AffNet model implementation

CNN-based affine shape estimator.

AffNet model implementation in PyTorch for ECCV2018 paper "Repeatability Is Not Enough: 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

Paper figures reproduction

To reproduce Figure 1 in paper, run notebook

To reproduce Figure 2-3 in paper, run notebooks here

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

WBS example

Example is in [notebook](examples/hesaffnet/WBS demo.ipynb)

Citation

Please cite us if you use this code:

@inproceedings{AffNet2017,
 author = {Dmytro Mishkin, Filip Radenovic, Jiri Matas},
    title = "{Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability}",
    year = 2018,
    month = sep,
    booktitle = {Proceedings of ECCV}
    }

batrlatom/affnet