AutoPercenty3/test/paddle2onnx/PaddleOCR/benchmark/PaddleOCR_DBNet

Real-time Scene Text Detection with Differentiable Binarization

note: some code is inherited from WenmuZhou/DBNet.pytorch

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.paddle.git
cd DBNet.paddle/

or

Install Manually

conda create -n dbnet python=3.6
conda activate dbnet

conda install ipython pip

# python dependencies
pip install -r requirement.txt

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

Requirements

• paddlepaddle 2.4+

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

Export Model

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

use following script to export inference model

python tools/export_model.py --config_file config/icdar2015_resnet50_FPN_DBhead_polyLR.yaml -o trainer.resume_checkpoint=model_best.pth trainer.output_dir=output/infer

Paddle Inference infer

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

use following script to export inference model

python tools/infer.py --model-dir=output/infer/ --img-path imgs/paper/db.jpg 

Performance

ICDAR 2015

only train on ICDAR2015 dataset

Method	image size (short size)	learning rate	Precision (%)	Recall (%)	F-measure (%)	FPS
ImageNet-resnet50-FPN-DBHead（torch）	736	1e-3	90.19	78.14	83.88	27
ImageNet-resnet50-FPN-DBHead（paddle）	736	1e-3	89.47	79.03	83.92	27
ImageNet-resnet50-FPN-DBHead（paddle_amp）	736	1e-3	88.62	79.95	84.06	27

examples

TBD

reference

1. https://arxiv.org/pdf/1911.08947.pdf
2. https://github.com/WenmuZhou/DBNet.pytorch

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