update README

traffic_signs_detection/traffic_signs_recognition/README.md

@@ -6,7 +6,10 @@ This module is a part of a [Traffic-Sign Recognition and Classification](https:/
 
 ## Module Overview
 In this module I trained a neural network to detect and classify different traffic signs using PyTorch, YOLOv3 and OpenCV.<br>
-In this project, I used the German Traffic Sign Detection Benchmark ([GTSBB](https://benchmark.ini.rub.de/gtsdb_news.html)) dataset.
+I based my program on the German Traffic Sign Detection Benchmark ([GTSBB](https://benchmark.ini.rub.de/gtsdb_news.html)) dataset.
+
+An example of output video:<br>
+    ![Traffic-Sign Recognition](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/tree/master/traffic_signs_detection/traffic_signs_recognition/output_video/traffic-sign.gif)
 
 
 ## User Guide
@@ -41,4 +44,6 @@ The benchmark has the following properties:
 3. More than 50,000 images in total (~35,000 training images, ~4000 validation images, and ~13,000 testing images).
 4. Large, lifelike database.
 
-The dataset can be found on the [INI Benchmark website](https://benchmark.ini.rub.de/?section=gtsrb&subsection=news), or downloaded directly through [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html).
+The dataset can be found on the [INI Benchmark website](https://benchmark.ini.rub.de/?section=gtsrb&subsection=news), or downloaded directly through [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html).
+
+![The German Traffic Sign Detection Benchmark](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/tree/master/traffic_signs_detection/traffic_signs_recognition/output_video/dataset.png)