/CarND-Semantic-Segmentation

Advanced deep learning project for Self-Driving Car Engineer nanodegree

Primary LanguagePython

Semantic Segmentation

The solution uses the pretrained VGG network with 1x1 convolutions and two skip layers, as suggested by the project setup.

Segmentation results:

sample1 sample2 sample3 sample4 sample5 sample6 sample7

Future TODOs that weren't completed due to time constraints:

  • optimise the model, tune hyperparameters, add regularization
  • test the segmentation on own road images
  • complete optional tasks
  • add a mIoU metric

Introduction

In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN).

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

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 (all images from the most recent run)

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