Rock-Paper-Scissors Image Classification 🎮

This project is a Deep Learning model built to classify images of the popular game Rock-Paper-Scissors 🪨📄✂️.
It uses a Convolutional Neural Network (CNN) trained on a dataset from Kaggle and deployed with a simple Gradio interface for interactive testing.

---

📌 Project Overview

• Built a CNN model using TensorFlow/Keras.
• Applied Data Augmentation techniques (rotation, shift, zoom, shear) to improve generalization.
• Achieved ~99% validation accuracy 🎯.
• Created a Gradio app that allows users to upload an image and instantly get a prediction (Rock / Paper / Scissors).

---

⚙️ Tech Stack

• Python 🐍
• TensorFlow / Keras
• Gradio
• NumPy, Matplotlib

---

📂 Dataset

The dataset was taken from Kaggle and contains:

• paper/ : 712 images
• rock/ : 726 images
• scissors/ : 750 images

---