text: _base_ = [
      '../../_base_/datasets/dota_detection_v2.0_obb.py',
      '../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
  ]
  
  # model settings
  model = dict(
      type='FasterRCNNOBB',
      pretrained='torchvision://resnet50',
      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=True),
          norm_eval=True,
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          num_outs=5),
      rpn_head=dict(
          type='RPNHead',
          in_channels=256,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              scales=[8],
              ratios=[0.5, 1.0, 2.0],
              strides=[4, 8, 16, 32, 64]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[1.0, 1.0, 1.0, 1.0]),
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
      roi_head=dict(
          type='StandardRoIHeadRbbox',
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          bbox_head=dict(
              type='Shared2FCBBoxHeadRbbox',
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=18,
              bbox_coder=dict(
                  type='DeltaXYWHABBoxCoder',
                  target_means=[0., 0., 0., 0., 0.],
                  target_stds=[0.1, 0.1, 0.1, 0.1, 0.1]),
              reg_class_agnostic=False,
              loss_cls=dict(
                  type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
              loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0))),
      # model training and testing settings
      train_cfg=dict(
          rpn=dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.7,
                  neg_iou_thr=0.3,
                  min_pos_iou=0.3,
                  match_low_quality=True,
                  gpu_assign_thr=500,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=256,
                  pos_fraction=0.5,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=False),
              allowed_border=0,
              pos_weight=-1,
              debug=False),
          rpn_proposal=dict(
              nms_pre=2000,
              max_per_img=2000,
              nms=dict(type='nms', iou_threshold=0.7),
              min_bbox_size=0),
          rcnn=dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.5,
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  match_low_quality=False,
                  gpu_assign_thr=500,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              pos_weight=-1,
              debug=False)),
      test_cfg=dict(
          rpn=dict(
              nms_pre=2000,
              max_per_img=2000,
              nms=dict(type='nms', iou_threshold=0.7),
              min_bbox_size=0),
          rcnn=dict(
              score_thr=0.05,
              nms=dict(type='nms_rotated', iou_threshold=0.1),
              max_per_img=2000)
          # soft-nms is also supported for rcnn testing
          # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
      )
  )
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
  
  