Card grading and identification Ai

[maniac_73]

Card not cars lol can’t edit title typo

. Whipped this up quickly in ChatGPT for fun. Try it out and tell me what you think https://chatgpt.com/g/g-6Xukfe94M-grademycard


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[Leon]

Originally Posted by maniac_73

Card not cars lol can’t edit title typo

. Whipped this up quickly in ChatGPT for fun. Try it out and tell me what you think https://chatgpt.com/g/g-6Xukfe94M-grademycard


Sent from my iPhone using Tapatalk

Title fixed.

To edit the title of threads ..go to edit it and look on the lower right for the words "go advanced"...click on those words and you can edit the title.

Also, it looks like we have to sign up to read it?

[rdwyer]

I like it. Uploaded a front and back and it gave me all kinds of info on the card.

[G1911]

I have tried to use GPT 4o for cards before, and it has mostly failed and returned false information for me, when using simply the basic chat without any preprogramming or memory saved knowledge. I am fairly impressed this time with this tool here. I did 3 tests in my boxing realm, before hitting the image limit on my free personal account. I tested with 3 cards pretty much randomly pulled from my boxing archive, making sure each is a decent quality picture and that it is from a known set documented on the internet, so that GPT could reasonably get it right if it was able to reverse image search and connect to similar results online, and then use that to pull sales data and identifications. Obviously no AI tool is going to be able to pull data for undocumented cards.


Test 1 - wrong. I uploaded an E80 caramel card, which it claimed was a T218 (T218 looks very different and doesn't have multiple subjects on a card). GPT does this a lot - if it isn't sure but returns some results, it just picks a claim and runs with it instead of admitting it doesn't know. It did correctly read the names and then pull information about them both, which was cool. The pricing information was, of course, just completely made up and meaningless.

Test 2 - Passed! Correctly identified it. "Shifted slightly to the left" and the grade on this miscut weren't very accurate, but it did identify the card.

Test 3 - This was supposed to be the hard one of the three designed to fail, but possible to succeed. N332 is a known set, but Mitchell here only surfaced with a full recognizable copy of the card with the ad a few months ago, not giving it much to pull from the internet. I am quite impressed it got it right. It also correctly defined it as "rare" in the verbiage, where it just praised me for the other 2 cards that are not rare. It gave the same pricing information for N310 and N332, which looks like GPT just making things up like it often does when it can't quite pull enough data but doesn't find 0 data.


So it identified 2 of the 3, 2 of the 3 had accurate grading and for the failed grade (N310) it still identified many of the condition issues, and the pricing didn't seem to really work. Pretty good, considering that I am giving it recognizable but not famous cards that most collectors wouldn't use. Whatever it can do for N332 is likely to be better for a 1956 Topps. This is cool

[maniac_73]

Thanks Guys, I really am impressed with the quality of results versus some of the older models. As I keep fine tuning it I think it will even get better. Definitely will never be perfect but a fun little project for me as continue to explore AI.

[Goudey]

did you fine-tune 4o is that the base model with a knowledge base?