Tensorflow Object detection API: Print detected class as output to terminal

Dat and Omar..I have a basic question.. When we print the array, it contains an array of the top 100 scores and classes.. Of this only 2 to 3 are actually displayed in the output image (with bounded boxes and accuracy). How can I subset only those values that are actually displayed in the output image? Is it possible or do we need to set a fixed accuracy threshold? (and risk losing some objects that are displayed in the output image).

Well that's very easy. The classes are encrypted in the category_index which is a dict, so you could do something like this:

with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
for image_path in TEST_IMAGE_PATHS:
  image = Image.open(image_path)
  # the array based representation of the image will be used later in order to prepare the
  # result image with boxes and labels on it.
  image_np = load_image_into_numpy_array(image)
  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
  image_np_expanded = np.expand_dims(image_np, axis=0)
  image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
  # Each box represents a part of the image where a particular object was detected.
  boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
  # Each score represent how level of confidence for each of the objects.
  # Score is shown on the result image, together with the class label.
  scores = detection_graph.get_tensor_by_name('detection_scores:0')
  classes = detection_graph.get_tensor_by_name('detection_classes:0')
  num_detections = detection_graph.get_tensor_by_name('num_detections:0')
  # Actual detection.
  (boxes, scores, classes, num_detections) = sess.run(
      [boxes, scores, classes, num_detections],
      feed_dict={image_tensor: image_np_expanded})

  # Here output the category as string and score to terminal
  print([category_index.get(i) for i in classes[0]])
  print(scores)

Simply, go to the utils directory in object_detection folder and open the script visualization_utils.py. You will find a function namely visualize_boxes_and_labels_on_image_array, add a print command in the end of function to print the variable class_name(print(class_name)). Now run your code and see the magic.