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< br >
[Ultralytics ](https://ultralytics.com ) [YOLOv8 ](https://github.com/ultralytics/ultralytics ) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。
我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 < a href = "https://docs.ultralytics.com/" > 文档< / a > 了解详细信息，在 < a href = "https://github.com/ultralytics/ultralytics/issues/new/choose" > GitHub< / a > 上提交问题以获得支持，并加入我们的 < a href = "https://ultralytics.com/discord" > Discord< / a > 社区进行问题和讨论！
如需申请企业许可，请在 [Ultralytics Licensing ](https://ultralytics.com/license ) 处填写表格
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< / div >
## <div align="center">文档</div>
请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档 ](https://docs.ultralytics.com ) 上有关训练、验证、预测和部署的完整文档。
< details open >
< summary > 安装< / summary >
使用Pip在一个[**Python>=3.8**](https://www.python.org/)环境中安装`ultralytics`包，此环境还需包含[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。这也会安装所有必要的[依赖项](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt)。
[](https://badge.fury.io/py/ultralytics) [](https://pepy.tech/project/ultralytics)
```bash
pip install ultralytics
```
如需使用包括[Conda](https://anaconda.org/conda-forge/ultralytics)、[Docker](https://hub.docker.com/r/ultralytics/ultralytics)和Git在内的其他安装方法，请参考[快速入门指南](https://docs.ultralytics.com/quickstart)。
< / details >
< details open >
< summary > Usage< / summary >
#### CLI
YOLOv8 可以在命令行界面（CLI）中直接使用，只需输入 `yolo` 命令：
```bash
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
```
`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。
#### 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
# 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）
# 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
```
查看 YOLOv8 [Python 文档 ](https://docs.ultralytics.com/usage/python )以获取更多示例。
< / details >
## <div align="center">模型</div>
在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测 ](https://docs.ultralytics.com/tasks/detect )，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类 ](https://docs.ultralytics.com/tasks/classify )模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
< img width = "1024" src = "https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt = "Ultralytics YOLO supported tasks" >
所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时会自动从最新的Ultralytics [发布版本 ](https://github.com/ultralytics/assets/releases )下载。
< details open > < summary > 检测 (COCO)< / summary >
查看[检测文档](https://docs.ultralytics.com/tasks/detect/)以获取这些在[COCO](https://docs.ultralytics.com/datasets/detect/coco/)上训练的模型的使用示例，其中包括80个预训练类别。
| 模型 | 尺寸< br > < sup > (像素) | mAP< sup > val< br > 50-95 | 速度< br > < sup > CPU ONNX< br > (ms) | 速度< br > < sup > A100 TensorRT< br > (ms) | 参数< 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 > ** 值是基于单模型单尺度在 [COCO val2017 ](http://cocodataset.org ) 数据集上的结果。
< br > 通过 `yolo val detect data=coco.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d ](https://aws.amazon.com/ec2/instance-types/p4/ ) 实例对 COCO val 图像进行平均计算的。
< br > 通过 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现
< / details >
< details > < summary > 检测（Open Image V7）< / summary >
查看[检测文档](https://docs.ultralytics.com/tasks/detect/)以获取这些在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)上训练的模型的使用示例，其中包括600个预训练类别。
| 模型 | 尺寸< br > < sup > (像素) | mAP< sup > 验证< br > 50-95 | 速度< br > < sup > CPU ONNX< br > (毫秒) | 速度< br > < sup > A100 TensorRT< br > (毫秒) | 参数< br > < sup > (M) | 浮点运算< br > < sup > (B) |
| ----------------------------------------------------------------------------------------- | --------------- | ------------------- | --------------------------- | -------------------------------- | -------------- | ---------------- |
| [YOLOv8n ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-oiv7.pt ) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
| [YOLOv8s ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-oiv7.pt ) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
| [YOLOv8m ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-oiv7.pt ) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
| [YOLOv8l ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-oiv7.pt ) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| [YOLOv8x ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-oiv7.pt ) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
- **mAP< sup > 验证</ sup > ** 值适用于在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)数据集上的单模型单尺度。
< br > 通过 `yolo val detect data=open-images-v7.yaml device=0` 以复现。
- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对Open Image V7验证图像进行平均测算。
< br > 通过 `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu` 以复现。
< / details >
< details > < summary > 分割 (COCO)< / summary >
查看[分割文档](https://docs.ultralytics.com/tasks/segment/)以获取这些在[COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/)上训练的模型的使用示例，其中包括80个预训练类别。
| 模型 | 尺寸< br > < sup > (像素) | mAP< sup > box< br > 50-95 | mAP< sup > mask< br > 50-95 | 速度< br > < sup > CPU ONNX< br > (ms) | 速度< br > < sup > A100 TensorRT< br > (ms) | 参数< 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 > ** 值是基于单模型单尺度在 [COCO val2017 ](http://cocodataset.org ) 数据集上的结果。
< br > 通过 `yolo val segment data=coco-seg.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d ](https://aws.amazon.com/ec2/instance-types/p4/ ) 实例对 COCO val 图像进行平均计算的。
< br > 通过 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` 复现
< / details >
< details > < summary > 姿态 (COCO)< / summary >
查看[姿态文档](https://docs.ultralytics.com/tasks/pose/)以获取这些在[COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/)上训练的模型的使用示例，其中包括1个预训练类别，即人。
| 模型 | 尺寸< br > < sup > (像素) | mAP< sup > pose< br > 50-95 | mAP< sup > pose< br > 50 | 速度< br > < sup > CPU ONNX< br > (ms) | 速度< br > < sup > A100 TensorRT< br > (ms) | 参数< br > < sup > (M) | FLOPs< br > < sup > (B) |
| ---------------------------------------------------------------------------------------------------- | --------------- | --------------------- | ------------------ | --------------------------- | -------------------------------- | -------------- | ----------------- |
| [YOLOv8n-pose ](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 |
| [YOLOv8s-pose ](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 |
| [YOLOv8m-pose ](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 |
| [YOLOv8l-pose ](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 |
| [YOLOv8x-pose ](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 |
| [YOLOv8x-pose-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 |
- **mAP< sup > val</ sup > ** 值是基于单模型单尺度在 [COCO Keypoints val2017 ](http://cocodataset.org ) 数据集上的结果。
< br > 通过 `yolo val pose data=coco-pose.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d ](https://aws.amazon.com/ec2/instance-types/p4/ ) 实例对 COCO val 图像进行平均计算的。
< br > 通过 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现
< / details >
< details > < summary > 分类 (ImageNet)< / summary >
查看[分类文档](https://docs.ultralytics.com/tasks/classify/)以获取这些在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/)上训练的模型的使用示例，其中包括1000个预训练类别。
| 模型 | 尺寸< br > < sup > (像素) | acc< br > < sup > top1 | acc< br > < sup > top5 | 速度< br > < sup > CPU ONNX< br > (ms) | 速度< br > < sup > A100 TensorRT< br > (ms) | 参数< 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** 值是模型在 [ImageNet ](https://www.image-net.org/ ) 数据集验证集上的准确率。
< br > 通过 `yolo val classify data=path/to/ImageNet device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d ](https://aws.amazon.com/ec2/instance-types/p4/ ) 实例对 ImageNet val 图像进行平均计算的。
< br > 通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现
< / details >
## <div align="center">集成</div>
我们与领先的AI平台的关键整合扩展了Ultralytics产品的功能，增强了数据集标签化、训练、可视化和模型管理等任务。探索Ultralytics如何与[Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic以及[OpenVINO](https://docs.ultralytics.com/integrations/openvino)合作，优化您的AI工作流程。
< br >
< a href = "https://bit.ly/ultralytics_hub" target = "_blank" >
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< br >
< br >
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< / div >
| 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 |
## <div align="center">Ultralytics HUB</div>
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！
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## <div align="center">贡献</div>
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](https://docs.ultralytics.com/help/contributing)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！🙏
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## <div align="center">许可证</div>
Ultralytics 提供两种许可证选项以适应各种使用场景：
- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件以了解更多细节。
- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing ](https://ultralytics.com/license )与我们联系。
## <div align="center">联系方式</div>
对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues ](https://github.com/ultralytics/ultralytics/issues )，并加入我们的 [Discord ](https://ultralytics.com/discord ) 社区进行问题和讨论！
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