Annotate images

Label your dataset images with tags for classification or bounding boxes for object detection. You need a dataset with images before you can annotate.

Tag images for classification

Tags are labels that apply to an entire image. Use them when you are building a classification model – for example, labeling images as “good-part” or “defective-part”.

Web UI:

1. In the DATA tab, click an image to open it in the detail view.
2. On the right side panel, find the Tags section.
3. Click the + button next to Tags.
4. Type a tag name (for example, good-part) and press Enter.
5. The tag is saved immediately. Repeat for each image.

To tag multiple images at once, use the SDK to add tags programmatically (see the code example below).

async def main():
    viam_client = await connect()
    data_client = viam_client.data_client

    binary_data_ids = ["binary-data-id-1", "binary-data-id-2"]

    await data_client.add_tags_to_binary_data_by_ids(
        tags=["good-part"],
        binary_ids=binary_data_ids,
    )
    print("Tags added.")

    viam_client.close()

binaryIDs := []string{"binary-data-id-1", "binary-data-id-2"}

err = dataClient.AddTagsToBinaryDataByIDs(
    ctx,
    []string{"good-part"},
    binaryIDs,
)
if err != nil {
    logger.Fatal(err)
}
fmt.Println("Tags added.")

You can get binary data IDs by querying for images using the data client’s binary_data_by_filter method, which returns objects that include the binary data ID.

Choose tag names that are clear, consistent, and descriptive. Use lowercase with hyphens (for example, good-part, defective-part, no-part). Avoid vague names like type1 or other.

Draw bounding boxes for object detection

Bounding boxes mark the location of specific objects within an image. Use them when you are building an object detection model – for example, detecting packages on a conveyor belt.

Web UI:

1. Open your dataset and click an image to open the detail view.
2. In the side panel, click the Actions tab.
3. Click Annotate to enter annotation mode.
4. Select or create a label (for example, package).
5. Hold Cmd (macOS) or Ctrl (Windows/Linux) and click-and-drag on the image to draw a rectangle around the object.
6. The bounding box appears with your selected label. Adjust the box edges by dragging them if needed.
7. Repeat for every object in the image that should be detected.
8. Move to the next image and repeat.

Draw tight bounding boxes that closely fit the object. Do not include excessive background. If an image contains multiple objects, draw a separate bounding box for each one.

async def main():
    viam_client = await connect()
    data_client = viam_client.data_client

    bbox_id = await data_client.add_bounding_box_to_image_by_id(
        binary_id="binary-data-id-1",
        label="package",
        x_min_normalized=0.15,
        y_min_normalized=0.20,
        x_max_normalized=0.85,
        y_max_normalized=0.90,
    )
    print(f"Added bounding box: {bbox_id}")

    viam_client.close()

bboxID, err := dataClient.AddBoundingBoxToImageByID(
    ctx,
    "binary-data-id-1",
    "package",
    0.15, // x_min_normalized
    0.20, // y_min_normalized
    0.85, // x_max_normalized
    0.90, // y_max_normalized
)
if err != nil {
    logger.Fatal(err)
}
fmt.Printf("Added bounding box: %s\n", bboxID)

Coordinates are normalized 0.0-1.0, where (0.0, 0.0) is the top-left corner and (1.0, 1.0) is the bottom-right corner.

Troubleshooting

What’s next

• Automate annotation – speed up labeling by using an existing ML model to generate predictions automatically.
• Train a model – use your labeled dataset to train a classification or object detection model.