ultralytics 8.0.211 README language links (ultralytics#6370)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

Author: 3 people

README.md

@@ -4,7 +4,7 @@
       <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
   </p>
 
-[English](README.md) | [简体中文](README.zh-CN.md)
+[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/)
 <br>
 
 <div>
@@ -16,8 +16,8 @@
     <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>
-  </div>
-  <br>
+</div>
+<br>
 
 [Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
 
@@ -44,7 +44,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
 </div>
 </div>
 
-## <div align="center">Documentation</div>
+## Documentation
 
 See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
 
@@ -94,11 +94,11 @@ results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
 path = model.export(format="onnx")  # export the model to ONNX format
 ```
 
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
+See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
 
 </details>
 
-## <div align="center">Models</div>
+## Models
 
 YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
 
@@ -203,7 +203,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
 
 </details>
 
-## <div align="center">Integrations</div>
+## Integrations
 
 Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow.
 
@@ -231,14 +231,14 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
 | :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
 | Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
 
-## <div align="center">Ultralytics HUB</div>
+## Ultralytics HUB
 
 Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
 
-## <div align="center">Contribute</div>
+## Contribute
 
 We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
 
@@ -247,14 +247,14 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou
 <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>
+## License
 
 Ultralytics offers two licensing options to accommodate diverse use cases:
 
 - **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
 - **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license).
 
-## <div align="center">Contact</div>
+## Contact
 
 For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://ultralytics.com/discord) community for questions and discussions!
 

README.zh-CN.md

@@ -4,7 +4,7 @@
       <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
   </p>
 
-[English](README.md) | [简体中文](README.zh-CN.md)
+[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/)
 <br>
 
 <div>
@@ -16,8 +16,8 @@
     <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>
-  </div>
-  <br>
+</div>
+<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。
 
@@ -44,7 +44,7 @@
 </div>
 </div>
 
-## <div align="center">文档</div>
+## 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) 上有关训练、验证、预测和部署的完整文档。
 
@@ -94,11 +94,11 @@ results = model("https://ultralytics.com/images/bus.jpg")  # 对图像进行预
 success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 ```
 
-[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/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。
+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>
+## 模型
 
 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。
 
@@ -202,7 +202,7 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 </details>
 
-## <div align="center">集成</div>
+## 集成
 
 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。
 
@@ -230,14 +230,14 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 | :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
 | 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>
+## 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！
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
 
-## <div align="center">贡献</div>
+## 贡献
 
 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！🙏
 
@@ -246,14 +246,14 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 <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">许可证</div>
+## 许可证
 
 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>
+## 联系方式
 
 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！
 

docs/en/guides/azureml-quickstart.md

@@ -77,7 +77,7 @@ Train a detection model for 10 epochs with an initial learning_rate of 0.01:
 yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
 ```
 
-You can find more [instructions to use the Ultralytics CLI here](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli).
+You can find more [instructions to use the Ultralytics CLI here](../quickstart.md#use-ultralytics-with-cli).
 
 ## Quickstart from a Notebook
 
@@ -114,15 +114,15 @@ pip install onnx>=1.12.0
 
 Note that we need to use the `source activate yolov8env` for all the %%bash cells, to make sure that the %%bash cell uses environment we want.
 
-Run some predictions using the [Ultralytics CLI](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli):
+Run some predictions using the [Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli):
 
 ```bash
 %%bash
 source activate yolov8env
 yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
-Or with the [Ultralytics Python interface](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-python), for example to train the model:
+Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with-python), for example to train the model:
 
 ```python
 from ultralytics import YOLO

docs/en/guides/conda-quickstart.md

@@ -129,4 +129,4 @@ And that's it! Your Conda installation will now use `libmamba` as the solver, wh
 
 ---
 
-Congratulations! You have successfully set up a Conda environment, installed the Ultralytics package, and are now ready to explore its rich functionalities. Feel free to dive deeper into the [Ultralytics documentation](https://docs.ultralytics.com/) for more advanced tutorials and examples.
+Congratulations! You have successfully set up a Conda environment, installed the Ultralytics package, and are now ready to explore its rich functionalities. Feel free to dive deeper into the [Ultralytics documentation](../index.md) for more advanced tutorials and examples.

docs/en/guides/docker-quickstart.md

@@ -116,4 +116,4 @@ Replace `/path/on/host` with the directory path on your local machine and `/path
 
 ---
 
-Congratulations! You're now set up to use Ultralytics with Docker and ready to take advantage of its powerful capabilities. For alternate installation methods, feel free to explore the [Ultralytics quickstart documentation](https://docs.ultralytics.com/quickstart/).
+Congratulations! You're now set up to use Ultralytics with Docker and ready to take advantage of its powerful capabilities. For alternate installation methods, feel free to explore the [Ultralytics quickstart documentation](../quickstart.md).

docs/en/guides/hyperparameter-tuning.md

@@ -23,7 +23,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are
   <img width="640" src="https://user-images.githubusercontent.com/26833433/263858934-4f109a2f-82d9-4d08-8bd6-6fd1ff520bcd.png" alt="Hyperparameter Tuning Visual">
 </p>
 
-For a full list of augmentation hyperparameters used in YOLOv8 please refer to [https://docs.ultralytics.com/usage/cfg/#augmentation](https://docs.ultralytics.com/usage/cfg/#augmentation).
+For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation).
 
 ### Genetic Evolution and Mutation
 
@@ -200,7 +200,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
 ### Further Reading
 
 1. [Hyperparameter Optimization in Wikipedia](https://en.wikipedia.org/wiki/Hyperparameter_optimization)
-2. [YOLOv5 Hyperparameter Evolution Guide](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
-3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](https://docs.ultralytics.com/integrations/ray-tune/)
+2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md)
+3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md)
 
 For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord).

docs/en/guides/index.md

@@ -31,6 +31,6 @@ Here's a compilation of in-depth guides to help you master different aspects of
 
 We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our guides, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.
 
-To get started, please read our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!
+To get started, please read our [Contributing Guide](../help/contributing.md) for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!
 
 Let's work together to make the Ultralytics YOLO ecosystem more robust and versatile 🙏!