‘The development of autonomous cars is in full progress. For the autonomous cars to be able to detect where other cars are, many machine learning models are utilised. One problem in the fi€eld of object detection, is that a human has to tell where objects are in images, for a machine to be able to learn to detect objects in images it has never seen before. To tackle this problem synthetic images can be created, where the ground truth of where objects are in images is known, without having to use human knowledge. Th‘is thesis study the approach of using a translation model to translate images to look more like real photographs. Several object detection models are then used to evaluate if training on the translated images increases the generalisation to the real world photographs. In some cases the results show that the translated images help to increase the performance on real world photographs when detecting cars in images of street scenes.