Instructions to use feyninc/FeyNobg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- nobg
How to use feyninc/FeyNobg with nobg:
pip install nobg
import torch from loadimg import load_img from nobg import AutoModel, AutoProcessor model = AutoModel.from_pretrained("feyninc/FeyNobg").eval() processor = AutoProcessor.from_pretrained("feyninc/FeyNobg") image = load_img("input.jpg").convert("RGB") inputs = processor(image, return_tensors="pt") with torch.no_grad(): outputs = model(pixel_values=inputs["pixel_values"]) alpha = processor.post_process_alpha_matting(outputs, target_sizes=[(image.height, image.width)])[0] processor.cutout(image, alpha).save("output.png") - Notebooks
- Google Colab
- Kaggle
RMBG2.0 is still better
#3
by MishaF - opened
How did you benchmark? Can you share?
In a blind test benchmark we ran across many categories, https://huggingface.co/briaai/RMBG-2.0 is clearly winning