juba7/BEN_Base

Model Provided by ParmaLLC

The base model is publicly available and free to use for commercial use on HuggingFace:

• 🤗 PramaLLC/BEN

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: 0.0270
• DICE: 0.8989
• IOU: 0.8506
• BER: 0.0496
• ACC: 0.9740

BEN_Base (94 million parameters):

• MAE: 0.0309
• DICE: 0.8806
• IOU: 0.8371
• BER: 0.0516
• ACC: 0.9718

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