Object Detection Model Search Using NAS for Task Adaptation
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Solution Overview
Problem
Existing object detection algorithms use fixed network structures that fail to adapt to different tasks and data characteristics, limiting their accuracy and flexibility.
Innovation Solution
An automatic modeling method using Neural Architecture Search (NAS) to dynamically select optimal network structures for object detection models, integrating a recurrent neural network (RNN) controller to adjust parameters based on task rewards, enhancing the detection model's adaptability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed network structure is used for object detection, then the model structure is simple and easy to implement, but the model cannot adapt to different tasks and data characteristics, resulting in limited accuracy
Solution Approach 1:
The patent implements dynamic model structure by using Neural Architecture Search (NAS) to automatically search for optimal network architectures tailored to specific tasks. The system dynamically generates and evaluates different model structures using a controller network, allowing the object detection model to adapt its architecture to different tasks and data characteristics rather than using a fixed structure.
Solution Approach 2:
The system employs self-service through automated model architecture search where the NAS framework autonomously explores and selects optimal network structures without manual intervention. The controller network automatically adjusts architectural parameters based on task requirements, enabling the system to self-optimize its model structure for different object detection scenarios.
2Measurement precision
If different network structures are searched for different tasks using NAS, then the object detection accuracy is improved, but the training time and computational resources increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training a controller network that learns to predict optimal architectural parameters before actual model training. The controller is trained on a subset of data or using distilled knowledge from pre-trained models, enabling it to quickly suggest appropriate architectures without requiring extensive trial-and-error searching during the main training phase, thus reducing overall training time.
Solution Approach 2:
The system implements feedback mechanisms where the performance of searched models is evaluated and fed back to the controller network. This feedback loop allows the controller to learn from previous search results and improve its architecture suggestions, reducing the number of iterations needed to find optimal models and thereby decreasing total training time while maintaining high accuracy.
Data Source
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AI summary
An automatic modeling method for object detection model, the method includes: S1, searching for an NAS network model according to a predetermined first neural network; S2, training a first object detection model by using a training set, evaluating the current first object detection model by using a validation set after a number of training times reaches a first preset number of times, and outputting an evaluation result, wherein the first object detection model is a model obtained by detection part fusion of the NAS network model and a second object detection model;S3, calculating a reward corresponding to the current first object detection model; S4, adjusting parameters used for searching for the NAS network model according to the reward: repeating the steps S1-S4 for a second preset number of times, and determining an optimal first obj ect detection model according to the evaluation result. Further disclosed in the present invention is an automatic modeling device. The method and device provided by the present invention can search for different models according to different tasks, and improve an object detection effect.