Rock, Paper, Scissors Image Classification with ML5.js and Next.js

This project demonstrates an Image Classification application that uses ML5.js to classify images of hand gestures representing Rock, Paper, or Scissors. The application is built with Next.js for the front-end framework, and Tailwind CSS for a modern, responsive design.

Features

• Rock, Paper, Scissors Classification: Classifies hand gestures into one of three categories: Rock, Paper, or Scissors.
• ML5.js: Utilizes the ML5.js library, which simplifies machine learning tasks in the browser using TensorFlow.js.
• Next.js: A React-based framework for building fast and SEO-friendly web applications with server-side rendering.
• Tailwind CSS: A utility-first CSS framework for rapid, customizable, and responsive styling.

Tech Stack

• Next.js: React framework for building the app with server-side rendering and static site generation.
• Tailwind CSS: For styling with a focus on utility-first, responsive design.
• ML5.js: A machine learning library built on top of TensorFlow.js, used for image classification.
• JavaScript/React: Front-end technologies used for creating the user interface.

Demo

Getting Started

To get started with the project, follow these steps:

Prerequisites

• Node.js (v14 or above)
• npm or Yarn package manager

Installation

1. Clone the repository: ```bash git clone https://github.com/yourusername/rock-paper-scissors-classification.git