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Code for CVPR15 paper "Learning to Compare Image Patches via Convolutional Neural Networks"

This package allows researches to apply the described networks to match image patches and extract corresponding patches.

We tried to make the code as easy to use as possible. The original models were trained with Torch ( http:https://torch.ch ) and we release the models in Torch7 format, and in addition to that we have C++ bindings which do not require Torch installation. Thus we provide example code how to use the models in MATLAB and with OpenCV http:https://opencv.org

CREDITS, LICENSE, CITATION

Copyright © 2015 Ecole des Ponts, Universite Paris-Est

All Rights Reserved. A license to use and copy this software and its documentation solely for your internal research and evaluation purposes, without fee and without a signed licensing agreement, is hereby granted upon your download of the software, through which you agree to the following:

  1. the above copyright notice, this paragraph and the following three paragraphs will prominently appear in all internal copies and modifications;
  2. no rights to sublicense or further distribute this software are granted;
  3. no rights to modify this software are granted; and
  4. no rights to assign this license are granted.

Please Contact Prof. Nikos Komodakis, 6 Avenue Blaise Pascal - Cite Descartes, Champs-sur-Marne, 77455 Marne-la-Vallee cedex 2, France, Office B003 Tel : +33164152173, Fax: +33164152186 for commercial licensing opportunities, or for further distribution, modification or license rights.

Created by Sergey Zagoruyko and Nikos Komodakis. http:https://imagine.enpc.fr/~komodakn/

Please cite the paper below if you use this code in your research.

Sergey Zagoruyko, Nikos Komodakis "Learning to Compare Image Patches via Convolutional Neural Networks", ArXiv:1504.03641. http:https://arxiv.org/abs/1504.03641

Installation

The code was tested to work in Linux (Ubuntu 14.04) and OS X 10.10, although we release all the source code to enable usage in other operating systems.

Models

We provide the models in 3 formats, two are Torch7 "nn" and "cudnn" formats and one is binary format with weights only. The table from the paper is here for convenience.

Train set Test set 2ch 2ch2stream 2chdeep siam siam2stream
yosemite notredame 2.74 2.11 2.43 5.62 5.23
yosemite liberty 8.59 7.2 7.4 13.48 11.34
notredame yosemite 6.04 4.09 4.38 13.23 10.44
notredame liberty 6.04 4.85 4.56 8.77 6.45
liberty yosemite 7 5 6.18 14.76 9.39
liberty notredame 2.76 1.9 2.77 4.04 2.82

To save time downloading models one by one run the script (downloads ~375MB):

./download_pack.sh

nn format

Models in nn format can be loaded and used without CUDA support in Torch. To enable CUDA support model:cuda() call required.

Train Set 2ch 2ch2stream 2chdeep siam siam2stream
yosemite 3.49 MB 9.74 MB 4.15 MB 7.95 MB 22.36 MB
notredame 3.49 MB 9.74 MB 4.15 MB 7.95 MB 22.36 MB
liberty 3.49 MB 9.74 MB 4.15 MB 7.95 MB 22.36 MB

cudnn format

Models in cudnn format are faster, but need a special library from NVIDIA. Check https://github.com/soumith/cudnn.torch

Train Set 2ch 2ch2stream 2chdeep siam siam2stream
yosemite 3.75 MB 9.85 MB 4.15 MB 5.62 MB 17.46 MB
notredame 3.75 MB 9.85 MB 4.15 MB 5.62 MB 17.71 MB
liberty 3.75 MB 9.85 MB 4.15 MB 5.62 MB 17.71 MB

binary format

Train Set 2ch 2ch2stream 2chdeep siam siam2stream
yosemite 3.48 MB 8.98 MB 4.13 MB 4.47 MB 13.17 MB
notredame 3.48 MB 8.98 MB 4.13 MB 4.47 MB 13.17 MB
liberty 3.48 MB 8.98 MB 4.13 MB 4.47 MB 13.17 MB

C++ code

We release CUDA code for the time being, CPU code might be added in the future. To install it you need to have CUDA 7.0 with the up-to-date CUDA driver. Clone and compile this repository it with:

git clone --recursive https://github.com/szagoruyko/cvpr15deepmatch
cd cvpr15deepmatch
mkdir build; cd build;
cmake .. -DCMAKE_INSTALL_PREFIX=../install
make -j4 install

MATLAB

OpenCV

OpenCV example is here to demonstrate how to use the deep CNN models to match image patches, how to preprocess the patches and use the proposed API.