Abstract: A system may be configured to perform label recollection, e.g., by automatically snapping, via a trained ML model, a set of vector labels by aligning one or more of the labels to an image, the alignment being performed at a quality that satisfies a criterion. Before this automatic snapping or matching of vectorized labels with reference imagery, this ML model may obtain training data from an output of another trained ML model. In another context, a computer-implemented method is disclosed for creating training data that better aligns labels with corresponding image features. This training data, created with reduced effort yet increased quality, may then be fed into to existing models, resulting in an automated pipeline.