class_docstr: Interface for object detection/segmentation evaluators.
  
  The evaluator should be able to accumulate the model outputs and compute
  the relevant metrics.
class_docstr_tok: ["Interface","for","object","detection\/segmentation","evaluators",".","The","evaluator","should","be","able","to","accumulate","the","model","outputs","and","compute","the","relevant","metrics","."]
docstr: Prints a summary of computed metrics to standard output.
  
  This should be called after `evaluate()`.
  
  :return: None
code: def summarize(self):
      """
      Prints a summary of computed metrics to standard output.
  
      This should be called after `evaluate()`.
  
      :return: None
      """
      pass
  
imports: import os
  from pathlib import Path
  import torch
  import json
  from typing import Any, Generic, List, Union, TypeVar, Tuple, Dict, TYPE_CHECKING, Type, Callable, Sequence, Optional
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from torch import Tensor
  from json import JSONEncoder
  from torch.utils.data import Subset, ConcatDataset
  from typing_extensions import Protocol
  from avalanche.evaluation import PluginMetric
  from avalanche.evaluation.metric_results import MetricValue
  from avalanche.evaluation.metric_utils import get_metric_name
label_desc: Use image node_id 3 for calling the DetectionEvaluator obj's underlying member method code with example usage: obj.summarize() without return types