Trained model is not giving the always the same result for the same input

vision
1

Hello guys,
So I trained a model and I trained it but after wards during inference time I do not get always the same result but this is just for some images. I find this strange and I did not find any logical explanation for this.
Any idea would be appreciated.
Thank you in advance.
Peace

2

Do you have any dropout layers enabled during inference? Make sure the model is in eval() mode.

3

Hello,
thank you for your answer but I do not have any dropout layer.
I cannot seem to find the reason that I am getting that.

4

Hi,

You may have batch normalization layers. If you have, model.eval() followed by a context block of torch.no_grad() is the proper way of doing the evaluation. Otherwise, new batch statistics will be used in the evaluation phase(/ test phase) which will produce different results.

Thanks