An image classification network training method based on massive single-class and single-amplitude images

Abstract

The invention discloses an image classification network training method based on massive single-class and single-amplitude images. The training data sets of double data forms of the single-class and single images and the single-class and multi-amplitude images are used to train an image classification network of the massive single-class and single-amplitude images alternately and cyclically, a training data input layer is replaced with two network layers of one input layer of a training data set 1 and two input layers of a training data set 2. When the number of training iterations is odd, thetraining data set is used as the input data of the image classification network base on the massive single-class and single-amplitude images, a dynamic loss function based on iteration times adopts an inter-class distance loss function to train the network. When the number of iterations is even, the training data set 2 is used as the input data of the image classification network base on the massive single-class and single-amplitude images, and the dynamic loss function based on the iteration times combines the center loss and Soft-max loss functions as the loss functions of the training network to train the network, thereby obtaining an image classification model.

Description

technical field [0001] The invention relates to technical fields such as computer vision, pattern recognition, and machine learning, in particular to an image classification network training method based on a single-type single-frame image. Background technique [0002] Machine learning methods are widely used in image analysis to complete specific tasks on new data, such as classification, recognition, and segmentation, by training a model on a given data set. Commonly used algorithms include Support Vector Machine (SVM), Hidden Markov (HMM) and Artificial Neural Network. However, traditional machine learning algorithms rely on prior knowledge to manually extract features from raw data to train models. Due to the difficulty of feature selection, the model may have overfitting problems, and the generalization ability is difficult to guarantee; on the other hand, traditional models are difficult to adapt to large-scale data sets, and the model scalability is poor. [0003] ...

AI Technical Summary

Problems solved by technology

However, DeepID uses the face ID card dataset to conduct experiments, and the effect is not very satisfactory.

This is because the face

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