What are Recipe & Ontology?
What is a recipe ?
The data Recipe is a list of instructions to be performed on a given item.
The Recipe has 3 main roles:
- Create a frictionless labeling instruction medium
- Enable Automatic UI simplifications and adjustments
- Enable automatic quality checking
The recipe is composed of two main componments: the ontology and the instructions.
The ontology of a dataset is the building block of your model, and will help you define the object detection your trained model provides.
It is a label map in its basic form that comes with more powerful capabilities, it is a part of the recipe containing the labels and attributes.
Labels (like classes) are the names you use to classify your annotations .
Attributes allow additional independent degrees of freedom while building a world definition.
While it's your job to define attributes according to your business needs, at Dataloop we typically look at the definition as the "answer sentence" to the following model question:
<subject (label)> <verb(attribute)> <adjective/Noun (attribute/label)>
The ontology of a dataset is the building block of your model and will help you define the object detection your trained model provides.
The Recipe hold settings for instructions for our Studio:
- Limiting tools for specific labels
- Setting up UI controls for the Studio
- Selecting Item views for specific items