Lyft Challenge

Image Segmentation using VGG net in Keras.

Inspired from https://github.com/divamgupta/image-segmentation-keras

Implementation of Deep Image Segmentation model for Lyft challenge in keras.

Models

• VGG Segnet

Getting Started

Prerequisites

• Keras 2.0
• opencv for python

sudo apt-get install python-opencv
sudo pip install --upgrade keras
sudo pip install pydot
sudo pip install graphviz
sudo apt install graphviz

Preparing the data for training

mkdir data cd data wet https://s3-us-west-1.amazonaws.com/udacity-selfdrivingcar/Lyft_Challenge/Training+Data/lyft_training_data.tar.gz -O dataset.tar.gz tar -xvzf dataset.tar.gz

Visualizing the prepared data

You can also visualize your prepared annotations for verification of the prepared data.

./run.sh visualize

Downloading the Pretrained VGG Weights

You need to download the pretrained VGG-16 weights trained on imagenet if you want to use VGG based models

mkdir data
cd data
wget "https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels.h5"
wget "https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5"

Training the Model

To train the model run the following command:

./run.sh train

Getting the predictions

To get the predictions of a trained model

./run.sh predict <model_id>