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?