Fixed OpenVINO Docs formatting (#14773)

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

docs/en/datasets/classify/cifar10.md

@@ -100,22 +100,22 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
 
     === "Python"
 
-    ```python
-    from ultralytics import YOLO
+        ```python
+        from ultralytics import YOLO
 
-    # Load a model
-    model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        # Load a model
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
-    # Train the model
-    results = model.train(data="cifar10", epochs=100, imgsz=32)
-    ```
+        # Train the model
+        results = model.train(data="cifar10", epochs=100, imgsz=32)
+        ```
 
     === "CLI"
 
-    ```bash
-    # Start training from a pretrained *.pt model
-    yolo detect train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
-    ```
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        ```
 
 For more details, refer to the model [Training](../../modes/train.md) page.
 

docs/en/datasets/classify/imagenette.md

@@ -126,22 +126,22 @@ To train a YOLO model on the ImageNette dataset for 100 epochs, you can use the
 
     === "Python"
 
-    ```python
-    from ultralytics import YOLO
+        ```python
+        from ultralytics import YOLO
 
-    # Load a model
-    model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        # Load a model
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
-    # Train the model
-    results = model.train(data="imagenette", epochs=100, imgsz=224)
-    ```
+        # Train the model
+        results = model.train(data="imagenette", epochs=100, imgsz=224)
+        ```
 
     === "CLI"
 
-    ```bash
-    # Start training from a pretrained *.pt model
-    yolo detect train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
-    ```
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        ```
 
 For more details, see the [Training](../../modes/train.md) documentation page.
 

docs/en/datasets/track/index.md

@@ -39,18 +39,18 @@ To use Multi-Object Tracking with Ultralytics YOLO, you can start by using the P
 
     === "Python"
     
-    ```python
-    from ultralytics import YOLO
+        ```python
+        from ultralytics import YOLO
 
-    model = YOLO("yolov8n.pt")  # Load the YOLOv8 model
-    results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
-    ```
+        model = YOLO("yolov8n.pt")  # Load the YOLOv8 model
+        results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
+        ```
 
     === "CLI"
 
-    ```bash
-    yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3 iou=0.5 show
-    ```
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3 iou=0.5 show
+        ```
     
 These commands load the YOLOv8 model and use it for tracking objects in the given video source with specific confidence (`conf`) and Intersection over Union (`iou`) thresholds. For more details, refer to the [track mode documentation](../../modes/track.md).