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/models/fast-sam.md

@@ -40,7 +40,7 @@ The FastSAM models are easy to integrate into your Python applications. Ultralyt
 
 To perform object detection on an image, use the `predict` method as shown below:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
         ```python
@@ -87,7 +87,7 @@ This snippet demonstrates the simplicity of loading a pre-trained model and runn
 
 Validation of the model on a dataset can be done as follows:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
         ```python
@@ -168,7 +168,7 @@ Additionally, you can try FastSAM through a [Colab demo](https://colab.research.
 
 We would like to acknowledge the FastSAM authors for their significant contributions in the field of real-time instance segmentation:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/index.md

@@ -37,7 +37,7 @@ Here are some of the key models supported:
 
 ## Getting Started: Usage Examples
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/models/mobile-sam.md

@@ -61,7 +61,7 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
 
 ### Point Prompt
 
-!!! example ""
+!!! Example ""
 
     === "Python"
         ```python
@@ -76,7 +76,7 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
 
 ### Box Prompt
 
-!!! example ""
+!!! Example ""
 
     === "Python"
         ```python
@@ -95,7 +95,7 @@ We have implemented `MobileSAM` and `SAM` using the same API. For more usage inf
 
 If you find MobileSAM useful in your research or development work, please consider citing our paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/rtdetr.md

@@ -30,7 +30,7 @@ The Ultralytics Python API provides pre-trained PaddlePaddle RT-DETR models with
 
 You can use RT-DETR for object detection tasks using the `ultralytics` pip package. The following is a sample code snippet showing how to use RT-DETR models for training and inference:
 
-!!! example ""
+!!! Example ""
 
     This example provides simple inference code for RT-DETR. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using RT-DETR with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
@@ -81,7 +81,7 @@ You can use RT-DETR for object detection tasks using the `ultralytics` pip packa
 
 If you use Baidu's RT-DETR in your research or development work, please cite the [original paper](https://arxiv.org/abs/2304.08069):
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/sam.md

@@ -32,7 +32,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
 
 ### SAM prediction example
 
-!!! example "Segment with prompts"
+!!! Example "Segment with prompts"
 
     Segment image with given prompts.
 
@@ -54,7 +54,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
         model('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
         ```
 
-!!! example "Segment everything"
+!!! Example "Segment everything"
 
     Segment the whole image.
 
@@ -82,7 +82,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
 
 - The logic here is to segment the whole image if you don't pass any prompts(bboxes/points/masks).
 
-!!! example "SAMPredictor example"
+!!! Example "SAMPredictor example"
 
     This way you can set image once and run prompts inference multiple times without running image encoder multiple times.
 
@@ -152,7 +152,7 @@ This comparison shows the order-of-magnitude differences in the model sizes and
 
 Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
         ```python
@@ -187,7 +187,7 @@ Auto-annotation is a key feature of SAM, allowing users to generate a [segmentat
 
 To auto-annotate your dataset with the Ultralytics framework, use the `auto_annotate` function as shown below:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
         ```python
@@ -212,7 +212,7 @@ Auto-annotation with pre-trained models can dramatically cut down the time and e
 
 If you find SAM useful in your research or development work, please consider citing our paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/yolo-nas.md

@@ -44,7 +44,7 @@ The following examples show how to use YOLO-NAS models with the `ultralytics` pa
 
 In this example we validate YOLO-NAS-s on the COCO8 dataset.
 
-!!! example ""
+!!! Example ""
 
     This example provides simple inference and validation code for YOLO-NAS. For handling inference results see [Predict](../modes/predict.md) mode. For using YOLO-NAS with additional modes see [Val](../modes/val.md) and [Export](../modes/export.md). YOLO-NAS on the `ultralytics` package does not support training.
 
@@ -106,7 +106,7 @@ Harness the power of the YOLO-NAS models to drive your object detection tasks to
 
 If you employ YOLO-NAS in your research or development work, please cite SuperGradients:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/yolov3.md

@@ -51,7 +51,7 @@ TODO
 
 You can use YOLOv3 for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv3 model for inference:
 
-!!! example ""
+!!! Example ""
 
     This example provides simple inference code for YOLOv3. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv3 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
@@ -91,7 +91,7 @@ You can use YOLOv3 for object detection tasks using the Ultralytics repository.
 
 If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/yolov4.md

@@ -53,7 +53,7 @@ YOLOv4 is a powerful and efficient object detection model that strikes a balance
 
 We would like to acknowledge the YOLOv4 authors for their significant contributions in the field of real-time object detection:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/yolov5.md

@@ -56,7 +56,7 @@ YOLOv5u represents an advancement in object detection methodologies. Originating
 
 You can use YOLOv5u for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv5u model for inference:
 
-!!! example ""
+!!! Example ""
 
     This example provides simple inference code for YOLOv5. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv5 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
@@ -96,7 +96,7 @@ You can use YOLOv5u for object detection tasks using the Ultralytics repository.
 
 If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
         ```bibtex

docs/en/models/yolov6.md

@@ -37,7 +37,7 @@ YOLOv6 also provides quantized models for different precisions and models optimi
 
 You can use YOLOv6 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv6 models for training:
 
-!!! example ""
+!!! Example ""
 
     This example provides simple training code for YOLOv6. For more options including training settings see [Train](../modes/train.md) mode. For using YOLOv6 with additional modes see [Predict](../modes/predict.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
@@ -95,7 +95,7 @@ You can use YOLOv6 for object detection tasks using the Ultralytics pip package.
 
 We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/yolov7.md

@@ -49,7 +49,7 @@ We regret any inconvenience this may cause and will strive to update this docume
 
 We would like to acknowledge the YOLOv7 authors for their significant contributions in the field of real-time object detection:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/models/yolov8.md

@@ -95,7 +95,7 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
 
 You can use YOLOv8 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv8 models for inference:
 
-!!! example ""
+!!! Example ""
 
     This example provides simple inference code for YOLOv8. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv8 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
@@ -135,7 +135,7 @@ You can use YOLOv8 for object detection tasks using the Ultralytics pip package.
 
 If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"