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City-scale-Road-Audit

Website - https://cvit.iiit.ac.in/research/projects/cvit-projects/city-scale-road-audit

Publications

'City-Scale Road Audit System using Deep Learning' accepted at IROS 2018 - https://arxiv.org/abs/1811.10210

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

Packages

For instructions please refer to the README on each folder:

  • train contains tools for training the network for semantic segmentation.Use python main_release_iros.py --savedir <save_dir> --datadir <data_dir> --num-epochs <> --batch-size <> --decoder --iouVal
  • trained_models Contains the trained models used in the papers. NOTE: the pytorch version is slightly different from the torch models.

Requirements:

  • The dataset: Download the "leftImg8bit" for the RGB images and the "gtFine" for the labels.
  • Python 3.6: If you don't have Python3.6 in your system, I recommend installing it with Anaconda
  • PyTorch 0.2 and above: 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/

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