text: # model settings
  input_size = 300
  model = dict(
      type='SingleStageDetector',
  #     pretrained='open-mmlab://vgg16_caffe',
      backbone=dict(
          type='SSDVGG',
          input_size=input_size,
          depth=16,
          with_last_pool=False,
          ceil_mode=True,
          out_indices=(3, 4),
          out_feature_indices=(22, 34),
          l2_norm_scale=20),
      neck=None,
      bbox_head=dict(
          type='SSDHead',
          input_size=input_size,
          in_channels=(512, 1024, 512, 256, 256, 256),
          num_classes=81,
          anchor_strides=(8, 16, 32, 64, 100, 300),
          basesize_ratio_range=(0.15, 0.9),
          anchor_ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
          target_means=(.0, .0, .0, .0),
          target_stds=(0.1, 0.1, 0.2, 0.2)))
  cudnn_benchmark = True
  train_cfg = dict(
      assigner=dict(
          type='MaxIoUAssigner',
          pos_iou_thr=0.5,
          neg_iou_thr=0.5,
          min_pos_iou=0.,
          ignore_iof_thr=-1,
          gt_max_assign_all=False),
      smoothl1_beta=1.,
      allowed_border=-1,
      pos_weight=-1,
      neg_pos_ratio=3,
      debug=False)
  test_cfg = dict(
      nms=dict(type='nms', iou_thr=0.45),
      min_bbox_size=0,
      score_thr=0.02,
      max_per_img=200)
  # model training and testing settings
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'data/coco/'
  img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
  data = dict(
      imgs_per_gpu=8,
      workers_per_gpu=3,
      train=dict(
          type='RepeatDataset',
          times=5,
          dataset=dict(
              type=dataset_type,
              ann_file=data_root + 'annotations/instances_train2017.json',
              img_prefix=data_root + 'train2017/',
              img_scale=(300, 300),
              img_norm_cfg=img_norm_cfg,
              size_divisor=None,
              flip_ratio=0.5,
              with_mask=False,
              with_crowd=False,
              with_label=True,
              test_mode=False,
              extra_aug=dict(
                  photo_metric_distortion=dict(
                      brightness_delta=32,
                      contrast_range=(0.5, 1.5),
                      saturation_range=(0.5, 1.5),
                      hue_delta=18),
                  expand=dict(
                      mean=img_norm_cfg['mean'],
                      to_rgb=img_norm_cfg['to_rgb'],
                      ratio_range=(1, 4)),
                  random_crop=dict(
                      min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3)),
              resize_keep_ratio=False)),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(300, 300),
          img_norm_cfg=img_norm_cfg,
          size_divisor=None,
          flip_ratio=0,
          with_mask=False,
          with_label=False,
          test_mode=True,
          resize_keep_ratio=False),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(300, 300),
          img_norm_cfg=img_norm_cfg,
          size_divisor=None,
          flip_ratio=0,
          with_mask=False,
          with_label=False,
          test_mode=True,
          resize_keep_ratio=False))
  # optimizer
  optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
  optimizer_config = dict()
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=1.0 / 3,
      step=[16, 22])
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  # runtime settings
  total_epochs = 24
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/ssd300_coco'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  