An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Currently, min_max values for normalization are updated every validation epoch. This update keeps irrelevant values from previous epochs by comparing new and old ones:
self.max = torch.max(self.max, torch.max(predictions))
It seems like it does not affect most of the models, but with at least EfficientAD, the final anomaly maps after training are normalized the wrong way (see #2139 or this discussion).
This fix resets min_max values at the beginning of every validation epoch to ensure that only values from the recent one will be used for normalization.
🛠️ Fixes #2027, thank you @CarlosNacher for pointing this problem out! Partially fixes #2139.
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abc-125
commented
1 week ago
📝 Description
Currently, min_max values for normalization are updated every validation epoch. This update keeps irrelevant values from previous epochs by comparing new and old ones:
self.max = torch.max(self.max, torch.max(predictions))It seems like it does not affect most of the models, but with at least EfficientAD, the final anomaly maps after training are normalized the wrong way (see #2139 or this discussion).
This fix resets min_max values at the beginning of every validation epoch to ensure that only values from the recent one will be used for normalization.
🛠️ Fixes #2027, thank you @CarlosNacher for pointing this problem out! Partially fixes #2139.
✨ Changes
Select what type of change your PR is:
✅ Checklist
Before you submit your pull request, please make sure you have completed the following steps:
For more information about code review checklists, see the Code Review Checklist.