Machine Learning

A Performance Baseline for Deep Feature Matching

DFM

Python (Pytorch) and Matlab (MatConvNet) implementations of our paper DFM: A Performance Baseline for Deep Feature Matching at CVPR 2021 Image Matching Workshop.

hierarchical_refinement

Setup Environment

We strongly recommend using Anaconda. Open a terminal in ./python folder, and simply run the following lines to create the environment:

conda env create -f environment.yml
conda activte dfm

Dependencies
If you do not use conda, DFM needs the following dependencies:
(Versions are not strict; however, we have tried DFM with these specific versions.)

  • python=3.7.1
  • pytorch=1.7.1
  • torchvision=0.8.2
  • cudatoolkit=11.0
  • matplotlib=3.3.4
  • pillow=8.2.0
  • opencv=3.4.2
  • ipykernel=5.3.4
  • pyyaml=5.4.1

Enjoy with DFM!

Now you are ready to test DFM by the following command:

python dfm.py --input_pairs image_pairs.txt

You should make the image_pairs.txt file as following:

1A> 1B>2A> 2B>
.
.
.nA> n**B>

If you want to run

 

 

 

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