Automated Card Grading - Surface

[Throttlesteer]

I think the idea is cool and it certainly will help with spotting alterations and doctoring. But at some point, I find myself shaking my head at the level of technology being applied to assess something far beyond the human eye or even a loupe. I totally understand the monetary concerns with today's "market". But I still struggle internally with applying micron measurements and machine learning to pieces of cardboard. I know, it's my issue.

Good luck with the company!

[vintagebaseballcardguy]

Originally Posted by Throttlesteer

I think the idea is cool and it certainly will help with spotting alterations and doctoring. But at some point, I find myself shaking my head at the level of technology being applied to assess something far beyond the human eye or even a loupe. I totally understand the monetary concerns with today's "market". But I still struggle internally with applying micron measurements and machine learning to pieces of cardboard. I know, it's my issue.

Good luck with the company!

You are not alone.

[vintagetoppsguy]

I know this thread is about the card's surface, but I have a question about the card's size. How does it know the difference between a factory and non-factory cut? Some cards are just naturally cut smaller from the factory, so can it distinguish the difference between a card that was cut short at the factory to a card that was trimmed?

[swarmee]

If you train it properly, then yes, it should be able to determine factory cuts vs. non-factory cuts.
Would be interesting to see if it could be trained to spot fake rough cuts like the 1952 Topps Look-n-See cards were given. But I could definitely see it catching the fake teeth given to the SI4K Tiger Woods RCs that both PSA and BGS missed on high-valued cards.

[drcy]

But can it make a good cup of coffee?

[Bigdaddy]

Being an engineer, I really like what you are doing. We know for a fact that dogs can smell things that are below our threshold, and we can train them to alert us to certain substances - drugs, cancers, odors left on clothing, etc. We also know that man made sensors can detect things we cannot see with our unaided eyes or fingers or other receptors, whether that is through greater magnification or sensitivity or bandwidth. We can't see radio waves, but our radios can certainly detect them. Imagine if we only let doctors use their hands and eyes and ears to diagnose our ailments.

The ML approach to grading is the next logical step in finding a way to accurately, consistently and without bias, grade a card based upon a set of known rules. It will take time for the system to learn, and hopefully the vast majority of that takes place in the lab before it is rolled out. Changes to the grading algorithm will happen over time, but that is no different than our current crop of companies using their own grading scale and changing it over time. PSA is known to be tougher in present day than it was on the earlier flips. How many times have I heard about a card in an older flip "That card would never get that grade if it was submitted today"? A pinhole used to automatically downgrade a card to a '1', now it could be a '2'. And we as a community have accepted those random changes.

Personally, I think a big challenge is to come up with an acceptable algorithm that takes into account all the items you can detect and then producing a number (1-10) that represents a measure of the cards 'goodness', or proximity to perfection. In the end, it will have to pass the eye test of hobbyists. I think that's one big downfall of the current companies - the ability to quantify 'eye appeal'.

Bravo young Kevin, for daring to introduce new technology into this hobby. Thank you and I wish you well.

[BlueSky]

How are you dealing with potential ML bias in the training and synthetic data? Can you describe the accuracy (precision - recall) for various sets/cards?