| 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 | |
| ```python | |
| 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 | |
| 1. Clone Repo | |
| 2. Install requirements.txt | |