Card grading and identification Ai

[sports-cards-forever]

This is fascinating. I think it's the future

[Snowman]

I uploaded a few cards. It's not good at judging the centering for some reason.

[maniac_73]

Originally Posted by Snowman

I uploaded a few cards. It's not good at judging the centering for some reason.

You're right. Just added PSA centering standards for it to use as a guideline. Seeing if that fixes it

[Bigdaddy]

I uploaded a 1964 Topps Sandy Koufax and it identified it as a 1966 Topps. Most of the information seemed to be in the ballpark except for the year of issue.

[prestigecollectibles]

I tried doing a Japanese baseball card
1949 JF 35i Asayama Fusen Gum Type 8--Kaoru Betto

I was pleasantly surprised by the results. It shows great potential. Although it couldn’t recognize the player or team names written in Japanese and the card isn’t a menko, I’m excited to see how it develops in the future.

[Leon]

Originally Posted by Bigdaddy

I uploaded a 1964 Topps Sandy Koufax and it identified it as a 1966 Topps. Most of the information seemed to be in the ballpark except for the year of issue.

It's only a matter of time for a lot more refinement in the AI area. It's in it's infancy still. I have mixed feelings on it, myself.
.

[Snowman]

Originally Posted by maniac_73

You're right. Just added PSA centering standards for it to use as a guideline. Seeing if that fixes it

I'm not sure how it works (I haven't looked into it), but if it's a LLM algorithm, then it might just be flagging every vintage card as being off-center because 95% of vintage cards are in fact off-center and it can achieve 95% accuracy by simply guessing that. It does not appear to be actually measuring the borders. Perhaps that can be fixed though.

[steve B]

I would think centering would be easy. But probably also has to be specifically programmed.

[Snowman]

Originally Posted by steve B

I would think centering would be easy. But probably also has to be specifically programmed.

It's pretty easy with convolutional neural networks (CNNs). You use them to detect where the borders end and begin (called edge detection) on each side and then count the pixels between the edges. But it's clear from the results that it's not doing that, so that's why I suggested that it's doing the typical ChatGPT LLM approach and simply bluffing because most cards are in fact OC.

[maniac_73]

Originally Posted by Snowman

It's pretty easy with convolutional neural networks (CNNs). You use them to detect where the borders end and begin (called edge detection) on each side and then count the pixels between the edges. But it's clear from the results that it's not doing that, so that's why I suggested that it's doing the typical ChatGPT LLM approach and simply bluffing because most cards are in fact OC.

Its using the OpenAI LLM. Not using Image Recognition transformer. The parameters are just a percentage of each side. Would be interesting to see how an Image Recogntion LLM would work as you're right it does the recogntion by pixel which would in theory be a much more accurate method when fine tuned properly.
This is a just a Custom GPT I was playing with and am tuning as I go. A quick thing for fun to see what is possible.