Introduction

What do you want your AI system to do? How will it accomplish this? What methods are you going to use?

The real world is messy. Commercial applications require a level of detail that’s hyper domain specific. There are many ways to structure this. In general, these structures are defined in the Schema. Further, the Schema provides “pivot points” to adapt and change sub-components over time to better fit current needs.

The Schema is important to get right because the rest of the system, including raw data, is defined in relation to the Schema.

Schema is the paradigm for encoding Who, What, Where, How & Why. Schema is the overall representation of Labels, Attributes, and their Relation to each other. It’s how we represent the meaning of what something is, where it is, and more.

Sections

Diffgram provides a series of concepts to work with Schema

Schemas - Top level organizing objects containing multiple labels and attributes.
Objects - Business objects of multiple media files
Labels - High level meaning
Attributes - The bulk of Annotations

Schemas

Schema Setup

API References

New Schema

Schemas List

Objects

Compound Files Ingestion

Compound File

Labels

Create Your First Label