code: def __init__(
      self,
      dataset,
      stage="train",
      wandb_log=False,
      use_tensorboard: bool = False,
  ):
      super(ObjectDetectionTracker, self).__init__(
          stage, wandb_log, use_tensorboard
      )
      self._num_classes = dataset.num_classes
      self._dataset = dataset
      self.reset(stage)
      self._metric_func = {
          "loss": min,
          "acc": max,
          "pos": max,
          "neg": min,
          "map": max,
      }
  
imports: from typing import Dict, List, Any
  import torchnet
  import torch
  from collections import OrderedDict
  from torch_points3d.models.model_interface import TrackerInterface
  from torch_points3d.metrics.base_tracker import BaseTracker, meter_value
  from torch_points3d.metrics.meters import APMeter
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from torch_points3d.modules.VoteNet import VoteNetResults
  from torch_points3d.datasets.object_detection.box_data import BoxData
  from .box_detection.ap import eval_detection
label_desc: Use image node_id 1 to create a new ObjectDetectionTracker object from inherited base classes: BaseTracker with example: obj = ObjectDetectionTracker(dataset, stage, wandb_log, use_tensorboard)