text: _base_ = [
      '../_base_/models/ssd300.py', '../_base_/datasets/ShipRSImageNet_Level1_detection.py',
      '../_base_/schedules/schedule_100e.py', '../_base_/default_runtime.py'
  ]
  input_size = 512
  
  model = dict(
      backbone=dict(input_size=input_size),
      bbox_head=dict(
          num_classes=4,
          in_channels=(512, 1024, 512, 256, 256, 256, 256),
          anchor_generator=dict(
              type='SSDAnchorGenerator',
              scale_major=False,
              input_size=input_size,
              basesize_ratio_range=(0.1, 0.9),
              strides=[8, 16, 32, 64, 128, 256, 512],
              ratios=[[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]])))
  # dataset settings
  dataset_type = 'ShipRSImageNet_Level1'
  data_root = './data/ShipRSImageNet/'
  img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='PhotoMetricDistortion',
          brightness_delta=32,
          contrast_range=(0.5, 1.5),
          saturation_range=(0.5, 1.5),
          hue_delta=18),
      dict(
          type='Expand',
          mean=img_norm_cfg['mean'],
          to_rgb=img_norm_cfg['to_rgb'],
          ratio_range=(1, 4)),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
          min_crop_size=0.3),
      dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='RandomFlip', flip_ratio=0.5),
      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='Normalize', **img_norm_cfg),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=8,
      workers_per_gpu=2,
      train=dict(
          _delete_=True,
          type='RepeatDataset',
          times=5,
          dataset=dict(
              type=dataset_type,
              ann_file=data_root + 'COCO_Format/ShipRSImageNet_bbox_train_level_1.json',
              img_prefix=data_root + 'VOC_Format/JPEGImages/',
              pipeline=train_pipeline)),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4)
  optimizer_config = dict(_delete_=True)
  
  checkpoint_config = dict(interval=20)
  
  evaluation = dict(interval=50, metric='bbox')