Fine-grained classification method and device of target object and electronic equipment

Abstract

The embodiment of the invention provides a fine-grained classification method and device for a target object and electronic equipment, and the method comprises the steps: extracting a feature vector which represents the feature of the target object by utilizing a convolutional neural network model based on an image of the target object; On the basis of the feature vectors, retrieving a standard feature vector set corresponding to a standard image library, and obtaining a fine-grained classification result of the target object, Wherein the convolutional neural network model is obtained by training based on a cross entropy loss function and a triple loss function in advance. According to the embodiment of the invention, the convolutional neural network is trained based on the cross entropy loss function and the triple loss function, and the trained convolutional neural network is adopted to realize the extraction process of the image features, so that the generalization ability of the classification algorithm can still be ensured under the condition of small data volume, and the classification accuracy is improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image recognition, and more specifically, relate to a fine-grained classification method, device, and electronic equipment for target objects. Background technique [0002] Image recognition technology refers to the technology of using computers to process, analyze and understand images to identify targets and objects in various patterns. Fine-grained image classification (Fine-Grained Categorization), also known as sub-category image classification (Sub-Category Recognition), is a popular research topic in the fields of computer vision and pattern recognition in recent years. A more detailed division of subcategories. Due to the subtle inter-class differences and large intra-class differences among subcategories, traditional classification algorithms have to rely on a large amount of human-labeled information. In recent years, with the development of deep learning, deep convolutio...

AI Technical Summary

Problems solved by technology

However, this method is prone to overfitting when the amount of data is small, and it can only have good results in the current data. The effect in actual application scenarios is often unsatisfactory, and the generalization ability is poor.

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