Reformat Markdown code blocks (#12795)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/obb/dota-v2.md

@@ -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"

docs/en/datasets/obb/dota8.md

@@ -34,10 +34,10 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-obb.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-obb.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='dota8.yaml', epochs=100, imgsz=640)
+        results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/obb/index.md

@@ -40,10 +40,10 @@ To train a model using these OBB formats:
         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"
@@ -78,7 +78,7 @@ Transitioning labels from the DOTA dataset format to the YOLO OBB format can be
         ```python
         from ultralytics.data.converter import convert_dota_to_yolo_obb
 
-        convert_dota_to_yolo_obb('path/to/DOTA')
+        convert_dota_to_yolo_obb("path/to/DOTA")
         ```
 
 This conversion mechanism is instrumental for datasets in the DOTA format, ensuring alignment with the Ultralytics YOLO OBB format.