| # Hyperparameters for VOC fine-tuning | |
| # python train.py --batch 64 --cfg '' --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 | |
| # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials | |
| lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) | |
| momentum: 0.94 # SGD momentum/Adam beta1 | |
| weight_decay: 0.0005 # optimizer weight decay 5e-4 | |
| giou: 0.05 # GIoU loss gain | |
| cls: 0.2 # cls loss gain | |
| cls_pw: 1.0 # cls BCELoss positive_weight | |
| obj: 0.3 # 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: 1.0 # image rotation (+/- deg) | |
| translate: 0.1 # image translation (+/- fraction) | |
| scale: 0.6 # image scale (+/- gain) | |
| shear: 1.0 # image shear (+/- deg) | |
| perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 | |
| flipud: 0.01 # image flip up-down (probability) | |
| fliplr: 0.5 # image flip left-right (probability) | |
| mixup: 0.2 # image mixup (probability) | |