Are you getting good predictions? If yes, great - accept them and add any missing bounding boxes. If the projections do not satisfy you, annotate 10 more images using the bounding box tool and try again.
As you continue to label, the Object Detection assistant will learn and improve - so keep using and testing it.
When you see that the predictions made by the assistant are consistently good, you might want to apply automated labeling to batch-process the rest of your dataset.
Quick side note: you can also use the Polygon or DEXTR tool, but neither is great for annotating non-object regions. Hasty does not support many polygons grouped into one, while DEXTR is pre-trained on Instance Segmentation data.
To use Panoptic Segmentation in Hasty, you basically have to perform both Instance and Semantic segmentation.
Start by annotating images until both assistants are unlocked. A tip would be to try the ATOM segmenter, as it is well-suited to both IS and SES tasks.
Use the guidance provided above to get your first annotations done.
When both the Instance Segmentation Assistant and the Semantic Segmentation Assistant become available, try both and see how they perform.
As usual, keep adding more data to increase the performance of both assistants.
When one or both of the assistants perform well, you can use auto-labeling to label the rest of the images in one click. Please note that you need to start two separate auto-labeling runs to perform a complete Panoptic Segmentation.