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--- |
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license: apache-2.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- BEN |
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- background-remove |
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- mask-generation |
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- Dichotomous image segmentation |
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- background remove |
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- foreground |
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- background |
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--- |
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# BEN - Background Erase Network (Beta Base Model) |
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BEN is a deep learning model designed to automatically remove backgrounds from images, producing both a mask and a foreground image. |
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- MADE IN AMERICA |
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## Quick Start Code (Inside Cloned Repo) |
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```python |
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import model |
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from PIL import Image |
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import torch |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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file = "./image.png" # input image |
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model = model.BEN_Base().to(device).eval() #init pipeline |
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model.loadcheckpoints("./BEN_Base.pth") |
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image = Image.open(file) |
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with torch.no_grad(): |
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mask, foreground = model.inference(image) |
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mask.save("./mask.png") |
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foreground.save("./foreground.png") |
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``` |
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# BEN SOA Benchmarks on Disk 5k Eval |
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### BEN_Base + BEN_Refiner (commercial model please contact us for more information): |
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- MAE: 0.0283 |
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- DICE: 0.8976 |
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- IOU: 0.8430 |
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- BER: 0.0542 |
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- ACC: 0.9725 |
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### BEN_Base (94 million parameters): |
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- MAE: 0.0331 |
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- DICE: 0.8743 |
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- IOU: 0.8301 |
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- BER: 0.0560 |
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- ACC: 0.9700 |
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### MVANet (old SOTA): |
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- MAE: 0.0353 |
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- DICE: 0.8676 |
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- IOU: 0.8104 |
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- BER: 0.0639 |
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- ACC: 0.9660 |
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### BiRefNet(not tested in house): |
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- MAE: 0.038 |
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### InSPyReNet (not tested in house): |
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- MAE: 0.042 |
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## Features |
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- Background removal from images |
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- Generates both binary mask and foreground image |
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- CUDA support for GPU acceleration |
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- Simple API for easy integration |
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## Installation |
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1. Clone Repo |
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2. Install requirements.txt |
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