--- 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 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 "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](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/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/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset. [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/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/v8.2.0/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/v8.2.0/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/v8.2.0/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/v8.2.0/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/v8.2.0/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/v8.2.0/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](https://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 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](../datasets/pose/index.md). To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use [JSON2YOLO](https://github.com/ultralytics/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](../modes/predict.md) 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. | Format | `format` Argument | Model | Metadata | Arguments | | ------------------------------------------------- | ----------------- | ------------------------------ | -------- | -------------------------------------------------------------------- | | [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-pose.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-pose.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-pose.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-pose.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | See full `export` details in the [Export](../modes/export.md) 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](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) and can be used for various pose estimation tasks. For more information, visit the [Pose Estimation Page](#pose-estimation). ### 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. ```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) # Train the model results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640) ``` For comprehensive details on training, refer to the [Train Section](#train). ### 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: ```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 ``` For more information, visit the [Val Section](#val). ### 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). ```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") ``` Refer to the [Export Section](#export) 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 mAPpose50-95 of 50.4 and an mAPpose50 of 80.1. For a complete list and performance details, visit the [Models Section](#models).