Segmentation output issues about cellseg_models.pytorch HOT 5 OPEN

Hello,

Those results are very odd indeed. Did you use some specific normalization during training? When you run inference with the Inferer-classes, you should also pass the same normalization method as a parameter to the class e.g. normalization="min-max". Usually, when I get weird results the reason is that I've forgot to use the normalization parameter.

Regarding your other questions:

1. Yes, you can use sliding window inferer on 1gpu. However, the input images can't be super large like 30000x30000px. The reason is that the output will have the same shape as the input and it can't fit to gpu memory. I'll take a look if this could be avoided in the future.

2. And yes, for now you need to train from scratch. In the future, I'll set up a hugginface space for pre-trained models that could be loaded directly. Once I just have the time.

from cellseg_models.pytorch.

Hello,

Those results are very odd indeed. Did you use some specific normalization during training? When you run inference with the Inferer-classes, you should also pass the same normalization method as a parameter to the class e.g. normalization="min-max". Usually, when I get weird results the reason is that I've forgot the normalization parameter.

Regarding your other questions:

1. Yes, you can use sliding window inferer on 1gpu. However, the input images can't be super large like 30000x30000px. The reason is that the output will have the same shape as the input and it can't fit to gpu memory. I'll take a look if this could be avoided in the future.

2. And yes, for now you need to train from scratch. In the future, I'll set up a hugginface space for pre-trained models that could be loaded directly. Once I just have the time.

from cellseg_models.pytorch.

Also, I noticed that you were using some parameters that don't exist like 'overlap' and 'patch'. Use 'stride' and 'patch_size' instead.

from cellseg_models.pytorch.

Thank you for your quick response! I see, okay, so I will have to train all the models before running inference on my dataset. The overlap and patch parameters are used to manually create patches so that I can work with ResizeInferer, without having to actually resize(like in one of your examples). 'Stride' and 'patch_size' can be used with the Sliding Window Inferer, however what would their role be in the Resize Inferer? I will train these and let you know if things change!

from cellseg_models.pytorch.

You're right, ResizeInferer does not use stride and patch_size parameters. My bad.

from cellseg_models.pytorch.