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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。 如需申请企业许可，请在 [Ultralytics 授权](https://ultralytics.com/license) 完成表格。
##
文档
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。
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)。 ```bash pip install ultralytics ```
Usage #### 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 ``` [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。
##
Models
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。 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。
检测 查看 [检测文档](https://docs.ultralytics.com/tasks/detect/) 以获取使用这些模型的示例。 | 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 | - **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`
分割 查看 [分割文档](https://docs.ultralytics.com/tasks/segment/) 以获取使用这些模型的示例。 | 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 | - **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`
分类 查看 [分类文档](https://docs.ultralytics.com/tasks/classify/) 以获取使用这些模型的示例。 | 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 | - **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`
姿态 查看 [姿态文档](https://docs.ultralytics.com/tasks/) 以获取使用这些模型的示例。 | 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 | - **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/)。