Add CLI commands for `predict` and `train` YOLOv10 models. (#13940)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/models/yolov10.md

@@ -122,31 +122,56 @@ Here is a detailed comparison of YOLOv10 variants with other state-of-the-art mo
 
 For predicting new images with YOLOv10:
 
-```python
+!!! Example
-from ultralytics import YOLO
 
-# Load a pre-trained YOLOv10n model
+    === "Python"
-model = YOLO("yolov10n.pt")
 
-# Perform object detection on an image
+        ```python
-results = model("image.jpg")
+        from ultralytics import YOLO
 
-# Display the results
+        # Load a pre-trained YOLOv10n model
-results[0].show()
+        model = YOLO("yolov10n.pt")
-```
+
+        # Perform object detection on an image
+        results = model("image.jpg")
+
+        # Display the results
+        results[0].show()
+        ``` 
+
+    === "CLI"
+
+        ```bash
+        # Load a COCO-pretrained YOLOv10n model and run inference on the 'bus.jpg' image
+        yolo detect predict model=yolov10n.pt source=path/to/bus.jpg
+        ```
 
 For training YOLOv10 on a custom dataset:
 
-```python
+!!! Example
-from ultralytics import YOLO
 
-# Load YOLOv10n model from scratch
+    === "Python"
-model = YOLO("yolov10n.yaml")
 
-# Train the model
+        ```python
-model.train(data="coco8.yaml", epochs=100, imgsz=640)
+        from ultralytics import YOLO
-```
 
+        # Load YOLOv10n model from scratch
+        model = YOLO("yolov10n.yaml")
+
+        # Train the model
+        model.train(data="coco8.yaml", epochs=100, imgsz=640)
+        ```
+    
+    === "CLI"
+
+        ```bash
+        # Build a YOLOv10n model from scratch and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov10n.yaml data=coco8.yaml epochs=100 imgsz=640
+
+        # Build a YOLOv10n model from scratch and run inference on the 'bus.jpg' image
+        yolo predict model=yolov10n.yaml source=path/to/bus.jpg
+        ```
+        
 ## Supported Tasks and Modes
 
 The YOLOv10 models series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.