Add Hindi हिन्दी and Arabic العربية Docs translations (#6428)

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/classify/caltech101.md

@@ -28,7 +28,7 @@ The Caltech-101 dataset is extensively used for training and evaluating deep lea
 
 To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -61,7 +61,7 @@ The example showcases the variety and complexity of the objects in the Caltech-1
 
 If you use the Caltech-101 dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/classify/caltech256.md

@@ -28,7 +28,7 @@ The Caltech-256 dataset is extensively used for training and evaluating deep lea
 
 To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -61,7 +61,7 @@ The example showcases the diversity and complexity of the objects in the Caltech
 
 If you use the Caltech-256 dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/classify/cifar10.md

@@ -31,7 +31,7 @@ The CIFAR-10 dataset is widely used for training and evaluating deep learning mo
 
 To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -64,7 +64,7 @@ The example showcases the variety and complexity of the objects in the CIFAR-10
 
 If you use the CIFAR-10 dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/classify/cifar100.md

@@ -31,7 +31,7 @@ The CIFAR-100 dataset is extensively used for training and evaluating deep learn
 
 To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -64,7 +64,7 @@ The example showcases the variety and complexity of the objects in the CIFAR-100
 
 If you use the CIFAR-100 dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/classify/fashion-mnist.md

@@ -45,7 +45,7 @@ The Fashion-MNIST dataset is widely used for training and evaluating deep learni
 
 To train a CNN model on the Fashion-MNIST dataset for 100 epochs with an image size of 28x28, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 

docs/en/datasets/classify/imagenet.md

@@ -31,7 +31,7 @@ The ImageNet dataset is widely used for training and evaluating deep learning mo
 
 To train a deep learning model on the ImageNet dataset for 100 epochs with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -64,7 +64,7 @@ The example showcases the variety and complexity of the images in the ImageNet d
 
 If you use the ImageNet dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/classify/imagenet10.md

@@ -27,7 +27,7 @@ The ImageNet10 dataset is useful for quickly testing and debugging computer visi
 
 To test a deep learning model on the ImageNet10 dataset with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Test Example"
+!!! Example "Test Example"
 
     === "Python"
 
@@ -59,7 +59,7 @@ The example showcases the variety and complexity of the images in the ImageNet10
 
 If you use the ImageNet10 dataset in your research or development work, please cite the original ImageNet paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/classify/imagenette.md

@@ -29,7 +29,7 @@ The ImageNette dataset is widely used for training and evaluating deep learning
 
 To train a model on the ImageNette dataset for 100 epochs with a standard image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -64,7 +64,7 @@ For faster prototyping and training, the ImageNette dataset is also available in
 
 To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imagenette320' in the training command. The following code snippets illustrate this:
 
-!!! example "Train Example with ImageNette160"
+!!! Example "Train Example with ImageNette160"
 
     === "Python"
 
@@ -85,7 +85,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
         yolo detect train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
         ```
 
-!!! example "Train Example with ImageNette320"
+!!! Example "Train Example with ImageNette320"
 
     === "Python"
 

docs/en/datasets/classify/imagewoof.md

@@ -26,7 +26,7 @@ The ImageWoof dataset is widely used for training and evaluating deep learning m
 
 To train a CNN model on the ImageWoof dataset for 100 epochs with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 

docs/en/datasets/classify/index.md

@@ -80,7 +80,7 @@ In this example, the `train` directory contains subdirectories for each class in
 
 ## Usage
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/datasets/classify/mnist.md

@@ -34,7 +34,7 @@ The MNIST dataset is widely used for training and evaluating deep learning model
 
 To train a CNN model on the MNIST dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -69,7 +69,7 @@ If you use the MNIST dataset in your
 
 research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/argoverse.md

@@ -8,7 +8,7 @@ keywords: Argoverse dataset, autonomous driving, YOLO, 3D tracking, motion forec
 
 The [Argoverse](https://www.argoverse.org/) dataset is a collection of data designed to support research in autonomous driving tasks, such as 3D tracking, motion forecasting, and stereo depth estimation. Developed by Argo AI, the dataset provides a wide range of high-quality sensor data, including high-resolution images, LiDAR point clouds, and map data.
 
-!!! note
+!!! Note
 
     The Argoverse dataset *.zip file required for training was removed from Amazon S3 after the shutdown of Argo AI by Ford, but we have made it available for manual download on [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
 
@@ -35,7 +35,7 @@ The Argoverse dataset is widely used for training and evaluating deep learning m
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Argoverse dataset, the `Argoverse.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml).
 
-!!! example "ultralytics/cfg/datasets/Argoverse.yaml"
+!!! Example "ultralytics/cfg/datasets/Argoverse.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/Argoverse.yaml"
@@ -45,7 +45,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -80,7 +80,7 @@ The example showcases the variety and complexity of the data in the Argoverse da
 
 If you use the Argoverse dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/coco.md

@@ -31,7 +31,7 @@ The COCO dataset is widely used for training and evaluating deep learning models
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco.yaml"
+!!! Example "ultralytics/cfg/datasets/coco.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco.yaml"
@@ -41,7 +41,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -76,7 +76,7 @@ The example showcases the variety and complexity of the images in the COCO datas
 
 If you use the COCO dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/coco8.md

@@ -17,7 +17,7 @@ and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8 dataset, the `coco8.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco8.yaml"
+!!! Example "ultralytics/cfg/datasets/coco8.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco8.yaml"
@@ -27,7 +27,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8 data
 
 If you use the COCO dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/globalwheat2020.md

@@ -30,7 +30,7 @@ The Global Wheat Head Dataset is widely used for training and evaluating deep le
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Global Wheat Head Dataset, the `GlobalWheat2020.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml).
 
-!!! example "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
+!!! Example "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
@@ -40,7 +40,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -75,7 +75,7 @@ The example showcases the variety and complexity of the data in the Global Wheat
 
 If you use the Global Wheat Head Dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/index.md

@@ -48,7 +48,7 @@ When using the Ultralytics YOLO format, organize your training and validation im
 
 Here's how you can use these formats to train your model:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -93,7 +93,7 @@ If you have your own dataset and would like to use it for training detection mod
 
 You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/datasets/detect/objects365.md

@@ -30,7 +30,7 @@ The Objects365 dataset is widely used for training and evaluating deep learning
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Objects365 Dataset, the `Objects365.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml).
 
-!!! example "ultralytics/cfg/datasets/Objects365.yaml"
+!!! Example "ultralytics/cfg/datasets/Objects365.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/Objects365.yaml"
@@ -40,7 +40,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -75,7 +75,7 @@ The example showcases the variety and complexity of the data in the Objects365 d
 
 If you use the Objects365 dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/open-images-v7.md

@@ -40,7 +40,7 @@ Open Images V7 is a cornerstone for training and evaluating state-of-the-art mod
 
 Typically, datasets come with a YAML (Yet Another Markup Language) file that delineates the dataset's configuration. For the case of Open Images V7, a hypothetical `OpenImagesV7.yaml` might exist. For accurate paths and configurations, one should refer to the dataset's official repository or documentation.
 
-!!! example "OpenImagesV7.yaml"
+!!! Example "OpenImagesV7.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/open-images-v7.yaml"
@@ -50,7 +50,7 @@ Typically, datasets come with a YAML (Yet Another Markup Language) file that del
 
 To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! warning
+!!! Warning
 
     The complete Open Images V7 dataset comprises 1,743,042 training images and 41,620 validation images, requiring approximately **561 GB of storage space** upon download.
 
@@ -59,7 +59,7 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an im
     - Verify that your device has enough storage capacity.
     - Ensure a robust and speedy internet connection.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -94,7 +94,7 @@ Researchers can gain invaluable insights into the array of computer vision chall
 
 For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/sku-110k.md

@@ -32,7 +32,7 @@ The SKU-110k dataset is widely used for training and evaluating deep learning mo
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the SKU-110K dataset, the `SKU-110K.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml).
 
-!!! example "ultralytics/cfg/datasets/SKU-110K.yaml"
+!!! Example "ultralytics/cfg/datasets/SKU-110K.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/SKU-110K.yaml"
@@ -42,7 +42,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -77,7 +77,7 @@ The example showcases the variety and complexity of the data in the SKU-110k dat
 
 If you use the SKU-110k dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/visdrone.md

@@ -28,7 +28,7 @@ The VisDrone dataset is widely used for training and evaluating deep learning mo
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Visdrone dataset, the `VisDrone.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
 
-!!! example "ultralytics/cfg/datasets/VisDrone.yaml"
+!!! Example "ultralytics/cfg/datasets/VisDrone.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/VisDrone.yaml"
@@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -73,7 +73,7 @@ The example showcases the variety and complexity of the data in the VisDrone dat
 
 If you use the VisDrone dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/voc.md

@@ -31,7 +31,7 @@ The VOC dataset is widely used for training and evaluating deep learning models
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the VOC dataset, the `VOC.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml).
 
-!!! example "ultralytics/cfg/datasets/VOC.yaml"
+!!! Example "ultralytics/cfg/datasets/VOC.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/VOC.yaml"
@@ -41,7 +41,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -77,7 +77,7 @@ The example showcases the variety and complexity of the images in the VOC datase
 
 If you use the VOC dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/detect/xview.md

@@ -34,7 +34,7 @@ The xView dataset is widely used for training and evaluating deep learning model
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the xView dataset, the `xView.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml).
 
-!!! example "ultralytics/cfg/datasets/xView.yaml"
+!!! Example "ultralytics/cfg/datasets/xView.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/xView.yaml"
@@ -44,7 +44,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a model on the xView dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -79,7 +79,7 @@ The example showcases the variety and complexity of the data in the xView datase
 
 If you use the xView dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/index.md

@@ -78,7 +78,7 @@ Contributing a new dataset involves several steps to ensure that it aligns well
 
 3. **Export Annotations**: Convert these annotations into the YOLO *.txt file format which Ultralytics supports.
 
-4. **Organize Dataset**: Arrange your dataset into the correct folder structure. You should have `train/` and `val/` top-level directories, and within each, an `images/` and `labels/` sub-directory.
+4. **Organize Dataset**: Arrange your dataset into the correct folder structure. You should have `train/` and `val/` top-level directories, and within each, an `images/` and `labels/` subdirectory.
 
     ```
     dataset/
@@ -100,7 +100,7 @@ Contributing a new dataset involves several steps to ensure that it aligns well
 
 ### Example Code to Optimize and Zip a Dataset
 
-!!! example "Optimize and Zip a Dataset"
+!!! Example "Optimize and Zip a Dataset"
 
     === "Python"
 

docs/en/datasets/obb/dota-v2.md

@@ -60,7 +60,7 @@ DOTA v2 serves as a benchmark for training and evaluating models specifically ta
 
 Typically, datasets incorporate a YAML (Yet Another Markup Language) file detailing the dataset's configuration. For DOTA v2, a hypothetical `DOTAv2.yaml` could be used. For accurate paths and configurations, it's vital to consult the dataset's official repository or documentation.
 
-!!! example "DOTAv2.yaml"
+!!! Example "DOTAv2.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/DOTAv2.yaml"
@@ -70,11 +70,11 @@ Typically, datasets incorporate a YAML (Yet Another Markup Language) file detail
 
 To train a model on the DOTA v2 dataset, you can utilize the following code snippets. Always refer to your model's documentation for a thorough list of available arguments.
 
-!!! warning
+!!! Warning
 
     Please note that all images and associated annotations in the DOTAv2 dataset can be used for academic purposes, but commercial use is prohibited. Your understanding and respect for the dataset creators' wishes are greatly appreciated!
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -109,7 +109,7 @@ The dataset's richness offers invaluable insights into object detection challeng
 
 For those leveraging DOTA v2 in their endeavors, it's pertinent to cite the relevant research papers:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/obb/index.md

@@ -32,7 +32,7 @@ An example of a `*.txt` label file for the above image, which contains an object
 
 To train a model using these OBB formats:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -69,7 +69,7 @@ For those looking to introduce their own datasets with oriented bounding boxes,
 
 Transitioning labels from the DOTA dataset format to the YOLO OBB format can be achieved with this script:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/datasets/pose/coco.md

@@ -32,7 +32,7 @@ The COCO-Pose dataset is specifically used for training and evaluating deep lear
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Pose dataset, the `coco-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco-pose.yaml"
+!!! Example "ultralytics/cfg/datasets/coco-pose.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco-pose.yaml"
@@ -42,7 +42,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -77,7 +77,7 @@ The example showcases the variety and complexity of the images in the COCO-Pose
 
 If you use the COCO-Pose dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/pose/coco8-pose.md

@@ -17,7 +17,7 @@ and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Pose dataset, the `coco8-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco8-pose.yaml"
+!!! Example "ultralytics/cfg/datasets/coco8-pose.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco8-pose.yaml"
@@ -27,7 +27,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8-Pose
 
 If you use the COCO dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/pose/index.md

@@ -64,7 +64,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
 ## Usage
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -125,7 +125,7 @@ If you have your own dataset and would like to use it for training pose estimati
 
 Ultralytics provides a convenient conversion tool to convert labels from the popular COCO dataset format to YOLO format:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/datasets/pose/tiger-pose.md

@@ -19,7 +19,7 @@ and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 A YAML (Yet Another Markup Language) file serves as the means to specify the configuration details of a dataset. It encompasses crucial data such as file paths, class definitions, and other pertinent information. Specifically, for the `tiger-pose.yaml` file, you can check [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
 
-!!! example "ultralytics/cfg/datasets/tiger-pose.yaml"
+!!! Example "ultralytics/cfg/datasets/tiger-pose.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/tiger-pose.yaml"
@@ -29,7 +29,7 @@ A YAML (Yet Another Markup Language) file serves as the means to specify the con
 
 To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the Tiger-Pose
 
 ## Inference Example
 
-!!! example "Inference Example"
+!!! Example "Inference Example"
 
     === "Python"
 

docs/en/datasets/segment/coco.md

@@ -31,7 +31,7 @@ COCO-Seg is widely used for training and evaluating deep learning models in inst
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Seg dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco.yaml"
+!!! Example "ultralytics/cfg/datasets/coco.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco.yaml"
@@ -41,7 +41,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -76,7 +76,7 @@ The example showcases the variety and complexity of the images in the COCO-Seg d
 
 If you use the COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/segment/coco8-seg.md

@@ -17,7 +17,7 @@ and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Seg dataset, the `coco8-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco8-seg.yaml"
+!!! Example "ultralytics/cfg/datasets/coco8-seg.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
@@ -27,7 +27,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8-Seg
 
 If you use the COCO dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/segment/index.md

@@ -33,7 +33,7 @@ Here is an example of the YOLO dataset format for a single image with two object
 1 0.504 0.000 0.501 0.004 0.498 0.004 0.493 0.010 0.492 0.0104
 ```
 
-!!! tip "Tip"
+!!! Tip "Tip"
 
       - The length of each row does **not** have to be equal.
       - Each segmentation label must have a **minimum of 3 xy points**: `<class-index> <x1> <y1> <x2> <y2> <x3> <y3>`
@@ -66,7 +66,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
 ## Usage
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -101,7 +101,7 @@ If you have your own dataset and would like to use it for training segmentation
 
 You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -123,7 +123,7 @@ Auto-annotation is an essential feature that allows you to generate a segmentati
 
 To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/datasets/track/index.md

@@ -12,7 +12,7 @@ Multi-Object Detector doesn't need standalone training and directly supports pre
 
 ## Usage
 
-!!! example ""
+!!! Example ""
 
     === "Python"