ultralytics 8.0.151 add DOTAv2.yaml for OBB training (#4258)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>

docs/modes/train.md

@@ -31,7 +31,7 @@ Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Argum
         model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
 
         # Train the model
-        model.train(data='coco128.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco128.yaml', epochs=100, imgsz=640)
         ```
     === "CLI"
 
@@ -61,7 +61,7 @@ The training device can be specified using the `device` argument. If no argument
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
 
         # Train the model with 2 GPUs
-        model.train(data='coco128.yaml', epochs=100, imgsz=640, device=[0, 1])
+        results = model.train(data='coco128.yaml', epochs=100, imgsz=640, device=[0, 1])
         ```
     === "CLI"
 
@@ -87,7 +87,7 @@ To enable training on Apple M1 and M2 chips, you should specify 'mps' as your de
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
 
         # Train the model with 2 GPUs
-        model.train(data='coco128.yaml', epochs=100, imgsz=640, device='mps')
+        results = model.train(data='coco128.yaml', epochs=100, imgsz=640, device='mps')
         ```
     === "CLI"
 
@@ -119,7 +119,7 @@ Below is an example of how to resume an interrupted training using Python and vi
         model = YOLO('path/to/last.pt')  # load a partially trained model
 
         # Resume training
-        model.train(resume=True)
+        results = model.train(resume=True)
         ```
     === "CLI"