Methods and apparatus for grading images of collectables using image segmentation and image analysis

A method, comprising:

• applying a machine learning (ML) model to an image of an object to generate a plurality of defect confidence levels, each defect confidence level from the plurality of defect confidence levels (1) associated with a unique portion of the image from a plurality of unique portions of the image, and (2) indicating a likelihood that at least one defect is present within that unique portion of the image, the ML model trained using a training dataset that includes at least one image and at least one synthetic image generated by modifying a brightness of the at least one image;
• and
• causing the image to be displayed.

A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the instructions comprising code to cause the processor to:

• augment an image of an object to generate a synthetic image of the object, the image being shifted to generate the synthetic image;
• and
• train a set of machine learning models based on the image and the synthetic image, each machine learning model from the set of machine learning models configured to generate a grade for an image attribute from a set of image attributes, the set of image attributes including at least one of an edge, a corner, a center, or a surface