title: How to use Tensorflows object detection model zoo with pretrained models but missing label files (.pbtxt)
body: <p>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 <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md" rel="nofollow noreferrer">Tensorflow detection model zoo</a> 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.</p>
  
  <p>My Code so far:</p>
  
  <pre><code>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])
  </code></pre>