text: _base_ = [
      '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
  ]
  
  # 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='TTFHead',
          num_classes=80,
          in_channels=128,
          feat_channels=(128, 64),
          stacked_convs=(2, 2),
          offset_base=16,
          area_cfg=dict(
              type='log',
              agnostic=True,
              gaussian=True,
              alpha=0.54,
              beta=0.54
          ),
          loss_heatmap=dict(
              type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
          loss_bbox=dict(type='CenterGIoULoss', loss_weight=5.0)))
  cudnn_benchmark = True
  # training and testing settings
  train_cfg = dict(
      vis_every_n_iters=100,
      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(
      samples_per_gpu=12,
      workers_per_gpu=2,
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=0.0004,
                   paramwise_cfg=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(interval=1)
  # runtime settings
  total_epochs = 12