PyTorch MNIST Handwritten Digit Recognition: A Complete Practice from Training to Deployment

This project demonstrates an end-to-end PyTorch MNIST handwritten digit recognition practice, covering the complete workflow from data preparation, model training to Streamlit web deployment. It is suitable for beginners to learn the full lifecycle of a machine learning project. The core value lies in its concise architecture and interactive experience, allowing users to draw digits directly in the browser and get recognition results in real time.

Handwritten digit recognition is a classic introductory problem in the field of computer vision, and the MNIST dataset is the standard test benchmark for this problem. This project provides a complete end-to-end implementation, with core value in its concise and complete architecture design: using PyTorch to build the neural network and Streamlit to implement the interactive interface, transforming abstract deep learning concepts into an intuitive 'draw-and-get-result' interactive demonstration.

Data Engineering

Load the MNIST dataset (60,000 training images, 10,000 test images, 28x28 grayscale). Normalize pixel values to the [-1,1] range using transforms.Compose, and manage batches (size 32) with DataLoader.

Model Architecture

Use a Multi-Layer Perceptron (MLP): Flatten layer converts 28x28 images into 784-dimensional vectors → hidden layer (128 nodes + ReLU activation) → output layer (10 nodes corresponding to classes 0-9). The structure is simple and suitable for teaching demonstrations.

Training Strategy

Choose CrossEntropyLoss (standard loss for multi-classification) as the loss function, and Adam as the optimizer (learning rate 0.001, fast convergence, low sensitivity to learning rate).

Model Persistence

Save model weights via state_dict (only stores parameters, small file size, good compatibility). Code example: torch.save(model.state_dict(), "DigitClassifier.pth").

Web Deployment

Build an interactive application using Streamlit: provides canvas components, image preprocessing, real-time inference, and result display functions. The advantage is its concise API, no front-end knowledge required.

Project Structure

Clear file organization: app.py (deployment UI), train.py (training script), DigitClassifier.pth (model weights), requirements.txt (dependency list), etc. Separates training and deployment logic.

Learning Value

• End-to-end workflow experience: covers the entire process from data loading, model definition, training to deployment
• Introduction to production practice: demonstrates methods to deploy models as interactive applications
• Code organization reference: clear file structure and comments

Extension Directions

• Upgrade the model to a Convolutional Neural Network (CNN)
• Add data augmentation strategies
• Show multi-model comparison
• Confidence visualization

This project is a carefully designed introductory computer vision project, which demonstrates the complete process from model training to web deployment in a concise way. The code is clear, the documentation is complete, and it is easy to run. It has high practical value as a learning material or a prototype starting point.