text: # model settings
  model = dict(
      type='TTFNet',
      pretrained='./pretrain/darknet53.pth',
      backbone=dict(
          type='DarknetV3',
          layers=[1, 2, 8, 8, 4],
          inplanes=[3, 32, 64, 128, 256, 512],
          planes=[32, 64, 128, 256, 512, 1024],
          norm_cfg=dict(type='BN'),
          out_indices=(1, 2, 3, 4),
          frozen_stages=1,
          norm_eval=False),
      neck=None,
      bbox_head=dict(
          type='TTFv3Head',
          inplanes=(128, 256, 512, 1024),
          planes=(256, 128, 64),
          down_ratio=(16, 8, 4),
          hm_head_channels=((128, 128), (128, 128), (64, 64)),
          wh_head_channels=((32, 32), (32, 32), (32, 32)),
          num_classes=81,
          shortcut_cfg=(1, 2, 3),
          s16_shortcut_twice=True,
          wh_scale_factor=(8., 8., 8.),
          alpha=0.6,
          beta=0.6,
          hm_weight=(1.4, 1.4, 1.),
          wh_weight=(7., 7., 5.),
          length_range=((128, 512), (48, 128), (1, 48)),
          train_branch=(True, True, True),
          inf_branch=(True, True, True),
          use_simple_nms=True,
          fast_nms=False,
          max_objs=128,
          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=12,
      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=0.0008,
      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/ttfv3net_d53_1x'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  