README.md

---
license: other
license_name: bria-rmbg-1.4
license_link: https://bria.ai/bria-huggingface-model-license-agreement/
pipeline_tag: image-segmentation
tags:
- remove background
- background
- background-removal
- Pytorch
- vision
- legal liability
- transformers

extra_gated_description: RMBG v1.4 is available as a source-available model for non-commercial use
extra_gated_heading: "Fill in this form to get instant access"
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  By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: checkbox
---

# BRIA Background Removal v1.4 Model Card

RMBG v1.4 is our state-of-the-art background removal model, 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 v1.4 is available as a source-available model for non-commercial use. 

[CLICK HERE FOR A DEMO](https://huggingface.co./spaces/briaai/BRIA-RMBG-1.4)
![examples](t4.png)

### Model Description

- **Developed by:** [BRIA AI](https://bria.ai/)
- **Model type:** Background Removal 
- **License:** [bria-rmbg-1.4](https://bria.ai/bria-huggingface-model-license-agreement/)
  - The model is released under a Creative Commons license for non-commercial use.
  - Commercial use is subject to a commercial agreement with BRIA. [Contact Us](https://bria.ai/contact-us) for more information. 

- **Model Description:** BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.
- **BRIA:** Resources for more information: [BRIA AI](https://bria.ai/)



## Training data
Bria-RMBG model was trained with over 12,000 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

![examples](results.png)


## Architecture

RMBG v1.4 is developed on the [IS-Net](https://github.com/xuebinqin/DIS) enhanced with our unique training scheme and proprietary dataset. 
These modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.

## Installation
```bash
pip install -qr https://huggingface.co./briaai/RMBG-1.4/resolve/main/requirements.txt
```

## Usage

Either load the pipeline
```python
from transformers import pipeline
image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
pillow_mask = pipe(image_path, return_mask = True) # outputs a pillow mask
pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
```

Or load the model 
```python
from transformers import AutoModelForImageSegmentation
from torchvision.transforms.functional import normalize
model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4",trust_remote_code=True)
def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
    if len(im.shape) < 3:
        im = im[:, :, np.newaxis]
    # orig_im_size=im.shape[0:2]
    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
    im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear')
    image = torch.divide(im_tensor,255.0)
    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
    return image

def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
    result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result-mi)/(ma-mi)
    im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
    im_array = np.squeeze(im_array)
    return im_array

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

# prepare input
image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
orig_im = io.imread(image_path)
orig_im_size = orig_im.shape[0:2]
image = preprocess_image(orig_im, model_input_size).to(device)

# inference 
result=model(image)

# post process
result_image = postprocess_image(result[0][0], orig_im_size)

# save result
pil_im = Image.fromarray(result_image)
no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
orig_image = Image.open(image_path)
no_bg_image.paste(orig_image, mask=pil_im)
```