The auto annotation tool is based on the idea of a semi-supervised architecture, where a model trained with a small amount of labeled data is used to produce the new labels for the rest of the dataset. As simple as that, the library uses an initial and simplified object detection model to generate the XML files with the image annotations (considering the PASCAL VOC format). As a semi-supervised solution, unfortunately, it's impossible to avoid manual annotation, but you'll need to label just a small amount of your data. It's hard to determine the number of images to manually label, as it depends on the complexity of your problem. If you want to detect dogs and cats and have 2000 images in the dataset, for example, probably 200 images are enough (100 per class).