Method and apparatus for training image processing model, and image classifying method and apparatus

An image processing device, comprising a processor and a memory storing one or more instructions thereon, wherein the one or more instructions are loaded and executed by the processor to implement:

• acquiring a plurality of original image sets, wherein each of the original image sets comprises a plurality of original images of a same category, and categories of the original images comprised in different original image sets are different;
• acquiring a training sample set by cropping the plurality of original images in the plurality of original image sets, wherein the training sample set comprises a plurality of training samples, and each of the training samples represents one original image, or a subimage acquired by cropping the original image;
• determining a plurality of positive sample pairs and a plurality of negative sample pairs from the training sample set, wherein each of the positive sample pairs comprises two training samples acquired based on different original images in the same original image set, and each of the negative sample pairs comprises two training samples acquired based on the original images in different original image sets;
• and
• training the image processing model based on the plurality of positive sample pairs and the plurality of negative sample pairs.

A non-transitory computer-readable storage medium storing one or more instructions thereon, wherein the one or more instructions are loaded and executed by a processor to implement:

• acquiring a plurality of original image sets, wherein each of the original image sets comprises a plurality of original images of a same category, and categories of the original images comprised in different original image sets are different;
• acquiring a training sample set by cropping the plurality of original images in the plurality of original image sets, wherein the training sample set comprises a plurality of training samples, and each of the training samples represents one original image, or a subimage acquired by cropping the original image;
• determining a plurality of positive sample pairs and a plurality of negative sample pairs from the training sample set, wherein each of the positive sample pairs comprises two training samples acquired based on different original images in the same original image set, and each of the negative sample pairs comprises two training samples acquired based on the original images in different original image sets;
• and
• training the image processing model based on the plurality of positive sample pairs and the plurality of negative sample pairs.