Keypoints

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

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

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.

PythonCLI

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)

# 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.

PythonCLI

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

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.

PythonCLI

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

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.

PythonCLI

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')

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/`	✅