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150 lines
6.6 KiB
150 lines
6.6 KiB
Key Point Estimation is a task that involves identifying the location of specific points in an image, usually referred
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to as keypoints. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive
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features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]`
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coordinates.
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
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The output of a keypoint detector is a set of points that represent the keypoints on the object in the image, usually
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along with the confidence scores for each point. Keypoint estimation is a good choice when you need to identify specific
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parts of an object in a scene, and their location in relation to each other.
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!!! tip "Tip"
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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.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
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## Train TODO
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Train an OpenPose model on a custom dataset of keypoints using the OpenPose framework. For more information on how to
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train an OpenPose model on a custom dataset, see the OpenPose Training page.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.yaml') # build a new model from YAML
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
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# Train the model
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Build a new model from YAML and start training from scratch
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yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
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# Start training from a pretrained *.pt model
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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# Build a new model from YAML, transfer pretrained weights to it and start training
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yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
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```
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## Val TODO
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Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
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training `data` and arguments as model attributes.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics.box.map # map50-95
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metrics.box.map50 # map50
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metrics.box.map75 # map75
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metrics.box.maps # a list contains map50-95 of each category
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```
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=== "CLI"
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```bash
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yolo detect val model=yolov8n.pt # val official model
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yolo detect val model=path/to/best.pt # val custom model
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```
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## Predict TODO
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Use a trained YOLOv8n model to run predictions on images.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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=== "CLI"
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```bash
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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## Export TODO
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format='onnx')
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```
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=== "CLI"
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```bash
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yolo export model=yolov8n.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models,
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i.e. `yolo predict model=yolov8n-pose.onnx`.
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| Format | `format` Argument | Model | Metadata |
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|--------------------------------------------------------------------|-------------------|---------------------------|----------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ |
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