Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification or salient object subitizing. In this paper, we study the problem of salient object subutizing, i.e. predicting the number of salient objects in a synthetic images (SID4VAM and Toronto). This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1-4). This means that the subitized information will tell us the number of featured objects in a given image, and will thus subsequently obtain the location or appearance information of the featured objects, and everything will be done within a weakly supervised configuration.