At Scale, we pride ourselves in developing superior tooling and implementing forward-looking solutions for problems our customers on the cutting edge of computer vision, including self-driving cars, AR/VR, and drones encounter. For example, our 2D image annotation endpoints now support both per-annotation attributes and label hierarchies, allowing you to get richer labeled data out of Scale.
This post will briefly outline the differences between the two.
Defining per-annotation attributes for a task helps you extract additional structured information about individual annotations, beyond what a single selected label can provide.
The attribute interface can also adapt dynamically to previously-selected attribute choices. For example, only if workers answer “No” to “Is the car currently parked?” will they be asked “ Which direction is the car heading?” If they answer “yes” to “Is the car currently parked?” the interface will skip the question “Which direction is the car heading?”
Nesting labels can also help you extract additional structured information from your data. By default, 2D annotation tasks sent to Scale have a limit of 7 flat labels to reduce the cognitive load on our human labelers and keep their accuracy high. But using subchoices, a worker can, for example, first categorize an object as pedestrian or vehicle, and based on that choice select the specific type of pedestrian or vehicle.
Ready to get started? Take a look at our Annotation Attributes documentation for how to include attributes in your tasks, and for how to use attribute conditions to dynamically request specific attributes. Or see our Nested Labels documentation to learn how to specify large lists of labels on annotation tasks.