# Image Segmentation

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.

WePOINTS is a series of multimodal models developed by the WeChat AI team, aimed at creating a unified framework that accommodates various modalities. These models utilize the latest advancements and technologies in multimodal modeling to promote seamless integration of content understanding and generation. The WePOINTS project provides not only models but also pre-trained datasets, evaluation tools, and usage tutorials, making it a significant contribution to the field of multimodal artificial intelligence.

RMBG-2.0 is a background removal model developed by BRIA AI, aimed at effectively separating the foreground and background in images. The model is trained on a curated dataset including general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use and capable of driving large-scale content creation for enterprises. Its accuracy, efficiency, and versatility are comparable to leading open-source models. RMBG-2.0 is available as source code for non-commercial use.

The finegrain-object-cutter is an image editing tool based on the Hugging Face Spaces platform that utilizes advanced machine learning technology for fine-grained object cutting in images. Its main advantages are high precision and user-friendliness, allowing users to accomplish complex editing tasks with simple operations. It is particularly well-suited for designers and developers needing detailed image processing and can be widely applied in fields such as image editing, augmented reality, and virtual reality.

BiRefNet is a model dedicated to high-precision image segmentation, employing bilateral reference technology to achieve high-resolution binary image segmentation. This technology is widely applied across numerous fields, including education, healthcare, and geography, particularly in situations requiring accurate image segmentation for further analysis, such as in medical imaging and autonomous vehicles.

ComfyUI-segment-anything-2 is an image segmentation library based on the segment-anything-2 model, allowing users to easily perform image segmentation tasks through ComfyUI nodes. The library is currently in development but its core functionalities are available. By automatically downloading models and integrating them into ComfyUI, it provides users with a straightforward image segmentation solution.

ViTMatte is an image segmentation system based on a pretrained pure vision transformer (Plain Vision Transformers, ViTs). It optimizes the balance between performance and computational efficiency by utilizing a hybrid attention mechanism and convolutional neck, and introduces a detail capture module to supplement the detailed information required for segmentation. ViTMatte is the first work to harness the potential of ViT in the field of image segmentation through simple adaptation, inheriting the advantages of ViT in terms of pretraining strategy, concise architecture design, and flexible inference strategy. In the Composition-1k and Distinctions-646, the most commonly used image segmentation benchmark tests, ViTMatte achieves state-of-the-art performance and surpasses previous works significantly.

AI Smart Image Segmentation is a Figma plugin that utilizes advanced Segment Anything Model (SAM) and ?? Transformers.js technology, providing designers and artists with an interactive and precise image segmentation tool. It simplifies the process of extracting objects or regions from images through click-based interactions, significantly boosting design efficiency and unleashing creativity. The plugin is free to use and open-source, allowing users to customize and contribute to its development.

EmerDiff is a novel diffusion model designed to generate fine-grained segmentation maps by leveraging semantic knowledge extracted from diffusion models, without requiring additional training. This model leverages semantic knowledge extracted from Stable Diffusion (SD) to overcome the challenge of directly extracting pixel-level semantic relationships from low-dimensional feature maps. It utilizes these relationships to construct segmentation maps at the image resolution. Extensive experiments have demonstrated that the generated segmentation maps are clear and capture detailed aspects of images, indicating the presence of highly accurate pixel-level semantic knowledge within diffusion models.

ActAnywhere is a generative model for automatically generating video backgrounds that match the motion and appearance of the foreground subject. This task involves synthesizing backgrounds that are consistent with the foreground subject's movement and appearance while also aligning with the artist's creative intent. ActAnywhere leverages the power of large-scale video diffusion models, specifically tailored for this task. It takes a sequence of foreground subject segmentation as input, uses an image as a conditioning frame describing the desired scene, and generates a coherent video that aligns with the conditioning frame, achieving realistic foreground-background interaction. The model is trained on a large-scale human-object interaction video dataset. Extensive evaluations demonstrate its superior performance compared to baselines and its ability to generalize to diverse distribution samples, including non-human subjects.

image-matting is an online image segmentation tool based on deep learning. It can achieve image segmentation for portraits and general scenarios, extract the main object in the image and output the corresponding background image, foreground image, and mask. The tool uses Model Studio's cv_unet_image-matting and cv_unet_universal-matting models to achieve high-quality image segmentation effects. It offers a simple and convenient online segmentation experience, supporting both image upload segmentation and URL segmentation. It can be widely applied in image editing, portrait processing on e-commerce platforms and other scenarios.

SAM is a C++ image segmentation model built from scratch. It performs pixel-level segmentation on images, identifying object boundaries without requiring any additional code or annotations. Based on Meta's Segment Anything Model, SAM leverages a Transformer architecture for end-to-end image segmentation prediction. It offers a simple and easy-to-use C++ interface, supporting both command-line and graphical user interface modes. SAM efficiently runs on CPUs, boasting a compact model size while maintaining good segmentation accuracy. It's ideal for deploying and utilizing image segmentation models in resource-constrained embedded environments where high performance is required but GPUs are unavailable.

SAM is a promptable segmentation system that can achieve zero-shot generalization for unfamiliar objects and images without additional training. It utilizes various input prompts to perform a wide range of segmentation tasks without further training. Its promptable design allows for flexible integration with other systems. Trained on 11 million images, it has 1 billion segmentation masks. Its efficient modular design enables inference within milliseconds. The Segment Anything Model (SAM) enables image segmentation based on text commands and others, recognizes all objects and provides one-click image masking. Simply upload an image and click on an object for recognition.

Skyglass is an AI-powered image processing tool that offers functions like image recognition, image enhancement, and image segmentation. It helps users quickly optimize and process images, boosting workflow efficiency. With flexible pricing, Skyglass caters to both individual and business users, aiming to provide an efficient and user-friendly image processing solution.