ml-lab / affnet

Code and weights for local feature affine shape estimation paper "Learning Discriminative Affine Regions via Discriminability"

AffNet model implementation

CNN-based affine shape estimator.

AffNet model implementation in PyTorch for TechReport "Learning Discriminative Affine Regions via Discriminability"

AffNet generates up to twice more correspondeces compared to Baumberg iterations

Retrieval on Oxford5k, mAP

Detector + Descriptor	BoW	BoW + SV	BoW + SV + QE	HQE + MA
HesAff + RootSIFT	55.1	63.0	78.4	88.0
HesAff + HardNet++	60.8	69.6	84.5	88.3
HesAffNet + HardNet++	68.3	77.8	89.0	89.5

Datasets and Training

To download datasets and start learning affnet:

git clone https://github.com/ducha-aiki/affnet
./run_me.sh

Pre-trained models

Pre-trained models can be found in folder pretrained: AffNet.pth

Usage example

We provide two examples, how to estimate affine shape with AffNet. First, on patch-column file, in HPatches format, i.e. grayscale image with w = patchSize and h = nPatches * patchSize

cd examples/just_shape
python detect_affine_shape.py imgs/face.png out.txt

Out file format is upright affine frame a11 0 a21 a22

Second, AffNet inside pytorch implementation of Hessian-Affine

2000 is number of regions to detect.

cd examples/hesaffnet
python hesaffnet.py img/cat.png ells-affnet.txt 2000
python hesaffBaum.py img/cat.png ells-Baumberg.txt 2000

output ells-affnet.txt is Oxford affine format

1.0
128
x y a b c 

Citation

Please cite us if you use this code:

@article{AffNet2017,
 author = {Dmytro Mishkin, Filip Radenovic, Jiri Matas},
    title = "{Learning Discriminative Affine Regions via Discriminability}",
     year = 2017,
    month = nov}