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EAST-Pytorch

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This is a project of EAST text detector implemented by Pytorch

// repository documentation

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# EAST: An Efficient and Accurate Scene Text Detector

### Introduction
This is a Pytorch re-implementation of [EAST: An Efficient and Accurate Scene Text Detector](https://arxiv.org/abs/1704.03155v2).
The features are summarized blow:
+ Only **RBOX** part is implemented.
+ Incidental Scene Text Detection Challenge using only training images from ICDAR 2015 and 2013.
+ Differences from original paper
     + Use Mobilenet-v2 / ResNet-50
     + Use dice loss (optimize IoU of segmentation) rather than balanced cross entropy
     + Use linear learning rate decay rather than staged learning rate decay
+ Every parameter is written in `config.py`, you should change it before you run this project
+ The pre-trained model byprovided achieves ( Mobilenet-v2-**75.05**, ResNet-50-**81.32**) F1-score on ICDAR 2015
+ Speed on 720p (resolution of 1280x720) images:
	+ Graphic card: GTX 1080 Ti
	+ Network fprop: **~15 ms**/**~50 ms**
	+ NMS (C++): **~6ms**/**~6ms**
	+ Overall: **~43 fps**/**~16 fps**

Thanks for the code of authors ([@argman](https://github.com/argman)) and ([@songdejia](https://github.com/songdejia)), thanks for the help of my partners Wei Baole, Yang Yirong, Liu Hao, Wang Wei and Ma Yuting.

### Contents
1. [Installation](#installation)
2. [Download](#download)
3. [Train](#train)
4. [Test](#test)
5. [Demo](#demo)
6. [Compute-hmean](#compute-hmean)
7. [Examples](#examples)

### Installation
1. Any version of pytorch version > 0.4.1 should be ok.
2. Other librarys are instructed in `requirements.txt`.

### Download
This project provides pre-trained models and datasets in  [BaiduYun link](https://pan.baidu.com/s/19yXbaWp0TvEdtsrAERo1Qg), keyword: yu00 or [Google drive link](https://drive.google.com/file/d/1oApGwPfuzuAfwNu2jSnWPIw88XBigHgc/view?usp=sharing) :

1. **backbone_net**: 
	The folder contains pretrained backbone net of Mobilenet-v2 / ResNet-50 which should put into 	  `.\tmp\backbone_net`
2. **pretrain-model-EAST**: 
	The folder contains pretrained Model of EAST which should put into `.\tmp`, you should also change files `model.py` and `train.py`
3. **train-dataset-ICDAR15,13**: 
	The folder contains train dataset of ICDAR15,13 which should put into `.\dataset\train`
4. **test-dataset-ICDAR15**: 
	The folder contains test dataset of ICDAR15 which should put into `.\dataset\test`
5. **test-groudtruth-ICDAR15**: 
	The folder contains groundtruth labels of test dataset ICDAR15 which should put into `.\dataset\test_compute_hmean`

### Train
If you want to train the model, you should change `config.py` parameter. 
1. `train_data_path` is the path of train dataset, put train dataset in this folder. 
2. Depending on your hardware configuration, set parameters of `train_batch_size_per_gpu` `num_workers` `gpu_ids` and `gpu`. 
3. Of course you should specify the pre-training model of backbone_net in `pretrained_basemodel_path` and `pretrained`. 
4. If you want to resume the model of EAST, you should specify the path of `checkpoint` and `resume`. 
5. On the other hand, you could also adjust the setting of other overparameters, such learning rate, weight decay, decay_steps and so on.
6. Then run
```
python train.py
```
*Note: you should change the train and test datasets format same as provided in this project, which the gt text files have same names as image files. In this project, only `.jpg` format image files is accepted. Of course, you can change the code of project.*

### Test
If you want to test the model, you should also change `config.py` parameter.
1. `test_img_path` is the path of test dataset, put test dataset in this folder. `res_img_path` is the path of result which will saved result of images files and txt files.
2. You should also specify the pretrained model in `checkpoint`.
3. Then run
```
python eval.py
```

### Demo
If you only want to test some demos, you downloaded the pre-trained model provided in this project and change `config.py`
1. Put demo images in `.\demo\test_img`, and specify the path of `test_img_path` and `res_img_path`, you will find result in `.\demo\result_img`
2. You should also specify the pretrained model in `checkpoint`.
3. Then run 
```
python eval.py
```

### Compute-hmean
1. Put groudtruth of `gt.zip` in `.\dataset\test_compute_hmean`
2. Change parameter of `config.py`, specify the path of `compute_hmean_path`
3. Then run
```
python compute_hmean.py
```
*Note: The result will show in the screen, also record in `.\dataset\test_compute_hmean\log_epoch_hmean.txt`*

### Examples
Here are some test examples on icdar2015, enjoy the beautiful text boxes by mobilenet-v2 EAST!

![image_1](https://raw.githubusercontent.com/mingliangzhang2018/EAST-Pytorch/master/demo/result_img/img_1.jpg)
![image_2](https://raw.githubusercontent.com/mingliangzhang2018/EAST-Pytorch/master/demo/result_img/img_2.jpg)
![image_16](https://raw.githubusercontent.com/mingliangzhang2018/EAST-Pytorch/master/demo/result_img/img_16.jpg)

Please let me know if you encounter any issues(my email zhangmingliang2018@ia.ac.cn).

EAST-Pytorch

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