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.
code: def __init__(self):
      self.__model_type = ""
      self.__model_path = ""
      self.__model_labels = []
      self.__model_anchors = []
      self.__detection_config_json_path = ""
      self.__input_size = 416
      self.__object_threshold = 0.4
      self.__nms_threshold = 0.4
      self.__model = None
      self.__detection_utils = CustomDetectionUtils(labels=[])
  
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 1 to create a new CustomObjectDetection object  with example: obj = CustomObjectDetection()