Pytorch code for semantic segmentation using ERFNet
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README.md

ERFNet (PyTorch version)

This code is a toolbox that uses PyTorch for training and evaluating the ERFNet architecture for semantic segmentation.

For the Original Torch version please go HERE

NOTE: This PyTorch version has a slightly better result than the ones in the Torch version (used in the paper): 72.1 IoU in Val set and 69.8 IoU in test set.

Example segmentation

Publications

If you use this software in your research, please cite our publications:

"Efficient ConvNet for Real-time Semantic Segmentation", E. Romera, J. M. Alvarez, L. M. Bergasa and R. Arroyo, IEEE Intelligent Vehicles Symposium (IV), pp. 1789-1794, Redondo Beach (California, USA), June 2017. [Best Student Paper Award], [pdf]

"ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation", E. Romera, J. M. Alvarez, L. M. Bergasa and R. Arroyo, Transactions on Intelligent Transportation Systems (T-ITS), December 2017. [pdf]

Packages

For instructions please refer to the README on each folder:

  • train contains tools for training the network for semantic segmentation.
  • eval contains tools for evaluating/visualizing the network's output.
  • imagenet Contains script and model for pretraining ERFNet's encoder in Imagenet.
  • trained_models Contains the trained models used in the papers. NOTE: the pytorch version is slightly different from the torch models.

Requirements:

  • The Cityscapes dataset: Download the "leftImg8bit" for the RGB images and the "gtFine" for the labels. Please note that for training you should use the "_labelTrainIds" and not the "_labelIds", you can download the cityscapes scripts and use the conversor to generate trainIds from labelIds
  • Python 3.6: If you don't have Python3.6 in your system, I recommend installing it with Anaconda
  • PyTorch: Make sure to install the Pytorch version for Python 3.6 with CUDA support (code only tested for CUDA 8.0).
  • Additional Python packages: numpy, matplotlib, Pillow, torchvision and visdom (optional for --visualize flag)

In Anaconda you can install with:

conda install numpy matplotlib torchvision Pillow
conda install -c conda-forge visdom

If you use Pip (make sure to have it configured for Python3.6) you can install with:

pip install numpy matplotlib torchvision Pillow visdom

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here: http://creativecommons.org/licenses/by-nc/4.0/