nobg 0.2.0

Open source background removal models for image matting

nobg

A library for image and video matting with HuggingFace Hub integration.

Installation

pip install nobg

Usage

AutoModel

Automatically detect and load the correct model architecture from a HuggingFace repo:

from nobg import AutoModel

model = AutoModel.from_pretrained("nobg/BiRefNet")

Load a specific model

from nobg import AutoModel

model = AutoModel.from_pretrained("nobg/BiRefNet")

AutoProcessor

AutoProcessor loads the matching image processor for a repo. It handles the resize + ImageNet normalization on the way in, and turns raw model logits back into an alpha matte at the original resolution on the way out.

from nobg import AutoProcessor

processor = AutoProcessor.from_pretrained("nobg/FeyNobg")

Remove a background with FeyNobg

nobg/FeyNobg is the strongest published model. The processor produces the normalized pixel_values the model expects and post-processes the raw logits (B, 1, 1024, 1024) into an alpha matte you can composite onto the image.

import torch
from loadimg import load_img

from nobg import AutoModel, AutoProcessor

model = AutoModel.from_pretrained("nobg/FeyNobg").eval()
processor = AutoProcessor.from_pretrained("nobg/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 matte in [0, 1] resized back to the original image size
alpha = processor.post_process_alpha_matting(
    outputs, target_sizes=[(image.height, image.width)]
)[0]

# Composite the cutout onto a transparent background
processor.cutout(image, alpha).save("output.png")

Input	Output

Push to HuggingFace Hub

model.push_to_hub("your-username/model-name")

Citation

@software{nobg,
  title={nobg: Open Source Background Removal Models for Image and Video Matting},
  author={Hichri, Hafedh},
  year={2026},
  url={https://github.com/feyninc/nobg},
  license={Apache-2.0},
}