class_docstr: Base class for configuring :class:`SegmentationEvaluation` instances.
  
  Args:
      pred_field: the name of the field containing the predicted
          :class:`fiftyone.core.labels.Segmentation` instances
      gt_field: the name of the field containing the ground truth
          :class:`fiftyone.core.labels.Segmentation` instances
      compute_dice (False): whether to compute the Dice coefficient for each
          sample
class_docstr_tok: ["Base","class","for","configuring",":","class",":","`","SegmentationEvaluation","`","instances",".","Args",":","pred_field",":","the","name","of","the","field","containing","the","predicted",":","class",":","`","fiftyone.core.labels.Segmentation","`","instances","gt_field",":","the","name","of","the","field","containing","the","ground","truth",":","class",":","`","fiftyone.core.labels.Segmentation","`","instances","compute_dice","(","False",")",":","whether","to","compute","the","Dice","coefficient","for","each","sample"]
code: def __init__(
      self, pred_field, gt_field, compute_dice=False, **kwargs
  ):
      super().__init__(**kwargs)
      self.pred_field = pred_field
      self.gt_field = gt_field
      self.compute_dice = compute_dice
  
imports: from copy import deepcopy
  import logging
  import inspect
  import warnings
  import numpy
  import sklearn.metrics
  import eta.core.image
  import eta.core.utils
  import fiftyone
  import fiftyone.core.evaluation
  import fiftyone.core.fields
  import fiftyone.core.labels
  import fiftyone.core.utils
  import fiftyone.core.validation
  from .base import BaseEvaluationResults
label_desc: Use image node_id 1 to create a new SegmentationEvaluationConfig object from inherited base classes: foe with example: obj = SegmentationEvaluationConfig(pred_field, gt_field, compute_dice)