# Set up your environment

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/)
- [Set up your environment](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/env-setup-1/)
- [Train and Test the rock-paper-scissors Model](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/fine-tune-2/)
- [Run the model on Corstone-320 FVP](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/fvp-3/)
- [Next Steps](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_next-steps/)

This Learning Path is a direct follow-up to [Introduction to TinyML on Arm using PyTorch and ExecuTorch](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/). 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](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/4-build-model/) 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.

![Image Alt Text:CNN architecture](/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/typical_cnn.png)

Typical CNN architecture by Aphex34, licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/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](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/). 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.