Image Recognition Model Training via Confidence-Based Sample Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image recognition models require a large number of manually tagged samples for training, leading to low training efficiency and high labor costs due to the need for complete manual tagging.
Innovation Solution
A method that uses an initial small set of tagged positive and negative image samples to train an image recognition model, selecting samples with confidence levels within a preset interval for manual tagging, reducing the number of samples needed and enhancing the model's recognition capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If completely manual tagging is adopted for all image samples, then the image recognition model achieves adequate recognition capability, but the training efficiency is low and labor cost is high
Solution Approach 1:
The system enables the image recognition model to automatically evaluate its own confidence levels on untagged samples and self-select which samples need manual tagging, reducing dependency on complete manual tagging while maintaining recognition capability
Solution Approach 2:
The system uses confidence levels as feedback to determine which samples require manual tagging, creating a closed-loop process where the model's uncertainty guides the tagging effort allocation
2Reliability
If completely manual tagging is adopted for all image samples, then the image recognition model achieves adequate recognition capability, but the labor cost is high
Solution Approach 1:
Instead of tagging all samples, the system tags only the necessary portion of samples that fall within the confidence interval, achieving adequate recognition capability with reduced manual labeling effort
Solution Approach 2:
The system changes the parameter from complete manual tagging to selective tagging based on confidence level thresholds, reducing the quantity of manual work required
3Reliability
If a large number of tagged image samples are used for training, then the image recognition model achieves high accuracy, but the training time is increased
Solution Approach 1:
The system uses only the necessary number of tagged samples (those within the confidence interval) for training, avoiding the time cost of training on all possible samples while maintaining high accuracy
Data Source
Figure 1~2
Figure 3A~3B
Figure 4
AI summary
A method and device for training an image recognition model, and a storage medium are provided. The method includes that: an image recognition model is trained by using a tagged image sample set; multiple to-be-tagged image samples in a to-be-tagged image sample set are recognized by using the presently trained image recognition model to obtain a confidence of a recognition result corresponding to each of the multiple to-be-tagged image samples; at least one to-be-tagged image sample of which the confidence is in a preset interval is selected as at least one to-be-tagged image sample to form a first to-be-tagged image sample set; tags of to-be-tagged image samples in the first to-be-tagged image sample set is acquired; the tagged image sample set is updated according to the acquired tags; and the image recognition model is trained by using the updated tagged image sample set.