/MNIST-Digit-Classifier

MNIST handwritten digit classifier

Primary LanguageJupyter Notebook

MNIST-Digit-Classifier

This is an image classification project that classify handwritten digits using the MNIST dataset.

It is a simple project which makes use of a simple architecture in order to correctly classify handwritten digits with a high test accuracy of 97%.

accuracy score

Table of contents

Technologies

  • python 3.6
  • opencv-python-headless 4.5.3.56
  • matplotlib 3.4.3
  • numpy 1.21.2
  • pillow 7.0.0
  • bokeh 2.1.1
  • torch 1.11.0
  • torchvision 0.12.0
  • tqdm 4.63.0
  • ipywidgets 7.7.0
  • livelossplot 0.5.4
  • pytest 7.1.1
  • pandas 1.3.5
  • seaborn 0.11.2
  • jupyter 1.0.0
  • ipykernel 4.10.0
    NB: Please note that the version numbers noted here are not strict.

Setup

The project is easy to setup and run.

  • First you want to ensure you have python installed on your machine.

  • Secondly having anaconda setup on your system is highly recommended.

  • Install the project dependencies using the requirement.txt file: (or do it through the notebook)

    pip install -r requirements.txt

  • Run the notebook cells one after the other.

Here is an example of the model being used to predict the digit written in an image:

test