A way to reduce labelling effort and improve accuracy for object detection is class grouping. In this research, we experiment with creating hierarchical tree structures of grouped classes (super-classes). Our objective is to find out what the effects are of grouping classes in terms of accuracy and labelling effort. First we show what accuracy improvement can be gained from different grouping strategies. Then we show what the difference is in accuracy gain for a hierarchical tree structure on FasterRCNN, RetinaNet and YOLOv8. Next up we introduce a new layer in the tree structure with new super-classes and we show the difference in the tree with two and with three layers. After that we compare the accuracy for predicting only super-classes. Lastly, we want to take predicting super-classes one step further by showing that class grouping can reduce labelling effort for real life applications like autonomous cars.