note: some code is inherited from MhLiao/DB
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
conda env create -f environment.yml
git clone https://github.com/WenmuZhou/DBNet.pytorch.git
cd DBNet.pytorch/
or
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/
- pytorch 1.4+
- torchvision 0.5+
- gcc 4.9+
TBD
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
- 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
eval.py is used to test model on test dataset
- config
model_path
in eval.sh - use following script to test
bash eval.sh
predict.py Can be used to inference on all images in a folder
- config
model_path
,input_folder
,output_folder
in predict.sh - 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.
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 |
TBD
- mutil gpu training
- https://arxiv.org/pdf/1911.08947.pdf
- https://github.com/WenmuZhou/PANet.pytorch
- https://github.com/MhLiao/DB
If this repository helps you,please star it. Thanks.