PyRetri/README.md

# PyRetri

## Introduction

PyRetri is a novel deep learning-based image retrieval toolbox based on PyTorch, which is designed for researchers and engineers.

![image](image/overview.png)

### Major Features

Pyretri is a versatile  deep learning-based image retrieval toolbox designed with simplicity, flexibility in mind.

- **Modular Design**: We decompose the deep learning-based image retrieval into several stages and one can easily construct a image retrieval pipeline by 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 are also suitable for person re-identification.
- **Combinations Search Tool**: We provide the pipeline combinations search scripts to help users to find possible combinations of approaches with various hyper-parameters. 

### Supported Methods

The toolbox supports several popluar methods and one can design and add their own modules.

- **Pre-processing**
  - DirectReszie, PadResize, ShorterResize
  - CenterCrop, TenCrop
  - TwoFlip
  - ToTensor, ToCaffeTensor
  - Normalize
- **Feature Representation**
  - GAP, GMP
  - [R-MAC](https://arxiv.org/pdf/1511.05879.pdf), [SPoC](https://arxiv.org/pdf/1510.07493.pdf), [CroW](https://arxiv.org/pdf/1512.04065.pdf), [GeM](https://pdfs.semanticscholar.org/a2ca/e0ed91d8a3298b3209fc7ea0a4248b914386.pdf), [SCDA](http://www.weixiushen.com/publication/tip17SCDA.pdf), [PWA](https://arxiv.org/abs/1705.01247)
  - [PCB](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yifan_Sun_Beyond_Part_Models_ECCV_2018_paper.pdf)
- **Post-processing**
  - [SVD](https://link.springer.com/chapter/10.1007%2F978-3-662-39778-7_10), [PCA](http://pzs.dstu.dp.ua/DataMining/pca/bibl/Principal%20components%20analysis.pdf)
  - [DBA](https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf)
  - [QE](https://www.robots.ox.ac.uk/~vgg/publications/papers/chum07b.pdf), [K-reciprocal](https://arxiv.org/pdf/1701.08398.pdf)

## License

This project is released under the [Apache 2.0 license](https://github.com/hby96/pyretri/blob/master/LICENSE).

## Installation

Please refer to [INSTALL.md](docs/INSTALL.md) for installation and dataset preparation.

## Get Started

Please see [GETTING_STARTED.md](docs/GETTING_STARTED.md) for the basic usage of PyRetri.

## Model Zoo

Results and models are available in [MODEL_ZOO.md](docs/MODEL_ZOO.md).

## Citation

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

```shell

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## 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](mailto:weixs.gm@gmail.com)

Renjie Song:[songrenjie@megvii.com](mailto:songrenjie@megvii.com)

Benyi Hu: [hby0906@stu.xjtu.edu.cn](mailto:hby0906@stu.xjtu.edu.cn)