Method for processing sample data, electronic device and storage medium

An electronic device, comprising:

• at least one processor;
• and
• a memory connected in communication with the at least one processor;
• wherein the memory stores an instruction executable by the at least one processor, and the instruction, when executed by the at least one processor, enables the at least one processor to execute:
• obtaining a plurality of first working images of a spinning box;
• for each first working image in the plurality of first working images, determining the first working image as a defect image of the spinning box in a case where a difference between the first working image and a normal image of the spinning box satisfies a first condition;
• obtaining a first sample data set based on all defect images in the plurality of first working images;
• wherein the first sample data set is used to train a defect detection model of the spinning box, and the defect detection model is used to detect whether a second working image of the spinning box has a defect;
• determining a second sample data set from the first sample data set based on a feature quantity of each image in the first sample data set in a case where a confidence of the defect detection model does not satisfy a second condition, by:
• clustering all images in the first sample data set to obtain a plurality of clusters;
• for each cluster in the plurality of clusters, determining a target image in the cluster based on a feature quantity of each image in the cluster;
• and
• obtaining the second sample data set based on the target image in each cluster;
• and
• updating the defect detection model based on the second sample data set.

A non-transitory computer-readable storage medium storing a computer instruction thereon, wherein the computer instruction is used to cause a computer to execute:

• obtaining a plurality of first working images of a spinning box;
• for each first working image in the plurality of first working images, determining the first working image as a defect image of the spinning box in a case where a difference between the first working image and a normal image of the spinning box satisfies a first condition;
• obtaining a first sample data set based on all defect images in the plurality of first working images;
• wherein the first sample data set is used to train a defect detection model of the spinning box, and the defect detection model is used to detect whether a second working image of the spinning box has a defect;
• determining a second sample data set from the first sample data set based on a feature quantity of each image in the first sample data set in a case where a confidence of the defect detection model does not satisfy a second condition, by:
• clustering all images in the first sample data set to obtain a plurality of clusters;
• for each cluster in the plurality of clusters, determining a target image in the cluster based on a feature quantity of each image in the cluster;
• and
• obtaining the second sample data set based on the target image in each cluster;
• and
• updating the defect detection model based on the second sample data set.