Maybe I am missing something obvious, but if a pre-trained network’s input image size is 300x300 or 500x500, how would the performance of the classification/segmentation inference be affected when I feed a oversampled image from 224px.
While I did observe differences in outputs, are there experiments which can be done to understand how these perturbations in image sizes can affect the model inferene.