Dataset of 400 pomegranate tree (Punica granatum L. ‘Wonderful’) images, with the corresponding fruit masks. The dataset is designed for training artificial intelligence models for instance segmentation. The pictures were collected by means of mobile devices (smartphones), in random trees, from different distances, orientations and in varying lighting conditions. The resolution of the images and masks is 640x480 pixels. The dataset is divided into training (70%), validation (15%) and test set (15%). Stratification was performed in 3 periods of the season to ensure that all fruit ripening stages were present in each subset. Masks consist of a very detailed manual annotation of the visible part for each of the fruits in the images.