This is an differentiable pytorch implementation of SIFT patch descriptor.

Last update: Dec 24, 2022

Overview

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can be used for descriptop-based learning shape of affine feature.

UPD 08/2019 : pytorch-sift is added to kornia and available by kornia.features.SIFTDescriptor

There are different implementations of the SIFT on the web. I tried to match Michal Perdoch implementation, which gives high quality features for image retrieval CVPR2009. However, on planar datasets, it is inferior to vlfeat implementation. The main difference is gaussian weighting window parameters, so I have made a vlfeat-like version too. MP version weights patch center much more (see image below, left) and additionally crops everything outside the circular region. Right is vlfeat version

descriptor_mp_mode = SIFTNet(patch_size = 65,
                        sigma_type= 'hesamp',
                        masktype='CircularGauss')

descriptor_vlfeat_mode = SIFTNet(patch_size = 65,
                        sigma_type= 'vlfeat',
                        masktype='Gauss')

Results:

OPENCV-SIFT - mAP 
   Easy     Hard      Tough     mean
-------  -------  ---------  -------
0.47788  0.20997  0.0967711  0.26154

VLFeat-SIFT - mAP 
    Easy      Hard      Tough      mean
--------  --------  ---------  --------
0.466584  0.203966  0.0935743  0.254708

PYTORCH-SIFT-VLFEAT-65 - mAP 
    Easy      Hard      Tough      mean
--------  --------  ---------  --------
0.472563  0.202458  0.0910371  0.255353

NUMPY-SIFT-VLFEAT-65 - mAP 
    Easy      Hard      Tough      mean
--------  --------  ---------  --------
0.449431  0.197918  0.0905395  0.245963

PYTORCH-SIFT-MP-65 - mAP 
    Easy      Hard      Tough      mean
--------  --------  ---------  --------
0.430887  0.184834  0.0832707  0.232997

NUMPY-SIFT-MP-65 - mAP 
    Easy     Hard      Tough      mean
--------  -------  ---------  --------
0.417296  0.18114  0.0820582  0.226832

Speed:

0.00246 s per 65x65 patch - numpy SIFT
0.00028 s per 65x65 patch - C++ SIFT
0.00074 s per 65x65 patch - CPU, 256 patches per batch
0.00038 s per 65x65 patch - GPU (GM940, mobile), 256 patches per batch
0.00038 s per 65x65 patch - GPU (GM940, mobile), 256 patches per batch

If you use this code for academic purposes, please cite the following paper:

@InProceedings{AffNet2018,
    title = {Repeatability Is Not Enough: Learning Affine Regions via Discriminability},
    author = {Dmytro Mishkin, Filip Radenovic, Jiri Matas},
    booktitle = {Proceedings of ECCV},
    year = 2018,
    month = sep
}

This is an differentiable pytorch implementation of SIFT patch descriptor.

Related tags

Overview

Owner

Dmytro Mishkin

PyNIF3D is an open-source PyTorch-based library for research on neural implicit functions (NIF)-based 3D geometry representation.

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Pytorch implementation of Distributed Proximal Policy Optimization

Official implementations of EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis.

Use Jax functions in Pytorch with DLPack

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

PyTorch implementations of normalizing flow and its variants.

A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.

A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

pip install antialiased-cnns to improve stability and accuracy

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

An implementation of Performer, a linear attention-based transformer, in Pytorch

This is an differentiable pytorch implementation of SIFT patch descriptor.

PyTorch extensions for fast R&D prototyping and Kaggle farming

TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards

Riemannian Adaptive Optimization Methods with pytorch optim

Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd

Unofficial PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training

An optimizer that trains as fast as Adam and as good as SGD.

PyTorch framework A simple and complete framework for PyTorch, providing a variety of data loading and simple task solutions that are easy to extend and migrate