class_docstr: Base class for segmentation evaluation methods.
  
  Args:
      config: a :class:`SegmentationEvaluationConfig`
class_docstr_tok: ["Base","class","for","segmentation","evaluation","methods",".","Args",":","config",":","a",":","class",":","`","SegmentationEvaluationConfig","`"]
docstr: Evaluates the predicted segmentation masks in the given samples with
  respect to the specified ground truth masks.
  
  Args:
      samples: a :class:`fiftyone.core.collections.SampleCollection`
      eval_key (None): an evaluation key for this evaluation
      mask_targets (None): a dict mapping mask values to labels. May
          contain a subset of the possible classes if you wish to
          evaluate a subset of the semantic classes. By default, the
          observed pixel values are used as labels
  
  Returns:
      a :class:`SegmentationResults` instance
docstr_tok: ["Evaluates","the","predicted","segmentation","masks","in","the","given","samples","with","respect","to","the","specified","ground","truth","masks",".","Args",":","samples",":","a",":","class",":","`","fiftyone.core.collections.SampleCollection","`","eval_key","(","None",")",":","an","evaluation","key","for","this","evaluation","mask_targets","(","None",")",":","a","dict","mapping","mask","values","to","labels",".","May","contain","a","subset","of","the","possible","classes","if","you","wish","to","evaluate","a","subset","of","the","semantic","classes",".","By","default",",","the","observed","pixel","values","are","used","as","labels","Returns",":","a",":","class",":","`","SegmentationResults","`","instance"]
code: def evaluate_samples(self, samples, eval_key=None, mask_targets=None):
      """Evaluates the predicted segmentation masks in the given samples with
      respect to the specified ground truth masks.
  
      Args:
          samples: a :class:`fiftyone.core.collections.SampleCollection`
          eval_key (None): an evaluation key for this evaluation
          mask_targets (None): a dict mapping mask values to labels. May
              contain a subset of the possible classes if you wish to
              evaluate a subset of the semantic classes. By default, the
              observed pixel values are used as labels
  
      Returns:
          a :class:`SegmentationResults` instance
      """
      pass
  
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 2 for calling the SegmentationEvaluation obj's underlying member method code with example usage: obj.evaluate_samples(samples, eval_key, mask_targets) without return types