# Hyperparameters for COCO training from scratch | |
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 | |
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials | |
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) | |
lrf: 0.2 # 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 | |
giou: 0.05 # box loss gain | |
cls: 0.5 # cls loss gain | |
cls_pw: 1.0 # cls BCELoss positive_weight | |
obj: 1.0 # 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: 0 # anchors per output grid (0 to ignore) | |
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) | |
hsv_h: 0.015 # image HSV-Hue augmentation (fraction) | |
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) | |
hsv_v: 0.4 # image HSV-Value augmentation (fraction) | |
degrees: 0.0 # image rotation (+/- deg) | |
translate: 0.1 # image translation (+/- fraction) | |
scale: 0.5 # image scale (+/- gain) | |
shear: 0.0 # image shear (+/- deg) | |
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 | |
flipud: 0.0 # image flip up-down (probability) | |
fliplr: 0.5 # image flip left-right (probability) | |
mosaic: 1.0 # image mosaic (probability) | |
mixup: 0.0 # image mixup (probability) | |