In this work, we propose a method to detect swimmers in unconstrained swimming video, using a Unet-based model trained on a small dataset. Our main motivation is to make the method accessible without spending much time or money in annotation or computation while maintaining performance. As a result, our model reaches topperformances in detection without requiring for intrusive sensors. The swimming videos can be recorded from various locations with different settings (distance and angle to the pool, light conditions, reflections, camera resolution), which alleviates a lot of the usual video capture constraints. Every algorithm described in the paper is accessible online at https://github.com/njacquelin/swimmers detection.