Updated VOC hyperparameters (#6732)

* Update hyps

* Update hyp.VOC.yaml

* Update pathlib

* Update hyps

* Update hyps

* Update hyps

* Update hyps
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Glenn Jocher authored and GitHub committed 2022-02-22 12:11:18 +01:00
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for Objects365 training
# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
lr0: 0.00258
lrf: 0.17
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# YOLOv5 🚀 by Ultralytics, GPL-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: 319
# Last generation: 434
# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss
# 0.86236, 0.86184, 0.91274, 0.72647, 0.0077056, 0.0042449, 0.0013846
lr0: 0.0033
lrf: 0.15184
momentum: 0.74747
weight_decay: 0.00025
warmup_epochs: 3.4278
warmup_momentum: 0.59032
warmup_bias_lr: 0.18742
box: 0.02
cls: 0.21563
cls_pw: 0.5
obj: 0.50843
obj_pw: 0.6729
iou_t: 0.2
anchor_t: 3.4172
fl_gamma: 0.0
hsv_h: 0.01032
hsv_s: 0.5562
hsv_v: 0.28255
degrees: 0.0
translate: 0.04575
scale: 0.73711
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 0.87158
mixup: 0.04294
copy_paste: 0.0
anchors: 3.3556
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for VOC finetuning
# python train.py --batch 64 --weights yolov5m.pt --data VOC.yaml --img 512 --epochs 50
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
# Hyperparameter Evolution Results
# Generations: 306
# P R mAP.5 mAP.5:.95 box obj cls
# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
lr0: 0.0032
lrf: 0.12
momentum: 0.843
weight_decay: 0.00036
warmup_epochs: 2.0
warmup_momentum: 0.5
warmup_bias_lr: 0.05
box: 0.0296
cls: 0.243
cls_pw: 0.631
obj: 0.301
obj_pw: 0.911
iou_t: 0.2
anchor_t: 2.91
# anchors: 3.63
fl_gamma: 0.0
hsv_h: 0.0138
hsv_s: 0.664
hsv_v: 0.464
degrees: 0.373
translate: 0.245
scale: 0.898
shear: 0.602
perspective: 0.0
flipud: 0.00856
fliplr: 0.5
mosaic: 1.0
mixup: 0.243
copy_paste: 0.0
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Hyperparameters for COCO training from scratch
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # 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
# anchors: 3 # anchors per output layer (0 to ignore)
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: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)