# Searching for the Ideal Recipe for Preparing Synthetic Data in the Multi-Object Detection Problem
## 4 Discussion
Diversity in the data was achieved through the incorporation of different 3D models, multiple models in a single scene, object occlusions, random arrangement of models, random positioning of camera views, diverse backgrounds, and lighting. This diversity, practically unreachable in reality, fills a crucial gap when actual images are unavailable. Promising methods for data generation are generative models, such as generative adversarial networks [38] and diffusion models [39], which are currently used in many applications. This type of methodology currently serves mainly as a data augmentation method, which creates coherent and logical images based on already provided pictures. Future development of these methods may make them an alternative to generating synthetic data and will speed up the process of preparing training data. The strategy of training on synthetic data significantly affects the results and influences the layers of neural networks [16]. Notably, the comparison between a detector trained on real data and one trained on synthetic data revealed the highest similarity in the early layers, while the most significant difference was observed in the head part of the network. Feature extractors (first layers) are only responsible for detecting various shapes that are identical for similar problems. This observation was used for a simple yet effective approach [14], where the layers responsible for feature extraction were frozen in pretrained models on real images. Subsequently, only the remaining layers were trained on synthetic data. The effective application of simulation data, treated as synthetic data, plays a pivotal role in enhancing the quality of training data preparation. On one hand, it facilitates the generation of substantial datasets that enrich real data with diverse sets in various aspects. On the other hand, synthetic data become crucial when real data for a specific class is lacking in any existing dataset. However, a significant challenge lies in appropriately structuring the classifier architecture to bridge the gap between the domains of real and synthetic data. The presented work delves into extensive research on the application of synthetic data in the detection of numerous objects. The selected list of objects includes classes with available data in publicly accessible benchmarks and classes requiring manual resource search. The utilization of synthetic data as a form of data augmentation is increasingly prevalent today. Image augmentation, as discussed in [9], is a popular way to boost dataset quality. It involves rotating, scaling, adjusting color, and blending images to create more labeled data, preventing overfitting during training. Such a process can be carried out in each epoch, providing rich data every iteration. With numerous data generation scenarios available, synthetic data finds practical applications in detecting various objects. Current knowledge enables the generation of realistic data, and as successive graphics engines are released, the photorealism of simulations is expected to improve. Nevertheless, a significant challenge for the future lies in achieving appropriate domain transfer, domain bias, and solutions for overfitting to construct a classifier based solely on synthetic images that remain effective on real data.