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diff --git a/ml/dlib/python_examples/cnn_face_detector.py b/ml/dlib/python_examples/cnn_face_detector.py
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--- a/ml/dlib/python_examples/cnn_face_detector.py
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-#!/usr/bin/python
-# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-#
-#   This example shows how to run a CNN based face detector using dlib.  The
-#   example loads a pretrained model and uses it to find faces in images.  The
-#   CNN model is much more accurate than the HOG based model shown in the
-#   face_detector.py example, but takes much more computational power to
-#   run, and is meant to be executed on a GPU to attain reasonable speed.
-#
-#   You can download the pre-trained model from:
-#       http://dlib.net/files/mmod_human_face_detector.dat.bz2
-#
-#   The examples/faces folder contains some jpg images of people.  You can run
-#   this program on them and see the detections by executing the
-#   following command:
-#       ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg
-#
-#
-# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
-#   You can install dlib using the command:
-#       pip install dlib
-#
-#   Alternatively, if you want to compile dlib yourself then go into the dlib
-#   root folder and run:
-#       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA
-#   if you have a CPU that supports AVX instructions, you have an Nvidia GPU
-#   and you have CUDA installed since this makes things run *much* faster.
-#
-#   Compiling dlib should work on any operating system so long as you have
-#   CMake installed.  On Ubuntu, this can be done easily by running the
-#   command:
-#       sudo apt-get install cmake
-#
-#   Also note that this example requires scikit-image which can be installed
-#   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html.
-
-import sys
-import dlib
-from skimage import io
-
-if len(sys.argv) < 3:
-    print(
-        "Call this program like this:\n"
-        "   ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg\n"
-        "You can get the mmod_human_face_detector.dat file from:\n"
-        "    http://dlib.net/files/mmod_human_face_detector.dat.bz2")
-    exit()
-
-cnn_face_detector = dlib.cnn_face_detection_model_v1(sys.argv[1])
-win = dlib.image_window()
-
-for f in sys.argv[2:]:
-    print("Processing file: {}".format(f))
-    img = io.imread(f)
-    # The 1 in the second argument indicates that we should upsample the image
-    # 1 time.  This will make everything bigger and allow us to detect more
-    # faces.
-    dets = cnn_face_detector(img, 1)
-    '''
-    This detector returns a mmod_rectangles object. This object contains a list of mmod_rectangle objects.
-    These objects can be accessed by simply iterating over the mmod_rectangles object
-    The mmod_rectangle object has two member variables, a dlib.rectangle object, and a confidence score.
-    
-    It is also possible to pass a list of images to the detector.
-        - like this: dets = cnn_face_detector([image list], upsample_num, batch_size = 128)
-
-    In this case it will return a mmod_rectangless object.
-    This object behaves just like a list of lists and can be iterated over.
-    '''
-    print("Number of faces detected: {}".format(len(dets)))
-    for i, d in enumerate(dets):
-        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(
-            i, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence))
-
-    rects = dlib.rectangles()
-    rects.extend([d.rect for d in dets])
-
-    win.clear_overlay()
-    win.set_image(img)
-    win.add_overlay(rects)
-    dlib.hit_enter_to_continue()