Image recognition model generation method and device, storage medium and electronic equipment

Abstract

The invention relates to an image recognition model generation method and device, a storage medium and electronic equipment, and the method comprises the steps: taking to-be-labeled image samples corresponding to human body parts as the input of a pre-trained sub-model, so as to obtain the recognition probability of whether each to-be-labeled image sample contains the human body parts or not; screening out a target sample from the to-be-labeled image samples according to the recognition probability and the image similarity between each to-be-labeled image sample and a plurality of labeled image samples corresponding to the human body parts; obtaining a labeled image sample corresponding to each target sample; and retraining the sub-model through the plurality of labeled image samples and atest set to generate an image recognition model. According to the method, the recognition probability of the image samples can be determined through the pre-trained recognition model, and the model is retrained through the pattern sample screened according to the recognition probability and the similarity characteristics of the image samples, so that the image annotation cost is reduced while themodel training effect is ensured.

Description

technical field [0001] The present disclosure relates to the field of information management, and in particular, to a method, device, storage medium and electronic equipment for generating an image recognition model. Background technique [0002] With the continuous development of artificial intelligence, more and more industries have begun to replace manual work with artificial intelligence models. The core of the artificial intelligence model is the training and application of the machine learning model. For a data-driven machine learning model, its training data usually includes business data (corresponding to the input data of the machine learning model) and classification or prediction results related to the business data (corresponding to the output of the machine learning model), while the training data The labeling of classification or prediction results is a key factor that enables machine learning models to have corresponding functions. Applied in the medical ind...

AI Technical Summary

Problems solved by technology

However, due to the constraints of labor time and personal privacy, the cost of manually labeling a large number of image samples is very high, resulting in high training costs for image recognition models and poor training efficiency.

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