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https://github.com/elskow/digitrecognizerfromscratch
Mid-Term AI Project Assignment
https://github.com/elskow/digitrecognizerfromscratch
flask mnist-dataset nextjs pytorch
Last synced: 7 days ago
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Mid-Term AI Project Assignment
- Host: GitHub
- URL: https://github.com/elskow/digitrecognizerfromscratch
- Owner: elskow
- Created: 2023-10-21T10:58:44.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-24T16:26:51.000Z (about 1 year ago)
- Last Synced: 2023-10-24T19:28:43.969Z (about 1 year ago)
- Topics: flask, mnist-dataset, nextjs, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 93.2 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Digit Recognizer from Scratch + Web UI
## Introduction
Welcome to the Digit Recognizer project, a comprehensive implementation of a handwritten digit recognition system from scratch using Python and PyTorch. The core of this project is a Convolutional Neural Network (CNN) model trained on the MNIST dataset, a widely used benchmark for image classification tasks. Our model employs the popular ResNet architecture, which is known for its exceptional performance and scalability.
The project originated as a part of the mid-term project for the Artificial Intelligence course at the esteemed State University of Surabaya. It offers a real-world application of machine learning and web development, seamlessly blending the two domains.
## Key Features
- State-of-the-Art Accuracy: Our model has been meticulously trained and tested on the MNIST dataset, achieving an impressive accuracy of 99.01% on the test set. This high accuracy demonstrates the model's ability to recognize handwritten digits with exceptional precision.
- Web User Interface: We've gone a step further by developing a user-friendly web interface to showcase the digit recognition capabilities. This allows users to draw or input handwritten digits and witness the model's recognition accuracy in real-time.
- Seamless Deployment: The model and web interface are seamlessly integrated using Flask, a powerful web framework for Python. You can run the web application locally on your machine, offering an interactive experience.
## How to run
Follow these steps to set up and run the Digit Recognizer project on your local machine:
1. **Clone the Repository**: Begin by cloning this repository to your local machine.
2. **Navigate to the Web App Directory**: Change your working directory to the "web-app" folder within the cloned repository.
```bash
cd web-app
```
3. **Install Dependencies**: To ensure all necessary dependencies are installed, run the following commands. This will take care of both frontend and backend dependencies.
```bash
npm install && npm run setup
```
4. **Run** the **Backend**
```bash
npm run be
```
5. **Run** the **Frontend**
```bash
npm run fe
```
6. Access the Web Interface: Open your web browser and visit http://localhost:3000 to start using the Digit Recognizer.