Semantic Segmentation

Discription

Labeling pixels on a road in images using a Fully Convolutional Network (FCN).

Architecture / Rubic points

Load pre-trained vgg model

Function load_vgg loads loads pre-trained vgg model.

Learn the correct features from the images

The project has layers functions implemented.

Optimize the neural network

The optimize function for the network is cross-entropy, and an Adam optimizer is used.

    cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
    optimizer = tf.train.AdamOptimizer(learning_rate)
    train_op = optimizer.minimize(cross_entropy_loss)

Train the neural network

The train_nn function is implemented and prints time and loss per epoch/epochs of training.

Train the model correctly with reasonable hyper parameters

The project trains model correctly, about 48s per epoch, 48sx40 epochs in total.

Final hyperparamters used for training.

    L2_REG = 1e-5
    STDEV = 1e-2
    KEEP_PROB = 0.8
    LEARNING_RATE = 1e-4
    EPOCHS = 40
    BATCH_SIZE = 8
    IMAGE_SHAPE_KITI = (160,576)
    NUM_CLASSES = 2

Correctly labeling the road

Results from the test images. From the GIF below, A pre-trained VGG-16 network combined with a fully convolutional network will successfully label the road. Performance was also improved through the use of skip connections and adding element-wise to upsampled lower-level layers.

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

You may also need Python Image Library (PIL) for SciPy's imresize function.

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.

QuickStart

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.