Fast Image Retrieval

Documentation Status



Fast Image Retrieval is an open source image retrieval framework 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 both 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.


This project is released under BSD 3-Clause License.


Please refer to Changelog for more detail.

Implemented method/backbone/datasets


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

Loss (Method)


Method Config Template Loss Name
ADSH adsh.yaml adsh
BiHalf bihalf-supervised.yaml bihalf-supervised
Cross Entropy ce.yaml ce
CSQ csq.yaml csq
DBDH dbdh.yaml dbdh
DFH dfh.yaml dfh
DPN dpn.yaml dpn
DPSH dpsh.yaml dpsh
DTSH dtsh.yaml dtsh
GreedyHash greedyhash.yaml greedyhash
HashNet hashnet.yaml hashnet
JMLH jmlh.yaml jmlh
MIHash mihash.yaml mihash
OrthoCos(OrthoHash) orthocos.yaml orthocos
OrthoArc(OrthoHash) orthoarc.yaml orthoarc
SDH sdh.yaml sdh
SDH-C sdhc.yaml sdhc


Method Config Template Loss Name
BiHalf bihalf.yaml bihalf
CIBHash cibhash.yaml cibhash
GreedyHash greedyhash-unsupervised.yaml greedyhash-unsupervised
SSDH ssdh.yaml ssdh
TBH tbh.yaml tbh

Shallow (Non-Deep learning methods)

Method Config Template Loss Name
ITQ itq.yaml itq
LsH lsh.yaml lsh
PCAHash pca.yaml pca
SH sh.yaml sh


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


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


Please head up to Tutorials Docs for guidance.


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

  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},

  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)},


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.


View Github