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diff --git a/src/ml/dlib/python_examples/cnn_face_detector.py b/src/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()