This GitHub Repository contains my final project for Udacity's Machine Learning Engineer Nanodegree
This is a Chest X-Ray (CXR) classification API. Building on previous work of [1], the CovNet model for this ML project utilizes a pre-trained EfficientNet-b1 to extract features and a fine-tuned Fast.ai classifier to differentiate between infection classes (Normal, Viral Pneumonia, or COVID-19) with 95% test accuracy.
- mle-capstone-data (submodule)
- mle-capstone-modeling (submodule)
- mle-capstone-deployment (submodule)
- project propsal (pdf)
- project report (pdf)
- README.md (here)
Each submodule provides an .ipynb file for a detailed walkthrough of that project phase.
In the data submodule, to generate the COVIDx dataset, you'll neet a kaggle account. If you don't have an account, you can create one at kaggle.com. From there, from the top right menu, visit My Account > API > create New API Token > ... to get a JSON hardfile of your API Token.
In the deployment submodule, if you'd like to run the quick start demo notebook, you'll also need to create a ngrok account and API token.