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.
 

docs/en/datasets/detect/roboflow-100.md

@@ -203,7 +203,7 @@ The **Roboflow 100** dataset is accessible on [GitHub](https://github.com/robofl
 
 When using the Roboflow 100 dataset in your research, ensure to properly cite it. Here is the recommended citation:
 
-!!! Quote
+!!! Quote ""
 
     === "BibTeX"
 

docs/en/datasets/detect/visdrone.md

@@ -159,16 +159,19 @@ The configuration file for the VisDrone dataset, `VisDrone.yaml`, can be found i
 
 If you use the VisDrone dataset in your research or development work, please cite the following paper:
 
-!!! Quote "BibTeX"
+!!! Quote ""
 
-    ```bibtex
-    @ARTICLE{9573394,
-      author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
-      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
-      title={Detection and Tracking Meet Drones Challenge},
-      year={2021},
-      volume={},
-      number={},
-      pages={1-1},
-      doi={10.1109/TPAMI.2021.3119563}}
-    ```
+    === "BibTeX"
+
+        ```bibtex
+        @ARTICLE{9573394,
+          author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+          journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+          title={Detection and Tracking Meet Drones Challenge},
+          year={2021},
+          volume={},
+          number={},
+          pages={1-1},
+          doi={10.1109/TPAMI.2021.3119563}
+        }
+        ```

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

@@ -135,19 +135,23 @@ Ultralytics YOLO offers advanced real-time object detection, segmentation, and c
 
 If you incorporate the Crack Segmentation Dataset into your research, please use the following BibTeX reference:
 
-```bibtex
-@misc{ crack-bphdr_dataset,
-    title = { crack Dataset },
-    type = { Open Source Dataset },
-    author = { University },
-    howpublished = { \url{ https://universe.roboflow.com/university-bswxt/crack-bphdr } },
-    url = { https://universe.roboflow.com/university-bswxt/crack-bphdr },
-    journal = { Roboflow Universe },
-    publisher = { Roboflow },
-    year = { 2022 },
-    month = { dec },
-    note = { visited on 2024-01-23 },
-}
-```
+!!! Quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{ crack-bphdr_dataset,
+            title = { crack Dataset },
+            type = { Open Source Dataset },
+            author = { University },
+            howpublished = { \url{ https://universe.roboflow.com/university-bswxt/crack-bphdr } },
+            url = { https://universe.roboflow.com/university-bswxt/crack-bphdr },
+            journal = { Roboflow Universe },
+            publisher = { Roboflow },
+            year = { 2022 },
+            month = { dec },
+            note = { visited on 2024-01-23 },
+        }
+        ```
 
 This citation format ensures proper accreditation to the creators of the dataset and acknowledges its use in your research.

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

@@ -99,24 +99,28 @@ The [Roboflow Package Segmentation Dataset](https://universe.roboflow.com/factor
 
 ### How do I train an Ultralytics YOLOv8 model on the Package Segmentation Dataset?
 
-You can train an Ultralytics YOLOv8n model using both Python and CLI methods. For Python, use the snippet below:
+You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Use the snippets below:
 
-```python
-from ultralytics import YOLO
+!!! Example "Train Example"
 
-# Load a model
-model = YOLO("yolov8n-seg.pt")  # load a pretrained model
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
 
-# Train the model
-results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
-```
+        # Load a model
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model
 
-For CLI:
+        # Train the model
+        results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
+        ```
 
-```bash
-# Start training from a pretrained *.pt model
-yolo segment train data=package-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
-```
+    === "CLI"
+        
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo segment train data=package-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        ```
 
 Refer to the model [Training](../../modes/train.md) page for more details.