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