snyy

As you read the rest of this answer, keep in mind that Critic attaches to your agent as a plugin. Plugins can have hooks (https://code.claude.com/docs/en/hooks and https://learn.chatgpt.com/docs/hooks) that pass instruction to an agent at predefined boundaries.

Now, Critic doesn't interfere with the actual process of code-writing. Your agent is free to write code as it wants. Critic's plugin simply observes the agent during its turn and at certain boundaries, nudges it to write a narrative. The plugin also comes with skills on how to write a concise narrative and how to identify "complex code" that needs annotation. Critic also prevents an agent from ending its turn until it publishes a quality narrative (we use heuristics like word count and format parsing to measure this).

All together, this results in a narrative that is short, direct, and easy to skim through.

Recapping here: Our first train was on 4,000 images from the MaskFactory dataset alone. This improved some benchmarks but regressed on others. We took this as a sign of narrow datasets causing unintended specialization.

In our next run, we assembled 26.1K images from 10 different datasets. We capped the amount of images that could come from one source, to prevent a single type of example from dominating. This composite set covered several cases like crowded scenes, camouflage, high-res subjects, fine objects like hair, blurred backgrounds, etc. We then shuffled everything together and trained FeyNoBg.

FeyNoBg is an "automatic" model. It automatically detects foreground elements and segments the image. Most of the time, this includes all foreground elements. As you can see in the freekick example (https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9Q...), the model output includes the ball, Messi, and the Liverpool defenders. In your example, FeyNoBg will segment around the person plus the couch.

> I only want the person [including props]

The alternative to automatic models are prompt models and those serve the exact use case you're describing. These allow you to specify the foreground element to include. Everything else is removed. That's the next step for FeyNoBg, converting it from an automatic to a prompt model. Now, answering your question:

> How does it stack up against Adobe's model?

We're better on automatic background removal. Support for selecting a subset of foreground elements is coming soon

We've released projects under the MIT license before, most notably https://github.com/feyninc/chonkie. While we're not trying to directly monetize on this work, credit goes a long way and helps in other operations.

Recently, attributed usage is shrinking. To be clear, this is not a shrinkage in actual use of our software, just how many people acknowledge that they rely on it.

cc-by-nc is a protection against that. We've been very honest about our work being on top of BiRefNet as we want to extend the original creators the same courtesy. I have no issues if individuals fork/finetune/or otherwise build on top of any open source projects we release, irrespective of license. At minimum, we want acknowledgment if a company chooses to use our software in production.

All I ask is you make a PR to https://github.com/feyninc/nobg with your results. We'd love to see what you make, contribute in any way we can, and share onwards.

https://github.com/feyninc/nobg/issues