YOLOv8-16bit/docs/tasks/keypoints.md

Key Point 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 keypoint detector is a set of points that represent the keypoints on the object in the image, usually along with the confidence scores for each point. Keypoint 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.

!!! tip "Tip"

YOLOv8 _keypoints_ models use the `-kpts` suffix, i.e. `yolov8n-kpts.pt`. These models are trained on the COCO dataset and are suitable for a variety of keypoint estimation tasks.

Models{ .md-button .md-button--primary}

Train TODO

Train an OpenPose model on a custom dataset of keypoints using the OpenPose framework. For more information on how to train an OpenPose model on a custom dataset, see the OpenPose Training page.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n.yaml')  # build a new model from YAML
    model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
    model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
    
    # Train the model
    model.train(data='coco128.yaml', epochs=100, imgsz=640)
    ```
=== "CLI"

    ```bash
    # Build a new model from YAML and start training from scratch
    yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640

    # Start training from a pretrained *.pt model
    yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640

    # Build a new model from YAML, transfer pretrained weights to it and start training
    yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
    ```

Val TODO

Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the model retains it's training data and arguments as model attributes.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n.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 detect val model=yolov8n.pt  # val official model
    yolo detect val model=path/to/best.pt  # val custom model
    ```

Predict TODO

Use a trained YOLOv8n model to run predictions on images.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n.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 detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'  # predict with official model
    yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'  # predict with custom model
    ```

Read more details of predict in our Predict page.

Export TODO

Export a YOLOv8n model to a different format like ONNX, CoreML, etc.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n.pt')  # load an official model
    model = YOLO('path/to/best.pt')  # load a custom trained
    
    # Export the model
    model.export(format='onnx')
    ```
=== "CLI"

    ```bash
    yolo export model=yolov8n.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 predict or validate directly on exported models, i.e. yolo predict model=yolov8n-pose.onnx.

Format	format Argument	Model	Metadata
PyTorch	-	yolov8n.pt	✅
TorchScript	torchscript	yolov8n.torchscript	✅
ONNX	onnx	yolov8n.onnx	✅
OpenVINO	openvino	yolov8n_openvino_model/	✅
TensorRT	engine	yolov8n.engine	✅
CoreML	coreml	yolov8n.mlmodel	✅
TF SavedModel	saved_model	yolov8n_saved_model/	✅
TF GraphDef	pb	yolov8n.pb	❌
TF Lite	tflite	yolov8n.tflite	✅
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅
TF.js	tfjs	yolov8n_web_model/	✅
PaddlePaddle	paddle	yolov8n_paddle_model/	✅