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

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Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2023-08-10 00:55:36 +02:00 • committed by GitHub
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docs/datasets/classify/cifar10.md
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@ -42,7 +42,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='cifar10', epochs=100, imgsz=32)
results = model.train(data='cifar10', epochs=100, imgsz=32)
```
=== "CLI"
@ -64,13 +64,17 @@ The example showcases the variety and complexity of the objects in the CIFAR-10
If you use the CIFAR-10 dataset in your research or development work, please cite the following paper:
```bibtex
@TECHREPORT{Krizhevsky09learningmultiple,
author={Alex Krizhevsky},
title={Learning multiple layers of features from tiny images},
institution={},
year={2009}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@TECHREPORT{Krizhevsky09learningmultiple,
author={Alex Krizhevsky},
title={Learning multiple layers of features from tiny images},
institution={},
year={2009}
}
```
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).