> For AI agents: guidance on navigating Viam documentation is available at https://docs.viam.com/llms.txt. # Wrap-up and next steps Review the dice reader you built and find where to take your models next. > Source: https://docs.viam.com/tutorials/dice-vision/wrap-up/ Your webcam reads dice with a model you trained on your own images, and a module running on your machine turns what it reads into Yahtzee scores. You also know, from measurements rather than a guess, which of two models does the job better. ## What you built {#what-you-built} - Four datasets: a hand-labeled starter set, an auto-labeled training set, and clean and hard test datasets. - A starter model that labeled most of your training images for you. - Two dice detectors, one from built-in training and one from a YOLO training script, scored the same way on the same test datasets. - A deployed vision service running the winning model on your machine. - An inline module that reads a settled roll and scores it. ## What you learned {#what-you-learned} - 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. ## What's next {#whats-next} - [Automate annotation](/train/automate-annotation/): auto-label new images with your best model, add images from the conditions where it struggled, retrain, and evaluate the new version on the same test datasets. - [Custom training scripts](/train/custom-training-scripts/): write your own training script, or fork `yolo-training` and try a larger `imgsz` to help with small pips. - [Configure a vision service](/vision/configure/): see the other kinds of vision services, such as classifiers and segmenters. - [Miniature Palletizing with the SO-ARM101](/tutorials/so-arm101-palletizing/): give your machine an arm, and you're one step from a robot that rolls its own dice. To keep going with the scorer, add a command to your module that keeps a scorecard across 13 rounds, so you can play a full game against your machine.