Skip to content

A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization

License

Notifications You must be signed in to change notification settings

dcdethan/DBNet.pytorch

 
 

Repository files navigation

Real-time Scene Text Detection with Differentiable Binarization

note: some code is inherited from MhLiao/DB

中文解读

network

update

2020-06-07: 添加灰度图训练,训练灰度图时需要在配置里移除dataset.args.transforms.Normalize

Install Using Conda

conda env create -f environment.yml
git clone https://github.com/WenmuZhou/DBNet.pytorch.git
cd DBNet.pytorch/

or

Install Manually

conda create -n dbnet python=3.6
conda activate dbnet

conda install ipython pip

# python dependencies
pip install -r requirement.txt

# install PyTorch with cuda-10.1
# Note that you can change the cudatoolkit version to the version you want.
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

# clone repo
git clone https://github.com/WenmuZhou/DBNet.pytorch.git
cd DBNet.pytorch/

Requirements

  • pytorch 1.4+
  • torchvision 0.5+
  • gcc 4.9+

Download

TBD

Data Preparation

Training data: prepare a text train.txt in the following format, use '\t' as a separator

./datasets/train/img/001.jpg	./datasets/train/gt/001.txt

Validation data: prepare a text test.txt in the following format, use '\t' as a separator

./datasets/test/img/001.jpg	./datasets/test/gt/001.txt
  • Store images in the img folder
  • Store groundtruth in the gt folder

The groundtruth can be .txt files, with the following format:

x1, y1, x2, y2, x3, y3, x4, y4, annotation

Train

  1. config the dataset['train']['dataset'['data_path']',dataset['validate']['dataset'['data_path']in config/icdar2015_resnet18_fpn_DBhead_polyLR.yaml
  • . single gpu train
bash singlel_gpu_train.sh
  • . Multi-gpu training
bash multi_gpu_train.sh

Test

eval.py is used to test model on test dataset

  1. config model_path in eval.sh
  2. use following script to test
bash eval.sh

Predict

predict.py Can be used to inference on all images in a folder

  1. config model_path,input_folder,output_folder in predict.sh
  2. use following script to predict
bash predict.sh

You can change the model_path in the predict.sh file to your model location.

tips: if result is not good, you can change thre in predict.sh

The project is still under development.

Performance

only train on ICDAR2015 dataset

Method image size (short size) learning rate Precision (%) Recall (%) F-measure (%) FPS
SynthText-Defrom-ResNet-18(paper) 736 0.007 86.8 78.4 82.3 48
ImageNet-resnet18-FPN-DBHead 736 1e-3 87.03 75.06 80.6 43
ImageNet-Defrom-Resnet18-FPN-DBHead 736 1e-3 88.61 73.84 80.56 36
ImageNet-resnet50-FPN-DBHead 736 1e-3 88.06 77.14 82.24 27
ImageNet-resnest50-FPN-DBHead 736 1e-3 88.18 76.27 81.78 27

examples

TBD

todo

  • mutil gpu training

reference

  1. https://arxiv.org/pdf/1911.08947.pdf
  2. https://github.com/WenmuZhou/PANet.pytorch
  3. https://github.com/MhLiao/DB

If this repository helps you,please star it. Thanks.

About

A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.5%
  • Shell 0.5%