What is Segment Anything?
Segment Anything is a Meta AI research project that introduces a new AI model called Segment Anything Model (SAM), which can "cut out" any object in an image with a single click. SAM is a promptable segmentation system with zero-shot generalization to unfamiliar objects and images, without the need for additional training.
Feature of Segment Anything
Segment Anything Model (SAM) has several features, including:
- Promptable design that enables flexible integration with other systems
- Extensible outputs that can be used as inputs to other AI systems
- Zero-shot generalization to unfamiliar objects and images without requiring additional training
- Ability to take input prompts from other systems, such as object detectors or user gaze from an AR/VR headset
How to use Segment Anything
You can try the demo of Segment Anything to see how it works. SAM uses a variety of input prompts, including points, boxes, and text, to segment objects in an image. You can also use SAM to automatically segment everything in an image or generate multiple valid masks for ambiguous prompts.
Price of Segment Anything
The price of Segment Anything is not explicitly stated, but it is a research project by Meta AI, and the model is open-sourced on GitHub.
Helpful Tips for Segment Anything
- SAM can be used for a wide range of segmentation tasks without the need for additional training
- The model can be integrated with other AI systems to enable more complex tasks, such as object tracking in videos or image editing applications
- The dataset used to train SAM includes over 1.1 billion segmentation masks collected on ~11 million licensed and privacy-preserving images
Frequently Asked Questions about Segment Anything
- What type of prompts are supported by SAM?
- Foreground/background points, bounding box, mask, and text prompts
- What is the structure of the SAM model?
- A ViT-H image encoder, a prompt encoder, and a lightweight transformer-based mask decoder
- What platforms does the model use?
- PyTorch and ONNX runtime
- How big is the model?
- The image encoder has 632M parameters, and the prompt encoder and mask decoder have 4M parameters
- How long does inference take?
- The image encoder takes ~0.15 seconds on an NVIDIA A100 GPU, and the prompt encoder and mask decoder take ~50ms on CPU in the browser using multithreaded SIMD execution