Visual object recognition

A system for training an object-recognition neural network to identify an object in a computer-readable image, the system comprising:

• a memory;
• and
• a processor communicatively coupled to the memory, wherein the processor is configured to:
• assign a first neural network for determining a visual alignment model of the images, wherein the visual alignment model is used to determine a normalized alignment of an object in input images;
• assign a second neural network for determining a visual representation model of the images, wherein the visual representation model is used to recognize the object in the input images;
• determine the visual alignment model by training the first neural network;
• determine the visual representation model by training the second neural network;
• determine a combined object recognition model by training a combination of the first neural network and the second neural network;
• and
• recognize the object in the computer-readable image based on the combined object recognition model by passing the computer-readable image through each of the combined neural networks.

A computer program product for training an object-recognition neural network to identify an object in a computer-readable image, the computer program product comprising a non-transitory computer readable storage medium, the computer readable storage medium comprising computer executable instructions, wherein the computer readable storage medium comprises instructions to:

• assign a first neural network for determining a visual alignment model of the images, wherein the visual alignment model is used to determine a normalized alignment of an object in input images;
• assign a second neural network for determining a visual representation model of the images, wherein the visual representation model is used to recognize the object in the input images;
• determine the visual alignment model by training the first neural network;
• determine the visual representation model by training the second neural network;
• determine a combined object recognition model by training a combination of the first neural network and the second neural network;
• and
• recognize the object in the computer-readable image based on the combined object recognition model by passing the computer-readable image through each of the combined neural networks.