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Both padim and patchcore uses a pretrained backbone (e.g. resnet). You could use whatever backbone you want. For example you could train a resnet on your own data for some other task, like detection. But I'm not sure this would generate better results than using the pretrained nets in PyTorch (but it's possible).
The model fitting in this package however aims at generating a representation of the features to be classified from such a pretrained net. In the case of padim it creates a multivariate normal distribution of the features. And in patchcore it generates a core set of features.
Hey ppl!
Is it possible to train the model from scratch ?
How?
Thanks!
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