Having datasets that are as clean as possible is crucial to optimizing the performance achieved by your models. A clean dataset is a dataset for which there are no annotation errors. When working on datasets of tens of thousands or even millions of images, it is very complicated in practice to achieve this. Indeed, annotating images with bounding boxes and with visual concepts is a tedious task, and like all painful and repetitive tasks, we humans make mistakes when we do them on large volumes. On the Deepomatic platform, you can run audits on your annotations that will display images for which there are potential errors.