for unsupervised anomaly detection at pixel level, train dataset should contain only normal data and test data should include both normal and anomaly data with their masks.
* Train--> Normal
* Test:
- Normal
- Not normal
* ground_truth of test data
How can I define a custom dataset for that and process it for training and evaluation?
Can I define a dataloader for train images and a custom dataset for test (images+ground_truth)?