Automatic efficient small model selection for monocular depth estimation

Abstract

Systems and methods are described for selecting models to perform monocular depth estimation. A computing device may receive a plurality of image and select an image from the plurality of images to evaluate a first machine-learning model and a plurality of machine-learning models are smaller than the first machine-learning model. The computing device may process the image using a first machine-learning model to generate a first predicted result. The computing device may also process the image using the plurality of machine-learning models to generate at least a second predicted result and a third test data set. The computing device may select a second machine-learning model from the plurality of machine-learning models based on a comparison of the first predicted result with the at least the second predicted result and the third test data set. The computing device may then process the plurality of images using the second machine-learning model.