license: other
tags:
- background-removal
- Pytorch
- vision
BRIA Background Removal v1.4 Model Card
100% automatically Background removal capability across all categories and image types that capture the variety of the world. Built and validated on a comprehensive dataset containing an equal distribution of general stock images, eComm, gaming and ads.
Model Description
- Developed by: BRIA AI
- Model type: Background removal image-to-image model
- License: bria-rmbg-1.4
- Model Description: BRIA RMBG 1.4 is an image-to-image model trained exclusively on a professional-grade dataset. It is designed and built for commercial use, subject to a commercial agreement with BRIA.
- Resources for more information: BRIA AI
Get Access
BRIA RMBG 1.4 is available under the BRIA RMBG 1.4 License Agreement. To access the model, please contact us. By submitting this form, you agree to BRIA’s Privacy policy and Terms & conditions.
Training data
Bria-RMBG model was trained over 12000 high quality, high resolution, fully licensed images. The training set as well as the validation benchmark if a holistic representation of the commercial world containing a distribution of general stock images, eComm, gaming and ads.
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% |
All images were manualy labeled pixel-wise accuratly.
Qualitative Evaluation
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)