BEN - Background Erase Network (Beta Base Model)

BEN is a deep learning model designed to automatically remove backgrounds from images, producing both a mask and a foreground image.

• For access to commercial model email at sales@pramadevelopment.com
• Website: https://pramadevelopment.com/
• Follow on X: https://x.com/PramaResearch/

BEN SOA Benchmarks on Disk 5k Eval

BEN_Base + BEN_Refiner (commercial model please contact us for more information):

• MAE: 0.0283
• DICE: 0.8976
• IOU: 0.8430
• BER: 0.0542
• ACC: 0.9725

BEN_Base (94 million parameters):

• MAE: 0.0331
• DICE: 0.8743
• IOU: 0.8301
• BER: 0.0560
• ACC: 0.9700

MVANet (old SOTA):

• MAE: 0.0353
• DICE: 0.8676
• IOU: 0.8104
• BER: 0.0639
• ACC: 0.9660

BiRefNet(not tested in house):

• MAE: 0.038

InSPyReNet (not tested in house):

• MAE: 0.042

Features

• Background removal from images
• Generates both binary mask and foreground image
• CUDA support for GPU acceleration
• Simple API for easy integration

Installation

Step 1: Clone the repository

git clone https://github.com/newgenai79/BEN

Step 2: Navigate inside the cloned repository

cd BEN

Step 3: Create virtual environment

python -m venv venv

Step 4: Activate virtual environment

venv\scripts\activate

Step 5: Install wheel package

pip install wheel

Step 6: Install requirements

pip install -r requirements.txt

Step 7: Download model

https://huggingface.co/PramaLLC/BEN/resolve/main/BEN_Base.pth?download=true

Step 8: Launch Gradio WebUI

venv\scripts\activate

python app.py