End-to-End Image Classification Training With Reference Outputs
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Solution Overview
Problem
Current image classification methods suffer from high calculation complexity due to independent processes for feature extraction and classification, leading to increased resource consumption and reduced efficiency.
Innovation Solution
An image classification model is trained end-to-end by constructing a reference classification result based on the model's output, adjusting parameters to minimize the total error value between the classification results and the reference, thereby reducing complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image features are extracted by a neural network and then classified by an independent classification module, then the classification can be performed, but the calculation complexity is high and resource consumption increases
Solution Approach 1:
The patent merges the feature extraction process and classification process into a single unified model. The model directly outputs classification results from input images without requiring separate feature extraction and classification modules, thereby reducing calculation complexity while maintaining classification accuracy.
2Reliability
If image features are extracted by a neural network and then classified by an independent classification module, then the classification can be performed, but resource consumption increases
Solution Approach 1:
The patent combines feature extraction and classification into one integrated model, eliminating the need for separate processing stages. This integration reduces computational overhead and resource consumption while preserving the ability to achieve accurate classification results.
3Reliability
If image features are extracted by a neural network and then classified by an independent classification module, then the classification can be performed, but the classification efficiency is reduced
Solution Approach 1:
The patent integrates feature extraction and classification operations into a single model that processes images end-to-end. This merging eliminates the sequential processing bottleneck of separate modules, improving classification efficiency while maintaining accuracy.
4Device complexity
If a unified model directly outputs classification results, then the complexity of the image classification process is lowered, but the model structure needs to be simplified
Solution Approach 1:
The patent extracts and removes unnecessary complex components from the model structure. By eliminating the need for separate feature extraction modules and complex multi-stage processing, the model achieves a simplified architecture that is easier to design and implement while maintaining low process complexity.
Data Source
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AI summary
This application discloses an image classification model training method and apparatus, a computer device, and a storage medium, which belong to the field of image processing technologies. In this application, classification results of each image outputted by an image classification model are obtained. When the classification results outputted by the image classification model do not meet a reference condition, a reference classification result is constructed based on the classification results outputted by the image classification model. Because the reference classification result can indicate a probability that images belong to each class, a parameter of the image classification model is updated to obtain a trained image classification model based on a total error value between the classification results of the each image and the reference classification result.