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  1. # Hyperparameters for COCO training from scratch
  2. # python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
  3. # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
  4. lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
  5. momentum: 0.937 # SGD momentum/Adam beta1
  6. weight_decay: 0.0005 # optimizer weight decay 5e-4
  7. giou: 0.05 # GIoU loss gain
  8. cls: 0.5 # cls loss gain
  9. cls_pw: 1.0 # cls BCELoss positive_weight
  10. obj: 1.0 # obj loss gain (scale with pixels)
  11. obj_pw: 1.0 # obj BCELoss positive_weight
  12. iou_t: 0.20 # IoU training threshold
  13. anchor_t: 4.0 # anchor-multiple threshold
  14. fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
  15. hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
  16. hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
  17. hsv_v: 0.4 # image HSV-Value augmentation (fraction)
  18. degrees: 0.0 # image rotation (+/- deg)
  19. translate: 0.1 # image translation (+/- fraction)
  20. scale: 0.5 # image scale (+/- gain)
  21. shear: 0.0 # image shear (+/- deg)
  22. perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
  23. flipud: 0.0 # image flip up-down (probability)
  24. fliplr: 0.5 # image flip left-right (probability)
  25. mixup: 0.0 # image mixup (probability)