Model Metric Visualization for Detecting Update Regressions
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Solution Overview
Problem
Developing and updating machine-learned models for autonomous vehicle navigation can be challenging, as it is difficult to determine whether updates result in improvements or regressions, especially with minor changes.
Innovation Solution
A metric visualization system is used to store and compare metric data associated with machine-learned models, allowing for the determination of whether an update has improved or regressed the model by visualizing differences in model outputs relative to ground truth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learned models are updated to improve autonomous vehicle navigation, then model performance may be enhanced, but it becomes difficult to determine whether the update resulted in an improvement or regression
Solution Approach 1:
The system implements feedback by automatically comparing metric data from updated models against baseline models, providing visual feedback through color-coded indicators (green for improvement, red for regression) that clearly communicate whether updates resulted in performance improvements or regressions across multiple metrics simultaneously
Solution Approach 2:
The patent introduces an intermediary evaluation system that acts as a mediator between model updates and performance assessment. This intermediary system automatically computes and visualizes metric differences, eliminating the difficulty of direct comparison and providing an objective intermediate representation of model performance changes
2Reliability
If multiple metrics are computed to evaluate model outputs, then evaluation comprehensiveness is improved, but interpretation of results becomes more difficult
Solution Approach 1:
The system segments the evaluation results by displaying multiple individual metrics (such as precision, recall, F1 score, IoU) separately with their own visual indicators, allowing each metric to be interpreted independently while collectively providing comprehensive evaluation coverage
Solution Approach 2:
The patent merges multiple metric evaluations into a unified visual interface that simultaneously displays all metrics with consistent color-coding and formatting, combining the comprehensiveness of multiple metrics with the simplicity of a single integrated view for easy interpretation
Data Source
AI summary
Techniques for evaluating and validating progress of training machine-learned models may include storing, in a database, metric data associated with outputs from machine-learned models based on sensor data inputs. For instance, the metric data may include first metric data associated with a first bounding box output by a machine-learned model and second metric data associated with a second bounding box output by an updated version of the machine-learned model. The techniques also include a graphical user interface (GUI) for presenting visualizations of the metric data that improve the ability to evaluate the performance of a machine-learned model. In some examples, an indication of a request to evaluate the updated version of the machine-learned model may be received via the GUI. Based on the indication, the GUI may cause presentation of visualization(s) of difference(s) between first metric(s) of the first metric data and second metric(s) of the second metric data.