About This Site
Unified model for segmenting objects across images and videos with high precision. Meta Segment Anything Model 2 (SAM 2) is the first unified model for segmenting objects across images and videos. It allows users to select objects in any image or video frame using a click, box, or mask as input. SAM 2 is designed for fast, precise object selection and offers state-of-the-art performance for object segmentation in both images and videos. The models are open source under an Apache 2.0 license.
Alternatives
Segment Anything
SAM is a promptable AI segmentation system for zero-shot generalization to objects and images.
Segment Anything (SAM) is a promptable segmentation system developed by Meta AI that offers zero-shot generalization to unfamiliar objects and images without requiring additional training. It allows users to "cut out" any object in any image with a single click. SAM uses a variety of input prompts to perform a wide range of segmentation tasks. The model was trained on millions of images and masks collected through a model-in-the-loop "data engine."
Image to Layers
AI tool for separating images into editable transparent layers
ImageLayered is an AI-powered image decomposition tool that separates flat images into named, editable transparent RGBA layers. It can isolate subjects, text, objects, backgrounds, and other visual elements, while reconstructing hidden areas behind extracted objects. Users can independently move, recolor, replace, edit, or export each layer without requiring Photoshop skills.
Key Features AI
Core Features
Unified image and video segmentation
Interactive object selection using clicks, boxes, or masks
Real-time interactivity and results
Robust zero-shot performance on unfamiliar videos and images
State-of-the-art performance for object segmentation
Advantages
Unified model for images and videos
Open-source and available under Apache 2.0 license
Strong zero-shot performance
Real-time interactivity
State-of-the-art segmentation performance
缺点：May require additional prompts for optimal results in complex scenes
缺点：Performance may vary depending on video quality and object visibility
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