Description

This project details a comprehensive image classification project for rock-paper-scissors using TensorFlow and Keras, covering the entire workflow from Google Drive integration and dataset preparation to model training and deployment. It systematically guides through data downloading, splitting, and augmentation, followed by the creation and training of a Convolutional Neural Network (CNN) model, and finally, saving the trained model.

Beyond model development, the notebook also provides a user-friendly feature to upload new images for real-time class prediction, complete with a clear visualization of the prediction results using Matplotlib. This makes it a complete solution for understanding and implementing an image classification pipeline, from raw data to actionable insights.