#3 Product of the WeekNovember 12, 2019
Discussion
Would you recommend this product?
37 Reviews
Hey everyone 👋
I am really proud to share Trace with you today. We started using it in-house and saved our design team over 100 hours already.
Using Trace requires no design experience. Upload your image and the technology automatically removes the background. It takes less than 10 seconds.
We want to help you make your awesome photos more awesome. Not everyone is a designer, but everyone wants to make beautiful things. Trace cuts out the subject (like your prized pet) so it can be used in all kinds of different projects.
The tool is completely free so try it and let us know what you think!
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Just tried in a number of different images , it works great .. no need for tweaking , very fast & effective. Good job!
@bernardamus thanks so much for your feedback!
I've tried) It works good!
@kotokur_dev awesome! Thanks for trying 🙂
@kotokur_dev
That's the ultimate test I guess:)
Has anyone compared it to https://www.remove.bg/ ? Is it better?
@baidoct it works well for products and people but Smart Pix is better generally: https://floom.app/service/smart-pix
@baidoct @robmoore tried Floom's smart-pix and it simply gave me two FAILED errors on both photos I tried. Trace handled both these photos perfectly. I'm used to painstakingly removing backgrounds with Photoshop and the results Trace gave were as good if not better than I could have done in a fraction of the time. Based on my results today, I will use Trace from here on. Smart Pix didn't even try to remove the bg from my photos. Great job Trace team!
@baidoct @patrick_villella smart-pix was temporarily down from the traffic, sorry about that, all images that failed have been reprocessed :)
This is such an awesome example of "SEO project marketing" (shoutout @harrydry) and I'm sure it'll drive loads of new customers to Sticker Mule.
I was a big fan of remove.bg when it came out - good to see deep learning models being democratized and making their way into real world products!
I'm curious if you could give any detail on the model you're using - are you using UNet and what dataset did you use?
Here's an open source implementation of background removal using UNet if anyone is interested:
https://github.com/eti-p-doray/u...
Congratulations on the launch! :)
