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The Label Schema (or Ontology) is the structural blueprint of a project. It defines the rules, classifications, and metadata requirements for every annotation. PixlHub provides a highly flexible schema builder, allowing for granular data collection through nested hierarchies and diverse input types.

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Building the Ontology

A project is organized around Classes (e.g., “Vehicle,” “Sign,” “Obstacle”). To capture detailed information without cluttering the class list, each class can be refined with multiple Attributes. The schema supports several input types to ensure data is structured correctly:
  • Text: For manual entries like license plates or serial numbers.
  • Number: For numerical values, counts, or measurements.
  • Radio: For selecting a single option from a list.
  • Checkbox: For simple boolean (True/False) properties.
  • Dropdown: For choosing one option from a large list.
  • Multi-select: For applying multiple relevant tags to a single object.
Label Schema 1

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Nested (Child) Attributes

To manage complex data requirements, PixlHub utilizes Child Attributes. This feature introduces conditional logic to the annotation process, ensuring that the interface remains clean and focused.
  • Conditional Logic: Child attributes only appear when a specific “Parent” option is selected. For example, selecting “Vehicle Type: Truck” can trigger a sub-menu for “Trailer Type,” which would remain hidden if “Sedan” were selected instead.
  • UI Optimization: By hiding irrelevant fields, child attributes reduce cognitive load for annotators, minimizing errors and speeding up the labeling process.
  • Deep Hierarchies: Managers can create multiple levels of nesting to capture high-fidelity data for specialized AI models.

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Global Attributes

While standard attributes describe specific objects, Global Attributes are used to define metadata for the entire asset or task. These are ideal for capturing situational context, such as:
  • Environment: Weather conditions (Rain, Snow), lighting (Day, Night), or location type.
  • Data Quality: Image clarity, sensor noise levels, or “Unlabelable” flags.
  • Scene Classification: Identifying the overall category of the frame (e.g., Urban, Rural, Highway).
Labelschema Globalattr

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Customization & Efficiency

The Label Schema includes features designed to maximize annotator throughput and maintain visual organization:
  • Color Assignment: Each class can be assigned a unique color. This makes the canvas instantly readable and helps distinguish between overlapping objects at a glance.
  • Keyboard Shortcuts: Classes and attributes can be mapped to specific hotkeys (e.g., 1, 2, 3, 4). This allows annotators to switch between labels and confirm attributes without moving the mouse, significantly reducing the time-per-task.