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

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docs/datasets/classify/imagenet10.md
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@ -38,7 +38,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='imagenet10', epochs=5, imgsz=224)
results = model.train(data='imagenet10', epochs=5, imgsz=224)
```
=== "CLI"
@ -59,16 +59,20 @@ The example showcases the variety and complexity of the images in the ImageNet10
If you use the ImageNet10 dataset in your research or development work, please cite the original ImageNet paper:
```bibtex
@article{ILSVRC15,
author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
title={ImageNet Large Scale Visual Recognition Challenge},
year={2015},
journal={International Journal of Computer Vision (IJCV)},
volume={115},
number={3},
pages={211-252}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@article{ILSVRC15,
author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
title={ImageNet Large Scale Visual Recognition Challenge},
year={2015},
journal={International Journal of Computer Vision (IJCV)},
volume={115},
number={3},
pages={211-252}
}
```
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).