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

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docs/datasets/detect/coco8.md
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@ -42,7 +42,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
```
=== "CLI"
@ -66,15 +66,19 @@ The example showcases the variety and complexity of the images in the COCO8 data
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).