Awesome
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.
<p align="center"> <img src="https://raw.githubusercontent.com/sunshineatnoon/Paper-Collection/master/images/FCN1.png" width="50%" > </p>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>