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

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docs/datasets/classify/caltech256.md
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@ -39,7 +39,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='caltech256', epochs=100, imgsz=416)
results = model.train(data='caltech256', epochs=100, imgsz=416)
```
=== "CLI"
@ -61,13 +61,17 @@ The example showcases the diversity and complexity of the objects in the Caltech
If you use the Caltech-256 dataset in your research or development work, please cite the following paper:
```bibtex
@article{griffin2007caltech,
title={Caltech-256 object category dataset},
author={Griffin, Gregory and Holub, Alex and Perona, Pietro},
year={2007}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@article{griffin2007caltech,
title={Caltech-256 object category dataset},
author={Griffin, Gregory and Holub, Alex and Perona, Pietro},
year={2007}
}
```
We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the