RMBG v2.0 is our new state-of-the-art background removal model significantly improves RMBG v1.4. The model is 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.
Model Description: BRIA RMBG-2.0 is a dichotomous image segmentation model trained exclusively on a professional-grade dataset. The model output includes a single-channel 8-bit grayscale alpha matte, where each pixel value indicates the opacity level of the corresponding pixel in the original image. This non-binary output approach offers developers the flexibility to define custom thresholds for foreground-background separation, catering to varied use cases requirements and enhancing integration into complex pipelines.
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
Open source models comparison
diagram
examples
Architecture
RMBG-2.0 is developed on the BiRefNet architecture enhanced with our proprietary dataset and training scheme. This training data significantly improves the model’s accuracy and effectiveness for background-removal task.
If you use this model in your research, please cite:
@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}
}
