Rock-Paper-Scissors Classification

A web application that classifies images of hand gestures as rock, paper, or scissors using a machine learning model.

Building model

Google Colab Notebook

Dataset

This project uses the Rock-Paper-Scissors dataset from Kaggle.

Prerequisites

• Python 3.11
• TensorFlow 2.x
• Flask
• Pillow (PIL)

Installation

1. Clone the repository:

   git clone https://github.com/zave52/rock-paper-scissors-classification.git
   cd rock-paper-scissors-classification

2. Install Git LFS: This repository uses Git Large File Storage (LFS) for the model files.

   • Install Git LFS following the official instructions
   • After installation, set up LFS and pull the model files:

   git lfs install
   git lfs pull

3. Create and activate a virtual environment (optional but recommended):

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate

4. Install dependencies:

   pip install -r requirements.txt

5. Create environment variables:

   cp .env.sample .env

   Then edit the .env file with your settings.

6. Make sure the directory structure includes:

   • static/uploads/ for uploaded images
   • static/models/ with the trained model file

How to Run

1. Activate your virtual environment if you're using one:

   source venv/bin/activate  # On Windows, use: venv\Scripts\activate

2. Start the Flask development server:

   flask run

3. Open your web browser and navigate to:

   http://127.0.0.1:5000/
   

Application Preview

Usage

1. Upload an image of a hand showing either rock, paper, or scissors gesture
2. The application will predict which gesture is shown in the image
3. Results are displayed with the predicted class and confidence score

Project Structure

• app.py: Flask application entry point
• classifier.py: Image classification logic
• static/: Static files (CSS, JS, models, uploaded images)
• templates/: HTML templates
• requirements.txt: Project dependencies