About This Site
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."
Alternatives
Meta Segment Anything Model 2
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.
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
Promptable segmentation with zero-shot generalization
Interactive point and box prompts
Automatic segmentation of entire images
Integration with other AI systems
Extensible outputs for use in other applications
Advantages
Zero-shot generalization to unfamiliar objects and images
Flexible promptable design
Efficient model design for web-browser use
Large dataset for training (SA-1B)
Integration with other AI systems
缺点：Currently only supports images or individual video frames
缺点：Requires a GPU for efficient image encoder inference
缺点：Does not produce mask labels, only object masks
缺点：Text prompts are explored in the paper but not released
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