---
license: other
license_name: bria-rmbg-1.4
license_link: https://bria.ai/bria-huggingface-model-license-agreement/

tags:
- remove background
- background
- background removal
- Pytorch
- vision
- legal liability

extra_gated_prompt: This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you.
extra_gated_fields:
  Name: text
  Company/Org name: text
  Org Type (Early/Growth Startup, Enterprise, Academy): text
  Role: text
  Country: text
  Email: text
  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. 
It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount. 

Developed by BRIA AI, RMBG v1.4 is available as an open-source 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 an open-source 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 an 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)

- **Inference Time :** 1 sec on Nvidia A10 GPU

## Architecture

RMBG v1.4 is developed on the [DIS neural network architecture](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
git clone https://huggingface.co/briaai/RMBG-1.4
cd RMBG-1.4/
pip install -r requirements.txt
```

## Usage

```python
from skimage import io
import torch, os
from PIL import Image
from briarmbg import BriaRMBG
from utilities import preprocess_image, postprocess_image

model_path = f"{os.path.dirname(os.path.abspath(__file__))}/model.pth"
im_path = f"{os.path.dirname(os.path.abspath(__file__))}/example_input.jpg"

net = BriaRMBG()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.load_state_dict(torch.load(model_path, map_location=device))
net.eval()    

# prepare input
model_input_size = [1024,1024]
orig_im = io.imread(im_path)
orig_im_size = orig_im.shape[0:2]
image = preprocess_image(orig_im, model_input_size).to(device)

# inference 
result=net(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(im_path)
no_bg_image.paste(orig_image, mask=pil_im)
no_bg_image.save("example_image_no_bg.png")
```