class_docstr: COCO-style evaluation.
  
  Args:
      config: a :class:`COCOEvaluationConfig`
class_docstr_tok: ["COCO-style","evaluation",".","Args",":","config",":","a",":","class",":","`","COCOEvaluationConfig","`"]
docstr: Generates aggregate evaluation results for the samples.
  
  If ``self.config.compute_mAP`` is True, this method performs COCO-style
  evaluation as in :meth:`evaluate` to generate precision and recall
  sweeps over the range of IoU thresholds in ``self.config.iou_threshs``.
  In this case, a :class:`COCODetectionResults` instance is returned that
  can compute mAP and PR curves.
  
  Args:
      samples: a :class:`fiftyone.core.collections.SampleCollection`
      matches: a list of
          ``(gt_label, pred_label, iou, pred_confidence, gt_id, pred_id)``
          matches. Either label can be ``None`` to indicate an unmatched
          object
      eval_key (None): the evaluation key for this evaluation
      classes (None): the list of possible classes. If not provided, the
          observed ground truth/predicted labels are used for results
          purposes
      missing (None): a missing label string. Any unmatched objects are
          given this label for results purposes
  
  Returns:
      a :class:`DetectionResults`
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code: def generate_results(
      self, samples, matches, eval_key=None, classes=None, missing=None
  ):
      """Generates aggregate evaluation results for the samples.
  
      If ``self.config.compute_mAP`` is True, this method performs COCO-style
      evaluation as in :meth:`evaluate` to generate precision and recall
      sweeps over the range of IoU thresholds in ``self.config.iou_threshs``.
      In this case, a :class:`COCODetectionResults` instance is returned that
      can compute mAP and PR curves.
  
      Args:
          samples: a :class:`fiftyone.core.collections.SampleCollection`
          matches: a list of
              ``(gt_label, pred_label, iou, pred_confidence, gt_id, pred_id)``
              matches. Either label can be ``None`` to indicate an unmatched
              object
          eval_key (None): the evaluation key for this evaluation
          classes (None): the list of possible classes. If not provided, the
              observed ground truth/predicted labels are used for results
              purposes
          missing (None): a missing label string. Any unmatched objects are
              given this label for results purposes
  
      Returns:
          a :class:`DetectionResults`
      """
      if not self.config.compute_mAP:
          return DetectionResults(
              samples,
              self.config,
              eval_key,
              matches,
              classes=classes,
              missing=missing,
              backend=self,
          )
  
      (
          precision,
          recall,
          thresholds,
          iou_threshs,
          classes,
      ) = _compute_pr_curves(samples, self.config, classes=classes)
  
      return COCODetectionResults(
          samples,
          self.config,
          eval_key,
          matches,
          precision,
          recall,
          iou_threshs,
          classes,
          thresholds=thresholds,
          missing=missing,
          backend=self,
      )
  
imports: import logging
  from collections import defaultdict
  import numpy
  import eta.core.utils
  import fiftyone.core.plots
  import fiftyone.utils.iou
  from .detection import DetectionEvaluation, DetectionEvaluationConfig, DetectionResults
label_desc: Use image node_id 3 for calling the COCOEvaluation obj's underlying member method code with example usage: obj.generate_results(samples, matches, eval_key, classes, missing) and returns: COCODetectionResults, DetectionResults