Restricted multi-scale inference for machine learning

Abstract

Techniques for utilizing multiple scales of images as input to machine learning (ML) models are discussed herein. Operations can include providing an image associated with a first scale to a first ML model. An output of the first ML model can include a first bounding box indicative of a first region of the image representing a first object, with the first bounding box falling within a first range of sizes. Next, a scaled image can be generated by scaling the image. The scaled image can be provided to a second ML model, which can output a second bounding box indicative of a second region of the image representing a second object, the second bounding falling within a second range of sizes. Thus, inputting a scaled image to a same ML model (or to different ML models) can result in different detected features in the images.