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Hardnet descriptor model - "Working hard to know your neighbor's margins: Local descriptor learning loss"
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| 2026-07-24 | 533 |
| 2026-07-25 | 533 |
| 2026-07-28 | 533 |
| 2026-07-30 | 533 |
| 2026-08-06 | 533 |
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# HardNet model implementation HardNet model implementation in PyTorch for NIPS 2017 paper ["Working hard to know your neighbor's margins: Local descriptor learning loss"](https://arxiv.org/abs/1705.10872) [poster](http://cmp.felk.cvut.cz/~mishkdmy/posters/hardnet2017.pdf), [slides](http://cmp.felk.cvut.cz/~mishkdmy/slides/HardNet2017.pdf) ## An example how to compile HardNet to Torchscript to be used in C++ code [Notebook](notebook/convert_HardNet_to_JIT.ipynb) ## Update April 2025 The PhotoTour dataset is [no longer available](https://github.com/pytorch/vision/issues/8960) at original links. We use a [mirror from CTU in Prague](https://cmp.felk.cvut.cz/~mishkdmy/datasets/BrownPhotoTour/) 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: - [ASMK](https://github.com/gtolias/asmk) - [HQE](https://github.com/gtolias/hqe) - [VISE](https://gitlab.com/vgg/vise) ## Benchmark on [HPatches](https://github.com/hpatches/hpatches-benchmark), mAP  ## Retrieval on Oxford5k, mAP, Hessian-Affine detector | Descriptor | BoW | BoW + SV | BoW + SV + QE | HQE + MA | | ----- | ---- | ---- | ---- | ---- | | [TFeatLib](https://github.com/vbalnt/tfeat) | 46.7 | 55.6 | 72.2 | n/a | | [RootSIFT](http://ieeexplore.ieee.org/document/6248018/) | 55.1 | 63.0 | 78.4 | 88.0 | | [L2NetLib+](https://github.com/yuruntian/L2-Net) | 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](https://github.com/ducha-aiki/affnet) + 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: ```bash 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](pretrained). ## 3rd party pre-trained models Rahul Mitra presented new [large-scale patch PS-dataset](https://github.com/rmitra/PS-Dataset) and trained even better HardNet on it. Original weights in torch format are [here](https://www.dropbox.com/s/q89g5kfuke6c348/hardnet-PS.t7?dl=1). Converted PyTorch version is [here](pretrained/3rd_party/HardNetPS/).  ## Which weights should I use? For practical applications, we recommend [HardNet++](https://github.com/DagnyT/hardnet/blob/master/pretrained/pretrained_all_datasets/HardNet%2B%2B.pth). For comparison with other descriptors, which are trained on Liberty Brown dataset, we recommend [HardNetLib+](pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth). 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](https://github.com/hpatches/hpatches-benchmark) 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](https://github.com/ducha-aiki/affnet) -- learned local affine shape estimator. ##
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Dmytro Mishkin · Czech Technical University in Prague · Czech Republic
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Would you bet a product on this? Bounded 0–100 and slow moving.
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