Hi Nelson, i hope this message finds you well. I'm curious on what is your take on this.
I am very satisfied with results i obtain with pad_maps = True and padding=False.
However, as you know, pad_maps creates a "frame" that pads the resulting anomaly map and it seems that anomalies contained in the padded region are not seen by the model.
Playing with these two pad_maps and padding parameters i realized that, regardless of pad_maps value, it's padding that determines if the padded region is ignored or not. So, if i set padding=True the model also sees anomalies close to the borders of the image. Also, if I set padding=True and pad_maps=False I obtain precise segmentations and i also see defects close to borders. To me it seems that pad_maps is needed only to correct the segmentations "translation" that is caused by padding=False.
So why i'm writing this message and i'm not simply using padding=True and pad_maps=False?
The problem is that, regardless of the pad_maps (so we can forget about it, because it's getting confusing 😁 ), padding=True significantly worsen the results. I mean, it seems that the model learns less things.
So the first thing that came to my mind is that maybe the Teacher has been pretrained with padding=False, so it maybe has a different architecture with respect to the Student with padding=True that i'm trying to use.
But based on the code in this repo, it seems you pretrained the Teacher using padding=True and then you use a Student with padding=False when you do the efficientad training. Is this true?
This image shows what i obtain with padding=True and pad_maps=False . It segments perfectly the fake defects i am using to do these tests. Unfortunately the overall performance on the real defects in the rest of the test test is significantly worse wrt the model trained with padding=False (you can see it also in this image: the model struggles to segment the real defect on the bottom).
What do you think? What could be the reason why padding=True jeopardizes my trainings?
Hi Nelson, i hope this message finds you well. I'm curious on what is your take on this.
I am very satisfied with results i obtain with pad_maps = True and padding=False.
However, as you know, pad_maps creates a "frame" that pads the resulting anomaly map and it seems that anomalies contained in the padded region are not seen by the model.
Playing with these two pad_maps and padding parameters i realized that, regardless of pad_maps value, it's padding that determines if the padded region is ignored or not. So, if i set padding=True the model also sees anomalies close to the borders of the image. Also, if I set padding=True and pad_maps=False I obtain precise segmentations and i also see defects close to borders. To me it seems that pad_maps is needed only to correct the segmentations "translation" that is caused by padding=False.
So why i'm writing this message and i'm not simply using padding=True and pad_maps=False?
The problem is that, regardless of the pad_maps (so we can forget about it, because it's getting confusing 😁 ), padding=True significantly worsen the results. I mean, it seems that the model learns less things.
So the first thing that came to my mind is that maybe the Teacher has been pretrained with padding=False, so it maybe has a different architecture with respect to the Student with padding=True that i'm trying to use.
But based on the code in this repo, it seems you pretrained the Teacher using padding=True and then you use a Student with padding=False when you do the efficientad training. Is this true?
This image shows what i obtain with padding=True and pad_maps=False . It segments perfectly the fake defects i am using to do these tests. Unfortunately the overall performance on the real defects in the rest of the test test is significantly worse wrt the model trained with padding=False (you can see it also in this image: the model struggles to segment the real defect on the bottom).
What do you think? What could be the reason why padding=True jeopardizes my trainings?