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code_test/configs/rpn/rpn_r50_fpn_1x_coco.py

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  1. _base_ = [

  2.     '../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',

  3.     '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'

  4. ]

  5. img_norm_cfg = dict(

  6.     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

  7. train_pipeline = [

  8.     dict(type='LoadImageFromFile'),

  9.     dict(type='LoadAnnotations', with_bbox=True, with_label=False),

  10.     dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),

  11.     dict(type='RandomFlip', flip_ratio=0.5),

  12.     dict(type='Normalize', **img_norm_cfg),

  13.     dict(type='Pad', size_divisor=32),

  14.     dict(type='DefaultFormatBundle'),

  15.     dict(type='Collect', keys=['img', 'gt_bboxes']),

  16. ]

  17. data = dict(train=dict(pipeline=train_pipeline))

  18. evaluation = dict(interval=1, metric='proposal_fast')