Add Docs languages zh, es, ru, pt, fr, de, ja, ko (#6316)

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>

docs/en/datasets/detect/index.md

@@ -72,16 +72,16 @@ Here's how you can use these formats to train your model:
 
 Here is a list of the supported datasets and a brief description for each:
 
-- [**Argoverse**](./argoverse.md): A collection of sensor data collected from autonomous vehicles. It contains 3D tracking annotations for car objects.
-- [**COCO**](./coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
-- [**COCO8**](./coco8.md): A smaller subset of the COCO dataset, COCO8 is more lightweight and faster to train.
-- [**GlobalWheat2020**](./globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
-- [**Objects365**](./objects365.md): A large-scale object detection dataset with 365 object categories and 600k images, aimed at advancing object detection research.
-- [**OpenImagesV7**](./open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
-- [**SKU-110K**](./sku-110k.md): A dataset containing images of densely packed retail products, intended for retail environment object detection.
-- [**VisDrone**](./visdrone.md): A dataset focusing on drone-based images, containing various object categories like cars, pedestrians, and cyclists.
-- [**VOC**](./voc.md): PASCAL VOC is a popular object detection dataset with 20 object categories including vehicles, animals, and furniture.
-- [**xView**](./xview.md): A dataset containing high-resolution satellite imagery, designed for the detection of various object classes in overhead views.
+- [**Argoverse**](argoverse.md): A collection of sensor data collected from autonomous vehicles. It contains 3D tracking annotations for car objects.
+- [**COCO**](coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
+- [**COCO8**](coco8.md): A smaller subset of the COCO dataset, COCO8 is more lightweight and faster to train.
+- [**GlobalWheat2020**](globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
+- [**Objects365**](objects365.md): A large-scale object detection dataset with 365 object categories and 600k images, aimed at advancing object detection research.
+- [**OpenImagesV7**](open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
+- [**SKU-110K**](sku-110k.md): A dataset containing images of densely packed retail products, intended for retail environment object detection.
+- [**VisDrone**](visdrone.md): A dataset focusing on drone-based images, containing various object categories like cars, pedestrians, and cyclists.
+- [**VOC**](voc.md): PASCAL VOC is a popular object detection dataset with 20 object categories including vehicles, animals, and furniture.
+- [**xView**](xview.md): A dataset containing high-resolution satellite imagery, designed for the detection of various object classes in overhead views.
 
 ### Adding your own dataset
 

docs/en/datasets/obb/index.md

@@ -57,7 +57,7 @@ To train a model using these OBB formats:
 
 Currently, the following datasets with Oriented Bounding Boxes are supported:
 
-- [**DOTA v2**](./dota-v2.md): DOTA (A Large-scale Dataset for Object Detection in Aerial Images) version 2, emphasizes detection from aerial perspectives and contains oriented bounding boxes with 1.7 million instances and 11,268 images.
+- [**DOTA v2**](dota-v2.md): DOTA (A Large-scale Dataset for Object Detection in Aerial Images) version 2, emphasizes detection from aerial perspectives and contains oriented bounding boxes with 1.7 million instances and 11,268 images.
 
 ### Incorporating your own OBB dataset
 

docs/en/datasets/pose/index.md

@@ -96,7 +96,7 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for
 - **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
 - **Usage**: Suitable for training human pose estimation models.
 - **Additional Notes**: The dataset is rich and diverse, containing over 200k labeled images.
-- [Read more about COCO-Pose](./coco.md)
+- [Read more about COCO-Pose](coco.md)
 
 ### COCO8-Pose
 
@@ -106,7 +106,7 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for
 - **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
 - **Usage**: Suitable for testing and debugging object detection models, or for experimenting with new detection approaches.
 - **Additional Notes**: COCO8-Pose is ideal for sanity checks and CI checks.
-- [Read more about COCO8-Pose](./coco8-pose.md)
+- [Read more about COCO8-Pose](coco8-pose.md)
 
 ### Tiger-Pose
 
@@ -115,7 +115,7 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for
 - **Number of Classes**: 1 (Tiger).
 - **Keypoints**: 12 keypoints.
 - **Usage**: Great for animal pose or any other pose that is not human-based.
-- [Read more about Tiger-Pose](./tiger-pose.md)
+- [Read more about Tiger-Pose](tiger-pose.md)
 
 ### Adding your own dataset
 

docs/en/datasets/segment/index.md

@@ -143,6 +143,6 @@ Certainly, here is the table updated with code snippets:
 | `device`     | `str, optional`         | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                        | `''`           |
 | `output_dir` | `str or None, optional` | Directory to save the annotated results. Defaults to a `'labels'` folder in the same directory as `'data'`. | `None`         |
 
-The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
+The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](../../models/sam.md), the device to run the models on, and the output directory for saving the annotated results.
 
 By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.