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- # 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)
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