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PyTorch implementation of DSNT

Project description

PyTorch DSNT

This repository contains the official implementation of the differentiable spatial to numerical (DSNT) layer and related operations.

$ pip install dsntnn


Please refer to the basic usage guide.


Running examples

$ python3 examples

HTML reports will be saved in the examples/ directory. Please note that the dsntnn package must be installed with pip install for the examples to run correctly.

Building documentation

$ mkdocs build

Running tests

$ python3 test     # Run tests
$ python3 coverage # Run tests with code coverage

Other implementations

If you write your own implementation of DSNT, please let me know so that I can add it to the list. I would also greatly appreciate it if you could add the following notice to your implementation's README:

Code in this project implements ideas presented in the research paper "Numerical Coordinate Regression with Convolutional Neural Networks" by Nibali et al. If you use it in your own research project, please be sure to cite the original paper appropriately.

License and citation

(C) 2017 Aiden Nibali

This project is open source under the terms of the Apache License 2.0.

If you use any part of this work in a research project, please cite the following paper:

  title={Numerical Coordinate Regression with Convolutional Neural Networks},
  author={Nibali, Aiden and He, Zhen and Morgan, Stuart and Prendergast, Luke},
  journal={arXiv preprint arXiv:1801.07372},

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