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---
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.
- For access to our commercial model email us at sales@pramadevelopment.com
- Our website: https://pramadevelopment.com/
- Follow us on X: https://x.com/PramaResearch/
## 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
### 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
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