Image labeling method, classification model training method and computer equipment

An image labeling and classification model technology, applied in the field of image processing, can solve problems such as low labeling efficiency, poor quality of new data labeling, and low efficiency of CNN model retraining, so as to improve training efficiency, improve efficiency, and improve labeling efficiency. Effect

Pending Publication Date: 2022-01-25
TCL CORPORATION
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Problems solved by technology

During retraining, all these new data need to be labeled. The number of new data is large, and the quality of labeling of new data is good or bad. It is necessary to adjust the

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  • Image labeling method, classification model training method and computer equipment
  • Image labeling method, classification model training method and computer equipment
  • Image labeling method, classification model training method and computer equipment

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[0032] In order to make those skilled in the art better understand the present invention, the following embodiment of the present invention in conjunction with the accompanying drawings of embodiments, the technical solutions in the embodiments of the present invention will be clearly and completely described, obviously, the described embodiments are merely some embodiments of the present invention rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art are in the range of the present invention without making creative labor premise.

[0033] The inventors has found that the image defect detection in industrial tasks, generally using a convolutional neural network model, the convolutional neural network trained to identify defects obtained can be achieved. With the passage of time, there will be new data are generated, due to the convolution neural network before training get no training ba...

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Abstract

The invention relates to an image labeling method, a classification model training method and computer equipment. The image labeling method comprises the steps of obtaining a to-be-labelled image and an labeled image, wherein the labelled image comprises a target label; determining to-be-labelled image feature parameters corresponding to the to-be-labelled image and labelled image feature parameters corresponding to the labelled image, where the labelled image feature parameter comprises a target label; and determining a label corresponding to the to-be-labeled image according to the feature parameters of the to-be-labeled image and the feature parameters of the labeled image. According to the invention, the to-be-labeled image is labeled through the labeled image, and the labeling quality of the to-be-labeled image is unified with the labeling quality of the labeled image, so that the labeling quality of the to-be-labeled image is relatively high, repeated labeling adjustment is not needed, and the labeling efficiency is greatly improved.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to an image labeling method, a classification model training method, and computer equipment. Background technique [0002] With the rapid development and gradual maturity of artificial intelligence technology, the application of artificial intelligence technology such as deep learning in the field of image processing technology is becoming more and more extensive. For example, in the application of deep learning technology in image classification tasks, through convolutional neural network ( CNN) model realizes the image classification task. Although the CNN model solves the image classification task by extracting image features, the training of the CNN model depends on a large amount of training data, the quantity of the training data and the labeling quality of the training data. effect has a greater impact. [0003] In the existing technology, in the process o...

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Application Information

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IPC IPC(8): G06V10/762G06V10/94G06V10/764G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/40G06F18/23G06F18/241
Inventor 俞大海李嘉豪
Owner TCL CORPORATION
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