Method, device, and computer program product for training image classification model

An electronic device, comprising:

• at least one processor;
• and
• a memory coupled to the at least one processor and having instructions stored therein, wherein the instructions, when executed by the at least one processor, cause the device to perform actions comprising:
• determining a first classification result obtained by an image classification model on pixel points of a sample image in a first training iteration, wherein the first classification result indicates that each of the pixel points belongs to one of a plurality of object classes;
• determining a second classification result obtained by the image classification model on the pixel points of the sample image in a second training iteration;
• determining, based on the first classification result and the second classification result, a first set of recall rates and a second set of recall rates for the plurality of object classes;
• and
• adjusting, based on the first set of recall rates and the second set of recall rates, the image classification model to obtain a trained image classification model.

A computer program product tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein the machine-executable instructions, when executed by a machine, cause the machine to perform a method for training an image classification model, the method comprising:

• determining a first classification result obtained by the image classification model on pixel points of a sample image in a first training iteration, wherein the first classification result indicates that each of the pixel points belongs to one of a plurality of object classes;
• determining a second classification result obtained by the image classification model on the pixel points of the sample image in a second training iteration;
• determining, based on the first classification result and the second classification result, a first set of recall rates and a second set of recall rates for the plurality of object classes;
• and
• adjusting, based on the first set of recall rates and the second set of recall rates, the image classification model to obtain a trained image classification model.