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<div align="center">
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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.txt](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
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pip install ultralytics
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```
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</details>
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<details open>
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<summary>使用方法</summary>
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YOLOv8 可以直接在命令行界面（CLI）中使用 `yolo` 命令运行：
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```bash
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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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)
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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
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yolo task=detect mode=train model=yolov8n.pt args...
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classify predict yolov8n-cls.yaml args...
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segment val yolov8n-seg.yaml args...
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export yolov8n.pt format=onnx args...
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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/)：
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```python
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from ultralytics import YOLO
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# 加载模型
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model = YOLO("yolov8n.yaml") # 从头开始构建新模型
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model = YOLO("yolov8n.pt") # 加载预训练模型（推荐用于训练）
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# Use the model
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results = model.train(data="coco128.yaml", epochs=3) # 训练模型
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results = model.val() # 在验证集上评估模型性能
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results = model("https://ultralytics.com/images/bus.jpg") # 预测图像
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success = model.export(format="onnx") # 将模型导出为 ONNX 格式
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```
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[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从
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Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
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### 已知问题 / 待办事项
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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
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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)。
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- [x] TensorFlow 导出
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- [x] DDP 恢复训练
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- [ ] [arxiv.org](https://arxiv.org) 论文
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</details>
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## <div align="center">模型</div>
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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。
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第一次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从
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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>
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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) |
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| ------------------------------------------------------------------------------------ | --------------- | -------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- |
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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。
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<br>复现命令 `yolo val detect 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。
|
|
|
<br>复现命令 `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
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|
|
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|
</details>
|
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|
<details><summary>实例分割</summary>
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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) |
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|
| -------------------------------------------------------------------------------------------- | --------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- |
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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 |
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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 |
|
|
|
|
|
|
- **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) 。
|
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|
## <div align="center">联系我们</div>
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请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)
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或 [Ultralytics Community Forum](https://community.ultralytics.com) 以报告 YOLOv8 错误和请求功能。
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<br>
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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="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" 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="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" 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="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" 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="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" 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="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" 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="3%" alt="" /></a>
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</div>
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