#End-to-End Computer Vision Framework

The End-to-End CV Framework (EECVF) goal is to offer a flexible and dynamic tool for researching and testing concepts without the need for the user to handle the interconnections through the system. To better overcome the continuous development of the EECVF it is constructed as a modular and scalable concept.

The term “End-to-End” describes in our case the ability of the framework to execute several stages of a CV process as a “one click” solution. The framework we propose has the capability to create-train-evaluate a ML model, run a CV application using the model, evaluate the results and plot the results without any intervention of the user. All of the steps being done in parallel with documenting debug information desired by the user.

During the past decade, Python (an interpreted, high-level programming language) has arguably become the de facto standard for exploratory, interactive, and computation-driven scientific research. We chose Python because of the capabilities to interconnect several elements of our environment. To make EECVF more accessible, the users can just use the setup_framework.py module that will install all the needed libraries and dependencies.

If you are using the code/data provided here in a publication, please consider citing our papers.

@article{orhei2021end,
  title={End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education},
  author={Orhei, Ciprian and Vert, Silviu and Mocofan, Muguras and Vasiu, Radu},
  journal={Sensors},
  volume={21},
  number={11},
  pages={3691},
  year={2021},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@inproceedings{orhei2020end,
    title={End-to-End Computer Vision Framework},
    author={Orhei, Ciprian and Mocofan, Muguras and Vert, Silviu and Vasiu, Radu},
    booktitle={2020 International Symposium on Electronics and Telecommunications (ISETC)},
    pages={1--4},
    year={2020},
    organization={IEEE}
}