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>
2023-11-18 21:51:47 +01:00 · 2023-11-18 21:51:47 +01:00 · 02bf8003a8
commit 02bf8003a8
parent b6baae584c
337 changed files with 6584 additions and 777 deletions
--- a/docs/en/datasets/classify/caltech101.md
+++ b/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"

--- a/docs/en/datasets/classify/caltech256.md
+++ b/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"

--- a/docs/en/datasets/classify/cifar10.md
+++ b/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"

--- a/docs/en/datasets/classify/cifar100.md
+++ b/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"

--- a/docs/en/datasets/classify/fashion-mnist.md
+++ b/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"

--- a/docs/en/datasets/classify/imagenet.md
+++ b/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"

--- a/docs/en/datasets/classify/imagenet10.md
+++ b/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"

--- a/docs/en/datasets/classify/imagenette.md
+++ b/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"

--- a/docs/en/datasets/classify/imagewoof.md
+++ b/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"

--- a/docs/en/datasets/classify/index.md
+++ b/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"

--- a/docs/en/datasets/classify/mnist.md
+++ b/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"

--- a/docs/en/datasets/detect/argoverse.md
+++ b/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"

--- a/docs/en/datasets/detect/coco.md
+++ b/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"

--- a/docs/en/datasets/detect/coco8.md
+++ b/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"

--- a/docs/en/datasets/detect/globalwheat2020.md
+++ b/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"

--- a/docs/en/datasets/detect/index.md
+++ b/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"

--- a/docs/en/datasets/detect/objects365.md
+++ b/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"

--- a/docs/en/datasets/detect/open-images-v7.md
+++ b/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"

--- a/docs/en/datasets/detect/sku-110k.md
+++ b/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"

--- a/docs/en/datasets/detect/visdrone.md
+++ b/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"

--- a/docs/en/datasets/detect/voc.md
+++ b/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"

--- a/docs/en/datasets/detect/xview.md
+++ b/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"

--- a/docs/en/datasets/index.md
+++ b/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"

--- a/docs/en/datasets/obb/dota-v2.md
+++ b/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"

--- a/docs/en/datasets/obb/index.md
+++ b/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"

--- a/docs/en/datasets/pose/coco.md
+++ b/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"

--- a/docs/en/datasets/pose/coco8-pose.md
+++ b/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"

--- a/docs/en/datasets/pose/index.md
+++ b/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"

--- a/docs/en/datasets/pose/tiger-pose.md
+++ b/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"

--- a/docs/en/datasets/segment/coco.md
+++ b/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"

--- a/docs/en/datasets/segment/coco8-seg.md
+++ b/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"

--- a/docs/en/datasets/segment/index.md
+++ b/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"

--- a/docs/en/datasets/track/index.md
+++ b/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"

--- a/docs/en/guides/hyperparameter-tuning.md
+++ b/docs/en/guides/hyperparameter-tuning.md
@ -69,7 +69,7 @@ The process is repeated until either the set number of iterations is reached or

 Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLOv8n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.

-!!! example ""
+!!! Example ""

    === "Python"

--- a/docs/en/guides/sahi-tiled-inference.md
+++ b/docs/en/guides/sahi-tiled-inference.md
@ -167,7 +167,7 @@ That's it! Now you're equipped to use YOLOv8 with SAHI for both standard and sli

 If you use SAHI in your research or development work, please cite the original SAHI paper and acknowledge the authors:

-!!! note ""
+!!! Note ""

    === "BibTeX"

--- a/docs/en/help/privacy.md
+++ b/docs/en/help/privacy.md
@ -39,7 +39,7 @@ We take several measures to ensure the privacy and security of the data you entr

 [Sentry](https://sentry.io/) is a developer-centric error tracking software that aids in identifying, diagnosing, and resolving issues in real-time, ensuring the robustness and reliability of applications. Within our package, it plays a crucial role by providing insights through crash reporting, significantly contributing to the stability and ongoing refinement of our software.

-!!! note
+!!! Note

    Crash reporting via Sentry is activated only if the `sentry-sdk` Python package is pre-installed on your system. This package isn't included in the `ultralytics` prerequisites and won't be installed automatically by Ultralytics.

@ -74,7 +74,7 @@ To opt out of sending analytics and crash reports, you can simply set `sync=Fals

 To gain insight into the current configuration of your settings, you can view them directly:

-!!! example "View settings"
+!!! Example "View settings"

    === "Python"
        You can use Python to view your settings. Start by importing the `settings` object from the `ultralytics` module. Print and return settings using the following commands:
@ -98,7 +98,7 @@ To gain insight into the current configuration of your settings, you can view th

 Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:

-!!! example "Update settings"
+!!! Example "Update settings"

    === "Python"
        Within the Python environment, call the `update` method on the `settings` object to change your settings:
--- a/docs/en/hub/app/android.md
+++ b/docs/en/hub/app/android.md
@ -43,7 +43,7 @@ FP16 (or half-precision) quantization converts the model's 32-bit floating-point

 INT8 (or 8-bit integer) quantization further reduces the model's size and computation requirements by converting its 32-bit floating-point numbers to 8-bit integers. This quantization method can result in a significant speedup, but it may lead to a slight reduction in mean average precision (mAP) due to the lower numerical precision.

-!!! tip "mAP Reduction in INT8 Models"
+!!! Tip "mAP Reduction in INT8 Models"

    The reduced numerical precision in INT8 models can lead to some loss of information during the quantization process, which may result in a slight decrease in mAP. However, this trade-off is often acceptable considering the substantial performance gains offered by INT8 quantization.

--- a/docs/en/hub/datasets.md
+++ b/docs/en/hub/datasets.md
@ -30,7 +30,7 @@ You can download our [COCO8](https://github.com/ultralytics/hub/blob/main/exampl

 The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format.

-!!! example "coco8.yaml"
+!!! Example "coco8.yaml"

    ```yaml
    --8<-- "ultralytics/cfg/datasets/coco8.yaml"
@ -92,7 +92,7 @@ Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) on y

 ## Share Dataset

-!!! info "Info"
+!!! Info "Info"

    Ultralytics HUB's sharing functionality provides a convenient way to share datasets with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.

--- a/docs/en/hub/inference_api.md
+++ b/docs/en/hub/inference_api.md
@ -106,7 +106,7 @@ The JSON list contains information about the detected objects, their coordinates

 YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.

-!!! example "Detect Model JSON Response"
+!!! Example "Detect Model JSON Response"

    === "Local"
        ```python
@ -200,7 +200,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca

 YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.

-!!! example "Segment Model JSON Response"
+!!! Example "Segment Model JSON Response"

    === "Local"
        ```python
@ -337,7 +337,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr

 YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.

-!!! example "Pose Model JSON Response"
+!!! Example "Pose Model JSON Response"

    === "Local"
        ```python
--- a/docs/en/hub/models.md
+++ b/docs/en/hub/models.md
@ -58,7 +58,7 @@ In this step, you have to choose the project in which you want to create your mo

    ![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the project name](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_7.jpg)

-!!! info "Info"
+!!! Info "Info"

    You can read more about the available [YOLOv8](https://docs.ultralytics.com/models/yolov8) (and [YOLOv5](https://docs.ultralytics.com/models/yolov5)) architectures in our documentation.

@ -146,7 +146,7 @@ You can export your model to 13 different formats, including ONNX, OpenVINO, Cor

 ## Share Model

-!!! info "Info"
+!!! Info "Info"

    Ultralytics HUB's sharing functionality provides a convenient way to share models with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.

--- a/docs/en/hub/projects.md
+++ b/docs/en/hub/projects.md
@ -44,7 +44,7 @@ Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) insi

 ## Share Project

-!!! info "Info"
+!!! Info "Info"

    Ultralytics HUB's sharing functionality provides a convenient way to share projects with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.

@ -68,7 +68,7 @@ Set the general access to "Unlisted" and click **Save**.

 ![Ultralytics HUB screenshot of the Share Project dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_3.jpg)

-!!! warning "Warning"
+!!! Warning "Warning"

    When changing the general access of a project, the general access of the models inside the project will be changed as well.

@ -108,7 +108,7 @@ Navigate to the Project page of the project you want to delete, open the project

    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Delete option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_2.jpg)

-!!! warning "Warning"
+!!! Warning "Warning"

    When deleting a project, the models inside the project will be deleted as well.

--- a/docs/en/integrations/comet.md
+++ b/docs/en/integrations/comet.md
@ -26,7 +26,7 @@ By combining Ultralytics YOLOv8 with Comet ML, you unlock a range of benefits. T

 To install the required packages, run:

-!!! tip "Installation"
+!!! Tip "Installation"

    === "CLI"

@ -39,7 +39,7 @@ To install the required packages, run:

 After installing the required packages, you’ll need to sign up, get a [Comet API Key](https://www.comet.com/signup), and configure it.

-!!! tip "Configuring Comet ML"
+!!! Tip "Configuring Comet ML"

    === "CLI"

@ -61,7 +61,7 @@ comet_ml.init(project_name="comet-example-yolov8-coco128")

 Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.

-!!! example "Usage"
+!!! Example "Usage"

    === "Python"

--- a/docs/en/integrations/mlflow.md
+++ b/docs/en/integrations/mlflow.md
@ -34,7 +34,7 @@ pip install mlflow

 Make sure that MLflow logging is enabled in Ultralytics settings. Usually, this is controlled by the settings `mflow` key. See the [settings](https://docs.ultralytics.com/quickstart/#ultralytics-settings) page for more info.

-!!! example "Update Ultralytics MLflow Settings"
+!!! Example "Update Ultralytics MLflow Settings"

    === "Python"
        Within the Python environment, call the `update` method on the `settings` object to change your settings:
--- a/docs/en/integrations/openvino.md
+++ b/docs/en/integrations/openvino.md
@ -27,7 +27,7 @@ OpenVINO, short for Open Visual Inference & Neural Network Optimization toolkit,

 Export a YOLOv8n model to OpenVINO format and run inference with the exported model.

-!!! example ""
+!!! Example ""

    === "Python"

@ -101,7 +101,7 @@ For more detailed steps and code snippets, refer to the [OpenVINO documentation]

 YOLOv8 benchmarks below were run by the Ultralytics team on 4 different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX and OpenVINO. Benchmarks were run on Intel Flex and Arc GPUs, and on Intel Xeon CPUs at FP32 precision (with the `half=False` argument).

-!!! note
+!!! Note

    The benchmarking results below are for reference and might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run.

@ -251,7 +251,7 @@ Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.

 To reproduce the Ultralytics benchmarks above on all export [formats](../modes/export.md) run this code:

-!!! example ""
+!!! Example ""

    === "Python"

--- a/docs/en/integrations/ray-tune.md
+++ b/docs/en/integrations/ray-tune.md
@ -28,7 +28,7 @@ YOLOv8 also allows optional integration with [Weights & Biases](https://wandb.ai

 To install the required packages, run:

-!!! tip "Installation"
+!!! Tip "Installation"

    === "CLI"

@ -42,7 +42,7 @@ To install the required packages, run:

 ## Usage

-!!! example "Usage"
+!!! Example "Usage"

    === "Python"

@ -103,7 +103,7 @@ The following table lists the default search space parameters for hyperparameter

 In this example, we demonstrate how to use a custom search space for hyperparameter tuning with Ray Tune and YOLOv8. By providing a custom search space, you can focus the tuning process on specific hyperparameters of interest.

-!!! example "Usage"
+!!! Example "Usage"

    ```python
    from ultralytics import YOLO
--- a/docs/en/integrations/roboflow.md
+++ b/docs/en/integrations/roboflow.md
@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Roboflow, vector analysis, confusion matrix, data

 [Roboflow](https://roboflow.com/?ref=ultralytics) has everything you need to build and deploy computer vision models. Connect Roboflow at any step in your pipeline with APIs and SDKs, or use the end-to-end interface to automate the entire process from image to inference. Whether you’re in need of [data labeling](https://roboflow.com/annotate?ref=ultralytics), [model training](https://roboflow.com/train?ref=ultralytics), or [model deployment](https://roboflow.com/deploy?ref=ultralytics), Roboflow gives you building blocks to bring custom computer vision solutions to your project.

-!!! warning
+!!! Warning

    Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing.

--- a/docs/en/models/fast-sam.md
+++ b/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"

--- a/docs/en/models/index.md
+++ b/docs/en/models/index.md
@ -37,7 +37,7 @@ Here are some of the key models supported:

 ## Getting Started: Usage Examples

-!!! example ""
+!!! Example ""

    === "Python"

--- a/docs/en/models/mobile-sam.md
+++ b/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"

--- a/docs/en/models/rtdetr.md
+++ b/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"

--- a/docs/en/models/sam.md
+++ b/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"

--- a/docs/en/models/yolo-nas.md
+++ b/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"

--- a/docs/en/models/yolov3.md
+++ b/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"

--- a/docs/en/models/yolov4.md
+++ b/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"

--- a/docs/en/models/yolov5.md
+++ b/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
--- a/docs/en/models/yolov6.md
+++ b/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"

--- a/docs/en/models/yolov7.md
+++ b/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"

--- a/docs/en/models/yolov8.md
+++ b/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"

--- a/docs/en/modes/benchmark.md
+++ b/docs/en/modes/benchmark.md
@ -32,7 +32,7 @@ Once your model is trained and validated, the next logical step is to evaluate i
 - **OpenVINO:** For Intel hardware optimization
 - **CoreML, TensorFlow SavedModel, and More:** For diverse deployment needs.

-!!! tip "Tip"
+!!! Tip "Tip"

    * Export to ONNX or OpenVINO for up to 3x CPU speedup.
    * Export to TensorRT for up to 5x GPU speedup.
@ -41,7 +41,7 @@ Once your model is trained and validated, the next logical step is to evaluate i

 Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a full list of export arguments.

-!!! example ""
+!!! Example ""

    === "Python"

--- a/docs/en/modes/export.md
+++ b/docs/en/modes/export.md
@ -39,7 +39,7 @@ Here are some of the standout functionalities:
 - **Optimized Inference:** Exported models are optimized for quicker inference times.
 - **Tutorial Videos:** In-depth guides and tutorials for a smooth exporting experience.

-!!! tip "Tip"
+!!! Tip "Tip"

    * Export to ONNX or OpenVINO for up to 3x CPU speedup.
    * Export to TensorRT for up to 5x GPU speedup.
@ -48,7 +48,7 @@ Here are some of the standout functionalities:

 Export a YOLOv8n model to a different format like ONNX or TensorRT. See Arguments section below for a full list of export arguments.

-!!! example ""
+!!! Example ""

    === "Python"

--- a/docs/en/modes/predict.md
+++ b/docs/en/modes/predict.md
@ -50,7 +50,7 @@ YOLOv8's predict mode is designed to be robust and versatile, featuring:

 Ultralytics YOLO models return either a Python list of `Results` objects, or a memory-efficient Python generator of `Results` objects when `stream=True` is passed to the model during inference:

-!!! example "Predict"
+!!! Example "Predict"

    === "Return a list with `stream=False`"
        ```python
@ -92,7 +92,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m

 YOLOv8 can process different types of input sources for inference, as shown in the table below. The sources include static images, video streams, and various data formats. The table also indicates whether each source can be used in streaming mode with the argument `stream=True` ✅. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory.

-!!! tip "Tip"
+!!! Tip "Tip"

    Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.

@ -115,7 +115,7 @@ YOLOv8 can process different types of input sources for inference, as shown in t

 Below are code examples for using each source type:

-!!! example "Prediction sources"
+!!! Example "Prediction sources"

    === "image"
        Run inference on an image file.
@ -327,7 +327,7 @@ Below are code examples for using each source type:

 `model.predict()` accepts multiple arguments that can be passed at inference time to override defaults:

-!!! example
+!!! Example

    ```python
    from ultralytics import YOLO
@ -411,7 +411,7 @@ The below table contains valid Ultralytics video formats.

 All Ultralytics `predict()` calls will return a list of `Results` objects:

-!!! example "Results"
+!!! Example "Results"

    ```python
    from ultralytics import YOLO
@ -463,7 +463,7 @@ For more details see the `Results` class [documentation](../reference/engine/res

 `Boxes` object can be used to index, manipulate, and convert bounding boxes to different formats.

-!!! example "Boxes"
+!!! Example "Boxes"

    ```python
    from ultralytics import YOLO
@ -501,7 +501,7 @@ For more details see the `Boxes` class [documentation](../reference/engine/resul

 `Masks` object can be used index, manipulate and convert masks to segments.

-!!! example "Masks"
+!!! Example "Masks"

    ```python
    from ultralytics import YOLO
@ -534,7 +534,7 @@ For more details see the `Masks` class [documentation](../reference/engine/resul

 `Keypoints` object can be used index, manipulate and normalize coordinates.

-!!! example "Keypoints"
+!!! Example "Keypoints"

    ```python
    from ultralytics import YOLO
@ -568,7 +568,7 @@ For more details see the `Keypoints` class [documentation](../reference/engine/r

 `Probs` object can be used index, get `top1` and `top5` indices and scores of classification.

-!!! example "Probs"
+!!! Example "Probs"

    ```python
    from ultralytics import YOLO
@ -603,7 +603,7 @@ For more details see the `Probs` class [documentation](../reference/engine/resul

 You can use the `plot()` method of a `Result` objects to visualize predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the `Results` object onto a numpy array that can then be shown or saved.

-!!! example "Plotting"
+!!! Example "Plotting"

    ```python
    from PIL import Image
@ -647,7 +647,7 @@ Ensuring thread safety during inference is crucial when you are running multiple

 When using YOLO models in a multi-threaded application, it's important to instantiate separate model objects for each thread or employ thread-local storage to prevent conflicts:

-!!! example "Thread-Safe Inference"
+!!! Example "Thread-Safe Inference"

    Instantiate a single model inside each thread for thread-safe inference:
    ```python
@ -672,7 +672,7 @@ For an in-depth look at thread-safe inference with YOLO models and step-by-step

 Here's a Python script using OpenCV (`cv2`) and YOLOv8 to run inference on video frames. This script assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`).

-!!! example "Streaming for-loop"
+!!! Example "Streaming for-loop"

    ```python
    import cv2
--- a/docs/en/modes/track.md
+++ b/docs/en/modes/track.md
@ -58,7 +58,7 @@ The default tracker is BoT-SORT.

 To run the tracker on video streams, use a trained Detect, Segment or Pose model such as YOLOv8n, YOLOv8n-seg and YOLOv8n-pose.

-!!! example ""
+!!! Example ""

    === "Python"

@ -97,7 +97,7 @@ As can be seen in the above usage, tracking is available for all Detect, Segment

 Tracking configuration shares properties with Predict mode, such as `conf`, `iou`, and `show`. For further configurations, refer to the [Predict](../modes/predict.md#inference-arguments) model page.

-!!! example ""
+!!! Example ""

    === "Python"

@ -120,7 +120,7 @@ Tracking configuration shares properties with Predict mode, such as `conf`, `iou

 Ultralytics also allows you to use a modified tracker configuration file. To do this, simply make a copy of a tracker config file (for example, `custom_tracker.yaml`) from [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) and modify any configurations (except the `tracker_type`) as per your needs.

-!!! example ""
+!!! Example ""

    === "Python"

@ -147,7 +147,7 @@ For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/tr

 Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker that the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image.

-!!! example "Streaming for-loop with tracking"
+!!! Example "Streaming for-loop with tracking"

    ```python
    import cv2
@ -195,7 +195,7 @@ Visualizing object tracks over consecutive frames can provide valuable insights

 In the following example, we demonstrate how to utilize YOLOv8's tracking capabilities to plot the movement of detected objects across multiple video frames. This script involves opening a video file, reading it frame by frame, and utilizing the YOLO model to identify and track various objects. By retaining the center points of the detected bounding boxes and connecting them, we can draw lines that represent the paths followed by the tracked objects.

-!!! example "Plotting tracks over multiple video frames"
+!!! Example "Plotting tracks over multiple video frames"

    ```python
    from collections import defaultdict
@ -272,7 +272,7 @@ The `daemon=True` parameter in `threading.Thread` means that these threads will

 Finally, after all threads have completed their task, the windows displaying the results are closed using `cv2.destroyAllWindows()`.

-!!! example "Streaming for-loop with tracking"
+!!! Example "Streaming for-loop with tracking"

    ```python
    import threading
--- a/docs/en/modes/train.md
+++ b/docs/en/modes/train.md
@ -41,7 +41,7 @@ The following are some notable features of YOLOv8's Train mode:
 - **Hyperparameter Configuration:** The option to modify hyperparameters through YAML configuration files or CLI arguments.
 - **Visualization and Monitoring:** Real-time tracking of training metrics and visualization of the learning process for better insights.

-!!! tip "Tip"
+!!! Tip "Tip"

    * YOLOv8 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml`

@ -49,7 +49,7 @@ The following are some notable features of YOLOv8's Train mode:

 Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. The training device can be specified using the `device` argument. If no argument is passed GPU `device=0` will be used if available, otherwise `device=cpu` will be used. See Arguments section below for a full list of training arguments.

-!!! example "Single-GPU and CPU Training Example"
+!!! Example "Single-GPU and CPU Training Example"

    Device is determined automatically. If a GPU is available then it will be used, otherwise training will start on CPU.

@ -84,7 +84,7 @@ Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. The train

 Multi-GPU training allows for more efficient utilization of available hardware resources by distributing the training load across multiple GPUs. This feature is available through both the Python API and the command-line interface. To enable multi-GPU training, specify the GPU device IDs you wish to use.

-!!! example "Multi-GPU Training Example"
+!!! Example "Multi-GPU Training Example"

    To train with 2 GPUs, CUDA devices 0 and 1 use the following commands. Expand to additional GPUs as required.

@ -113,7 +113,7 @@ With the support for Apple M1 and M2 chips integrated in the Ultralytics YOLO mo

 To enable training on Apple M1 and M2 chips, you should specify 'mps' as your device when initiating the training process. Below is an example of how you could do this in Python and via the command line:

-!!! example "MPS Training Example"
+!!! Example "MPS Training Example"

    === "Python"

@ -146,7 +146,7 @@ You can easily resume training in Ultralytics YOLO by setting the `resume` argum

 Below is an example of how to resume an interrupted training using Python and via the command line:

-!!! example "Resume Training Example"
+!!! Example "Resume Training Example"

    === "Python"

@ -236,7 +236,7 @@ To use a logger, select it from the dropdown menu in the code snippet above and

 To use Comet:

-!!! example ""
+!!! Example ""

    === "Python"
        ```python
@ -254,7 +254,7 @@ Remember to sign in to your Comet account on their website and get your API key.

 To use ClearML:

-!!! example ""
+!!! Example ""

    === "Python"
        ```python
@ -272,7 +272,7 @@ After running this script, you will need to sign in to your ClearML account on t

 To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb):

-!!! example ""
+!!! Example ""

    === "CLI"
        ```bash
@ -282,7 +282,7 @@ To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ul

 To use TensorBoard locally run the below command and view results at http://localhost:6006/.

-!!! example ""
+!!! Example ""

    === "CLI"
        ```bash
--- a/docs/en/modes/val.md
+++ b/docs/en/modes/val.md
@ -30,7 +30,7 @@ These are the notable functionalities offered by YOLOv8's Val mode:
 - **CLI and Python API:** Choose from command-line interface or Python API based on your preference for validation.
 - **Data Compatibility:** Works seamlessly with datasets used during the training phase as well as custom datasets.

-!!! tip "Tip"
+!!! Tip "Tip"

    * YOLOv8 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolov8n.pt` or `model('yolov8n.pt').val()`

@ -38,7 +38,7 @@ These are the notable functionalities offered by YOLOv8's Val mode:

 Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.

-!!! example ""
+!!! Example ""

    === "Python"

--- a/docs/en/quickstart.md
+++ b/docs/en/quickstart.md
@ -8,7 +8,7 @@ keywords: Ultralytics installation, pip install Ultralytics, Docker install Ultr

 Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.

-!!! example "Install"
+!!! Example "Install"

    === "Pip install (recommended)"
        Install the `ultralytics` package using pip, or update an existing installation by running `pip install -U ultralytics`. Visit the Python Package Index (PyPI) for more details on the `ultralytics` package: [https://pypi.org/project/ultralytics/](https://pypi.org/project/ultralytics/).
@ -39,7 +39,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
        conda install -c conda-forge ultralytics
        ```

-        !!! note
+        !!! Note

            If you are installing in a CUDA environment best practice is to install `ultralytics`, `pytorch` and `pytorch-cuda` in the same command to allow the conda package manager to resolve any conflicts, or else to install `pytorch-cuda` last to allow it override the CPU-specific `pytorch` package if necessary.
            ```bash
@ -129,7 +129,7 @@ See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralyt
  <strong>Watch:</strong> Ultralytics YOLO Quick Start Guide
 </p>

-!!! tip "Tip"
+!!! Tip "Tip"

    PyTorch requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally).

@ -141,7 +141,7 @@ See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralyt

 The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.

-!!! example
+!!! Example

    === "Syntax"

@ -196,7 +196,7 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
        yolo cfg
        ```

-!!! warning "Warning"
+!!! Warning "Warning"

    Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces between pairs. Do not use `--` argument prefixes or commas `,` between arguments.

@ -213,7 +213,7 @@ YOLOv8's Python interface allows for seamless integration into your Python proje

 For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code. Check out the [Python Guide](usage/python.md) to learn more about using YOLOv8 within your Python projects.

-!!! example
+!!! Example

    ```python
    from ultralytics import YOLO
@ -247,7 +247,7 @@ The Ultralytics library provides a powerful settings management system to enable

 To gain insight into the current configuration of your settings, you can view them directly:

-!!! example "View settings"
+!!! Example "View settings"

    === "Python"
        You can use Python to view your settings. Start by importing the `settings` object from the `ultralytics` module. Print and return settings using the following commands:
@ -271,7 +271,7 @@ To gain insight into the current configuration of your settings, you can view th

 Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:

-!!! example "Update settings"
+!!! Example "Update settings"

    === "Python"
        Within the Python environment, call the `update` method on the `settings` object to change your settings:
--- a/docs/en/reference/cfg/init.md
+++ b/docs/en/reference/cfg/init.md
@ -5,7 +5,7 @@ keywords: Ultralytics, YOLO, Configuration, cfg2dict, handle_deprecation, merge_

 # Reference for `ultralytics/cfg/__init__.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/__init__.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/__init__.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/cfg/__init__.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/annotator.md
+++ b/docs/en/reference/data/annotator.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Auto-Annotate, Machine Learning, AI, Annotation, Data Pro

 # Reference for `ultralytics/data/annotator.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/annotator.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/augment.md
+++ b/docs/en/reference/data/augment.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Data Augmentation, BaseTransform, MixUp, RandomHSV, Lette

 # Reference for `ultralytics/data/augment.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/augment.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/base.md
+++ b/docs/en/reference/data/base.md
@ -5,7 +5,7 @@ keywords: Ultralytics, docs, BaseDataset, data manipulation, dataset creation

 # Reference for `ultralytics/data/base.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/base.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/build.md
+++ b/docs/en/reference/data/build.md
@ -5,7 +5,7 @@ keywords: Ultralytics, YOLO v3, Data build, DataLoader, InfiniteDataLoader, seed

 # Reference for `ultralytics/data/build.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/build.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/converter.md
+++ b/docs/en/reference/data/converter.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Data Converter, coco91_to_coco80_class, merge_multi_segme

 # Reference for `ultralytics/data/converter.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/converter.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/dataset.md
+++ b/docs/en/reference/data/dataset.md
@ -5,7 +5,7 @@ keywords: Ultralytics, YOLO, YOLODataset, SemanticDataset, data handling, data m

 # Reference for `ultralytics/data/dataset.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/dataset.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/dataset.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/dataset.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/loaders.md
+++ b/docs/en/reference/data/loaders.md
@ -5,7 +5,7 @@ keywords: Ultralytics, data loaders, LoadStreams, LoadImages, LoadTensor, YOLO,

 # Reference for `ultralytics/data/loaders.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/loaders.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/loaders.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/loaders.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/data/utils.md
+++ b/docs/en/reference/data/utils.md
@ -5,7 +5,7 @@ keywords: Ultralytics, data utils, YOLO, img2label_paths, exif_size, polygon2mas

 # Reference for `ultralytics/data/utils.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/utils.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/engine/exporter.md
+++ b/docs/en/reference/engine/exporter.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Exporter, IOSDetectModel, Export Formats, Try export

 # Reference for `ultralytics/engine/exporter.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/exporter.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/exporter.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/engine/exporter.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/engine/model.md
+++ b/docs/en/reference/engine/model.md
@ -5,7 +5,7 @@ keywords: Ultralytics, YOLO, engine model, documentation, guide, implementation,

 # Reference for `ultralytics/engine/model.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/model.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/engine/model.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/engine/predictor.md
+++ b/docs/en/reference/engine/predictor.md
@ -5,7 +5,7 @@ keywords: Ultralytics, BasePredictor, YOLO, prediction, engine

 # Reference for `ultralytics/engine/predictor.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/predictor.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/predictor.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/engine/predictor.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/engine/results.md
+++ b/docs/en/reference/engine/results.md
@ -5,7 +5,7 @@ keywords: Ultralytics, engine, results, base tensor, boxes, keypoints

 # Reference for `ultralytics/engine/results.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/results.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/results.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/engine/results.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/engine/trainer.md
+++ b/docs/en/reference/engine/trainer.md
@ -5,7 +5,7 @@ keywords: Ultralytics, BaseTrainer, Machine Learning, Training Control, Python l

 # Reference for `ultralytics/engine/trainer.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/trainer.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/trainer.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/engine/trainer.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/engine/tuner.md
+++ b/docs/en/reference/engine/tuner.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Tuner, YOLO, hyperparameter tuning, optimization, object

 # Reference for `ultralytics/engine/tuner.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/tuner.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/tuner.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/engine/tuner.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/engine/validator.md
+++ b/docs/en/reference/engine/validator.md
@ -5,7 +5,7 @@ keywords: Ultralytics, BaseValidator, Ultralytics engine, module, components

 # Reference for `ultralytics/engine/validator.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/validator.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/validator.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/engine/validator.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/hub/init.md
+++ b/docs/en/reference/hub/init.md
@ -5,7 +5,7 @@ keywords: Ultralytics, hub functions, model export, dataset check, reset model,

 # Reference for `ultralytics/hub/__init__.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/__init__.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/__init__.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/hub/__init__.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/hub/auth.md
+++ b/docs/en/reference/hub/auth.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Auth, API documentation, User Authentication, AI, Machine

 # Reference for `ultralytics/hub/auth.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/auth.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/auth.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/hub/auth.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/hub/session.md
+++ b/docs/en/reference/hub/session.md
@ -5,7 +5,7 @@ keywords: Ultralytics, HUBTrainingSession, Documentation, Model Training, AI, Ma

 # Reference for `ultralytics/hub/session.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/session.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/session.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/hub/session.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/hub/utils.md
+++ b/docs/en/reference/hub/utils.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Events, request_with_credentials, smart_request, Ultralyt

 # Reference for `ultralytics/hub/utils.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/utils.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/hub/utils.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/fastsam/model.md
+++ b/docs/en/reference/models/fastsam/model.md
@ -5,7 +5,7 @@ keywords: Ultralytics, FastSAM model, Model documentation, Efficient model train

 # Reference for `ultralytics/models/fastsam/model.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/model.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/fastsam/model.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/fastsam/predict.md
+++ b/docs/en/reference/models/fastsam/predict.md
@ -5,7 +5,7 @@ keywords: Ultralytics, FastSAMPredictor, predictive modeling, AI optimization, m

 # Reference for `ultralytics/models/fastsam/predict.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/predict.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/fastsam/predict.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/fastsam/prompt.md
+++ b/docs/en/reference/models/fastsam/prompt.md
@ -5,7 +5,7 @@ keywords: Ultralytics, FastSAMPrompt, machine learning, model, guide, documentat

 # Reference for `ultralytics/models/fastsam/prompt.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/prompt.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/prompt.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/fastsam/prompt.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/fastsam/utils.md
+++ b/docs/en/reference/models/fastsam/utils.md
@ -5,7 +5,7 @@ keywords: Ultralytics, bounding boxes, Bboxes, image borders, object detection,

 # Reference for `ultralytics/models/fastsam/utils.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/utils.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/fastsam/utils.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/fastsam/val.md
+++ b/docs/en/reference/models/fastsam/val.md
@ -5,7 +5,7 @@ keywords: Ultralytics, FastSAMValidator, model, synthetic, AI, machine learning,

 # Reference for `ultralytics/models/fastsam/val.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/val.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/fastsam/val.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/nas/model.md
+++ b/docs/en/reference/models/nas/model.md
@ -5,7 +5,7 @@ keywords: Ultralytics, NAS model, NAS guide, machine learning, model documentati

 # Reference for `ultralytics/models/nas/model.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/model.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/nas/model.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/nas/predict.md
+++ b/docs/en/reference/models/nas/predict.md
@ -5,7 +5,7 @@ keywords: NASPredictor, Ultralytics, Ultralytics model, model architecture, effi

 # Reference for `ultralytics/models/nas/predict.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/predict.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/nas/predict.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/nas/val.md
+++ b/docs/en/reference/models/nas/val.md
@ -5,7 +5,7 @@ keywords: Ultralytics, NASValidator, models.nas.val.NASValidator, AI models, all

 # Reference for `ultralytics/models/nas/val.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/val.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/nas/val.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/rtdetr/model.md
+++ b/docs/en/reference/models/rtdetr/model.md
@ -5,7 +5,7 @@ keywords: Ultralytics, RTDETR model, Ultralytics models, object detection, Ultra

 # Reference for `ultralytics/models/rtdetr/model.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/model.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/rtdetr/model.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/rtdetr/predict.md
+++ b/docs/en/reference/models/rtdetr/predict.md
@ -5,7 +5,7 @@ keywords: Ultralytics, RTDETRPredictor, model documentation, guide, real-time ob

 # Reference for `ultralytics/models/rtdetr/predict.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/predict.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/rtdetr/predict.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/rtdetr/train.md
+++ b/docs/en/reference/models/rtdetr/train.md
@ -5,7 +5,7 @@ keywords: Ultralytics, RTDETRTrainer, model training, Ultralytics models, PyTorc

 # Reference for `ultralytics/models/rtdetr/train.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/train.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/train.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/rtdetr/train.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/rtdetr/val.md
+++ b/docs/en/reference/models/rtdetr/val.md
@ -5,7 +5,7 @@ keywords: Ultralytics, RTDETRDataset, RTDETRValidator, real-time object detectio

 # Reference for `ultralytics/models/rtdetr/val.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/val.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/rtdetr/val.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/sam/amg.md
+++ b/docs/en/reference/models/sam/amg.md
@ -5,7 +5,7 @@ keywords: Ultralytics, Mask Data, Transformation, Encoding, RLE encoding, Image

 # Reference for `ultralytics/models/sam/amg.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/amg.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/amg.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/sam/amg.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/sam/build.md
+++ b/docs/en/reference/models/sam/build.md
@ -5,7 +5,7 @@ keywords: Ultralytics, SAM, build sam, vision transformer, vits, build_sam_vit_l

 # Reference for `ultralytics/models/sam/build.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/build.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/build.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/sam/build.py) 🛠️. Thank you 🙏!

--- a/docs/en/reference/models/sam/model.md
+++ b/docs/en/reference/models/sam/model.md
@ -5,7 +5,7 @@ keywords: Ultralytics, YOLO, SAM Model, Documentations, Machine Learning, AI, Co

 # Reference for `ultralytics/models/sam/model.py`

-!!! note
+!!! Note

    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/model.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/models/sam/model.py) 🛠️. Thank you 🙏!

--- a/Show more
+++ b/Show more