This is a pytorch-based implementation for paper ContourNet (CVPR2020). ContourNet is a contour-based text detector which represents text region with a set of contour points. This repository is built on the pytorch maskrcnn.

• Release code
• Document for Installation
• Trained models
• Document for testing and training
• Evaluation
• Experiment on more datasets
• re-organize and clean the parameters

2020/5/6 We upload the models on Drive.

We recommend you to use Anaconda BaiduYun Link(passward:1y3v) or Geogle Drive to manage your libraries.

  conda create --name ContourNet
  conda activate ContourNet
  conda install ipython
  pip install ninja yacs cython matplotlib tqdm scipy shapely networkx pandas
  conda install pytorch=1.0 torchvision=0.2 cudatoolkit=9.0 -c pytorch
  conda install -c menpo opencv
  export INSTALL_DIR=$PWDcd $INSTALL_DIR
  git clone https://github.com/cocodataset/cocoapi.git
  cd cocoapi/PythonAPI
  python setup.py build_ext install
  cd $INSTALL_DIR
  git clone https://github.com/wangyuxin87/ContourNet.git
  cd ContourNet
  python setup.py build develop

Prepare data follow COCO format or you can download our IC15dataset-BAIDU (passward:ect5) or Geogle Drive, and unzip it in

   datasets/.

You need to modify maskrcnn_benchmark/config/paths_catalog.py to point to the location where your dataset is stored.

Download ResNet50 model (ImageNet)-BAIDU(passward:edt8) or ResNet50 model(ImageNet)-Drive and put it in ContourNet/.

Put the folder in

   output/.

You need to set the resolution to 1200x2000 in maskrcnn_benchmark/data/transformstransforms.py (line 50 to 52). Then run

   bash test_contour.sh

Put bo.json to ic15_evaluate/, then run

   cd ic15_evaluate
   conda deactivate
   pip2 install polygon2
   conda install zip
   python2 eval_ic15

As mentioned in our paper, we only use offical training images to train our model, data augmentation includes random crop, rotate etc. There are 2 strategies to initialize the parameters in the backbone:1) use the ResNet50 model (ImageNet)-BAIDU(passward:edt8) or ResNet50 model (ImageNet)-Drive, this is provided by Yuliang, which is ONLY an ImageNet Model With a few iterations on ic15 training data for a stable initialization.2) Use model only pre-trained on ImageNet(modify the WEIGHT to catalog://ImageNetPretrained/MSRA/R-50 in config/r50_baseline.yaml). In this repository, we use the first one to train the model on this dataset.

Run

   bash train_contour.sh

Change the ROTATE_PROB_TRAIN to 0.3 and ROTATE_DEGREE to 10 in config/r50_baseline.yaml (corresponding modification also needs to be done in maskrcnn_benchmark/data/transformstransforms.py from line 312 to 317), then finetune the model for more 10500 steps (lr starts from 2.5e-4 and dot 0.1 when step = [5k,10k]).

If you find our method useful for your reserach, please cite

  @article{wang2020contournet,
  title={ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection},
  author={Wang, Yuxin and Xie, Hongtao and Zha, Zhengjun and Xing, Mengting and Fu, Zilong and Zhang, Yongdong},
  journal={CVPR},
  year={2020}

}

Suggestions and discussions are greatly welcome. Please contact the authors by sending email to [email protected]