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/datasets/pose/coco.md

@@ -53,7 +53,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an im
         model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
 
         # Train the model
-        model.train(data='coco-pose.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco-pose.yaml', epochs=100, imgsz=640)
         ```
 
     === "CLI"
@@ -77,15 +77,19 @@ The example showcases the variety and complexity of the images in the COCO-Pose
 
 If you use the COCO-Pose dataset in your research or development work, please cite the following paper:
 
-```bibtex
-@misc{lin2015microsoft,
-      title={Microsoft COCO: Common Objects in Context},
-      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
-      year={2015},
-      eprint={1405.0312},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
 
 We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

docs/datasets/pose/coco8-pose.md

@@ -42,7 +42,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
         model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
 
         # Train the model
-        model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
         ```
 
     === "CLI"
@@ -66,15 +66,19 @@ The example showcases the variety and complexity of the images in the COCO8-Pose
 
 If you use the COCO dataset in your research or development work, please cite the following paper:
 
-```bibtex
-@misc{lin2015microsoft,
-      title={Microsoft COCO: Common Objects in Context},
-      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
-      year={2015},
-      eprint={1405.0312},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
 
 We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

docs/datasets/pose/index.md

@@ -78,7 +78,7 @@ For example if we assume five keypoints of facial landmark: [left eye, right eye
         model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
 
         # Train the model
-        model.train(data='coco128-pose.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco128-pose.yaml', epochs=100, imgsz=640)
         ```
     === "CLI"