This is a modular framework to ease training of neural networks for semantic segmentation. A separation of network design, training and evaluation is reflected by the software architecture. All parameters to describe the training procedure and evaluation phase are stored in a YAML config file and serve for documentation and reproduction of results. Therefore, changing the network architecture will ideally affect only a single line in the config file.

New networks can easily be integrated into the framework as well as custom datasets, augmentation strategies etc. See the section Design Principles for further details.

When designing a neural network for semantic segmentation, you will likely ask yourself the following questions

• How should my architecture look like?
• Which state of the art architectures are available?
• Which data will I use to train my network?
• How should I preprocess my images to feed them into the network?

These questions are further followed by

• Which data augmentation can I apply to my dataset?

and in the end after training

• How can I evaluate my network on (multiple) dataset(s)

This frameworks aims to provide an easy to use solution for all those questions and automates evaluation.

Although the currently available networks are implemented in Keras, the framework is designed such that it is framework-agnostic. For data augmentation, img_aug is used.

pip install -r requirements.txt 

 export CITYSCAPES_DATASET="..."
 ./thirdparty/cityscapesScripts/cityscapesscripts/preparation/createTrainIdLabelImgs.py

There is a single entrypoint for all tasks. See the output of

python main.py -h

for a complete list of options.

To train your network, you need to provide a config file like the one you find in the toplevel directory of this repo. Then, simply execute

python main.py train --config='...'

That's it.

To test and evaluate your network, simply replace train by those commands as the first argument.

• Add tests for all components