https://hub.docker.com/r/xxxxxxxxxxxxxxxxxxxx/cosseglink/ sudo docker run --runtime=nvidia -it -v /home/jerry/workspace/seglink:/workspace -v /home/jerry/workspace/datasets/scene_text/icdar_2015_incidental:/mnt/datasets/scene_text/icdar_2015_incidental/ xxxxxxxxxxxxxxxxxxxx/cosseglink:0.1 bash source activate py36 cd workspace/ python manage.py train exp/sgd finetune_ic15
Detecting Oriented Text in Natural Images by Linking Segments (https://arxiv.org/abs/1703.06520).
The project is written in Python3 and C++ and relies on TensorFlow v1.0 or newer. We have only tested it on Ubuntu 14.04. If you are using other Linux versions, we suggest using Docker. CMake (version >= 2.8) is required to compile the C++ code. Install TensorFlow (GPU-enabled) by following the instructions on https://www.tensorflow.org/install/. The project requires no other Python packages.
On Ubuntu 14.04, install the required packages by
sudo apt-get install cmake
sudo pip install --upgrade tensorflow-gpu
The project uses manage.py to execute commands for compiling code and running training and testing programs. For installation, execute
./manage.py build_op
in the project directory to compile the custom TensorFlow operators written in C++. To remove the compiled binaries, execute
./manage.py clean_op
See ``tool/create_datasets.py''
./manage.py <exp-directory> train
See ``evaluate.py''