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

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docs/datasets/detect/globalwheat2020.md
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@ -51,7 +51,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
results = model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
```
=== "CLI"
@ -75,13 +75,17 @@ The example showcases the variety and complexity of the data in the Global Wheat
If you use the Global Wheat Head Dataset in your research or development work, please cite the following paper:
```bibtex
@article{david2020global,
title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
journal={arXiv preprint arXiv:2005.02162},
year={2020}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@article{david2020global,
title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
journal={arXiv preprint arXiv:2005.02162},
year={2020}
}
```
We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).