/ebl-ai-api

Primary LanguagePythonMIT LicenseMIT

ebl-ai-api

Data+Code is part of Paper Sign Detection for Cuneiform Tablets from Yunus Cobanoglu, Luis Sáenz, Ilya Khait, Enrique Jiménez please contact us for access to data on Zenodoo DOI and paper as it is under currently under review. See https://github.com/ElectronicBabylonianLiterature/cuneiform-ocr/blob/main/README.md for overview and general information of all repositories associated with the paper from above.

Build Status Maintainability

Ebl Ai Api

Server deploying a deep learning model for inference on detecting bounding boxes on cuneiform sign tablets For training please refer to cuneiform-sign-detection repo

Table of contents

Setup

Requirements:

  • sudo apt-get install ffmpeg libsm6 libxext6  -y  
(may be needed for open-cv python)

  • Python 3.9

python3 -m venv ./.venv

pyre-configuration specifies paths specifically to .venv directory

pip3 install -r requirements

Run

python3 ebl_ai/check_installation.py

to check pytorch, mmcv, mmdet and mmocr installation.

Model

Checkpoints and Model Config

Running the tests

  • Use command black ebl_ai_api to format code.
  • Use command flake8 for linting.
  • Use command pytest to run all tests.
  • Use command pyre check for type-checking.

Running the server

waitress-serve --port=8001 --call ebl.app:get_app

Acknowledgements