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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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14
docs/en/datasets/obb/dota-v2.md
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@ -60,7 +60,7 @@ DOTA serves as a benchmark for training and evaluating models specifically tailo
Typically, datasets incorporate a YAML (Yet Another Markup Language) file detailing the dataset's configuration. For DOTA v1 and DOTA v1.5, Ultralytics provides `DOTAv1.yaml` and `DOTAv1.5.yaml` files. For additional details on these as well as DOTA v2 please consult DOTA's official repository and documentation.
!!! Example "DOTAv1.yaml"
!!! example "DOTAv1.yaml"
```yaml
--8<-- "ultralytics/cfg/datasets/DOTAv1.yaml"
@ -70,7 +70,7 @@ Typically, datasets incorporate a YAML (Yet Another Markup Language) file detail
To train DOTA dataset, we split original DOTA images with high-resolution into images with 1024x1024 resolution in multiscale way.
!!! Example "Split images"
!!! example "Split images"
=== "Python"
@ -97,11 +97,11 @@ To train DOTA dataset, we split original DOTA images with high-resolution into i
To train a model on the DOTA v1 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 DOTAv1 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"
@ -136,7 +136,7 @@ The dataset's richness offers invaluable insights into object detection challeng
For those leveraging DOTA in their endeavors, it's pertinent to cite the relevant research papers:
!!! Quote ""
!!! quote ""
=== "BibTeX"
@ -169,7 +169,7 @@ DOTA utilizes Oriented Bounding Boxes (OBB) for annotation, which are represente
To train a model on the DOTA dataset, you can use the following example with Ultralytics YOLO:
!!! Example "Train Example"
!!! example "Train Example"
=== "Python"
@ -204,7 +204,7 @@ For a detailed comparison and additional specifics, check the [dataset versions
DOTA images, which can be very large, are split into smaller resolutions for manageable training. Here's a Python snippet to split images:
!!! Example
!!! example
=== "Python"

8
docs/en/datasets/obb/dota8.md
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@ -16,7 +16,7 @@ This dataset is intended for use with Ultralytics [HUB](https://hub.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 DOTA8 dataset, the `dota8.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml).
!!! Example "ultralytics/cfg/datasets/dota8.yaml"
!!! example "ultralytics/cfg/datasets/dota8.yaml"
```yaml
--8<-- "ultralytics/cfg/datasets/dota8.yaml"
@ -26,7 +26,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
To train a YOLOv8n-obb model on the DOTA8 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"
@ -61,7 +61,7 @@ The example showcases the variety and complexity of the images in the DOTA8 data
If you use the DOTA dataset in your research or development work, please cite the following paper:
!!! Quote ""
!!! quote ""
=== "BibTeX"
@ -90,7 +90,7 @@ The DOTA8 dataset is a small, versatile oriented object detection dataset made u
To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
!!! Example "Train Example"
!!! example "Train Example"
=== "Python"

6
docs/en/datasets/obb/index.md
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@ -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"
@ -70,7 +70,7 @@ 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"
@ -106,7 +106,7 @@ This script will reformat your DOTA annotations into a YOLO-compatible format.
Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the Ultralytics API to train the model. Here's an example in both Python and CLI:
!!! Example
!!! example
=== "Python"