Improve Docs dataset layout issues (#15696)

Co-authored-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/datasets/classify/imagewoof.md

@@ -59,18 +59,29 @@ ImageWoof dataset comes in three different sizes to accommodate various research
 
 To use these variants in your training, simply replace 'imagewoof' in the dataset argument with 'imagewoof320' or 'imagewoof160'. For example:
 
-```python
-from ultralytics import YOLO
+!!! Example "Example"
 
-# Load a model
-model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+    === "Python"
 
-# For medium-sized dataset
-model.train(data="imagewoof320", epochs=100, imgsz=224)
+        ```python
+        from ultralytics import YOLO
 
-# For small-sized dataset
-model.train(data="imagewoof160", epochs=100, imgsz=224)
-```
+        # Load a model
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # For medium-sized dataset
+        model.train(data="imagewoof320", epochs=100, imgsz=224)
+
+        # For small-sized dataset
+        model.train(data="imagewoof160", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Load a pretrained model and train on the small-sized dataset
+        yolo classify train model=yolov8n-cls.pt data=imagewoof320 epochs=100 imgsz=224
+        ```
 
 It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.