Method and device for training multi-label classification model, and storage medium
By segmenting and filtering single-label image datasets, images that do not meet the preset standards are selected for multi-label annotation to form a target sub-training set. This solves the problems of high manpower cost and low efficiency in multi-label classification model training and achieves efficient multi-label classification model training.
CN115713640BActive Publication Date: 2026-07-03BEIJING XIAOMI MOBILE SOFTWARE CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING XIAOMI MOBILE SOFTWARE CO LTD
- Filing Date
- 2021-08-18
- Publication Date
- 2026-07-03
AI Technical Summary
Technical Problem
Traditional multi-label classification models rely on multi-label image datasets for training, resulting in high manual annotation costs and low training efficiency.
Method used
The single-label image dataset is divided into multiple parts. A multi-label classification model is used to infer the category of each sub-training set. Images that do not meet the preset standards are selected for multi-label annotation to form the target sub-training set, and the model is trained on the target sub-training set.
Benefits of technology
It reduces the number of images that need to be labeled, saves labeling costs, improves training efficiency, and achieves training results close to those of labeling all batches of data.
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Smart Images
Figure CN115713640B_ABST
Abstract
The present disclosure relates to a multi-label classification model training method and device, and a storage medium. The method comprises: dividing a single-label picture dataset into multiple parts to obtain multiple original sub-training sets; sequentially performing the following processing on each original sub-training set: performing class inference on the current original sub-training set according to the current multi-label classification model to obtain the inference result of each picture in the current original sub-training set, and selecting at least one picture to be labeled from the current original sub-training set according to the label category and the inference result of each picture, wherein the at least one picture to be labeled meets a to-be-labeled condition; obtaining at least one labeled picture according to the picture to be labeled, and forming a target sub-training set by using the at least one labeled picture; and training the current multi-label classification model according to the target sub-training set. The technical solution selects a small amount of pictures for labeling, maximizes the training effect of labeling all batch data, thereby saving labeling costs and improving model training efficiency.
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