Released September, 2021

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Documentation: https://fast-image-retrieval.readthedocs.io/en/latest/

Introduction

Fast Image Retrieval (FIRe) is an open source image retrieval project release by Center of Image and Signal Processing Lab (CISiP Lab), Universiti Malaya. This framework implements most of the major binary hashing methods, together with different popular backbone networks and public datasets.

Major features

• One for All

  Herein, we unified (i) various binary hashing methods, (ii) different backbone, and (iii) multiple datasets under a single framework to ease the research and benchmarking in this domain. It supports popular binary hashing methods, e.g. HashNet, GreedyHash, DPN, OrthoHash, etc. - Modularity

  We break the framework into parts so that one can easily implement their own method by joining up the components.

License

This project is released under BSD 3-Clause License.

Changelog

Please refer to Changelog for more detail.

Implemented method/backbone/datasets

Backbone

1. Alexnet
2. VGG{16}
3. ResNet{18,34,50,101,152}

Loss (Method)

Supervised

|Method|Config Template|Loss Name|64bit ImageNet AlexNet ([email protected])| |---|---|---|---| |ADSH|adsh.yaml|adsh|0.645| |BiHalf|bihalf-supervised.yaml|bihalf-supervised|0.684| |Cross Entropy|ce.yaml|ce|0.434| |CSQ|csq.yaml|csq|0.686| |DFH|dfh.yaml|dfh|0.689| |DPN|dpn.yaml|dpn|0.692| |DPSH|dpsh.yaml|dpsh|0.599| |DTSH|dtsh.yaml|dtsh|0.608| |GreedyHash|greedyhash.yaml|greedyhash|0.667| |HashNet|hashnet.yaml|hashnet|0.588| |JMLH|jmlh.yaml|jmlh|0.664| |OrthoCos(OrthoHash)|orthocos.yaml|orthocos|0.701| |OrthoArc(OrthoHash)|orthoarc.yaml|orthoarc|0.698| |SDH-C|sdhc.yaml|sdhc|0.639|

Unsupervised

|Method|Config Template|Loss Name|64bit ImageNet AlexNet ([email protected])| |---|---|---|---| |BiHalf|bihalf.yaml|bihalf|0.403| |CIBHash|cibhash.yaml|cibhash|0.322|0.686401 |GreedyHash|greedyhash-unsupervised.yaml|greedyhash-unsupervised|0.407| |SSDH|ssdh.yaml|ssdh|0.146| |TBH|tbh.yaml|tbh|0.324|

Shallow (Non-Deep learning methods)

|Method|Config Template|Loss Name|64bit ImageNet AlexNet ([email protected])| |---|---|---|---| |IMH|imh.yaml|imh|0.467| |ITQ|itq.yaml|itq|0.402| |LsH|lsh.yaml|lsh|0.206| |PCAHash|pca.yaml|pca|0.405| |SH|sh.yaml|sh|0.350|

{warning} Shallow methods only works with descriptor datasets. We will upload the descriptor datasets and

Datasets

|Dataset|Name in framework| |---|---| |ImageNet100|imagenet100| |NUS-WIDE|nuswide| |MS-COCO|coco| |MIRFLICKR/Flickr25k|mirflickr| |Stanford Online Product|sop| |Cars dataset|cars| |CIFAR10|cifar10|

Installation

Please head up to Get Started Docs for guides on setup conda environment and installation.

Tutorials

Please head up to Tutorials Docs for guidance.

Reference

If you find this framework useful in your research, please consider cite this project.

``` @inproceedings{dpn2020, title={Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes.}, author={Fan, Lixin and Ng, Kam Woh and Ju, Ce and Zhang, Tianyu and Chan, Chee Seng}, booktitle={IJCAI}, pages={825--831}, year={2020} }

@inproceedings{orthohash2021, title={One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective}, author={Hoe, Jiun Tian and Ng, Kam Woh and Zhang, Tianyu and Chan, Chee Seng and Song, Yi-Zhe and Xiang, Tao}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, year={2021} } ```

Contributing

We welcome the contributions to improve this project. Please file your suggestions/issues by creating new issues or send us a pull request for your new changes/improvement/features/fixes.