🧠 Digit Predictor with PyTorch & C++ Inference

Welcome to Digit Predictor – a simple but cool demo that shows how to train a PyTorch model in Python and run it in fast C++!

It's not production code – just a toy example to help you get started with native inference using TorchScript. It’s great for learning how to bridge Python & C++.

🛠️ What’s Inside?

• ✅ A PyTorch training script (Python)
• ✅ A C++ inference program using LibTorch
• ✅ Preprocessing with OpenCV
• ✅ Digit classification from 28×28 grayscale images (MNIST-style) with simple CNN
• ✅ TorchScript serialization

🧪 Prerequisites

To make things smooth, I recommend the following setup:

Component	Version
Python	3.8+
PyTorch	2.5.1
LibTorch (C++)	2.5.1 CPU
OpenCV (C++)	Any recent
Compiler	MSVC

• ⚠️ Make sure your LibTorch and PyTorch versions match exactly. Mismatched versions may cause cryptic errors.
• ⚠️ Also I used a debug CPU version of LibTorch.
• ⚠️ And don't forget to create a virtual environment:

⦁ python -m venv torch_venv
⦁ Source torch_venv/scripts/activate
⦁ pip install torch torchvision torchaudio 

🧑‍🏫 How It Works

1. 🔧 Train & Save the Model (Python)

Run the training script to:

• Train a basic CNN on MNIST
• Export it as a TorchScript .pt file
• Save the model in CPU format for C++ inference

cd training
python train.py

This creates model_cpu.pt in your working directory.

Training losses look like this:

2. ⚙️ Run Inference (C++)

Build soulution using CMake. Before that you should download LibTorch and OpenCV for C++.

Afterwards, in CMAKE-UI configure and generate a solution.

Compile and run the C++ program using Visual Studio. It will:

• Load the model using torch::jit::load
• Read a 28×28 grayscale image, preprocess it
• Run prediction
• Print the predicted digit
• You are awesome!