metadata

license: apache-2.0
pipeline_tag: image-segmentation
tags:
  - BEN
  - background-remove
  - mask-generation
  - Dichotomous image segmentation
  - background remove
  - foreground
  - background

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.

MADE IN AMERICA

Quick Start Code

import model
from PIL import Image
import torch


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

file = "./image2.png" # input image

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

model.loadcheckpoints("./BEN_Base.pth")
image = Image.open(file)
with torch.no_grad():
    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

Clone Repo
Install requirements.txt