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‎.DS_Store

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‎.gitignore

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.idea/
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data
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data/
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csvs
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csvs/
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*.csv
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*.xlsx
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*.ipynb
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*.pth
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*.zip
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logs/
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__pycache__/

‎Dockerfile

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FROM pytorch/pytorch:1.8.0-cuda11.1-cudnn8-devel
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RUN apt-get update
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RUN apt-get install ffmpeg libsm6 libxext6 libgl1-mesa-glx -y
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RUN pip install jupyterlab
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RUN pip install opencv-python
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RUN pip install scikit-image
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RUN pip install scikit-learn
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RUN pip install seaborn
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RUN pip install pandas
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RUN pip install imutils
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RUN pip install craft-text-detector
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RUN pip install tensorflow
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RUN pip install kornia
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RUN pip install pytorch_memlab
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RUN apt-get install bc unzip git -y
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# if use accimage
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RUN pip install --prefix=/opt/intel/ipp ipp-devel
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RUN pip install git+https://github.com/pytorch/accimage
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ENV LD_LIBRARY_PATH=/opt/intel/ipp/lib:$LD_LIBRARY_PATH
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# if use pillow-simd
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RUN pip uninstall -y pillow
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RUN CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
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RUN pip install umap-learn
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RUN pip install faiss-gpu
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# setup user [ to avoid -u $(id -u):$(id -g) ]
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RUN groupadd -g 1000 user
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RUN useradd -g 1000 -u 1000 -ms /bin/bash user
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EXPOSE 6006
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EXPOSE 8123
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USER user
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WORKDIR /workspace

‎README.md

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# Fast Image Retrieval
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## Introduction
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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 implemented most major binary hashing methods, together with major backbone networks and major datasets.
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### Major features
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- **One for All**
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We unified various binary hashing methods, different backbone, and multiple datasets under a same single framework, easing the benchmark and research. It supports popular binary hashing methods, e.g. HashNet, GreedyHash, DPN, OrthoHash, etc.
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- **Modularity**
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We break the framework into parts so that one can easily implement their own method by joining up the components.
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## License
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[comment]: <> (Add license)
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This project is released under [BSD-3 license]().
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## Changelog
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[comment]: <> (Change to docs link)
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Please refer to [Changelog](?) for more detail.
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## Implemented method/backbone/datasets
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[//]: <> (This is copied from docs/implementation.md)
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### Backbone
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1. Alexnet
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2. VGG{16}
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3. ResNet{18,34,50,101,152}
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### Loss (Method)
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#### Supervised
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|Method|Config Template|Loss Name|
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|---|---|---|
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|ADSH|adsh.yaml|adsh|
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|BiHalf|bihalf-supervised.yaml|bihalf-supervised|
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|Cross Entropy|ce.yaml|ce|
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|CSQ|csq.yaml|csq|
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|DBDH|dbdh.yaml|dbdh|
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|DFH|dfh.yaml|dfh|
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|DPN|dpn.yaml|dpn|
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|DPSH|dpsh.yaml|dpsh|
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|DTSH|dtsh.yaml|dtsh|
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|GreedyHash|greedyhash.yaml|greedyhash|
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|HashNet|hashnet.yaml|hashnet|
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|JMLH|jmlh.yaml|jmlh|
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|MIHash|mihash.yaml|mihash|
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|OrthoCos(OrthoHash)|orthocos.yaml|orthocos|
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|OrthoArc(OrthoHash)|orthoarc.yaml|orthoarc|
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|SDH|sdh.yaml|sdh|
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|SDH-C|sdhc.yaml|sdhc|
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#### Unsupervised
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|Method|Config Template|Loss Name|
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|---|---|---|
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|BiHalf|bihalf.yaml|bihalf|
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|CIBHash|cibhash.yaml|cibhash|
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|GreedyHash|greedyhash-unsupervised.yaml|greedyhash-unsupervised|
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|SSDH|ssdh.yaml|ssdh|
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|TBH|tbh.yaml|tbh|
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#### Shallow (Non-Deep learning methods)
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|Method|Config Template|Loss Name|
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|---|---|---|
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|ITQ|itq.yaml|itq|
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|LsH|lsh.yaml|lsh|
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|PCAHash|pca.yaml|pca|
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|SH|sh.yaml|sh|
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### Datasets
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|Dataset|Name in framework|
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|---|---|
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|ImageNet100|imagenet|
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|NUS-WIDE|nuswide|
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|MS-COCO|coco|
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|MIRFLICKR/Flickr25k|mirflickr|
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|Stanford Online Product|sop|
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|Cars dataset|cars|
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|CIFAR10|cifar10|
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## Installation
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[comment]: <> (Change to docs link)
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Please head up to [get_started.md](docs/get_started.md) for guides on setup conda environment and installation.
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## Tutorials
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[comment]: <> (Change to docs link)
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Please head up to [tutorials.md](docs/tutorials.md) for guidances.
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[comment]: <> (## Citation)
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[comment]: <> (If you find this framework useful in your research, please consider cite this project.)
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[comment]: <> (```)
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[comment]: <> (@misc{)
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[comment]: <> (```)
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## Contributing
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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.

‎analysis/__init__.py

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