Image Classification is a final submission on "Belajar Machine Learning untuk Pemula" from Dicoding course. This program is an implementation of image classification using machine learning techniques such as Data Preprocessing, Image Augmentation, Convolutional Neural Networks (CNN), Model Training, Model Evaluation and Visualization, and Prediction on New Images.
For the dataset, we using Rock-Paper-Scissors dataset that you can download it on https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip or using this wget command: https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip.
This dataset includes of total 3000 labeled images, with 1,000 images for each class (Rock, Paper, Scissors).
- Python
- TensorFlow
- Keras
- NumPy
- Scikit-Learn
- Matplotlib
- Clone this repository.
- Make sure you have already Python 3 and required packages such as numpy, matplotlib, scikit-learn, and tensorflow using this command:
pip install - Extract the dataset that you have already download to your favorite directory.
- Open the VScode and please download the Jupyter notebook extension before you run it.
- Finally, you can run the cells by execute the code step-by-step.