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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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MatthewNoyce 2024-09-06 16:33:26 +01:00 • committed by GitHub
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docs/en/modes/val.md
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@ -41,7 +41,7 @@ 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()`
@ -49,7 +49,7 @@ These are the notable functionalities offered by YOLOv8's Val mode:
Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the `model` retains its training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
!!! Example
!!! example
=== "Python"
@ -102,7 +102,7 @@ Each of these settings plays a vital role in the validation process, allowing fo
The below examples showcase YOLO model validation with custom arguments in Python and CLI.
!!! Example
!!! example
=== "Python"