Ai Labels, also known as label detection, is a feature that detects and identifies a broad range of entities within an image. These entities can be objects, places, activities, animals, or other types of elements present in the image.
A machine learning model is used to recognize and classify these entities by assigning them appropriate labels with a confidence score indicating the probability that the label is accurate.
For example, given an image of a modern interior , it might return labels such as “Plant” , “Chair”, “Wood”, “Room” and “Home ” , each with an associated confidence score.
Another feature that focuses on identifying and locating individual instances of objects within an image is Ai Objects.
Unlike label detection, which provides general labels for the entire image, object detection is more granular and returns information about the specific objects present, including their confidence scores.
In this example, there are 3 specific objects that can be found in the image: “Picture frame”,
“Сhair” and “Table”.
Difference Between Ai Labels and Ai Objects
The main difference between Ai Labels and Ai Objects lies in the granularity of the information provided by each feature:
- Ai Labels provides a general understanding of the image content by identifying and classifying various entities present in the image.
- It assigns relevant labels with confidence scores but does not provide information about individual object instances.
- Ai Objects focuses on detecting and locating specific instances of objects within an image.
Ai Labels helps to get a broader understanding of an image’s content, while Ai Objects is used for more detailed analysis, such as identifying and locating individual objects within the image.