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 our commercial model please email us at [email protected]
• Our website: https://pramadevelopment.com/
• Follow us on X: https://x.com/PramaResearch/
• MADE IN AMERICA

Quick Start Code (Inside Cloned Repo)

import model
from PIL import Image
import torch


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

file = "./image.png" # input image

model = model.BEN_Base().to(device).eval() #init pipeline

model.loadcheckpoints("./BEN_Base.pth")
image = Image.open(file)
mask, foreground = model.inference(image)

mask.save("./mask.png")
foreground.save("./foreground.png")

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

1. Clone Repo
2. Install requirements.txt