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Merge pull request #4 from aash1999/cbam-imp
adding hyp and model files as mentioned in paper
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | ||
# Hyperparameters for low-augmentation COCO training from scratch | ||
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear | ||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials | ||
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lr0: 0.001 # initial learning rate (SGD=1E-2, Adam=1E-3) | ||
lrf: 0.01 # 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: 0.25 # cls loss gain | ||
cls_pw: 1.0 # cls BCELoss positive_weight | ||
obj: 0.5 # 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: 0.4 # image HSV-Hue augmentation (fraction) | ||
hsv_s: 0.3 # image HSV-Saturation augmentation (fraction) | ||
hsv_v: 0.5 # image HSV-Value augmentation (fraction) | ||
degrees: 0.2 # image rotation (+/- deg) | ||
translate: 0.1 # image translation (+/- fraction) | ||
scale: 0.4 # 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)s | ||
mixup: 0.2 # image mixup (probability) | ||
copy_paste: 0.1 # segment copy-paste (probability) |
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license | ||
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# Parameters | ||
nc: 10 # number of classes | ||
depth_multiple: 0.33 # model depth multiple | ||
width_multiple: 0.50 # layer channel multiple | ||
anchors: | ||
- [2.9434,4.0435, 3.8626,8.5592, 6.8534, 5.9391] | ||
- [10,13, 16,30, 33,23] # P3/8 | ||
- [30,61, 62,45, 59,119] # P4/16 | ||
- [116,90, 156,198, 373,326] # P5/32 | ||
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# YOLOv5 v6.0 backbone | ||
backbone: | ||
# [from, number, module, args] | ||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 | ||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 | ||
[-1, 3, C3, [128]], | ||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 | ||
[-1, 6, C3, [256]], | ||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 | ||
[-1, 9, C3, [512]], | ||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 | ||
[-1, 3, C3, [1024]], | ||
[-1, 3, CBAMBottleneck, [1024, 3]], | ||
[-1, 1, SPPF, [1024, 5]], # 10 | ||
] | ||
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# YOLOv5 v6.0 head | ||
head: | ||
[[-1, 1, Involution, [1024, 1, 1]], | ||
[-1, 1, Conv, [512, 1, 1]], | ||
[-1, 1, nn.Upsample, [None, 2, 'nearest']], | ||
[[-1, 6], 1, Concat, [1]], # cat backbone P4 | ||
[-1, 3, C3, [512, False]], # 15 | ||
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[-1, 1, Conv, [512, 1, 1]], | ||
[-1, 1, nn.Upsample, [None, 2, 'nearest']], | ||
[[-1, 4], 1, Concat, [1]], # cat backbone P3 | ||
[-1, 3, C3, [512, False]], # 19 | ||
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[-1, 1, Conv, [256, 1, 1]], | ||
[-1, 1, nn.Upsample, [None, 2, 'nearest']], | ||
[[-1, 2], 1, Concat, [1]], | ||
[-1, 3, C3, [256, False]], # 23 160*160 p2 head | ||
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[-1, 1, Conv, [256, 3, 2]], | ||
[[-1, 19], 1, Concat, [1]], | ||
[-1, 3, C3, [512, False]], # 26 80*80 p3 head | ||
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[-1, 1, Conv, [256, 3, 2]], | ||
[[-1, 15], 1, Concat, [1]], | ||
[-1, 3, C3, [256, False]], # 29 40*40 p4 head | ||
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[-1, 1, Conv, [512, 3, 2]], | ||
[[-1, 11], 1, Concat, [1]], | ||
[-1, 3, C3, [1024, False]], # 32 20*20 p5 head | ||
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[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) | ||
] |