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  #1  
Old 03-30-2020, 03:23 PM
kevinlenane kevinlenane is offline
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It's a good question - my plan here is to start narrow and then expand - the rarer the set the more scant the data for sure. It's something that we'd have to expand set by set starting with the cross section of popularity and value.

I will post a Dropbox link if anyone does want to post picture. I really appreciate all the above advice - I'll try to see if the images on VCP and the existing grading companies are large enough to warrant a web scrape...
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  #2  
Old 03-30-2020, 04:35 PM
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tonyo tonyo is offline
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I have high res scans of my former 33 goudey near set on google docs. I can give you access to view and download. But, the scans include multiple cards per scan. Do you need it to be one card per scan/photo ?

Last edited by tonyo; 03-31-2020 at 10:36 AM.
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Old 03-30-2020, 06:45 PM
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perezfan perezfan is offline
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Heritage has extremely high resolution scans as well. They've sold thousands of Goudeys in all grades over the years.
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  #4  
Old 03-31-2020, 10:17 AM
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Are you using AI?
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  #5  
Old 03-31-2020, 11:14 AM
kevinlenane kevinlenane is offline
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Ill take a look at those scans - my email is kevinlenane@gmail.com for Google Docs access. Thank you for the offers - high res scans could work but the best thing is just close up pictures taken on a smart phone and uploaded to the below Google Drive link. There is an example in that google drive folder of the front and back of the cad:

https://drive.google.com/drive/u/0/f...GxCM13GRpy13B_

To explain a bit more on the image size preference - the general idea is to get the software working such that a general consumer can authenticate a card on their own smartphone device. So to create the best machine vision pipeline - the training data should be around that same resolution (which is sufficient for these purposes) Obviously id like to get my hands on some bulk imagery shot on a smart-phone but i will gladly dig through scans that could be similar size and use them as well if thats more what people have here. Alternatively if a site like VCP stores cards at a similar resolution to smart phone pictures the I would run a web scrape and grab a ton of images en masse - so thanks again for all the suggestions.

If someone out there happens to be sitting on a huge pile of raw Goudeys I would be willing to compensate someone for their time taking pictures of them. Obviously im trying to be somewhat economical in the POC stage given the but the real limiter right now is training data. I may need to come up with some creative ways to get this imagery if web scraping doesnt work - maybe compensating photos with actual cards or some other motivational exercise
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Old 03-31-2020, 11:19 AM
kevinlenane kevinlenane is offline
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A couple of other answers/clarifications - it does seem like most folks have scans here on Net54 but they seem to be generally of their own collection and are of a huge variety of resolutions. I chose smartphone photos as a base as I would assume they are the most readily available and consistent (in terms of resolution) when it really counts which is during a transaction. This would include a purely web transaction and even more so during any in-person transaction as most would have a smartphone on their person to authenticate live.

On the AI question - right now Im just using machine vision and basic blocking and tackling with no real AI/MI. I can see at some point in the future the system being able to identify other card markers using some form of AI and also potentially learning things from comparison between automated grades and numerical grades that this would give out. It could learn new things and significant relationships between authentication and condition as well. If this ever gets to the investment stage lord knows Ill have to talk about AI a lot - but for now its hard enough to just train up the machine vision with screaming kids in the background...

Last edited by kevinlenane; 03-31-2020 at 11:23 AM.
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  #7  
Old 03-31-2020, 01:40 PM
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Jobu Jobu is offline
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CardTarget, which is free, also has a lot of scans.
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