DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments

DeepDIVA is an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment with a very limited amount of information or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source and accessible as Web Service through DIVAServices.

Additional resources

Getting started

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/DIVA-DIA/DeepDIVA.git

Run the script:

bash setup_environment.sh

Reload your environment variables from .bashrc with: source ~/.bashrc

Verifying Everything Works

To verify the correctness of the procecdure you can run a small experiment. Activate the DeepDIVA python environment:

source activate deepdiva

Download the MNIST dataset:

python util/data/get_a_dataset.py mnist --output-folder toy_dataset

Train a simple Convolutional Neural Network on the MNIST dataset using the command:

python template/RunMe.py --output-folder log --dataset-folder toy_dataset/MNIST --lr 0.1 --ignoregit --no-cuda

Citing us

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}
}

License