`ultralytics 8.0.72` faster Windows trainings and corrupt cache fix (#1912)
Co-authored-by: andreaswimmer <53872150+andreaswimmer@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/1934/head v8.0.72
Glenn Jocher
2 years ago
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GitHub
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<div align="center"> |
# YOLOv8 Pose Models |
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<p> |
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<a href="https://ultralytics.com/yolov8" target="_blank"> |
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<img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a> |
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</p> |
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[English](README.md) | [简体中文](README.zh-CN.md) |
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<br> |
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<div> |
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<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a> |
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a> |
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<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> |
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<br> |
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<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> |
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<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |
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<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> |
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</div> |
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<br> |
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[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) 是由 [Ultralytics](https://ultralytics.com) 开发的一个前沿的 |
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SOTA 模型。它在以前成功的 YOLO 版本基础上，引入了新的功能和改进，进一步提升了其性能和灵活性。YOLOv8 |
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基于快速、准确和易于使用的设计理念，使其成为广泛的目标检测、图像分割和图像分类任务的绝佳选择。 |
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如果要申请企业许可证，请填写 [Ultralytics 许可](https://ultralytics.com/license)。 |
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<div align="center"> |
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<a href="https://github.com/ultralytics" style="text-decoration:none;"> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> |
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<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;"> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> |
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<a href="https://twitter.com/ultralytics" style="text-decoration:none;"> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> |
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<a href="https://youtube.com/ultralytics" style="text-decoration:none;"> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> |
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<a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;"> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> |
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<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;"> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a> |
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</div> |
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</div> |
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## <div align="center">文档</div> |
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有关训练、测试和部署的完整文档见[YOLOv8 Docs](https://docs.ultralytics.com)。请参阅下面的快速入门示例。 |
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<details open> |
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<summary>安装</summary> |
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Pip 安装包含所有 [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) 的 |
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ultralytics 包，环境要求 [**Python>=3.7**](https://www.python.org/)，且 [\*\*PyTorch>=1.7 |
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\*\*](https://pytorch.org/get-started/locally/)。 |
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```bash |
Pose estimation is a task that involves identifying the location of specific points in an image, usually referred |
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pip install ultralytics |
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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</details> |
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png"> |
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<details open> |
The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually |
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<summary>使用方法</summary> |
along with the confidence scores for each point. Pose 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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YOLOv8 可以直接在命令行界面（CLI）中使用 `yolo` 命令运行： |
**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. |
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```bash |
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8) |
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' |
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YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on |
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the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify |
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models are pretrained on |
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the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset. |
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest |
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Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use. |
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| 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) | |
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| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org) |
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dataset. Reproduce by `yolo val pose data=coco-pose.yaml device=0` |
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) |
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instance. Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu` |
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## Train |
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Train a YOLOv8-pose model on the COCO128-pose dataset. |
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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-pose.yaml") # build a new model from YAML |
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model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training) |
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model = YOLO("yolov8n-pose.yaml").load( |
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"yolov8n-pose.pt" |
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) # build from YAML and transfer weights |
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# Train the model |
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model.train(data="coco8-pose.yaml", epochs=100, imgsz=640) |
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``` |
``` |
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`yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com) |
### CLI |
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中可用`yolo`[参数](https://docs.ultralytics.com/usage/cfg/)的完整列表。 |
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```bash |
```bash |
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yolo task=detect mode=train model=yolov8n.pt args... |
# Build a new model from YAML and start training from scratch |
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classify predict yolov8n-cls.yaml args... |
yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml epochs=100 imgsz=640 |
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segment val yolov8n-seg.yaml args... |
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export yolov8n.pt format=onnx args... |
# Start training from a pretrained *.pt model |
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yolo pose train data=coco8-pose.yaml model=yolov8n-pose.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 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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YOLOv8 也可以在 Python 环境中直接使用，并接受与上面 CLI 例子中相同的[参数](https://docs.ultralytics.com/usage/cfg/)： |
## Val |
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Validate trained YOLOv8n-pose model accuracy on the COCO128-pose dataset. No argument need to passed as the `model` |
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retains it's training `data` and arguments as model attributes. |
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### Python |
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```python |
```python |
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from ultralytics import YOLO |
from ultralytics import YOLO |
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# 加载模型 |
# Load a model |
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model = YOLO("yolov8n.yaml") # 从头开始构建新模型 |
model = YOLO("yolov8n-pose.pt") # load an official model |
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model = YOLO("yolov8n.pt") # 加载预训练模型（推荐用于训练） |
model = YOLO("path/to/best.pt") # load a custom model |
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# Use the model |
# Validate the model |
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results = model.train(data="coco128.yaml", epochs=3) # 训练模型 |
metrics = model.val() # no arguments needed, dataset and settings remembered |
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results = model.val() # 在验证集上评估模型性能 |
metrics.box.map # map50-95 |
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results = model("https://ultralytics.com/images/bus.jpg") # 预测图像 |
metrics.box.map50 # map50 |
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success = model.export(format="onnx") # 将模型导出为 ONNX 格式 |
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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[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从 |
### CLI |
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Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。 |
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</details> |
```bash |
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yolo pose val model=yolov8n-pose.pt # val official model |
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## <div align="center">模型</div> |
yolo pose val model=path/to/best.pt # val custom model |
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``` |
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所有 YOLOv8 的预训练模型都可以在这里找到。目标检测和分割模型是在 COCO 数据集上预训练的，而分类模型是在 ImageNet 数据集上预训练的。 |
## Predict |
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第一次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从 |
Use a trained YOLOv8n-pose model to run predictions on images. |
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Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。 |
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<details open><summary>目标检测</summary> |
### Python |
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| 模型 | 尺寸<br><sup>（像素） | mAP<sup>val<br>50-95 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(B) | |
```python |
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from ultralytics import YOLO |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 | |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 | |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 | |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | |
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- **mAP<sup>val</sup>** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。 |
# Load a model |
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<br>复现命令 `yolo val detect data=coco.yaml device=0` |
model = YOLO("yolov8n-pose.pt") # load an official model |
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- **推理速度**使用 COCO |
model = YOLO("path/to/best.pt") # load a custom model |
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验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。 |
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<br>复现命令 `yolo val detect data=coco128.yaml batch=1 device=0|cpu` |
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</details> |
# 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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<details><summary>实例分割</summary> |
### CLI |
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| 模型 | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(B) | |
```bash |
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yolo pose predict model=yolov8n-pose.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model |
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| [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 | |
yolo pose predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model |
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| [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 | |
``` |
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| [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 | |
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| [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 | |
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| [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 | |
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- **mAP<sup>val</sup>** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。 |
See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page. |
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<br>复现命令 `yolo val segment data=coco.yaml device=0` |
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- **推理速度**使用 COCO |
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验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。 |
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<br>复现命令 `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu` |
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</details> |
## Export |
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<details><summary>分类</summary> |
Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc. |
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| 模型 | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(B) at 640 | |
### Python |
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| [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 | |
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| [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 | |
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| [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 | |
|
||||||
|
|
||||||
- **acc** 都在 [ImageNet](https://www.image-net.org/) 数据集上，使用单模型单尺度测试得到。 |
```python |
||||||
<br>复现命令 `yolo val classify data=path/to/ImageNet device=0` |
from ultralytics import YOLO |
||||||
- **推理速度**使用 ImageNet |
|
||||||
验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。 |
|
||||||
<br>复现命令 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` |
|
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|
|
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</details> |
# Load a model |
||||||
|
model = YOLO("yolov8n-pose.pt") # load an official model |
||||||
|
model = YOLO("path/to/best.pt") # load a custom trained |
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|
|
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<details><summary>Pose</summary> |
# Export the model |
||||||
|
model.export(format="onnx") |
||||||
|
``` |
||||||
|
|
||||||
See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with these models. |
### CLI |
||||||
|
|
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) | |
```bash |
||||||
| ---------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | |
yolo export model=yolov8n-pose.pt format=onnx # export official model |
||||||
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | 131.8 | 1.18 | 3.3 | 9.2 | |
yolo export model=path/to/best.pt format=onnx # export custom trained model |
||||||
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | 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 | 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 | 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 | 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 | 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) |
Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models, |
||||||
dataset. |
i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your model after export completes. |
||||||
<br>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/) |
| Format | `format` Argument | Model | Metadata | |
||||||
instance. |
| ------------------------------------------------------------------ | ----------------- | ------------------------------ | -------- | |
||||||
<br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu` |
| [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | |
||||||
|
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | |
||||||
</details> |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-pose.onnx` | ✅ | |
||||||
|
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ | |
||||||
## <div align="center">模块集成</div> |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-pose.engine` | ✅ | |
||||||
|
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-pose.mlmodel` | ✅ | |
||||||
<br> |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | |
||||||
<a href="https://bit.ly/ultralytics_hub" target="_blank"> |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-pose.pb` | ❌ | |
||||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a> |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-pose.tflite` | ✅ | |
||||||
<br> |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | |
||||||
<br> |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | |
||||||
|
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | |
||||||
<div align="center"> |
|
||||||
<a href="https://roboflow.com/?ref=ultralytics"> |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. |
||||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> |
|
||||||
<a href="https://cutt.ly/yolov5-readme-clearml"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> |
|
||||||
<a href="https://bit.ly/yolov8-readme-comet"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> |
|
||||||
<a href="https://bit.ly/yolov5-neuralmagic"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a> |
|
||||||
</div> |
|
||||||
|
|
||||||
| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | |
|
||||||
| :--------------------------------------------------------------------------------: | :-------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | |
|
||||||
| 将您的自定义数据集进行标注并直接导出到 YOLOv8 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv8 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov8-readme-comet)可让您保存 YOLOv8 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv8 推理的速度最高可提高6倍 | |
|
||||||
|
|
||||||
## <div align="center">Ultralytics HUB</div> |
|
||||||
|
|
||||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们⭐ **新**的无代码解决方案，用于可视化数据集，训练 YOLOv8🚀 |
|
||||||
模型，并以无缝体验方式部署到现实世界。现在开始**免费**! |
|
||||||
还可以通过下载 [Ultralytics App](https://ultralytics.com/app_install) 在你的 iOS 或 Android 设备上运行 YOLOv8 模型! |
|
||||||
|
|
||||||
<a href="https://bit.ly/ultralytics_hub" target="_blank"> |
|
||||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a> |
|
||||||
|
|
||||||
## <div align="center">贡献</div> |
|
||||||
|
|
||||||
我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv8 做出贡献。请看我们的 [贡献指南](CONTRIBUTING.md) |
|
||||||
，并填写 [调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) |
|
||||||
向我们发送您的体验反馈。感谢我们所有的贡献者！ |
|
||||||
|
|
||||||
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 --> |
|
||||||
|
|
||||||
<a href="https://github.com/ultralytics/yolov5/graphs/contributors"> |
|
||||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a> |
|
||||||
|
|
||||||
## <div align="center">License</div> |
|
||||||
|
|
||||||
YOLOv8 在两种不同的 License 下可用： |
|
||||||
|
|
||||||
- **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件的详细信息。 |
|
||||||
- **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI |
|
||||||
模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。 |
|
||||||
|
|
||||||
## <div align="center">联系我们</div> |
|
||||||
|
|
||||||
请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) |
|
||||||
或 [Ultralytics Community Forum](https://community.ultralytics.com) 以报告 YOLOv8 错误和请求功能。 |
|
||||||
|
|
||||||
<br> |
|
||||||
<div align="center"> |
|
||||||
<a href="https://github.com/ultralytics" style="text-decoration:none;"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> |
|
||||||
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> |
|
||||||
<a href="https://twitter.com/ultralytics" style="text-decoration:none;"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> |
|
||||||
<a href="https://youtube.com/ultralytics" style="text-decoration:none;"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> |
|
||||||
<a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="" /></a> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> |
|
||||||
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;"> |
|
||||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a> |
|
||||||
</div> |
|
||||||
|
|||||||
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Reference in new issue