It's tough to judge without seeing examples of the targets and the user photos, but I'm curious if this could be done with just old-school SIFT. If it really is exactly the same image in the in the corpus and on the wall, does a neural embedding model really buy you a lot? A small number of high confidence tie points seems like it'd be all you need, but it probably depends a lot on just how challenging the user photos are.
How we used GPT-4o for image detection with 350 similar illustrations
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Re: How we used GPT-4o for image detection with 350 similar illustrations
#12First time for me posting this kind of story - I thought it would make an interesting case on solving a hard computer vision problem with a crafty product engineer team.
Just a small feedback… I have switched to the reader mode because the font used is very challenging to read for me.
Also, having a blog post about image detection, and not showing a single picture in the whole post was quite frustrating.
Re: How we used GPT-4o for image detection with 350 similar illustrations
#13I mean, cool tech, but why not just print a QR code next to each illustration?
just in: using gpt4o to read QRs
Re: How we used GPT-4o for image detection with 350 similar illustrations
#14A bit tangential, but I think we will see a good chunk of small teams building competing products in different software business segments, by just doubling on productivity and offering a cheaper option due to less operational overhead (reads: paying engineers). I can think of at least two businesses that can be competed in costs if the team can automate a good chunk of it.
> I can think of at least two businesses that can be competed in costs if the team can automate a good chunk of it.
And which would those be?
Re: How we used GPT-4o for image detection with 350 similar illustrations
#15Earlier quoted context omitted.
Just a small feedback… I have switched to the reader mode because the font used is very challenging to read for me.
Also, having a blog post about image detection, and not showing a single picture in the whole post was quite frustrating.
Especially given the detailed description surely the author could just generate a similar image
Re: How we used GPT-4o for image detection with 350 similar illustrations
#16A bit tangential, but I think we will see a good chunk of small teams building competing products in different software business segments, by just doubling on productivity and offering a cheaper option due to less operational overhead (reads: paying engineers). I can think of at least two businesses that can be competed in costs if the team can automate a good chunk of it.
> I can think of at least two businesses that can be competed in costs if the team can automate a good chunk of it. And which would those be?
We both know I didn't write it down with the hopes that I'll act on the at some point in the near future, and want to avoid my imaginary competitors. Even though, in reality, I will ponder about it for another week or two, give up without actually getting anything done, then regret for never trying :)
Re: How we used GPT-4o for image detection with 350 similar illustrations
#17It's tough to judge without seeing examples of the targets and the user photos, but I'm curious if this could be done with just old-school SIFT. If it really is exactly the same image in the in the corpus and on the wall, does a neural embedding model really buy you a lot? A small number of high confidence tie points seems like it'd be all you need, but it probably depends a lot on just how challenging the user photo…
I find a lot of applied AI use-cases to be "same as this other method, but more expensive".
Re: How we used GPT-4o for image detection with 350 similar illustrations
#18Cool real life use Case.
Don't think lmms usually get applied reasonably where they should be and I am glad that a generic knn model also was used to simplify costs and also just more suitable.
Re: How we used GPT-4o for image detection with 350 similar illustrations
#19It's tough to judge without seeing examples of the targets and the user photos, but I'm curious if this could be done with just old-school SIFT. If it really is exactly the same image in the in the corpus and on the wall, does a neural embedding model really buy you a lot? A small number of high confidence tie points seems like it'd be all you need, but it probably depends a lot on just how challenging the user photo…
I find a lot of applied AI use-cases to be "same as this other method, but more expensive".
Better to spend $100 in op-ex money than spend $1 in cap-ex money reading a journal paper, especially if it lets you tell investors "AI." :p
Re: How we used GPT-4o for image detection with 350 similar illustrations
#20A bit tangential, but I think we will see a good chunk of small teams building competing products in different software business segments, by just doubling on productivity and offering a cheaper option due to less operational overhead (reads: paying engineers). I can think of at least two businesses that can be competed in costs if the team can automate a good chunk of it.
> I can think of at least two businesses that can be competed in costs if the team can automate a good chunk of it. And which would those be?
Job applications, recruiter outreach and initial screening calls. I heard of an AI interviewer via voice chat on a reddit thread recently.