option
Home
Flash News
Content
JohnYoung
JohnYoung
July 30, 2026

Feyn team launched FeyNoBg, a new SOTA background removal model, and open-sourced the NoBg library. Built on BiRefNet, it expands feature extraction modules from 18 to 24 (263M params). Trained on 10 datasets (26,100 images), it achieves top S-measure on 4 of 8 benchmarks. The NoBg library offers higher throughput, lower latency, and less memory than BiRefNet. Available on Hugging Face and GitHub.

Feyn team launched FeyNoBg, a new SOTA background removal model, and open-sourced the NoBg library. Built on BiRefNet, it expands feature extraction modules from 18 to 24 (263M params). Trained on 10 datasets (26,100 images), it achieves top S-measure on 4 of 8 benchmarks. The NoBg library offers higher throughput, lower latency, and less memory than BiRefNet. Available on Hugging Face and GitHub. Feyn team launched FeyNoBg, a new SOTA background removal model, and open-sourced the NoBg library. Built on BiRefNet, it expands feature extraction modules from 18 to 24 (263M params). Trained on 10 datasets (26,100 images), it achieves top S-measure on 4 of 8 benchmarks. The NoBg library offers higher throughput, lower latency, and less memory than BiRefNet. Available on Hugging Face and GitHub. Feyn team launched FeyNoBg, a new SOTA background removal model, and open-sourced the NoBg library. Built on BiRefNet, it expands feature extraction modules from 18 to 24 (263M params). Trained on 10 datasets (26,100 images), it achieves top S-measure on 4 of 8 benchmarks. The NoBg library offers higher throughput, lower latency, and less memory than BiRefNet. Available on Hugging Face and GitHub.
Comments (0)
0/300
OR