PyRetri/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](teaser_image/overview.png)

### 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**
  - Global Average Pooling (GAP), Global Max Pooling (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://arxiv.org/pdf/1711.02512.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](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.

```
@article{PyRetri,
  title = {PyRetri: A PyTorch-based Library for Image Retrieval by Deep Convolutional Neural Networks},
  author = {Hu, Benyi and Song, Ren-Jie and Wei, Xiu-Shen and Yao, Yazhou and Hua, Xian-Sheng and Liu, Yuehu},
  year = {2020}
}
```

## 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)

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

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