Insects such as Diptera are capable of highly complex aerial maneuvers and rapid responses to environmental stimuli, making them a subject of interest for studies in flight dynamics and motor control. To accurately quantify these movements, high-speed cameras are employed, capturing footage at 5000 frames per second. This results in a vast amount of data, necessitating the development of automated analysis models. In this work, we present a segmentation model specifically designed for the wings of Diptera species, aiming to track their wingbeats with high precision. The model’s performance was evaluated on species included in the training set as well as on species only included in the testset, allowing us to assess its generalization capabilities. Our results show that the model achieves a 95th percentile error of 7.8 pixels, while operating at a speed 2500 times faster than manual human analysis. This significant improvement in speed highlights the potential for our model to facilitate large-scale, high-speed analysis of insect flight, with implications for both biological research and bio-inspired engineering applications.