This framework is an evolved fork of DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments. The major differences are the full adoption of an object oriented programming design, the polishing of the workflow, the introduction of an optimized inference-use case and a better isolation between the tasks.
This work has been conducted during an internship at V7, London, UK.
If you use our software, please cite our paper as:
@inproceedings{albertipondenkandath2018deepdiva,
title={{DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments}},
author={Alberti, Michele and Pondenkandath, Vinaychandran and W{\"u}rsch, Marcel and Ingold, Rolf and Liwicki, Marcus},
booktitle={2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)},
pages={423--428},
year={2018},
organization={IEEE}
}Our work is on GNU Lesser General Public License v3.0
In order to get the framework up and running it is only necessary to clone the latest version of the repository:
git clone https://github.com/v7labs/Gale.gitRun the script:
bash setup_environment.shReload your environment variables from .bashrc with: source ~/.bashrc
Some runners require additional packages. To install them, simply run the extend_environment.sh script in the folder
of the respective runner.