This project involves End-to-end ML-project. from developing a Convolutional Neural Network (CNN) model based on the ResNet50 architecture to detect COVID-19 from X-ray images to building the API with the help if FastAPI. with containerinzation using Docker
The model was trained on a dataset with 148 examples and achieved an accuracy of 97.5%.
-
code.ipynb
: Contains all the code for data preprocessing, model training, and evaluation. -
COVID DataSet.zip
: The dataset used for training and testing the model. -
Dockerfile
with the containerization of the project. -
The
/app
directory have all the file related to the Ops- With
/app/main.py
as the file containing FastAPI. - With
/app/model.h5
the trained model. - With
/app/requirement.txt
having the required libraries.
- With
- Accuracy: 97.5%
- Training Data: 148 X-ray images
-
- Clone the Repository: Clone this repository
- Open the Repository: Go to the cloned repository
- Run Docker Image: use
$ docker run covid-19-detection
in the bash. - Check the FastAPI: Check the API at the
/predict/docs
-
- Clone the Repository: Clone this repository
- Open the Repository: Go to the cloned repository
- Go to the app:use
$ cd app
in the bash. - Install required libraries run
$ pip install -r requirements.txt
- Run the app: use
$ uvicorn main:app
in the bash. - Check the FastAPI: Check the API at the
/predict/docs
This project is licensed under the Apache License 2.0. See the LICENSE file for details.