/tfeat

TFeat descriptor models for BMVC 2016 paper "Learning local feature descriptors with triplets and shallow convolutional neural networks"

Primary LanguagePython

TFeat shallow convolutional patch descriptor

Code for the BMVC 2016 paper Learning local feature descriptors with triplets and shallow convolutional neural networks

Network description

We provide 4 variants of the TFeat descriptor trained with combinations of different loss functions, and with and without in-triplet anchor swap. For more details check the paper.

network description
tfeat-ratio ratio w/out anchor swap
tfeat-ratio* ratio with anchor swap
tfeat-margin margin w/out anchor swap
tfeat-margin* margin with anchor swap

To download the networks run the get_nets.sh script

sh get_nets.sh

[New] Example usage - Tensorflow

Example on how to use and train the network using Tensorflow can be found here

Example usage - torch

Example on how to use the TFeat descriptor in Torch can be found here

Example usage - python

tfeat_demo.py shows how to use the TFeat descriptor using python and openCV.

To use TFeat to detect an object object_img.png in a video input_video.webm using feature point matching

python tfeat_demo.py nets/tfeat_liberty_margin_star.t7 input_video.webm object_img.png'

Real-time Matching demo

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Real-time demo on using TFeat

More information

More information and the full training code can be found in the pnnet repository