# YOLOv8 Pose Models
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.
< img width = "1024" src = "https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png" >
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.
**Pro 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< br > < sup > (pixels) | mAP< sup > pose< br > 50-95 | mAP< sup > pose< br > 50 | Speed< br > < sup > CPU ONNX< br > (ms) | Speed< br > < sup > A100 TensorRT< br > (ms) | params< br > < sup > (M) | FLOPs< br > < sup > (B) |
| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-pose ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt ) | 640 | 49.7 | 79.7 | 131.8 | 1.18 | 3.3 | 9.2 |
| [YOLOv8s-pose ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt ) | 640 | 59.2 | 85.8 | 233.2 | 1.42 | 11.6 | 30.2 |
| [YOLOv8m-pose ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt ) | 640 | 63.6 | 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 | 67.0 | 89.9 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLOv8x-pose ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt ) | 640 | 68.9 | 90.4 | 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 | 71.5 | 91.3 | 4088.7 | 10.04 | 99.1 | 1066.4 |
- **mAP< sup > val</ sup > ** 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.
### 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
```
## 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.
### 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.
### 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.
### 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 |
| ------------------------------------------------------------------ | ----------------- | ------------------------------ | -------- |
| [PyTorch ](https://pytorch.org/ ) | - | `yolov8n-pose.pt` | ✅ |
| [TorchScript ](https://pytorch.org/docs/stable/jit.html ) | `torchscript` | `yolov8n-pose.torchscript` | ✅ |
| [ONNX ](https://onnx.ai/ ) | `onnx` | `yolov8n-pose.onnx` | ✅ |
| [OpenVINO ](https://docs.openvino.ai/latest/index.html ) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ |
| [TensorRT ](https://developer.nvidia.com/tensorrt ) | `engine` | `yolov8n-pose.engine` | ✅ |
| [CoreML ](https://github.com/apple/coremltools ) | `coreml` | `yolov8n-pose.mlmodel` | ✅ |
| [TF SavedModel ](https://www.tensorflow.org/guide/saved_model ) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ |
| [TF GraphDef ](https://www.tensorflow.org/api_docs/python/tf/Graph ) | `pb` | `yolov8n-pose.pb` | ❌ |
| [TF Lite ](https://www.tensorflow.org/lite ) | `tflite` | `yolov8n-pose.tflite` | ✅ |
| [TF Edge TPU ](https://coral.ai/docs/edgetpu/models-intro/ ) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ |
| [TF.js ](https://www.tensorflow.org/js ) | `tfjs` | `yolov8n-pose_web_model/` | ✅ |
| [PaddlePaddle ](https://github.com/PaddlePaddle ) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ |
See full `export` details in the [Export ](https://docs.ultralytics.com/modes/export/ ) page.
