text: _base_ = [
      '../../../_base_/datasets/fine_tune_based/few_shot_voc.py',
      '../../../_base_/schedules/schedule.py', '../../tfa_r101_fpn.py',
      '../../../_base_/default_runtime.py'
  ]
  # classes splits are predefined in FewShotVOCDataset
  # FewShotVOCDefaultDataset predefine ann_cfg for model reproducibility.
  data = dict(
      train=dict(
          type='FewShotVOCDefaultDataset',
          ann_cfg=[dict(method='TFA', setting='SPLIT3_1SHOT')],
          num_novel_shots=1,
          num_base_shots=1,
          classes='ALL_CLASSES_SPLIT3'),
      val=dict(classes='ALL_CLASSES_SPLIT3'),
      test=dict(classes='ALL_CLASSES_SPLIT3'))
  evaluation = dict(
      interval=4000,
      class_splits=['BASE_CLASSES_SPLIT3', 'NOVEL_CLASSES_SPLIT3'])
  checkpoint_config = dict(interval=4000)
  optimizer = dict(lr=0.001)
  lr_config = dict(
      warmup=None, step=[
          3000,
      ])
  runner = dict(max_iters=4000)
  # base model needs to be initialized with following script:
  #   tools/detection/misc/initialize_bbox_head.py
  # please refer to configs/detection/tfa/README.md for more details.
  load_from = ('work_dirs/tfa_r101_fpn_voc-split3_base-training/'
               'base_model_random_init_bbox_head.pth')
  