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# Hyperparameters for VOC fine-tuning
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# python train.py --batch 64 --cfg '' --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
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# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
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lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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giou: 0.05 # GIoU loss gain
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cls: 0.5 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 1.0 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.1 # image translation (+/- fraction)
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scale: 0.5 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mixup: 0.0 # image mixup (probability)
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