Awesome

EAST: An Efficient and Accurate Scene Text Detector

Description:

This version will be updated soon, please pay attention to this work. The motivation of this version is to build a easy-training model. This version can automatically update best_model by comparing current hmean and the former. At the same time, we can see evaluation info about every sample easily.

• 1.train
• 2.predict
• 3.compress
• 4.compute Hmean(if Hmean is higher than before, update best_weight.pkl)
• 5.visualization(blue, green, red)
• 6.multi-scale test (update soon) multi-scale vis. (vis with score, scales)

Thanks

The version is ported from argman/EAST, from Tensorflow to Pytorch

Check On Website

If you have no confidence of the result of our program, you could use submit.zip to submit on website,then you can see result of every image.

Performance

• right -- green || wrong -- red || miss -- blue

• recall/precision/hmean for every test image

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.
• A fast Locality-Aware NMS in C++ provided by the paper's author.(g++/gcc version 6.0 + will be ok)
• Evalution see here for the detailed results.
• Differences from original paper
  • Use ResNet-50 rather than PVANET
  • Use dice loss (optimize IoU of segmentation) rather than balanced cross entropy
  • Use linear learning rate decay rather than staged learning rate decay

Thanks for the author's (@zxytim) help! Please cite his paper if you find this useful.

Contents

1. Installation
2. Download
3. Prepare dataset/pretrain
4. Test
5. Train
6. Examples

Installation

1. Any version of pytorch version > 0.4.0 should be ok.

Download

1. Pretrained model is not provided temporarily. Web site is updating now, please continue to pay attention

Prepare dataset/pretrain weight

[1]. dataset(you need to prepare for dataset for train and test) suggestions: you could do a soft-link to root_to_this_program/dataset/train/img/*.jpg

• -- train ./dataset/train/img/img_###.jpg ./dataset/train/gt/img_###.txt (you need to change name)
• -- test ./data/test/img_###.jpg (img only)
• -- gt.zip ./result/gt.zip(ICDAR15 gt.zip is avaliable on website

** Note: you can download dataset here

• -- ICDAR15
• -- ICDAR13

[2]. pretrained

• In config.py set resume True and set checkpoint path/to/weight/file
• I will provide pretrianed weight soon

[3]. check GPUs and CPUs you can use following to check aviliable gpu, this is for train

watch -n 0.1 nvidia-smi

then, you will see 2,3 is avaliable, modify config.py gpu_ids = [0,1], gpu = 2, and modify run.sh - CUDA_VISIBLE_DEVICES=2,3

Train

If you want to train the model, you should provide the dataset path in config.py and run

sh run.py

** Note: you should modify run.sh to specify your gpu id

If you have more than one gpu, you can pass gpu ids to gpu_list(like gpu_list=0,1,2,3) in config.py

** Note: you should change the gt text file of icdar2015's filename to img_*.txt instead of gt_img_*.txt(or you can change the code in icdar.py), and some extra characters should be removed from the file. See the examples in training_samples/**

Test

By default, we set train-eval process into integer. If you want to use eval independently, you can do it by yourself. Any question can contact me.

Examples

Here are some test examples on icdar2015, enjoy the beautiful text boxes!