allthingskryptonite

I am inclined to call it a 5.0 For reference, look at this raw 5.0 from mycomicshop inventory. Looks similar to me.

Indeed. The buyer should understand this, it's only $12.

X-men #112 CGC 9.8 White @ 15% off

As per the rules one book per page. FF 8: 2.5+ On first look the book appears structurally nicer than my grade. But with the water damage, "15", ink marks, and an almost book length cb that starts from comics code through puppet master's hand and beyond, I'm currently at 2.5

Captain Marvel #26 8.0 ow $80 Detective Comics #396 8.0 ow/w $50 @ 10% off

First full appearance of the PayPal ID after cameo on page 21.

What you just described is very much doable. We need good quality scans -- and a lot of it. Grading companies like CGC or auction houses will have large corpus of images on which someone can train a network. My point with the centerfold missing example was to highlight that we still have to rely on human intervention. A grader who inspects the book for defects and note them. Regarding my comments on mycomicshop -- it publishes major defects only for relatively lower grade raw books. It does not provide general grader notes for all books.

Yes to perform the first part one would need grader notes as text, and then extraction of relevant features. Such information might be hard to come by. One avenue that I can think of is crawling mycomicshop website for raw books and their grader notes. This exercise is just to verify that given a set of simple features, can we make okayish predictions for grades? I believe the answer to this would be yes. On second thought, the problem the OP wants to attempt -- predicting grades from purely image scans -- appears extremely challenging. Imagine a book that appears F/VF but has centerfold missing, it is a 0.5. If we only have image scans and no additional info regarding the missing centerfold then getting the correct prediction (0.5) is almost impossible.

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This sounds like a very interesting project. I can't go through the entire thread right now, but I sure will sometime tomorrow. I understand a bit of Neural Networks and comic book grading, so jotting down my initial thought. I would break the problem in two stages. First I would start with something simple. There are nuances that affect comic book grades. Conditions like mold, foxing, stains, staple rust, staple pop, centerfold detached, water damage, subscription crease, residue -- these affect the grade. I would train a simple feed-forward neural network trained on one hot vector of features, where a feature is a condition being present or absent (mold, foxing, stains, staple rust, staple pop, centerfold detached, water damage, subscription crease, residue), which we would extract from grader notes (text data). Although this is a very primitive design, this simple setup would be able to make some decent predictions. Imagine how different features in your one hot vector help train the simple classifier: Is cover missing? Yes or No. If Yes, then grade is always 0.3 Pages missing? Centerfold missing? Yes or No. If Yes, then most likely a 0.5 Subscription crease? Book length crease? Yes or No. If Yes, then grade most likely in range ~4.0 Once I get this primitive design to work with a reasonable accuracy, then I would attempt the bigger problem. Trying to design a CNN that takes image data as input to predict the comic book grade, with the one hot vector of features being the intermediate latent variables. The accuracy of the network would depend on the amount of data you have. But I think it will be able to predict ranges, like a book being in vicinity of GD, VG, F or VF or so on. Let us know if you make any progress.

@ 30% off

@ 25% off, if available.

@25% off

@ 25% off

Take LL 10, 12, 16A @ 25% off if the deal is still there.

Take @ 20% off