license: other
licence_name: bria-rmbg-1.4
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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 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
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")