Processing method for image recognition model and related product

An electronic device, comprising:

• a processor, and a memory communicatively connected to the processor;
• wherein the memory stores computer-executed instructions;
• the processor, when executing the computer-executed instructions stored in the memory, is configured to:
• obtain an image sample;
• determine a target object positioning box of the image sample, wherein the target object positioning box covers an area where an effective feature of the image sample is located;
• adjust a pixel value of at least one pixel within the target object positioning box to obtain a preprocessed image sample;
• and
• train a network model based on the preprocessed image sample to obtain the image recognition model;
• wherein the processor is configured to:
• adjust the pixel value of the at least one pixel within the target object positioning box of each image sample, and form the at least one preprocessed image sample into a preprocessed image sample set;
• wherein an amount of the feature map information is N, and N is a natural number greater than or equal to 1, and the processor is configured to:
• for each piece of the feature map information, perform a normalization processing on the feature map information to obtain a heat map, determine a target point with a heat value greater than a preset threshold from the heat map, and determine an initial target object positioning box based on the target point, wherein the heat map contains at least one pixel, a magnitude of a heat value of the pixel represents a probability that the pixel contains an effective feature, and the heat value of the at least one pixel is within a preset interval;
• and
• determine an average value of the N initial target object positioning boxes to obtain the target object positioning box.

A non-transitory computer-readable storage medium with computer-executed instructions stored therein, wherein a processor, when executing the computer-executed instructions, is configured to:

• obtain an image sample;
• determine a target object positioning box of the image sample, wherein the target object positioning box covers an area where an effective feature of the image sample is located;
• adjust a pixel value of at least one pixel within the target object positioning box to obtain a preprocessed image sample;
• and
• train a network model based on the preprocessed image sample to obtain the image recognition model;
• wherein the processor is configured to:
• adjust the pixel value of the at least one pixel within the target object positioning box of each image sample, and form the at least one preprocessed image sample into a preprocessed image sample set;
• wherein an amount of the feature map information is N, and N is a natural number greater than or equal to 1, and the processor is configured to:
• for each piece of the feature map information, perform a normalization processing on the feature map information to obtain a heat map, determine a target point with a heat value greater than a preset threshold from the heat map, and determine an initial target object positioning box based on the target point, wherein the heat map contains at least one pixel, a magnitude of a heat value of the pixel represents a probability that the pixel contains an effective feature, and the heat value of the at least one pixel is within a preset interval;
• and
• determine an average value of the N initial target object positioning boxes to obtain the target object positioning box.