🧠 KMNIST CNN Classifier

A simple convolutional neural network built with PyTorch to classify Japanese Hiragana characters from the KMNIST dataset. This project demonstrates basic image classification on a non-Latin dataset using a custom CNN.

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📌 Project Overview

• 🔤 10-class classification task (Hiragana characters)
• 📈 Trained with PyTorch on CPU
• 🧪 Evaluates accuracy and visualizes correct vs. incorrect predictions
• 🖼️ Uses Matplotlib to plot prediction results

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🧱 Model Summary

The CNN model is defined in model.py and includes:

• 5 convolutional layers with ReLU activation
• Max pooling after each conv layer
• Fully connected layers with ReLU
• nn.LazyLinear used for dynamic input sizing

Example:

model = CNN(input_layer=1, hidden_layer=200, output_layer=10)

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🧪 Example Output

After training, a 5×5 grid shows predictions:

• ✅ Green titles: Correct predictions
• ❌ Red titles: Incorrect predictions

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🚀 Getting Started

1. Clone this repository

git clone https://github.com/your-username/kmnist-cnn-classifier.git
cd kmnist-cnn-classifier

2. Install requirements

pip install -r requirements.txt

On Windows, if you encounter SSL certificate issues with KMNIST, add this in your code:

import ssl
ssl._create_default_https_context = ssl._create_unverified_context

3. Run training

python main.py

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🧾 Requirements

Listed in requirements.txt:

• torch>=2.0.0
• torchvision>=0.15.0
• matplotlib>=3.5.0

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📁 Project Structure

├── main.py              # Training and evaluation loop  
├── model.py             # CNN model definition  
├── images/              # (Optional) folder for output plots  
├── data/                # Auto-downloaded KMNIST dataset  
├── requirements.txt     # Package dependencies  
└── README.md

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🧠 Credits

• KMNIST dataset from ROIS-DS Center for Open Data in the Humanities
• Built using PyTorch and Matplotlib

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📜 License

This project is licensed under the MIT License.