class_docstr: Wrapper around [cocoapi evaluator](https://github.com/cocodataset/cocoapi)
  
  Calculates average precision.
  
  # Arguments
      metric_type: Dependent on the task you're solving.
      print_summary: If `True`, prints a table with statistics.
      show_pbar: If `True` shows pbar when preparing the data for evaluation.
code: def __init__(
      self,
      metric_type: COCOMetricType = COCOMetricType.bbox,
      iou_thresholds: Optional[Sequence[float]] = None,
      print_summary: bool = False,
      show_pbar: bool = False,
  ):
      self.metric_type = metric_type
      self.iou_thresholds = iou_thresholds
      self.print_summary = print_summary
      self.show_pbar = show_pbar
      self._records, self._preds = [], []
  
file_path: icevision/icevision/metrics/coco_metric/coco_metric.py
imports: from icevision.imports import *
  from icevision.utils import *
  from icevision.data import *
  from icevision.metrics.metric import *
label_desc: Use image node_id 1 to create a new COCOMetric object from inherited base classes: Metric with example: obj = COCOMetric(metric_type, iou_thresholds, print_summary, show_pbar)