My goal is to optimize a pre-trained model from TFHub for inference. Therefore I would like to use an object detection model with multiple outputs:
https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1
where the archive contains a SavedModel file
https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1?tf-hub-format=compressed
I came across the methods optimize_for_inference and freeze_graph, but read on the following thread that is is no longer supported in TF2:
https://stackoverflow.com/a/56384808/11687201
So how is optimization for inference done with TF2?
The plan is to use this one of the pre-trained networks for transfer learning and use this network later on with a hardware accelerator, the converter for this hardware requires a frozen graph as input.
