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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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6
docs/en/yolov5/tutorials/train_custom_data.md
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@ -25,7 +25,7 @@ pip install -r requirements.txt # install
Creating a custom model to detect your objects is an iterative process of collecting and organizing images, labeling your objects of interest, training a model, deploying it into the wild to make predictions, and then using that deployed model to collect examples of edge cases to repeat and improve.
!!! Question "Licensing"
!!! question "Licensing"
Ultralytics offers two licensing options:
@ -137,11 +137,11 @@ Train a YOLOv5s model on COCO128 by specifying dataset, batch-size, image size a
python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt
```
!!! Tip "Tip"
!!! tip "Tip"
💡 Add `--cache ram` or `--cache disk` to speed up training (requires significant RAM/disk resources).
!!! Tip "Tip"
!!! tip "Tip"
💡 Always train from a local dataset. Mounted or network drives like Google Drive will be very slow.