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pan_r18_ic15_finetune.py
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pan_r18_ic15_finetune.py
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model = dict(
type='PAN',
backbone=dict(
type='resnet18',
pretrained=True
),
neck=dict(
type='FPEM_v1',
in_channels=(64, 128, 256, 512),
out_channels=128
),
detection_head=dict(
type='PA_Head',
in_channels=512,
hidden_dim=128,
num_classes=6,
loss_text=dict(
type='DiceLoss',
loss_weight=1.0
),
loss_kernel=dict(
type='DiceLoss',
loss_weight=0.5
),
loss_emb=dict(
type='EmbLoss_v1',
feature_dim=4,
loss_weight=0.25
)
)
)
data = dict(
batch_size=16,
train=dict(
type='PAN_IC15',
split='train',
is_transform=True,
img_size=736,
short_size=736,
kernel_scale=0.5,
read_type='cv2'
),
test=dict(
type='PAN_IC15',
split='test',
short_size=736,
read_type='cv2'
)
)
train_cfg = dict(
lr=1e-3,
schedule='polylr',
epoch=600,
optimizer='Adam',
pretrain='checkpoints/pan_r18_synth/checkpoint_1ep.pth.tar'
)
test_cfg = dict(
min_score=0.85,
min_area=16,
bbox_type='rect',
result_path='outputs/submit_ic15.zip'
)