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%.
- Project description
- Technologies used
- Setup
- Image example
- 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.
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.