ZQL9711

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. Developed by BRIA AI, RMBG v2.0 is available as a source-available model for non-commercial use. Bria RMBG2.0 is availabe everywhere you build, either as source-code and weights, ComfyUI nodes or API endpoints. - Purchase: for commercial license simply click Here. - API Endpoint: Bria.ai, fal.ai - ComfyUI: Use it in workflows Join our Discord community for more information, tutorials, tools, and to connect with other users! - Developed by: BRIA AI - Model type: Background Removal - License: Creative Commons Attribution–Non-Commercial (CC BY-NC 4.0) - The model is released under a CC BY-NC 4.0 license for non-commercial use. - Commercial use is subject to a commercial agreement with BRIA. Available here Purchase: to purchase a commercial license simply click Here. - Model Description: BRIA RMBG-2.0 is a dichotomous image 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. | 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 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: