The quality and diversity of training datasets significantly influence the performance of neural networks, particularly in object detection tasks that rely on annotated images to learn from marked regions. While synthetic image data reduces the need for extensive manual annotation, the domain gap between synthetic and real-world data remains a challenge, often leading to reduced model performance on real-world images. This study compares the image regions considered important for object detection by analyzing the predictive behavior of Transformer networks trained on synthetic versus real image data. Using a shared set of test images and a feature visualization method, the analysis focuses on the size, quantity, and spatial distribution of regions of high attention. Additionally, the study explores the influence of generative artificial intelligence techniques—specifically Stable Diffusion—on the predictive behavior of models trained on synthetic data. Furthermore, an occlusion-based evaluation method is proposed to assess model behavior by selectively masking regions of high attention. Findings suggest a positive correlation between the number of large regions of high attention—specifically, those exceeding \(\frac{1}{16}\) of the bounding box area—used for object detection and model performance. Augmentation with Stable Diffusion appears to enhance the model’s robustness to the occlusion of regions of high-attention in the images.