Systems and methods for rapid development of object detector models

A system for developing a merged machine learned model for classification and detection of one or more previously specified objects and at least one newly specified object comprising

• one or more processors and associated storage coupled to the one or more processors and having stored therein instructions executable by the processors wherein the instructions when executed comprise
• a first machine learned model configured as a system production model capable of detecting and classifying one or more previously specified objects identified through the use of at least some anchor bounding boxes,
• a second machine learned model configured as an iterated model capable, following training, of detecting and classifying at least one newly specified object,
• a system production training dataset representative of the previously specified objects and a second training dataset representative of a newly specified object identified through the use of at least some bounding boxes,
• the processors being operable when executing the instructions to process, in both the system production model and the iterated model, at least the system production dataset and the second training dataset, to generate a system training output and an iterated training output, respectively, to optimize the system training output and the iterated training output by applying classification and regression algorithms to the system training output and the iterated training output to generate an optimized training output, and to supply the optimized training output as the merged machine learned model configured to classify and detect at least some of the one or more previously specified objects and at least one of the newly specified objects.

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• provide a system production model comprising a first machine learned model capable of detecting and classifying one or more previously specified objects identified through the use of at least some anchor bounding boxes,
• provide an iterated model comprising a second machine learned model capable, following training, of detecting and classifying at least one newly specified object,
• provide a system production training dataset representative of the previously specified objects to the system production model and the iterated model,
• provide a second training dataset representative of a newly specified object identified through the use of at least some bounding boxes to the system production model and the iterated model,
• process, in both the system production model and the iterated model, at least the system production dataset and the second training dataset and generate a system training output and an iterated training output, respectively,
• optimize the system training output and the iterated training output by applying classification and regression algorithms to the system training output and the iterated training output to generate an optimized training output,
• supply the optimized training output as the merged machine learned model configured to classify and detect at least some of the one or more previously specified objects and at least one of the newly specified objects.