Clarify transformations for image models at inference time

[googlecreativelab]

Clarify transformations for image models at inference time

[irealva]

Thanks for sharing Kevin! We'll look into this as soon a bit of work clears up. Some great thoughts and suggestions in here.

[irealva]

Hi Kevin, sorry for the delay on this. Would you want to take a stab at adding a preprocess function like this perhaps to utils and making a PR?

[roca77]

This is definitely needed; I can train models but in production I am just shooting in the dark...This causes a lot of inaccurate prediction at inference because the input images would be reprocessed in a different fashion,...

Please advise

[kevinrobinson]

@roca77 I don't work on the project, and just ran into this when playing around with it a while ago. I don't have any other suggestions other than what I've written above, and I'm not sure that code is even the same as it was over a year ago :)

If you want help with something specific, you could try sharing a code example (eg, through CodePen or Glitch or a GitHub project) that illustrates and describes the problem you're seeing. I'm not sure you're running into exactly the same issue described here.

[roca77]

@kevinrobinson Well, yes I am experiencing this issue. I build an image classifier but at inference I am not what sort of processing to apply to the input image. I know the images using in training are cropped to a square, but nothing about the dimension, scaling. I wish those information could be made public so I could implement them in my javascript code.

[michaelrcrowl]

@irealva , based on this thread, do I understand correctly that besides the cropping to square, Teachable Machine does not augment the data in any other way? No random rotations, flips, etc?