Image Recognition Training With Mixed Annotation Levels

The annotation process is segmented into two distinct types: first annotations providing detailed information (object masks, precise boundaries) and second annotations providing less detailed information (bounding boxes, basic labels). This segmentation allows different levels of annotation detail to be applied to different image sets, reducing overall annotation time while maintaining recognition accuracy through the combination of both annotation types in training the algorithm.

Different quality levels of annotations are applied locally to different subsets of training images. Some images receive comprehensive first annotations with detailed object masks and precise boundaries, while other images receive simplified second annotations with bounding boxes and basic labels. This local quality differentiation ensures sufficient training data coverage while significantly improving annotation productivity, as second annotations can be applied much faster than first annotations.

The annotation approach is made dynamic by allowing flexible combination of first and second annotations based on specific needs. The system can adaptively select which images receive which annotation type, enabling scalable training data preparation that balances quality and resource requirements. This dynamic approach reduces overall process complexity compared to uniformly applying detailed manual annotation to all images.