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license: other
licence_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
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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 various use cases. Developed by BRIA AI, RMBG v1.4 is available as an open-source tool for non-commercial use.

CLICK HERE FOR A DEMO

Model Description

Developed by: BRIA AI
Model type: Background Removal
License: bria-rmbg-1.4
- The model is open 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 an saliency segmentation model trained exclusively on a professional-grade dataset.
BRIA: Resources for more information: BRIA AI

Training data

Bria-RMBG model was trained over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images. 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

Inference Time : 1 sec on Nvidia A10 GPU

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

import numpy as np
from skimage import io
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
from briarmbg import BriaRMBG
from PIL import Image

model_path = "./model.pth"
im_path = "./example_input.jpg"

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

# prepare input
model_input_size=[1024,1024]
im = io.imread(im_path)
if len(im.shape) < 3:
    im = im[:, :, np.newaxis]
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').type(torch.uint8)
image = torch.divide(im_tensor,255.0)
image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])

if torch.cuda.is_available():
    image=image.cuda()

# inference 
result=net(image)

# post process
result = torch.squeeze(F.interpolate(result[0][0], size=im_size, mode='bilinear') ,0)
ma = torch.max(result)
mi = torch.min(result)
result = (result-mi)/(ma-mi)

# save result
im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
pil_im = Image.fromarray(np.squeeze(im_array))
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")