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"