Update LICENSE to AGPL-3.0 (#2031)
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@ -19,7 +19,7 @@ pip install -r requirements.txt # install
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YOLOv5 has about 30 hyperparameters used for various training settings. These are defined in `*.yaml` files in the `/data/hyps` directory. Better initial guesses will produce better final results, so it is important to initialize these values properly before evolving. If in doubt, simply use the default values, which are optimized for YOLOv5 COCO training from scratch.
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```yaml
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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# Hyperparameters for low-augmentation COCO training from scratch
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# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
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# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
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