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https://github.com/songdejia/EAST
This is a pytorch re-implementation of EAST: An Efficient and Accurate Scene Text Detector.
https://github.com/songdejia/EAST
deeplearning east icdar ocr pytorch textdetection
Last synced: 10 days ago
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This is a pytorch re-implementation of EAST: An Efficient and Accurate Scene Text Detector.
- Host: GitHub
- URL: https://github.com/songdejia/EAST
- Owner: songdejia
- License: mit
- Created: 2018-08-29T07:26:05.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-04-06T06:58:22.000Z (over 5 years ago)
- Last Synced: 2024-08-02T11:15:40.048Z (3 months ago)
- Topics: deeplearning, east, icdar, ocr, pytorch, textdetection
- Language: Python
- Homepage:
- Size: 8.14 MB
- Stars: 573
- Watchers: 13
- Forks: 153
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://github.com/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](http://rrc.cvc.uab.es/?ch=2&com=mymethods&task=1),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](https://arxiv.org/abs/1704.03155v2).
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](http://rrc.cvc.uab.es/?ch=4&com=evaluation&view=method_samples&task=1&m=29855>v=1) 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](https://github.com/zxytim)) help!
Please cite his [paper](https://arxiv.org/abs/1704.03155v2) if you find this useful.
### Contents
1. [Installation](#installation)
2. [Download](#download)
3. [Prepare dataset/pretrain](#dataset)
4. [Test](#train)
5. [Train](#test)
6. [Examples](#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](http://rrc.cvc.uab.es/?ch=2&com=mymethods&task=1)
** Note: you can download dataset here
+ -- [ICDAR15](http://rrc.cvc.uab.es/?ch=4&com=downloads)
+ -- [ICDAR13](http://rrc.cvc.uab.es/?ch=2&com=downloads)
[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!
