class_docstr: This is the object detection class for using your custom trained models. It supports your custom trained YOLOv3 model and allows to you to perform object detection in images.
docstr: 'setModelPath' is used to specify the filepath to your custom detection model
  :param detection_model_path: path to the .h5 model file.
      Usually is one of those under <data_directory>/models/detection_model-ex-ddd--loss-dddd.ddd.h5
  :return: None
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code: def setModelPath(self, detection_model_path):
      """
      'setModelPath' is used to specify the filepath to your custom detection model
      :param detection_model_path: path to the .h5 model file.
          Usually is one of those under <data_directory>/models/detection_model-ex-ddd--loss-dddd.ddd.h5
      :return: None
      """
      self.__model_path = detection_model_path
  
imports: import os
  import re
  import numpy
  import json
  from imageai.Detection.Custom.voc import parse_voc_annotation
  from imageai.Detection.YOLO.yolov3 import yolov3_main, yolov3_train, dummy_loss
  from imageai.Detection.Custom.generator import BatchGenerator
  from imageai.Detection.Custom.utils.utils import normalize, evaluate, makedirs
  from tensorflow.keras.callbacks import ReduceLROnPlateau
  from tensorflow.keras.optimizers import Adam
  from imageai.Detection.Custom.callbacks import CustomModelCheckpoint
  from imageai.Detection.Custom.utils.multi_gpu_model import multi_gpu_model
  from imageai.Detection.Custom.gen_anchors import generateAnchors
  import tensorflow
  from tensorflow.keras.models import load_model
  from tensorflow.keras import Input
  from tensorflow.keras.callbacks import TensorBoard
  import tensorflow.keras.backend
  import cv2
label_desc: Use image node_id 3 for calling the CustomObjectDetection obj's underlying member method code with example usage: obj.setModelPath(detection_model_path) without return types