--- comments: true description: Learn how to use YOLOv8 pose estimation models to identify the position of keypoints on objects in an image, and how to train, validate, predict, and export these models for use with various formats such as ONNX or CoreML. keywords: YOLOv8, Pose Models, Keypoint Detection, COCO dataset, COCO val2017, Amazon EC2 P4d, PyTorch --- 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. !!! 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/datasets/coco-pose.yaml) dataset and are suitable for a variety of pose estimation tasks. ## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8) YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset. [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) 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](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 | | [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 | | [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 | | [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 | | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 | | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 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](http://cocodataset.org) dataset.
Reproduce by `yolo val pose data=coco-pose.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 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 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](../datasets/pose/index.md). To convert your existing dataset from other formats( like COCO etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) 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 "" === "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](https://docs.ultralytics.com/modes/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 # 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 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](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - | | [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize` | | [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-pose.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ | `imgsz`, `half` | | [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-pose.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | | [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-pose.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` | | [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | `imgsz`, `keras` | | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz` | | [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-pose.tflite` | ✅ | `imgsz`, `half`, `int8` | | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz` | | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz` | | [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz` | See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.