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_base_ = './rpn_r50_fpn_1x_coco.py'
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model = dict(
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backbone=dict(
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norm_cfg=dict(requires_grad=False),
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norm_eval=True,
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style='caffe',
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init_cfg=dict(
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type='Pretrained',
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checkpoint='open-mmlab://detectron2/resnet50_caffe')))
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# use caffe img_norm
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img_norm_cfg = dict(
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mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True, with_label=False),
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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