Awesome
This is an python implementation of SIFT patch descriptor. It is derived from Michal Perdoch C++ implementation at https://github.com/perdoch/hesaff
The SIFT descriptor code is protected under a US Patent 6,711,293. A license MUST be obtained from the University of British Columbia for use of SIFT code, files numpy_sift.py, in commercial applications (see LICENSE.SIFT for details)
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. MP version weights patch center much more (see image below, left) and additionally crops everything outside the circular region. Right is vlfeat version.
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 - pytorch SIFTCPU, 256 patches per batch
- 0.00038 s per 65x65 patch - pytorch SIFT GPU (GM940, mobile), 256 patches per batch
If you use this code for academic purposes, please cite the following paper:
@article{HardNet2017,
author = {Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas},
title = "{Working hard to know your neighbor's margins: Local descriptor learning loss}",
booktitle = {Proceedings of NIPS},
year = 2017,
month = dec}