@@ -143,8 +143,11 @@ To train a YOLO11 model using JupyterLab:
 5. Visualize training results using JupyterLab's built-in plotting capabilities:
 
     ```python
-    %matplotlib inline
+    import matplotlib
+
     from ultralytics.utils.plotting import plot_results
+
+    matplotlib.use("inline")  # or 'notebook' for interactive
     plot_results(results)
     ```
 

@@ -325,7 +325,7 @@ To use YOLOv7 ONNX model with Ultralytics:
 
 2. Install the `TensorRT` Python package:
 
-    ```python
+    ```bash
     pip install tensorrt
     ```
 

@@ -43,14 +43,6 @@ keywords: ultralytics, plotting, utilities, documentation, data visualization, a
 
 <br><br><hr><br>
 
-## ::: ultralytics.utils.plotting.output_to_target
-
-<br><br><hr><br>
-
-## ::: ultralytics.utils.plotting.output_to_rotated_target
-
-<br><br><hr><br>
-
 ## ::: ultralytics.utils.plotting.feature_visualization
 
 <br><br>

@@ -76,16 +76,21 @@ Train YOLO11n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralyti
 
     Ultralytics YOLO classification uses [torchvision.transforms.RandomResizedCrop](https://docs.pytorch.org/vision/stable/generated/torchvision.transforms.RandomResizedCrop.html) for training augmentation and [torchvision.transforms.CenterCrop](https://docs.pytorch.org/vision/stable/generated/torchvision.transforms.CenterCrop.html) for validation/inference.
     For images with extreme aspect ratios, consider using [torchvision.transforms.Resize](https://docs.pytorch.org/vision/stable/generated/torchvision.transforms.Resize.html) instead. The example below shows how to customize augmentations for classification training.
+
     ```python
     import torch
     import torchvision.transforms as T
 
+    from ultralytics import YOLO
     from ultralytics.data.dataset import ClassificationDataset
     from ultralytics.models.yolo.classify import ClassificationTrainer
 
 
     class CustomizedDataset(ClassificationDataset):
+        """A customized dataset class for image classification with enhanced data augmentation transforms."""
+
         def __init__(self, root: str, args, augment: bool = False, prefix: str = ""):
+            """Initialize a customized classification dataset with enhanced data augmentation transforms."""
             super().__init__(root, args, augment, prefix)
             train_transforms = T.Compose(
                 [
@@ -110,12 +115,13 @@ Train YOLO11n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralyti
 
 
     class CustomizedTrainer(ClassificationTrainer):
+        """A customized trainer class for YOLO classification models with enhanced dataset handling."""
+
         def build_dataset(self, img_path: str, mode: str = "train", batch=None):
+            """Build a customized dataset for classification training or validation."""
             return CustomizedDataset(root=img_path, args=self.args, augment=mode == "train", prefix=mode)
 
 
-    from ultralytics import YOLO
-
     model = YOLO("yolo11n-cls.pt")
     model.train(data="imagenet1000", trainer=CustomizedTrainer, epochs=10, imgsz=224, batch=64)
     ```