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.
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.gitRun the script:
bash setup_environment.shReload your environment variables from .bashrc with: source ~/.bashrc
To verify the correctness of the procecdure you can run a small experiment. Activate the DeepDIVA python environment:
source activate deepdivaDownload the MNIST dataset:
python util/data/get_a_dataset.py mnist --output-folder toy_datasetTrain 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-cudaIf you use our software, please cite our paper as (will be updated soon):
@inproceedings{albertipondenkandath2018deepdiva,
archivePrefix = {arXiv},
arxivId = {1805.00329},
eprint = {1805.00329},
author = {Alberti, Michele and Pondenkandath, Vinaychandran and Würsch, Marcel and Ingold, Rolf and Liwicki, Marcus},
title = {{DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments}},
year = {2018},
month = {apr},
}Our work is on GNU Lesser General Public License v3.0