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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 examples

Model Description

  • Developed by: BRIA AI

  • Model type: Background Removal

  • License: bria-rmbg-1.4

    • 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 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

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

Architecture

RMBG v1.4 is developed on the IS-Net 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

git clone https://huggingface.co/briaai/RMBG-1.4
cd RMBG-1.4/
pip install -r requirements.txt

Usage

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

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 = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
net.to(device)

# 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")
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