We merged different datasets from Roboflow and converted them to the YOLO annotation format, enabling the use of the Ultralytics ecosystem of pretrained YOLO models and facilitating rapid deployment of custom-trained models on mobile devices. The combined dataset form Roboflow comprises 3,459 images distributed across the three classes. The majority of samples (2,593) correspond to power sockets, while the smallest subset (151) belongs to the power strip class. All images in the prepared dataset exhibit varying resolutions, lighting conditions, and orientations. The prepared dataset was partitioned into 80% for training, 10% for testing, and 10% for validation. Datasets used:https://universe.roboflow.com/test-gxlza/power-strip-cosfvhttps://universe.roboflow.com/test-gvutb/socket_rockethttps://universe.roboflow.com/yolov5-power-socket-detection/power-socket-detection-rofcvhttps://universe.roboflow.com/timengelbracht/spotlight-light-switch-dataset