mingliangzhang2018 / EAST-Pytorch

This is a project of EAST text detector implemented by Pytorch

EAST: An Efficient and Accurate Scene Text Detector

Introduction

This is a Pytorch re-implementation of EAST: An Efficient and Accurate Scene Text Detector. 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) and (@songdejia), thanks for the help of my partners Wei Baole, Yang Yirong, Liu Hao, Wang Wei and Ma Yuting.

Contents

1. Installation
2. Download
3. Train
4. Test
5. Demo
6. Compute-hmean
7. 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, keyword: yu00 or Google drive link :

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!

Please let me know if you encounter any issues(my email [e-mail]).