Striveworks Blog The Challenge—and Cost—of Annotating Image Segments

Many object-detection models are trained on data with simple annotations, created by dropping rectangular bounding boxes around an object of interest. It’s fast, easy, and (computationally) less intensive than some other approaches to annotation. And for counting stars in the sky or finding ships in the ocean, that can be enough. But sometimes, you need a model to be able to identify objects with precision—think self-driving cars or tumor diagnosis. In these situations, using bounding boxes to annotate training data may not be precise enough.