Object Detection Model Optimization via Component Weight Training
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Solution Overview
Problem
Existing machine learning model optimization methods require extensive computing time and resources, and existing techniques for configuring and training model portions do not effectively evaluate interactions between components, leading to suboptimal performance.
Innovation Solution
A method for automated end-to-end machine learning model optimization that trains component parameters, backbone component weights, and link weights using stochastic gradient descent, while holding other parameters constant, to configure and train a model efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If every possible model permutation is configured and trained to generate an optimized model, then model optimization performance is improved, but computing time and resources increase excessively
Solution Approach 1:
The patent segments the model architecture into distinct portions (backbone, neck, head) and trains them separately using different datasets. This allows each portion to be optimized independently, reducing the computational burden of training complete model permutations while still achieving comprehensive optimization through iterative assembly and evaluation.
2Loss of time
If model portions are configured and trained separately, then computing time is reduced, but interactions between portions are not evaluated leading to suboptimal performance
Solution Approach 1:
The patent implements continuous optimization through iterative cycles where trained model portions are assembled into complete models, evaluated on validation data, and used to guide further training of individual portions. This continuous feedback loop ensures that interactions between portions are evaluated and optimized over time, maintaining model performance while reducing overall computing time.
Solution Approach 2:
The system uses validation data to evaluate assembled model portions and feeds this performance information back into the training process. The validation results guide which portions to retrain and how to adjust them, ensuring that interactions between portions are continuously optimized without requiring exhaustive permutation testing.
3Adaptability or versatility
If a human developer selects model configuration manually, then flexibility and adaptability are maintained, but the process is time-consuming and may not achieve optimal results
Solution Approach 1:
The system enables automated model optimization where the computational process itself performs the selection and configuration of model portions based on performance feedback. Instead of relying on human developers to manually evaluate configurations, the system automatically trains, assembles, and optimizes model portions using validation data, achieving both time savings and optimal results.
Data Source
AI summary
Using a training portion of a dataset, a set of component parameters comprising parameters of a component of an object detection model are trained. Using the trained set of component parameters, a set of backbone component weights comprising weights of component types in a backbone portion of the object detection model are trained. Using the trained set of component parameters, a set of backbone link weights comprising weights of links within the backbone portion are trained. Using the trained set of component parameters, a set of head component weights comprising weights of component types in a head portion of the object detection model are trained. Using the trained sets of component parameters, backbone component weights, backbone link weights, and head component weights, a trained object detection model is configured and trained to perform object detection.