How to use a trained deeplearning model at different resolutions?
YEAH, you can do it in high resolution image. But the small resolution is easy to train and it is easy for the model to find the features of the image. Training in small resolution models saves your time and makes your model faster since it has the less number of parameters. HD images contains large amount of pixels, so if you train your model in higher resolution images, it makes your training and model slower as it contains large number of parameters due to the presence of higher number of pixels and it makes difficult for your model to find features in the high resolution image. So, mostly your are advisable to use lower resolution instead of higher resolution.
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?