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- # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
- # Hyperparameters when using Albumentations frameworks
- # python train.py --hyp hyp.no-augmentation.yaml
- # See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
- lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
- lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
- momentum: 0.937 # SGD momentum/Adam beta1
- weight_decay: 0.0005 # optimizer weight decay 5e-4
- warmup_epochs: 3.0 # warmup epochs (fractions ok)
- warmup_momentum: 0.8 # warmup initial momentum
- warmup_bias_lr: 0.1 # warmup initial bias lr
- box: 0.05 # box loss gain
- cls: 0.3 # cls loss gain
- cls_pw: 1.0 # cls BCELoss positive_weight
- obj: 0.7 # obj loss gain (scale with pixels)
- obj_pw: 1.0 # obj BCELoss positive_weight
- iou_t: 0.20 # IoU training threshold
- anchor_t: 4.0 # anchor-multiple threshold
- # anchors: 3 # anchors per output layer (0 to ignore)
- # this parameters are all zero since we want to use albumentation framework
- fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
- hsv_h: 0 # image HSV-Hue augmentation (fraction)
- hsv_s: 0 # image HSV-Saturation augmentation (fraction)
- hsv_v: 0 # image HSV-Value augmentation (fraction)
- degrees: 0.0 # image rotation (+/- deg)
- translate: 0 # image translation (+/- fraction)
- scale: 0 # image scale (+/- gain)
- shear: 0 # image shear (+/- deg)
- perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
- flipud: 0.0 # image flip up-down (probability)
- fliplr: 0.0 # image flip left-right (probability)
- mosaic: 0.0 # image mosaic (probability)
- mixup: 0.0 # image mixup (probability)
- copy_paste: 0.0 # segment copy-paste (probability)
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