Tutorials: Train a dice reader and score Yahtzee

Fri, 06 Nov 2026 00:00:00 +0000

In this workshop you teach a webcam to read dice, then use what it sees to score Yahtzee. You capture and label your own images, train two detection models two different ways, measure which one reads dice better, and build a Yahtzee scorer on the winner that runs on your machine.

Dice are a good first vision project. They’re cheap and the same everywhere, a pretrained model can’t read them, and there’s never any doubt about what a die shows.

You’ll learn how to:

Add images from a camera to a dataset
Label images, and use a starter model to label more of them for you
Train an object detection model with built-in training
Train a YOLO model with a training script from the registry
Evaluate models on held-out test datasets and compare the results
Choose a confidence threshold from evaluation results
Deploy a model with the ML model and vision services
Build an inline module that scores rolls from your model’s detections

Requirements

Hardware

A USB webcam connected to your computer, such as a Logitech C920. Any webcam that viam-server can stream from works.
Five or more dice whose pips stand out clearly from the die, such as white pips on colored dice. Any color works; the model learns the colors you train it on.
A way to hold the webcam about 30 cm above your workspace, and later about 45 cm. A stand, a clamp, a 3D-printed post or tape all work.
A plain background to roll on, such as a dice tray or a sheet of plain paper.
A Mac or Linux computer that runs viam-server.

Software

A Viam account and a machine running viam-server on your computer. See Set up your first machine.

What you should know first

How components, services and machines fit together: Viam platform overview.
What training, datasets and models are in Viam: Train ML models.

Check your setup

Everything in this workshop happens in the Viam app, including the code in Phase 10, so you don’t need to install anything on your computer besides viam-server.

Open your machine’s page in the Viam app and check that it shows Live. If it shows Offline, check that viam-server is running on your computer.

Phases

The phase times are hands-on time. Each training job also runs for some time in the cloud, and the phases tell you what to do while you wait.

Phase	What you end with	Time
1. Capture a starter set from your webcam	The webcam streams in the `camera` card’s Test section and 30 images of dice are in a dataset.	About 20 minutes
2. Label the starter set	Every die in the starter set has a box and a value.	About 20 minutes
3. Train a starter and capture more	A starter model trains while you capture about 70 more images.	About 20 minutes
4. Auto-label the training set	The starter model labels your training set, you correct it, and you add your starter images. Milestone: a model you trained labels your data.	About 20 minutes
5. Train a built-in model	A TFLite detection model trains on the full training set.	About 10 minutes
6. Train a YOLO model from the registry	A YOLO model trains on the same dataset.	About 15 minutes
7. Build the test datasets	A clean and a hard test dataset, labeled and checked.	About 25 minutes
8. Evaluate both models and pick a winner	Four evaluations and a chosen model and threshold. Milestone: a measured answer to which model reads dice better.	About 20 minutes
9. Deploy the winning model	The winning model draws boxes on the live camera feed.	About 15 minutes
10. Score a Yahtzee roll	An inline module reads a roll and returns its score. Milestone: an application built on your own model.	About 20 minutes

When you finish, the wrap-up suggests where to go next.