learndb VS rembg

I ran into the same problem while building https://learnawesome.org/ . Forget the broad class of "learning resources", even the "books" category doesn't have a usable unique ID. Not everything gets an ISBN for example. There's also the ambiguity between a "work" and an "edition" of a work.

This is probably why OpenLibrary supports mapping of books with 40+ identifiers: https://github.com/internetarchive/openlibrary/blob/master/o...

I loved using Svelte for these two open-source projects but haven't yet an opportunity to use it at work:

- An SVG-based graph dataset editor: https://codeberg.org/nilesh/grapher

- A curated collection of educational resources: https://github.com/learn-awesome/learndb

Compared to React, developing with Svelte felt like a breath of fresh air.

A couple of my tools that don't require signup:

- Grapher (a visual editor for graph datasets with features like nested nodes, custom attributes on nodes and edges, and export in Cytoscape JSON or SVG format. Accessible at: https://grapherx.netlify.app/

- LearnDB (A curated collection of links to educational resources organized by topics, formats, reviews and other tags). Accessible at: https://learndb.vercel.app/

For those who're curious, the app is mostly completed - although the dataset could be enriched further. You can try it online here: https://learnawesome.vercel.app/ but it's designed to work best when run locally: https://github.com/learn-awesome/learndb

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