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WongGawa
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support v12
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‎configs/yolov12/hyp.scratch.l.yaml‎

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optimizer:
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optimizer: sgd
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lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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momentum: 0.937 # SGD momentum/Adam beta1
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nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm
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loss_scale: 1.0 # loss scale for optimizer
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warmup_epochs: 3 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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min_warmup_step: 1000 # minimum warmup step
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group_param: yolov8 # group param strategy
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gp_weight_decay: 0.0005 # group param weight decay 5e-4
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start_factor: 1.0
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end_factor: 0.01
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loss:
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name: YOLOv12Loss
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box: 7.5 # box loss gain
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cls: 0.5 # cls loss gain
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dfl: 1.5 # dfl loss gain
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reg_max: 16
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data:
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num_parallel_workers: 8
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# multi-stage data augment
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train_transforms: {
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stage_epochs: [ 590, 10 ],
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trans_list: [
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[
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.5},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: mixup, alpha: 32.0, beta: 32.0, prob: 0.15, pre_transform: [
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.5},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0}, ]
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},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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],
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[
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{func_name: letterbox, scaleup: True},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]]
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}
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test_transforms: [
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{func_name: letterbox, scaleup: False, only_image: True},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]

‎configs/yolov12/hyp.scratch.m.yaml‎

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optimizer:
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optimizer: sgd
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lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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momentum: 0.937 # SGD momentum/Adam beta1
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nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm
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loss_scale: 1.0 # loss scale for optimizer
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warmup_epochs: 3 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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min_warmup_step: 1000 # minimum warmup step
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group_param: yolov8 # group param strategy
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gp_weight_decay: 0.0005 # group param weight decay 5e-4
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start_factor: 1.0
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end_factor: 0.01
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loss:
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name: YOLOv12Loss
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box: 7.5 # box loss gain
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cls: 0.5 # cls loss gain
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dfl: 1.5 # dfl loss gain
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reg_max: 16
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data:
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num_parallel_workers: 8
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# multi-stage data augment
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train_transforms: {
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stage_epochs: [ 590, 10 ],
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trans_list: [
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[
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.4},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: mixup, alpha: 32.0, beta: 32.0, prob: 0.15, pre_transform: [
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.4},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0}, ]
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},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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],
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[
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{func_name: letterbox, scaleup: True},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]]
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}
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test_transforms: [
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{func_name: letterbox, scaleup: False, only_image: True},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]

‎configs/yolov12/hyp.scratch.n.yaml‎

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optimizer:
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optimizer: sgd
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lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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momentum: 0.937 # SGD momentum/Adam beta1
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nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm
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loss_scale: 1.0 # loss scale for optimizer
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warmup_epochs: 3 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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min_warmup_step: 1000 # minimum warmup step
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group_param: yolov8 # group param strategy
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gp_weight_decay: 0.0005 # group param weight decay 5e-4
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start_factor: 1.0
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end_factor: 0.01
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loss:
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name: YOLOv12Loss
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box: 7.5 # box loss gain
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cls: 0.5 # cls loss gain
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dfl: 1.5 # dfl loss gain
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reg_max: 16
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data:
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num_parallel_workers: 8
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# multi-stage data augment
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train_transforms: {
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stage_epochs: [ 590, 10 ],
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trans_list: [
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[
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.1},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.5, shear: 0.0},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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],
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[
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{func_name: letterbox, scaleup: True},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.5, shear: 0.0},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]]
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}
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test_transforms: [
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{func_name: letterbox, scaleup: False, only_image: True},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]

‎configs/yolov12/hyp.scratch.s.yaml‎

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optimizer:
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optimizer: sgd
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lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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momentum: 0.937 # SGD momentum/Adam beta1
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nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm
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loss_scale: 1.0 # loss scale for optimizer
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warmup_epochs: 3 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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min_warmup_step: 1000 # minimum warmup step
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group_param: yolov8 # group param strategy
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gp_weight_decay: 0.0005 # group param weight decay 5e-4
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start_factor: 1.0
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end_factor: 0.01
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loss:
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name: YOLOv12Loss
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box: 7.5 # box loss gain
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cls: 0.5 # cls loss gain
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dfl: 1.5 # dfl loss gain
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reg_max: 16
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data:
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num_parallel_workers: 8
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# multi-stage data augment
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train_transforms: {
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stage_epochs: [ 590, 10 ],
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trans_list: [
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[
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.15},
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{func_name: resample_segments},
34+
{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: mixup, alpha: 32.0, beta: 32.0, prob: 0.05, pre_transform: [
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{func_name: mosaic, prob: 1.0},
37+
{func_name: copy_paste, prob: 0.15},
38+
{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0}, ]
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},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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],
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[
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{func_name: letterbox, scaleup: True},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
55+
{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]]
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}
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test_transforms: [
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{func_name: letterbox, scaleup: False, only_image: True},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]

‎configs/yolov12/hyp.scratch.x.yaml‎

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optimizer:
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optimizer: sgd
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lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
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momentum: 0.937 # SGD momentum/Adam beta1
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nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm
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loss_scale: 1.0 # loss scale for optimizer
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warmup_epochs: 3 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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min_warmup_step: 1000 # minimum warmup step
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group_param: yolov8 # group param strategy
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gp_weight_decay: 0.0005 # group param weight decay 5e-4
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start_factor: 1.0
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end_factor: 0.01
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loss:
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name: YOLOv12Loss
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box: 7.5 # box loss gain
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cls: 0.5 # cls loss gain
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dfl: 1.5 # dfl loss gain
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reg_max: 16
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data:
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num_parallel_workers: 8
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# multi-stage data augment
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train_transforms: {
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stage_epochs: [ 590, 10 ],
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trans_list: [
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[
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.6},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: mixup, alpha: 32.0, beta: 32.0, prob: 0.2, pre_transform: [
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{func_name: mosaic, prob: 1.0},
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{func_name: copy_paste, prob: 0.6},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0}, ]
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},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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],
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[
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{func_name: letterbox, scaleup: True},
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{func_name: resample_segments},
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{func_name: random_perspective, prob: 1.0, degrees: 0.0, translate: 0.1, scale: 0.9, shear: 0.0},
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{func_name: albumentations},
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{func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4},
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{func_name: fliplr, prob: 0.5},
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{func_name: label_norm, xyxy2xywh_: True},
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{func_name: label_pad, padding_size: 160, padding_value: -1},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]]
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}
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test_transforms: [
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{func_name: letterbox, scaleup: False, only_image: True},
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{func_name: image_norm, scale: 255.},
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{func_name: image_transpose, bgr2rgb: True, hwc2chw: True}
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]

‎configs/yolov12/yolov12-base.yaml‎

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epochs: 600 # total train epochs
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per_batch_size: 32 # 32 * 8 = 256
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img_size: 640
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conf_free: True
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iou_thres: 0.7
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ms_loss_scaler: dynamic
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ms_loss_scaler_value: 65536.0
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clip_grad: True
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anchor_base: False
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opencv_threads_num: 0 # opencv: disable threading optimizations
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network:
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model_name: yolov12
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nc: 80 # number of classes
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reg_max: 16
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stride: [8, 16, 32]
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# YOLOv12n backbone
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, ConvNormAct, [64, 3, 2]] # 0-P1/2
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- [-1, 1, ConvNormAct, [128, 3, 2]] # 1-P2/4
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- [-1, 2, C3k2, [256, False, 0.25]]
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- [-1, 1, ConvNormAct, [256, 3, 2]] # 3-P3/8
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- [-1, 2, C3k2, [512, False, 0.25]]
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- [-1, 1, ConvNormAct, [512, 3, 2]] # 5-P4/16
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- [-1, 4, A2C2f, [512, True, 4]]
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- [-1, 1, ConvNormAct, [1024, 3, 2]] # 7-P5/32
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- [-1, 4, A2C2f, [1024, True, 1]]
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# YOLOv12n head
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head:
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- [-1, 1, Upsample, [None, 2, 'nearest']]
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- [[-1, 6], 1, Concat, [1]] # cat backbone P4
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- [-1, 2, A2C2f, [512, False, -1]] # 11
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- [-1, 1, Upsample, [None, 2, 'nearest']]
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- [[-1, 4], 1, Concat, [1] ] # cat backbone P3
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- [-1, 2, A2C2f, [256, False, -1]] # 14
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- [-1, 1, ConvNormAct, [256, 3, 2]]
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- [[ -1, 11], 1, Concat, [1]] # cat head P4
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- [-1, 2, A2C2f, [512, False, -1]] # 17
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- [-1, 1, ConvNormAct, [512, 3, 2]]
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- [[-1, 8], 1, Concat, [1]] # cat head P5
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- [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
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- [[14, 17, 20], 1, YOLOv12Head, [nc, reg_max, stride]] # Detect(P3, P4, P5)

‎configs/yolov12/yolov12l.yaml‎

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__BASE__: [
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'../coco.yaml',
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'./hyp.scratch.l.yaml',
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'./yolov12-base.yaml',
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]
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overflow_still_update: False
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network:
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scale: l
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depth_multiple: 1.00 # model depth multiple
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width_multiple: 1.00 # layer channel multiple
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max_channels: 512

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