Model overview
BEN2 (Background Erase Network) introduces a novel approach to foreground segmentation through its innovative Confidence Guided Matting (CGM) pipeline. The architecture employs a refiner network that targets and processes pixels where the base model exhibits lower confidence levels, resulting in more precise and reliable matting results. This model is built on BEN: [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-vd?p=ben-using-confidence-guided-matting-for) 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: - 🌐 backgrounderase.com - For access to our commercial model email us at sales@backgrounderase.com - Our website: https://backgrounderase.com/ - Follow us on X: https://x.com/PramaResearch/
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。