/CRAFT-Re-reimplementation

CRAFT算法的训练

Primary LanguagePython

CRAFT-Reimplementation

Note：If you have any problems, please comment. Or you can join us weChat group. The QR code will update in issues #49 .

Reimplementation：Character Region Awareness for Text Detection Reimplementation based on Pytorch

Character Region Awareness for Text Detection

Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee (Submitted on 3 Apr 2019)

The full paper is available at: https://arxiv.org/pdf/1904.01941.pdf

Install Requirements:

1、PyTroch>=0.4.1
2、torchvision>=0.2.1
3、opencv-python>=3.4.2
4、check requiremtns.txt
5、4 nvidia GPUs(we use 4 nvidia titanX)

pre-trained model:

NOTE: There are old pre-trained models, I will upload the new results pre-trained models' link.
Syndata:Syndata for baidu drive || Syndata for google drive
Syndata+IC15:Syndata+IC15 for baidu drive || Syndata+IC15 for google drive
Syndata+IC13+IC17:Syndata+IC13+IC17 for baidu drive|| Syndata+IC13+IC17 for google drive

Training

Note: When you train the IC15-Data or MLT-Data, please see the annotation in data_loader.py line 92 and line 108-112.

Train for Syndata

  • download the Syndata(I will give the link)
  • change the path in basernet/vgg16_bn.py file:

(/data/CRAFT-pytorch/vgg16_bn-6c64b313.pth -> /your_path/vgg16_bn-6c64b313.pth).You can download the model here.baidu||google

  • change the path in trainSyndata.py file:

(1、/data/CRAFT-pytorch/SynthText -> /your_path/SynthText 2、/data/CRAFT-pytorch/synweights/synweights -> /your_path/real_weights)

  • Run python trainSyndata.py

Train for IC15 data based on Syndata pre-trained model

  • download the IC15 data, rename the image file and the gt file for ch4_training_images and ch4_training_localization_transcription_gt,respectively.
  • change the path in basernet/vgg16_bn.py file:

(/data/CRAFT-pytorch/vgg16_bn-6c64b313.pth -> /your_path/vgg16_bn-6c64b313.pth).You can download the model here.baidu||google

  • change the path in trainic15data.py file:

(1、/data/CRAFT-pytorch/SynthText -> /your_path/SynthText 2、/data/CRAFT-pytorch/real_weights -> /your_path/real_weights)

  • change the path in trainic15data.py file:

(1、/data/CRAFT-pytorch/1-7.pth -> /your_path/your_pre-trained_model_name 2、/data/CRAFT-pytorch/icdar1317 -> /your_ic15data_path/)

  • Run python trainic15data.py

Train for IC13+17 data based on Syndata pre-trained model

  • download the MLT data, rename the image file and the gt file,respectively.
  • change the path in basernet/vgg16_bn.py file:

(/data/CRAFT-pytorch/vgg16_bn-6c64b313.pth -> /your_path/vgg16_bn-6c64b313.pth).You can download the model here.baidu||google

  • change the path in trainic-MLT_data.py file:

(1、/data/CRAFT-pytorch/SynthText -> /your_path/SynthText 2、savemodel path-> your savemodel path)

  • change the path in trainic-MLT_data.py file:

(1、/data/CRAFT-pytorch/1-7.pth -> /your_path/your_pre-trained_model_name 2、/data/CRAFT-pytorch/icdar1317 -> /your_ic15data_path/)

  • Run python trainic-MLT_data.py

If you want to train for weak supervised use our Syndate pre-trained model:

1、You should first download the pre_trained model trained in the Syndata baidu||google.
2、change the data path and pre-trained model path.
3、run python trainic15data.py

This code supprts for Syndata and icdar2015, and we will release the training code for IC13 and IC17 as soon as possible.

Methods dataset Recall precision H-mean
Syndata ICDAR13 71.93% 81.31% 76.33%
Syndata+IC15 ICDAR15 76.12% 84.55% 80.11%
Syndata+MLT(deteval) ICDAR13 86.81% 95.28% 90.85%
Syndata+MLT(deteval)(new gaussian map method) ICDAR13 90.67% 94.56% 92.57%
Syndata+IC15(new gaussian map method) ICDAR15 80.36% 84.25% 82.26%

We have released the latest code with new gaussian map and random crop algorithm.

Note:new gaussian map method can split the inference gaussian region score map
Sample:

Note:We have solved the problem about detecting big word. Now we are training the model. And any issues or advice are welcome.

Sample:

###weChat QR code

Contributing to the project

We will release training code as soon as possible， and we have not yet reached the results given in the author's paper. Any pull requests or issues are welcome. We also hope that you could give us some advice for the project.

Acknowledgement

Thanks for Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee excellent work and code for test. In this repo, we use the author repo's basenet and test code.

License

For commercial use, please contact us.