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RMBG-2.0

Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
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RMBG-2.0 / README.md
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metadata
license: other
license_name: bria-rmbg-2.0
license_link: https://bria.ai/bria-huggingface-model-license-agreement/
pipeline_tag: image-segmentation
tags:
  - remove background
  - background
  - background-removal
  - Pytorch
  - vision
  - legal liability
  - transformers

BRIA Background Removal v2.0 Model Card

RMBG v2.0 is our new state-of-the-art background removal model, designed to effectively separate foreground from background in a range of categories and image types. This model has been trained on a carefully selected dataset, which includes: general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale. The accuracy, efficiency, and versatility currently rival leading source-available models. It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount.

Developed by BRIA AI, RMBG v2.0 is available as a source-available model for non-commercial use.

CLICK HERE FOR A DEMO examples

Model Details

Model Description

  • Developed by: BRIA AI

  • Model type: Background Removal

  • License: bria-rmbg-2.0

    • The model is released under a Creative Commons license for non-commercial use.
    • Commercial use is subject to a commercial agreement with BRIA. Contact Us for more information.

  • Model Description: BRIA RMBG-2.0 is a segmentation model trained exclusively on a professional-grade dataset.

  • BRIA: Resources for more information: BRIA AI

Training data

Bria-RMBG model was trained with over 15,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images. Our benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities. For clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.

Distribution of images:

Category Distribution
Objects only 45.11%
People with objects/animals 25.24%
People only 17.35%
people/objects/animals with text 8.52%
Text only 2.52%
Animals only 1.89%
Category Distribution
Photorealistic 87.70%
Non-Photorealistic 12.30%
Category Distribution
Non Solid Background 52.05%
Solid Background 47.95%
Category Distribution
Single main foreground object 51.42%
Multiple objects in the foreground 48.58%

Qualitative Evaluation

examples

Architecture RMBG-2.0 is developed on the BiRefNet enhanced with our proprietary dataset. This training data significantly improve the model’s accuracy and effectiveness for background-removal task.

Model Description

  • Developed by: BRIA AI
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: Background Removal
  • Language(s) (NLP): [More Information Needed]
  • License: [More Information Needed]
  • Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use 

from PIL import Image
import matplotlib.pyplot as plt
import torch
from torchvision import transforms
from models.birefnet import BiRefNet

birefnet = BiRefNet.from_pretrained('briaai/RMBG-2.0')
torch.set_float32_matmul_precision(['high', 'highest'][0])
birefnet.to('cuda')
birefnet.eval()

# Data settings
image_size = (1024, 1024)
transform_image = transforms.Compose([
    transforms.Resize(image_size),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

image = Image.open(input_image_path)
input_images = transform_image(image).unsqueeze(0).to('cuda')

# Prediction
with torch.no_grad():
    preds = birefnet(input_images)[-1].sigmoid().cpu()
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
mask = pred_pil.resize(image.size)
image.putalpha(mask)

image.save("no_bg_image.png")

Citation 

@article{BiRefNet,
  title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
  author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
  journal={CAAI Artificial Intelligence Research},
  year={2024}
}