public-apis VS rembg

For inspirations on what type of agents I should build, I turned to the Public APIs GitHub repository which has a curated lists of free APIs. I narrowed my search for an API that does not require sign-up or an API key and returns useful information. I ultimately decided to use the Free Currency Exchange Rates API, which seemed promising upon some basic testing.

✅ public-apis/public-apis : https://github.com/public-apis/public-apis

Public-Apis Github Repo — A list of free public APIs.

Interesting thread at https://github.com/public-apis/public-apis/issues/3104

Use curl to access a free public API and get a random joke, cat fact, or whatever.

I remember the forking of https://github.com/public-apis/public-apis and the long git issue discussions. The company owning the repository stopped maintaining it but didn't give up control either. Over the years you've put in a lot of work in publicapis.dev and that is much appreciated.

Also, that isn't really a list of "contributors", but of "organisations with the most stars". Those are different things.

For example "public-apis"[1] didn't "contribute" anything as that's not a person, and looking at GitHub[2] there are a bunch of substantial contributors (the person who created the organisation/repo only has 12 commits by the way);

[1]: https://opensource-heroes.com/o/public-apis

[2]: https://github.com/public-apis/public-apis/graphs/contributo...

A bit conflicted as the linked video is also linked from the actual rembg repo but it seems way faster and more detailed to just read the readme at that repo first, and maybe use a video if something doesn’t make sense.

There are open source tools like rembg (https://github.com/danielgatis/rembg) which call into pre-trained models.

You can use Rembg extension to remove background automatically: https://github.com/danielgatis/rembg

Hi guys 📷 I am new to ml deployment. Can anyone help about production deployment ? I made fastapi docker app which removes background from image (https://github.com/danielgatis/rembg). It uses u2net segmentation. I tried aws ec2, lambda, googld cloudrun and so far ec2(t2-large) is the fastest but still too slow. Also, it costs way more than I expected. Are there any other solution that I can deploy ml app with as low as possible? Where do you guys mostly deploy ml app?

I am currently using rembg https://github.com/danielgatis/rembg

rembg

A python script with rembg could work

4.) rembg -- backgrounds be gone. If there isn't an extension in Automatic1111 for this yet, there should be (I haven't checked recently). Same deal as midas, you can point it at a folder and zap the backgrounds off of all your images. Useful in combo with midas and imagemagick if, for instance, you want images of an object on a white background (Stable Diffusion training via LoRAs/Dreambooth may not benefit, but other things like GANs prefer that sort of training image). Useful if you want to "compose" a scene and you have images of dispirate objects/people you want in that scene.

Ran into another issue with " LoadLibrary failed with error 126 Here's the solution: https://github.com/danielgatis/rembg/issues/312