Tensorflow port model from 1.x to 2.x

Question

this is a basic question.

Currently I am using one available 1.x model for object detection. For this I am re-training this model with my own data and can detect the objects I want.

I would like to port all my logic to 2.x version in order to use the new released tools for converting models to TFLite.

Do I need to retrain the weights of the reference model (coco for example) once I modify the code to 2.0? Or only retrain my customized data?

if yes, is there any recommendation to do this without much effort?

Thanks for the advice

Answer 1

Luckily for all users, tensorflow has a lot of documentation and the developers of tensorflow thought you would ask this question and therefore have answered it already for you. This post should help you perfectly migrating your model from 1.x to 2.x.

To sum it up quickly, if you are using high level APIs like keras it is basically no work at all. If you want to make use of of the performance improvements made in tensorflow 2 or if you are not using said high level APIs it might be a bit more work.

Weights of your network should generally not have to be retrained, except if you want to change your model obviously. If you want to just use the same model but then in tensorflow 2, the link above should help you transfer your code to tensorflow 2 and you should not have to retrain the weights of your model.