https://github.com/zackproser/pytorch-mnist
https://github.com/zackproser/pytorch-mnist
Last synced: 11 days ago
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- Host: GitHub
- URL: https://github.com/zackproser/pytorch-mnist
- Owner: zackproser
- License: mit
- Created: 2024-07-28T18:27:49.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-29T02:30:53.000Z (over 1 year ago)
- Last Synced: 2025-01-15T01:07:46.177Z (over 1 year ago)
- Language: Python
- Size: 23.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Network Digit Recognizer
A Python project that trains a neural network to recognize hand-drawn digits between 0 and 9 using the MNIST dataset, and provides a FastAPI web service for predictions.
## Overview
This project uses PyTorch to create and train a neural network for digit recognition.
The trained model can accurately classify hand-drawn digits from the MNIST dataset. Additionally, it includes a FastAPI web service that allows users to make predictions on uploaded images and view sample test images with predictions.
## Features
- PyTorch-based neural network architecture
- Training script with configurable hyperparameters
- Evaluation metrics for model performance
- Ability to save and load trained models
- FastAPI web service for making predictions on uploaded images
- Endpoint to view sample test images with predictions
## Technology Stack
- Python 3.x
- PyTorch
- FastAPI
- Pillow (PIL)
- NumPy
- Matplotlib (for visualization)
- Dataset: MNIST
## How It Works
1. The MNIST dataset is used to train the neural network model.
2. The trained model is saved and can be loaded for predictions.
3. A FastAPI web service provides two main endpoints:
- `/predict`: Accepts an uploaded image and returns the predicted digit.
- `/test_images`: Generates a plot of sample test images with predictions.
## Getting Started
### Prerequisites
- Python 3.x
- pip
### Installation
1. Clone the repository:
```
git clone https://github.com/yourusername/mnist-digit-recognizer.git
cd mnist-digit-recognizer
```
2. Install dependencies:
```
pip install -r requirements.txt
```
### Running the Web Service
To start the FastAPI web service, run:
```
python main.py
```
The service will be available at `http://localhost:8000`.
### API Endpoints
- `POST /predict`: Upload an image file to get a prediction.
- `GET /test_images`: View a plot of sample test images with predictions.
## Usage
### Making Predictions
To make a prediction on an image, send a POST request to `/predict` with the image file:
```
curl -X POST -F "file=@path/to/your/image.png" http://localhost:8000/predict
```
### Viewing Test Images
To view sample test images with predictions, open the following URL in your browser:
```
http://localhost:8000/test_images
```
## License
This project is open source and available under the [MIT License](LICENSE).