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# model settings
model = dict(
type='Detection',
pretrained=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(4, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch'),
head=dict(
type='DETRHead',
transformer=dict(
type='DetrTransformer',
in_channels=2048,
num_queries=100,
d_model=256,
nhead=8,
num_encoder_layers=6,
num_decoder_layers=6,
dim_feedforward=2048,
dropout=0.1,
activation='relu',
normalize_before=False,
return_intermediate_dec=True,
),
num_classes=80,
in_channels=2048,
embed_dims=256,
eos_coef=0.1,
cost_dict=dict(
cost_class=1,
cost_bbox=5,
cost_giou=2,
),
weight_dict=dict(
loss_ce=1,
loss_bbox=5,
loss_giou=2,
)))