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.
Per-Annotation Attributes
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?”
Label Hierarchies
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
documentation for how to include attributes in your tasks, and for how to use
attribute conditions to dynamically request specific attributes. Or see our
to learn how to specify large lists of labels on annotation tasks.
