text: _base_ = [
      '../_base_/datasets/visdrone_detection.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  # model settings
  model = dict(
      type='FCOS',
      pretrained='open-mmlab://detectron/resnet50_caffe',
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=dict(type='BN', requires_grad=False),
          norm_eval=True,
          style='caffe'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs=True,
          extra_convs_on_inputs=False,  # use P5
          num_outs=5,
          relu_before_extra_convs=True),
      bbox_head=dict(
          type='FCOSHead',
          num_classes=10,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          strides=[8, 16, 32, 64, 128],
          loss_cls=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_bbox=dict(type='IoULoss', loss_weight=1.0),
          loss_centerness=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
      # training and testing settings
      train_cfg=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.5,
              neg_iou_thr=0.4,
              min_pos_iou=0,
              ignore_iof_thr=-1),
          allowed_border=-1,
          pos_weight=-1,
          debug=False),
      test_cfg=dict(
          nms_pre=1000,
          min_bbox_size=0,
          score_thr=0.05,
          nms=dict(type='nms', iou_threshold=0.5),
          max_per_img=100))
  img_norm_cfg = dict(
      mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
      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=(1333, 800),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              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=2,
      workers_per_gpu=2,
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
  optimizer_config = dict(
      _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='constant',
      warmup_iters=500,
      warmup_ratio=1.0 / 3,
      step=[8, 11])
  runner = dict(type='EpochBasedRunner', max_epochs=12)
  