[FIXED] How to use a trained deeplearning model at different resolutions?

Issue

I have trained a model for image segmentation task on 320x240x3 resolution images using tensorflow 2.x. I am wondering if there is a way to use the same model or tweak the model to make it work on different resolutions?

I have to use a model trained on a 320x240 resolution for Full HD (1920x1080) and SD(1280x720) images but as the GPU Memory is not sufficient to train the model at the specified resolutions with my architecture, I have trained it on 320x240 images.

I am looking for a scalable solution that works at all the resolutions. Any Suggestions?

Solution

The answer to your question is no: you cannot use a model trained at a particular resolution to be used at different resolution; in essence, this is why we train the models at different resolutions, to check the performance and possibly improve it.

The suggestion below omits one crucial aspect: that, depending on the task at hand, increasing the resolution can considerably improve the results in object detection and image segmentation, particularly if you have small objects.

The only solution for your problem, considering the GPU memory constraint, is to try to split the initial image into smaller parts (or maybe tiles) and train per part(say 320x240) and then reconstruct the initial image; otherwise, there is no other solution than to increase the GPU memory in order to train at higher resolutions.

There are indeed architectures that enable you to send an image at a different resolution (downscaling and upscaling (for display purposes)) behind the curtains <-> for example in some YOLO models), there is indeed a resizing operation being performed in the background --- do not be under the misapprehension that you are actually doing the inference at a different size.

PS: I understood your question after reading it a couple of times; I suggest that you modify a little bit the details w.r.t the resolution.

Answered By - Timbus Calin