text: _base_ = [
      "../_base_/datasets/coco_detection.py",
      "../_base_/schedules/schedule_1x.py",
      "../_base_/default_runtime.py",
  ]
  model = dict(
      type="PAA",
      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,
          start_level=1,
          add_extra_convs="on_output",
          num_outs=5,
      ),
      bbox_head=dict(
          type="PAAHead",
          reg_decoded_bbox=True,
          score_voting=True,
          topk=9,
          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],
          ),
          bbox_coder=dict(
              type="DeltaXYWHBBoxCoder",
              target_means=[0.0, 0.0, 0.0, 0.0],
              target_stds=[0.1, 0.1, 0.2, 0.2],
          ),
          loss_cls=dict(
              type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
          ),
          loss_bbox=dict(type="GIoULoss", loss_weight=1.3),
          loss_centerness=dict(
              type="CrossEntropyLoss", use_sigmoid=True, loss_weight=0.5
          ),
      ),
  )
  # training and testing settings
  train_cfg = dict(
      assigner=dict(
          type="MaxIoUAssigner",
          pos_iou_thr=0.1,
          neg_iou_thr=0.1,
          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=0.6),
      max_per_img=100,
  )
  # optimizer
  optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)
  