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

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Glenn Jocher 2023-08-10 00:55:36 +02:00 • committed by GitHub
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docs/datasets/pose/coco.md
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@ -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).