Method for detecting image classification output result

Abstract

The invention relates to a method for detecting an image classification output result. The method comprises the following steps of: constructing a first classification network with a multi-layer network by a method of analyzing network layers layer by layer; wherein the substrate comprises a convolution layer and a full connection layer; enabling the convolution layer and the full connection layerto perform linear and nonlinear processing on the original image to obtain a feature map, and converting the feature map to form introspection network training data; if the prediction result of the first classification network is wrong, indicating the training label is 0; if the prediction result of the first classification network is correct, indicating the training label is 1; inputting the training sample into an introspection network; and S3, inputting the output result of the first classification network into a trained introspection network after processing and conversion in the step S2,and judging whether the output result of the first classification network is correct or not through the output of the introspection network. The output feature map of the first classification networkis recognized through the introspection network, and then whether the output result of the first classification network is correct or not can be judged.

Description

technical field [0001] The invention relates to the technical field of computers, in particular to a method for detecting output results of image classification. Background technique [0002] By learning translation-invariant features and parameter sharing, using convolutional neural networks (CNN, ConvolutionalNeural Networks) to perform image classification tasks greatly improves classification performance and reduces the number of learning parameters, making the network more generalized and less prone to overfitting . However, the performance of these networks is still highly dependent on the datasets they are trained on. A dataset that deviates from the norm can lead to incorrect predictions, mainly because the network has not seen similar images when it was trained. When these networks use a softmax to compress the output between [0,1] and force the scores to become probabilities that sum to 1, even though all categories have lower scores, due to the normalization exp...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies of the prior art and provide a detection method for the output result of image classification to solve the problem in the prior art that it is impossible to judge whether the output result of the classification image is accurate

Images