text: _base_ = [
      '../_base_/datasets/coco_detection_mstrain_480_960.py',
      '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
  ]
  model = dict(
      type='GFL',
      backbone=dict(
          type='ResNet',
          depth=101,
          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',
          init_cfg=dict(type='Pretrained',
                        checkpoint='torchvision://resnet101')),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs='on_output',
          num_outs=5),
      bbox_head=dict(
          type='GFLHead',
          num_classes=80,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              ratios=[1.0],
              octave_base_scale=8,
              scales_per_octave=1,
              strides=[8, 16, 32, 64, 128]),
          loss_cls=dict(
              type='QualityFocalLoss',
              use_sigmoid=False,
              beta=2.0,
              loss_weight=1.0),
          loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
          reg_max=16,
          use_dgqp=True,
          reg_topk=4,
          reg_channels=64,
          add_mean=True,
          loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
      # training and testing settings
      train_cfg=dict(
          assigner=dict(type='ATSSAssigner', topk=9),
          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=0.6),
          max_per_img=100))
  # optimizer
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  