Annotate

Use the interface on the DATA page to annotate your images. Always follow best practices when you label your images:

More data means better models

Incorporate as much data as you practically can to improve your model’s overall performance.

Include counterexamples

Include images with and without the object you’re looking to classify. This helps the model distinguish the target object from the background and reduces the chances of false positives by teaching the model what the object is not.

Avoid class imbalance

Don’t train excessively on one specific type or class, make sure each category has a roughly equal number of images. For instance, if you’re training a dog detector, include images of various dog breeds to avoid bias towards one breed. An imbalanced dataset can lead the model to favor one class over others, reducing its overall accuracy.

Match training images to intended use case

Use images that reflect the quality and conditions of your production environment. For example, if you plan to use a low-quality camera in production, train with low-quality images. Similarly, if your model will run all day, capture images in daylight, nighttime, dusk, and dawn conditions.

Vary angles and distances

Include image examples from every angle and distance that you expect the model to handle.

Viam enables you to annotate images for the following machine learning methods:

Classification determines a descriptive tag or set of tags for an image. For example, classification could help you identify:

  • whether an image of a food display appears full, empty, or average
  • the quality of manufacturing output: good or bad
  • what combination of toppings exists on a pizza: pepperoni, sausage and pepper, or pineapple and ham and mushroom

Viam supports single and multiple label classification. To create a training set for classification, annotate tags to describe your images.

To tag an image:

  1. Click on an image, then click the + next to the Tags option.

  2. Add one or more tags to your image.

Repeat these steps for all images in the dataset.

Object detection identifies and determines the location of certain objects in an image. For example, object detection could help you identify:

  • how many pizza objects appear on a counter
  • the number of bicycle and pedestrian objects on a greenway
  • which plant objects are popular with deer in your garden

To create a training set for object detection, annotate bounding boxes to teach your model to identify objects that you want to detect in future images.

To label an object with a bounding box:

  1. Click on an image, then click the Annotate button in right side menu.

  2. Choose an existing label or create a new label.

  3. Holding the command key (on macOS), or the control key (on Linux and Windows), click and drag on the image to create the bounding box:

Repeat these steps for all images in the dataset.

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