✊✋✌ Rock Paper Scissors Classification

🚀 This project is a deep learning model designed to classify hand gestures into Rock, Paper, or Scissors. It was developed as part of a bootcamp final project to apply computer vision techniques for real-world classification tasks.

📌 Project Overview

This project aims to recognize hand gestures using deep learning. The model classifies images into three categories:
✔ Rock
✔ Paper
✔ Scissors

🛠 Technologies Used

• Programming Language: Python 🐍
• Libraries & Frameworks: TensorFlow, Keras, OpenCV, NumPy, Matplotlib
• Model Architecture: Convolutional Neural Network (CNN) 🧠

📊 Dataset

• The dataset consists of hand gesture images representing Rock, Paper, and Scissors.
• It includes diverse hand positions, backgrounds, and lighting conditions to improve generalization.
• Augmentation techniques such as rotation, flipping, and brightness adjustment were applied.

🔥 Model Training

• Implemented a CNN model with multiple convolutional layers for feature extraction.
• Used ReLU activation for non-linearity and softmax for classification.
• Optimized the model with Adam optimizer and evaluated using accuracy and loss metrics.

🎯 Results

• Achieved high classification accuracy on test data.
• The model successfully distinguishes between Rock, Paper, and Scissors gestures.
• Deployed and tested using real-time webcam input.

📌 How to Run

1. Clone this repository:

   git clone https://github.com/your-username/rock-paper-scissors-classification.git