# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# Hyperparameters for VOC training

# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve

# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials

# YOLOv5 Hyperparameter Evolution Results

# Best generation: 467

# Last generation: 996

#    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss

#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865

lr0: 0.00334

lrf: 0.15135

momentum: 0.74832

weight_decay: 0.00025

warmup_epochs: 3.3835

warmup_momentum: 0.59462

warmup_bias_lr: 0.18657

box: 0.02

cls: 0.21638

cls_pw: 0.5

obj: 0.51728

obj_pw: 0.67198

iou_t: 0.2

anchor_t: 3.3744

fl_gamma: 0.0

hsv_h: 0.01041

hsv_s: 0.54703

hsv_v: 0.27739

degrees: 0.0

translate: 0.04591

scale: 0.75544

shear: 0.0

perspective: 0.0

flipud: 0.0

fliplr: 0.5

mosaic: 0.85834

mixup: 0.04266

copy_paste: 0.0

anchors: 3.412