35 lines
1.7 KiB
YAML
35 lines
1.7 KiB
YAML
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
|
# Hyperparameters for low-augmentation COCO training from scratch
|
|
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
|
|
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
|
|
|
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
|
lrf: 0.01 # 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: 0.5 # 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: 3 # anchors per output layer (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)
|
|
copy_paste: 0.0 # segment copy-paste (probability)
|