text: _base_ = [
      '../../../_base_/datasets/cub200/per_15_sz224_bs24.py',
      '../../../_base_/default_runtime.py',
  ]
  
  # model settings
  model = dict(
      type='SelfTuning',
      queue_size=32,
      proj_dim=1024,
      class_num=200,
      momentum=0.999,
      temperature=0.07,
      pretrained="work_dirs/my_pretrains/official/resnet50_pytorch.pth",
      backbone=dict(
          type='ResNet',
          depth=50,
          in_channels=3,
          out_indices=(3,),  # no conv-1, x-1: stage-x
          norm_cfg=dict(type='BN'),
      ),
      neck=dict(
          type='MoCoV2Neck',
          in_channels=2048,
          hid_channels=2048,
          out_channels=1024,
          with_avg_pool=True),
      head_cls=dict(
          type='ClsHead',
          loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
          with_avg_pool=True, in_channels=2048, num_classes=200)
  )
  
  # optimizer
  optimizer = dict(type='SGD',
                  lr=0.001, momentum=0.9, weight_decay=0.0001, nesterov=False,
                  paramwise_options={'head_cls': dict(lr_mult=10)})  # classification head
  optimizer_config = dict(grad_clip=None)
  
  # learning policy
  lr_config = dict(policy='CosineAnnealing', min_lr=0.)
  
  # runtime settings
  runner = dict(type='EpochBasedRunner', max_epochs=130)
  