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diff --git a/ml/dlib/examples/random_cropper_ex.cpp b/ml/dlib/examples/random_cropper_ex.cpp
deleted file mode 100644
index 6b020058d..000000000
--- a/ml/dlib/examples/random_cropper_ex.cpp
+++ /dev/null
@@ -1,99 +0,0 @@
-// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-/*
-    When you are training a convolutional neural network using the loss_mmod loss
-    layer, you need to generate a bunch of identically sized training images.  The
-    random_cropper is a convenient tool to help you crop out a bunch of
-    identically sized images from a training dataset.
-
-    This example shows you what it does exactly and talks about some of its options.
-*/
-
-
-#include <iostream>
-#include <dlib/data_io.h>
-#include <dlib/gui_widgets.h>
-#include <dlib/image_transforms.h>
-
-using namespace std;
-using namespace dlib;
-
-// ----------------------------------------------------------------------------------------
-
-int main(int argc, char** argv) try
-{
-    if (argc != 2)
-    {
-        cout << "Give an image dataset XML file to run this program." << endl;
-        cout << "For example, if you are running from the examples folder then run this program by typing" << endl;
-        cout << "   ./random_cropper_ex faces/training.xml" << endl;
-        cout << endl;
-        return 0;
-    }
-
-    // First lets load a dataset
-    std::vector<matrix<rgb_pixel>> images;
-    std::vector<std::vector<mmod_rect>> boxes;
-    load_image_dataset(images, boxes, argv[1]);
-
-    // Here we make our random_cropper.  It has a number of options. 
-    random_cropper cropper;
-    // We can tell it how big we want the cropped images to be.
-    cropper.set_chip_dims(400,400);
-    // Also, when doing cropping, it will map the object annotations from the
-    // dataset to the cropped image as well as perform random scale jittering.
-    // You can tell it how much scale jittering you would like by saying "please
-    // make the objects in the crops have a min and max size of such and such".
-    // You do that by calling these two functions.  Here we are saying we want the
-    // objects in our crops to be no more than 0.8*400 pixels in height and width.
-    cropper.set_max_object_size(0.8);
-    // And also that they shouldn't be too small. Specifically, each object's smallest
-    // dimension (i.e. height or width) should be at least 60 pixels and at least one of
-    // the dimensions must be at least 80 pixels.  So the smallest objects the cropper will
-    // output will be either 80x60 or 60x80.
-    cropper.set_min_object_size(80,60);
-    // The cropper can also randomly mirror and rotate crops, which we ask it to
-    // perform as well.
-    cropper.set_randomly_flip(true);
-    cropper.set_max_rotation_degrees(50);
-    // This fraction of crops are from random parts of images, rather than being centered
-    // on some object.
-    cropper.set_background_crops_fraction(0.2);
-
-    // Now ask the cropper to generate a bunch of crops.  The output is stored in
-    // crops and crop_boxes.
-    std::vector<matrix<rgb_pixel>> crops;
-    std::vector<std::vector<mmod_rect>> crop_boxes;
-    // Make 1000 crops.
-    cropper(1000, images, boxes, crops, crop_boxes);
-
-    // Finally, lets look at the results
-    image_window win;
-    for (size_t i = 0; i < crops.size(); ++i)
-    {
-        win.clear_overlay();
-        win.set_image(crops[i]);
-        for (auto b : crop_boxes[i])
-        {
-            // Note that mmod_rect has an ignore field.  If an object was labeled
-            // ignore in boxes then it will still be labeled as ignore in
-            // crop_boxes.  Moreover, objects that are not well contained within
-            // the crop are also set to ignore.
-            if (b.ignore)
-                win.add_overlay(b.rect, rgb_pixel(255,255,0)); // draw ignored boxes as orange 
-            else
-                win.add_overlay(b.rect, rgb_pixel(255,0,0));   // draw other boxes as red
-        }
-        cout << "Hit enter to view the next random crop.";
-        cin.get();
-    }
-
-}
-catch(std::exception& e)
-{
-    cout << e.what() << endl;
-}
-
-
-
-
-