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# 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
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