tao-hunter
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RMBG-2.0

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----
-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>&nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/briaai/">Hugging Face</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://blog.bria.ai/">Blog</a> &nbsp&nbsp
-<br>
-🖥️ <a href="https://huggingface.co/spaces/briaai/BRIA-RMBG-2.0">Demo</a>&nbsp&nbsp| &nbsp&nbsp <a href="https://github.com/Bria-AI/RMBG-2.0">Github</a>&nbsp&nbsp
-</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:** To purchase a commercial license for RMBG V2.0 **or** an API package [Click Here](https://share-eu1.hsforms.com/2sj9FVZTGSFmFRibDLhr_ZAf4e04).
-- **API Endpoint**: [Bria.ai](https://docs.bria.ai/image-editing/v2-endpoints/background-remove), [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)
-![examples](t4.png)
-## 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)
-- **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
-![diagram](diagram1.png)
-![examples](collage5.png)
-### 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>