Annotation modes

The General tab contains the essential settings for your project. Configuring these first is recommended before moving on.

Label Modes

Choose a label mode based on your task:

Mode	Annotation	Use Case
Classes	One or more class labels per image	Quality control, sorting, counting
Objects	Bounding boxes with class labels	Locating and identifying multiple objects
Segmentation	Pixel-precise masks per object	Surface defects, medical imaging, precise region delineation

Converting Labels to Classification

Switching the label mode from Objects or Segmentation to Classes converts the dataset at training time (annotation files on disk are not modified). For finer control, keep the original label mode and select a Prediction Type or Segmentation Type in the Model Settings tab:

Prediction Type	Behavior
All Present Object-Classes	Selects all classes present in the image
Class with Largest Combined Object Area	Selects the class with the largest total bounding box area
Class with Most Objects	Selects the most frequently occurring class
At Least One Object? (Y/N)	Binary: any object present or not