Different classification result between DIGITS and jetson-inference

Hi, recently I trained a classifier using DIGITS (LeNet) to classify license plate characters (31 characters and numbers). I got good accuracy of 98% and 0.1 loss.

When I test the model I got different prediction between DIGITS and jetson-inference on my Jetson Nano.
0-X

I resized the images to 28x28 and to gray scale with openCV before testing.
With this image I got 67,6% for class: “2” and 31,91% for class “X” running on DIGITS, but jetson-inference example give me 81.108% of class: “X”.

I tried switching between FP16 and FP32, traning with no mean substraction but nothing changed.

What could be the problems here?
In the preprocessing phase before prediction, what does DIGITS do?

Thanks!

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I got same problem, anyone solved ??