Method for training an object recognition model in a computing device

Abstract

An object recognition model training method in a computing device is disclosed. In the present disclosure, an object of interest, which is an object for object recognition, is designated, and an object of non-interest excluding the object of interest is generated and used as learning data for the object recognition model. In the process of training the object recognition model, when an erroneously detected object occurs, the object recognition model may be retrained by automatically converting the erroneously detected object to the object of non-interest without feedback of the erroneous detection to the user. Accordingly, user convenience for processing the erroneously detected object is improved, which increases reliability of the object recognition model. This disclosure can be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.