In this project, I have labeled the pixels of a road in images using a Fully Convolutional Network (FCN). To do this, I used VGG16 as an encoder, then 1x1 convolutions which replace the fully connected layers in the original VGG16 model, followed by a decoder, consisting of series of transposed convolutions and skip connections. The idea is that after the image goes through the encoder, a vector representing its semantics will be created and then this vector will be decoded into an image again from the decoder. The architecture for the decoder can be found in the layers method in main.py. L2 regularization has been used in the decoder to avoid overfitting.

• Learning rate: 1e-4
• Dropout with keep probability: 0.5
• Epochs: 20

The training was done on a p2.xlarge instance in AWS and took ~30 minutes to train. Full logs from the training are available in the file train.log. The loss function is 1.01993 in the beginning and it steadily goes down until it reaches 0.019856 after 20 epochs.

Original	Semantic segmentation

Make sure you have the following is installed:

• Python 3
• TensorFlow
• NumPy
• SciPy

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.

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 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.

1. Ensure you've passed all the unit tests.
2. Ensure you pass all points on the rubric.
3. Submit the following in a zip file.

• helper.py
• main.py
• project_tests.py
• Newest inference images from runs folder (all images from the most recent run)

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