Detecting continuous gravitational waves using machine learning approaches is an active research topic. With signal strengths between 0.1% and 2%, this classification task is very difficult. The presence of noise makes it impossible even for humans to distinguish between data with and with- out traces of continuous gravitational waves. The European Gravitational Observatory (EGO) formulated this problem as a Kaggle challenge. As participants, we present our approach in this paper. In particular, we focus on our innovative data generation solution, which provides great flexibility while maintaining efficiency and training accuracy. Our generated training data is fully compatible with state-of-the-art image classifiers.