| --- |
| license: other |
| license_name: bria-rmbg-2.0 |
| license_link: https://creativecommons.org/licenses/by-nc/4.0/deed.en |
| pipeline_tag: image-segmentation |
| tags: |
| - remove background |
| - background |
| - background-removal |
| - Pytorch |
| - vision |
| - legal liability |
| - transformers |
| - transformers.js |
| extra_gated_description: >- |
| Bria AI Model weights are open source for non commercial use only, per the |
| provided [license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). |
| extra_gated_heading: Fill in this form to immediatly access the model for non commercial use |
| extra_gated_fields: |
| Name: text |
| Email: text |
| Company/Org name: text |
| Company Website URL: text |
| Discord user: text |
| I agree to BRIA’s Privacy policy, Terms & conditions, and acknowledge Non commercial use to be Personal use / Academy / Non profit (direct or indirect): checkbox |
| --- |
| |
| # BRIA Background Removal v2.0 Model Card |
| <p align="center"><img src="https://platform.bria.ai/assets/Bria-logo-5e0c53b1.svg" alt="BRIA Logo" width="200" /></p> |
|
|
| <!-- RMBG Card wrapper --> |
| <div class="rmbg-card" style="position: relative; border-radius: 12px; overflow: hidden;"> |
|
|
| <!-- FIBO Promo Banner (Top) --> |
| <a |
| href="https://huggingface.co/briaai/FIBO" |
| target="_blank" |
| rel="noopener" |
| aria-label="Explore FIBO on Hugging Face" |
| style=" |
| position: absolute; |
| top: 0; |
| left: 0; |
| width: 100%; |
| display: flex; |
| align-items: center; |
| justify-content: center; |
| gap: 10px; |
| background: linear-gradient(90deg, #fff6b7 0%, #fde047 100%); |
| color: #1f2937; |
| text-decoration: none; |
| font-family: Inter, system-ui, -apple-system, Segoe UI, Roboto, Arial, sans-serif; |
| font-weight: 600; |
| font-size: 13px; |
| padding: 10px 0; |
| border-bottom: 1px solid rgba(0,0,0,0.08); |
| box-shadow: 0 2px 8px rgba(0,0,0,0.08); |
| z-index: 10; |
| "> |
| <img |
| src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" |
| alt="Hugging Face" |
| width="18" |
| height="18" |
| style="display:block" |
| /> |
| <span>✨ Discover <strong>FIBO</strong> on Hugging Face</span> |
| </a> |
| |
| <!-- ... your RMBG content below ... --> |
| <p align="center"> |
| 💜 <a href="https://go.bria.ai/46gzn20"><b>Bria AI</b></a>   |   🤗 <a href="https://huggingface.co/briaai/">Hugging Face</a>    |    📑 <a href="https://blog.bria.ai/">Blog</a>    |
| <br> |
| 🖥️ <a href="https://huggingface.co/spaces/briaai/BRIA-RMBG-2.0">Demo</a>  |    <a href="https://github.com/Bria-AI/RMBG-2.0">Github</a>   |
| </p> |
| |
| 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. |
|
|
| ### Get Access |
|
|
| Bria RMBG2.0 is availabe everywhere you build, either as source-code and weights, ComfyUI nodes or API endpoints. |
|
|
| - **Purchase:** for commercial license [Contant us](https://bria.ai/contact-us). |
| - **API Endpoint**: [Bria.ai](https://platform.bria.ai/console/api/image-editing), [fal.ai](https://fal.ai/models/fal-ai/bria/background/remove), [Replicate](https://replicate.com/bria/remove-background) |
| - **ComfyUI**: [Use it in workflows](https://github.com/Bria-AI/ComfyUI-BRIA-API) |
| - **GitHub**: [github.com/Bria-AI/RMBG-2.0](https://github.com/Bria-AI/RMBG-2.0) |
|
|
| For more information, please visit our [website](https://bria.ai/). |
|
|
| Join our [Discord community](https://discord.gg/Nxe9YW9zHS) for more information, tutorials, tools, and to connect with other users! |
|
|
| [CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-2.0) |
|
|
|
|
|
|
|  |
|
|
| ## Model Details |
| ##### |
| ### Model Description |
|
|
| - **Developed by:** [BRIA AI](https://bria.ai/) |
| - **Model type:** Background Removal |
| - **License:** [Creative Commons Attribution–Non-Commercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/deed.en) |
| - 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](https://share-eu1.hsforms.com/2sj9FVZTGSFmFRibDLhr_ZAf4e04?utm_campaign=RMBG%202.0&utm_source=Hugging%20face&utm_medium=hyperlink&utm_content=RMBG%20Hugging%20Face%20purchase%20form) |
|
|
|
|
| - **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:** Resources for more information: [BRIA AI](https://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 |
| Open source models comparison |
|  |
|  |
|
|
| ### Architecture |
| RMBG-2.0 is developed on the [BiRefNet](https://github.com/ZhengPeng7/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.<br> |
| 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} |
| } |
| ``` |
|
|
| #### Requirements |
| ```bash |
| torch |
| torchvision |
| pillow |
| kornia |
| transformers |
| ``` |
|
|
| ### Usage |
|
|
| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
|
|
|
|
| ```python |
| from PIL import Image |
| import torch |
| from torchvision import transforms |
| from transformers import AutoModelForImageSegmentation |
| |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True).eval().to(device) |
| |
| # 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(device) |
| |
| # Prediction |
| with torch.no_grad(): |
| preds = model(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") |
| ``` |
|
|
|
|
| </div> |
|
|
