The problem I have with these is that they do 98% of the removal well, but flub the last 2%. This has been the case with every one of the online and app flavors I have used (note I refuse to subscribe to Adobe.)
My use case is mineral photos, as it turns out. And I would be very surprised if the AI had been trained on these. The sad thing is -- mineral photo backgrounds tend to be very simple and smoothly-varying. Should be a slam dunk. Ah well.
A one-shot background remover doesn't give you the opportunity to interact with it and suggest that it got things wrong here and there.
Yes, I tried one of my mineral photos, and the app made several errors that it shouldn't have, as the foreground was clearly distinct from the background.
If you don't mind a bit of manual effort, Photopea's magic cut is pretty good for this kind of scenario. It is similar to the AI tools except you can designate what to lose or keep by highlighting sections.
(I'm a dev on the project.) We have another background-removal service, ClippingMagic.com, that is built around an editor to let you fix the errors in the automatic result. You may want to give it a try for your mineral photos.
Smooth background is likely worse because it carries not much background semantic. A wood table might do better there. Anyway the white box approach does so much more to item display photos, including all around soft lighting, background removal ai still are quite a way from those.
As someone who is actively working on using SAM, I would say that it leaves a weird border (2-5 pixels wide) of the cut out object so depending on the tasks, it may or may not be suitable. And in the demo, the server returned a way better image embedding than the open source one from Vit_h. There are a lot of issues on github talking about this. So take what you see on the demo page with a grain of salt.
iOS photos has this built-in AI thing where you double tap a photo and it separates the subject into a png with alpha. Imgur made available only a very low res version of your shot but still this is what I got, seems pretty good actually: https://wormhole.app/mbrY3#ab0qbAz7p26SYR0CEONDpQ
My use case is mineral photos, as it turns out. And I would be very surprised if the AI had been trained on these. The sad thing is -- mineral photo backgrounds tend to be very simple and smoothly-varying. Should be a slam dunk. Ah well.
A one-shot background remover doesn't give you the opportunity to interact with it and suggest that it got things wrong here and there.
Yes, I tried one of my mineral photos, and the app made several errors that it shouldn't have, as the foreground was clearly distinct from the background.
I don't know if I'm allowed to post a photo link, but here it is: https://imgur.com/a/V9H1pRH .