/CarND-Semantic-Segmentation-1

A fully convolutional network for road classification

Primary LanguagePython

Semantic Segmentation

Introduction

In this project the pixels of a road in images are labelled using a Fully Convolutional Network (FCN). The network uses the architecture described in Long et al. and is trained on the Kitti Road dataset.

Some of the results are shown below:

sample sample sample sample

The code performs a hyperparameter search using 200 epochs for training each network. A test of the trained network on road conditions very different to the training data can be found here project_video

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

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.
  3. Submit the following in a zip file.
  • helper.py
  • main.py
  • project_tests.py
  • Newest inference images from runs folder