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Thank you very much for sharing the code, excellent work.
I'm trying to train the framework using synthetic data for domain generalization with unlabeled real data. Is there any documentation regarding this?
When using my own data (synthetic and real), both with a binary segmentation mask (grayscale), I am having problems with the IoU and Acc metrics, which have the value "Nan", the only class with values (above 90% inclusive) is background. Any post-processing recommendations for the labels?
Thank you so much, this is an amazing work!
The text was updated successfully, but these errors were encountered:
LuanSilvaTec
changed the title
Binary instance segmentation masks and domain generalization with real data unlabeled
Binary instance segmentation masks and domain generalization with unlabeled real data
Mar 23, 2024
Thank you very much for sharing the code, excellent work.
I'm trying to train the framework using synthetic data for domain generalization with unlabeled real data. Is there any documentation regarding this?
When using my own data (synthetic and real), both with a binary segmentation mask (grayscale), I am having problems with the IoU and Acc metrics, which have the value "Nan", the only class with values (above 90% inclusive) is background. Any post-processing recommendations for the labels?
Thank you so much, this is an amazing work!
The text was updated successfully, but these errors were encountered: