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* Update LICENSE to AGPL-3.0 This pull request updates the license of the YOLOv5 project from GNU General Public License v3.0 (GPL-3.0) to GNU Affero General Public License v3.0 (AGPL-3.0). We at Ultralytics have decided to make this change in order to better protect our intellectual property and ensure that any modifications made to the YOLOv5 source code will be shared back with the community when used over a network. AGPL-3.0 is very similar to GPL-3.0, but with an additional clause to address the use of software over a network. This change ensures that if someone modifies YOLOv5 and provides it as a service over a network (e.g., through a web application or API), they must also make the source code of their modified version available to users of the service. This update includes the following changes: - Replace the `LICENSE` file with the AGPL-3.0 license text - Update the license reference in the `README.md` file - Update the license headers in source code files We believe that this change will promote a more collaborative environment and help drive further innovation within the YOLOv5 community. Please review the changes and let us know if you have any questions or concerns. Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update headers to AGPL-3.0 --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
35 lines
1.7 KiB
YAML
35 lines
1.7 KiB
YAML
# 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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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 0.05 # box loss gain
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cls: 0.5 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 1.0 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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# anchors: 3 # anchors per output layer (0 to ignore)
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.1 # image translation (+/- fraction)
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scale: 0.5 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.0 # image mixup (probability)
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copy_paste: 0.0 # segment copy-paste (probability)
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