metadata

title: Mnist Comparison
emoji: 📚
colorFrom: purple
colorTo: gray
sdk: streamlit
sdk_version: 1.37.0
app_file: app.py
pinned: false
license: mit

MNIST Streamlit

This is a simple Streamlit app that demonstrates the differences between neural nets trained on the MNIST

There are three models saved locally available in the models directory:

autokeras_model.keras
mnist_12.onnx
mnist_model.keras

The mnist_model.keras is a simple 300x300 neural net trained over 35 epochs.

The autokeras_model.keras is a more complex model generated by running the Autokeras image classifier class.

Meanwhile, the mnist_12.onnx model is a pre-trained model from theOnnx model zoo. Onnx provides detailed information about how the model was created in the repository on GitHub.

The application allows you to:

Select which model you want to use for predicting a handwritten digit
Select your stroke width of the digit you draw
Draw a specific digit within a canvas

Once you draw a digit, the model will be loaded, asked to make a prediction on your input, and provide:

The name of the model used to make the prediction
A prediction (the top prediction from it's probability distribution)
The time the model took to predict
The time it took to load the model
The probability distribution of predictions as a bar chart and table

Usage

To run the Streamlit app locally using Poetry, clone the repository, cd into the created directory, and run the following commands:

poetry shell
poetry install
streamlit run app.py

If you don't have Poetry installed, never fear! There is a requirements.txt file that you may use to install the necessary packages with Pip. Simply create a new virtual environment and run:

pip install -r requirements.txt