Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasoned by their high robustness against meteorological effects, such as rain, snow, or fog, and the radar’s ability to measure relative radial velocity differences via the Doppler effect. The cause for these advantages, namely the large wavelength, is also one of the drawbacks of radar sensors. Compared to camera or lidar sensor, a lot more surfaces in a typical traffic scenario appear flat relative to the radar’s emitted signal. This results in multi-path reflections or so called ghost detections in the radar signal. Ghost objects pose a major source for potential false positive detections in a vehicle’s perception pipeline. Therefore, it is important to be able to segregate multi-path reflections from direct ones. Here we present a dataset with detailed manual annotations for different kinds of ghost detections. We hope that our dataset encourages more researchers to engage in the fields of multi-path object suppression or exploitation. Paper: https://ieeexplore.ieee.org/document/9636338 (10.1109/IROS51168.2021.9636338) Accompanying github repository: https://github.com/flkraus/ghosts Documentation and further information can be found on the github repo.