Automated cropping of images using a machine learning predictor

A system comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the system to carry out operations of a machine learning (ML) predictor program, wherein the operations include:
• receiving as input to the ML predictor program a first plurality of training raw images, each respective training raw image of the first plurality being associated with a respective set of training master images, each training master image of a given respective set of training master images indicating respective cropping characteristics defined for the associated respective training raw image, wherein the ML predictor program is configured to generate predicted cropping characteristics for any given input image, and wherein cropping characteristics for any particular input image comprise coordinates of cropping boundaries with respect to the particular input image prior to cropping;
• training the ML predictor program to predict cropping characteristics for each respective training raw image based on expected cropping characteristics represented in the associated respective set of training master images;
• subsequent to training the ML predictor program with the first plurality of training raw images, applying the trained ML predictor program to a second plurality of runtime raw images input to the trained the ML predictor program in order to generate for each respective runtime raw image of the second plurality a respective set of runtime cropping characteristics, wherein the respective set of runtime cropping characteristics for each respective runtime raw image comprises one or more subsets of cropping coordinates for the respective runtime raw image, and wherein each subset corresponds to a different cropped version of the respective runtime raw image;
• and
• storing, in non-transitory computer-readable memory, the second plurality of runtime raw images together with the respective set of runtime cropping characteristics for each respective runtime raw image of the second plurality.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more processors of a system, cause the system to carry out operations of a machine learning (ML) predictor program, wherein the operations include:

• receiving as input to the ML predictor program a first plurality of training raw images, each respective training raw image of the first plurality being associated with a respective set of training master images, each training master image of a given respective set of training master images indicating respective cropping characteristics defined for the associated respective training raw image, wherein the ML predictor program is configured to generate predicted cropping characteristics for any given input image, and wherein cropping characteristics for any particular input image comprise coordinates of cropping boundaries with respect to the particular input image prior to cropping;
• training the ML predictor program to predict cropping characteristics for each respective training raw image based on expected cropping characteristics represented in the associated respective set of training master images;
• subsequent to training the ML predictor program with the first plurality of training raw images, applying the trained ML predictor program to a second plurality of runtime raw images input to the trained the ML predictor program in order to generate for each respective runtime raw image of the second plurality a respective set of runtime cropping characteristics, wherein the respective set of runtime cropping characteristics for each respective runtime raw image comprises one or more subsets of cropping coordinates for the respective runtime raw image, and wherein each subset corresponds to a different cropped version of the respective runtime raw image;
• and
• storing, in non-transitory computer-readable memory, the second plurality of runtime raw images together with the respective set of runtime cropping characteristics for each respective runtime raw image of the second plurality.