text: import os.path as osp
  
  from cvpods.configs.rcnn_fpn_config import RCNNFPNConfig
  
  _config_dict = dict(
      MODEL=dict(
          WEIGHTS="/path/to/your/model.pth",
          MASK_ON=False,
          RESNETS=dict(DEPTH=101),
          ROI_HEADS=dict(
              NUM_CLASSES=80,
              COSINE_ON=True,
              COSINE_SCALE=20,
              BOX_REG_ON=True,
              FREEZE_FEAT=True,
              BASEDET_BONUS=.1,
              CONSISTENCY_COEFF=0.1,
          ),
          BACKBONE=dict(FREEZE=True),
          PROPOSAL_GENERATOR=dict(FREEZE_FEAT=True, FREEZE_BOX=True),
      ),
      GLOBAL=dict(DUMP_TEST=True),
      DATASETS=dict(
          TRAIN=("cocofsdet_2014_trainval_all_30shot_seed8",),
          TEST=("cocofsdet_2014_test_all",),
      ),
      SOLVER=dict(
          IMS_PER_BATCH=16,
          OPTIMIZER=dict(
              BASE_LR=0.05,
          ),
          LR_SCHEDULER=dict(
              STEPS=(7500,),
              MAX_ITER=7500,
              WARMUP_ITER=1000,
          ),
      ),
      INPUT=dict(
          AUG=dict(
              TRAIN_PIPELINES=[
                  ("ResizeShortestEdge",
                   dict(short_edge_length=(640, 672, 704, 736, 768, 800),
                        max_size=1333, sample_style="choice")),
                  ("RandomFlip", dict()),
              ],
              TEST_PIPELINES=[
                  ("ResizeShortestEdge",
                   dict(short_edge_length=800, max_size=1333, sample_style="choice")),
              ],
          )
      ),
      OUTPUT_DIR=osp.join(
          '/data/Outputs/model_logs/cvpods_playground',
          osp.split(osp.realpath(__file__))[0].split("playground/")[-1]),
  )
  
  
  class FasterRCNNConfig(RCNNFPNConfig):
      def __init__(self):
          super(FasterRCNNConfig, self).__init__()
          self._register_configuration(_config_dict)
  
  
  config = FasterRCNNConfig()
  