|
- # 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)
- momentum: 0.937 # SGD momentum/Adam beta1
- weight_decay: 0.0005 # optimizer weight decay 5e-4
- giou: 0.05 # GIoU 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
- 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)
- mixup: 0.0 # image mixup (probability)
|
