/CarND-Semantic-Segmentation

Primary LanguagePython

Semantic Segmentation

Introduction

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

Segmentation

Figure: Results after 10 epochs of training with augmentation (Command to reproduce: python main.py 10 16 0.5)

Check road segmentation with 4k video on youtube:

Road segmentation for Self-Driving Car - Dataset #2

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.

4K video data

wget https://drive.google.com/open?id=0B_6iW8KaJFXOQmhaWU56dlBDY28

Training

Run the following command to run the project:

python main.py

Parameters:

  • number of epoch
  • batch size
  • keep probability
#python main.py  EPOCHS  BATCH_SIZE  DROPOUT
python main.py 10 16 0.5

Training loss

Figure: Training loss after 10 epochs of training with augmentation (Command to reproduce: python main.py 10 16 0.5)

Data augmentation

get_batches_fn() in helper.py

  • flipping every image
  • translating +/- 100 in x axis and +/- 30 in y axis
  • brightness +/- 150 (in 0-255 range)

Example batch:

Example batch