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Self Driving Car Engineer Project 12 - Semantic Segmentation

Benjamin Söllner, 27 Dec 2017


Fun Project Header Image


In this project, I label the pixels of a road in images using a Fully Convolutional Network (FCN). The fully convolutional network consists of a pre- trained encoder based on VGG-16 and a decoder that is trained on a GPU. The network is implemented using Tensorflow on Python. All code is in main.py.

Labeled Image from Validation Set

Labeled Image from Validation Set

Labeled Image from Validation Set

Environment

Frameworks and Packages

Make sure you have the following is installed:

Dataset

Download the Kitti Road dataset 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.

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.

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Project 12 "Semantic Segmentation" of Udacity's "Self Driving Car Engineer" Nanodegree

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