An Object Class defines a specific category or type that an object belongs to, grouping it by shared characteristics, behaviors, or functions. In fields like Computer Vision or Object Detection, it helps systems recognize and distinguish entities within a given context, providing a fundamental layer for understanding scenes and data.
The primary purpose of an object class is to provide a standardized way to categorize diverse real-world entities. This categorization allows for generalization, meaning that a system trained on examples of a "cat" can identify various breeds of cats it has never seen before, as long as they share the defining characteristics of the "cat" class. It simplifies complex environments by organizing them into manageable, conceptual units.
Common examples of object classes include "person," "car," "bicycle," "tree," "building," or "traffic light." Each class represents a collection of objects that are sufficiently similar to be treated as belonging to the same group for a given task. For instance, all instances of vehicles used for transport on roads might fall under a "Vehicle" class, which could then be further subdivided into "Car," "Truck," or "Motorcycle" classes.
In Machine Learning workflows, object classes are crucial during the Training phase. Data Labeling involves annotating datasets by assigning a specific object class to each relevant entity within images or videos. This labeled data serves as the ground truth, enabling algorithms to learn the visual patterns and features associated with each defined class.
Once a model is trained, it uses its learned understanding of object classes during the Inference phase. When presented with new, unseen data, the model attempts to classify detected objects into one of its known classes. For example, an object detection model might draw a bounding box around a detected item and label it as "dog" or "chair" based on its internal representations of those classes.
The definition and granularity of object classes can vary greatly depending on the application. Some applications might require broad classes like "animal" or "furniture," while others demand highly specific distinctions such as "Golden Retriever" or "Eames chair." Establishing a clear Taxonomy of classes is often a critical preliminary step in any vision or classification project.
