text: _base_ = [
      '../_base_/datasets/coco_detection.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  model = dict(
      type='GFL',
      pretrained='torchvision://resnet101',
      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'),
      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='GFocalHead',
          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=0.250),
          reg_max=16,
          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)
  
  
  # learning policy
  lr_config = dict(step=[16, 22])
  total_epochs = 24
  # multi-scale training
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='Resize',
          img_scale=[(1333, 480), (1333, 960)],
          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']),
  ]
  data = dict(train=dict(pipeline=train_pipeline))
  
  
  dataset_type = 'CocoDataset'
  data_root = 'data/coco/'
  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(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2017.json',
          img_prefix=data_root + 'train2017/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline))
  evaluation = dict(interval=1, metric='bbox')
  