text: _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  model = dict(
      backbone=dict(
          type='ResNeSt',
          stem_channels=64,
          depth=50,
          radix=2,
          reduction_factor=4,
          avg_down_stride=True,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=norm_cfg,
          norm_eval=False,
          style='pytorch',
          init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
      roi_head=dict(
          bbox_head=[
              dict(
                  type='Shared4Conv1FCBBoxHead',
                  in_channels=256,
                  conv_out_channels=256,
                  fc_out_channels=1024,
                  norm_cfg=norm_cfg,
                  roi_feat_size=7,
                  num_classes=1,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.1, 0.1, 0.2, 0.2]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                                 loss_weight=1.0)),
              dict(
                  type='Shared4Conv1FCBBoxHead',
                  in_channels=256,
                  conv_out_channels=256,
                  fc_out_channels=1024,
                  norm_cfg=norm_cfg,
                  roi_feat_size=7,
                  num_classes=1,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.05, 0.05, 0.1, 0.1]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                                 loss_weight=1.0)),
              dict(
                  type='Shared4Conv1FCBBoxHead',
                  in_channels=256,
                  conv_out_channels=256,
                  fc_out_channels=1024,
                  norm_cfg=norm_cfg,
                  roi_feat_size=7,
                  num_classes=1,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.033, 0.033, 0.067, 0.067]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
          ], ))
  # # use ResNeSt img_norm
  img_norm_cfg = dict(
      mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='LoadAnnotations',
          with_bbox=True,
          with_mask=False,
          poly2mask=False),
      dict(
          type='Resize',
          img_scale=[(1333, 640), (1333, 800)],
          multiscale_mode='range',
          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(
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  