Gofind AI

Snap a picture and find similar items to buy instantly

4 followers

Snap a picture and find similar items to buy instantly

4 followers

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Shopping

Launch tags:iOS•Productivity•Fashion

Launch Team

Render Sandboxes: a computer for every agentIsolated compute, persistent state and a real filesystem

Promoted

#DefeatByTweet

I think a potential compelling use case is integrating this with Instagram likes data to create a new mobile-first pinterest

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10yr ago

FashionBot by GoFind.AI

Maker

@xanderschultz great idea! it already works with a screenshot & upload from instagram

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10yr ago

DoodleLens

Results are in the [ballpark] I didn't get any matches after a few dozen photos, but I do like where this is going. Good mvp.

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10yr ago

FashionBot by GoFind.AI

Maker

@capsuledev1 thanks, working on it!

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10yr ago

This would be a good idea when I am shopping for someone I am not quite sure about. Getting ideas on the spot could be a good inspiration especially with the holiday season coming up

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10yr ago

FashionBot by GoFind.AI

Maker

Thanks for all the feedback. After the successful 2 week beta, we are now launching the next version this week all new features and integration with social media. I may make another ph post for that

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10yr ago

Do you have an android app? If not then when are you planning to launch it?

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10yr ago

FashionBot by GoFind.AI

Maker

@ank2302 Android version is releasing this week.

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10yr ago

How does this differ from Pomika? http://pomika.com/tech

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10yr ago

We are actually more interested in how to integrate with Instagram than to create an app. If you are curious about our technology I can say that is mostly composed by two pieces: a deep learning descriptor called deepDNA and the street to shop solver that uses deepDNA. DeepDNA is every efficient in computation and merges image features and text features (word embeddings) so contains a lot of information. It captures incredible semantics, not just only visual features. The recommendations are mindblowing. But our biggest technology improvement is probably how we work with natural images. Mostly all the visual technologies out there are doing the following pipeline: Product extraction and reverse image search. We started doing this also. We developed technology based on top of state-of-the-art segmentation / object detection papers... However my opinion is that this pipeline is very hard to make it work for every single scenario (partial products to full human images). It can work apparently well, but again, very difficult to make it work always. We also want to cover every space, including home design and annotate images does not sound even realistic to me. So we had to think out of the box to not doing what we consider a mistake. We still use RPN's but more as an attention model. I would say that our approach to solve the street to shop problem is more RNN than CNN. Believe me, we are working hard to make that game changer ;)

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10yr ago

FashionBot by GoFind.AI

Maker

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10yr ago

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