/Rock-Paper-Scissor-Image-Classification-Using-Model-Deployment

Primary LanguageJupyter Notebook

Rock Paper Scissor Image Classification Using Model Deployment

This project aims to classify hand movements with similarities to Rock Paper Scissor movements using TensorFlow and deployment models.

Project Criteria

  • The dataset to be used is free, but has at least 1000 images.
  • The dataset has never been used in a machine learning class submission before.
  • The dataset is divided into 80% train set and 20% test set.
  • The model must use a sequential model.
  • The model must use Conv2D Maxpooling Layer.
  • The accuracy of the training and validation set is at least 80%.
  • Using Callbacks.
  • Make a plot against the accuracy and loss of the model.
  • Write code to save the model into TF-Lite format.

Dataset

This project uses the Rock Paper Scissor dataset from Dicoding Academy.

Libraries used (python)

TensorFlow
Keras
Numpy
Matplotlib
zipfile
os
glob
warnings