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Open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥
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README.md

PyRetri

Introduction

PyRetri (pronounced as [ˈperɪˈtriː]) is a unified deep learning based image retrieval toolbox based on PyTorch, which is designed for researchers and engineers.

image

Major Features

PyRetri is a versatile deep learning based image retrieval toolbox designed with simplicity and flexibility in mind.

  • Modular Design: We decompose the deep learning based image retrieval into several stages and users can easily construct an image retrieval pipeline by selecting and combining different modules.
  • Flexible Loading: The toolbox is able to adapt to load several types of model parameters, including parameters with the same keys and shape, parameters with different keys, and parameters with the same keys but different shapes.
  • Support of Multiple Methods: The toolbox directly supports several popluar methods designed for deep learning based image retrieval, which is also suitable for person re-identification.
  • Combinations Search Tool: We provide the pipeline combinations search scripts to help users to find the optimal combinations of these supported methods with various hyper-parameters.

Supported Methods

The toolbox supports popluar and prominent methods of image retrieval and users can also design and add their own modules.

  • Pre-processing
    • DirectReszie, PadResize, ShorterResize
    • CenterCrop, TenCrop
    • TwoFlip
    • ToTensor, ToCaffeTensor
    • Normalize
  • Feature Representation
  • Post-processing

License

This project is released under the Apache 2.0 license.

Installation

Please refer to INSTALL.md for installation and dataset preparation.

Get Started

Please see GETTING_STARTED.md for the basic usage of PyRetri.

Model Zoo

Results and models are available in MODEL_ZOO.md.

Citation

If you use this toolbox in your research, please cite this project.

Contacts

If you have any questions about our work, please do not hesitate to contact us by emails.

Xiu-Shen Wei: weixs.gm@gmail.com

Benyi Hu: hby0906@stu.xjtu.edu.cn

Renjie Song: songrenjie@megvii.com