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Simple ML web app using FastAPI for the backend and Streamlit for the frontend

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ML App for Classifying Images

The model was training on CIFAR10 and thus only supports the following classes:

["airplane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]

You can download model weights, download the code, run docker-compose to build and run the backend and frontend with the following steps:

Download model weights

Place the model weights in folder image_classification_ml_app/state_dicts.

Model weights to Google drive for weights of simple VGG model trained on CIFAR10 dataset.

Install locally

git clone git@github.com:jonasdieker/image_classification_ml_app.git
cd image_classification_ml_app
docker-compose up -d --build

Then go to http://localhost:8501/ to interact with the web app and classify some images!

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Simple ML web app using FastAPI for the backend and Streamlit for the frontend

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