diff --git a/samples/realnet_face_detection_embedded.c b/samples/realnet_face_detection_embedded.c
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+/*
+ * Programming introduction with the SOD Embedded RealNets API (Frontal Facial detection).
+ * Copyright (C) PixLab | Symisc Systems, https://sod.pixlab.io
+ */
+/*
+* Compile this file together with the SOD embedded source code to generate
+* the executable. For example:
+*
+*  gcc sod.c realnet_face_detection_embedded.c -lm -Ofast -march=native -Wall -std=c99 -o sod_realnet_face_detect
+*  
+* Under Microsoft Visual Studio (>= 2015), just drop `sod.c` and its accompanying
+* header files on your source tree and you're done. If you have any trouble
+* integrating SOD in your project, please submit a support request at:
+* https://sod.pixlab.io/support.html
+*/
+/*
+* This simple program is a quick introduction on how to embed and start
+* experimenting with SOD without having to do a lot of tedious
+* reading and configuration.
+*
+* Make sure you have the latest release of SOD from:
+*  https://pixlab.io/downloads
+* The SOD Embedded C/C++ documentation is available at:
+*  https://sod.pixlab.io/api.html
+*/
+#include <stdio.h>
+#include "sod.h"
+/*
+ * The following include is shipped with the built-in realnet embedded face detection model. 
+ * There is no need to bring an independent model in order to detect faces at real-time.
+ *
+ * All you need, is to register the model hex array defined in "sod_face_realnet.h" via 
+ * `sod_realnet_load_model_from_mem()` and use the same C/C++ as for detecting faces
+ * via Realnets as shown below in this sample.
+ *
+ * This feature is available in the commercial version of the library.
+ * You can obtain your commercial license from https://pixlab.io/downloads.
+ *
+ * Advantages includes:
+ *
+ *	Multi-core CPU support for all platforms - Up to 3 ~ 10 times faster processing speed.
+ *	Built-in (C Code), high performance RealNets frontal face detector.
+ *	75 days of integration & technical assistance.
+ *	Royalty-free commercial licenses without any GPL restrictions.
+ *	Application source code stays private.
+ */
+#include "sod_face_realnet.h"
+int main(int argc, char *argv[])
+{
+	 /* Input image (pass a path or use the test image shipped with the samples ZIP archive) */
+	const char *zFile = argc > 1 ? argv[1] : "./realnet_faces.jpg";
+	/*
+	* By default, RealNets are designed to process video streams thanks
+	* to their very fast processing speed. However, for the sake of simplicity
+	* we'll stick with images for this programming intro to RealNets.
+	*/
+	sod_realnet *pNet; /* Realnet handle */
+	int i,rc;
+	/*
+	 * Allocate a new RealNet handle */
+	rc = sod_realnet_create(&pNet);
+	if (rc != SOD_OK) return rc;
+	/* 
+	 * Load our RealNet model from memory. No need to bring an external one like the previous sample.
+	 *
+	 * You can train your own RealNet model on your CPU using the training interfaces [sod_realnet_train_start()]
+	 * or download pre-trained models like this one from https://pixlab.io/downloads
+	 */
+	rc = sod_realnet_load_model_from_mem(pNet, face_model, face_model_length, 0);
+	if (rc != SOD_OK) return rc;
+	/* Load the target image in grayscale colorspace */
+	sod_img img = sod_img_load_grayscale(zFile);
+	if (img.data == 0) {
+		puts("Cannot load image");
+		return 0;
+	}
+	/* Load a full color copy of the target image so we draw rose boxes
+	 * Note that drawing on grayscale images is also supported.
+	 */
+	sod_img color = sod_img_load_color(zFile);
+	/*
+	 * convert the grayscale image to blob.
+	 */
+	unsigned char *zBlob = sod_image_to_blob(img);
+	/* 
+	 * Bounding boxes array
+	 */
+	sod_box *aBoxes;
+	int nbox;
+	/* 
+	 * Perform Real-Time detection on this blob
+	 */
+	rc = sod_realnet_detect(pNet, zBlob, img.w, img.h, &aBoxes, &nbox);
+	if (rc != SOD_OK) return rc;
+	/* Consume result */
+	printf("%d potential face(s) were detected..\n", nbox);
+	for (i = 0; i < nbox; i++) {
+		/* Ignore low score detection */
+		if (aBoxes[i].score < 5.0) continue;
+		/* Report current object */
+		printf("(%s) x:%d y:%d w:%d h:%d prob:%f\n", aBoxes[i].zName, aBoxes[i].x, aBoxes[i].y, aBoxes[i].w, aBoxes[i].h, aBoxes[i].score);
+		/* Draw a rose box on the target coordinates */
+		sod_image_draw_bbox_width(color, aBoxes[i], 3, 255., 0, 225.);
+		//sod_image_draw_circle(color, aBoxes[i].x + (aBoxes[i].w / 2), aBoxes[i].y + (aBoxes[i].h / 2), aBoxes[i].w, 255., 0, 225.);
+	}
+	/* Save the detection result */
+	sod_img_save_as_png(color, argc > 2 ? argv[2] : "./out.png");
+	/* cleanup */
+	sod_free_image(img);
+	sod_free_image(color);
+	sod_image_free_blob(zBlob);
+	sod_realnet_destroy(pNet);
+	return 0;
+}