GoKawiilOriginal Background removed ↔ Original Background removed ↔ Original Background removed ↔ ← Bicycles Windblown hair Free kick →
We’re introducing FeyNoBg, a state-of-the-art model for automatic background removal. Across eight benchmarks, it posts the best published S-measure on four and comes within 2% of the leader on the rest.
Others better FeyNoBg better UHRSD-TE +2.5% HRSOD-TE +2.3% DIS5K +1.1% DAVIS-S +0.2% DUTS-TE -0.8% COD10K-TE -1.2% DUT-OMRON -1.7% CAMO-TE -1.9% -2% 0% +2.5% Benchmark UHRSD-TE FeyNoBg 0.981 BiRefNet 0.957 Description Tests salient-object masks in ultra-high-resolution images, including 4K and 8K scenes.
We’re also releasing NoBg, the library we used to train our model. Use NoBg to run FeyNoBg or train your own background removal model.
Both are open source. Download FeyNoBg on Hugging Face, or build with NoBg on GitHub.
Removal Requires Combination
In your computer, images are stored as grids of pixels. The goal of a background removal algorithm is to predict an opacity value for each pixel such that background pixels become transparent, foreground pixels remain opaque, and boundary pixels become translucent.
Producing this opacity map requires two skills. First, the model has to separate the foreground from the background. When the subject stands against a plain backdrop, this can be done easily by comparing colors. However, in low contrast, crowded, or camouflaged images, the model has to use shape, texture, and context to recognize which pixels form the subject.
Second, the model has to trace the foreground’s boundary. In simple images, a sharp change in color or texture provides a strong edge signal. But real boundaries are more difficult. Hair, fur, thin wires, and motion blur can blend foreground and background elements together. The model has to measure how much of an edge pixel belongs to the subject and set its opacity accordingly. This is called image matting.
Generally, these skills are taught with different kinds of focused data. This creates a failure point. A poor training mix can produce unbalanced models where improvements in one skill come at the expense of the other. Outputs either miss parts of the subject or lack clean edges.
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