Fixrm

🚀 BRIA Background Removal v1.4 Model Card

RMBG v1.4 is our state-of-the-art background removal model. It can effectively separate the foreground from the background across various categories and image types. Trained on a carefully - selected dataset that includes general stock images, e - commerce, gaming, and advertising content, this model is suitable for commercial use cases in large - scale enterprise content creation. Its accuracy, efficiency, and versatility rival those of leading source - available models. It is an ideal choice when content safety, legally licensed datasets, and bias mitigation are of utmost importance.

Developed by BRIA AI, RMBG v1.4 is available as a source - available model for non - commercial use.

CLICK HERE FOR A DEMO

✨ Features

• High - performance Background Removal: Capable of accurately separating foreground from background in a wide range of image types.
• Commercial - ready: Suitable for large - scale enterprise content creation.
• Ethical and Safe: Trained on legally licensed datasets with bias mitigation in mind.

📦 Installation

pip install -qr https://huggingface.co/briaai/RMBG-1.4/resolve/main/requirements.txt

💻 Usage Examples

Basic Usage

from transformers import pipeline
image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
pillow_mask = pipe(image_path, return_mask = True) # outputs a pillow mask
pillow_image = pipe(image_path) # applies mask on input and returns a pillow image

Advanced Usage

from transformers import AutoModelForImageSegmentation
from torchvision.transforms.functional import normalize
model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4",trust_remote_code=True)
def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
    if len(im.shape) < 3:
        im = im[:, :, np.newaxis]
    # orig_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')
    image = torch.divide(im_tensor,255.0)
    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
    return image

def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
    result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result-mi)/(ma-mi)
    im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
    im_array = np.squeeze(im_array)
    return im_array

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

# prepare input
image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
orig_im = io.imread(image_path)
orig_im_size = orig_im.shape[0:2]
image = preprocess_image(orig_im, model_input_size).to(device)

# inference 
result=model(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(image_path)
no_bg_image.paste(orig_image, mask=pil_im)

📚 Documentation

Model Description

Training data

The 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:

Qualitative Evaluation

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.

📄 License

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](https://bria.ai/contact - us) for more information.