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.

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?


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I got same problem, anyone solved ??