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This project implements a Convolutional Neural Network (CNN) for classifying handwritten digits from the MNIST dataset. The model is built using PyTorch, with added features for interpretability and prediction visualization. The project also includes a REST API for image-based predictions and is configured for deployment with Gunicorn.
Features
Digit Classification: Predicts digits (0-9) from input images.
Probability Distribution: Displays class probabilities for predictions.
Interpretability: Visualizes the learned features from convolutional layers.
REST API: Submit images via a simple API for predictions.
Deployment: Configured with Gunicorn for production use.
Handwritten Digit Classification
This project implements a Convolutional Neural Network (CNN) for classifying handwritten digits from the MNIST dataset. The model is built using PyTorch, with added features for interpretability and prediction visualization. The project also includes a REST API for image-based predictions and is configured for deployment with Gunicorn.
Features