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OCTAGAN OCTA Quality Assessment

Example application using ONNX Runtime Web & Progressive Web Application (PWA) principles to deploy an OCTA image grade classifier CNN in-browser, on client devices, with offline functionality.

For JHU RAIL Usage (Running locally)

  1. Clone this repo, and on the command line, change directory to onnx-pwa-demo/
  2. Install Node Version Manager from this link
  3. Install node package manager: nvm install --lts
  4. Run the setup script: npm run setup
  5. Run the local dev server: netlify dev
  6. (Hint) if you want to use webgpu, ensure that you are running a supported browser

Converting pytorch models to onnx files

  1. Please follow this demo
  2. Host you model on huggingface.
  3. Go the public/models.js and add a new model. Use the existing model as a template. You will need to write your own pre/post processing functions
  4. Write a new model test file based on for your new model public/super_resolution-test.spec.js to ensure your model is working correctly
  5. (Hint) if your model doesn't work, it's probably the pre and post processing functions double check those.

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