/Semantic_Segmentation

Primary LanguagePython

Semantic Segmentation

Introduction

This is my 2nd project for Term 3 semantic segmentation using a Fully Convolutional Network (FCN).

Approach

The encoder is VGG16 pretrained model. Decoder is fully convolutional network FCN8. Data is from KITTI road dataset.

Archetecture

A pre-trained VGG-16 was followed by, instead of full connected layers, a 1x1 convolution with depth equal to number of classes (road/nonroad). Skip connections was used to improve performance. In this case, as the paper suggests, 1x1 convolution of layer 4 and 2x2 upsampled layers (output of layer 7). Same applied to 1x1 convolution of layer 3 and 2x2 conv2d transpose upsample of output of previous layer. Each convolution and transpose convolution layer include a kernel initializer and regularizer.

Augmentation of image

Vertical image flipping was used as agumentation to improve the training dataset.

Training parameter setting

keep_prob: 0.5 learning_rate: 0.00 epochs: 100 batch_size: 5

Training loss was reduced from initial 1.5 to 0.3 after 50 epochs, further reduced to 0.08 at 100 epochs.

Sample image output

alt text

alt text

alt text

alt text

alt text

alt text

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)

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.