> For AI agents: guidance on navigating Viam documentation is available at https://docs.viam.com/llms.txt. # Phase 2: Label the starter set Draw a bounding box around every die in the starter dataset and label each box with the die's value. > Source: https://docs.viam.com/tutorials/dice-vision/label-the-starter-set/ At the end of this phase, every die in `dice-starter` has a box around it labeled with its value, at least 10 times for each face. That's the minimum a training job accepts, and it's what the starter model in [Phase 3](/tutorials/dice-vision/train-a-starter-model/) learns from. This is the only time you label a whole dataset by hand, so it's worth being careful: about 100 boxes, one die at a time. ## Draw a box on every die {#draw-a-box-on-every-die} Use the labels `1`, `2`, `3`, `4`, `5` and `6`, for the number of pips on the top face. 1. On the **DATASETS** page, open `dice-starter` and click the first image. 1. In the side panel, click the **Actions** tab, then click **Annotate**. 1. Select or create the label for the first die's top face, for example `4`. 1. Hold **Cmd** (macOS) or **Ctrl** (Linux) and drag a rectangle tightly around the die. 1. Repeat for every die in the image, then move to the next image. For more on the annotation tools, see [Annotate images](/train/annotate-data/). Follow these rules on every image, here and in later phases: - Box the whole die, edge to edge, with as little background as possible. - Label the face pointing at the camera, not a side face. Your boxes should look like these, with each one tight around a die, including dice that touch: ![Three images from a labeled starter dataset, with a tight box around every die and each box labeled with the die's top face: four dice spread apart, three dice in a diagonal line, and four dice touching in a cluster.](/tutorials/dice-vision/annotated-examples.jpg) > **Checkpoint:** > > Every image in `dice-starter` shows a labeled box on each die. > If you find a die with no box, go back and box it; otherwise the model learns that a die can be background. ## Check the label counts {#check-the-label-counts} 1. On the **DATASETS** page, open `dice-starter`. 1. In the dataset's side panel, find **Bounding box labels**. Each row shows how many images use the label and, in parentheses, how many boxes have it. ![The dice-starter dataset page with 30 images. The side panel's Bounding box labels list shows labels 1 to 6, each with at least 10 boxes, from 3 with 17 images and 23 labels down to 4 with 10 images and 13 labels.](/tutorials/dice-vision/dice-starter-labels.webp) The **Unlabeled** count under **Classifications and tags** counts images with no classification tag. This workshop uses bounding boxes, not tags, so that count stays at 30. It doesn't affect training: when you train an object detection model, the 20% unlabeled limit counts only images with no box for any label you train on, and classification tags don't count. > **Checkpoint:** > > Each label, `1` through `6`, has at least 10 boxes, and **Bounding box labels** shows no **Unlabeled** row. > If you see a label you didn't mean to create, such as `l` or `six`, relabel its boxes and delete it. > If a face has fewer, go back to the **Test** section of the `camera` card on the **CONFIGURE** tab, add a few images where that face is on top, and label them. > A training job won't start with fewer than 10 boxes for any label you train on. In the next phase, you train a starter model on these 30 images, and capture the rest of your training images while it trains.