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Reformat Markdown code blocks (#12795)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-05-18 18:58:06 +02:00 • committed by GitHub
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22
docs/en/datasets/obb/dota-v2.md
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@ -75,21 +75,21 @@ To train DOTA dataset, we split original DOTA images with high-resolution into i
=== "Python"
```python
from ultralytics.data.split_dota import split_trainval, split_test
from ultralytics.data.split_dota import split_test, split_trainval
# split train and val set, with labels.
split_trainval(
data_root='path/to/DOTAv1.0/',
save_dir='path/to/DOTAv1.0-split/',
rates=[0.5, 1.0, 1.5], # multiscale
gap=500
data_root="path/to/DOTAv1.0/",
save_dir="path/to/DOTAv1.0-split/",
rates=[0.5, 1.0, 1.5], # multiscale
gap=500,
)
# split test set, without labels.
split_test(
data_root='path/to/DOTAv1.0/',
save_dir='path/to/DOTAv1.0-split/',
rates=[0.5, 1.0, 1.5], # multiscale
gap=500
data_root="path/to/DOTAv1.0/",
save_dir="path/to/DOTAv1.0-split/",
rates=[0.5, 1.0, 1.5], # multiscale
gap=500,
)
```
@ -109,10 +109,10 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
from ultralytics import YOLO
# Create a new YOLOv8n-OBB model from scratch
model = YOLO('yolov8n-obb.yaml')
model = YOLO("yolov8n-obb.yaml")
# Train the model on the DOTAv2 dataset
results = model.train(data='DOTAv1.yaml', epochs=100, imgsz=640)
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=640)
```
=== "CLI"