Perform multi-class segmentation with pixel-labels, which only contain single class

vision
1

For example, we have class number 3 in total.
But the training set only contains classes like, [1,0,0],[0,1,0], and [0,0,1]
What can i do to improve the performance?

2

The number of targets matches the number of classes, so I’m unsure what the issue is.
In case you are struggling to get any improvements in your model training I would recommend to try to overfit a small dataset (e.g. just 10 samples) by playing around with some hyperparameters first.

3

But there is no traning samples containing multi-class.
When inference, there may be [1,1,1] containing 3 class, it not work well.
I try to use cutmix to increase the diversity of training samples, but it improved a little.