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

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docs/datasets/classify/caltech101.md
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@ -39,7 +39,7 @@ To train a YOLO model on the Caltech-101 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='caltech101', epochs=100, imgsz=416)
results = model.train(data='caltech101', epochs=100, imgsz=416)
```
=== "CLI"
@ -61,17 +61,21 @@ The example showcases the variety and complexity of the objects in the Caltech-1
If you use the Caltech-101 dataset in your research or development work, please cite the following paper:
```bibtex
@article{fei2007learning,
title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
journal={Computer vision and Image understanding},
volume={106},
number={1},
pages={59--70},
year={2007},
publisher={Elsevier}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@article{fei2007learning,
title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
journal={Computer vision and Image understanding},
volume={106},
number={1},
pages={59--70},
year={2007},
publisher={Elsevier}
}
```
We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).