# 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)
