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---
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license: other
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license_name: bria-rmbg-1.4
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license_link: https://bria.ai/bria-huggingface-model-license-agreement/
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pipeline_tag: image-segmentation
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tags:
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- remove background
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- background
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- background-removal
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- Pytorch
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- vision
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- legal liability
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- transformers
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extra_gated_description: RMBG v1.4 is available as a source-available model for non-commercial use
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extra_gated_heading: "Fill in this form to get instant access"
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extra_gated_fields:
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Name: text
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Company/Org name: text
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Org Type (Early/Growth Startup, Enterprise, Academy): text
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Role: text
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Country: text
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Email: text
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By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: checkbox
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---
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# BRIA Background Removal v1.4 Model Card
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RMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of
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categories and image types. This model has been trained on a carefully selected dataset, which includes:
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general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale.
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The accuracy, efficiency, and versatility currently rival leading source-available models.
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It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount.
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Developed by BRIA AI, RMBG v1.4 is available as a source-available model for non-commercial use.
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[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-1.4)
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
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### Model Description
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- **Developed by:** [BRIA AI](https://bria.ai/)
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- **Model type:** Background Removal
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- **License:** [bria-rmbg-1.4](https://bria.ai/bria-huggingface-model-license-agreement/)
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- The model is released under a Creative Commons license for non-commercial use.
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- Commercial use is subject to a commercial agreement with BRIA. [Contact Us](https://bria.ai/contact-us) for more information.
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- **Model Description:** BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.
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- **BRIA:** Resources for more information: [BRIA AI](https://bria.ai/)
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## Training data
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Bria-RMBG model was trained with over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.
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Our benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.
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For clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.
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### Distribution of images:
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| Category | Distribution |
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| -----------------------------------| -----------------------------------:|
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| Objects only | 45.11% |
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| People with objects/animals | 25.24% |
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| People only | 17.35% |
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| people/objects/animals with text | 8.52% |
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| Text only | 2.52% |
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| Animals only | 1.89% |
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| Category | Distribution |
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| -----------------------------------| -----------------------------------------:|
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| Photorealistic | 87.70% |
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| Non-Photorealistic | 12.30% |
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| Category | Distribution |
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| -----------------------------------| -----------------------------------:|
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| Non Solid Background | 52.05% |
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| Solid Background | 47.95%
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| Category | Distribution |
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| -----------------------------------| -----------------------------------:|
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| Single main foreground object | 51.42% |
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| Multiple objects in the foreground | 48.58% |
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## Qualitative Evaluation
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
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## Architecture
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RMBG v1.4 is developed on the [IS-Net](https://github.com/xuebinqin/DIS) enhanced with our unique training scheme and proprietary dataset.
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These modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.
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## Installation
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```bash
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pip install -qr https://huggingface.co/briaai/RMBG-1.4/resolve/main/requirements.txt
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```
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## Usage
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Either load the pipeline
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```python
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from transformers import pipeline
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image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
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pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
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pillow_mask = pipe(image_path, return_mask = True) # outputs a pillow mask
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pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
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```
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Or load the model
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```python
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from transformers import AutoModelForImageSegmentation
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from torchvision.transforms.functional import normalize
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model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4",trust_remote_code=True)
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def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
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if len(im.shape) < 3:
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im = im[:, :, np.newaxis]
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# orig_im_size=im.shape[0:2]
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im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
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im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear')
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image = torch.divide(im_tensor,255.0)
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image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
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return image
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def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
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result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
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ma = torch.max(result)
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mi = torch.min(result)
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result = (result-mi)/(ma-mi)
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im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
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im_array = np.squeeze(im_array)
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return im_array
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# prepare input
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image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
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orig_im = io.imread(image_path)
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orig_im_size = orig_im.shape[0:2]
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image = preprocess_image(orig_im, model_input_size).to(device)
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# inference
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result=model(image)
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# post process
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result_image = postprocess_image(result[0][0], orig_im_size)
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# save result
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pil_im = Image.fromarray(result_image)
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no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
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orig_image = Image.open(image_path)
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no_bg_image.paste(orig_image, mask=pil_im)
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```
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