Reformat Markdown code blocks (#12795)

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

docs/en/datasets/segment/carparts-seg.md

@@ -55,10 +55,10 @@ To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='carparts-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/coco.md

@@ -59,10 +59,10 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/coco8-seg.md

@@ -34,10 +34,10 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an ima
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/crack-seg.md

@@ -44,10 +44,10 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 epo
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='crack-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/index.md

@@ -74,10 +74,10 @@ The `train` and `val` fields specify the paths to the directories containing the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
         ```
     === "CLI"
 
@@ -117,7 +117,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
         ```python
         from ultralytics.data.converter import convert_coco
 
-        convert_coco(labels_dir='path/to/coco/annotations/', use_segments=True)
+        convert_coco(labels_dir="path/to/coco/annotations/", use_segments=True)
         ```
 
 This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
@@ -139,7 +139,7 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
         ```python
         from ultralytics.data.annotator import auto_annotate
 
-        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
+        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
         ```
 
 Certainly, here is the table updated with code snippets:

docs/en/datasets/segment/package-seg.md

@@ -44,10 +44,10 @@ To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 e
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='package-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"