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In the domain of printed circuit board (PCB) X-ray inspection, the effectiveness of deep learning models greatly depends on the availability and quality of annotated data. The utilization of data augmentation techniques, particularly through the utilization of synthetic data, has emerged as a promising strategy to improve model performance and alleviate the burden of manual annotation. However, a significant question remains unanswered: What is the optimal amount of synthetic data required to effectively augment the dataset and enhance model performance? This study introduces the Synthetic Data Tuner, a comprehensive framework developed to address this crucial question and optimize the performance of deep learning models for PCB X-ray inspection tasks. By employing a combination of cutting-edge deep learning architectures and advanced data augmentation techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), the Synthetic Data Tuner systematically assesses the impact of different levels of synthetic data integration on model accuracy, robustness, and generalization. Through extensive experimentation and rigorous evaluation procedures, our results illustrate the intricate relationship between the quantity of synthetic data and model performance. We elucidate the phenomenon of diminishing returns, where model performance reaches a saturation point beyond a specific threshold of synthetic data augmentation. Moreover, we determine the optimal balance between synthetic and real data, achieving a harmonious equilibrium that maximizes performance improvements while mitigating the risk of overfitting. Additionally, our findings emphasize the significance of data diversity and quality in the generation of synthetic data, highlighting the importance of domain-specific knowledge and context-aware augmentation techniques. By providing insights into the complex interplay between synthetic data augmentation and deep learning model performance, the Synthetic Data Tuner not only advances the current state-of-the-art in PCB X-ray inspection but also offers valuable insights and methodologies applicable to various computer vision and industrial inspection domains.
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