Synthetic data has shown to be an effective proxy for real data in order to train computer vision algorithms when acquiring labeled data is costly or impossible. Ship detection and classification from satellite imagery and surveillance video is one such area, and images generated using gaming engines such as Unity3D have been used successfully to circumvent the need for annotated real data. However, there is a lack of understanding of the effect of rendering quality of 3D models on algorithms that use synthetic data. In this work, we investigate how the level of detail (LOD) of objects in a maritime scene affects ship classification algorithms. To study this systematically, we create datasets featuring objects with varying LODs and observe their significance in computer vision algorithms. Specifically, we evaluate the impact of mismatched LOD datasets on classification algorithms, and investigate the effect of low or high LOD datasets on a model's ability to transfer to real data. The LOD of 3D objects are quantified using image quality metrics while the performance of computer vision algorithms is compared using accuracy metrics.