FeyNoBg beats BiRefNet on 4 of 8 removal benchmarks

FeyNoBg beat BiRefNet on four of eight background-removal benchmarks, per Feyn Labs' July 21 release of the model and its NoBg training library.

The wins aren't close calls. Feyn Labs reports S-measure scores of 0.981 vs 0.957 on UHRSD-TE and 0.983 vs 0.961 on HRSOD-TE. It also leads on DIS5K (0.961) and DAVIS-S (0.977). On the remaining four benchmarks, FeyNoBg trails by under 2%.

Under the hood, the change is simple: Feyn Labs added six blocks to the third feature-extractor stage of BiRefNet's 222M-parameter architecture, landing at 263M parameters.

Weights are public at feyninc/FeyNobg, and the training code that produced them is open at github.com/feyninc/nobg as the NoBg library. Anyone can pull the model, rerun the benchmark comparisons, or retrain it with different data instead of taking Feyn Labs' numbers on faith.

What's actually being tested

Adding 41M parameters bought four wins and four near-ties within 2%. That's a specific trade. The open NoBg library is what makes it checkable: pull the code, retrain it, and see whether the results hold outside Feyn Labs' own runs.