WorkComputer Vision

Rock-Paper-Scissors Classifier

A deep learning image classifier that identifies hand gestures for Rock, Paper, and Scissors with over 99% validation accuracy.

SourceCompleted

Built using TensorFlow and Keras, this CNN-based model classifies images of hand gestures into rock, paper, or scissors.

The dataset is sourced from a public GitHub repository and processed using custom data generators with augmentation.

Model architecture includes multiple Conv2D and MaxPooling layers, followed by dropout and dense layers for classification.

Training achieves 99.22% validation accuracy with visualized training history for performance tracking.

Includes an image prediction module for real-time gesture recognition using uploaded images.

What it does

Dataset Processor: Downloads and prepares the Rock-Paper-Scissors dataset with validation split and augmentation.
Model Builder: Defines and trains a CNN model with dropout and softmax output for gesture classification.
Training History Plot: Visualizes accuracy and loss trends across 75 epochs.
Image Predictor: Predicts uploaded image class using the trained model and displays results with matplotlib.
Performance Metrics: Achieves 99.22% validation accuracy with low loss, ensuring robust classification.