Update README.md

project_12_road_segmentation/README.md

@@ -2,6 +2,13 @@
 ### Introduction
 In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN).
 
+<p align="center">
+ <img src="./img/example.png" alt="Overview" width="75%" height="75%">
+ <br>Qualitative results.
+</p>
+
+
+
 ### Setup
 ##### Frameworks and Packages
 Make sure you have the following is installed:
@@ -12,25 +19,11 @@ Make sure you have the following is installed:
 ##### Dataset
 Download the [Kitti Road dataset](http://www.cvlibs.net/datasets/kitti/eval_road.php) from [here](http://www.cvlibs.net/download.php?file=data_road.zip).  Extract the dataset in the `data` folder.  This will create the folder `data_road` with all the training a test images.
 
-### Start
-##### Implement
-Implement the code in the `main.py` module indicated by the "TODO" comments.
-The comments indicated with "OPTIONAL" tag are not required to complete.
-##### Run
+### Run
+
 Run the following command to run the project:
 ```
 python main.py
 ```
 **Note** If running this in Jupyter Notebook system messages, such as those regarding test status, may appear in the terminal rather than the notebook.
-
-### Submission
-1. Ensure you've passed all the unit tests.
-2. Ensure you pass all points on [the rubric](https://review.udacity.com/#!/rubrics/989/view).
-3. Submit the following in a zip file.
- - `helper.py`
- - `main.py`
- - `project_tests.py`
- - Newest inference images from `runs` folder
 
- ## How to write a README
-A well written README file can enhance your project and portfolio.  Develop your abilities to create professional README files by completing [this free course](https://www.udacity.com/course/writing-readmes--ud777).