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https://github.com/ducha-aiki/affnet
Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"
https://github.com/ducha-aiki/affnet
affine-shape-estimator computer-vision convolutional-networks convolutional-neural-networks deep-learning hessian image-matching image-retrieval local-features pytorch
Last synced: 7 days ago
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Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"
- Host: GitHub
- URL: https://github.com/ducha-aiki/affnet
- Owner: ducha-aiki
- License: mit
- Created: 2017-11-17T19:07:21.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-14T10:06:51.000Z (over 2 years ago)
- Last Synced: 2025-01-08T05:25:55.354Z (14 days ago)
- Topics: affine-shape-estimator, computer-vision, convolutional-networks, convolutional-neural-networks, deep-learning, hessian, image-matching, image-retrieval, local-features, pytorch
- Language: Python
- Homepage:
- Size: 61.2 MB
- Stars: 268
- Watchers: 18
- Forks: 46
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://arxiv.org/abs/1711.06704)"
## Update: pytorch 1.4 version
The master branch is the one, which produced ECCV-paper results, python 2.7 and pytorch 0.4.0
[Here](https://github.com/ducha-aiki/affnet/tree/pytorch1-4_python3) is the one, which successfully runs on python 3.7, pytorch 1.4.0
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](https://github.com/perdoch/hesaff) + [RootSIFT](http://ieeexplore.ieee.org/document/6248018/) | 55.1 | 63.0 | 78.4 | 88.0 |
| [HesAff](https://github.com/perdoch/hesaff) + [HardNet++](https://github.com/DagnyT/hardnet) | 60.8 | 69.6 | 84.5 | 88.3 |
| HesAffNet + [HardNet++](https://github.com/DagnyT/hardnet) | **68.3** | **77.8** | **89.0** | **89.5** |
## Datasets and Training
To download datasets and start learning affnet:
```bash
git clone https://github.com/ducha-aiki/affnet
./run_me.sh
```
## Paper figures reproduction
To reproduce Figure 1 in paper, run [notebook](examples/toy_example_figure1/Figure1.ipynb)
To reproduce Figure 2-3 in paper, run notebooks [here](examples/direct_shape_optimization)
```bash
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](https://github.com/hpatches/hpatches-benchmark) 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](http://www.robots.ox.ac.uk/~vgg/research/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}
}
```