< div align = "center" >
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< a href = "https://ultralytics.com/yolov8" target = "_blank" >
< img width = "850" src = "https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" > < / a >
< / p >
[English ](README.md ) | [简体中文 ](README.zh-CN.md )
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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 >
< a href = "https://zenodo.org/badge/latestdoi/264818686" > < img src = "https://zenodo.org/badge/264818686.svg" alt = "YOLOv8 Citation" > < / a >
< 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 >
< br >
< a href = "https://console.paperspace.com/github/ultralytics/ultralytics" > < img src = "https://assets.paperspace.io/img/gradient-badge.svg" alt = "Run on Gradient" / > < / a >
< 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 >
< 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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< br >
[Ultralytics YOLOv8 ](https://github.com/ultralytics/ultralytics ) 是由 [Ultralytics ](https://ultralytics.com ) 开发的一个前沿的
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
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。
如果要申请企业许可证，请填写 [Ultralytics 许可 ](https://ultralytics.com/license )。
< 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 = "2%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "2%" 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 = "2%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "2%" 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 = "2%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "2%" alt = "" / >
< a href = "https://www.producthunt.com/@glenn_jocher" style = "text-decoration:none;" >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width = "2%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "2%" 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 = "2%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "2%" alt = "" / >
< a href = "https://www.facebook.com/ultralytics" style = "text-decoration:none;" >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width = "2%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "2%" 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 = "2%" alt = "" / > < / a >
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## <div align="center">文档</div>
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。
< details open >
< summary > 安装< / summary >
Pip 安装包含所有 [requirements.txt ](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt ) 的
ultralytics 包，环境要求 [**Python>=3.7** ](https://www.python.org/ )，且 [\*\*PyTorch>=1.7
\*\*](https://pytorch.org/get-started/locally/)。
```bash
pip install ultralytics
```
< / details >
< details open >
< summary > 使用方法< / summary >
YOLOv8 可以直接在命令行界面（CLI）中使用 `yolo` 命令运行：
```bash
yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
```
`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 )
中可用`yolo`[参数](https://docs.ultralytics.com/usage/cfg/)的完整列表。
```bash
yolo task=detect mode=train model=yolov8n.pt args...
classify predict yolov8n-cls.yaml args...
segment val yolov8n-seg.yaml args...
export yolov8n.pt format=onnx args...
```
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/)：
```python
from ultralytics import YOLO
# 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（推荐用于训练）
# Use the model
results = model.train(data="coco128.yaml", epochs=3) # 训练模型
results = model.val() # 在验证集上评估模型性能
results = model("https://ultralytics.com/images/bus.jpg") # 预测图像
success = model.export(format="onnx") # 将模型导出为 ONNX 格式
```
[模型 ](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models ) 会从
Ultralytics [发布页 ](https://github.com/ultralytics/ultralytics/releases ) 自动下载。
### 已知问题 / 待办事项
我们仍在努力完善 YOLOv8 的几个部分！我们的目标是尽快完成这些工作，使 YOLOv8 的功能设置达到YOLOv5
的水平，包括对所有相同格式的导出和推理。我们还在写一篇 YOLOv8 的论文，一旦完成，我们将提交给 [arxiv.org ](https://arxiv.org )。
- [x] TensorFlow 导出
- [x] DDP 恢复训练
- [ ] [arxiv.org ](https://arxiv.org ) 论文
< / details >
## <div align="center">模型</div>
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。
第一次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从
Ultralytics [发布页 ](https://github.com/ultralytics/ultralytics/releases ) 自动下载。
< details open > < summary > 目标检测< / summary >
| 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) |
| ------------------------------------------------------------------------------------ | --------------- | -------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- |
| [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 |
- **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。
< br > 复现命令 `yolo val detect data=coco.yaml device=0`
- **推理速度**使用 COCO
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 detect data=coco128.yaml batch=1 device=0/cpu`
< / details >
< details > < summary > 实例分割< / summary >
| 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) |
| -------------------------------------------------------------------------------------------- | --------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- |
| [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 |
- **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。
< br > 复现命令 `yolo val segment data=coco.yaml device=0`
- **推理速度**使用 COCO
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 segment data=coco128-seg.yaml batch=1 device=0/cpu`
< / details >
< details > < summary > 分类< / summary >
| 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 |
| -------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | --------------- | ------------------------ |
| [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 |
- **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。
< br > 复现命令 `yolo val classify data=path/to/ImageNet device=0`
- **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`
< / details >
## <div align="center">模块集成</div>
< br >
< a href = "https://bit.ly/ultralytics_hub" target = "_blank" >
< img width = "100%" src = "https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" > < / a >
< br >
< br >
< div align = "center" >
< a href = "https://roboflow.com/?ref=ultralytics" >
< 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/yolov5-readme-comet2" >
< 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/yolov5-readme-comet2)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://www.producthunt.com/@glenn_jocher" style = "text-decoration:none;" >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.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.facebook.com/ultralytics" style = "text-decoration:none;" >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.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 >
