MNIST Interactive Model Analyzer
A hands-on tool for visualizing and testing handwriting recognition models.

This project provides an interactive 28x28 drawing grid, perfectly matching the MNIST dataset's input dimensions. It allows developers and enthusiasts to draw digits and receive real-time predictions from their own custom-trained TensorFlow or PyTorch models, offering a tangible way to assess model performance.

Core Features

Interactive Drawing

A custom-built canvas allows for intuitive drawing and erasing, with a blur effect to simulate natural handwriting and improve model accuracy.

Real-Time Analysis

Get instant predictions and confidence scores as you draw. The tool also includes a visualizer to display neuron activation values in real-time.

Model Agnostic

Supports both TensorFlow and PyTorch models with a flexible backend system. Custom conversion functions allow for compatibility with various model input formats.

Easy Integration

The entire tool is available as a simple-to-install Pip package, allowing for quick integration into any Python project requiring interactive model testing.

Technologies Used

PythonTensorFlowPyTorchNumPyTkinter