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

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Glenn Jocher 2023-08-10 00:55:36 +02:00 • committed by GitHub
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docs/datasets/detect/sku-110k.md
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@ -53,7 +53,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image si
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
results = model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
```
=== "CLI"
@ -77,13 +77,17 @@ The example showcases the variety and complexity of the data in the SKU-110k dat
If you use the SKU-110k dataset in your research or development work, please cite the following paper:
```bibtex
@inproceedings{goldman2019dense,
author = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
title = {Precise Detection in Densely Packed Scenes},
booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
year = {2019}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@inproceedings{goldman2019dense,
author = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
title = {Precise Detection in Densely Packed Scenes},
booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
year = {2019}
}
```
We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).