text: # model settings
  model = dict(
      type='TTFNet',
      pretrained='modelzoo://resnet18',
      backbone=dict(
          type='ResNet',
          depth=18,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_eval=False,
          style='pytorch'),
      neck=None,
      bbox_head=dict(
          type='TTFLevelHead',
          inplanes=(64, 128, 256, 512),
          planes=(256, 128, 64),
          down_ratio_b1=8,
          down_ratio_b2=4,
          hm_head_channels=(128, 64),
          wh_head_channels=(32, 32),
          hm_head_conv_num=(2, 2),
          wh_head_conv_num=(2, 2),
          num_classes=81,
          wh_scale_factor_b1=8.,
          wh_scale_factor_b2=8.,
          shortcut_cfg=(1, 2, 3),
          extra_shortcut_cfg=3,
          alpha=0.6,
          beta=0.6,
          max_objs=128,
          hm_weight_b1=1.,
          wh_weight_b1=5.,
          hm_weight_b2=1.,
          wh_weight_b2=5.,
          b1_min_length=64,
          b2_max_length=64,
          mdcn_before_s8=True,
          train_branch=['b1'],
          inf_branch=['b1'],
          use_simple_nms=True,
          conv_cfg=None,
          norm_cfg=dict(type='BN')))
  cudnn_benchmark = True
  # training and testing settings
  train_cfg = dict(debug=False)
  test_cfg = dict(score_thr=0.01, max_per_img=100)
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'data/coco/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(512, 512),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=False),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      imgs_per_gpu=16,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2017.json',
          img_prefix=data_root + 'train2017/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD',
      lr=0.002,
      momentum=0.9,
      weight_decay=0.0004,
      paramwise_options=dict(bias_lr_mult=2., bias_decay_mult=0.))
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=1.0 / 5,
      step=[9, 11])
  checkpoint_config = dict(save_every_n_steps=200, max_to_keep=1, keep_every_n_epochs=9)
  # yapf:disable
  log_config = dict(interval=20)
  # yapf:enable
  # runtime settings
  total_epochs = 12
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/ttfv2net_r18_1x'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  