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update code
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@Chilicyy
Chilicyy committed Jan 6, 2023
1 parent 2a66b22 commit 5122fd7
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55 changes: 55 additions & 0 deletions configs/experiment/yolov6t.py
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# YOLOv6t model
model = dict(
type='YOLOv6t',
pretrained=None,
depth_multiple=0.33,
width_multiple=0.375,
backbone=dict(
type='EfficientRep',
num_repeats=[1, 6, 12, 18, 6],
out_channels=[64, 128, 256, 512, 1024],
),
neck=dict(
type='RepPANNeck',
num_repeats=[12, 12, 12, 12],
out_channels=[256, 128, 128, 256, 256, 512],
),
head=dict(
type='EffiDeHead',
in_channels=[128, 256, 512],
num_layers=3,
begin_indices=24,
anchors=1,
out_indices=[17, 20, 23],
strides=[8, 16, 32],
iou_type='siou',
use_dfl=False,
reg_max=0 #if use_dfl is False, please set reg_max to 0
)
)

solver = dict(
optim='SGD',
lr_scheduler='Cosine',
lr0=0.01,
lrf=0.01,
momentum=0.937,
weight_decay=0.0005,
warmup_epochs=3.0,
warmup_momentum=0.8,
warmup_bias_lr=0.1
)

data_aug = dict(
hsv_h=0.015,
hsv_s=0.7,
hsv_v=0.4,
degrees=0.0,
translate=0.1,
scale=0.5,
shear=0.0,
flipud=0.0,
fliplr=0.5,
mosaic=1.0,
mixup=0.0,
)
55 changes: 55 additions & 0 deletions configs/experiment/yolov6t_finetune.py
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# YOLOv6t model
model = dict(
type='YOLOv6t',
pretrained='weights/yolov6t.pt',
depth_multiple=0.33,
width_multiple=0.375,
backbone=dict(
type='EfficientRep',
num_repeats=[1, 6, 12, 18, 6],
out_channels=[64, 128, 256, 512, 1024],
),
neck=dict(
type='RepPANNeck',
num_repeats=[12, 12, 12, 12],
out_channels=[256, 128, 128, 256, 256, 512],
),
head=dict(
type='EffiDeHead',
in_channels=[128, 256, 512],
num_layers=3,
begin_indices=24,
anchors=1,
out_indices=[17, 20, 23],
strides=[8, 16, 32],
iou_type='siou',
use_dfl=False,
reg_max=0 #if use_dfl is False, please set reg_max to 0
)
)

solver = dict(
optim='SGD',
lr_scheduler='Cosine',
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
)

data_aug = dict(
hsv_h=0.0138,
hsv_s=0.664,
hsv_v=0.464,
degrees=0.373,
translate=0.245,
scale=0.898,
shear=0.602,
flipud=0.00856,
fliplr=0.5,
mosaic=1.0,
mixup=0.243,
)
62 changes: 62 additions & 0 deletions configs/yolov6l6.py
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# YOLOv6l6 model
model = dict(
type='YOLOv6l6',
pretrained=None,
depth_multiple=1.0,
width_multiple=1.0,
backbone=dict(
type='CSPBepBackbone_P6',
num_repeats=[1, 6, 12, 18, 6, 6],
out_channels=[64, 128, 256, 512, 768, 1024],
csp_e=float(1)/2,
fuse_P2=True,
),
neck=dict(
type='CSPRepBiFPANNeck_P6',
num_repeats=[12, 12, 12, 12, 12, 12],
out_channels=[512, 256, 128, 256, 512, 1024],
csp_e=float(1)/2,
),
head=dict(
type='EffiDeHead',
in_channels=[128, 256, 512, 1024],
num_layers=4,
anchors=1,
strides=[8, 16, 32, 64],
atss_warmup_epoch=4,
iou_type='giou',
use_dfl=True,
reg_max=16, #if use_dfl is False, please set reg_max to 0
distill_weight={
'class': 1.0,
'dfl': 1.0,
},
)
)

solver = dict(
optim='SGD',
lr_scheduler='Cosine',
lr0=0.01,
lrf=0.01,
momentum=0.937,
weight_decay=0.0005,
warmup_epochs=3.0,
warmup_momentum=0.8,
warmup_bias_lr=0.1
)

data_aug = dict(
hsv_h=0.015,
hsv_s=0.7,
hsv_v=0.4,
degrees=0.0,
translate=0.1,
scale=0.9,
shear=0.0,
flipud=0.0,
fliplr=0.5,
mosaic=1.0,
mixup=0.2,
)
training_mode = "conv_silu"
62 changes: 62 additions & 0 deletions configs/yolov6l6_finetune.py
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# YOLOv6l6 model
model = dict(
type='YOLOv6l6',
pretrained='weights/yolov6l6.pt',
depth_multiple=1.0,
width_multiple=1.0,
backbone=dict(
type='CSPBepBackbone_P6',
num_repeats=[1, 6, 12, 18, 6, 6],
out_channels=[64, 128, 256, 512, 768, 1024],
csp_e=float(1)/2,
fuse_P2=True,
),
neck=dict(
type='CSPRepBiFPANNeck_P6',
num_repeats=[12, 12, 12, 12, 12, 12],
out_channels=[512, 256, 128, 256, 512, 1024],
csp_e=float(1)/2,
),
head=dict(
type='EffiDeHead',
in_channels=[128, 256, 512, 1024],
num_layers=4,
anchors=1,
strides=[8, 16, 32, 64],
atss_warmup_epoch=4,
iou_type='giou',
use_dfl=True,
reg_max=16, #if use_dfl is False, please set reg_max to 0
distill_weight={
'class': 1.0,
'dfl': 1.0,
},
)
)

solver = dict(
optim='SGD',
lr_scheduler='Cosine',
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
)

data_aug = dict(
hsv_h=0.0138,
hsv_s=0.664,
hsv_v=0.464,
degrees=0.373,
translate=0.245,
scale=0.898,
shear=0.602,
flipud=0.00856,
fliplr=0.5,
mosaic=1.0,
mixup=0.243,
)
training_mode = "conv_silu"
61 changes: 61 additions & 0 deletions configs/yolov6m6.py
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# YOLOv6m6 model
model = dict(
type='YOLOv6m6',
pretrained=None,
depth_multiple=0.60,
width_multiple=0.75,
backbone=dict(
type='CSPBepBackbone_P6',
num_repeats=[1, 6, 12, 18, 6, 6],
out_channels=[64, 128, 256, 512, 768, 1024],
csp_e=float(2)/3,
fuse_P2=True,
),
neck=dict(
type='CSPRepBiFPANNeck_P6',
num_repeats=[12, 12, 12, 12, 12, 12],
out_channels=[512, 256, 128, 256, 512, 1024],
csp_e=float(2)/3,
),
head=dict(
type='EffiDeHead',
in_channels=[128, 256, 512, 1024],
num_layers=4,
anchors=1,
strides=[8, 16, 32, 64],
atss_warmup_epoch=4,
iou_type='giou',
use_dfl=True,
reg_max=16, #if use_dfl is False, please set reg_max to 0
distill_weight={
'class': 1.0,
'dfl': 1.0,
},
)
)

solver = dict(
optim='SGD',
lr_scheduler='Cosine',
lr0=0.01,
lrf=0.01,
momentum=0.937,
weight_decay=0.0005,
warmup_epochs=3.0,
warmup_momentum=0.8,
warmup_bias_lr=0.1
)

data_aug = dict(
hsv_h=0.015,
hsv_s=0.7,
hsv_v=0.4,
degrees=0.0,
translate=0.1,
scale=0.9,
shear=0.0,
flipud=0.0,
fliplr=0.5,
mosaic=1.0,
mixup=0.1,
)
61 changes: 61 additions & 0 deletions configs/yolov6m6_finetune.py
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# YOLOv6m6 model
model = dict(
type='YOLOv6m6',
pretrained='weights/yolov6m6.pt',
depth_multiple=0.60,
width_multiple=0.75,
backbone=dict(
type='CSPBepBackbone_P6',
num_repeats=[1, 6, 12, 18, 6, 6],
out_channels=[64, 128, 256, 512, 768, 1024],
csp_e=float(2)/3,
fuse_P2=True,
),
neck=dict(
type='CSPRepBiFPANNeck_P6',
num_repeats=[12, 12, 12, 12, 12, 12],
out_channels=[512, 256, 128, 256, 512, 1024],
csp_e=float(2)/3,
),
head=dict(
type='EffiDeHead',
in_channels=[128, 256, 512, 1024],
num_layers=4,
anchors=1,
strides=[8, 16, 32, 64],
atss_warmup_epoch=4,
iou_type='giou',
use_dfl=True,
reg_max=16, #if use_dfl is False, please set reg_max to 0
distill_weight={
'class': 1.0,
'dfl': 1.0,
},
)
)

solver = dict(
optim='SGD',
lr_scheduler='Cosine',
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
)

data_aug = dict(
hsv_h=0.0138,
hsv_s=0.664,
hsv_v=0.464,
degrees=0.373,
translate=0.245,
scale=0.898,
shear=0.602,
flipud=0.00856,
fliplr=0.5,
mosaic=1.0,
mixup=0.243,
)
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