This is a final project for Dicoding that I worked on independently. This project is a part of the requirements for graduating from the Machine Learning Developer class that I attended. The dataset used was obtained from the Dicoding platform.
A link to the data used in the project.
- Objectives
- Methodology
- Expected Outcomes
The primary objectives of this project are:
- Gather and preprocess a comprehensive dataset of hand images representing rock, paper, and scissors gestures, ensuring a diverse range of hand shapes, sizes, and skin tones.
- Design and train a convolutional neural network (CNN) tailored for image classification to accurately distinguish between the three hand image categories.
- Assess the model's accuracy and performance using a dedicated validation dataset, employing metrics of accuracy and loss on both training and validation data.
- Data Collection and Preprocessing
- Model Development
- Model Evaluation
An image classification model that can accurately distinguish 3 categories of images: rock, paper, and scissors.
From this rock-paper-scissors image classification project, I gained a deeper understanding of machine learning, image processing, and techniques related to classification tasks
Please do not steal my work. It took countless cups of coffee and many sleepless nights to create. Thank you.
©️ Zevanna Vangelyn (Velyn)