Abstract: Embodiments described herein are directed to an adaptive AI model for 3D object detection using synthetic training data. For example, an ML model is trained to detect certain items of interest based on a training set that is synthetically generated in real time during the training process. The training set comprises a plurality of images depicting containers that are virtually packed with items of interest. Each image of the training set is a composite of an image comprising a container that is packed with items of non-interest and an image comprising an item of interest scanned in isolation. A plurality of such images is generated during any given training iteration of the ML model. Once trained, the ML model is configured to detect items of interest in actual containers and output a classification indicative of a likelihood that a container comprises an item of interest.