Return metrics, Update docs (#846)

docs/cli.md

@@ -35,103 +35,41 @@ the [Configuration](cfg.md) page.
 
 !!! example ""
 
-    === "CLI"
-    
-        ```bash
-        yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
-        ```
-
-    === "Python"
-    
-        ```python
-        from ultralytics import YOLO
-        
-        # Load a model
-        model = YOLO("yolov8n.yaml")  # build a new model from scratch
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
-        
-        # Train the model
-        results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
-        ```
-
+      ```bash
+      yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
+      yolo detect train resume model=last.pt  # resume training
+      ```
 ## Val
 
 Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
 training `data` and arguments as model attributes.
 
 !!! example ""
-
-    === "CLI"
-    
-        ```bash
-        yolo detect val model=yolov8n.pt  # val official model
-        yolo detect val model=path/to/best.pt  # val custom model
-        ```
-
-    === "Python"
-    
-        ```python
-        from ultralytics import YOLO
-        
-        # Load a model
-        model = YOLO("yolov8n.pt")  # load an official model
-        model = YOLO("path/to/best.pt")  # load a custom model
-        
-        # Validate the model
-        results = model.val()  # no arguments needed, dataset and settings remembered
-        ```
-
+  
+      ```bash
+      yolo detect val model=yolov8n.pt  # val official model
+      yolo detect val model=path/to/best.pt  # val custom model
+      ```
 ## Predict
 
 Use a trained YOLOv8n model to run predictions on images.
 
 !!! example ""
 
-    === "CLI"
-    
-        ```bash
-        yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"  # predict with official model
-        yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model
-        ```
-
-    === "Python"
-    
-        ```python
-        from ultralytics import YOLO
-        
-        # Load a model
-        model = YOLO("yolov8n.pt")  # load an official model
-        model = YOLO("path/to/best.pt")  # load a custom model
-        
-        # Predict with the model
-        results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
-        ```
-
+      ```bash
+      yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"  # predict with official model
+      yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model
+      ```
 ## Export
 
 Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
 
 !!! example ""
-
-    === "CLI"
     
-        ```bash
-        yolo export model=yolov8n.pt format=onnx  # export official model
-        yolo export model=path/to/best.pt format=onnx  # export custom trained model
-        ```
-
-    === "Python"
-    
-        ```python
-        from ultralytics import YOLO
-        
-        # Load a model
-        model = YOLO("yolov8n.pt")  # load an official model
-        model = YOLO("path/to/best.pt")  # load a custom trained
-        
-        # Export the model
-        model.export(format="onnx")
-        ```
+      ```bash
+      yolo export model=yolov8n.pt format=onnx  # export official model
+      yolo export model=path/to/best.pt format=onnx  # export custom trained model
+      ```
 
     Available YOLOv8 export formats include:
     
@@ -194,4 +132,4 @@ like `imgsz=320` in this example:
         ```bash
         yolo copy-cfg
         yolo cfg=default_copy.yaml imgsz=320
-        ```
+        ```