186 lines
12 KiB
Markdown
186 lines
12 KiB
Markdown
---
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comments: true
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description: 学习如何使用Ultralytics YOLOv8进行姿态估计任务。找到预训练模型，学习如何训练、验证、预测以及导出你自己的模型。
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keywords: Ultralytics, YOLO, YOLOv8, 姿态估计, 关键点检测, 物体检测, 预训练模型, 机器学习, 人工智能
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---
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# 姿态估计
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418616-9811ac0b-a4a7-452a-8aba-484ba32bb4a8.png" alt="姿态估计示例">
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姿态估计是一项任务，其涉及识别图像中特定点的位置，通常被称为关键点。这些关键点可以代表物体的各种部位，如关节、地标或其他显著特征。关键点的位置通常表示为一组2D `[x, y]` 或3D `[x, y, visible]` 坐标。
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姿态估计模型的输出是一组点集，这些点代表图像中物体上的关键点，通常还包括每个点的置信度得分。当你需要在场景中识别物体的特定部位及其相互之间的位置时，姿态估计是一个不错的选择。
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<p align="center">
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<br>
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<iframe width="720" height="405" src="https://www.youtube.com/embed/Y28xXQmju64?si=pCY4ZwejZFu6Z4kZ"
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title="YouTube视频播放器" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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允许全屏>
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</iframe>
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<br>
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<strong>观看：</strong>使用Ultralytics YOLOv8进行姿态估计。
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</p>
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!!! Tip "提示"
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YOLOv8 _姿态_ 模型使用 `-pose` 后缀，例如 `yolov8n-pose.pt`。这些模型在 [COCO关键点](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) 数据集上进行了训练，并且适用于各种姿态估计任务。
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## [模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
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这里展示了YOLOv8预训练的姿态模型。检测、分割和姿态模型在 [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) 数据集上进行预训练，而分类模型则在 [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) 数据集上进行预训练。
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[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) 在首次使用时将自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>姿态<br>50-95 | mAP<sup>姿态<br>50 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>A100 TensorRT<br>(毫秒) | 参数<br><sup>(M) | 浮点数运算<br><sup>(B) |
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|----------------------------------------------------------------------------------------------------|-----------------|---------------------|------------------|-----------------------------|----------------------------------|----------------|-------------------|
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| [YOLOv8n-姿态](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 |
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| [YOLOv8s-姿态](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 |
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| [YOLOv8m-姿态](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 |
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| [YOLOv8l-姿态](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 |
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| [YOLOv8x-姿态](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 |
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| [YOLOv8x-姿态-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 |
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- **mAP<sup>val</sup>** 值适用于[COCO 关键点 val2017](http://cocodataset.org)数据集上的单模型单尺度。
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<br>通过执行 `yolo val pose data=coco-pose.yaml device=0` 来复现。
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- **速度** 是在 [亚马逊EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例上使用COCO val图像的平均值。
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<br>通过执行 `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu` 来复现。
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## 训练
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在COCO128姿态数据集上训练一个YOLOv8姿态模型。
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!!! Example "示例"
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=== "Python"
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```python
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from ultralytics import YOLO
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# 加载模型
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model = YOLO('yolov8n-pose.yaml') # 从YAML构建一个新模型
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model = YOLO('yolov8n-pose.pt') # 加载一个预训练模型（推荐用于训练）
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model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # 从YAML构建并传输权重
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# 训练模型
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results = model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# 从YAML构建一个新模型并从头开始训练
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yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml epochs=100 imgsz=640
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# 从一个预训练的*.pt模型开始训练
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yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
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# 从YAML构建一个新模型，传输预训练权重并开始训练
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yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml pretrained=yolov8n-pose.pt epochs=100 imgsz=640
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```
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### 数据集格式
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YOLO姿态数据集格式可详细找到在[数据集指南](/../datasets/pose/index.md)中。若要将您现有的数据集从其他格式（如COCO等）转换为YOLO格式，请使用Ultralytics的 [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) 工具。
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## 验证
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在COCO128姿态数据集上验证训练好的YOLOv8n姿态模型的准确性。没有参数需要传递，因为`模型`保存了其训练`数据`和参数作为模型属性。
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!!! Example "示例"
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=== "Python"
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```python
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from ultralytics import YOLO
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# 加载模型
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model = YOLO('yolov8n-pose.pt') # 加载官方模型
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model = YOLO('path/to/best.pt') # 加载自定义模型
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# 验证模型
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metrics = model.val() # 无需参数，数据集和设置都记住了
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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 # 包含每个类别map50-95的列表
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```
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=== "CLI"
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```bash
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yolo pose val model=yolov8n-pose.pt # 验证官方模型
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yolo pose val model=path/to/best.pt # 验证自定义模型
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```
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## 预测
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使用训练好的YOLOv8n姿态模型在图片上运行预测。
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!!! Example "示例"
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=== "Python"
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```python
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from ultralytics import YOLO
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# 加载模型
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model = YOLO('yolov8n-pose.pt') # 加载官方模型
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model = YOLO('path/to/best.pt') # 加载自定义模型
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# 用模型进行预测
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results = model('https://ultralytics.com/images/bus.jpg') # 在一张图片上预测
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```
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=== "CLI"
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```bash
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yolo pose predict model=yolov8n-pose.pt source='https://ultralytics.com/images/bus.jpg' # 用官方模型预测
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yolo pose predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # 用自定义模型预测
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```
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在[预测](https://docs.ultralytics.com/modes/predict/)页面中查看完整的`预测`模式细节。
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## 导出
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将YOLOv8n姿态模型导出为ONNX、CoreML等不同格式。
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!!! Example "示例"
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=== "Python"
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```python
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from ultralytics import YOLO
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# 加载模型
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model = YOLO('yolov8n-pose.pt') # 加载官方模型
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model = YOLO('path/to/best.pt') # 加载自定义训练好的模型
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# 导出模型
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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-pose.pt format=onnx # 导出官方模型
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yolo export model=path/to/best.pt format=onnx # 导出自定义训练好的模型
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```
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以下表格中有可用的YOLOv8姿态导出格式。您可以直接在导出的模型上进行预测或验证，例如 `yolo predict model=yolov8n-pose.onnx`。导出完成后，为您的模型显示用法示例。
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| 格式 | `format` 参数 | 模型 | 元数据 | 参数 |
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|--------------------------------------------------------------------|---------------|--------------------------------|-----|-----------------------------------------------------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-pose.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ | `imgsz`, `half` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-pose.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-pose.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-pose.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz` |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz` |
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| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz`, `half` |
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在[导出](https://docs.ultralytics.com/modes/export/) 页面中查看完整的`导出`细节。
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