Overview:

The Image Classification MNIST application is a robust tool crafted using advanced technologies such as TensorFlow 2, Django 3, and React 17. Designed to recognize handwritten digits from the MNIST dataset, this app combines a powerful machine learning model with an intuitive user interface. Whether you’re a developer eager to explore machine learning or an educator seeking a practical application for students, this app offers a rich learning experience.

With the integration of Django REST Framework and Material UI, users can enjoy seamless interaction and customization options, making it a versatile project for various use cases. Setting it up involves straightforward backend and frontend installations, ensuring that users can run the application efficiently on their local machines.

Features:

• Machine Learning Integration: Utilizes TensorFlow 2 to efficiently recognize and classify handwritten digits from the MNIST dataset.
• Full-Stack Architecture: Built with Django for the backend and React for the frontend, allowing for an organized, responsive application structure.
• Customizable User Interface: Users can easily change colors, fonts, logos, and text in the application to suit their branding or personal preferences.
• Easy Installation: The app provides clear steps for setting up both the backend and frontend, making it accessible even for beginners.
• Responsive Design: Implemented with Material UI, the app offers a modern and user-friendly interface across different devices.
• Educational Resource: Ideal for students and developers wanting to learn about machine learning applications and web development.
• Local Deployment: Run the application on a local server by simply accessing http://localhost:3000/, which simplifies testing and development.