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Semantic Segmentation - Udacity's Self-Driving Car Nanodegree Project
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README.md

Semantic Segmentation

Introduction

The objective of this project is to build a Fully Connected Convolutional Neural Net to identify the road in a collection of pictures. The encoding of the FCN will be provided by a pre-trained VGG16 model and the decoder will be built using 1x1 convolutions, upscaling and layer skipping.

This project is part of Udacity's Self Driving Car Nanodegree Course.

Checklist

  1. Ensure you've passed all the unit tests. [X]
  2. Ensure you pass all points on the rubric. [X]

Example1

Example2

Setup

Frameworks and Packages

Make sure you have the following is installed:

Dataset

Download the Kitti Road dataset from here. 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 the following command to run the project:

python main.py
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