Unified ai model training platform

A system configured to provide a neural network agnostic method for unified data annotation, the system comprising:

• a processor operatively coupled to a memory configured to store computer readable code that, when executed by the processor, causes the processor to:
• retrieve a dataset from one or more databases based on a configuration file, the dataset comprising image files extracted from one or more video feeds along with corresponding label files comprising information about one or more annotations added to the image files;
• determine that one or more of the image files and one or more of the label files are in a format that is not compatible with a required format of neural network architecture specified in the configuration file,
• reformat the one or more of the image files and the one or more of the label files, such that the dataset is formatted for the neural network architecture;
• select the neural network architecture from a repository based on the configuration file;
• train a machine learning (ML) model based on the formatted dataset and one or more hyperparameters;
• evaluate a performance of the ML model over one or more object detection metrics;
• adjust the one or more hyperparameters and iterating the training until the performance of the ML model meets a determined threshold;
• and
• once the performance of the ML model meets the determined threshold, convert the ML model to a file format that is compatible with a production platform.