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/cifar10.md

@@ -153,14 +153,18 @@ Each subset comprises images categorized into 10 classes, with their annotations
 
 If you use the CIFAR-10 dataset in your research or development projects, make sure to cite the following paper:
 
-```bibtex
-@TECHREPORT{Krizhevsky09learningmultiple,
-            author={Alex Krizhevsky},
-            title={Learning multiple layers of features from tiny images},
-            institution={},
-            year={2009}
-}
-```
+!!! Quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @TECHREPORT{Krizhevsky09learningmultiple,
+                    author={Alex Krizhevsky},
+                    title={Learning multiple layers of features from tiny images},
+                    institution={},
+                    year={2009}
+        }
+        ```
 
 Acknowledging the dataset's creators helps support continued research and development in the field. For more details, see the [citations and acknowledgments](#citations-and-acknowledgments) section.
 

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.