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diff --git a/ml/dlib/examples/dnn_mmod_dog_hipsterizer.cpp b/ml/dlib/examples/dnn_mmod_dog_hipsterizer.cpp
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+// 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 dog face detector using dlib.  The
+    example loads a pretrained model and uses it to find dog faces in images.
+    We also use the dlib::shape_predictor to find the location of the eyes and
+    nose and then draw glasses and a mustache onto each dog found :)
+    
+
+    Users who are just learning about dlib's deep learning API should read the
+    dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp examples to learn how
+    the API works.  For an introduction to the object detection method you
+    should read dnn_mmod_ex.cpp
+
+
+    
+    TRAINING THE MODEL
+        Finally, users interested in how the dog face detector was trained should
+        read the dnn_mmod_ex.cpp example program.  It should be noted that the
+        dog face detector used in this example uses a bigger training dataset and
+        larger CNN architecture than what is shown in dnn_mmod_ex.cpp, but
+        otherwise training is the same.  If you compare the net_type statements
+        in this file and dnn_mmod_ex.cpp you will see that they are very similar
+        except that the number of parameters has been increased.
+
+        Additionally, the following training parameters were different during
+        training: The following lines in dnn_mmod_ex.cpp were changed from
+            mmod_options options(face_boxes_train, 40,40);
+            trainer.set_iterations_without_progress_threshold(300);
+        to the following when training the model used in this example:
+            mmod_options options(face_boxes_train, 80,80);
+            trainer.set_iterations_without_progress_threshold(8000);
+
+        Also, the random_cropper was left at its default settings,  So we didn't
+        call these functions:
+            cropper.set_chip_dims(200, 200);
+            cropper.set_min_object_size(40,40);
+
+        The training data used to create the model is also available at 
+        http://dlib.net/files/data/CU_dogs_fully_labeled.tar.gz
+
+        Lastly, the shape_predictor was trained with default settings except we
+        used the following non-default settings: cascade depth=20, tree
+        depth=5, padding=0.2
+*/
+
+
+#include <iostream>
+#include <dlib/dnn.h>
+#include <dlib/data_io.h>
+#include <dlib/image_processing.h>
+#include <dlib/gui_widgets.h>
+
+
+using namespace std;
+using namespace dlib;
+
+// ----------------------------------------------------------------------------------------
+
+template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
+template <long num_filters, typename SUBNET> using con5  = con<num_filters,5,5,1,1,SUBNET>;
+
+template <typename SUBNET> using downsampler  = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>;
+template <typename SUBNET> using rcon5  = relu<affine<con5<45,SUBNET>>>;
+
+using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv) try
+{
+    if (argc < 3)
+    {
+        cout << "Call this program like this:" << endl;
+        cout << "./dnn_mmod_dog_hipsterizer mmod_dog_hipsterizer.dat faces/dogs.jpg" << endl;
+        cout << "\nYou can get the mmod_dog_hipsterizer.dat file from:\n";
+        cout << "http://dlib.net/files/mmod_dog_hipsterizer.dat.bz2" << endl;
+        return 0;
+    }
+
+
+    // load the models as well as glasses and mustache.
+    net_type net;
+    shape_predictor sp;
+    matrix<rgb_alpha_pixel> glasses, mustache;
+    deserialize(argv[1]) >> net >> sp >> glasses >> mustache;  
+    pyramid_up(glasses);
+    pyramid_up(mustache);
+
+    image_window win1(glasses);
+    image_window win2(mustache);
+    image_window win_wireframe, win_hipster;
+
+    // Now process each image, find dogs, and hipsterize them by drawing glasses and a
+    // mustache on each dog :)
+    for (int i = 2; i < argc; ++i)
+    {
+        matrix<rgb_pixel> img;
+        load_image(img, argv[i]);
+
+        // Upsampling the image will allow us to find smaller dog faces but will use more
+        // computational resources.
+        //pyramid_up(img); 
+
+        auto dets = net(img);
+        win_wireframe.clear_overlay();
+        win_wireframe.set_image(img);
+        // We will also draw a wireframe on each dog's face so you can see where the
+        // shape_predictor is identifying face landmarks.
+        std::vector<image_window::overlay_line> lines;
+        for (auto&& d : dets)
+        {
+            // get the landmarks for this dog's face
+            auto shape = sp(img, d.rect);
+
+            const rgb_pixel color(0,255,0);
+            auto top  = shape.part(0);
+            auto lear = shape.part(1);
+            auto leye = shape.part(2);
+            auto nose = shape.part(3);
+            auto rear = shape.part(4);
+            auto reye = shape.part(5);
+
+            // The locations of the left and right ends of the mustache.
+            auto lmustache = 1.3*(leye-reye)/2 + nose;
+            auto rmustache = 1.3*(reye-leye)/2 + nose;
+
+            // Draw the glasses onto the image.
+            std::vector<point> from = {2*point(176,36), 2*point(59,35)}, to = {leye, reye};
+            auto tform = find_similarity_transform(from, to);
+            for (long r = 0; r < glasses.nr(); ++r)
+            {
+                for (long c = 0; c < glasses.nc(); ++c)
+                {
+                    point p = tform(point(c,r));
+                    if (get_rect(img).contains(p))
+                        assign_pixel(img(p.y(),p.x()), glasses(r,c));
+                }
+            }
+
+            // Draw the mustache onto the image right under the dog's nose.
+            auto mrect = get_rect(mustache);
+            from = {mrect.tl_corner(), mrect.tr_corner()};
+            to = {rmustache, lmustache};
+            tform = find_similarity_transform(from, to);
+            for (long r = 0; r < mustache.nr(); ++r)
+            {
+                for (long c = 0; c < mustache.nc(); ++c)
+                {
+                    point p = tform(point(c,r));
+                    if (get_rect(img).contains(p))
+                        assign_pixel(img(p.y(),p.x()), mustache(r,c));
+                }
+            }
+
+
+            // Record the lines needed for the face wire frame.
+            lines.push_back(image_window::overlay_line(leye, nose, color));
+            lines.push_back(image_window::overlay_line(nose, reye, color));
+            lines.push_back(image_window::overlay_line(reye, leye, color));
+            lines.push_back(image_window::overlay_line(reye, rear, color));
+            lines.push_back(image_window::overlay_line(rear, top, color));
+            lines.push_back(image_window::overlay_line(top, lear,  color));
+            lines.push_back(image_window::overlay_line(lear, leye,  color));
+        }
+
+        win_wireframe.add_overlay(lines);
+        win_hipster.set_image(img);
+
+        cout << "Hit enter to process the next image." << endl;
+        cin.get();
+    }
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+
+
+
+