Abstract: Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.

Abstract: Systems, methods, and graphical user interfaces (GUIs) for ingesting and enriching data regarding a plurality of entities are provided. A first data set comprising company data and a second data set comprising customer data are ingested. The first data set is processed to generate a processed data set. The first data set may be processed by applying an entity matching technique, wherein one or more data elements are generated based on whether an entity of the first data set and an entity of the second data set are commonly associated. The first data set may additionally or alternatively be processed by applying a statistical matching technique, wherein one or more predicted data elements are generated based on similarity between an entity of the first data set and one or more entities of the second data set.

Abstract: Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.