/smg-detectron2

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smg-detectron2

This Python package provides a wrapper for Detectron2.

It is a submodule of smglib, the open-source Python framework associated with our drone research in the Cyber-Physical Systems group at the University of Oxford.

Installation (as part of smglib)

Note: Please read the top-level README for smglib before following these instructions.

  1. Open the terminal.

  2. Activate the Conda environment, e.g. conda activate smglib.

  3. If you haven't already installed PyTorch, install it now. In our case, we did this via:

    pip install https://download.pytorch.org/whl/cu111/torch-1.9.1%2Bcu111-cp37-cp37m-win_amd64.whl
pip install https://download.pytorch.org/whl/torchaudio-0.9.1-cp37-cp37m-win_amd64.whl
pip install https://download.pytorch.org/whl/cu111/torchvision-0.10.1%2Bcu111-cp37-cp37m-win_amd64.whl

    However, you may need a different version of PyTorch for your system, so change this as needed. (In particular, the latest version will generally be ok.)

  4. Install cudatoolkit, e.g. via conda install cudatoolkit==11.3.1 (the version you need may be different).

  5. Install Detectron2 as per here. If you run into any trouble:

    i. If you get the error ImportError: cannot import name '_nt_quote_args' from 'distutils.spawn', install setuptools version 59.6 or below. (See also here.)

    ii. Try applying the fixes in fix_torch_for_detectron2.sh. (With newer versions of PyTorch, they may no longer be needed.)

  6. Change to the <root>/smg-detectron2 directory.

  7. Check out the master branch.

  8. Run pip install -e . at the terminal.

Publications

If you build on this framework for your research, please cite the following paper:

@inproceedings{Golodetz2022TR,
author = {Stuart Golodetz and Madhu Vankadari* and Aluna Everitt* and Sangyun Shin* and Andrew Markham and Niki Trigoni},
title = {{Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV}},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
month = {October},
year = {2022}
}

Acknowledgements

This work was supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme, and by UKRI as part of the ACE-OPS grant. We would also like to thank Graham Taylor for the use of the Wytham Flight Lab, Philip Torr for the use of an Asus ZenFone AR, and Tommaso Cavallari for implementing TangoCapture.