ultralytics 8.0.211 README language links (#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>

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
 
@@ -114,7 +114,7 @@ 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
@@ -122,7 +122,7 @@ 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 🙏!

docs/en/guides/kfold-cross-validation.md

@@ -20,7 +20,7 @@ Without further ado, let's dive in!
 
 ## Setup
 
-- Your annotations should be in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
+- Your annotations should be in the [YOLO detection format](../datasets/detect/index.md).
 
 - This guide assumes that annotation files are locally available.
 
@@ -52,7 +52,7 @@ Without further ado, let's dive in!
     - The Ultralytics library: `pip install -U ultralytics`. Alternatively, you can clone the official [repo](https://github.com/ultralytics/ultralytics).
     - Scikit-learn, pandas, and PyYAML: `pip install -U scikit-learn pandas pyyaml`.
 
-2. Verify that your annotations are in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
+2. Verify that your annotations are in the [YOLO detection format](../datasets/detect/index.md).
 
     - For this tutorial, all annotation files are found in the `Fruit-Detection/labels` directory.
 

docs/en/guides/model-deployment-options.md

@@ -14,13 +14,13 @@ This guide walks you through YOLOv8’s deployment options and the essential fac
 
 ## How to Select the Right Deployment Option for Your YOLOv8 Model
 
-When it's time to deploy your YOLOv8 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLOv8 Modes documentation](https://docs.ultralytics.com/modes/export/#usage-examples), the model.export() function allows for converting your trained model into a variety of formats tailored to diverse environments and performance requirements.
+When it's time to deploy your YOLOv8 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLOv8 Modes documentation](../modes/export.md#usage-examples), the model.export() function allows for converting your trained model into a variety of formats tailored to diverse environments and performance requirements.
 
 The ideal format depends on your model's intended operational context, balancing speed, hardware constraints, and ease of integration. In the following section, we'll take a closer look at each export option, understanding when to choose each one.
 
 ### YOLOv8’s Deployment Options
 
-Let’s walk through the different YOLOv8 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](https://docs.ultralytics.com/modes/export/).
+Let’s walk through the different YOLOv8 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
 
 #### PyTorch
 
@@ -94,7 +94,7 @@ OpenVINO is an Intel toolkit designed to facilitate the deployment of deep learn
 
 - **Hardware Acceleration**: Tailored for acceleration on Intel hardware, leveraging dedicated instruction sets and hardware features.
 
-For more details on deployment using OpenVINO, refer to the Ultralytics Integration documentation: [Intel OpenVINO Export](https://docs.ultralytics.com/integrations/openvino/).
+For more details on deployment using OpenVINO, refer to the Ultralytics Integration documentation: [Intel OpenVINO Export](../integrations/openvino.md).
 
 #### TensorRT
 
@@ -260,7 +260,7 @@ ncnn is a high-performance neural network inference framework optimized for the
 
 ## Comparative Analysis of YOLOv8 Deployment Options
 
-The following table provides a snapshot of the various deployment options available for YOLOv8 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](https://docs.ultralytics.com/modes/export/#export-formats).
+The following table provides a snapshot of the various deployment options available for YOLOv8 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](../modes/export.md#export-formats).
 
 | Deployment Option | Performance Benchmarks                          | Compatibility and Integration                  | Community Support and Ecosystem               | Case Studies                               | Maintenance and Updates                     | Security Considerations                           | Hardware Acceleration              |
 |-------------------|-------------------------------------------------|------------------------------------------------|-----------------------------------------------|--------------------------------------------|---------------------------------------------|---------------------------------------------------|------------------------------------|
@@ -292,7 +292,7 @@ When you're getting started with YOLOv8, having a helpful community and support
 
 ### Official Documentation and Resources
 
-- **Ultralytics YOLOv8 Docs:** The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
+- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
 
 These resources will help you tackle challenges and stay updated on the latest trends and best practices in the YOLOv8 community.
 

docs/en/guides/yolo-common-issues.md

@@ -26,7 +26,7 @@ Installation errors can arise due to various reasons, such as incompatible versi
 
 - Consider using virtual environments to avoid conflicts.
 
-- Follow the [official installation guide](https://docs.ultralytics.com/quickstart/) step by step.
+- Follow the [official installation guide](../quickstart.md) step by step.
 
 Additionally, here are some common installation issues users have encountered, along with their respective solutions:
 
@@ -263,7 +263,7 @@ Engaging with a community of like-minded individuals can significantly enhance y
 
 ### Official Documentation and Resources
 
-**Ultralytics YOLOv8 Docs**: The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
+**Ultralytics YOLOv8 Docs**: The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
 
 These resources should provide a solid foundation for troubleshooting and improving your YOLOv8 projects, as well as connecting with others in the YOLOv8 community.
 

docs/en/guides/yolo-performance-metrics.md

@@ -26,7 +26,7 @@ Let’s start by discussing some metrics that are not only important to YOLOv8 b
 
 ## How to Calculate Metrics for YOLOv8 Model
 
-Now, we can explore [YOLOv8's Validation mode](https://docs.ultralytics.com/modes/val/) that can be used to compute the above discussed evaluation metrics.
+Now, we can explore [YOLOv8's Validation mode](../modes/val.md) that can be used to compute the above discussed evaluation metrics.
 
 Using the validation mode is simple. Once you have a trained model, you can invoke the model.val() function. This function will then process the validation dataset and return a variety of performance metrics. But what do these metrics mean? And how should you interpret them?
 
@@ -152,7 +152,7 @@ Tapping into a community of enthusiasts and experts can amplify your journey wit
 
 ### Official Documentation and Resources:
 
-- **Ultralytics YOLOv8 Docs:** The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
+- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
 
 Using these resources will not only guide you through any challenges but also keep you updated with the latest trends and best practices in the YOLOv8 community.