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Update to lowercase MkDocs admonitions (#15990)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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12
docs/en/models/fast-sam.md
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@ -60,7 +60,7 @@ 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"
@ -98,7 +98,7 @@ To perform object detection on an image, use the `predict` method as shown below
This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
!!! Example "FastSAMPredictor example"
!!! example "FastSAMPredictor example"
This way you can run inference on image and get all the segment `results` once and run prompts inference multiple times without running inference multiple times.
@ -120,7 +120,7 @@ This snippet demonstrates the simplicity of loading a pre-trained model and runn
text_results = predictor.prompt(everything_results, texts="a photo of a dog")
```
!!! Note
!!! note
All the returned `results` in above examples are [Results](../modes/predict.md#working-with-results) object which allows access predicted masks and source image easily.
@ -128,7 +128,7 @@ 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"
@ -155,7 +155,7 @@ Please note that FastSAM only supports detection and segmentation of a single cl
To perform object tracking on an image, use the `track` method as shown below:
!!! Example
!!! example
=== "Python"
@ -241,7 +241,7 @@ 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:
!!! Quote ""
!!! quote ""
=== "BibTeX"