Image Segmentation
BEN2
ONNX
Safetensors
PyTorch
BEN2
background-remove
mask-generation
Dichotomous image segmentation
background remove
foreground
background
remove background
model_hub_mixin
pytorch_model_hub_mixin
background removal
background-removal
Instructions to use PramaLLC/BEN2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BEN2
How to use PramaLLC/BEN2 with BEN2:
import requests from PIL import Image from ben2 import AutoModel url = "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg" image = Image.open(requests.get(url, stream=True).raw) model = AutoModel.from_pretrained("PramaLLC/BEN2") model.to("cuda").eval() foreground = model.inference(image) - Notebooks
- Google Colab
- Kaggle
updated readme
Browse files
README.md
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## BEN2 access
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BEN2 was trained on the DIS5k and our 22K proprietary segmentation dataset. Our enhanced model delivers superior performance in hair matting, 4K processing, object segmentation, and edge refinement. Our Base model is open source. To try the full model through our free web demo or integrate BEN2 into your project with our API:
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- 🌐 [backgrounderase.
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## Contact us
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- For access to our commercial model email us at sales@
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- Our website: https://
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- Follow us on X: https://x.com/PramaResearch/
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## BEN2 access
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BEN2 was trained on the DIS5k and our 22K proprietary segmentation dataset. Our enhanced model delivers superior performance in hair matting, 4K processing, object segmentation, and edge refinement. Our Base model is open source. To try the full model through our free web demo or integrate BEN2 into your project with our API:
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- 🌐 [backgrounderase.com](https://backgrounderase.com)
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## Contact us
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- For access to our commercial model email us at sales@backgrounderase.com
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- Our website: https://backgrounderase.com/
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- Follow us on X: https://x.com/PramaResearch/
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