Abstract: A machine-learning model (MLM) is trained to identify a given item identifier for an item and shelf dimensions of an empty space associated with the item from training images of a shelf. After training, real-time images of the shelf are provided as input to the MLM and the output provided by the MLM includes empty space identifiers, dimensions or pixel coordinates for each empty space identifier, and an item identifier for each empty space identifier. A quantity of each item identifier is determined based on known shelf dimensions that the corresponding item should occupy on a fully stocked shelf and based on the corresponding empty space dimensions for the empty space associated with the item. A real-time report is sent to store personnel and/or published on a website monitored by the store personnel. The report identifies the items, the shelves, and restocking item quantities that need restocked in the store.