Semantic Segmentation

• This repository is for udacity self-driving car nanodegree project - Semantic Segmentation.
• Implement this paper: "Fully Convolutional Networks for Semantic Segmentation (2015)"
• See FCN-VGG16.ipynb

Implementation Details

Network

FCN-8s with VGG16 as below figure.

Dataset

• Kitti Road dataset from here.

Hyperparameters

Learning rate, batch size and keep probability were tunned by random search. If you want to see code for this: Link

• Optimizer: Adam
• Learning rate: 0.0002395
• Deconvolution l2 regularization factor: 1e-3
• Batch size: 2
• Training epochs: 30
• Keep prob for dropout (VGG): 0.495

Results

Loss

After 30 epochs, loss became about 0.05

Nice results

These are pretty nice results. It seems like the network classify road area well.

Bad results

These are bad results. I believe that the results will be better using the following methods.

• Use more deeper network (e.g. ResNet)
• Augment given data or train network with another data (e.g. CityScape)
• Use different architecture (e.g. U-Net)
• Use post processing (e.g. CRF(Conditional Random Field))

Setup

Frameworks and Packages

Make sure you have the following is installed:

• Python 3
• TensorFlow
• NumPy
• SciPy