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https://github.com/codeamt/fastefficientcovidnet
CXR classification API with pre-trained EfficientNet-b1 and Fast.ai
https://github.com/codeamt/fastefficientcovidnet
Last synced: about 2 months ago
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CXR classification API with pre-trained EfficientNet-b1 and Fast.ai
- Host: GitHub
- URL: https://github.com/codeamt/fastefficientcovidnet
- Owner: codeamt
- Created: 2020-06-07T21:59:56.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-10T04:35:52.000Z (10 months ago)
- Last Synced: 2024-03-11T04:26:04.140Z (10 months ago)
- Language: Makefile
- Size: 2.36 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Fast Efficient CovidNet - An End-to-End Pipeline
This GitHub Repository contains my final project for Udacity's Machine Learning Engineer Nanodegree## About
This is a Chest X-Ray (CXR) classification API. Building on previous work of [[1]](https://arxiv.org/pdf/2003.09871v3.pdf), 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.
## Repo Contents:
- [mle-capstone-data (submodule)](https://github.com/codeamt/mle-capstone-data/tree/41128aef7f98d479fafc6c946f804b5e52d8c89f)
- [mle-capstone-modeling (submodule)](https://github.com/codeamt/mle-capstone-modeling/tree/6d2469b585b6d0d7535a33e734453b07846a5cb6)
- [mle-capstone-deployment (submodule)](https://github.com/codeamt/mle-capstone-deployment/tree/16741ab5f2c4670dff3c962cc0ba5b9580e8b9b2)
- [project propsal (pdf)](https://github.com/codeamt/udacity-mle-capstone-project/blob/master/proposal.pdf)
- [project report (pdf)](https://github.com/codeamt/udacity-mle-capstone-project/blob/master/report.pdf)
- [README.md (here)]()## Instructions:
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](https://www.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.