Paper-Rock-Scissors Image Classification

Overview

This project is a final project for the Dicoding course "Belajar Machine Learning untuk Pemula". It aims to classify images of rock, paper, and scissors gestures using machine learning techniques.

Key Features

• Utilizes deep learning for image classification.
• Dataset includes images of rock, paper, and scissors.
• Implemented using TensorFlow and Keras.

Key Steps

1. Dataset collection and preprocessing.
2. Model architecture design.
3. Model training and evaluation.
4. Validation of the model accuracy.

Model Architecture

The model uses a Convolutional Neural Network (CNN) architecture, which is ideal for image classification:

1. Input Layer: Accepts images of rock, paper, or scissors.
2. Convolutional Layers: These layers apply various filters to detect important features like edges and shapes in the image.
3. Pooling Layers: Reduce the spatial dimensions of the image, improving efficiency and reducing computational load.
4. Fully Connected Layers: These layers perform the final classification based on the learned features.
5. Output Layer: Predicts one of the three classes: rock, paper, or scissors.

Results

• The model achieved a validation accuracy of 95%.

Project Limitations

• The model is not capable of correctly predicting real-world data, as it was trained on a limited dataset of images.