Closed andreluizbvs closed 3 years ago
Hi,
I would assume that the code itself runs fine if you provide dummy values for the min_anomaly_size
of your dataset as well as generate random masks by sampling from np.random.randint
(0,1, size=(image.shape[0], image.shape[1]))
. This would of course give you nonsensical values for everything the gt-masks are used for (it's not used for image-level AD calculation of the Gaussian AD method). Sampling from np.random.randint
is necessary so ROC-computation does not error out when only identical values are present in the gt.
Refer here on where zero-value masks are generated for good
images, which also do not have ground-truth segmentation masks available.
Best,
Hi, if masks are not required in the gaussian model, why do we need it in the test_step?
Is it possible to test on my own dataset considering there are no ground truth masks? I created my own
my_ad_dataset.py
, but without success.In other words, I would like to apply this method only in image-level anomaly detection. Is it possible to do it without major code changes?