Semantic Segmentation

Introduction

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

• keep_prob = 0.8 , l_rate = 0.00001

1 Build the Neural Network

1. vgg model
2. layers
3. optimize

• optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
• train_op = optimizer.minimize(loss = cross_entropy_loss)

4. train_nn (4 pattern)

• keep_prob = 0.8 , l_rate = 0.00001
• keep_prob = 0.9 , l_rate = 0.00001
• keep_prob = 0.8 , l_rate = 0.00004
• keep_prob = 0.9 , l_rate = 0.00004

2 Neural Network Training

1.The number of epoch and batch size

• epochs = 200
• batch_size = 16

2.loss

• 

3 Spec etc.

• Ubuntu 16.04
• cuda 9.0.176-1
• cudnn 7.0.5.15-1
• python 3.5.2
• cpu i3-4130 CPU @ 3.40GHz × 4
• GPU GTX-1080
• 1 pattern processing time about 105 munites ( epochs = 250, batch_size = 16 )

Setup

GPU

main.py will check to make sure you are using GPU - if you don't have a GPU on your system, you can use AWS or another cloud computing platform.

Frameworks and Packages

Make sure you have the following is installed:

• Python 3
• TensorFlow
• NumPy
• SciPy

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