metadata
license: apache-2.0
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
from BEN import model
from PIL import Image
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
file = "./image2.jpg" # input image
model = model.BEN_Base().to(device).eval() #init pipeline
model.loadcheckpoints("./BEN/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:
- 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
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