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-# YOLOv8 Pose Models
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+[English](README.md) | [简体中文](README.zh-CN.md)
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+[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)，由 [Ultralytics](https://ultralytics.com) 开发，是一种尖端的、最先进（SOTA）的模型，它在之前 YOLO 版本的成功基础上进行了建设，并引入了新的特性和改进，以进一步提高性能和灵活性。YOLOv8 旨在快速、准确且易于使用，使其成为广泛的对象检测、图像分割和图像分类任务的绝佳选择。
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+如需申请企业许可，请在 [Ultralytics 授权](https://ultralytics.com/license) 完成表格。
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+##
文档
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+请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关培训、验证、预测和部署的完整文档。
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+
+安装
+
+在一个 [**Python>=3.7**](https://www.python.org/) 环境中，使用 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)，通过 pip 安装 ultralytics 软件包以及所有[依赖项](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt)。
-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.
-
-**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.
+```bash
+pip install ultralytics
+```
-## [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.
+
+Usage
-[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.
+#### CLI
-| 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 | 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 |
+YOLOv8 可以在命令行界面（CLI）中直接使用，只需输入 `yolo` 命令：
-- **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`
+```bash
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+```
-## Train
+`yolo` 可用于各种任务和模式，并接受其他参数，例如 `imgsz=640`。查看 YOLOv8 [CLI 文档](https://docs.ultralytics.com/usage/cli)以获取示例。
-Train a YOLOv8-pose model on the COCO128-pose dataset.
+#### Python
-### Python
+YOLOv8 也可以在 Python 环境中直接使用，并接受与上述 CLI 示例中相同的[参数](https://docs.ultralytics.com/usage/cfg/)：
```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
+# 加载模型
+model = YOLO("yolov8n.yaml") # 从头开始构建新模型
+model = YOLO("yolov8n.pt") # 加载预训练模型（建议用于训练）
-# 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
+# 使用模型
+model.train(data="coco128.yaml", epochs=3) # 训练模型
+metrics = model.val() # 在验证集上评估模型性能
+results = model("https://ultralytics.com/images/bus.jpg") # 对图像进行预测
+success = model.export(format="onnx") # 将模型导出为 ONNX 格式
```
-## 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
+[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。查看 YOLOv8 [Python 文档](https://docs.ultralytics.com/usage/python)以获取更多示例。
-```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
+##
Models
-# 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
-```
+所有的 YOLOv8 预训练模型都可以在此找到。检测、分割和姿态模型在 [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) 数据集上进行预训练，而分类模型在 [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) 数据集上进行预训练。
-### CLI
+在首次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。
-```bash
-yolo pose val model=yolov8n-pose.pt # val official model
-yolo pose val model=path/to/best.pt # val custom model
-```
+
检测
-## Predict
+查看 [检测文档](https://docs.ultralytics.com/tasks/detect/) 以获取使用这些模型的示例。
-Use a trained YOLOv8n-pose model to run predictions on images.
+| Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) |
+| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
-### Python
+- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+
Reproduce by `yolo val detect data=coco.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 detect data=coco128.yaml batch=1 device=0|cpu`
-```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
+
分割
-# Predict with the model
-results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
-```
+查看 [分割文档](https://docs.ultralytics.com/tasks/segment/) 以获取使用这些模型的示例。
-### CLI
+| Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) |
+| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
+| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
+| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
+| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
+| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
-```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
-```
+- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+
Reproduce by `yolo val segment data=coco.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 segment data=coco128-seg.yaml batch=1 device=0|cpu`
-See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
+
-## Export
+
分类
-Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
+查看 [分类文档](https://docs.ultralytics.com/tasks/classify/) 以获取使用这些模型的示例。
-### Python
+| Model | size
(pixels) | acc
top1 | acc
top5 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) at 640 |
+| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
+| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
+| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
+| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
+| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
+| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
-```python
-from ultralytics import YOLO
+- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
+
Reproduce by `yolo val classify data=path/to/ImageNet device=0`
+- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
+
Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
-# 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")
-```
+
姿态
-### CLI
+查看 [姿态文档](https://docs.ultralytics.com/tasks/) 以获取使用这些模型的示例。
-```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
-```
+| 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 | 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 |
-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.
+- **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`
+
+
+
+##
Integrations
+
+
+
+
+
+
+
+
+
+| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
+| :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
+| 使用 [Roboflow](https://roboflow.com/?ref=ultralytics) 将您的自定义数据集直接标记并导出至 YOLOv8 进行训练 | 使用 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！）自动跟踪、可视化，甚至远程训练 YOLOv8 | 免费且永久，[Comet](https://bit.ly/yolov8-readme-comet) 让您保存 YOLOv8 模型、恢复训练，并以交互式方式查看和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 使 YOLOv8 推理速度提高多达 6 倍 |
+
+##
Ultralytics HUB
+
+体验 [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://ultralytics.com/app_install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！
+
+
+
+
+##
Contribute
+
+我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](CONTRIBUTING.md)以开始使用，并填写我们的[调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏
+
+
+
+
+
+
+##
License
+
+YOLOv8 提供两种不同的许可证：
+
+- **GPL-3.0 许可证**：详细信息请参阅 [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件。
+- **企业许可证**：为商业产品开发提供更大的灵活性，无需遵循 GPL-3.0 的开源要求。典型的用例是将 Ultralytics 软件和 AI 模型嵌入商业产品和应用中。在 [Ultralytics 授权](https://ultralytics.com/license) 处申请企业许可证。
+
+##
Contact
+
+如需报告 YOLOv8 的错误或提出功能需求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) 或 [Ultralytics 社区论坛](https://community.ultralytics.com/)。
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