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Hi @khanhha , thanks for the repo and the pretrained model. I note in inference_unet.py that you perform once inference on the entire image and then on patches of images and then merge the patches to form the final picture.
N typically the patch-based inference performs better. Is there any reason you took the patch-based approach?
The text was updated successfully, but these errors were encountered:
Hi @khanhha , thanks for the repo and the pretrained model. I note in inference_unet.py that you perform once inference on the entire image and then on patches of images and then merge the patches to form the final picture.
N typically the patch-based inference performs better. Is there any reason you took the patch-based approach?
The text was updated successfully, but these errors were encountered: