BEN2

BEN2 is a deep learning-based image segmentation model focusing on background removal and foreground extraction.

CommonProductImageImage SegmentationBackground Removal

BEN2 (Background Erase Network) is an innovative image segmentation model that employs the Confidence Guided Matting (CGM) process. It utilizes a refinement network specifically designed to handle pixels with lower model confidence, achieving more precise cut-out effects. BEN2 excels in hair segmentation, 4K image processing, object segmentation, and edge refinement. Its base model is open-source, allowing users to try the complete model for free via API or web demonstration. The model's training data includes the DIS5k dataset and a 22K proprietary segmentation dataset, meeting various image processing needs.

Best AI Websites & Tools

BEN2

BEN2 Visit Over Time

BEN2 Visit Trend

BEN2 Visit Geography

BEN2 Traffic Sources

BEN2 Alternatives

RMBG-2.0 — A model designed for efficient separation of image foreground and background.

Video Background Removal — A video background removal tool for one-click background elimination.

CogView3-Plus-3B — A text-to-image generation model that supports high-resolution image generation.

AuraFlow v0.3 — Open-source text-to-image generation model

RMBG v1.4 — An open source PyTorch model for image background removal provided by BRIA AI

Image Matting — An online image segmentation tool based on deep learning.

Segment Anything — An online AI image masking tool that can extract any object from any image

VideoJAM — VideoJAM is a framework designed to enhance the motion coherence of video generation models.

DeepResearch123 — A navigation website for AI research resources, providing documents and practical case studies in the field of AI.

MatAnyone — MatAnyone is a stable video matting framework that supports target specification, suitable for complex backgrounds.

Open R1 — This is a fully open-source replication project of the DeepSeek-R1 model, aimed at assisting developers in recreating and building models based on R1.

Video Depth Anything — Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

Janus-Pro-7B — Janus-Pro-7B is an innovative autoregressive framework that unifies multimodal understanding and generation.

Janus-Pro-1B — Janus-Pro-1B is an autoregressive framework for unified multi-modal understanding and generation.

YuE-s1-7B-anneal-en-cot — YuE is an open-source music generation model that transforms lyrics into complete songs.

Tarsier — Tarsier is a large video language model developed by ByteDance that generates high-quality video descriptions.

Flux-Midjourney-Mix2-LoRA — A text-to-image generation model based on the Midjourney style, focusing on high-resolution and realistic image creation.

leapfusion-hunyuan-image2video — A novel image-to-video sampling technology based on the Hunyuan model, enabling high-quality video generation.

Llasa-3B — Llasa-3B is a text-to-speech synthesis model based on LLaMA that supports speech generation in both Chinese and English.

VideoLLaMA3 — VideoLLaMA3 is a cutting-edge multimodal foundational model focused on image and video understanding.

Momodel.cn — Online courses in Python, AI, large models, AI writing, and painting—easy entry for beginners.

Flex.1-alpha — A pre-trained model for text-to-image generation with 8 billion parameters and licensed under Apache 2.0.

Worlds of Frames — Frames is an advanced image generation foundational model launched by Runway, offering unprecedented style control and visual fidelity.

OmniThink — OmniThink is a framework that enhances the knowledge density of machine writing by simulating human thought processes.

MiniRAG — A simple retrieval-augmented generation framework that enables small models to achieve good RAG performance through heterogeneous graph indexing and lightweight topological enhanced retrieval.

Seaweed-APT — Seaweed-APT is a model that supports real-time step-by-step generation of 1280x720 24fps videos.

InternLM3-8B-Instruct — InternLM3-8B-Instruct is an open-source instruction model with 8 billion parameters designed for general-purpose use and advanced reasoning.

MiniMax-01 — A powerful language model with a total of 456 billion parameters, capable of processing context lengths of up to 4 million tokens.

rStar-Math — Showcases research results demonstrating how small language models can master mathematical reasoning abilities through self-evolution and deep thinking.