Image recognition method and apparatus, recognition model training method and apparatus, and storage medium

An image recognition apparatus, comprising a memory and a processor, the memory storing a computer program, when executing the computer program, the processor being configured top:

• obtain a target 3D image;
• input the target 3D image to a first recognition model, the first recognition model being configured to perform image recognition on the target 3D image to obtain a type of the target 3D image;
• and
• obtain the type of the target 3D image outputted by the first recognition model,
• wherein the first recognition model is obtained by:
• determining N target cubes from a 3D sample image to be rotated, N being a natural number greater than 1;
• determining a plurality of candidate permutation sequences of the N target cubes;
• obtaining, based on the 3D sample image, a target training sample, the target training sample comprising cubes obtained by rotating and sorting the N target cubes based on a target permutation sequence randomly selected from the candidate permutation sequences;
• using the target training sample without tag annotation to train an original recognition model and obtain a convolutional block of the second recognition model;
• and
• after the second recognition model is trained, using the 3D sample image with the tag annotation and using the convolutional block of the trained second recognition model as a convolutional block of the first recognition model, to train the first recognition model, the tag annotation including a type of the 3D sample.

A non-transitory computer storage medium, storing a computer program, the computer program, when executed, causing a processor to perform:

• obtaining a target three-dimensional (3D) image;
• inputting the target 3D image to a first recognition model, the first recognition model being configured to perform image recognition on the target 3D image to obtain a type of the target 3D image;
• and
• obtaining the type of the target 3D image outputted by the first recognition model,
• wherein the first recognition model is obtained by:
• determining N target cubes from a 3D sample image to be rotated, N being a natural number greater than 1;
• determining a plurality of candidate permutation sequences of the N target cubes;
• obtaining, based on the 3D sample image, a target training sample, the target training sample comprising cubes obtained by rotating and sorting the N target cubes based on a target permutation sequence randomly selected from the candidate permutation sequences;
• using the target training sample without tag annotation to train an original recognition model and obtain a convolutional block of the second recognition model;
• and
• after the second recognition model is trained, using the 3D sample image with the tag annotation and using the convolutional block of the trained second recognition model as a convolutional block of the first recognition model, to train the first recognition model, the tag annotation including a type of the 3D sample.