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:
- Download and extract the files gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip (11GB) from the CityScapes website.
- Clone the repository
- Change into the FRRN directory and run
$ python predict.py
- 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:
- Download and extract the files gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip (11GB) from the CityScapes website.
- Clone the repository
- Inspect the configuration at the beginning of the file
train.py
and change it if necessary. - Run
$ python train.py
The MIT License (MIT) Copyright (c) 2016 Tobias Pohlen
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