Update to lowercase MkDocs admonitions (#15990)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/datasets/classify/caltech101.md

@@ -28,7 +28,7 @@ The Caltech-101 dataset is extensively used for training and evaluating deep lea
 
 To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 
@@ -61,7 +61,7 @@ The example showcases the variety and complexity of the objects in the Caltech-1
 
 If you use the Caltech-101 dataset in your research or development work, please cite the following paper:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 
@@ -90,7 +90,7 @@ The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is widel
 
 To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the provided code snippets. For example, to train for 100 epochs:
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 
@@ -128,7 +128,7 @@ These features make it an excellent choice for training and evaluating object re
 
 Citing the Caltech-101 dataset in your research acknowledges the creators' contributions and provides a reference for others who might use the dataset. The recommended citation is:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 

docs/en/datasets/classify/caltech256.md

@@ -39,7 +39,7 @@ 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"
 
@@ -72,7 +72,7 @@ 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:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 
@@ -98,7 +98,7 @@ The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is a lar
 
 To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the following code snippets. Refer to the model [Training](../../modes/train.md) page for additional options.
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 

docs/en/datasets/classify/cifar10.md

@@ -42,7 +42,7 @@ 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"
 
@@ -75,7 +75,7 @@ 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:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 
@@ -96,7 +96,7 @@ We would like to acknowledge Alex Krizhevsky for creating and maintaining the CI
 
 To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow the examples provided for both Python and CLI. Here is a basic example to train your model for 100 epochs with an image size of 32x32 pixels:
 
-!!! Example
+!!! example
 
     === "Python"
 
@@ -153,7 +153,7 @@ Each subset comprises images categorized into 10 classes, with their annotations
 
 If you use the CIFAR-10 dataset in your research or development projects, make sure to cite the following paper:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 

docs/en/datasets/classify/cifar100.md

@@ -31,7 +31,7 @@ The CIFAR-100 dataset is extensively used for training and evaluating deep learn
 
 To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 
@@ -64,7 +64,7 @@ The example showcases the variety and complexity of the objects in the CIFAR-100
 
 If you use the CIFAR-100 dataset in your research or development work, please cite the following paper:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 
@@ -89,7 +89,7 @@ The [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) is a large
 
 You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI commands. Here's how:
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 

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

@@ -56,7 +56,7 @@ 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"
 
@@ -99,7 +99,7 @@ The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is
 
 To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use both Python and CLI commands. Here's a quick example to get you started:
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 

docs/en/datasets/classify/imagenet.md

@@ -41,7 +41,7 @@ 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"
 
@@ -74,7 +74,7 @@ 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:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 
@@ -102,7 +102,7 @@ The [ImageNet dataset](https://www.image-net.org/) is a large-scale database con
 
 To use a pretrained Ultralytics YOLO model for image classification on the ImageNet dataset, follow these steps:
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 

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"
 
@@ -58,7 +58,7 @@ The ImageNet10 dataset contains a subset of images from the original ImageNet da
 
 If you use the ImageNet10 dataset in your research or development work, please cite the original ImageNet paper:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 
@@ -86,7 +86,7 @@ The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/
 
 To test your deep learning model on the ImageNet10 dataset with an image size of 224x224, use the following code snippets.
 
-!!! Example "Test Example"
+!!! example "Test Example"
 
     === "Python"
 

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 classify train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
         ```
 
-!!! Example "Train Example with ImageNette320"
+!!! example "Train Example with ImageNette320"
 
     === "Python"
 
@@ -122,7 +122,7 @@ The [ImageNette dataset](https://github.com/fastai/imagenette) is a simplified s
 
 To train a YOLO model on the ImageNette dataset for 100 epochs, you can use the following commands. Make sure to have the Ultralytics YOLO environment set up.
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 
@@ -159,7 +159,7 @@ For more details on model training and dataset management, explore the [Dataset
 
 Yes, the ImageNette dataset is also available in two resized versions: ImageNette160 and ImageNette320. These versions help in faster prototyping and are especially useful when computational resources are limited.
 
-!!! Example "Train Example with ImageNette160"
+!!! example "Train Example with ImageNette160"
 
     === "Python"
 

docs/en/datasets/classify/imagewoof.md

@@ -26,7 +26,7 @@ The ImageWoof dataset is widely used for training and evaluating deep learning m
 
 To train a CNN model on the ImageWoof dataset for 100 epochs with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 
@@ -59,7 +59,7 @@ ImageWoof dataset comes in three different sizes to accommodate various research
 
 To use these variants in your training, simply replace 'imagewoof' in the dataset argument with 'imagewoof320' or 'imagewoof160'. For example:
 
-!!! Example "Example"
+!!! example "Example"
 
     === "Python"
 
@@ -109,7 +109,7 @@ The [ImageWoof](https://github.com/fastai/imagenette) dataset is a challenging s
 
 To train a Convolutional Neural Network (CNN) model on the ImageWoof dataset using Ultralytics YOLO for 100 epochs at an image size of 224x224, you can use the following code:
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 

docs/en/datasets/classify/index.md

@@ -78,7 +78,7 @@ This structured approach ensures that the model can effectively learn from well-
 
 ## Usage
 
-!!! Example
+!!! example
 
     === "Python"
 
@@ -194,7 +194,7 @@ For additional insights and real-world applications, you can explore [Ultralytic
 
 Training a model using Ultralytics YOLO can be done easily in both Python and CLI. Here's an example:
 
-!!! Example
+!!! example
 
     === "Python"
 

docs/en/datasets/classify/mnist.md

@@ -34,7 +34,7 @@ The MNIST dataset is widely used for training and evaluating deep learning model
 
 To train a CNN model on the MNIST dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"
 
@@ -69,7 +69,7 @@ If you use the MNIST dataset in your
 
 research or development work, please cite the following paper:
 
-!!! Quote ""
+!!! quote ""
 
     === "BibTeX"
 
@@ -95,7 +95,7 @@ The [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, or Modified National Ins
 
 To train a model on the MNIST dataset using Ultralytics YOLO, you can follow these steps:
 
-!!! Example "Train Example"
+!!! example "Train Example"
 
     === "Python"