Model Metric Visualization for Detecting Training Regressions
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Solution Overview
Problem
Developing trained machine-learned models for autonomous vehicle navigation is challenging, as it is difficult to determine whether updates improve or regress model performance, especially when minor improvements become less significant.
Innovation Solution
A database is used to store metric data comparing model outputs to ground truth, allowing for visualization of differences between updated and original models, enabling determination of improvements or regressions through normalized metric calculations and filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learned models are updated to improve performance, then model accuracy may improve, but it becomes difficult to determine whether the update resulted in an improvement or regression
Solution Approach 1:
The system segments model evaluation into multiple independent metric components (e.g., detection accuracy, localization precision, false positive rate) that can be individually tracked and compared across model versions. This segmentation allows for precise measurement of specific performance aspects while maintaining overall evaluation capability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring model performance metrics and providing visual feedback through graphs and tables that show improvements or regressions. This feedback loop enables developers to immediately assess whether model updates achieved desired performance improvements.
2Measurement precision
If comprehensive metric data is collected to evaluate model performance, then evaluation accuracy improves, but system complexity increases
Solution Approach 1:
The system merges multiple evaluation functions into a unified platform that simultaneously collects metric data, stores it in structured databases, processes comparisons, and generates visualizations. This consolidation reduces overall system complexity while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The evaluation system is designed as a universal platform that can assess multiple types of machine-learned models (object detection, segmentation, tracking) using the same metric collection and analysis infrastructure, reducing complexity through reuse of common components.
3Loss of information
If detailed metric comparisons are performed between model versions, then performance insight improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing metric data in structured formats as models are updated, rather than computing all comparisons from raw data each time. This preliminary organization of data significantly reduces processing time for subsequent version comparisons.
Solution Approach 2:
The system replaces manual or sequential metric comparison processes with automated computational algorithms that efficiently calculate performance differences across multiple metrics and model versions simultaneously, reducing evaluation time.
Data Source
AI summary
Techniques for evaluating and validating progress of training machine-learned models are described herein. The techniques 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.