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.
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
Model Description: BRIA RMBG 1.4 is an saliency segmentation model trained exclusively on a professional-grade dataset.
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
The model’s architecture is based on IS-Net. Yet, we employ a distinct training scheme and utilize our proprietary data for the training process, enhancing the model's effectiveness.
Usage
import os
import numpy as np
from skimage import io
from glob import glob
from tqdm import tqdm
import cv2
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
from models import BriaRMBG
input_size=[1024,1024]
net=BriaRMBG()
model_path = "./model.pth"
im_path = "./example_image.jpg"
result_path = "."
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_path))
net=net.cuda()
else:
net.load_state_dict(torch.load(model_path,map_location="cpu"))
net.eval()
# prepare input
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=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_name=im_path.split('/')[-1].split('.')[0]
im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
cv2.imwrite(os.path.join(result_path, im_name+".png"), im_array)