv8s-pose, YOLOv8m-pose, among others. These models differ in size, accuracy (mAP), and speed. For instance, the YOLOv8n-pose model achieves a mAP^pose50-95 of 50.4 and an mAP^pose50 of 80.1. For a complete list and performance details, visit the Models Section.