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Pose Estimation

Pose estimation examples

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

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

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.

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
  • mAPval 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

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)

# 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 it's training data and arguments as model attributes.

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
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=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

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

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

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')

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 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.

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

See full export details in the Export page.



Created 2023-11-12, Updated 2024-04-17
Authors: glenn-jocher (12), RizwanMunawar (1), AyushExel (1), Laughing-q (1)

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