In wireless sensing applications, such as Integrated Sensing and Communication (ISAC), one of the first crucial signal processing steps is the detection and estimation targets from a channel estimate. Effective algorithms in this context must be robust across a broad Signal-to-Noise Ratio (SNR) range, capable of handling an unknown number of targets, and computationally efficient for real-time implementation. During the last decade, different Machine Learning methods have emerged as promising solutions, either as standalone models or as complementing existing techniques. However, since models are often trained and evaluated on synthetic data from existing models, applying them to measurement is challenging. All the while, training directly on measurement data is prohibitive in complex propagation scenarios as a groundtruth is not available. Therefore, in this paper, we train a Convolutional Neural Network (CNN) approach for target detection and estimation on synthetic data and evaluate it on measurement data from a suburban outdoor measurement. Using knowledge of the environment as well as available groundtruth positions, we study the detection probability and accuracy of our approach. The results demonstrate that our approach works on measurement data and is suitable for joint detection and estimation of sensing targets in ISAC systems.