Create README.md

README.md

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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://bria.ai/contact-us).
+- **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://bria.ai/contact-us)
+
+
+- **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>