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# model settings
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model = dict(
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type='RetinaNet',
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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style='pytorch',
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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neck=dict(
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type='FPN',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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start_level=1,
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add_extra_convs='on_input',
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num_outs=5),
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bbox_head=dict(
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type='RetinaHead',
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num_classes=80,
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in_channels=256,
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stacked_convs=4,
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feat_channels=256,
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anchor_generator=dict(
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type='AnchorGenerator',
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octave_base_scale=4,
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scales_per_octave=3,
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ratios=[0.5, 1.0, 2.0],
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strides=[8, 16, 32, 64, 128]),
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[.0, .0, .0, .0],
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target_stds=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='FocalLoss',
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use_sigmoid=True,
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gamma=2.0,
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alpha=0.25,
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loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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# model training and testing settings
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train_cfg=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.5,
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neg_iou_thr=0.4,
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min_pos_iou=0,
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ignore_iof_thr=-1),
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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test_cfg=dict(
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nms_pre=1000,
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min_bbox_size=0,
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score_thr=0.05,
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nms=dict(type='nms', iou_threshold=0.5),
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max_per_img=100))
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