Set up your environment

Set up your environment for Tiny rock-paper-scissors on Arm

This Learning Path is a direct follow-up to Introduction to TinyML on Arm using PyTorch and ExecuTorch . While the previous Learning Path introduced the core concepts and toolchain, this one puts that knowledge into practice with a small, real-world example. You move from a simple Feedforward Neural Network to a practical computer vision task: a tiny rock-paper-scissors game that runs efficiently on Arm-based edge devices.

You will train a lightweight CNN to classify images of the letters R, P, and S as “rock,” “paper,” or “scissors.” The script uses a synthetic data renderer to create a large dataset of these images with various transformations and noise, eliminating the need for a massive real-world dataset.

What is a Convolutional Neural Network (CNN)?

A Convolutional Neural Network (CNN) is a type of deep neural network primarily used for analyzing visual imagery. Unlike traditional neural networks, CNNs are designed to process pixel data by using a mathematical operation called convolution. This allows them to automatically and adaptively learn spatial hierarchies of features from input images, from low-level features like edges and textures to high-level features like shapes and objects.

Typical CNN architecture by Aphex34, licensed under CC BY-SA 4.0 .

Common CNN applications include:

• Image classification: identify the main object in an image, such as classifying a photo as a cat or dog
• Object detection: locate specific objects in an image and draw bounding boxes
• Facial recognition: identify or verify individuals based on facial features

For the rock-paper-scissors game, you use a tiny CNN to classify the letters R, P, and S as the corresponding hand gestures.

Environment setup

To get started, complete the first three sections of Introduction to TinyML on Arm using PyTorch and ExecuTorch . This setup prepares your development environment and installs the required tools. Return here after running the ./examples/arm/run.sh script in the ExecuTorch repository.

If you just completed the earlier Learning Path, your virtual environment should still be active. If not, activate it:

    

        
        
source $HOME/executorch-venv/bin/activate

    

The prompt of your terminal now has (executorch-venv) as a prefix to indicate the virtual environment is active.

Install Python dependencies:

    

        
        
pip install numpy pillow torch

    

You’re now ready to create the model.