Question about Inference for Image segmentation
It's absolutely right to resize the input image to the model input size else, it will generate an error if you feed an image of different size to the model. Coming towards your question, you can solve this either by rescaling the model output to the original size of your input images. A simple technique can be resizing the masks but there can be better ways. OR
You can resize your input images and their Ground Truths (masks) to the model size, and so you won't need to rescale the model's output. I hope that answers the question !!!
during i trained my own model, i have a simple question.
origianl input image shape is (height : 434, width : 636), and i used resized image(416 x 416) for my train model(Unet++).
I wonder if it is right to resize the test image when inference step, How can I resize the model output to the original image size when comparing test output with original test image.
---------process
original input size : (434, 636)
train input size: (416, 416)

inference
test img -> resize (416, 416) -> test model -> test output(416,416) -> comparing test output with test img