Curious if you've compared the results with remove.bg, which has been around (and improving) for a few years?
And are you trying to become a competitor or is this something you're open-sourcing?
remove.bg seems to be more conservative in its approach; when given an image without an obvious “background” the subject of the image can be left with some chunks of background. Even with an obvious background, literally solid colored, one segment still was left over.
This tool produced much cleaner results for my attempts. However, it did remove a couple tiny elements that I would’ve considered foreground elements. Still, it cleanly removed the backgrounds, and it seems to work pretty okay with both photorealistic images and illustrations.
Sample size is low but I think there is merit in the approach.
I upload a hand holding a ring. It removed everything except the hand (which is the most logical) but if it included the hand it would still be a valid result.
Remove.bg was sold to Canva for an undisclosed sum - but one of Austrias biggest exits. So something between 20M to 40m is the guess.
And the "cursive" fallback font is defined by your browser, and it's often pretty... well... interesting.
And if my mom was still alive, she’d definitely be a regular customer. She spent probably years in Photoshop outlining jewelry to remove the background for thousands of products she sold online.
Same, Chrome/Windows desktop.
But hand labelling would be incredibly costly, far more than image classication.
There must be an interesting trick in generating training data.
Also, what exactly do the nets predict, since the foreground objects can have very complex contours, you'd need to predict an arbitrarily shaped polygon ?
Since you have experience with image manipulation using machine learning I’d love to get your advice for a project I want to start. I have a little business that sells 3D printed jewelry (https://lulimjewelry.com). My biggest seller is customers engraving their or their loved ones fingerprint on the ring. Most of those prints come in needing manual cleanup, which I can usually do in a few minutes.
I’d love to train a ML algorithm to do this, and I’ve been building up the before and after pictures over time using my manually cleaned up customer fingerprint images.
Can you give me suggestions or pointers on the sort of algorithm that may be best suited for this task? Just something to get me started down the correct path would be very helpful. Also, would you have a rough estimate on what a good number of training images would be? I want to know if I’m currently in the ballpark or if I need to create a ton more.
Not sure about using ML algorithm but you could look into “edge enhancement”. I think edge enhancement combined with a bit of tweaking of contrast, sharpness should give you good results.
Sharpening Filters is another one to look at. If you are looking to do this in an app, GPUImage library has sharpening filters. It might have edge enhancement filters too though I am not 100% certain.
Search for “edge enhancement online” for example for some tools. You can also look for their algorithms too. Photoshop and Photopea probably do them too.
Here’s some example I tested with an online image with edge enhancement. Your customers probably give you better quality and size images than this:
Using this tool:
https://pinetools.com/image-edge-enhancement
Uploading this image:
https://listverse-wpengine.netdna-ssl.com/wp-content/uploads...
gives this result:
Some are free (but then usually poor quality), others cost money (usually with some resolution limited freebie or credits-on-signup).
I run clippingmagic.com, which launched here on hn almost eight years ago now - time flies [1].
We were #1 until the recent surge in DL-based solutions. It’s more fun to be the disruptor, but getting disrupted sure brought some renewed focus - competition works ;)
Just updated our DL model - old dogs take a while, but we finally did get a fully automatic solution including hair, which means we’re the only option with both good full auto and an editor.
Hopefully the market will eventually notice that none of the solutions work 100%, and that a smart editor can really close the loop.
[0] https://removebackground.app/image?job_name=bb990210&count=2
Table has left, bits of person removed, and some other parts left.