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 (mAP@1K)
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 (mAP@1K)
BiHalf	bihalf.yaml	bihalf	0.403
CIBHash	cibhash.yaml	cibhash	0.322
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 (mAP@1K)
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

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.