text: # model settings
  model = dict(
      type='RPN',
      pretrained='/media/ser606/Data/DoubleCircle/model/vgg16-397923af.pth',
      backbone=dict(
          type='VGG',
          depth=16,
          num_stages=5,
          out_indices=[1, 2, 3, 4],
          frozen_stages=1),
      neck=dict(
          type='FPN',
          in_channels=[128, 256, 512, 512],
          out_channels=256,
          num_outs=4,
      ),
      rpn_head=dict(
          type='RPNHead',
          in_channels=256,
          feat_channels=256,
          anchor_scales=[8, 10, 12, 14],
          anchor_ratios=[1 / 0.5, 1.0],
          anchor_strides=[4, 8, 16, 32],
          anchor_base_sizes=[4, 8, 16, 32],
          target_means=[.0, .0, .0, .0],
          target_stds=[1.0, 1.0, 1.0, 1.0],
          use_sigmoid_cls=True))
  # model training and testing settings
  train_cfg = dict(
      rpn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.5,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=128,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=False,
              pos_balance_sampling=False,
              neg_balance_thr=0),
          allowed_border=0,
          pos_weight=-1,
          smoothl1_beta=1.0,
          debug=False))
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=20000,
          nms_post=20000,
          max_num=40,
          nms_thr=0.5,
          min_bbox_size=0))
  # dataset settings
  dataset_type = 'CaltechDataset'
  data_root = '/media/ser606/Data/DoubleCircle/datasets/Caltech/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  data = dict(
      imgs_per_gpu=6,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations-pkl/train-all.pkl',
          img_prefix=data_root + 'images/',
          img_scale=1.6,
          img_norm_cfg=img_norm_cfg,
          size_divisor=None,
          flip_ratio=0.5,
          with_mask=False,
          with_crowd=False,
          with_label=False),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations-pkl/test-all.pkl',
          img_prefix=data_root + 'images/',
          img_scale=1.6,
          img_norm_cfg=img_norm_cfg,
          size_divisor=None,
          flip_ratio=0,
          with_mask=False,
          with_crowd=False,
          with_label=False),
      test=dict(
          type='CocoDataset',
          ann_file=data_root + 'annotations-json/test-all.json',
          img_prefix=data_root + 'images/',
          img_scale=1.6,
          img_norm_cfg=img_norm_cfg,
          size_divisor=None,
          flip_ratio=0,
          with_mask=False,
          with_label=False,
          test_mode=True))
  # optimizer
  optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)
  # runner configs
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=5000,
      warmup_ratio=1.0 / 3,
      step=[8])
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=1000,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  # runtime settings
  total_epochs = 20
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = '../../work_dirs/rpn_vgg16_fpn_caltech'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  