Thanks @ptrblck,

model.eval() will use the running stats, which might be wrong or skewed depending on your dataset.

Do you mean that 100k input images with data augmentation (flip/rotations) for resnet20 or higher resnetXXX is not enougth ?

If you are concerned, if the batch norm layers give valid answers, could you please compare the same outputs with the same modes?
E.g. make sure that a fixed input creates the same output in model.eval() .
Note that you will not get the same outputs for the fixed data in model.train() , as the running stats will be updated.

In fact, notice that the last column of the series of 3x2 plots above show 1) the outputs with ‘model.eval()’ for the 1st script,  and also ‘model.eval()’ for the second script that loads the model parameters.
Aren’t they comparable?
Thanks