# RotNet

**Repository Path**: fkgeek/RotNet

## Basic Information

- **Project Name**: RotNet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-02
- **Last Updated**: 2021-12-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="right">
  Language:
    🇺🇸
  <a title="Chinese" href="./README.zh-CN.md">🇨🇳</a>
</div>

 <div align="center"><a title="" href="https://github.com/ZJCV/RotNet"><img align="center" src="./imgs/RotNet.png"></a></div>

<p align="center">
  «RotNet» realizes image rotation correction based on deep learning
<br>
<br>
  <a href="https://github.com/RichardLitt/standard-readme"><img src="https://img.shields.io/badge/standard--readme-OK-green.svg?style=flat-square"></a>
  <a href="https://conventionalcommits.org"><img src="https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow.svg"></a>
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</p>

![](./imgs/demo.png)

## Table of Contents

- [Table of Contents](#table-of-contents)
- [Background](#background)
- [Installation](#installation)
- [Usage](#usage)
  - [How to use](#how-to-use)
  - [How to add dataset](#how-to-add-dataset)
- [Maintainers](#maintainers)
- [Thanks](#thanks)
- [Contributing](#contributing)
- [License](#license)

## Background

Looking for information on the Internet, we found that the image rotation angle can be detected by deep learning algorithm. Refer to

* [d4nst/RotNet](https://github.com/d4nst/RotNet)
* [Correcting Image Orientation Using Convolutional Neural Networks](https://d4nst.github.io/2017/01/12/image-orientation/)
* [Image Orientation Estimation with Convolutional Networks](https://lmb.informatik.uni-freiburg.de/Publications/2015/FDB15/image_orientation.pdf)
* [UNSUPERVISED REPRESENTATION LEARNING BY PREDICTING IMAGE ROTATIONS](https://arxiv.org/pdf/1803.07728.pdf)

The corresponding implementation can't meet the current performance requirements, so I implement one myself

## Installation

```angular2html
$ pip install -r requirements.txt
```

## Usage

### How to use

* train

```
$ export PYTHONPATH=<root path>
$ CUDA_VISIBLE_DEVICES=0 python tools/train.py -cfg=configs/xxx.yaml
```

* test

```
$ export PYTHONPATH=<root path>
$ CUDA_VISIBLE_DEVICES=0 python demo/demo.py -cfg=demo/xxx.yaml
```

### How to add dataset

Suppose your dataset is in the following format

```
root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png

root/cat/123.png
root/cat/nsdf3.png
root/cat/asd932_.png
```

modify config_file like this

```
DATASET:
  NAME: 'GeneralDataset'
  TRAIN_ROOT: /path/to/train_root
  TEST_ROOT: /path/to/test_root
  TOP_K: (1, 5)
```

## Maintainers

* zhujian - *Initial work* - [zjykzj](https://github.com/zjykzj)

## Thanks

* [d4nst/RotNet](https://github.com/d4nst/RotNet)
* [ZJCV/ZCls](https://github.com/ZJCV/ZCls)

## Contributing

Anyone's participation is welcome! Open an [issue](https://github.com/ZJCV/RotNet/issues) or submit PRs.

Small note:

* Git submission specifications should be complied
  with [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/)
* If versioned, please conform to the [Semantic Versioning 2.0.0](https://semver.org) specification
* If editing the README, please conform to the [standard-readme](https://github.com/RichardLitt/standard-readme)
  specification.

## License

[Apache License 2.0](LICENSE) © 2020 zjykzj