Seeding/maintaining head machine learning models

A method, comprising:

• training a root machine learning model (MLM) on item images for items to produce item classification feature vectors as seed vectors;
• training a plurality of head MLMs on candidate item identifiers, seed vectors, and localized metadata specific to each of the head MLMs to produce predicted item identifiers;
• receiving transaction information for a transaction being performed at a terminal, a current candidate item identifier for an item of the transaction, and a current item image for the item;
• selecting a certain head MLM based on at least the transaction information;
• obtaining a current seed vector for the current item image from the root MLM;
• obtaining a current predicted item identifier for the item from the certain head MLM based on the current seed vector, the current candidate item identifier, and certain localized metadata;
• receiving an actual item identifier for the item from the terminal;
• and
• updating the certain localized metadata of the certain head MLM with the actual item identifier when the current predicted item identifier does not match the actual item identifier.

A system, comprising:

• at least one server comprising at least one processor and a non-transitory computer-readable storage medium;
• the non-transitory computer-readable storage medium comprising executable instructions;
• and
• the executable instructions when executed by at least one processor cause the at least one processor to perform operations, comprising:
• receiving, from a transaction terminal, transaction information for a transaction, an item image for an item of the transaction, and a candidate item identifier for the item;
• selecting a head machine learning model (MLM) from a plurality of head MLM based at least on the transaction information;
• providing the item image to a root MLM;
• receiving item classification data for a coarse grained item classification determined by the root MLM from the item image;
• providing the candidate item identifier and the item classification data to the head MLM;
• receiving a predicted item identifier from the head MLM based on localized metadata processed by the head MLM using the candidate item identifier and the item classification data;
• providing the predicted item identifier to the transaction terminal;
• receiving an actual item identifier for the item from the transaction terminal;
• and
• updating the localized metadata processed by the head MLM with the item classification data and the actual item identifier.