We've also developed a way that allows you to automatically remove the background class wherever it overlaps with any other class, allowing you to annotate more efficiently. Here's how it works:

Guidelines for partial annotation on arivis AI on the arivis Cloud

1. Use the brush tool to annotate several objects in a region.
2. Draw the background class around the annotated objects with the Autofill holes option active, covering as much background as possible.
3. The background class is automatically cropped where other annotations exist. Make sure that the background class doesn't cover any non-annotated objects.
3. If you annotate all objects in your image, draw one big background annotation around the entire image to crop all objects at once (with the brush tool and Autofill holes active).
4. Select the image from the list of images (right panel) for training.

If there are any regions where you want to make sure that the algorithm interprets them as background, it can make sense to create additional background annotations. These are examples of regions where additional background annotations can help the algorithm to learn how to segment more precisely:

• Narrow background lines between objects helps to separate them.
• Background annotations on image features that look similar to your classes help to prevent them from getting (wrongly) segmented.