Iteratively training a machine learning model

A method comprising:

• receiving a plurality of training images, the training images comprising a set of labeled training images and a set of unlabeled training images;
• training an object detection model using the set of labeled training images;
• applying one or more images of the set of unlabeled training images to the object detection model to generate a first output of the object detection model;
• transmitting the first output of the object detection model;
• receiving user feedback indicating accuracy of the first output of the object detection model;
• updating the set of labeled training images by labeling one or more images of the set of unlabeled training images according to the user feedback;
• and
• re-training the object detection model using the updated set of labeled training images.

A system comprising:

• a set of processors;
• and
• a non-transitory computer-readable storage medium comprising stored instructions, the instructions when executed by the set of processors cause the set of processors to perform operations comprising:
• receiving a plurality of training images, the training images comprising a set of labeled training images and a set of unlabeled training images, wherein labels of the labeled training images are received from a client device of a user;
• training an object detection model using the set of labeled training images;
• applying one or more images of the set of unlabeled training images to the object detection model to generate a first output of the object detection model;
• transmitting, to the client device, the first output of the object detection model;
• receiving, from the client device, user feedback indicating accuracy of the first output of the object detection model;
• updating the set of labeled training images by labeling one or more images of the set of unlabeled training images according to the user feedback;
• and
• re-training the object detection model using the updated set of labeled training images.