How to use Tensorflows object detection model zoo with pretrained models but missing label files (.pbtxt)

I'm trying to get the Tensorflow object detection running. Unfortunately, I have found that the pre-trained models of Tensorflow all come without label files. How can I get these files? All I want to do is test the object detection for a few pictures and get the labels displayed. The following code is what I have so far. Unfortunately, almost all of the tutorials use label files (.pbtxt) that I do not have. On the appropriate download page of Tensorflow Tensorflow detection model zoo is said that the label files are included in the download, but they are not. I have downloaded different models. None of the models had a label file. I would be very grateful if someone could help me.

My Code so far:

import tensorflow as tf
import cv2
import os

def get_frozen_graph(graph_file):
    """Read Frozen Graph file from disk."""
    with tf.gfile.FastGFile(graph_file, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
    return graph_def

# The TensorRT inference graph file downloaded from Colab or your local machine.
pb_fname = os.path.join(os.getcwd(), "faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28", "frozen_inference_graph.pb")
trt_graph = get_frozen_graph(pb_fname)

input_names = ['image_tensor']

# Create session and load graph
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
tf_sess = tf.Session(config=tf_config)
tf.import_graph_def(trt_graph, name='')

tf_input = tf_sess.graph.get_tensor_by_name(input_names[0] + ':0')
tf_scores = tf_sess.graph.get_tensor_by_name('detection_scores:0')
tf_boxes = tf_sess.graph.get_tensor_by_name('detection_boxes:0')
tf_classes = tf_sess.graph.get_tensor_by_name('detection_classes:0')
tf_num_detections = tf_sess.graph.get_tensor_by_name('num_detections:0')


IMAGE_PATH = os.path.join(os.getcwd(), "testimages", "000002_491724089556.png")
image = cv2.imread(IMAGE_PATH)
image = cv2.resize(image, (300, 300))

scores, boxes, classes, num_detections = tf_sess.run([tf_scores, tf_boxes, tf_classes, tf_num_detections], feed_dict={
    tf_input: image[None, ...]
})
boxes = boxes[0]  # index by 0 to remove batch dimension
scores = scores[0]
classes = classes[0]
num_detections = int(num_detections[0])