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README
MIT

HardNet model implementation

HardNet model implementation in PyTorch for NIPS 2017 paper "Working hard to know your neighbor's margins: Local descriptor learning loss" poster, slides

An example how to compile HardNet to Torchscript to be used in C++ code

Notebook

Update April 06 2018

We have added small shift and rot augmentation, which improves results up to 1mAP point on HPatches. It is in HardNet.py, turn on by --augmentation=True. All the weight will be updated soon. Version, which is trained on Brown + HPatches + PS datasets is in progress, stay tuned

Re: popular question about BoW retrieval engine

Unfortunately, it is proprietary and we cannot release it. But you can try the following open source repos, both Matlab-based:

Benchmark on HPatches, mAP

HPatches-results

Retrieval on Oxford5k, mAP, Hessian-Affine detector

Descriptor BoW BoW + SV BoW + SV + QE HQE + MA
TFeatLib 46.7 55.6 72.2 n/a
RootSIFT 55.1 63.0 78.4 88.0
L2NetLib+ 59.8 67.7 80.4 n/a
HardNetLibNIPS+ 59.8 68.6 83.0 88.2
HardNet++ 60.8 69.6 84.5 88.3
HesAffNet + HardNet++ 68.3 77.8 89.0 89.5

Requirements

Please use Python 2.7, install OpenCV and additional libraries from requirements.txt

Datasets and Training

To download datasets and start learning descriptor:

git clone https://github.com/DagnyT/hardnet
./code/run_me.sh

Logs are stored in tensorboard format in directory logs/

Pre-trained models

Pre-trained models can be found in folder pretrained.

3rd party pre-trained models

Rahul Mitra presented new large-scale patch PS-dataset and trained even better HardNet on it. Original weights in torch format are here.

Converted PyTorch version is here.

HardNet-Datasets-results

Which weights should I use?

For practical applications, we recommend HardNet++.

For comparison with other descriptors, which are trained on Liberty Brown dataset, we recommend HardNetLib+.

For the best descriptor, which is NOT trained on HPatches dataset, we recommend model by Mitra et.al., link in section above.

Usage example

We provide an example, how to describe patches with HardNet. Script expects patches in HPatches format, i.e. grayscale image with w = patch_size and h = n_patches * patch_size

cd examples
python extract_hardnet_desc_from_hpatches_file.py imgs/ref.png out.txt

or with Caffe:

cd examples/caffe
python extract_hardnetCaffe_desc_from_hpatches_file.py ../imgs/ref.png hardnet_caffe.txt

Projects, which use HardNet

AffNet -- learned local affine shape estimator.

Citation

Please cite us if you use this code:

@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}
MIT License Copyright (c) 2017 Anastasiia Mishchuk, Dmytro Mishkin Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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