This repository contains code to train and qualitatively evaluate Full Resolution Residual Networks (FRRNs) as described in Tobias Pohlen, Alexander Hermans, Markus Mathias, Bastian Leibe. Full Resolution Residual Networks for Semantic Segmentation in Street Scenes. arXiv:1611.08323.

Click here to watch our video.

In order to run the code, your setup has to meet the following minimum requirements:

• Python 3
  • Theano with CUDA
  • Lasagne
  • OpenCV
  • Scikit-Learn
  • Termcolor
  • Numpy

Step by step guide:

1. Download and extract the files gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip (11GB) from the CityScapes website.
2. Clone the repository
3. Change into the FRRN directory and run $ python predict.py
4. When being asked, enter the path to the directory where you extracted the dataset to and choose the default model file.

Step by step guide:

1. Download and extract the files gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip (11GB) from the CityScapes website.
2. Clone the repository
3. Inspect the configuration at the beginning of the file train.py and change it if necessary.
4. Run $ python train.py

The MIT License (MIT) Copyright (c) 2016 Tobias Pohlen

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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