
Using CNNs for Handbag Brand and Color Detection - gautamsarora
https://technology.condenast.com/story/handbag-brand-and-color-detection
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kmangutov
Although they describe the process of how they went about collecting the data
set, it would have been an extra special touch for them to release a
formatted/clean version of it!

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justinAlcon
Wow. It's really crazy how it can pick the handbag out even with complex
similar backgrounds.

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Omnipresent
I don't quite understand the object localization piece of the article. Are
there other resources on this topic that explain how do do the same thing
(object localization). Or tutorials/examples on this topic?

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jedvinss
It's based on this paper
[https://arxiv.org/pdf/1512.04150.pdf](https://arxiv.org/pdf/1512.04150.pdf)
The approach is different than the Faster R-CNN etc mentioned below as it is
not fully supervised, i.e. the position of the objected is not in the training
data. I can write a simple tutorial and link in the article and here later
today.

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Omnipresent
> I can write a simple tutorial and link in the article and here later today

That would be fantastic. Thanks!

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adamqureshi
I am curious if you can use this "process" to spot a fake. Fake handbags are a
BIG problem in the luxury accessories market. R-CNN. Object detection and
classification.

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dontreact
A big piece missing here is data augmentation. If they had more of it maybe
they wouldn’t need to do the separate final layer training.

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KGIII
I wonder if it will ever get good enough to detect counterfeit bags? My
understanding is that they are a prolific problem.

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nl
You shouldn't be getting downvoted for this.

Yes, this is an active area of research. There was a paper at KDD17 working on
this:
[https://dl.acm.org/citation.cfm?id=3098186&dl=ACM&coll=DL&CF...](https://dl.acm.org/citation.cfm?id=3098186&dl=ACM&coll=DL&CFID=827097581&CFTOKEN=86319338)

NVidia had a blog post about this:
[https://blogs.nvidia.com/blog/2017/08/03/detecting-
counterfe...](https://blogs.nvidia.com/blog/2017/08/03/detecting-counterfeit-
products/)

There's a French company with a product:
[http://cypheme.com/](http://cypheme.com/)

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KGIII
Neat, thanks! I could see a few valuable use cases, from customs to personal.

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baybal2
I have no idea why condenast got into tech, I believe it is simply getting
trendy with their crowd

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paulfryzel
Hi - Team Lead from Condé Nast here. You're right in that WIRED and New Yorker
readers are interested in ML, but that's not solely why we've invested. This
work is driven by our platform research team (FORE) which produces predictive
models for a [wide] variety of uses, primarily to deliver a better user
experience for our readers.

For example, these models are directly integrated into our search,
recommendation, SEO optimization engines. Those engines then power both our
in-house CMS as well as the modules you see on the brand websites.

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mac01021
I wonder how long it took to train.

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jedvinss
I took about a day on one aws p2.xlarge (one Tesla K80) starting from a pre-
trained inception model (trained and opened sourced by Googled on ImageNet
data).

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leeleathers
Killer work Johan and Paul!

