Text Detection Using py-faster-rcnn.
This repository is aimed at provide an example of training text-detection models using faster-rcnn
- Clone the repository
# Make sure to clone with --recursive
git clone --recursive https://github.com/jugg1024/Text-Detection-with-FRCN.git- Compile py-faster-rcnn
2.1 change the branch of py-faster-rcnn to text-detection-demo
cd $Text-Detection-with-FRCN/py-faster-rcnn
git checkout text-detection2.2 Build Caffe and pycaffe.
# ensure your enviroment support the training of caffeensure your enviroment support the training of caffe
cd $Text-Detection-with-FRCN/py-faster-rcnn/caffe-fast-rcnn
cp Makefile.config.example Makefile.config
# adjust the Makefile.config
make -j16 && make pycaffe # here only python api is used.
# test if caffe python api is ok.
cd python
python
>>> import caffe
>>> caffe.__version__
'1.0.0-rc3'2.3 Build the Cython modules.
cd $Text-Detection-with-FRCN/py-faster-rcnn/lib
make- Run text detection demo
3.1 download pre-trained model
URL: http://pan.baidu.com/s/1dE2Ori5 Extract Code: phxk
ln -s $DOWNLOAD_MODEL_PATH $Text-Detection-with-FRCN/model/vgg16_faster_rcnn_fine_tune_on_coco.caffemodel3.2 run demo
cd $Text-Detection-with-FRCN/
./script/text_detect_demo.shResults are on output_img
if you think the model is not ok, then you can trainning with your own dataset, take coco-text for example.
- training
4.1 download coco-text dataset
cd $Text-Detection-with-FRCN/datasets/script
./fetch_dataset.sh coco-text
# download it takes long!
# ensure you have both data and label
# for coco-text label is in COCO-text.json, and data is in train2014.zip4.2 download pre-train model
# finetune on this model, you can also use one model you train before
cd $Text-Detection-with-FRCN/py-faster-rcnn
./data/scripts/fetch_imagenet_models.sh
# download it takes long!4.3 format the data(you should write your code here)
# format the raw image and label into the type of pascal_voc
# follow the code in $Text-Detection-with-FRCN/datasets/script/format_annotation.py
cd $Text-Detection-with-FRCN/datasets/script
./format_annotation.py --dataset coco-text4.4 create a softlink the formatted data to working directorry
# link your data folder to train_data
cd $Text-Detection-with-FRCN/datasets/
ln -s train_data coco-text # $YOUR_DATA4.5 training
cd $Text-Detection-with-FRCN/py-faster-rcnn/
./experiments/scripts/faster_rcnn_end2end.sh 0 VGG16 pascal_voc
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