Real-time Scene Text Detection with Differentiable Binarization

note: some code is inherited from MhLiao/DB

中文解读

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

ICDAR 2015

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.