text: 
  IMAGE_KEY = "image_raw"
  MASK_KEY = "mask_raw"
  
  EPOCHS = 1
  TRAIN_STEPS = 1
  EVAL_STEPS = 2
  TRAIN_BATCH_SIZE = 4
  EVAL_BATCH_SIZE = 1
  HEIGHT = 320
  WIDTH = 320
  PRETRAINED_WEIGHTS = "imagenet"
  BACKBONE_TRAINABLE = False
  BACKBONE_NAME = "efficientnetb3"
  
  TOTAL_CLASSES = ['sky', 'building', 'pole', 'road', 'pavement', 
                 'tree', 'signsymbol', 'fence', 'car', 
                 'pedestrian', 'bicyclist', 'unlabelled']
  N_TOTAL_CLASSES = 12
  MODEL_CLASSES = ["sky", "building"]
  if len(MODEL_CLASSES) == 11:
      ALL_CLASSES = True
      MODEL_CLASSES = TOTAL_CLASSES
      N_MODEL_CLASSES = N_TOTAL_CLASSES
  else:
      ALL_CLASSES = False
      N_MODEL_CLASSES = len(MODEL_CLASSES) + 1