Abstract: Disclosed herein are methods and systems for automatic labeling of image data for machine learning training purposes. A method comprises retrieving navigation data and image data from a set of egos navigating through an environment comprising at least one feature; generating a three-dimensional (3D) model of the environment using the navigation data and image data of at least a subset of the set of egos, the 3D model comprising a virtual representation of the at least one feature of the environment; identifying a machine learning label associated with the at least one feature within the image data; receiving second navigation data and second image data from a second ego not included within the set of egos, the second ego navigating the environment, the second image data including the at least one feature; automatically generating a machine learning label for the at least one feature depicted within the second image data.

Abstract: Systems and methods can include a computing system receiving a bitstream of a video sequence and one or more indications indicative of one or more image frames of the video sequence, determining, by parsing the bitstream, timestamps, positions within the bitstream and types of image frames of the video sequence and determining, using the one or more indications and the timestamps, positions within the bitstream and types of the image frames of the video sequence, one or more segments of the bitstream for decoding to extract the one or more image frames. For an image frame of the one or more image frames, a corresponding segment represents a corresponding referencing chain of image frames of the video sequence. The computing system can decode the one or more segments of the bitstream and use the one or more image frames to train the ML model.

Abstract: Systems and methods for model evaluation and enhanced user interface for analyzing machine learning models. An example method includes obtaining information associated with a machine learning (ML) model, wherein the ML model is associated with autonomous or semi-autonomous operation of a vehicle; obtaining validation data, wherein the validation data includes one or more video sequences obtained from image sensors of an end-user vehicle; obtaining output via computing forward pass-through ML model using validation data, wherein the output indicates, at least, location information associated with objects detected via the ML model in the validation data; determining values associated with metrics based on the obtained output; and generating user interface information based on one or more of the determined values or obtained output.