Create a tflite model and expand its capabilities


I’m relatively new to tensorflowlite and I’m working on a project in which I’d like someone to point me to the right direction.

I am trying to use a pretrained model to use on tflite (MobileNetv2 for now) as the body and expand its capabilities by adding a latent replay buffer with a fully connected layer as the head which will allow me to perform continual learning. I want to perform object detection but instead of a specific image, I want to be able to have classes within my app and take pictures for those classes live within the app and perform on-device training and see how much I can minimize forgetting when adding a new class or new samples of the classes.

I would like some assistance on the following so I can go towards the right direction:

  1. How can I load my tflite model? Is there a way to do it directly on android studio or I should create it in TensorFlow and then convert it? If yes, what do I use? (I only saw something about TF task library)

  2. When I load the model, how can I add my replay buffer and perform on device training for the scenario mentioned above? (Is there any resource, or tf api/library that can help me achieve this?) I’ve seen on the tflite website a tutorial for object detection but I don’t know how I can alter it so I can be able to add my own classes and image samples live from the application and use those for object detection.

Any help or guidance is greatly appreciated. Thanks!