---
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 (Inside Cloned Repo)

```python
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

![Demo Results](demo.jpg)


### 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