Military vehicle recognition faces critical data scarcity due to operational security constraints and prohibitive collection costs. Classification of vehicles demands extensive training data rarely available in defence contexts. We propose a hybrid approach combining limited real-world data with scalable synthetic generation. Our methodology comprises: (1) a Blender-based pipeline generating high-resolution synthetic images with domain randomization across 3D models, lighting, and camera angles; (2) training transformer-based RF-DETR detectors on real-world and synthetic data, respectively; (3) an in-depth evaluation of the trained networks to determine the effect of synthetic data. Our approach utilizes a baseline RF-DETR detector trained on real-world imagery to compare against. Then we utilize the custom-made synthetic data generation pipeline to create an equally large synthetic dataset. This generated data is added to real data subsets, thus creating a mixed datasets containing varying percentages of real data. We created five datasets containing 5%, 10%, 25%, 50%, and 100%, respectively. With these new mixed datasets we train another set of RF-DETR detectors. Afterwards we evaluate the influence of the synthetic data by comparing the detectors across computer vision metrics.