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hardnet

Hardnet descriptor model - "Working hard to know your neighbor's margins: Local descriptor learning loss"

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创建于 2017-05-29 · 更新于 2026-09-23 · 今日第 17532 名
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DagnyT/hardnet 排名第 17,532,有 534 个星标。

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DagnyT/hardnet 排名第 17,532,此处有 8 个同类条目;HKUDS/LLMRec 排名第 17,533。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Architecture
关键词Computer vision
星标数534
分叉数101
贡献者3
创建日期2017-05-29
更新日期2026-09-23
1 天星标数0
7 天星标数0
排名17,532
上次排名17,316
排名变化-216
7 天星标排名16,155
上次 7 天星标排名16,162
7 天星标排名变化7
总星标排名18,377
上次总星标排名18,352
总星标排名变化-25
README 摘录

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 2025 The PhotoTour dataset is no longer available at original links. We use a mirror from CTU in Prague instead. Update April 06 2018 We have added small shift and rot augmentation, which improves results up to 1mAP

point on HPatches. 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. 7, install OpenCV and additional libraries from requirements. txt Datasets and Training To download datasets and start learning descriptor: 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. 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. Usage example We provide an example, how to describe patches with HardNet. Script expects patches in HPatches format, i. Citation Please

cite us if you use this code:

常见问题

记录了多少星标和分叉?

DagnyT/hardnet 有 534 个星标和 101 个分叉。

仓库排名是多少?

DagnyT/hardnet 排名第 17,532。

记录的语言和类别是什么?

DagnyT/hardnet 有 534 个星标,语言为 Python,类别为 Model Development。

哪个仓库排名最接近?

DagnyT/hardnet 与 HKUDS/LLMRec 比较,后者排名第 17,533,有 534 个星标。

README

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 2025

The PhotoTour dataset is no longer available at original links. We use a mirror from CTU in Prague instead.

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

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.

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:

@inproceedings{HardNet,
 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 NeurIPS},
 year = 2017,
 month = dec
}
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